pax_global_header00006660000000000000000000000064114732520660014520gustar00rootroot0000000000000052 comment=0ffd308877aaecf9455e8f9700926e9f6df4631a ants-1.9.2+svn680.dfsg/000077500000000000000000000000001147325206600144605ustar00rootroot00000000000000ants-1.9.2+svn680.dfsg/ANTSCopyright.txt000066400000000000000000000030001147325206600176500ustar00rootroot00000000000000ConsortiumOfANTS® - http://www.picsl.upenn.edu/ANTS/ Copyright (c) 2009-2010 (updated to current year ad infinitum) Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the consortium nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE CONSORTIUM AND CONTRIBUTORS ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ants-1.9.2+svn680.dfsg/COPYING.txt000066400000000000000000000027111147325206600163320ustar00rootroot00000000000000Copyright (c) 2009, 2010 ConsortiumOfANTS All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the consortium nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE CONSORTIUM AND CONTRIBUTORS ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. ants-1.9.2+svn680.dfsg/Examples/000077500000000000000000000000001147325206600162365ustar00rootroot00000000000000ants-1.9.2+svn680.dfsg/Examples/ANTS.cxx000066400000000000000000000240411147325206600175300ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: ANTS.cxx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:05 $ Version: $Revision: 1.19 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include "itkPICSLAdvancedNormalizationToolKit.h" #include "itkANTSImageTransformation.h" #include "itkANTSImageRegistrationOptimizer.h" #include template int ANTSex(int argc, char *argv[]) { typedef itk::PICSLAdvancedNormalizationToolKit RegistrationType; typename RegistrationType::Pointer registration = RegistrationType::New(); registration->ParseCommandLine( argc, argv ); std::cout << " Run Reg " << std::endl; try { registration->RunRegistration(); } catch(...) { std::cerr << "Exception thrown: ANTS" << std::endl; return EXIT_FAILURE; } registration->GetTransformationModel()->SetWriteComponentImages(true); registration->GetTransformationModel()->Write(); return EXIT_SUCCESS; } int main(int argc, char *argv[] ) { int dim = 2; // while(argc--) printf("%s\n", *argv++); if ( argc < 2 || ((argc == 2) && strcmp(argv[1], "--help") == 0)) { std::cout << " \n " << std::endl; std::cout << "Example usage: \n " << std::endl; std::cout << argv[0] << " ImageDimension -m MI[fixedimage.nii.gz,movingimage.nii.gz,1,32] -o Outputfname.nii.gz -i 30x20x0 -r Gauss[3,1] -t Elast[3] \n \n " << std::endl; std::cout << " Compulsory arguments:\n " << std::endl; std::cout << " ImageDimension: 2 or 3 (for 2 or 3 Dimensional registration)\n " << std::endl; std::cout << " -m: Type of similarity model used for registration. \n " << std::endl; std::cout << " For intramodal image registration, use: " << std::endl; std::cout << " CC = cross-correlation " << std::endl; std::cout << " MI = mutual information " << std::endl; std::cout << " PR = probability mapping " << std::endl; std::cout << " MSQ = mean square difference " << std::endl; std::cout << " \n " << std::endl; std::cout << " For intermodal image registration, use: " << std::endl; std::cout << " MI = mutual information " << std::endl; std::cout << " PR = probability mapping " << std::endl; std::cout << " \n " << std::endl; std::cout << " -o Outputfname.nii.gz: the name of the resulting image.\n " << std::endl; std::cout << " -i Max-iterations in format: JxKxL, where: " << std::endl; std::cout << " J = max iterations at coarsest resolution (here, reduce by power of 2^2) " << std::endl; std::cout << " K = middle resolution iterations (here,reduce by power of 2) " << std::endl; std::cout << " L = fine resolution iterations (here, full resolution). This level takes much more time per iteration!\n " << std::endl; std::cout << " Adding an extra value before JxKxL (i.e. resulting in IxJxKxL) would add another iteration level.\n " << std::endl; std::cout << " -r Regularization \n" << std::endl; std::cout << " -t Type of transformation model used for registration \n" << std::endl; std::cout << " For elastic image registration, use: " << std::endl; std::cout << " Elast = elastic transformation model (less deformation possible)\n " << std::endl; std::cout << " For diffeomorphic image registration, use: " << std::endl; std::cout << " Syn[GradStep,TimePoints,IntegrationStep] --geodesic 2 = SyN with time with arbitrary number of time points in time discretization " << std::endl; std::cout << " SyN[GradStep,2,IntegrationStep] = SyN with time optimized specifically for 2 time points in the time discretization " << std::endl; std::cout << " SyN[GradStep] = Greedy SyN, typicall GradStep=0.25 " << std::endl; std::cout << " Exp[GradStep,TimePoints] = Exponential " << std::endl; std::cout << " GreedyExp = Diffeomorphic Demons style exponential mapping " << std::endl; std::cout << " \n " << std::endl; std::cout << " Please use the `ANTS -h ` call or refer to the ANTS.pdf manual or antsIntroduction.sh script for additional information and typical values for transformation models\n " << std::endl; return 1; } else dim = atoi( argv[1] ); if ( dim <= 1 || dim > 3 ) { std::cout <<" You passed ImageDimension: " << dim << " . Please use only 2 or 3 (for 2 or 3 Dimensional registration) " << std::endl; argv[1]=(char*)("--help"); ANTSex<2>( argc,argv ); exit(1); } /** * Try the simple case of the call "ANTS fixedImage movingImage" */ if( argc == 3 && ( atoi( argv[1] ) != 2 || atoi( argv[1] ) != 3 ) ) { itk::ImageIOBase::Pointer fixedImageIO = itk::ImageIOFactory::CreateImageIO( argv[1], itk::ImageIOFactory::ReadMode ); if( fixedImageIO.IsNull() ) { std::cerr << "Invalid fixed image: " << argv[1] << std::endl; return EXIT_FAILURE; } itk::ImageIOBase::Pointer movingImageIO = itk::ImageIOFactory::CreateImageIO( argv[2], itk::ImageIOFactory::ReadMode ); if( movingImageIO.IsNull() ) { std::cerr << "Invalid moving image: " << argv[2] << std::endl; return EXIT_FAILURE; } fixedImageIO->SetFileName( argv[1] ); fixedImageIO->ReadImageInformation(); movingImageIO->SetFileName( argv[2] ); movingImageIO->ReadImageInformation(); unsigned int fdim = fixedImageIO->GetNumberOfDimensions(); unsigned int mdim = movingImageIO->GetNumberOfDimensions(); if( fdim != mdim ) { std::cerr << "Fixed image dimension does not equal " << "the moving image dimension (" << fdim << " != " << mdim << ")" << std::endl; return EXIT_FAILURE; } if( fdim != 2 && fdim != 3 ) { std::cerr << "Unsupported image dimension" << std::endl; return EXIT_FAILURE; } /** * After the checking, we can add the default parameters; */ std::string arguments; std::string transformation( " -t SyN[1.0] "); std::string regularization( " -r Gauss[3,0.5] "); std::string metric = std::string( " -m PR[" ) + std::string( argv[1] ) + std::string( "," ) + std::string( argv[2] ) + std::string( ",1,3] " ); std::string outputNaming( " -o ANTS.nii.gz " ); long maxSize = vnl_math_min( fixedImageIO->GetDimensions( 0 ), movingImageIO->GetDimensions( 0 ) ); for( unsigned int d = 1; d < fdim; d++ ) { long tmpMax = vnl_math_max( fixedImageIO->GetDimensions( d ), movingImageIO->GetDimensions( d ) ); if( maxSize < tmpMax ) { maxSize = tmpMax; } } unsigned int numberOfLevels = static_cast( vcl_log( (double)maxSize / 32 ) / vcl_log( (double) 2 ) ) + 1; std::string iterations( " -i " ); for( int n = numberOfLevels; n > 0; n-- ) { itk::OStringStream buf; unsigned long numberOfIterations = ( 5 << (n-1) ); buf << numberOfIterations << "x"; iterations += buf.str(); } iterations = iterations.substr( 0, iterations.length()-1 ); itk::OStringStream dimBuf; dimBuf << fdim; arguments.clear(); arguments = std::string( " ANTS " ) + dimBuf.str() + iterations + transformation + regularization + metric + outputNaming; dim = fdim; std::cout << arguments << std::endl; unsigned int my_argc = 0; std::string::size_type delimPos = 0; std::string::size_type tokenPos = 0; std::string::size_type pos = 0; while( true ) { delimPos = arguments.find_first_of( " ", pos ); tokenPos = arguments.find_first_not_of( " ", pos ); if( std::string::npos != delimPos && std::string::npos != tokenPos && tokenPos < delimPos ) { my_argc++; } else if( std::string::npos == delimPos ) { if( std::string::npos != tokenPos ) { my_argc++; } else { break; } } pos = delimPos + 1; } unsigned int arg_count = 0; char **my_argv = new char*[my_argc]; delimPos = 0; tokenPos = 0; pos = 0; while( true ) { delimPos = arguments.find_first_of( " ", pos ); tokenPos = arguments.find_first_not_of( " ", pos ); if( std::string::npos != delimPos && std::string::npos != tokenPos && tokenPos < delimPos ) { std::string arg = arguments.substr( pos, delimPos-pos ); my_argv[arg_count] = new char[arg.size() + 1]; strcpy( my_argv[arg_count], arg.c_str() ); arg_count++; } else if( std::string::npos == delimPos ) { if( std::string::npos != tokenPos ) { std::string arg = arguments.substr( pos ); my_argv[arg_count] = new char[arg.size() + 1]; strcpy( my_argv[arg_count], arg.c_str() ); arg_count++; } else { break; } } pos = delimPos + 1; } switch ( dim ) { case 3: ANTSex<3>( my_argc, my_argv ); break; default: ANTSex<2>( my_argc, my_argv ); } } else { switch ( dim ) { case 3: ANTSex<3>( argc, argv ); break; default: ANTSex<2>( argc, argv ); } } return EXIT_SUCCESS; } ants-1.9.2+svn680.dfsg/Examples/ANTSConformalMapping.cxx000066400000000000000000000402101147325206600227010ustar00rootroot00000000000000#include "antsCommandLineParser.h" #include "itkImage.h" #include "itkImageFileReader.h" #include "itkImageFileWriter.h" #include "itkFEM.h" #include "itkFEMLinearSystemWrapperItpack.h" #include "itkFEMElement3DC0LinearTriangularLaplaceBeltrami.h" #include "itkFEMElement3DC0LinearTriangularMembrane.h" #include "itkFEMDiscConformalMap.h" #include "vtkCallbackCommand.h" #include "vtkPolyDataWriter.h" #include "vtkPolyDataReader.h" #include #include #include #include #include template class CommandIterationUpdate : public itk::Command { public: typedef CommandIterationUpdate Self; typedef itk::Command Superclass; typedef itk::SmartPointer Pointer; itkNewMacro( Self ); protected: CommandIterationUpdate() {}; public: void Execute(itk::Object *caller, const itk::EventObject & event) { Execute( (const itk::Object *) caller, event); } void Execute(const itk::Object * object, const itk::EventObject & event) { const TFilter * filter = dynamic_cast< const TFilter * >( object ); if( typeid( event ) != typeid( itk::IterationEvent ) ) { return; } std::cout << "Iteration " << filter->GetElapsedIterations() << " (of " << filter->GetMaximumNumberOfIterations() << "): "; std::cout << filter->GetCurrentConvergenceMeasurement() << " (threshold = " << filter->GetConvergenceThreshold() << ")" << std::endl; } }; void InitializeCommandLineOptions( itk::ants::CommandLineParser *parser ) { typedef itk::ants::CommandLineParser::OptionType OptionType; { std::string description = std::string( "Two mesh images are specified as input - 1. defines the label mesh. must have a scalar attached to vertices named 'Label'. 2. defines the feature mesh. scalar name is 'Feature'. we put the 2nd mesh's values into the flat space." ) ; OptionType::Pointer option = OptionType::New(); option->SetLongName( "input-mesh" ); option->SetShortName( 'i' ); option->SetUsageOption( 0, "[InputMesh1.vtk,]" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "Display the mesh." ) ; OptionType::Pointer option = OptionType::New(); option->SetLongName( "display-mesh" ); option->SetShortName( 'd' ); option->SetUsageOption( 0, "[InputMesh1.vtk]" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "Inflation --- two params : \n 1. BandPass (smaller increases smoothing) -- e.g. 0.001. \n " ) + std::string( "2. number of iterations --- higher increases smoothing. " ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "inflate" ); option->SetShortName( 'f' ); option->SetUsageOption( 0, "[,]" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "SegmentationCost --- 3 params : \n 1. Float-Max-Cost : controls the size of the output region. \n " ) + std::string( "2. Float-Weight for distance cost = edge_length*W_d \n " ) + std::string( "3. Float-Weight for label cost = H(fabs( desiredLabel - localLabel ))*W_l*MaxCost \n where H is the heaviside function." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "segmentation-cost" ); option->SetShortName( 's' ); option->SetUsageOption( 0, "e.g. [40,1,0]" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "The output consists of one (or more) meshes ... 1. the flattened mesh with features mapped. 2. the extracted extrinisic mesh. 3. inflated mesh. "); OptionType::Pointer option = OptionType::New(); option->SetLongName( "output" ); option->SetShortName( 'o' ); option->SetUsageOption( 0, "[MyFlatMesh.vtk,,]" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "Map to a canonical domain : pass square or circle "); OptionType::Pointer option = OptionType::New(); option->SetLongName( "canonical-domain" ); option->SetShortName( 'c' ); option->SetUsageOption( 0, "[domain]" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "Print the help menu (short version)." ); OptionType::Pointer option = OptionType::New(); option->SetShortName( 'h' ); option->SetDescription( description ); option->AddValue( std::string( "0" ) ); parser->AddOption( option ); } { std::string description = std::string( "Print the help menu." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "help" ); option->SetDescription( description ); option->AddValue( std::string( "0" ) ); parser->AddOption( option ); } { std::string description = std::string( "Parameterize the boundary while searching for the boundary (a bit slow and not guaranteed to be doable). " ) + std::string( "If false, we try to parameterize after the searching is done. " ) + std::string( "This option is meaningless if you pass the boundary in as an option. " ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "param-while-searching" ); option->SetShortName( 'p' ); option->SetUsageOption( 0, "0 / 1 " ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "Which label (unsigned integer) to flatten. " ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "label-to-flatten" ); option->SetShortName( 'l' ); option->SetUsageOption( 0, "1" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "The name of a boundary parameterization file (not implemented)." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "boundary-param" ); option->SetShortName( 'b' ); option->SetUsageOption( 0, "[filename.vtk]" ); option->SetDescription( description ); parser->AddOption( option ); } } template int ANTSConformalMapping( itk::ants::CommandLineParser *parser ) { typedef float PixelType; typedef float RealType; typedef itk::Image ImageType; // we define the options in the InitializeCommandLineOptions function // and then use them here ... typedef vtkPolyData MeshType; vtkSmartPointer labelmesh=NULL; vtkSmartPointer featuremesh=NULL; vtkSmartPointer inflatedmesh=NULL; typedef itk::FEMDiscConformalMap ParamType; typename ParamType::Pointer flattener=ParamType::New(); flattener->SetDebug(false); flattener->SetSigma(1); // flattener->SetSurfaceMesh(vtkmesh); // first find out if the user wants to inflate the mesh ... unsigned int inflate_iterations=0; float inflate_param=0; typename itk::ants::CommandLineParser::OptionType::Pointer infOption = parser->GetOption( "inflate" ); if( infOption && infOption->GetNumberOfValues() > 0 ) { if( infOption->GetNumberOfParameters() == 2 ) { inflate_param = parser->Convert(infOption->GetParameter( 0 ) ); inflate_iterations = parser->Convert(infOption->GetParameter( 1 ) ); std::cout << " you will inflate before flattening with params " << inflate_param << " applied over " << inflate_iterations << " iterations. " << std::endl; } else { std::cerr << " wrong params for inflation. ignoring. " << std::endl; std::cerr << " " << infOption->GetDescription() << std::endl; return EXIT_FAILURE; } } float maxCost=40,distCostW=1,labelCostW=0; typename itk::ants::CommandLineParser::OptionType::Pointer costOption = parser->GetOption( "segmentation-cost" ); if( costOption && costOption->GetNumberOfValues() > 0 ) { if( costOption->GetNumberOfParameters() == 3 ) { maxCost = parser->Convert(costOption->GetParameter( 0 ) ); distCostW = parser->Convert(costOption->GetParameter( 1 ) ); labelCostW = parser->Convert(costOption->GetParameter( 2 ) ); } else { std::cerr << " wrong params for cost weights. " << std::endl; std::cerr << " " << costOption->GetDescription() << std::endl; return EXIT_FAILURE; } } typename itk::ants::CommandLineParser::OptionType::Pointer displayOption = parser->GetOption( "display-mesh" ); if( displayOption && displayOption->GetNumberOfValues() > 0 ) { if( displayOption->GetNumberOfParameters() > 0 ) { std::string dispm=displayOption->GetParameter( 0 ); std::cout << " render " << dispm << std::endl; // read the vtk file ... vtkPolyDataReader *fltReader = vtkPolyDataReader::New(); fltReader->SetFileName(dispm.c_str()); fltReader->Update(); vtkSmartPointer normalGenerator = vtkSmartPointer::New(); normalGenerator->SetInput(fltReader->GetOutput()); normalGenerator->Update(); vtkRenderer* ren1 = vtkRenderer::New(); vtkRenderWindow* renWin = vtkRenderWindow::New(); renWin->AddRenderer(ren1); vtkRenderWindowInteractor* inter = vtkRenderWindowInteractor::New(); inter->SetRenderWindow(renWin); vtkCallbackCommand *cbc = vtkCallbackCommand::New(); ren1 -> AddObserver(vtkCommand::KeyPressEvent, cbc); vtkDataSetMapper* mapper = vtkDataSetMapper::New(); mapper->SetInput( normalGenerator->GetOutput() ); mapper->SetScalarRange(0,255); vtkActor* actor = vtkActor::New(); actor->SetMapper(mapper); ren1->SetViewport(0.0, 0.0, 1.0, 1.0); ren1->AddActor(actor); renWin->Render(); inter->Start(); mapper->Delete(); actor->Delete(); ren1->Delete(); renWin->Delete(); inter->Delete(); fltReader->Delete(); return 0; } } /** * Initialization */ typename itk::ants::CommandLineParser::OptionType::Pointer inOption = parser->GetOption( "input-mesh" ); if( inOption && inOption->GetNumberOfParameters() == 2 ) { std::string innm=inOption->GetParameter( 0 ); vtkSmartPointer labReader = vtkSmartPointer::New(); labReader->SetFileName(innm.c_str()); labReader->Update(); labelmesh=vtkSmartPointer ( labReader->GetOutput() ) ; vtkDataArray* labels=labelmesh->GetPointData()->GetArray("Label"); if ( !labels ) { std::cout << " Cannot find vtk Array named 'Label' in " << innm << std::endl; std::cout <<" exiting " << std::endl; exit(1); } innm=inOption->GetParameter( 1 ); vtkSmartPointer fltReader = vtkSmartPointer::New(); fltReader->SetFileName(innm.c_str()); fltReader->Update(); featuremesh=vtkSmartPointer ( fltReader->GetOutput() ) ; vtkDataArray* feats=featuremesh->GetPointData()->GetArray("Feature"); if ( !feats ) { std::cout << " Cannot find vtk Array named 'Feature' in " << innm << std::endl; std::cout <<" continuing " << std::endl; } /** inflation */ if ( inflate_iterations > 0 ) { vtkSmartPointer smoother = vtkSmartPointer::New(); smoother->SetInput(labelmesh); smoother->SetNumberOfIterations( (int) inflate_iterations ); smoother->BoundarySmoothingOn(); smoother->FeatureEdgeSmoothingOff(); smoother->SetFeatureAngle(180.0); smoother->SetEdgeAngle(180.0); smoother->SetPassBand( inflate_param ); // smaller values increase smoothing smoother->NonManifoldSmoothingOn(); smoother->NormalizeCoordinatesOff(); smoother->Update(); inflatedmesh=vtkSmartPointer (smoother->GetOutput()); std::cout << " done smoothing "<< std::endl; flattener->SetSurfaceMesh(inflatedmesh); } else flattener->SetSurfaceMesh(labelmesh); flattener->SetSurfaceFeatureMesh(featuremesh); } bool paramws = parser->template Convert( parser->GetOption( "param-while-searching" )->GetValue() ); flattener->SetParamWhileSearching(paramws); unsigned int labeltoflatten = parser->template Convert( parser->GetOption( "label-to-flatten" )->GetValue() ); flattener->SetLabelToFlatten(labeltoflatten); std::string canonicaldomain=parser->GetOption( "canonical-domain" )->GetValue(); // canonicaldomain=ConvertToLowerCase( canonicaldomain ); std::cout << " you will map label " << labeltoflatten << " to a " << canonicaldomain << std::endl; if (canonicaldomain == std::string("circle") ) flattener->SetMapToCircle(); else if (canonicaldomain == std::string("square") ) flattener->SetMapToSquare(); else { std::cout <<" that domain is not an option -- exiting. " << std::endl; return 1; } std::string boundaryparam=parser->GetOption( "boundary-param" )->GetValue(); // do stuff -- but not implemented yet // flattener->SetDiscBoundaryList(NULL); std::cout << " you will flatten " << labeltoflatten << ". param while searching? " << paramws << std::endl; flattener->SetSigma(1); flattener->SetMaxCost(maxCost); flattener->SetDistanceCostWeight(distCostW); flattener->SetLabelCostWeight(labelCostW); std::cout << " MC " << maxCost << " DW " << distCostW << " LW " << labelCostW << std::endl; flattener->ExtractSurfaceDisc(); std::cout << " begin conformal mapping "; flattener->ConformalMap(); /** * output */ typename itk::ants::CommandLineParser::OptionType::Pointer outputOption = parser->GetOption( "output" ); if( outputOption && outputOption->GetNumberOfValues() > 0 ) { if( outputOption->GetNumberOfParameters() > 0 ) { for ( unsigned int p = 0 ; p < outputOption->GetNumberOfParameters() ; p++ ) { if ( p == 0 ) { vtkPolyDataWriter *writer = vtkPolyDataWriter::New(); writer->SetInput(flattener->m_DiskSurfaceMesh); std::string outnm=outputOption->GetParameter( p ); std::cout << " writing " << outnm << std::endl; writer->SetFileName(outnm.c_str()); writer->SetFileTypeToBinary(); if ( flattener->m_DiskSurfaceMesh ) writer->Update(); } if ( p == 1 ) { vtkPolyDataWriter *writer = vtkPolyDataWriter::New(); writer->SetInput(flattener->m_ExtractedSurfaceMesh); std::string outnm=outputOption->GetParameter( 1 ); std::cout << " writing " << outnm << std::endl; writer->SetFileName(outnm.c_str()); writer->SetFileTypeToBinary(); if ( flattener->m_ExtractedSurfaceMesh ) writer->Update(); } if ( p == 2 && inflatedmesh ) { vtkPolyDataWriter *writer = vtkPolyDataWriter::New(); writer->SetInput(inflatedmesh); std::string outnm=outputOption->GetParameter( 2 ); std::cout << " writing " << outnm << std::endl; writer->SetFileName(outnm.c_str()); writer->SetFileTypeToBinary(); writer->Update(); } } } } return EXIT_SUCCESS; } int main( int argc, char *argv[] ) { if ( argc < 2 ) { std::cout << "Usage: " << argv[0] << " args" << std::endl; std::cout << " try " << argv[0] << " --help or -h " << std::endl; exit( 1 ); } itk::ants::CommandLineParser::Pointer parser = itk::ants::CommandLineParser::New(); parser->SetCommand( argv[0] ); std::string commandDescription = std::string( "A tool for conformal mapping to various canonical coordinate systems: disc, square " ) + std::string( " operates on 3D vtk triangulated meshes.") + std::string(" Open problems include computation of, consistent orientation of and parameterization of the boundary-condition defining loop. Should we use curve matching ? Knot points? Min distortion? " ); parser->SetCommandDescription( commandDescription ); InitializeCommandLineOptions( parser ); parser->Parse( argc, argv ); if( argc < 2 || parser->Convert( parser->GetOption( "help" )->GetValue() ) ) { parser->PrintMenu( std::cout, 5, false ); exit( EXIT_FAILURE ); } else if( parser->Convert( parser->GetOption( 'h' )->GetValue() ) ) { parser->PrintMenu( std::cout, 5, true ); exit( EXIT_FAILURE ); } ANTSConformalMapping<3>( parser ); } ants-1.9.2+svn680.dfsg/Examples/ANTSIntegrateVectorField.cxx000066400000000000000000000374771147325206600235430ustar00rootroot00000000000000 #include "itkVectorIndexSelectionCastImageFilter.h" #include "itkImageRegionIteratorWithIndex.h" #include "vnl/algo/vnl_determinant.h" #include "itkWarpImageFilter.h" #include "itkImageFileWriter.h" // #include "itkScalarImageToHistogramGenerator.h" // #include "itkImageToHistogramGenerator.h" #include "itkRescaleIntensityImageFilter.h" #include "vnl/algo/vnl_determinant.h" #include "itkDiscreteGaussianImageFilter.h" #include "itkVectorLinearInterpolateImageFunction.h" #include "itkBinaryThresholdImageFilter.h" #include "itkDanielssonDistanceMapImageFilter.h" #include "itkGradientRecursiveGaussianImageFilter.h" #include "itkVectorCurvatureAnisotropicDiffusionImageFilter.h" #include "itkBinaryErodeImageFilter.h" #include "itkBinaryDilateImageFilter.h" #include "itkBinaryBallStructuringElement.h" #include "itkLaplacianRecursiveGaussianImageFilter.h" #include "ReadWriteImage.h" #include "itkGradientRecursiveGaussianImageFilter.h" template typename TImage::Pointer BinaryThreshold( typename TImage::PixelType low, typename TImage::PixelType high, typename TImage::PixelType replaceval, typename TImage::Pointer input) { //std::cout << " Binary Thresh " << std::endl; typedef typename TImage::PixelType PixelType; // Begin Threshold Image typedef itk::BinaryThresholdImageFilter InputThresholderType; typename InputThresholderType::Pointer inputThresholder = InputThresholderType::New(); inputThresholder->SetInput( input ); inputThresholder->SetInsideValue( replaceval ); int outval=0; if ((float) replaceval == (float) -1) outval=1; inputThresholder->SetOutsideValue( outval ); if (high < low) high=255; inputThresholder->SetLowerThreshold((PixelType) low ); inputThresholder->SetUpperThreshold((PixelType) high); inputThresholder->Update(); return inputThresholder->GetOutput(); } template typename TImage::Pointer GetVectorComponent(typename TField::Pointer field, unsigned int index) { // Initialize the Moving to the displacement field typedef TField FieldType; typedef TImage ImageType; typename ImageType::Pointer sfield=ImageType::New(); sfield->SetSpacing( field->GetSpacing() ); sfield->SetOrigin( field->GetOrigin() ); sfield->SetDirection( field->GetDirection() ); sfield->SetLargestPossibleRegion(field->GetLargestPossibleRegion() ); sfield->SetRequestedRegion(field->GetRequestedRegion() ); sfield->SetBufferedRegion( field->GetBufferedRegion() ); sfield->Allocate(); typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter( field, field->GetLargestPossibleRegion() ); for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter) { typename TField::PixelType v1=vfIter.Get(); sfield->SetPixel(vfIter.GetIndex(),v1[index]); } return sfield; } template typename TImage::Pointer SmoothImage(typename TImage::Pointer image, float sig) { // find min value typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter(image,image->GetLargestPossibleRegion() ); for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter) { typename TImage::PixelType v1=vfIter.Get(); if (vnl_math_isnan(v1)) vfIter.Set(0); } typedef itk::DiscreteGaussianImageFilter dgf; typename dgf::Pointer filter = dgf::New(); filter->SetVariance(sig); filter->SetUseImageSpacingOn(); filter->SetMaximumError(.01f); filter->SetInput(image); filter->Update(); typename TImage::Pointer out= filter->GetOutput(); return out; } template void SmoothDeformation(typename TImage::Pointer vectorimage, float sig) { typedef itk::Vector VectorType; typedef itk::Image ImageType; typename ImageType::Pointer subimgx=GetVectorComponent(vectorimage,0); subimgx=SmoothImage(subimgx,sig); typename ImageType::Pointer subimgy=GetVectorComponent(vectorimage,1); subimgy=SmoothImage(subimgy,sig); typename ImageType::Pointer subimgz=GetVectorComponent(vectorimage,2); subimgz=SmoothImage(subimgz,sig); typedef itk::ImageRegionIteratorWithIndex IteratorType; IteratorType Iterator( vectorimage, vectorimage->GetLargestPossibleRegion().GetSize() ); Iterator.GoToBegin(); while( !Iterator.IsAtEnd() ) { VectorType vec; vec[0]=subimgx->GetPixel(Iterator.GetIndex()); vec[1]=subimgy->GetPixel(Iterator.GetIndex()); vec[2]=subimgz->GetPixel(Iterator.GetIndex()); Iterator.Set(vec); ++Iterator; } return; } template float IntegrateLength( typename TImage::Pointer gmsurf, typename TImage::Pointer thickimage, typename TImage::IndexType velind, typename TField::Pointer lapgrad, float itime, float starttime, float finishtime, bool timedone, float deltaTime, typename TInterp::Pointer vinterp , typename TImage::SpacingType spacing, float vecsign, float timesign, float gradsign ) { typedef TField TimeVaryingVelocityFieldType; typedef typename TField::PixelType VectorType; typedef itk::ImageRegionIteratorWithIndex FieldIterator; typedef typename TField::IndexType DIndexType; typedef typename TField::PointType DPointType; typedef itk::VectorLinearInterpolateImageFunction DefaultInterpolatorType; VectorType zero; zero.Fill(0); VectorType disp; disp.Fill(0); unsigned int ct=0; DPointType pointIn1; DPointType pointIn2; typename DefaultInterpolatorType::ContinuousIndexType vcontind; DPointType pointIn3; enum { ImageDimension = TImage::ImageDimension }; typedef typename TImage::IndexType IndexType; unsigned int m_NumberOfTimePoints=2; IndexType index; for (unsigned int jj=0; jjGetSpacing()[jj]; } itime=starttime; timedone=false; float totalmag=0; while ( !timedone ) { float scale = 1;//*m_DT[timeind]/m_DS[timeind]; // std::cout << " scale " << scale << std::endl; double itimetn1 = itime - timesign*deltaTime*scale; double itimetn1h = itime - timesign*deltaTime*0.5*scale; if (itimetn1h < 0 ) itimetn1h=0; if (itimetn1h > m_NumberOfTimePoints-1 ) itimetn1h=m_NumberOfTimePoints-1; if (itimetn1 < 0 ) itimetn1=0; if (itimetn1 > m_NumberOfTimePoints-1 ) itimetn1=m_NumberOfTimePoints-1; // first get current position of particle IndexType index; for (unsigned int jj=0; jjGetSpacing()[jj]; } // std::cout << " ind " << index << std::endl; // now index the time varying field at that position. typename DefaultInterpolatorType::OutputType f1; f1.Fill(0); typename DefaultInterpolatorType::OutputType f2; f2.Fill(0); typename DefaultInterpolatorType::OutputType f3; f3.Fill(0); typename DefaultInterpolatorType::OutputType f4; f4.Fill(0); typename DefaultInterpolatorType::ContinuousIndexType Y1; typename DefaultInterpolatorType::ContinuousIndexType Y2; typename DefaultInterpolatorType::ContinuousIndexType Y3; typename DefaultInterpolatorType::ContinuousIndexType Y4; for (unsigned int jj=0; jjGetSpacing()[jj]; Y1[jj]=vcontind[jj]; Y2[jj]=vcontind[jj]; Y3[jj]=vcontind[jj]; Y4[jj]=vcontind[jj]; } //Y1[ImageDimension]=itimetn1; //Y2[ImageDimension]=itimetn1h; //Y3[ImageDimension]=itimetn1h; // Y4[ImageDimension]=itime; f1 = vinterp->EvaluateAtContinuousIndex( Y1 ); for (unsigned int jj=0; jj lapgrad->GetLargestPossibleRegion().GetSize()[jj] -2 ) isinside=false; if (isinside) f2 = vinterp->EvaluateAtContinuousIndex( Y2 ); for (unsigned int jj=0; jj lapgrad->GetLargestPossibleRegion().GetSize()[jj] -2 ) isinside=false; if (isinside) f3 = vinterp->EvaluateAtContinuousIndex( Y3 ); for (unsigned int jj=0; jj lapgrad->GetLargestPossibleRegion().GetSize()[jj] -2 ) isinside=false; if (isinside) f4 = vinterp->EvaluateAtContinuousIndex( Y4 ); for (unsigned int jj=0; jjGetPixel(myind) < 1 ) { timedone=true; } if ( ct > 1000 ) { std::cout << " stopping b/c exceed 1000 points " << voxmag << std::endl; timedone=true;} if ( voxmag < 0.1 ) { timedone=true;} } return totalmag; } template int IntegrateVectorField(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image DeformationFieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; double dT=0.001; float gradstep= 1./dT;//atof(argv[3])*(-1.0); std::string vectorfn = std::string(argv[1]); std::string roifn = std::string(argv[2]); int argct=3; std::string outname=std::string(argv[argct]); argct++; std::string lenoutname=std::string(""); if (argc > argct) lenoutname=std::string(argv[argct]); argct++; if (argc > argct) gradstep*=atof(argv[argct]); argct++; typename ImageType::Pointer ROIimage; ReadImage(ROIimage,roifn.c_str()); typename ImageType::Pointer thickimage; ReadImage(thickimage,roifn.c_str()); thickimage->FillBuffer(0); typename DeformationFieldType::Pointer VECimage; ReadImage(VECimage,vectorfn.c_str()); SpacingType spacing=ROIimage->GetSpacing(); typedef itk::ImageRegionIteratorWithIndex IteratorType; IteratorType Iterator( ROIimage, ROIimage->GetLargestPossibleRegion().GetSize() ); double timezero=0; //1 typename ImageType::SizeType s= ROIimage->GetLargestPossibleRegion().GetSize(); double timeone=1;//(s[ImageDimension]-1-timezero); float starttime=timezero;//timezero; float finishtime=timeone;//s[ImageDimension]-1;//timeone; typename DeformationFieldType::IndexType velind; float timesign=1.0; if (starttime > finishtime ) timesign= -1.0; typedef DeformationFieldType TimeVaryingVelocityFieldType; typedef itk::ImageRegionIteratorWithIndex FieldIterator; typedef typename DeformationFieldType::IndexType DIndexType; typedef typename DeformationFieldType::PointType DPointType; typedef typename TimeVaryingVelocityFieldType::IndexType VIndexType; typedef typename TimeVaryingVelocityFieldType::PointType VPointType; typedef itk::VectorLinearInterpolateImageFunction DefaultInterpolatorType; typedef itk::VectorLinearInterpolateImageFunction DefaultInterpolatorType2; typename DefaultInterpolatorType::Pointer vinterp = DefaultInterpolatorType::New(); typedef itk::LinearInterpolateImageFunction ScalarInterpolatorType; VectorType zero; zero.Fill(0); DPointType pointIn1; DPointType pointIn2; typename DefaultInterpolatorType::ContinuousIndexType vcontind; DPointType pointIn3; typedef itk::ImageRegionIteratorWithIndex VIteratorType; VIteratorType VIterator( VECimage, VECimage->GetLargestPossibleRegion().GetSize() ); VIterator.GoToBegin(); while( !VIterator.IsAtEnd() ) { VectorType vec=VIterator.Get(); float mag=0; for (unsigned int qq=0; qq 0) vec=vec/mag; VIterator.Set(vec*gradstep); ++VIterator; } Iterator.GoToBegin(); while( !Iterator.IsAtEnd() ) { velind=Iterator.GetIndex(); float itime = starttime; bool timedone = false; VectorType disp; disp.Fill(0.0); double deltaTime=dT,vecsign=1.0; float gradsign=1.0; if ( ROIimage->GetPixel(velind) == 2 ) { vinterp->SetInputImage(VECimage); gradsign=-1.0; vecsign=-1.0; float len1=IntegrateLength (ROIimage, thickimage, velind, VECimage, itime, starttime, finishtime, timedone, deltaTime, vinterp, spacing,vecsign, gradsign, timesign); gradsign=1.0; vecsign=1; float len2=IntegrateLength (ROIimage, thickimage, velind, VECimage, itime, starttime, finishtime, timedone, deltaTime, vinterp, spacing,vecsign, gradsign, timesign ); float totalength=len1+len2; thickimage->SetPixel(velind,totalength); if ( (totalength)> 0 ) std::cout << " len1 " << len1 << " len2 " << len2 << " ind " << velind << std::endl; } ++Iterator; } WriteImage(thickimage,lenoutname.c_str()); return 0; } int main(int argc, char *argv[]) { if ( argc < 4) { std::cout << "Usage: " << argv[0] << " VecImageIN.nii.gz ROIMaskIN.nii.gz FibersOUT.vtk LengthImageOUT.nii.gz " << std::endl; std::cout << " The vector field should have vectors as voxels , the ROI is an integer image, fibers out will be vtk text files .... " << std::endl; std::cout << " ROI-Mask controls where the integration is performed and the start point region ... " << std::endl; std::cout << " e.g. the brain will have value 1 , the ROI has value 2 , then all starting seed points " << std::endl; std::cout << " for the integration will start in the region labeled 2 and be constrained to the region labeled 1. " << std::endl; return 1; } std::string ifn = std::string(argv[1]); itk::ImageIOBase::Pointer imageIO = itk::ImageIOFactory::CreateImageIO(ifn.c_str(), itk::ImageIOFactory::ReadMode); imageIO->SetFileName(ifn.c_str()); imageIO->ReadImageInformation(); unsigned int dim = imageIO->GetNumberOfDimensions(); switch ( dim ) { case 2: IntegrateVectorField<2>(argc,argv); break; case 3: IntegrateVectorField<3>(argc,argv); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } return EXIT_SUCCESS; return 1; } ants-1.9.2+svn680.dfsg/Examples/ANTSJacobian.cxx000066400000000000000000000354701147325206600211670ustar00rootroot00000000000000 //#include "DoSomethingToImage.cxx" #include "itkVectorIndexSelectionCastImageFilter.h" #include "itkImageRegionIteratorWithIndex.h" #include "vnl/algo/vnl_determinant.h" #include "ReadWriteImage.h" #include "vnl/algo/vnl_determinant.h" #include "itkDiscreteGaussianImageFilter.h" #include "itkGradientRecursiveGaussianImageFilter.h" #include "itkVectorCurvatureAnisotropicDiffusionImageFilter.h" #include "itkVectorImageFileReader.h" #include "itkMatrixOffsetTransformBase.h" #include "itkWarpImageMultiTransformFilter.h" template typename TImage::Pointer VectorAniDiff(typename TImage::Pointer img, unsigned int iters) { double timeStep = 0.065; typedef TImage VectorImageType; typedef itk::VectorCurvatureAnisotropicDiffusionImageFilter< VectorImageType, VectorImageType > FilterType; typename FilterType::Pointer filter = FilterType::New(); filter->SetInput( img ); filter->SetNumberOfIterations( iters ); filter->SetTimeStep( timeStep ); filter->SetConductanceParameter(1.0); filter->Update(); // Software Guide : EndCodeSnippet return filter->GetOutput(); } template typename TImage::Pointer GenerateGridImage(TImage* img, unsigned int gridsize) { typedef TImage ImageType; enum { ImageDimension = TImage::ImageDimension }; itk::ImageRegionIteratorWithIndex wimIter( img, img->GetLargestPossibleRegion() ); wimIter.GoToBegin(); for( ; !wimIter.IsAtEnd(); ++wimIter ) wimIter.Set(2); wimIter.GoToBegin(); for( ; !wimIter.IsAtEnd(); ++wimIter ) { typename ImageType::IndexType ind=wimIter.GetIndex(); for (int i=0; i<2; i++) { if (ind[i] % gridsize == 0) wimIter.Set(0); // if (ind[i] % (gridsize+1) == 0) wimIter.Set(0);// tartan } } wimIter.GoToBegin(); for( ; !wimIter.IsAtEnd(); ++wimIter ) { typename ImageType::IndexType ind=wimIter.GetIndex(); typename ImageType::IndexType ind2=wimIter.GetIndex(); for (int i=0; i<2; i++) { ind2[i]=ind[i]-1; if (ind2[i] < 0) ind2[i]=0; } for (int i=0; i<2; i++) { //this creates a 3-d effect //if (ind[i] % gridsize == 0) img->SetPixel(ind2,2); //this gives double thickness if (ind[i] % gridsize == 0) img->SetPixel(ind2,0); } } return img; } template typename ImageType::Pointer ReadAnImage(char* fn) { // Read the image files begin typedef itk::ImageFileReader< ImageType > FileSourceType; typename FileSourceType::Pointer reffilter = FileSourceType::New(); reffilter->SetFileName( fn ); try { reffilter->Update(); } catch( ... ) { return NULL; } return reffilter->GetOutput(); } template typename TDeformationField::PixelType TransformVector(TDeformationField* field, typename TImage::IndexType index ) { enum { ImageDimension = TImage::ImageDimension }; typename TDeformationField::PixelType vec=field->GetPixel(index); typename TDeformationField::PixelType newvec; newvec.Fill(0); for (unsigned int row=0; rowGetDirection()[row][col]; return newvec; } template void ComputeJacobian(TDeformationField* field,char* fnm, char* maskfn, bool uselog=false, bool norm=false, bool use2ndorder=false) { typedef TImage ImageType; typedef TDeformationField FieldType; enum { ImageDimension = TImage::ImageDimension }; typedef itk::Image FloatImageType; typename FloatImageType::RegionType m_JacobianRegion; typename FloatImageType::Pointer mask=NULL; mask=ReadAnImage(maskfn); if (!field) { return; } typename TImage::SizeType s= field->GetLargestPossibleRegion().GetSize(); typename TImage::SpacingType sp= field->GetSpacing(); typename FloatImageType::Pointer m_FloatImage=NULL; m_FloatImage = FloatImageType::New(); m_FloatImage->SetLargestPossibleRegion( field->GetLargestPossibleRegion() ); m_FloatImage->SetBufferedRegion( field->GetLargestPossibleRegion().GetSize() ); m_FloatImage->SetSpacing(field->GetSpacing()); m_FloatImage->SetDirection( field->GetDirection() ); m_FloatImage->SetOrigin(field->GetOrigin()); m_FloatImage->Allocate(); m_FloatImage->FillBuffer(0); typename ImageType::Pointer grid = GenerateGridImage(m_FloatImage,7); if (grid) { typedef itk::MatrixOffsetTransformBase< double, ImageDimension, ImageDimension > TransformType; typedef itk::WarpImageMultiTransformFilter WarperType; typename WarperType::Pointer warper = WarperType::New(); warper->SetInput(grid); warper->SetEdgePaddingValue( 0); warper->SetSmoothScale(1); warper->PushBackDeformationFieldTransform(field); warper->SetOutputOrigin(field->GetOrigin()); warper->SetOutputSize(field->GetLargestPossibleRegion().GetSize()); warper->SetOutputSpacing(field->GetSpacing()); warper->SetOutputDirection(field->GetDirection()); warper->Update(); grid=warper->GetOutput(); typedef itk::ImageFileWriter writertype; typename writertype::Pointer writer = writertype::New(); std::string fng=std::string(fnm)+"grid.nii"; writer->SetFileName(fng.c_str()); writer->SetInput(grid); writer->Write(); std::cout << " Grid done "; } typename FloatImageType::SizeType m_FieldSize = field->GetLargestPossibleRegion().GetSize(); typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator wimIter( m_FloatImage, m_FloatImage->GetLargestPossibleRegion() ); wimIter.GoToBegin(); for( ; !wimIter.IsAtEnd(); ++wimIter ) wimIter.Set(1.0); typedef vnl_matrix MatrixType; MatrixType jMatrix,idMatrix,avgMatrix; jMatrix.set_size(ImageDimension,ImageDimension); avgMatrix.set_size(ImageDimension,ImageDimension); avgMatrix.fill(0); itk::ImageRegionIteratorWithIndex m_FieldIter( field, field->GetLargestPossibleRegion() ); typename TImage::IndexType rindex; typename TImage::IndexType ddrindex; typename TImage::IndexType ddlindex; typename TImage::IndexType difIndex[ImageDimension][2]; double det=0.0; unsigned int posoff=1; float difspace=1.0; float space=1.0; if (posoff == 0) difspace=1.0; typedef itk::Vector VectorType; typename FieldType::PixelType dPix; typename FieldType::PixelType lpix; typename FieldType::PixelType llpix; typename FieldType::PixelType rpix; typename FieldType::PixelType rrpix; typename FieldType::PixelType cpix; float volumeelt=1.0; for (int j=0;j(field,rindex); for(unsigned int row=0; row< ImageDimension;row++) { difIndex[row][0]=rindex; difIndex[row][1]=rindex; ddrindex=rindex; ddlindex=rindex; if ((unsigned int) rindex[row] < (unsigned int) m_FieldSize[row]-2) { difIndex[row][0][row]=rindex[row]+posoff; ddrindex[row]=rindex[row]+posoff*2; } if (rindex[row] > 1 ) { difIndex[row][1][row]=rindex[row]-1; ddlindex[row]=rindex[row]-2; } float h=1; space=1.0; // should use image spacing here? rpix = TransformVector(field,difIndex[row][1]); rpix = rpix*h+cpix*(1.-h); lpix = TransformVector(field,difIndex[row][0]); lpix = lpix*h+cpix*(1.-h); // dPix = ( rpix - lpix)*(1.0)/(2.0); rrpix = TransformVector(field,ddrindex); rrpix = rrpix*h+rpix*(1.-h); llpix = TransformVector(field,ddlindex); llpix = llpix*h+lpix*(1.-h); dPix=( lpix*(-8.0) + rpix*8.0 - rrpix + llpix )*(-1.0)*space/(12.0); //4th order centered difference if ( use2ndorder) dPix=( lpix - rpix )*(1.0)*space/(2.0*h); //4th order centered difference for(unsigned int col=0; col< ImageDimension;col++){ float val; if (row == col) val=dPix[col]/sp[col]+1.0; else val = dPix[col]/sp[col]; // std::cout << " row " << row << " col " << col << " val " << val << std::endl; jMatrix.put(col,row,val); avgMatrix.put(col,row,avgMatrix.get(col,row)+val); } } //the determinant of the jacobian matrix // std::cout << " get det " << std::endl; det = vnl_determinant(jMatrix); // float prodval = m_FloatImage->GetPixel(rindex); if (det < 0.0) det = 0; m_FloatImage->SetPixel(rindex, det ); //totaljac+=det; }//oktosample if } std::cout <<" avg Mat " << avgMatrix / (float)ct << std::endl; if (norm && mask) { std::cout <<" using mask && normalizing " << std::endl; /* typedef itk::DiscreteGaussianImageFilter dgf; float sig=2.0; typename FloatImageType::Pointer temp; { typename dgf::Pointer filter = dgf::New(); filter->SetVariance(sig); filter->SetUseImageSpacingOff(); filter->SetMaximumError(.01f); filter->SetInput(m_FloatImage); filter->Update(); // m_FloatImage=filter->GetOutput(); temp=filter->GetOutput(); } { typename dgf::Pointer filter = dgf::New(); filter->SetVariance(sig); filter->SetUseImageSpacingOff(); filter->SetMaximumError(.01f); filter->SetInput(temp); filter->Update(); // m_FloatImage=filter->GetOutput(); temp=filter->GetOutput(); } */ double total = 0.0; unsigned long ct = 0; for( m_FieldIter.GoToBegin() ; !m_FieldIter.IsAtEnd(); ++m_FieldIter ) { rindex=m_FieldIter.GetIndex(); if (mask->GetPixel(rindex) > 0 ) { total+=m_FloatImage->GetPixel(rindex); ct++; } else m_FloatImage->SetPixel(rindex,0); } total /= (double) ct; for( m_FieldIter.GoToBegin() ; !m_FieldIter.IsAtEnd(); ++m_FieldIter ) { rindex=m_FieldIter.GetIndex(); double val = m_FloatImage->GetPixel(rindex)/total; if (mask->GetPixel(rindex) > 0 ) m_FloatImage->SetPixel(rindex,val); else m_FloatImage->SetPixel(rindex,0); } } for( m_FieldIter.GoToBegin() ; !m_FieldIter.IsAtEnd(); ++m_FieldIter ) { rindex=m_FieldIter.GetIndex(); double val = m_FloatImage->GetPixel(rindex); if (uselog && val > 0) val=log(val); else if (uselog && val < 0) val = log(0.01); if (uselog ) m_FloatImage->SetPixel(rindex,val); } typedef itk::ImageFileWriter writertype; typename writertype::Pointer writer = writertype::New(); std::string fn=std::string(fnm)+"jacobian.nii"; if (uselog) fn=std::string(fnm)+"logjacobian.nii"; writer->SetFileName(fn.c_str()); writer->SetInput(m_FloatImage); writer->Write(); return; } template int Jacobian(int argc, char *argv[]) { // std::cout << " enter " << ImageDimension << std::endl; if ( argc < 3 ) { std::cout << "Usage: Jacobian gWarp outfile uselog maskfn normbytotalbool " << std::endl; return 1; } typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::LinearInterpolateImageFunction InterpolatorType; typedef itk::VectorImageFileReader ReaderType; //std::cout << "read warp " << std::string(argv[1]) << std::endl; typename ReaderType::Pointer reader = ReaderType::New(); reader->SetFileName( argv[1] ); reader->SetUseAvantsNamingConvention( true ); reader->Update(); typename FieldType::Pointer gWarp=reader->GetOutput(); // //std::cout << "read warp 2 " << std::endl; // typename FieldType::Pointer gWarp = ReadWarpFromFile(argv[1],"vec.nii"); // here hWarp is changed in place to be fWarp // Jacobian(gWarp,argv[2]); // std::cout << " vecanidiff " << std::endl; // gWarp = VectorAniDiff(gWarp , atoi(argv[3]) ); // std::cout << " vecanidiffdone " << std::endl; bool uselog=false; if (argc > 3) uselog=(bool)atoi(argv[3]); bool norm=false; if (argc > 5) norm=(bool)atoi(argv[5]); bool use2ndorder=true; if (argc > 6) use2ndorder=(bool)atoi(argv[6]); // std::cout << " name "<< argv[2] << " mask " << argv[4] << " norm " << norm << " Log " << uselog << std::endl; ComputeJacobian(gWarp,argv[2],argv[4],uselog,norm,use2ndorder); // DiffeomorphicJacobian(gWarp,1,argv[2]); // if (argc > 3) DiffeomorphicMetric(gWarp,argv[2]); return 0; } int main(int argc, char *argv[]) { if ( argc < 3 ) { std::cout << "Usage: " << argv[0] << " ImageDim gWarp outfile uselog maskfn normbytotalbool use-2nd-order-central-difference-boolean " << std::endl; std::cout <<" for example " << std::endl << " ANTSJacobian 3 myWarp.nii Output 1 templatebrainmask.nii 1 " << std::endl; std::cout << " the last 1 normalizes the jacobian by the total in the mask. use this to adjust for head size. " << std::endl; return 1; } switch( atoi( argv[1] ) ) { case 2: Jacobian<2>(argc-1,argv+1); break; case 3: Jacobian<3>(argc-1,argv+1); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } return EXIT_SUCCESS; return 1; } ants-1.9.2+svn680.dfsg/Examples/ANTSUseDeformationFieldToGetAffineTransform.cxx000066400000000000000000000361201147325206600273120ustar00rootroot00000000000000/** ANTS Landmarks used to initialize an affine transform ... */ #include #include #include #include "itkLandmarkBasedTransformInitializer.h" #include "itkImage.h" #include "itkImageIOBase.h" #include "itkImageIOFactory.h" #include #include #include "ReadWriteImage.h" #include "itkTransformFileWriter.h" #include "itkVectorImageFileReader.h" // #include "ANTS_affine_registration2.h" #include // #include #include "vnl/algo/vnl_qr.h" template void DumpTransformForANTS3D(const TransformAPointer &transform, StringType &ANTS_prefix); template void GetAffineTransformFromTwoPointSets3D(PointContainerType &fixedLandmarks, PointContainerType &movingLandmarks, TransformPointerType &transform); template void GetRigidTransformFromTwoPointSets3D(PointContainerType &fixedLandmarks, PointContainerType &movingLandmarks, TransformPointerType &transform); template void FetchLandmarkMappingFromDeformationField(const StringType &deformation_field_file_name, float load_ratio, PointContainerType &fixedLandmarks, PointContainerType &movingLandmarks, itk::Image::Pointer maskimg); // // The test specifies a bunch of fixed and moving landmarks and test if the // fixed landmarks after transform by the computed transform coincides // with the moving landmarks.... int DeformationFieldBasedTransformInitializer3D(int argc, char * argv[]) { const unsigned int Dim = 3; typedef itk::Point PointType; typedef itk::Image ImageType; typedef std::vector PointContainerType; const char *deformation_field_file_name = argv[1]; float load_ratio = atof(argv[2]); bool bRigid = (strcmp(argv[3], "rigid")==0); std::string ANTS_prefix(argv[4]); std::string maskfn=std::string(""); if (argc > 5 ) maskfn=std::string(argv[5]); std::cout << " mask " << maskfn << std::endl; // input PointContainerType fixedLandmarks, movingLandmarks; // output typedef itk::MatrixOffsetTransformBase< double, 3, 3> AffineTransformType; AffineTransformType::Pointer aff = AffineTransformType::New(); ImageType::Pointer maskimg=NULL; if ( maskfn.length() > 4 ) ReadImage(maskimg,maskfn.c_str()); FetchLandmarkMappingFromDeformationField(deformation_field_file_name, load_ratio, fixedLandmarks, movingLandmarks, maskimg); if (bRigid) GetRigidTransformFromTwoPointSets3D(fixedLandmarks, movingLandmarks, aff); else GetAffineTransformFromTwoPointSets3D(fixedLandmarks, movingLandmarks, aff); std::cout << "affine:" << aff; DumpTransformForANTS3D(aff, ANTS_prefix); return EXIT_SUCCESS; // initializer->SetFixedLandmarks(fixedLandmarks); // initializer->SetMovingLandmarks(movingLandmarks); // initializer->SetTransform( transform ); // initializer->InitializeTransform(); // // transform->Print(std::cout); // // // transform the transform to ANTS format // std::string ANTS_prefix(argv[4]); // // // typedef itk::MatrixOffsetTransformBase< double, 3, 3> AffineTransformType; // AffineTransformType::Pointer aff = AffineTransformType::New(); // GetAffineTransformFromTwoPointSets3D(fixedLandmarks, movingLandmarks, aff); // std::cout << "affine:" << aff; // // // if (bRigid) // DumpTransformForANTS3D(transform, ANTS_prefix); // else // DumpTransformForANTS3D(aff, ANTS_prefix); // // // return EXIT_SUCCESS; } template void GetRigidTransformFromTwoPointSets3D(PointContainerType &fixedLandmarks, PointContainerType &movingLandmarks, TransformPointerType &aff){ // Set the transform type.. typedef itk::VersorRigid3DTransform< double > TransformType; TransformType::Pointer transform = TransformType::New(); typedef float PixelType; const unsigned int Dimension = 3; typedef itk::Image< PixelType, Dimension > FixedImageType; typedef itk::Image< PixelType, Dimension > MovingImageType; typedef itk::Image< PixelType, Dimension > ImageType; typedef itk::LandmarkBasedTransformInitializer< TransformType, FixedImageType, MovingImageType > TransformInitializerType; TransformInitializerType::Pointer initializer = TransformInitializerType::New(); initializer->SetFixedLandmarks(fixedLandmarks); initializer->SetMovingLandmarks(movingLandmarks); initializer->SetTransform( transform ); initializer->InitializeTransform(); std::cout << "rigid: " << transform << std::endl; // ANTS transform file type // typedef itk::MatrixOffsetTransformBase< double, Dimension, Dimension > AffineTransformType; // typename AffineTransformType::Pointer aff = AffineTransformType::New(); PostConversionInAffine(transform, aff); } ////////// // x: fixedLandmarks // y: movingLandmarks // (A,t,c) : affine transform, A:3*3, t: 3*1 c: 3*1 (c is the center of all points in x) // y-c = A*(x-c) + t; // steps: // 1. c = average of points of x // 2. let y1 = y-c; x1 = x - c; x11 = [x1; 1 ... 1] // extend x11 // 3. minimize (y1-A1*x11)^2, A1 is a 3*4 matrix // 4. A = A1(1:3, 1:3), t = A1(1:3, 4); // step 3: // A11 = (y1*x11')*(x11*x11')^(-1) // type info: // assume PointContainerType is std::vector // assume TrnasformPointerType is MatrixOffsetTransformBase template void GetAffineTransformFromTwoPointSets3D(PointContainerType &fixedLandmarks, PointContainerType &movingLandmarks, TransformPointerType &transform){ const int Dim=3; int n = fixedLandmarks.size(); vnl_matrix y(Dim, n), x(Dim, n); for(int i=0; i c(Dim); for(int j=0; j y1(Dim, n), x11(Dim+1, n); for(int i=0; i x_tmp(Dim), x1_tmp(Dim+1); x_tmp = x.get_column(i)-c; for(int j=0; j A11(Dim, Dim+1); vnl_matrix x11t = x11.transpose(); //vnl_matrix_inverse tmp(x11 * x11t); // BA -- removed this -- not used? vnl_svd qr( x11t ); // can use vnl_qr A11 = qr.inverse() * (y1.transpose()); A11 = A11.transpose(); vnl_matrix A(Dim,Dim); A = A11.extract(Dim, Dim, 0, 0); // std::cout << "y=" << y << std::endl; // std::cout << "x=" << x << std::endl; // // std::cout << "y1=" << y1 << std::endl; // std::cout << "x11=" << x11 << std::endl; std::cout << "A11=" << A11 << std::endl; vnl_vector t = A11.get_column(Dim); typedef typename TransformPointerType::ObjectType TransformType; typedef typename TransformType::InputPointType PointType; typedef typename TransformType::OutputVectorType VectorType; typedef typename TransformType::MatrixType MatrixType; PointType center; for(int i=0;iSetCenter(center); transform->SetTranslation(translation); transform->SetMatrix(matrix); return; } //////////////////////////////////////////////////////////////////////// //Stripped from ANTS_affine_registration2.h template void WriteAffineTransformFile(TransformPointerType &transform, StringType filename){ itk::TransformFileWriter::Pointer transform_writer; transform_writer = itk::TransformFileWriter::New(); transform_writer->SetFileName(filename); transform_writer->SetInput(transform); try{ transform_writer->Update(); } catch( itk::ExceptionObject &err){ std::cout << "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!" << std::endl <<"Exception in writing tranform file: " << std::endl << filename << std::endl; return; } return; } //////////////////////////////////////////////////////////////////////// //Stripped from ANTS_affine_registration2.h template inline void PostConversionInAffine(RunningAffineTransformPointerType& transform_running, AffineTransformPointerType &transform){ typedef typename RunningAffineTransformPointerType::ObjectType RunningAffineTransformType; typedef typename AffineTransformPointerType::ObjectType AffineTransformType; transform->SetCenter(*(reinterpret_cast (const_cast (&(transform_running->GetCenter()))))); transform->SetTranslation(*(reinterpret_cast (const_cast (&(transform_running->GetTranslation()))))); transform->SetMatrix(*(reinterpret_cast (const_cast (&(transform_running->GetMatrix()))))); // std::cout << "transform_running" << transform_running << std::endl; // std::cout << "transform" << transform << std::endl; } template void DumpTransformForANTS3D(const TransformAPointer &transform, StringType &ANTS_prefix){ const int ImageDimension = 3; // ANTS transform file type typedef itk::MatrixOffsetTransformBase< double, ImageDimension, ImageDimension > AffineTransformType; AffineTransformType::Pointer transform_ANTS = AffineTransformType::New(); // typedef TransformAPointer::ObjectType TransformA; // std::cout << " writing " << ANTS_prefix << " affine " << std::endl; // std::string ANTS_affine_filename = ANTS_prefix + std::string( "Affine.txt" ); std::string ANTS_affine_filename = ANTS_prefix; std::cout << " writing ANTS affine file:" << ANTS_affine_filename << std::endl; PostConversionInAffine(transform, transform_ANTS); WriteAffineTransformFile(transform_ANTS, ANTS_affine_filename); } int DeformationFieldBasedTransformInitializer2D(int, char * []) { std::cout << " not implemented " << std::endl; return 1; /* typedef float PixelType; const unsigned int Dimension = 2; typedef itk::Image< PixelType, Dimension > FixedImageType; typedef itk::Image< PixelType, Dimension > MovingImageType; typedef itk::Image< PixelType, Dimension > ImageType; typename FixedImageType::Pointer fixedimage; typename MovingImageType::Pointer movingimage; ReadImage(fixedimage,argv[1]); ReadImage(movingimage,argv[2]); // Set the transform type.. typedef itk::Rigid2DTransform< double > TransformType; */ return EXIT_SUCCESS; } template void FetchLandmarkMappingFromDeformationField(const StringType &deformation_field_file_name, float load_ratio, PointContainerType &fixedLandmarks, PointContainerType &movingLandmarks,itk::Image::Pointer maskimg){ const unsigned int ImageDimension = 3; typedef typename PointContainerType::value_type PointType; typedef itk::Image ImageType; typedef itk::Vector VectorType; typedef itk::Image DeformationFieldType; typedef itk::VectorImageFileReader FieldReaderType; typename FieldReaderType::Pointer field_reader = FieldReaderType::New(); field_reader->SetFileName( deformation_field_file_name ); field_reader->Update(); typename DeformationFieldType::Pointer field = field_reader->GetOutput(); fixedLandmarks.clear(); movingLandmarks.clear(); unsigned int nb_voxels = 1; itk::Size field_size = field->GetLargestPossibleRegion().GetSize(); for(unsigned int i=0; i FieldIteratorType; FieldIteratorType it(field, field->GetLargestPossibleRegion()); srand ( time(NULL) ); it.GoToBegin(); unsigned int cnt = 0; for(; (!it.IsAtEnd()) & (cnt < nb_try_to_load); ++it){ bool getpoint=true; if (maskimg) if ( maskimg->GetPixel( it.GetIndex() ) < 0.5 ) getpoint=false; if (getpoint) { if (rand() % 32767 > load_ratio * 32767) continue; PointType point1, point2; // get the output image index typename DeformationFieldType::IndexType index = it.GetIndex(); field->TransformIndexToPhysicalPoint(index, point1 ); VectorType displacement = field->GetPixel(index); for(unsigned int j = 0; jSetFileName(fn.c_str()); // imageIO->ReadImageInformation(); int dim = 3; // switch ( imageIO->GetNumberOfDimensions() ) switch ( dim ) { case 2: DeformationFieldBasedTransformInitializer2D(argc,argv); break; case 3: DeformationFieldBasedTransformInitializer3D(argc,argv); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } return 0; } ants-1.9.2+svn680.dfsg/Examples/ANTSUseLandmarkImagesToGetAffineTransform.cxx000066400000000000000000000353461147325206600267670ustar00rootroot00000000000000/** ANTS Landmarks used to initialize an affine transform ... */ #include "itkLandmarkBasedTransformInitializer.h" #include "itkImage.h" #include "itkImageIOBase.h" #include "itkImageIOFactory.h" #include #include #include "ReadWriteImage.h" #include "itkTransformFileWriter.h" // #include "ANTS_affine_registration2.h" #include // #include #include "vnl/algo/vnl_qr.h" template void DumpTransformForANTS3D(const TransformAPointer &transform, StringType &ANTS_prefix); template void GetAffineTransformFromTwoPointSets3D(PointContainerType &fixedLandmarks, PointContainerType &movingLandmarks, TransformPointerType &transform); // // The test specifies a bunch of fixed and moving landmarks and test if the // fixed landmarks after transform by the computed transform coincides // with the moving landmarks.... int LandmarkBasedTransformInitializer3D(int, char * argv[]) { typedef float PixelType; const unsigned int Dimension = 3; typedef itk::Image< PixelType, Dimension > FixedImageType; typedef itk::Image< PixelType, Dimension > MovingImageType; typedef itk::Image< PixelType, Dimension > ImageType; typedef itk::ImageRegionIteratorWithIndex Iterator; ImageType::Pointer fixedimage; ImageType::Pointer movingimage; ReadImage(fixedimage,argv[1]); ReadImage(movingimage,argv[2]); bool bRigid = (strcmp(argv[3], "rigid")==0); /** get all of the relevant labels in the fixed image and moving image */ typedef std::vector LabelSetType; LabelSetType myFixLabelSet; LabelSetType myMovLabelSet; /** count the labels in the image */ Iterator It ( fixedimage,fixedimage->GetLargestPossibleRegion() ); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { PixelType label = It.Get(); if( fabs(label) > 0 ) { if( find( myFixLabelSet.begin(), myFixLabelSet.end(), label ) == myFixLabelSet.end() ) { // std::cout <<" f-label " << label << std::endl; myFixLabelSet.push_back( label ); } } } Iterator ItM ( movingimage,movingimage->GetLargestPossibleRegion() ); for( ItM.GoToBegin(); !ItM.IsAtEnd(); ++ItM ) { PixelType label = ItM.Get(); if( fabs(label) > 0 ) { if( find( myMovLabelSet.begin(), myMovLabelSet.end(), label ) == myMovLabelSet.end() ) { // std::cout <<" m-label " << label << std::endl; myMovLabelSet.push_back( label ); } } } std::sort(myFixLabelSet.begin(),myFixLabelSet.end()); std::sort(myMovLabelSet.begin(),myMovLabelSet.end()); LabelSetType::const_iterator fit; LabelSetType::const_iterator mit=myMovLabelSet.begin(); for( fit = myFixLabelSet.begin(); fit != myFixLabelSet.end(); ++fit ) { float fixlabel= *fit ; float movlabel= *mit ; std::cout << " fix-label " << fixlabel << " movlabel " << movlabel << std::endl; if ( movlabel != fixlabel ) { std::cout <<" labels do not match -- exiting " << std::endl; exit(1); } ++mit; } // Set the transform type.. typedef itk::VersorRigid3DTransform< double > TransformType; TransformType::Pointer transform = TransformType::New(); typedef itk::LandmarkBasedTransformInitializer< TransformType, FixedImageType, MovingImageType > TransformInitializerType; TransformInitializerType::Pointer initializer = TransformInitializerType::New(); // Set fixed and moving landmarks TransformInitializerType::LandmarkPointContainer fixedLandmarks; TransformInitializerType::LandmarkPointContainer movingLandmarks; TransformInitializerType::LandmarkPointType point; TransformInitializerType::LandmarkPointType tmp; // compute the CoM's of all the landmarks ImageType::SpacingType spacing=fixedimage->GetSpacing(); for( fit = myFixLabelSet.begin(); fit != myFixLabelSet.end(); ++fit ) { float currentlabel= *fit ; float totalct=0; TransformInitializerType::LandmarkPointType myCenterOfMass; myCenterOfMass.Fill(0); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { PixelType label = It.Get(); if( label == currentlabel ) { totalct++; // compute center of mass ImageType::PointType point; fixedimage->TransformIndexToPhysicalPoint(It.GetIndex(), point); for (unsigned int i=0; iGetSpacing(); for( mit = myMovLabelSet.begin(); mit != myMovLabelSet.end(); ++mit ) { float currentlabel= *mit ; float totalct=0; TransformInitializerType::LandmarkPointType myCenterOfMass; myCenterOfMass.Fill(0); for( ItM.GoToBegin(); !ItM.IsAtEnd(); ++ItM ) { PixelType label = ItM.Get(); if( label == currentlabel ) { totalct++; // compute center of mass ImageType::PointType point; movingimage->TransformIndexToPhysicalPoint(ItM.GetIndex(), point); for (unsigned int i=0; iSetFixedLandmarks(fixedLandmarks); initializer->SetMovingLandmarks(movingLandmarks); // initializer->SetFixedImage( fixedimage ); // initializer->SetMovingImage( movingimage ); initializer->SetTransform( transform ); initializer->InitializeTransform(); transform->Print(std::cout); // to transform a point // transform->TransformPoint( *fitr ) << std::endl; // transform the transform to ANTS format std::string ANTS_prefix(argv[4]); typedef itk::MatrixOffsetTransformBase< double, 3, 3> AffineTransformType; AffineTransformType::Pointer aff = AffineTransformType::New(); GetAffineTransformFromTwoPointSets3D(fixedLandmarks, movingLandmarks, aff); std::cout << "affine:" << aff; if (bRigid) DumpTransformForANTS3D(transform, ANTS_prefix); else DumpTransformForANTS3D(aff, ANTS_prefix); return EXIT_SUCCESS; } ////////// // x: fixedLandmarks // y: movingLandmarks // (A,t,c) : affine transform, A:3*3, t: 3*1 c: 3*1 (c is the center of all points in x) // y-c = A*(x-c) + t; // steps: // 1. c = average of points of x // 2. let y1 = y-c; x1 = x - c; x11 = [x1; 1 ... 1] // extend x11 // 3. minimize (y1-A1*x11)^2, A1 is a 3*4 matrix // 4. A = A1(1:3, 1:3), t = A1(1:3, 4); // step 3: // A11 = (y1*x11')*(x11*x11')^(-1) // type info: // assume PointContainerType is std::vector // assume TrnasformPointerType is MatrixOffsetTransformBase template void GetAffineTransformFromTwoPointSets3D(PointContainerType &fixedLandmarks, PointContainerType &movingLandmarks, TransformPointerType &transform){ const int Dim=3; int n = fixedLandmarks.size(); vnl_matrix y(Dim, n), x(Dim, n); for(int i=0; i c(Dim); for(int j=0; j y1(Dim, n), x11(Dim+1, n); for(int i=0; i x_tmp(Dim), x1_tmp(Dim+1); x_tmp = x.get_column(i)-c; for(int j=0; j A11(Dim, Dim+1); vnl_matrix x11t = x11.transpose(); //vnl_matrix_inverse tmp(x11 * x11t); // BA -- removed this -- not used? vnl_svd qr( x11t ); // can use vnl_qr A11 = qr.inverse() * (y1.transpose()); A11 = A11.transpose(); vnl_matrix A(Dim,Dim); A = A11.extract(Dim, Dim, 0, 0); std::cout << "y=" << y << std::endl; std::cout << "x=" << x << std::endl; std::cout << "y1=" << y1 << std::endl; std::cout << "x11=" << x11 << std::endl; std::cout << "A11=" << A11 << std::endl; vnl_vector t = A11.get_column(Dim); typedef typename TransformPointerType::ObjectType TransformType; typedef typename TransformType::InputPointType PointType; typedef typename TransformType::OutputVectorType VectorType; typedef typename TransformType::MatrixType MatrixType; PointType center; for(int i=0;iSetCenter(center); transform->SetTranslation(translation); transform->SetMatrix(matrix); return; } //////////////////////////////////////////////////////////////////////// //Stripped from ANTS_affine_registration2.h template void WriteAffineTransformFile(TransformPointerType &transform, StringType filename){ itk::TransformFileWriter::Pointer transform_writer; transform_writer = itk::TransformFileWriter::New(); transform_writer->SetFileName(filename); transform_writer->SetInput(transform); try{ transform_writer->Update(); } catch( itk::ExceptionObject &err){ std::cout << "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!" << std::endl <<"Exception in writing tranform file: " << std::endl << filename << std::endl; return; } return; } //////////////////////////////////////////////////////////////////////// //Stripped from ANTS_affine_registration2.h template inline void PostConversionInAffine(RunningAffineTransformPointerType& transform_running, AffineTransformPointerType &transform){ typedef typename RunningAffineTransformPointerType::ObjectType RunningAffineTransformType; typedef typename AffineTransformPointerType::ObjectType AffineTransformType; transform->SetCenter(*(reinterpret_cast (const_cast (&(transform_running->GetCenter()))))); transform->SetTranslation(*(reinterpret_cast (const_cast (&(transform_running->GetTranslation()))))); transform->SetMatrix(*(reinterpret_cast (const_cast (&(transform_running->GetMatrix()))))); // std::cout << "transform_running" << transform_running << std::endl; // std::cout << "transform" << transform << std::endl; } template void DumpTransformForANTS3D(const TransformAPointer &transform, StringType &ANTS_prefix){ const int ImageDimension = 3; // ANTS transform file type typedef itk::MatrixOffsetTransformBase< double, ImageDimension, ImageDimension > AffineTransformType; AffineTransformType::Pointer transform_ANTS = AffineTransformType::New(); // typedef TransformAPointer::ObjectType TransformA; // std::cout << " writing " << ANTS_prefix << " affine " << std::endl; // std::string ANTS_affine_filename = ANTS_prefix + std::string( "Affine.txt" ); std::string ANTS_affine_filename = ANTS_prefix; std::cout << " writing ANTS affine file:" << ANTS_affine_filename << std::endl; PostConversionInAffine(transform, transform_ANTS); WriteAffineTransformFile(transform_ANTS, ANTS_affine_filename); } int LandmarkBasedTransformInitializer2D(int, char * []) { std::cout << " not implemented " << std::endl; return 1; /* typedef float PixelType; const unsigned int Dimension = 2; typedef itk::Image< PixelType, Dimension > FixedImageType; typedef itk::Image< PixelType, Dimension > MovingImageType; typedef itk::Image< PixelType, Dimension > ImageType; typename FixedImageType::Pointer fixedimage; typename MovingImageType::Pointer movingimage; ReadImage(fixedimage,argv[1]); ReadImage(movingimage,argv[2]); // Set the transform type.. typedef itk::Rigid2DTransform< double > TransformType; */ return EXIT_SUCCESS; } int main(int argc, char *argv[]) { if ( argc < 3 ) { std::cout << "Usage: " << argv[0] << " FixedImageWithLabeledLandmarks.nii.gz MovingImageWithLabeledLandmarks.nii.gz [rigid | affine] OutAffine.txt " << std::endl; std::cout << " we expect the input images to be (1) N-ary (2) in the same physical space as the images you want to " << std::endl; std::cout << " register and (3 ) to have the same landmark points defined within them ... " << std::endl; std::cout << " landmarks will be defined from the center of mass of the labels in the input images . " << std::endl; std::cout << " You can use ITK-snap to generate the label images. " << std::endl; return 1; } // Get the image dimension std::string fn = std::string(argv[1]); itk::ImageIOBase::Pointer imageIO = itk::ImageIOFactory::CreateImageIO(fn.c_str(), itk::ImageIOFactory::ReadMode); imageIO->SetFileName(fn.c_str()); imageIO->ReadImageInformation(); switch ( imageIO->GetNumberOfDimensions() ) { case 2: LandmarkBasedTransformInitializer2D(argc,argv); break; case 3: LandmarkBasedTransformInitializer3D(argc,argv); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } return 0; } ants-1.9.2+svn680.dfsg/Examples/Atropos.cxx000066400000000000000000001374411147325206600204230ustar00rootroot00000000000000#include "itkImage.h" #include "itkImageFileReader.h" #include "itkImageFileWriter.h" #include "itkImageRegionIterator.h" #include "itkMaskImageFilter.h" #include "itkNumericSeriesFileNames.h" #include "itkVectorImage.h" #include "itkVectorIndexSelectionCastImageFilter.h" #include "antsAtroposSegmentationImageFilter.h" #include "antsBoxPlotQuantileListSampleFilter.h" #include "antsCommandLineOption.h" #include "antsCommandLineParser.h" #include "antsGaussianListSampleFunction.h" #include "antsLogEuclideanGaussianListSampleFunction.h" #include "antsGrubbsRosnerListSampleFilter.h" #include "antsHistogramParzenWindowsListSampleFunction.h" #include "antsListSampleToListSampleFilter.h" #include "antsManifoldParzenWindowsListSampleFunction.h" #include "antsPassThroughListSampleFilter.h" #include #include #include template class CommandIterationUpdate : public itk::Command { public: typedef CommandIterationUpdate Self; typedef itk::Command Superclass; typedef itk::SmartPointer Pointer; itkNewMacro( Self ); protected: CommandIterationUpdate() {}; public: void Execute(itk::Object *caller, const itk::EventObject & event) { Execute( (const itk::Object *) caller, event); } void Execute(const itk::Object * object, const itk::EventObject & event) { const TFilter * filter = dynamic_cast< const TFilter * >( object ); if( typeid( event ) != typeid( itk::IterationEvent ) ) { return; } std::cout << " Iteration " << filter->GetElapsedIterations() << " (of " << filter->GetMaximumNumberOfIterations() << "): "; std::cout << filter->GetCurrentConvergenceMeasurement() << " (threshold = " << filter->GetConvergenceThreshold() << ")" << std::endl; } }; void ConvertToLowerCase( std::string& str ) { std::transform( str.begin(), str.end(), str.begin(), tolower ); // You may need to cast the above line to (int(*)(int)) // tolower - this works as is on VC 7.1 but may not work on // other compilers } template int AtroposSegmentation( itk::ants::CommandLineParser *parser ) { typedef float PixelType; typedef float RealType; typedef itk::Image InputImageType; typedef unsigned char LabelType; typedef itk::Image LabelImageType; typedef itk::ants::AtroposSegmentationImageFilter SegmentationFilterType; typename SegmentationFilterType::Pointer segmenter = SegmentationFilterType::New(); typedef CommandIterationUpdate CommandType; typename CommandType::Pointer observer = CommandType::New(); segmenter->AddObserver( itk::IterationEvent(), observer ); //segmenter->DebugOn(); /** * memory-usage -- need to set before setting the prior probability images. */ typename itk::ants::CommandLineParser::OptionType::Pointer memoryOption = parser->GetOption( "minimize-memory-usage" ); if( memoryOption && memoryOption->GetNumberOfValues() > 0 ) { segmenter->SetMinimizeMemoryUsage( parser->Convert( memoryOption->GetValue() ) ); } /** * Initialization */ typename itk::ants::CommandLineParser::OptionType::Pointer initializationOption = parser->GetOption( "initialization" ); if( initializationOption && initializationOption->GetNumberOfParameters() < 1 ) { std::cerr << "Incorrect initialization option specification." << std::endl; std::cerr << " " << initializationOption->GetDescription() << std::endl; return EXIT_FAILURE; } else { segmenter->SetNumberOfClasses( parser->Convert( initializationOption->GetParameter( 0 ) ) ); std::string initializationStrategy = initializationOption->GetValue(); ConvertToLowerCase( initializationStrategy ); if( !initializationStrategy.compare( std::string( "random" ) ) ) { segmenter->SetInitializationStrategy( SegmentationFilterType::Random ); } else if( !initializationStrategy.compare( std::string( "otsu" ) ) ) { segmenter->SetInitializationStrategy( SegmentationFilterType::Otsu ); } else if( !initializationStrategy.compare( std::string( "kmeans" ) ) ) { segmenter->SetInitializationStrategy( SegmentationFilterType::KMeans ); if( initializationOption->GetNumberOfParameters() > 1 ) { std::vector clusterCenters = parser->ConvertVector( initializationOption->GetParameter( 1 ) ); if( clusterCenters.size() != segmenter->GetNumberOfClasses() ) { std::cerr << "The cluster center vector size does not equal the " << "specified number of classes." << std::endl; return EXIT_FAILURE; } else { typename SegmentationFilterType::ParametersType parameters; parameters.SetSize( segmenter->GetNumberOfClasses() ); for( unsigned int n = 0; n < parameters.GetSize(); n++ ) { parameters[n] = clusterCenters[n]; } segmenter->SetInitialKMeansParameters( parameters ); } } } else if( !initializationStrategy.compare( std::string( "priorprobabilityimages" ) ) ) { segmenter->SetInitializationStrategy( SegmentationFilterType::PriorProbabilityImages ); if( initializationOption->GetNumberOfParameters() < 3 ) { std::cerr << "Incorrect initialization option specification." << std::endl; std::cerr << " " << initializationOption->GetDescription() << std::endl; return EXIT_FAILURE; } segmenter->SetPriorProbabilityWeight( parser->Convert( initializationOption->GetParameter( 2 ) ) ); if( initializationOption->GetNumberOfParameters() > 3 ) { segmenter->SetProbabilityThreshold( parser->Convert( initializationOption->GetParameter( 3 ) ) ); } std::string filename = initializationOption->GetParameter( 1 ); if( filename.find( std::string( "%" ) ) != std::string::npos ) { itk::NumericSeriesFileNames::Pointer fileNamesCreator = itk::NumericSeriesFileNames::New(); fileNamesCreator->SetStartIndex( 1 ); fileNamesCreator->SetEndIndex( segmenter->GetNumberOfClasses() ); fileNamesCreator->SetSeriesFormat( filename.c_str() ); const std::vector & imageNames = fileNamesCreator->GetFileNames(); for ( unsigned int k = 0; k < imageNames.size(); k++ ) { typedef itk::ImageFileReader ReaderType; typename ReaderType::Pointer reader = ReaderType::New(); reader->SetFileName( imageNames[k].c_str() ); reader->Update(); segmenter->SetPriorProbabilityImage( k + 1, reader->GetOutput() ); } } else { typedef itk::VectorImage VectorImageType; typedef itk::ImageFileReader ReaderType; typename ReaderType::Pointer reader = ReaderType::New(); reader->SetFileName( filename.c_str() ); reader->Update(); if( reader->GetOutput()->GetNumberOfComponentsPerPixel() != segmenter->GetNumberOfClasses() ) { std::cerr << "The number of components does not match the number of " << "classes." << std::endl; return EXIT_FAILURE; } typedef itk::VectorIndexSelectionCastImageFilter CasterType; typename CasterType::Pointer caster = CasterType::New(); caster->SetInput( reader->GetOutput() ); for( unsigned int k = 0; k < segmenter->GetNumberOfClasses(); k++ ) { caster->SetIndex( k ); caster->Update(); segmenter->SetPriorProbabilityImage( k + 1, caster->GetOutput() ); } } if( initializationOption->GetNumberOfParameters() > 3 ) { segmenter->SetProbabilityThreshold( parser->Convert( initializationOption->GetParameter( 3 ) ) ); } } else if( !initializationStrategy.compare( std::string( "priorlabelimage" ) ) ) { segmenter->SetInitializationStrategy( SegmentationFilterType::PriorLabelImage ); if( initializationOption->GetNumberOfParameters() < 3 ) { std::cerr << "Incorrect initialization option specification." << std::endl; std::cerr << " " << initializationOption->GetDescription() << std::endl; return EXIT_FAILURE; } segmenter->SetPriorProbabilityWeight( parser->Convert( initializationOption->GetParameter( 2 ) ) ); std::string filename = initializationOption->GetParameter( 1 ); typedef itk::ImageFileReader ReaderType; typename ReaderType::Pointer reader = ReaderType::New(); reader->SetFileName( filename.c_str() ); reader->Update(); segmenter->SetPriorLabelImage( reader->GetOutput() ); } else { std::cerr << "Unrecognized initialization strategy request." << std::endl; return EXIT_FAILURE; } } /** * Posterior probability formulation */ typename itk::ants::CommandLineParser::OptionType::Pointer posteriorOption = parser->GetOption( "posterior-formulation" ); if( posteriorOption ) { if( posteriorOption->GetNumberOfParameters() > 0 ) { segmenter->SetUseMixtureModelProportions( parser->Convert( posteriorOption->GetParameter( 0 ) ) ); } std::string posteriorStrategy = posteriorOption->GetValue(); ConvertToLowerCase( posteriorStrategy ); if( !posteriorStrategy.compare( std::string( "socrates" ) ) ) { segmenter->SetPosteriorProbabilityFormulation( SegmentationFilterType::Socrates ); } else if( !posteriorStrategy.compare( std::string( "plato" ) ) ) { segmenter->SetPosteriorProbabilityFormulation( SegmentationFilterType::Plato ); } else if( !posteriorStrategy.compare( std::string( "aristotle" ) ) ) { segmenter->SetPosteriorProbabilityFormulation( SegmentationFilterType::Aristotle ); } } /** * convergence options */ typename itk::ants::CommandLineParser::OptionType::Pointer convergenceOption = parser->GetOption( "convergence" ); if( convergenceOption ) { if( convergenceOption->GetNumberOfParameters() > 0 ) { segmenter->SetMaximumNumberOfIterations( parser->Convert( convergenceOption->GetParameter( 0 ) ) ); } if( convergenceOption->GetNumberOfParameters() > 1 ) { segmenter->SetConvergenceThreshold( parser->Convert( convergenceOption->GetParameter( 1 ) ) ); } } /** * mask image */ typename itk::ants::CommandLineParser::OptionType::Pointer maskOption = parser->GetOption( "mask-image" ); if( maskOption && maskOption->GetNumberOfValues() > 0 ) { try { typedef itk::ImageFileReader ReaderType; typename ReaderType::Pointer reader = ReaderType::New(); reader->SetFileName( ( maskOption->GetValue() ).c_str() ); reader->Update(); segmenter->SetMaskImage( reader->GetOutput() ); } catch(...) {} } else { std::cerr << "An image mask is required. Specify a mask image" << " with the -x option." << std::endl; return EXIT_FAILURE; } /** * BSpline options */ typename itk::ants::CommandLineParser::OptionType::Pointer bsplineOption = parser->GetOption( "bspline" ); if( bsplineOption && bsplineOption->GetNumberOfValues() ) { if( bsplineOption->GetNumberOfParameters() > 0 ) { std::vector numLevels = parser->ConvertVector( bsplineOption->GetParameter( 0 ) ); typename SegmentationFilterType::ArrayType numberOfFittingLevels; if( numLevels.size() == 1 ) { numberOfFittingLevels.Fill( numLevels[0] ); } else if( numLevels.size() == ImageDimension ) { for( unsigned int d = 0; d < ImageDimension; d++ ) { numberOfFittingLevels[d] = numLevels[d]; } } else { std::cerr << "Incorrect number of levels" << std::endl; return EXIT_FAILURE; } segmenter->SetNumberOfLevels( numberOfFittingLevels ); } if( bsplineOption->GetNumberOfParameters() > 2 ) { segmenter->SetSplineOrder( parser->Convert( bsplineOption->GetParameter( 2 ) ) ); } if( bsplineOption->GetNumberOfParameters() > 1 ) { std::vector array = parser->ConvertVector( bsplineOption->GetParameter( 1 ) ); typename SegmentationFilterType::ArrayType numberOfControlPoints; if( array.size() == 1 ) { numberOfControlPoints.Fill( array[0] + segmenter->GetSplineOrder() ); } else if( array.size() == ImageDimension ) { for( unsigned int d = 0; d < ImageDimension; d++ ) { numberOfControlPoints[d] = array[d] + segmenter->GetSplineOrder(); } } else { std::cerr << "Incorrect mesh resolution" << std::endl; return EXIT_FAILURE; } segmenter->SetNumberOfControlPoints( numberOfControlPoints ); } } /** * labels */ typename itk::ants::CommandLineParser::OptionType::Pointer labelOption = parser->GetOption( "label-propagation" ); if( labelOption && labelOption->GetNumberOfValues() > 0 ) { if( labelOption->GetNumberOfValues() == 1 && ( labelOption->GetValue( 0 ) ).empty() ) { typename SegmentationFilterType::LabelParameterMapType labelMap; float labelLambda = parser->Convert( labelOption->GetParameter( 0, 0 ) ); float labelBoundaryProbability = 1.0; if( labelOption->GetNumberOfParameters( 0 ) > 1 ) { labelBoundaryProbability = parser->Convert( labelOption->GetParameter( 0, 1 ) ); if( labelBoundaryProbability < 0.0 ) { labelBoundaryProbability = 0.0; } if( labelBoundaryProbability > 1.0 ) { labelBoundaryProbability = 1.0; } } for( unsigned int n = 1; n <= segmenter->GetNumberOfClasses(); n++ ) { typename SegmentationFilterType::LabelParametersType labelPair; labelPair.first = labelLambda; labelPair.second = labelBoundaryProbability; labelMap[n] = labelPair; } segmenter->SetPriorLabelParameterMap( labelMap ); } else { typename SegmentationFilterType::LabelParameterMapType labelMap; for( unsigned int n = 0; n < labelOption->GetNumberOfValues(); n++ ) { typename SegmentationFilterType::LabelParametersType labelPair; float labelLambda = parser->Convert( labelOption->GetParameter( n, 0 ) ); float labelBoundaryProbability = 1.0; if( labelOption->GetNumberOfParameters( n ) > 1 ) { labelBoundaryProbability = parser->Convert( labelOption->GetParameter( n, 1 ) ); if( labelBoundaryProbability < 0.0 ) { labelBoundaryProbability = 0.0; } if( labelBoundaryProbability > 1.0 ) { labelBoundaryProbability = 1.0; } } labelPair.first = labelLambda; labelPair.second = labelBoundaryProbability; unsigned int whichClass = parser->Convert( labelOption->GetValue( n ) ); labelMap[whichClass] = labelPair; } segmenter->SetPriorLabelParameterMap( labelMap ); } } /** * intensity images */ typename itk::ants::CommandLineParser::OptionType::Pointer imageOption = parser->GetOption( "intensity-image" ); if( imageOption && imageOption->GetNumberOfValues() > 0 ) { unsigned int count = 0; for( int n = imageOption->GetNumberOfValues() - 1; n >= 0; n-- ) { typedef itk::ImageFileReader ReaderType; typename ReaderType::Pointer reader = ReaderType::New(); if( imageOption->GetNumberOfParameters( n ) > 0 ) { reader->SetFileName( imageOption->GetParameter( n, 0 ) ); } else { reader->SetFileName( imageOption->GetValue( n ) ); } reader->Update(); segmenter->SetIntensityImage( count, reader->GetOutput() ); if( imageOption->GetNumberOfParameters( count ) > 1 ) { segmenter->SetAdaptiveSmoothingWeight( count, parser->Convert( imageOption->GetParameter( count, 1 ) ) ); } else { segmenter->SetAdaptiveSmoothingWeight( count, 0.0 ); } count++; } } else { std::cerr << "No input images were specified. Specify an input image" << " with the -a option." << std::endl; return EXIT_FAILURE; } /** * MRF options */ typename itk::ants::CommandLineParser::OptionType::Pointer mrfOption = parser->GetOption( "mrf" ); if( mrfOption && mrfOption->GetNumberOfValues() > 0 ) { if( mrfOption->GetNumberOfParameters() > 0 ) { segmenter->SetMRFSmoothingFactor( parser->Convert( mrfOption->GetParameter( 0 ) ) ); } if( mrfOption->GetNumberOfParameters() > 1 ) { std::vector array = parser->ConvertVector( mrfOption->GetParameter( 1 ) ); typename SegmentationFilterType::ArrayType radius; if( array.size() == 1 ) { radius.Fill( array[0] ); } else if( array.size() == ImageDimension ) { for( unsigned int d = 0; d < ImageDimension; d++ ) { radius[d] = array[d]; } } else { std::cerr << "MRF radius size needs to be equal to the image dimension." << std::endl; return EXIT_FAILURE; } segmenter->SetMRFRadius( radius ); } } /** * euclidean distance */ typename itk::ants::CommandLineParser::OptionType::Pointer distanceOption = parser->GetOption( "use-euclidean-distance" ); if( distanceOption && distanceOption->GetNumberOfValues() > 0 ) { segmenter->SetUseEuclideanDistanceForPriorLabels( parser->Convert( distanceOption->GetValue() ) ); } /** * likelihood */ typename itk::ants::CommandLineParser::OptionType::Pointer likelihoodOption = parser->GetOption( "likelihood-model" ); if( likelihoodOption && likelihoodOption->GetNumberOfValues() > 0 ) { std::string likelihoodModel = likelihoodOption->GetValue(); ConvertToLowerCase( likelihoodModel ); if( !likelihoodModel.compare( std::string( "gaussian" ) ) ) { typedef typename SegmentationFilterType::SampleType SampleType; typedef itk::ants::Statistics::GaussianListSampleFunction LikelihoodType; for( unsigned int n = 0; n < segmenter->GetNumberOfClasses(); n++ ) { typename LikelihoodType::Pointer gaussianLikelihood = LikelihoodType::New(); segmenter->SetLikelihoodFunction( n, gaussianLikelihood ); } } else if( !likelihoodModel.compare( std::string( "manifoldparzenwindows" ) ) ) { typedef typename SegmentationFilterType::SampleType SampleType; typedef itk::ants::Statistics::ManifoldParzenWindowsListSampleFunction LikelihoodType; float regularizationSigma = 1.0; if( likelihoodOption->GetNumberOfParameters() > 0 ) { regularizationSigma = parser->Convert( likelihoodOption->GetParameter( 0 ) ); } unsigned int evalNeighborhood = 50; if( likelihoodOption->GetNumberOfParameters() > 1 ) { evalNeighborhood = parser->Convert( likelihoodOption->GetParameter( 1 ) ); } unsigned int covNeighborhood = 0; if( likelihoodOption->GetNumberOfParameters() > 2 ) { covNeighborhood = parser->Convert( likelihoodOption->GetParameter( 2 ) ); } float covSigma = 1.0; if( likelihoodOption->GetNumberOfParameters() > 3 ) { covSigma = parser->Convert( likelihoodOption->GetParameter( 3 ) ); } for( unsigned int n = 0; n < segmenter->GetNumberOfClasses(); n++ ) { typename LikelihoodType::Pointer mpwLikelihood = LikelihoodType::New(); mpwLikelihood->SetRegularizationSigma( regularizationSigma ); mpwLikelihood->SetEvaluationKNeighborhood( evalNeighborhood ); mpwLikelihood->SetCovarianceKNeighborhood( covNeighborhood ); mpwLikelihood->SetKernelSigma( covSigma ); segmenter->SetLikelihoodFunction( n, mpwLikelihood ); } } else if( !likelihoodModel.compare( std::string( "histogramparzenwindows" ) ) ) { typedef typename SegmentationFilterType::SampleType SampleType; typedef itk::ants::Statistics::HistogramParzenWindowsListSampleFunction LikelihoodType; float sigma = 1.0; if( likelihoodOption->GetNumberOfParameters() > 0 ) { sigma = parser->Convert( likelihoodOption->GetParameter( 0 ) ); } unsigned int numberOfBins = 32; if( likelihoodOption->GetNumberOfParameters() > 1 ) { numberOfBins = parser->Convert( likelihoodOption->GetParameter( 1 ) ); } for( unsigned int n = 0; n < segmenter->GetNumberOfClasses(); n++ ) { typename LikelihoodType::Pointer hpwLikelihood = LikelihoodType::New(); hpwLikelihood->SetSigma( sigma ); hpwLikelihood->SetNumberOfHistogramBins( numberOfBins ); segmenter->SetLikelihoodFunction( n, hpwLikelihood ); } } else if( !likelihoodModel.compare( std::string( "logeuclideangaussian" ) ) ) { if( segmenter->GetNumberOfIntensityImages() != static_cast( ImageDimension * ( ImageDimension + 1 ) / 2 ) ) { std::cerr << "Incorrect number of intensity images specified." << std::endl; return EXIT_FAILURE; } typedef typename SegmentationFilterType::SampleType SampleType; typedef itk::ants::Statistics::LogEuclideanGaussianListSampleFunction LikelihoodType; for( unsigned int n = 0; n < segmenter->GetNumberOfClasses(); n++ ) { typename LikelihoodType::Pointer gaussianLikelihood = LikelihoodType::New(); segmenter->SetLikelihoodFunction( n, gaussianLikelihood ); } } else { std::cerr << "Unrecognized likelihood model request." << std::endl; return EXIT_FAILURE; } } /** * outliers? */ typename itk::ants::CommandLineParser::OptionType::Pointer outlierOption = parser->GetOption( "winsorize-outliers" ); if( outlierOption && outlierOption->GetNumberOfValues() > 0 ) { std::string outlierStrategy = outlierOption->GetValue(); ConvertToLowerCase( outlierStrategy ); if( !outlierStrategy.compare( std::string( "boxplot" ) ) ) { typedef typename SegmentationFilterType::SampleType SampleType; typedef itk::ants::Statistics::BoxPlotQuantileListSampleFilter SampleFilterType; typename SampleFilterType::Pointer boxplotFilter = SampleFilterType::New(); if( outlierOption->GetNumberOfParameters( 0 ) > 0 ) { boxplotFilter->SetLowerPercentile( parser->Convert( outlierOption->GetParameter( 0 ) ) ); } if( outlierOption->GetNumberOfParameters( 0 ) > 1 ) { boxplotFilter->SetUpperPercentile( parser->Convert( outlierOption->GetParameter( 1 ) ) ); } if( outlierOption->GetNumberOfParameters( 0 ) > 2 ) { boxplotFilter->SetWhiskerScalingFactor( parser->Convert( outlierOption->GetParameter( 2 ) ) ); } segmenter->SetOutlierHandlingFilter( boxplotFilter ); } else if( !outlierStrategy.compare( std::string( "grubbsrosner" ) ) ) { typedef typename SegmentationFilterType::SampleType SampleType; typedef itk::ants::Statistics::GrubbsRosnerListSampleFilter SampleFilterType; typename SampleFilterType::Pointer grubbsFilter = SampleFilterType::New(); if( outlierOption->GetNumberOfParameters( 0 ) > 0 ) { grubbsFilter->SetSignificanceLevel( parser->Convert( outlierOption->GetParameter( 0 ) ) ); } if( outlierOption->GetNumberOfParameters( 0 ) > 1 ) { grubbsFilter->SetWinsorizingLevel( parser->Convert( outlierOption->GetParameter( 1 ) ) ); } segmenter->SetOutlierHandlingFilter( grubbsFilter ); } else { std::cerr << "Unrecognized outlier handling strategy request." << std::endl; return EXIT_FAILURE; } } try { std::cout << std::endl << "Progress: " << std::endl; segmenter->Update(); } catch( itk::ExceptionObject exp ) { std::cerr << exp << std::endl; return EXIT_FAILURE; } /** * output */ std::cout << std::endl << "Writing output:" << std::endl; typename itk::ants::CommandLineParser::OptionType::Pointer outputOption = parser->GetOption( "output" ); if( outputOption && outputOption->GetNumberOfValues() > 0 ) { if( outputOption->GetNumberOfParameters() == 0 ) { typedef itk::ImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetInput( segmenter->GetOutput() ); writer->SetFileName( ( outputOption->GetValue() ).c_str() ); writer->Update(); } if( outputOption->GetNumberOfParameters() > 0 ) { typedef itk::ImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetInput( segmenter->GetOutput() ); writer->SetFileName( ( outputOption->GetParameter( 0 ) ).c_str() ); writer->Update(); } if( outputOption->GetNumberOfParameters() > 1 ) { std::string filename = outputOption->GetParameter( 1 ); itk::NumericSeriesFileNames::Pointer fileNamesCreator = itk::NumericSeriesFileNames::New(); fileNamesCreator->SetStartIndex( 1 ); fileNamesCreator->SetEndIndex( segmenter->GetNumberOfClasses() ); fileNamesCreator->SetSeriesFormat( filename.c_str() ); const std::vector & imageNames = fileNamesCreator->GetFileNames(); for( unsigned int i = 0; i < imageNames.size(); i++ ) { std::cout << " Writing posterior image (class " << i + 1 << ")" << std::endl; typename InputImageType::Pointer probabilityImage = segmenter->GetPosteriorProbabilityImage( i + 1 ); if( segmenter->GetMaskImage() ) { typedef itk::MaskImageFilter MaskerType; typename MaskerType::Pointer masker = MaskerType::New(); masker->SetInput1( probabilityImage ); masker->SetInput2( segmenter->GetMaskImage() ); masker->SetOutsideValue( 0 ); masker->Update(); probabilityImage = masker->GetOutput(); } typedef itk::ImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetInput( probabilityImage ); writer->SetFileName( imageNames[i].c_str() ); writer->Update(); } } if( outputOption->GetNumberOfParameters() > 2 ) { std::string filename = outputOption->GetParameter( 2 ); itk::NumericSeriesFileNames::Pointer fileNamesCreator = itk::NumericSeriesFileNames::New(); fileNamesCreator->SetStartIndex( 1 ); fileNamesCreator->SetEndIndex( segmenter->GetNumberOfClasses() ); fileNamesCreator->SetSeriesFormat( filename.c_str() ); const std::vector & imageNames = fileNamesCreator->GetFileNames(); for( unsigned int i = 0; i < segmenter->GetNumberOfClasses(); i++ ) { std::cout << " Writing likelihood image (class " << i + 1 << ")" << std::endl; typename InputImageType::Pointer likelihoodImage = segmenter-> GetLikelihoodImage( i + 1 ); typedef itk::ImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetInput( likelihoodImage ); writer->SetFileName( imageNames[i].c_str() ); writer->Update(); } } if( outputOption->GetNumberOfParameters() > 3 ) { std::string filename = outputOption->GetParameter( 3 ); itk::NumericSeriesFileNames::Pointer fileNamesCreator = itk::NumericSeriesFileNames::New(); fileNamesCreator->SetStartIndex( 1 ); fileNamesCreator->SetEndIndex( segmenter->GetNumberOfClasses() ); fileNamesCreator->SetSeriesFormat( filename.c_str() ); const std::vector & imageNames = fileNamesCreator->GetFileNames(); for( unsigned int i = 0; i < segmenter->GetNumberOfClasses(); i++ ) { if( segmenter->GetPriorProbabilityImage( i + 1 ) || segmenter->GetPriorLabelImage() ) { std::cout << " Writing distance image (class " << i + 1 << ")" << std::endl; typename InputImageType::Pointer distanceImage = segmenter-> GetDistancePriorProbabilityImage( i + 1 ); typedef itk::ImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetInput( distanceImage ); writer->SetFileName( imageNames[i].c_str() ); writer->Update(); } } } if( outputOption->GetNumberOfParameters() > 4 ) { std::string filename = outputOption->GetParameter( 4 ); itk::NumericSeriesFileNames::Pointer fileNamesCreator = itk::NumericSeriesFileNames::New(); fileNamesCreator->SetStartIndex( 1 ); fileNamesCreator->SetEndIndex( segmenter->GetNumberOfClasses() ); fileNamesCreator->SetSeriesFormat( filename.c_str() ); const std::vector & imageNames = fileNamesCreator->GetFileNames(); if( segmenter->GetAdaptiveSmoothingWeight( 0 ) > 0.0 ) { for( unsigned int i = 0; i < segmenter->GetNumberOfClasses(); i++ ) { if( segmenter->GetPriorProbabilityImage( i + 1 ) || segmenter->GetPriorLabelImage() ) { std::cout << " Writing B-spline image (class " << i + 1 << ")" << std::endl; typename InputImageType::Pointer bsplineImage = segmenter-> GetSmoothIntensityImageFromPriorImage( 0, i + 1 ); typedef itk::ImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetInput( bsplineImage ); writer->SetFileName( imageNames[i].c_str() ); writer->Update(); } } } } } std::cout << std::endl; segmenter->Print( std::cout, 2 ); return EXIT_SUCCESS; } void InitializeCommandLineOptions( itk::ants::CommandLineParser *parser ) { typedef itk::ants::CommandLineParser::OptionType OptionType; { std::string description = std::string( "This option forces the image to be treated as a specified-" ) + std::string( "dimensional image. If not specified, Atropos tries to " ) + std::string( "infer the dimensionality from the first input image." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "image-dimensionality" ); option->SetShortName( 'd' ); option->SetUsageOption( 0, "2/3/4" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "One or more scalar images is specified for segmentation " ) + std::string( "using the -a/--intensity-image option. For segmentation " ) + std::string( "scenarios with no prior information, the first scalar " ) + std::string( "image encountered on the command line is used to order " ) + std::string( "labelings such that the class with the smallest intensity " ) + std::string( "signature is class \'1\' through class \'N\' which represents " ) + std::string( "the voxels with the largest intensity values. The " ) + std::string( "optional adaptive smoothing weight parameter is applicable " ) + std::string( "only when using prior label or probability images. This " ) + std::string( "scalar parameter is to be specified between [0,1] which " ) + std::string( "smooths each labeled region separately and modulates the " ) + std::string( "intensity measurement at each voxel in each intensity image " ) + std::string( "between the original intensity and its smoothed " ) + std::string( "counterpart. The smoothness parameters are governed by the " ) + std::string( "-b/--bspline option." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "intensity-image" ); option->SetShortName( 'a' ); option->SetUsageOption( 0, "[intensityImage,]" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "If the adaptive smoothing weights are > 0, the intensity " ) + std::string( "images are smoothed in calculating the likelihood values. " ) + std::string( "This is to account for subtle intensity differences " ) + std::string( "across the same tissue regions." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "bspline" ); option->SetShortName( 'b' ); option->SetUsageOption( 0, "[,,]" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "To initialize the FMM parameters, one of the following " ) + std::string( "options must be specified. If one does not have " ) + std::string( "prior label or probability images we recommend " ) + std::string( "using kmeans as it is typically faster than otsu and can " ) + std::string( "be used with multivariate initialization. However, since a " ) + std::string( "Euclidean distance on the inter cluster distances is used, one " ) + std::string( "might have to appropriately scale the additional input images. " ) + std::string( "Random initialization is meant purely for intellectual " ) + std::string( "curiosity. The prior weighting (specified in the range " ) + std::string( "[0,1]) is used to modulate the calculation of the " ) + std::string( "posterior probabilities between the likelihood*mrfprior " ) + std::string( "and the likelihood*mrfprior*prior. For specifying many " ) + std::string( "prior probability images for a multi-label segmentation, " ) + std::string( "we offer a minimize usage option (see -m). With that option " ) + std::string( "one can specify a prior probability threshold in which only " ) + std::string( "those pixels exceeding that threshold are stored in memory. "); OptionType::Pointer option = OptionType::New(); option->SetLongName( "initialization" ); option->SetShortName( 'i' ); option->SetUsageOption( 0, "Random[numberOfClasses]" ); option->SetUsageOption( 1, "Otsu[numberOfClasses]" ); option->SetUsageOption( 2, "KMeans[numberOfClasses,]" ); option->SetUsageOption( 3, "PriorProbabilityImages[numberOfClasses,fileSeriesFormat(index=1 to numberOfClasses) or vectorImage,priorWeighting,]" ); option->SetUsageOption( 4, "PriorLabelImage[numberOfClasses,labelImage,priorWeighting]" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "Different posterior probability formulations are possible ") + std::string( "which include the following: " ) + std::string( " Socrates: posteriorProbability = (spatialPrior)^priorWeight" ) + std::string( "*(likelihood*mrfPrior)^(1-priorWeight), " ) + std::string( " Plato: posteriorProbability = 1.0, " ) + std::string( " Aristotle: posteriorProbability = 1.0, " )/* + std::string( " Zeno: posteriorProbability = 1.0\n" ) + std::string( " Diogenes: posteriorProbability = 1.0\n" ) + std::string( " Thales: posteriorProbability = 1.0\n" ) + std::string( " Democritus: posteriorProbability = 1.0.\n" ) */; OptionType::Pointer option = OptionType::New(); option->SetLongName( "posterior-formulation" ); option->SetShortName( 'p' ); option->SetUsageOption( 0, "Socrates[]" ); option->SetUsageOption( 1, "Plato[]" ); option->SetUsageOption( 2, "Aristotle[]" ); // option->SetUsageOption( 3, "Zeno[]" ); // option->SetUsageOption( 4, "Diogenes[]" ); // option->SetUsageOption( 5, "Thales[]" ); // option->SetUsageOption( 6, "Democritus" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "The image mask (which is required) defines the region which " ) + std::string( "is to be labeled by the Atropos algorithm." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "mask-image" ); option->SetShortName( 'x' ); option->SetUsageOption( 0, "maskImageFilename" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "Convergence is determined by calculating the mean maximum " ) + std::string( "posterior probability over the region of interest at " ) + std::string( "each iteration. When this value decreases or increases " ) + std::string( "less than the specified threshold from the previous " ) + std::string( "iteration or the maximum number of iterations is exceeded " ) + std::string( "the program terminates."); OptionType::Pointer option = OptionType::New(); option->SetLongName( "convergence" ); option->SetShortName( 'c' ); option->SetUsageOption( 0, "[,]" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "Both parametric and non-parametric options exist in Atropos. " ) + std::string( "The Gaussian parametric option is commonly used " ) + std::string( "(e.g. SPM & FAST) where the mean and standard deviation " ) + std::string( "for the Gaussian of each class is calculated at each " ) + std::string( "iteration. Other groups use non-parametric approaches " ) + std::string( "exemplified by option 2. We recommend using options 1 " ) + std::string( "or 2 as they are fairly standard and the " ) + std::string( "default parameters work adequately." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "likelihood-model" ); option->SetShortName( 'k' ); option->SetUsageOption( 0, "Gaussian" ); option->SetUsageOption( 1, "HistogramParzenWindows[,]" ); option->SetUsageOption( 2, "ManifoldParzenWindows[,,,]" ); option->SetUsageOption( 3, "LogEuclideanGaussian" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "Markov random field (MRF) theory provides a general " ) + std::string( "framework for enforcing spatially contextual constraints " ) + std::string( "on the segmentation solution. The default smoothing " ) + std::string( "factor of 0.3 provides a moderate amount of smoothing. " ) + std::string( "Increasing this number causes more smoothing whereas " ) + std::string( "decreasing the number lessens the smoothing. The radius " ) + std::string( "parameter specifies the mrf neighborhood." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "mrf" ); option->SetShortName( 'm' ); option->SetUsageOption( 0, "[,]" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "The output consists of a labeled image where each voxel " ) + std::string( "in the masked region is assigned a label from 1, 2, " ) + std::string( "..., N. Optionally, one can also output the posterior " ) + std::string( "probability images specified in the same format as the " ) + std::string( "prior probability images, e.g. posterior%02d.nii.gz " ) + std::string( "(C-style file name formatting)." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "output" ); option->SetShortName( 'o' ); option->SetUsageOption( 0, "[classifiedImage,]" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "By default, memory usage is not minimized, however, if " ) + std::string( "this is needed, the various probability and distance " ) + std::string( "images are calculated on the fly instead of being " ) + std::string( "stored in memory at each iteration. Also, if prior " ) + std::string( "probability images are used, only the non-negligible " ) + std::string( "pixel values are stored in memory. " ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "minimize-memory-usage" ); option->SetShortName( 'u' ); option->SetUsageOption( 0, "(0)/1" ); option->SetDescription( description ); option->AddValue( std::string( "0" ) ); parser->AddOption( option ); } { std::string description = std::string( "To remove the effects of outliers in calculating the " ) + std::string( "weighted mean and weighted covariance, the user can " ) + std::string( "opt to remove the outliers through the options " ) + std::string( "specified below." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "winsorize-outliers" ); option->SetShortName( 'w' ); option->SetUsageOption( 0, "BoxPlot[,,]" ); option->SetUsageOption( 1, "GrubbsRosner[,]" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "Given prior label or probability images, the labels are " ) + std::string( "propagated throughout the masked region so that every " ) + std::string( "voxel in the mask is labeled. Propagation is done " ) + std::string( "by using a signed distance transform of the label. " ) + std::string( "Alternatively, propagation of the labels with the " ) + std::string( "fast marching filter respects the distance along the " ) + std::string( "shape of the mask (e.g. the sinuous sulci and gyri " ) + std::string( "of the cortex." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "use-euclidean-distance" ); option->SetShortName( 'e' ); option->SetUsageOption( 0, "(0)/1" ); option->SetDescription( description ); option->AddValue( std::string( "0" ) ); parser->AddOption( option ); } { std::string description = std::string( "The propagation of each prior label can be controlled " ) + std::string( "by the lambda and boundary probability parameters. The " ) + std::string( "latter parameter is the probability (in the range " ) + std::string( "[0,1]) of the label on the boundary which increases linearly " ) + std::string( "to a maximum value of 1.0 in the interior of the labeled " ) + std::string( "region. The former parameter dictates the exponential " ) + std::string( "decay of probability propagation outside the labeled " ) + std::string( "region from the boundary probability, i.e. " ) + std::string( "boundaryProbability*exp( -lambda * distance )." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "label-propagation" ); option->SetShortName( 'l' ); option->SetUsageOption( 0, "whichLabel[lambda=0.0,]" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "Print the help menu (short version)." ); OptionType::Pointer option = OptionType::New(); option->SetShortName( 'h' ); option->SetDescription( description ); option->AddValue( std::string( "0" ) ); parser->AddOption( option ); } { std::string description = std::string( "Print the help menu." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "help" ); option->SetDescription( description ); option->AddValue( std::string( "0" ) ); parser->AddOption( option ); } } int main( int argc, char *argv[] ) { itk::ants::CommandLineParser::Pointer parser = itk::ants::CommandLineParser::New(); parser->SetCommand( argv[0] ); std::string commandDescription = std::string( "A finite mixture modeling (FMM) segmentation approach " ) + std::string( "with possibilities for specifying prior constraints. " ) + std::string( "These prior constraints include the specification " ) + std::string( "of a prior label image, prior probability images " ) + std::string( "(one for each class), and/or an MRF prior to " ) + std::string( "enforce spatial smoothing of the labels. Similar algorithms " ) + std::string( "include FAST and SPM. " ); parser->SetCommandDescription( commandDescription ); InitializeCommandLineOptions( parser ); parser->Parse( argc, argv ); if( argc < 2 || parser->Convert( parser->GetOption( "help" )->GetValue() ) ) { parser->PrintMenu( std::cout, 5, false ); exit( EXIT_FAILURE ); } else if( parser->GetOption( 'h' ) && parser->Convert( parser->GetOption( 'h' )->GetValue() ) ) { parser->PrintMenu( std::cout, 5, true ); exit( EXIT_FAILURE ); } // Get dimensionality unsigned int dimension = 3; itk::ants::CommandLineParser::OptionType::Pointer dimOption = parser->GetOption( "image-dimensionality" ); if( dimOption && dimOption->GetNumberOfValues() > 0 ) { dimension = parser->Convert( dimOption->GetValue() ); } else { // Read in the first intensity image to get the image dimension. std::string filename; itk::ants::CommandLineParser::OptionType::Pointer imageOption = parser->GetOption( "intensity-image" ); if( imageOption && imageOption->GetNumberOfValues() > 0 ) { if( imageOption->GetNumberOfParameters( 0 ) > 0 ) { filename = imageOption->GetParameter( 0, 0 ); } else { filename = imageOption->GetValue( 0 ); } } else { std::cerr << "No input images were specified. Specify an input image" << " with the -a option" << std::endl; return( EXIT_FAILURE ); } itk::ImageIOBase::Pointer imageIO = itk::ImageIOFactory::CreateImageIO( filename.c_str(), itk::ImageIOFactory::ReadMode ); dimension = imageIO->GetNumberOfDimensions(); } std::cout << std::endl << "Running Atropos for " << dimension << "-dimensional images." << std::endl; switch( dimension ) { case 2: AtroposSegmentation<2>( parser ); break; case 3: AtroposSegmentation<3>( parser ); break; case 4: AtroposSegmentation<4>( parser ); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } } ants-1.9.2+svn680.dfsg/Examples/AverageImages.cxx000066400000000000000000000134451147325206600214710ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: AverageImages.cxx,v $ Language: C++ Date: $Date: 2009/01/27 23:25:24 $ Version: $Revision: 1.21 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ // We divide the 2nd input image by its mean and add it to the first // input image with weight 1/n. //The output overwrites the 1st img with the sum. //Note: could easily add variance computation //http://people.revoledu.com/kardi/tutorial/RecursiveStatistic/Time-Variance.htm #include "ReadWriteImage.h" #include "itkOptimalSharpeningImageFilter.h" template int AverageImages(unsigned int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; bool normalizei=atoi(argv[3]); float numberofimages=(float)argc - 4.; std::cout <<" Averaging " << numberofimages << " images " << std::endl; typename ImageType::Pointer averageimage = NULL; typename ImageType::Pointer image2 = NULL; typename ImageType::SizeType size; double meanval=1; size.Fill(0); unsigned int bigimage=0; for (unsigned int j=4; j< argc; j++) { // Get the image dimension std::string fn = std::string(argv[j]); std::cout <<" fn " << fn << std::endl; typename itk::ImageIOBase::Pointer imageIO = itk::ImageIOFactory::CreateImageIO(fn.c_str(), itk::ImageIOFactory::ReadMode); imageIO->SetFileName(fn.c_str()); imageIO->ReadImageInformation(); for (unsigned int i=0; iGetNumberOfDimensions(); i++) { if ( imageIO->GetDimensions(i) > size[i] ) { size[i]=imageIO->GetDimensions(i); bigimage=j; std::cout << " bigimage " << j << " size " << size << std::endl; } } } std:: cout << " largest image " << size << std::endl; ReadImage(averageimage,argv[bigimage]); averageimage->FillBuffer(0); for (unsigned int j=4; j< argc; j++) { // Get the image dimension std::string fn = std::string(argv[j]); // std::cout <<" fn " << fn << std::endl; ReadImage(image2,fn.c_str()); unsigned long ct = 0; if (normalizei) { meanval = 0.0; Iterator vfIter2( image2, image2->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { meanval+=vfIter2.Get(); ct++; } if (ct > 0) meanval /= (float)ct; if (meanval <= 0) meanval=1.0; } ct = 0; Iterator vfIter( image2, image2->GetLargestPossibleRegion() ); for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) { double val = vfIter.Get()/meanval *1.0/numberofimages; averageimage->SetPixel(vfIter.GetIndex() , val + averageimage->GetPixel(vfIter.GetIndex())); } } typedef itk::OptimalSharpeningImageFilter< ImageType, ImageType > sharpeningFilter; typename sharpeningFilter::Pointer shFilter = sharpeningFilter::New(); if (normalizei && argc > 3 ) { shFilter->SetInput( averageimage ); shFilter->SetSValue(0.5); averageimage = shFilter->GetOutput(); } std::cout << " writing output "; { typename writertype::Pointer writer = writertype::New(); writer->SetFileName(argv[2]); writer->SetInput( averageimage ); writer->Update(); } return 0; } int main(int argc, char *argv[]) { if ( argc < 3 ) { std::cout << "\n" << std::endl; std::cout << "Usage: \n" << std::endl; std::cout << argv[0] << " ImageDimension Outputfname.nii.gz Normalize \n" << std::endl; std::cout << " Compulsory arguments: \n" << std::endl; std::cout << " ImageDimension: 2 or 3 (for 2 or 3 dimensional input).\n " << std::endl; std::cout << " Outputfname.nii.gz: the name of the resulting image.\n" << std::endl; std::cout << " Normalize: 0 (false) or 1 (true); if true, the 2nd image is divided by its mean. This will select the largest image to average into.\n" << std::endl; std::cout << " Example Usage:\n" << std::endl; std::cout << argv[0] << " 3 average.nii.gz 1 *.nii.gz \n" << std::endl; std::cout << " \n" << std::endl; return 1; } int dim = atoi( argv[1] ); // Get the image dimension switch( atoi(argv[1])) { case 2: AverageImages<2>(argc,argv); break; case 3: AverageImages<3>(argc,argv); break; default: std::cerr <<" You passed ImageDimension: " << dim << " . Please use only 2 or 3 (for 2 or 3 Dimensional registration) " << std::endl; exit( EXIT_FAILURE ); } return 0; } ants-1.9.2+svn680.dfsg/Examples/AverageTensorImages.cxx000066400000000000000000000067141147325206600226650ustar00rootroot00000000000000#include "stdio.h" #include "itkImage.h" #include "itkImageRegionIteratorWithIndex.h" #include "ReadWriteImage.h" #include "TensorFunctions.h" template int AverageTensorImages(unsigned int argc, char *argv[]) { // typedef itk::Vector TensorType; typedef itk::SymmetricSecondRankTensor< float, 3 > TensorType; typedef itk::Image ImageType; typedef itk::ImageRegionIteratorWithIndex IteratorType; char * outputName = argv[2]; int mathtype = atoi(argv[3]); float numberofimages = (float)argc - 4.0; std::cout << "Averaging " << numberofimages << " images " << std::endl; typename ImageType::Pointer averageimage = NULL; typename ImageType::Pointer image2 = NULL; typename ImageType::SizeType size; size.Fill(0); unsigned int bigimage=0; for (unsigned int j=4; j< argc; j++) { // Get the image dimension std::string fn = std::string(argv[j]); std::cout <<" fn " << fn << std::endl; typename itk::ImageIOBase::Pointer imageIO = itk::ImageIOFactory::CreateImageIO(fn.c_str(), itk::ImageIOFactory::ReadMode); imageIO->SetFileName(fn.c_str()); imageIO->ReadImageInformation(); for (unsigned int i=0; iGetNumberOfDimensions(); i++) { if ( imageIO->GetDimensions(i) > size[i] ) { size[i]=imageIO->GetDimensions(i); bigimage=j; std::cout << " bigimage " << j << " size " << size << std::endl; } } } std:: cout << " largest image " << size << std::endl; bool logeuc = true; if (mathtype == 1) logeuc = false; TensorType nullTensor; nullTensor[0] = nullTensor[1] = nullTensor[2] = nullTensor[3] = nullTensor[4] = nullTensor[5] = 0; ReadTensorImage(averageimage,argv[bigimage],logeuc); averageimage->FillBuffer(nullTensor); for (unsigned int j=4; j< argc; j++) { std::string fn = std::string(argv[j]); ReadTensorImage(image2,fn.c_str(),logeuc); IteratorType vfIter( image2, image2->GetLargestPossibleRegion() ); for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) { TensorType val = vfIter.Get() / numberofimages; averageimage->SetPixel(vfIter.GetIndex(), val + averageimage->GetPixel(vfIter.GetIndex())); } } WriteTensorImage(averageimage,outputName,logeuc); return EXIT_SUCCESS; } // Main Program int main( int argc, char *argv[] ) { try { int dim = atoi(argv[1]); //char * outputName = argv[2]; //int mathtype = atoi(argv[3]); int numberofimages = argc - 4; if (numberofimages < 1) { std::cout << "Basic useage ex: " << std::endl; std::cout << argv[0] << " ImageDimension average.nii mathtype list-of-files-via-wildcard " << std::endl; std::cout << " e.g. \n AverageTensorImages 3 average.nii 1 *registered.nii " << std::endl; std::cout << " mathtype=[0=log-euclidean, 1=euclidean] " << std::endl; exit( EXIT_FAILURE ); } // Get the image dimension switch(dim) { case 2: AverageTensorImages<2>(argc,argv); break; case 3: AverageTensorImages<3>(argc,argv); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } return 0;; } catch( itk::ExceptionObject & err ) { std::cerr << "ExceptionObject caught !" << std::endl; std::cerr << err << std::endl; return EXIT_FAILURE; } } ants-1.9.2+svn680.dfsg/Examples/CMakeLists.txt000066400000000000000000001077701147325206600210120ustar00rootroot00000000000000cmake_minimum_required(VERSION 2.4) if(COMMAND cmake_policy) cmake_policy(SET CMP0003 NEW) endif(COMMAND cmake_policy) #Change PROJECT_NAME to the name of your project PROJECT(ANTS) OPTION(USE_ITK "Use ITK Libraries" ON) OPTION(USE_VTK "Use VTK Libraries" OFF) set (CMAKE_INCLUDE_DIRECTORIES_BEFORE ON) FIND_PATH(VTK_DIR UseVTK.cmake $ENV{VTK_DIR} ) # Set up ITK IF(USE_ITK) FIND_PACKAGE(ITK) IF(ITK_FOUND) INCLUDE(${ITK_USE_FILE}) ELSE(ITK_FOUND) MESSAGE(FATAL_ERROR "Cannot build without ITK. Please set ITK_DIR.") ENDIF(ITK_FOUND) ENDIF(USE_ITK) # Set up VTK IF(USE_VTK) FIND_PACKAGE(VTK) IF(VTK_FOUND) INCLUDE(${VTK_USE_FILE}) ELSE(VTK_FOUND) MESSAGE("Cannot build some programs without VTK. Please set VTK_DIR if you need these programs.") ENDIF(VTK_FOUND) ENDIF(USE_VTK) #The following lines are required to use Dart INCLUDE(CTest) ENABLE_TESTING() INCLUDE (${CMAKE_ROOT}/Modules/FindITK.cmake) IF (USE_ITK_FILE) INCLUDE(${USE_ITK_FILE}) ENDIF(USE_ITK_FILE) SET(DART_TESTING_TIMEOUT 1500) SET(PICSL_INCLUDE_DIRS ../Utilities ../ImageRegistration ../ImageSegmentation ../GraphTheory ../Tensor ../Temporary ) INCLUDE_DIRECTORIES(${PICSL_INCLUDE_DIRS}) LINK_DIRECTORIES( ${ITK_LIBRARY_PATH} ) # non-templated class -- this should be stored in a library and linked in... SET(UI_SOURCES "../Utilities/antsCommandLineParser" "../Utilities/antsCommandLineOption" ) ADD_EXECUTABLE(ANTS ANTS.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(ANTS ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(ANTSJacobian ANTSJacobian.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(ANTSJacobian ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(PrintHeader PrintHeader.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(PrintHeader ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(ResetDirection ResetDirection.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(ResetDirection ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) #ADD_EXECUTABLE(ResetDirection2 ResetDirection2.cxx ${UI_SOURCES}) #TARGET_LINK_LIBRARIES(ResetDirection2 ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) #ADD_EXECUTABLE(BoykovGraphCutFilter BoykovGraphCutFilter.cxx ${UI_SOURCES}) #TARGET_LINK_LIBRARIES(BoykovGraphCutFilter ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) #ADD_EXECUTABLE(GeneralLinearModel GeneralLinearModel.cxx ${UI_SOURCES}) #TARGET_LINK_LIBRARIES(GeneralLinearModel ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(ANTSUseLandmarkImagesToGetAffineTransform ANTSUseLandmarkImagesToGetAffineTransform.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(ANTSUseLandmarkImagesToGetAffineTransform ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(ANTSUseDeformationFieldToGetAffineTransform ANTSUseDeformationFieldToGetAffineTransform ${UI_SOURCES}) TARGET_LINK_LIBRARIES(ANTSUseDeformationFieldToGetAffineTransform ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) #ADD_EXECUTABLE(FDR FDR.cxx ${UI_SOURCES}) #TARGET_LINK_LIBRARIES(FDR ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(ImageMath ImageMath.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(ImageMath ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(SurfaceCurvature SurfaceCurvature.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(SurfaceCurvature ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(SurfaceBasedSmoothing SurfaceBasedSmoothing.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(SurfaceBasedSmoothing ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) # ADD_EXECUTABLE(test test.cxx) #TARGET_LINK_LIBRARIES(test) #ADD_EXECUTABLE(WarpImage WarpImage.cxx ${UI_SOURCES}) #TARGET_LINK_LIBRARIES(WarpImage ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(LaplacianThickness LaplacianThickness.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(LaplacianThickness ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(SetOrigin SetOrigin.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(SetOrigin ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(SetSpacing SetSpacing.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(SetSpacing ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(SetDirectionByMatrix SetDirectionByMatrix.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(SetDirectionByMatrix ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) #ADD_EXECUTABLE(WarpFunctionalImage WarpFunctionalImage.cxx ${UI_SOURCES}) #TARGET_LINK_LIBRARIES(WarpFunctionalImage ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(ConvertScalarImageToRGB ConvertScalarImageToRGB.cxx) TARGET_LINK_LIBRARIES(ConvertScalarImageToRGB ITKIO) ADD_EXECUTABLE(CreateWarpedGridImage CreateWarpedGridImage.cxx) TARGET_LINK_LIBRARIES(CreateWarpedGridImage ITKCommon ITKBasicFilters ITKIO) # ADD_EXECUTABLE(CreateJacobianDeterminantImage CreateJacobianDeterminantImage.cxx) # TARGET_LINK_LIBRARIES(CreateJacobianDeterminantImage ITKCommon ITKBasicFilters ITKIO) ADD_EXECUTABLE(MeasureImageSimilarity MeasureImageSimilarity.cxx) TARGET_LINK_LIBRARIES(MeasureImageSimilarity ITKCommon ITKBasicFilters ITKIO) ADD_EXECUTABLE(ConvertToJpg ConvertToJpg.cxx) TARGET_LINK_LIBRARIES(ConvertToJpg ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(ConvertImagePixelType ConvertImagePixelType.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(ConvertImagePixelType ITKCommon ITKBasicFilters ITKIO ) ADD_EXECUTABLE(AverageImages AverageImages.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(AverageImages ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(AverageTensorImages AverageTensorImages.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(AverageTensorImages ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(ImageSetStatistics ImageSetStatistics.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(ImageSetStatistics ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(ThresholdImage ThresholdImage.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(ThresholdImage ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(MultiplyImages MultiplyImages.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(MultiplyImages ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(SmoothImage SmoothImage.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(SmoothImage ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(ClusterImageStatistics ClusterImageStatistics.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(ClusterImageStatistics ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(LabelClustersUniquely LabelClustersUniquely.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(LabelClustersUniquely ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) #ADD_EXECUTABLE(StudentsTestOnImages StudentsTestOnImages.cxx ${UI_SOURCES}) #TARGET_LINK_LIBRARIES(StudentsTestOnImages ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(LabelOverlapMeasures LabelOverlapMeasures.cxx ) TARGET_LINK_LIBRARIES(LabelOverlapMeasures ITKIO) ADD_EXECUTABLE(MeasureMinMaxMean MeasureMinMaxMean.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(MeasureMinMaxMean ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(WarpImageMultiTransform WarpImageMultiTransform ${UI_SOURCES}) TARGET_LINK_LIBRARIES(WarpImageMultiTransform ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(ComposeMultiTransform ComposeMultiTransform ${UI_SOURCES}) TARGET_LINK_LIBRARIES(ComposeMultiTransform ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(StackSlices StackSlices.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(StackSlices ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(MemoryTest MemoryTest.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(MemoryTest ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) #ADD_EXECUTABLE(ANTSOrientImage ANTSOrientImage.cxx ${UI_SOURCES}) #TARGET_LINK_LIBRARIES(ANTSOrientImage ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(PermuteFlipImageOrientationAxes PermuteFlipImageOrientationAxes.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(PermuteFlipImageOrientationAxes ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics ITKFEM ) ADD_EXECUTABLE(ImageCompare ImageCompare.cxx) TARGET_LINK_LIBRARIES(ImageCompare ITKIO) ADD_EXECUTABLE(ResampleImageBySpacing ResampleImageBySpacing.cxx) TARGET_LINK_LIBRARIES(ResampleImageBySpacing ITKIO) #ADD_EXECUTABLE(ResampleImageByOtherImage ResampleImageByOtherImage.cxx) #TARGET_LINK_LIBRARIES(ResampleImageByOtherImage ITKIO) ADD_EXECUTABLE(CopyImageHeaderInformation CopyImageHeaderInformation.cxx) TARGET_LINK_LIBRARIES(CopyImageHeaderInformation ITKIO) ADD_EXECUTABLE(WarpTimeSeriesImageMultiTransform WarpTimeSeriesImageMultiTransform.cxx) TARGET_LINK_LIBRARIES(WarpTimeSeriesImageMultiTransform ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics) ADD_EXECUTABLE(ResampleImage ResampleImage.cxx ) TARGET_LINK_LIBRARIES(ResampleImage ITKNumerics ITKIO ITKCommon) ADD_EXECUTABLE(ExtractSliceFromImage ExtractSliceFromImage.cxx ) TARGET_LINK_LIBRARIES(ExtractSliceFromImage ITKNumerics ITKIO ITKCommon) ADD_EXECUTABLE(TileImages TileImages.cxx ) TARGET_LINK_LIBRARIES(TileImages ITKNumerics ITKIO ITKCommon) ADD_EXECUTABLE(WarpTensorImageMultiTransform WarpTensorImageMultiTransform.cxx) TARGET_LINK_LIBRARIES(WarpTensorImageMultiTransform ITKCommon ITKBasicFilters ITKIO ITKNumerics ITKStatistics) ADD_EXECUTABLE(ReorientTensorImage ReorientTensorImage.cxx) TARGET_LINK_LIBRARIES(ReorientTensorImage ITKCommon ITKBasicFilters ITKIO) ADD_EXECUTABLE(N3BiasFieldCorrection N3BiasFieldCorrection.cxx) TARGET_LINK_LIBRARIES(N3BiasFieldCorrection ITKIO ITKStatistics ITKNumerics) ADD_EXECUTABLE(N4BiasFieldCorrection N4BiasFieldCorrection.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(N4BiasFieldCorrection ITKIO ITKStatistics) ADD_EXECUTABLE(KellySlater KellySlater.cxx) TARGET_LINK_LIBRARIES(KellySlater ITKIO ITKStatistics) IF (USE_VTK) INCLUDE (${CMAKE_ROOT}/Modules/FindVTK.cmake) IF (USE_VTK_FILE) INCLUDE(${USE_VTK_FILE}) ADD_EXECUTABLE(ConvertVectorFieldToVTK ConvertVectorFieldToVTK.cxx ) TARGET_LINK_LIBRARIES(ConvertVectorFieldToVTK ITKNumerics ITKIO ITKCommon vtkIO) ADD_EXECUTABLE(GetMeshAndTopology GetMeshAndTopology.cxx) TARGET_LINK_LIBRARIES(GetMeshAndTopology ITKCommon ITKNumerics ITKFEM ITKIO ITKMetaIO vtkRendering vtksys vtkIO) ADD_EXECUTABLE(CheckTopology CheckTopology.cxx) TARGET_LINK_LIBRARIES(CheckTopology ITKCommon ITKNumerics ITKFEM ITKIO ITKMetaIO vtkRendering vtksys vtkIO) ADD_EXECUTABLE(WarpVTKPolyDataMultiTransform WarpVTKPolyDataMultiTransform) TARGET_LINK_LIBRARIES(WarpVTKPolyDataMultiTransform ITKIO vtksys vtkIO ) SET(FLAT_SRC ../Temporary/itkFEMElement3DC0LinearTriangular.cxx ../Temporary/itkFEMElement3DC0LinearTriangularLaplaceBeltrami.cxx ../Temporary/itkFEMElement3DC0LinearTriangularMembrane.cxx ) # Build the library ADD_LIBRARY(FLATFEM ${FLAT_SRC}) TARGET_LINK_LIBRARIES(FLATFEM ITKBasicFilters ITKIO) ADD_EXECUTABLE(ConformalMapping ConformalMapping.cxx) TARGET_LINK_LIBRARIES(ConformalMapping ITKCommon ITKNumerics ITKFEM FLATFEM ITKIO ITKMetaIO vtkRendering vtkIO) ADD_EXECUTABLE(ANTSConformalMapping ANTSConformalMapping.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(ANTSConformalMapping ITKCommon ITKNumerics ITKFEM FLATFEM ITKIO ITKMetaIO vtkRendering vtkIO) ENDIF (USE_VTK_FILE) ENDIF(USE_VTK) ADD_EXECUTABLE(ANTSIntegrateVectorField ANTSIntegrateVectorField.cxx) TARGET_LINK_LIBRARIES(ANTSIntegrateVectorField ITKIO ITKStatistics) IF( ITK_USE_REVIEW_STATISTICS ) ADD_EXECUTABLE(Atropos Atropos.cxx ${UI_SOURCES}) TARGET_LINK_LIBRARIES(Atropos ITKCommon ITKBasicFilters ITKIO ITKStatistics ) ENDIF( ITK_USE_REVIEW_STATISTICS ) #Set any libraries that your project depends on. #examples: ITKCommon, VTKRendering, etc SET(Libraries ITKIO ) #SET(CurrentExe "GlamorousGlue") #ADD_EXECUTABLE(${CurrentExe} GlamorousGlue.cxx) #TARGET_LINK_LIBRARIES(${CurrentExe} ${Libraries}) ## # Copy all the shell scripts in the Scripts/ directory to the bin directory ## OPTION( COPY_SCRIPT_FILES_TO_BIN_DIR "Copy the script files to the ANTS bin directory." ON ) IF( COPY_SCRIPT_FILES_TO_BIN_DIR ) FILE( GLOB SCRIPT_FILES "${CMAKE_SOURCE_DIR}/../Scripts/*.sh" ) FILE( GLOB PSCRIPT_FILES "${CMAKE_SOURCE_DIR}/../Scripts/*.pl" ) FOREACH( SCRIPT_FILE ${SCRIPT_FILES} ) ADD_CUSTOM_COMMAND( TARGET ANTS POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${SCRIPT_FILE} ${CMAKE_BINARY_DIR} ) ENDFOREACH( SCRIPT_FILE ) FOREACH( SCRIPT_FILE ${PSCRIPT_FILES} ) ADD_CUSTOM_COMMAND( TARGET ANTS POST_BUILD COMMAND ${CMAKE_COMMAND} -E copy ${SCRIPT_FILE} ${CMAKE_BINARY_DIR} ) ENDFOREACH( SCRIPT_FILE ) ENDIF( COPY_SCRIPT_FILES_TO_BIN_DIR ) ### # Perform testing ### SET(DATA_DIR ${CMAKE_SOURCE_DIR}/Data) SET(R16_IMAGE ${DATA_DIR}/r16slice.nii) SET(R64_IMAGE ${DATA_DIR}/r64slice.nii) SET(OUTPUT_PREFIX ${CMAKE_BINARY_DIR}/TEST) SET(WARP ${OUTPUT_PREFIX}Warp.nii.gz ${OUTPUT_PREFIX}Affine.txt ) SET(INVERSEWARP -i ${OUTPUT_PREFIX}Affine.txt ${OUTPUT_PREFIX}InverseWarp.nii.gz ) SET(WARP_IMAGE ${CMAKE_BINARY_DIR}/warped.nii.gz) SET(INVERSEWARP_IMAGE ${CMAKE_BINARY_DIR}/inversewarped.nii.gz) SET(DEVIL_IMAGE ${DATA_DIR}/Frown.nii) SET(ANGEL_IMAGE ${DATA_DIR}/Smile.nii) SET(SEG_IMAGE ${DATA_DIR}/nslice.nii.gz) ### # ANTS metric testing ### ADD_TEST(ANTS_CC_1 ANTS 2 -m PR[ ${R16_IMAGE}, ${R64_IMAGE}, 1 ,2 ] -r Gauss[ 3 , 0 ] -t SyN[ 0.5 ] -i 50x50x30 -o ${OUTPUT_PREFIX}.nii.gz) ADD_TEST(ANTS_CC_1_WARP WarpImageMultiTransform 2 ${R64_IMAGE} ${WARP_IMAGE} ${WARP} -R ${R16_IMAGE} ) ADD_TEST(ANTS_CC_1_JPG ConvertToJpg ${WARP_IMAGE} ANTSCC1.jpg) ADD_TEST(ANTS_CC_1_WARP_METRIC_0 MeasureImageSimilarity 2 0 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 11.9992 0.05) ADD_TEST(ANTS_CC_1_WARP_METRIC_1 MeasureImageSimilarity 2 1 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.61 0.05) ADD_TEST(ANTS_CC_1_WARP_METRIC_2 MeasureImageSimilarity 2 2 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.00038593 0.05) ADD_TEST(ANTS_CC_1_INVERSEWARP WarpImageMultiTransform 2 ${R16_IMAGE} ${INVERSEWARP_IMAGE} ${INVERSEWARP} -R ${R16_IMAGE} ) ADD_TEST(ANTS_CC_1_INVERSEWARP_METRIC_0 MeasureImageSimilarity 2 0 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 12.1606 0.05) ADD_TEST(ANTS_CC_1_INVERSEWARP_METRIC_1 MeasureImageSimilarity 2 1 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.61 0.05) ADD_TEST(ANTS_CC_1_INVERSEWARP_METRIC_2 MeasureImageSimilarity 2 2 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.000380545 0.05) ADD_TEST(ANTS_CC_2 ANTS 2 -m PR[${R16_IMAGE},${R64_IMAGE},1,4] -r Gauss[3,0] -t SyN[0.5] -i 50x50x30 -o ${OUTPUT_PREFIX}.nii.gz --go-faster true ) ADD_TEST(ANTS_CC_2_WARP WarpImageMultiTransform 2 ${R64_IMAGE} ${WARP_IMAGE} ${WARP} -R ${R16_IMAGE} ) ADD_TEST(ANTS_CC_2_JPG ConvertToJpg ${WARP_IMAGE} ANTSCC2.jpg) ADD_TEST(ANTS_CC_2_WARP_METRIC_0 MeasureImageSimilarity 2 0 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 11.7083 0.05) ADD_TEST(ANTS_CC_2_WARP_METRIC_1 MeasureImageSimilarity 2 1 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.62 0.05) ADD_TEST(ANTS_CC_2_WARP_METRIC_2 MeasureImageSimilarity 2 2 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.000461792 0.05) ADD_TEST(ANTS_CC_2_INVERSEWARP WarpImageMultiTransform 2 ${R16_IMAGE} ${INVERSEWARP_IMAGE} ${INVERSEWARP} -R ${R16_IMAGE} ) ADD_TEST(ANTS_CC_2_INVERSEWARP_METRIC_0 MeasureImageSimilarity 2 0 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 11.8012 0.05) ADD_TEST(ANTS_CC_2_INVERSEWARP_METRIC_1 MeasureImageSimilarity 2 1 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.62 0.05) ADD_TEST(ANTS_CC_2_INVERSEWARP_METRIC_2 MeasureImageSimilarity 2 2 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.000442839 0.05) #ADD_TEST(ANTS_CC_2_INVERSEWARP_METRIC_2 MeasureImageSimilarity 2 2 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -5 0.05) ADD_TEST(ANTS_CC_3 ANTS 2 -m CC[${R16_IMAGE},${R64_IMAGE},1,4] -r Gauss[3,0] -t SyN[0.5] -i 50x50x30 -o ${OUTPUT_PREFIX}.nii.gz --go-faster true ) ADD_TEST(ANTS_CC_3_WARP WarpImageMultiTransform 2 ${R64_IMAGE} ${WARP_IMAGE} ${WARP} -R ${R16_IMAGE} ) ADD_TEST(ANTS_CC_3_JPG ConvertToJpg ${WARP_IMAGE} ANTSCC2.jpg) ADD_TEST(ANTS_CC_3_WARP_METRIC_0 MeasureImageSimilarity 2 0 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 11.7083 0.05) ADD_TEST(ANTS_CC_3_WARP_METRIC_1 MeasureImageSimilarity 2 1 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.62 0.05) ADD_TEST(ANTS_CC_3_WARP_METRIC_2 MeasureImageSimilarity 2 2 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.000461792 0.05) ADD_TEST(ANTS_CC_3_INVERSEWARP WarpImageMultiTransform 2 ${R16_IMAGE} ${INVERSEWARP_IMAGE} ${INVERSEWARP} -R ${R16_IMAGE} ) ADD_TEST(ANTS_CC_3_INVERSEWARP_METRIC_0 MeasureImageSimilarity 2 0 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 11.8012 0.05) ADD_TEST(ANTS_CC_3_INVERSEWARP_METRIC_1 MeasureImageSimilarity 2 1 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.62 0.05) ADD_TEST(ANTS_CC_3_INVERSEWARP_METRIC_2 MeasureImageSimilarity 2 2 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.000442839 0.05) #ADD_TEST(ANTS_CC_3_INVERSEWARP_METRIC_2 MeasureImageSimilarity 2 2 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -5 0.05) ADD_TEST(ANTS_MSQ ANTS 2 -m MSQ[${R16_IMAGE},${R64_IMAGE},1,0] -r Gauss[2,0] -t SyN[0.5] -i 50x50x30 -o ${OUTPUT_PREFIX}.nii.gz) ADD_TEST(ANTS_MSQ_WARP WarpImageMultiTransform 2 ${R64_IMAGE} ${WARP_IMAGE} ${WARP} -R ${R16_IMAGE} ) ADD_TEST(ANTS_MSQ_JPG ConvertToJpg ${WARP_IMAGE} ANTSMSQ.jpg) ADD_TEST(ANTS_MSQ_WARP_METRIC_0 MeasureImageSimilarity 2 0 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 11.7416 0.05) ADD_TEST(ANTS_MSQ_WARP_METRIC_1 MeasureImageSimilarity 2 1 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.6 0.05) ADD_TEST(ANTS_MSQ_WARP_METRIC_2 MeasureImageSimilarity 2 2 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.000543902 0.05) ADD_TEST(ANTS_MSQ_INVERSEWARP WarpImageMultiTransform 2 ${R16_IMAGE} ${INVERSEWARP_IMAGE} ${INVERSEWARP} -R ${R16_IMAGE} ) ADD_TEST(ANTS_MSQ_INVERSEWARP_METRIC_0 MeasureImageSimilarity 2 0 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 11.7845 0.05) ADD_TEST(ANTS_MSQ_INVERSEWARP_METRIC_1 MeasureImageSimilarity 2 1 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.6 0.05) ADD_TEST(ANTS_MSQ_INVERSEWARP_METRIC_2 MeasureImageSimilarity 2 2 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.000501458 0.05) ADD_TEST(ANTS_MI_1 ANTS 2 -m MI[${R16_IMAGE},${R64_IMAGE},1,32] -r Gauss[3,0] -t SyN[0.25] -i 50x50x30 -o ${OUTPUT_PREFIX}.nii.gz) ADD_TEST(ANTS_MI_1_WARP WarpImageMultiTransform 2 ${R64_IMAGE} ${WARP_IMAGE} ${WARP} -R ${R16_IMAGE} ) ADD_TEST(ANTS_MI_1_JPG ConvertToJpg ${WARP_IMAGE} ANTSMI1.jpg) ADD_TEST(ANTS_MI_1_WARP_METRIC_0 MeasureImageSimilarity 2 0 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 12.4 0.05) ADD_TEST(ANTS_MI_1_WARP_METRIC_1 MeasureImageSimilarity 2 1 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.45 0.05) ADD_TEST(ANTS_MI_1_WARP_METRIC_2 MeasureImageSimilarity 2 2 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.000370686 0.05) ADD_TEST(ANTS_MI_1_INVERSEWARP WarpImageMultiTransform 2 ${R16_IMAGE} ${INVERSEWARP_IMAGE} ${INVERSEWARP} -R ${R16_IMAGE} ) ADD_TEST(ANTS_MI_1_INVERSEWARP_METRIC_0 MeasureImageSimilarity 2 0 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 12.6 0.05) ADD_TEST(ANTS_MI_1_INVERSEWARP_METRIC_1 MeasureImageSimilarity 2 1 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.45 0.05) ADD_TEST(ANTS_MI_1_INVERSEWARP_METRIC_2 MeasureImageSimilarity 2 2 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.000366879 0.05) ADD_TEST(ANTS_MI_2 ANTS 2 -m SMI[${R16_IMAGE},${R64_IMAGE},1,32] -r Gauss[3,0] -t SyN[0.25] -i 50x0x0 -o ${OUTPUT_PREFIX}.nii.gz) ADD_TEST(ANTS_MI_2_WARP WarpImageMultiTransform 2 ${R64_IMAGE} ${WARP_IMAGE} ${WARP} -R ${R16_IMAGE} ) ADD_TEST(ANTS_MI_2_JPG ConvertToJpg ${WARP_IMAGE} ANTSMI2.jpg) ADD_TEST(ANTS_MI_2_WARP_METRIC_0 MeasureImageSimilarity 2 0 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 12.4 0.05) ADD_TEST(ANTS_MI_2_WARP_METRIC_1 MeasureImageSimilarity 2 1 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.45 0.05) ADD_TEST(ANTS_MI_2_WARP_METRIC_2 MeasureImageSimilarity 2 2 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.000370686 0.05) ADD_TEST(ANTS_MI_2_INVERSEWARP WarpImageMultiTransform 2 ${R16_IMAGE} ${INVERSEWARP_IMAGE} ${INVERSEWARP} -R ${R16_IMAGE} ) ADD_TEST(ANTS_MI_2_INVERSEWARP_METRIC_0 MeasureImageSimilarity 2 0 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 12.6 0.05) ADD_TEST(ANTS_MI_2_INVERSEWARP_METRIC_1 MeasureImageSimilarity 2 1 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.45 0.05) ADD_TEST(ANTS_MI_2_INVERSEWARP_METRIC_2 MeasureImageSimilarity 2 2 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.000366879 0.05) ### # ANTS transform testing ### ADD_TEST(ANTS_ELASTIC ANTS 2 -m PR[${R16_IMAGE},${R64_IMAGE},1,2] -t Elast[1] -i 50x50x50 -r Gauss[0,1] -o ${OUTPUT_PREFIX}.nii.gz) ADD_TEST(ANTS_ELASTIC_WARP WarpImageMultiTransform 2 ${R64_IMAGE} ${WARP_IMAGE} ${WARP} -R ${R16_IMAGE} ) ADD_TEST(ANTS_ELASTIC_JPG ConvertToJpg ${WARP_IMAGE} ANTSELASTIC.jpg) ADD_TEST(ANTS_ELASTIC_WARP_METRIC_0 MeasureImageSimilarity 2 0 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 12.5 0.05) ADD_TEST(ANTS_ELASTIC_WARP_METRIC_1 MeasureImageSimilarity 2 1 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.4 0.05) ADD_TEST(ANTS_ELASTIC_WARP_METRIC_2 MeasureImageSimilarity 2 2 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.000459869 0.05) ADD_TEST(ANTS_GSYN ANTS 2 -m PR[${R16_IMAGE},${R64_IMAGE},1,2] -t SyN[0.25] -i 50x50x50 -r Gauss[3,0.0,32] -o ${OUTPUT_PREFIX}.nii.gz) ADD_TEST(ANTS_GSYN_WARP WarpImageMultiTransform 2 ${R64_IMAGE} ${WARP_IMAGE} ${WARP} -R ${R16_IMAGE} ) ADD_TEST(ANTS_GSYN_JPG ConvertToJpg ${WARP_IMAGE} ANTSGSYN.jpg) ADD_TEST(ANTS_GSYN_WARP_METRIC_0 MeasureImageSimilarity 2 0 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 11.7734 0.05) ADD_TEST(ANTS_GSYN_WARP_METRIC_1 MeasureImageSimilarity 2 1 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.6 0.05) ADD_TEST(ANTS_GSYN_WARP_METRIC_2 MeasureImageSimilarity 2 2 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.000478672 0.05) ADD_TEST(ANTS_GSYN_INVERSEWARP WarpImageMultiTransform 2 ${R16_IMAGE} ${INVERSEWARP_IMAGE} ${INVERSEWARP} -R ${R16_IMAGE} ) ADD_TEST(ANTS_GSYN_JPGINV ConvertToJpg ${INVERSEWARP_IMAGE} ANTSGSYNINV.jpg) ADD_TEST(ANTS_GSYN_INVERSEWARP_METRIC_0 MeasureImageSimilarity 2 0 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 12.0541 0.05) ADD_TEST(ANTS_GSYN_INVERSEWARP_METRIC_1 MeasureImageSimilarity 2 1 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.6 0.05) ADD_TEST(ANTS_GSYN_INVERSEWARP_METRIC_2 MeasureImageSimilarity 2 2 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.000475175 0.05) ADD_TEST(ANTS_EXP ANTS 2 -m PR[${R16_IMAGE},${R64_IMAGE},1,4] -t Exp[0.5,2,0.5] -i 50x50x50 -r Gauss[0.5,0.25] -o ${OUTPUT_PREFIX}.nii.gz) ADD_TEST(ANTS_EXP_WARP WarpImageMultiTransform 2 ${R64_IMAGE} ${WARP_IMAGE} ${WARP} -R ${R16_IMAGE} ) ADD_TEST(ANTS_EXP_JPG ConvertToJpg ${WARP_IMAGE} ANTSEXP.jpg) ADD_TEST(ANTS_EXP_WARP_METRIC_0 MeasureImageSimilarity 2 0 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 12 0.05) ADD_TEST(ANTS_EXP_WARP_METRIC_1 MeasureImageSimilarity 2 1 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.5 0.05) ADD_TEST(ANTS_EXP_WARP_METRIC_2 MeasureImageSimilarity 2 2 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.000423693 0.05) ADD_TEST(ANTS_EXP_INVERSEWARP WarpImageMultiTransform 2 ${R16_IMAGE} ${INVERSEWARP_IMAGE} ${INVERSEWARP} -R ${R16_IMAGE} ) ADD_TEST(ANTS_EXP_INVERSEWARP_METRIC_0 MeasureImageSimilarity 2 0 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 12.349 0.05) ADD_TEST(ANTS_EXP_INVERSEWARP_METRIC_1 MeasureImageSimilarity 2 1 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.5 0.05) ADD_TEST(ANTS_EXP_INVERSEWARP_METRIC_2 MeasureImageSimilarity 2 2 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.000339606 0.05) #ADD_TEST(ANTS_GSYN ANTS 2 -m PR[${R16_IMAGE},${R64_IMAGE},1,2] -t SyN[0.75] -i 50x50x50 -r Gauss[3,0.0,32] -o ${OUTPUT_PREFIX}.nii.gz) ADD_TEST(ANTS_SYN ANTS 2 -m PR[${R16_IMAGE},${R64_IMAGE},1,2] -t SyN[0.5,2,0.05] -i 50x50x50 -r Gauss[3,0.0,32] -o ${OUTPUT_PREFIX}.nii.gz) ADD_TEST(ANTS_SYN_WARP WarpImageMultiTransform 2 ${R64_IMAGE} ${WARP_IMAGE} ${WARP} -R ${R16_IMAGE} ) ADD_TEST(ANTS_SYN_JPG ConvertToJpg ${WARP_IMAGE} ANTSSYN.jpg) ADD_TEST(ANTS_SYN_WARP_METRIC_0 MeasureImageSimilarity 2 0 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 12.0239 0.05) ADD_TEST(ANTS_SYN_WARP_METRIC_1 MeasureImageSimilarity 2 1 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.6 0.05) ADD_TEST(ANTS_SYN_WARP_METRIC_2 MeasureImageSimilarity 2 2 ${R16_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.000461922 0.05) ADD_TEST(ANTS_SYN_INVERSEWARP WarpImageMultiTransform 2 ${R16_IMAGE} ${INVERSEWARP_IMAGE} ${INVERSEWARP} -R ${R16_IMAGE} ) ADD_TEST(ANTS_SYN_JPGINV ConvertToJpg ${INVERSEWARP_IMAGE} ANTSSYNINV.jpg) ADD_TEST(ANTS_SYN_INVERSEWARP_METRIC_0 MeasureImageSimilarity 2 0 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 12.5104 0.05) ADD_TEST(ANTS_SYN_INVERSEWARP_METRIC_1 MeasureImageSimilarity 2 1 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.6 0.05) ADD_TEST(ANTS_SYN_INVERSEWARP_METRIC_2 MeasureImageSimilarity 2 2 ${R64_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.000444279 0.05) ### # PSE sub-tests: Check to see if .txt files and .vtk files also run correctly ### SET(ANGEL_IMAGE_TXT ${DATA_DIR}/Smile.txt) SET(DEVIL_IMAGE_TXT ${DATA_DIR}/Frown.txt) SET(ANGEL_IMAGE_VTK ${DATA_DIR}/Smile.vtk) SET(DEVIL_IMAGE_VTK ${DATA_DIR}/Frown.vtk) ADD_TEST(ANTS_PSE_MSQ_TXT ANTS 2 -i 91x70x55x40x30 -r Gauss[3,0.,32] -t SyN[0.25] -m MSQ[${DEVIL_IMAGE},${ANGEL_IMAGE},1,0] -m PSE[${DEVIL_IMAGE},${ANGEL_IMAGE},${DEVIL_IMAGE_TXT},${ANGEL_IMAGE_TXT},1,0.33,11,1,25] --continue-affine 0 --number-of-affine-iterations 0 -o ${OUTPUT_PREFIX}.nii.gz) ADD_TEST(ANTS_PSE_MSQ_VTK ANTS 2 -i 91x70x55x40x30 -r Gauss[3,0.,32] -t SyN[0.25] -m MSQ[${DEVIL_IMAGE},${ANGEL_IMAGE},1,0] -m PSE[${DEVIL_IMAGE},${ANGEL_IMAGE},${DEVIL_IMAGE_VTK},${ANGEL_IMAGE_VTK},1,0.33,11,1,25] --continue-affine 0 --number-of-affine-iterations 0 -o ${OUTPUT_PREFIX}.nii.gz) ### # ANTS labeled data testing ### OPTION(RUN_LONG_TESTS "Run the time consuming tests." OFF ) IF(RUN_LONG_TESTS) ADD_TEST(ANTS_PSE_MSQ_IMG ANTS 2 -i 191x170x90x90x10 -r Gauss[6,0.25] -t SyN[1,2,0.1] -m PSE[${DEVIL_IMAGE},${ANGEL_IMAGE},${DEVIL_IMAGE},${ANGEL_IMAGE},0.25,0.1,100,0,10] -m MSQ[${DEVIL_IMAGE},${ANGEL_IMAGE},1,0.1] --continue-affine 0 --number-of-affine-iterations 0 --geodesic 2 -o ${OUTPUT_PREFIX}.nii.gz) ADD_TEST(ANTS_PSE_MSQ_IMG_WARP WarpImageMultiTransform 2 ${ANGEL_IMAGE} ${WARP_IMAGE} ${WARP} -R ${DEVIL_IMAGE} ) ADD_TEST(ANTS_PSE_JPG ConvertToJpg ${WARP_IMAGE} ANTSPSE.jpg) ADD_TEST(ANTS_PSE_MSQ_IMG_WARP_METRIC_0 MeasureImageSimilarity 2 0 ${DEVIL_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 0.0116083 0.05) ADD_TEST(ANTS_PSE_MSQ_IMG_WARP_METRIC_1 MeasureImageSimilarity 2 1 ${DEVIL_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.884041 0.05) ADD_TEST(ANTS_PSE_MSQ_IMG_WARP_METRIC_2 MeasureImageSimilarity 2 2 ${DEVIL_IMAGE} ${WARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.000710551 0.05) ADD_TEST(ANTS_PSE_MSQ_IMG_INVERSEWARP WarpImageMultiTransform 2 ${DEVIL_IMAGE} ${INVERSEWARP_IMAGE} ${INVERSEWARP} -R ${ANGEL_IMAGE} ) ADD_TEST(ANTS_PSE_JPGINV ConvertToJpg ${INVERSEWARP_IMAGE} ANTSPSEINV.jpg) ADD_TEST(ANTS_PSE_MSQ_IMG_INVERSEWARP_METRIC_0 MeasureImageSimilarity 2 0 ${ANGEL_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 0.0109054 0.05) ADD_TEST(ANTS_PSE_MSQ_IMG_INVERSEWARP_METRIC_1 MeasureImageSimilarity 2 1 ${ANGEL_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.901632 0.05) ADD_TEST(ANTS_PSE_MSQ_IMG_INVERSEWARP_METRIC_2 MeasureImageSimilarity 2 2 ${ANGEL_IMAGE} ${INVERSEWARP_IMAGE} ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz -0.000704717 0.05) ### # ANTS images with non-trival rotation header test ### #SET(ROT_REF_IMAGE ${DATA_DIR}/ref2.nii.gz) #SET(ROT_MOV_IMAGE ${DATA_DIR}/mov2.nii.gz) SET(ROT_REF_IMAGE ${DATA_DIR}/r16roth.nii.gz) SET(ROT_MOV_IMAGE ${DATA_DIR}/r64roth.nii.gz) SET(ROT_OUTPUT_PREFIX ${CMAKE_BINARY_DIR}/RotTEST) SET(ROT_WARP ${ROT_OUTPUT_PREFIX}Warp.nii.gz ${ROT_OUTPUT_PREFIX}Affine.txt ) SET(ROT_INVERSEWARP -i ${ROT_OUTPUT_PREFIX}Affine.txt ${ROT_OUTPUT_PREFIX}InverseWarp.nii.gz ) SET(ROT_WARP_IMAGE ${CMAKE_BINARY_DIR}/rotwarped.nii.gz) SET(ROT_INVERSEWARP_IMAGE ${CMAKE_BINARY_DIR}/rotinversewarped.nii.gz) SET(ROT_WARP_FILES ${ROT_OUTPUT_PREFIX}Warp*vec.nii.gz ${ROT_OUTPUT_PREFIX}InverseWarp*vec.nii.gz ${ROT_OUTPUT_PREFIX}Affine.txt ) #ADD_TEST(ANTS_ROT_SYN ANTS 3 -m MSQ[${ROT_REF_IMAGE},${ROT_MOV_IMAGE},1,0] -t SyN[0.5,2,0.05] -i 50x5x0 -r Gauss[3,0.] -o ${ROT_OUTPUT_PREFIX}.nii.gz) #ADD_TEST(ANTS_ROT_SYN_WARP WarpImageMultiTransform 3 ${ROT_MOV_IMAGE} ${ROT_WARP_IMAGE} -R ${ROT_REF_IMAGE} ${ROT_WARP} ) #ADD_TEST(ANTS_ROT_SYN_WARP_METRIC_0 MeasureImageSimilarity 3 0 ${ROT_REF_IMAGE} ${ROT_WARP_IMAGE} ${ROT_OUTPUT_PREFIX}log.txt ${ROT_OUTPUT_PREFIX}metric.nii.gz 35.4473 0.5) #ADD_TEST(ANTS_ROT_SYN_INVERSEWARP WarpImageMultiTransform 3 ${ROT_REF_IMAGE} ${ROT_INVERSEWARP_IMAGE} -R ${ROT_MOV_IMAGE} ${ROT_INVERSEWARP}) #ADD_TEST(ANTS_ROT_SYN_INVERSEWARP_METRIC_0 MeasureImageSimilarity 3 0 ${ROT_MOV_IMAGE} ${ROT_INVERSEWARP_IMAGE} ${ROT_OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 34.8184 0.5) #ADD_TEST(ANTS_ROT_SYN_CLEAN rm ${ROT_WARP_FILES}) ADD_TEST(ANTS_ROT_GSYN ANTS 3 -m MSQ[${ROT_REF_IMAGE},${ROT_MOV_IMAGE},1,0] -t SyN[0.25] -i 50x5x0 -r Gauss[3,0.0,32] -o ${ROT_OUTPUT_PREFIX}.nii.gz) ADD_TEST(ANTS_ROT_GSYN_WARP WarpImageMultiTransform 3 ${ROT_MOV_IMAGE} ${ROT_WARP_IMAGE} -R ${ROT_REF_IMAGE} ${ROT_WARP} ) ADD_TEST(ANTS_ROT_GSYN_WARP_METRIC_0 MeasureImageSimilarity 3 0 ${ROT_REF_IMAGE} ${ROT_WARP_IMAGE} ${ROT_OUTPUT_PREFIX}log.txt ${ROT_OUTPUT_PREFIX}metric.nii.gz 35.4473 0.5) ADD_TEST(ANTS_ROT_GSYN_INVERSEWARP WarpImageMultiTransform 3 ${ROT_REF_IMAGE} ${ROT_INVERSEWARP_IMAGE} -R ${ROT_MOV_IMAGE} ${ROT_INVERSEWARP}) ADD_TEST(ANTS_ROT_GSYN_INVERSEWARP_METRIC_0 MeasureImageSimilarity 3 0 ${ROT_MOV_IMAGE} ${ROT_INVERSEWARP_IMAGE} ${ROT_OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 34.8184 0.5) #ADD_TEST(ANTS_ROT_GSYN_CLEAN rm ${ROT_WARP_FILES}) ADD_TEST(ANTS_ROT_EXP ANTS 3 -m MSQ[${ROT_REF_IMAGE},${ROT_MOV_IMAGE},1,0] -t Exp[3,2] -i 50x5x1 -r Gauss[3,0.0,32] -o ${ROT_OUTPUT_PREFIX}.nii.gz) ADD_TEST(ANTS_ROT_EXP_WARP WarpImageMultiTransform 3 ${ROT_MOV_IMAGE} ${ROT_WARP_IMAGE} -R ${ROT_REF_IMAGE} ${ROT_WARP} ) ADD_TEST(ANTS_ROT_EXP_WARP_METRIC_0 MeasureImageSimilarity 3 0 ${ROT_REF_IMAGE} ${ROT_WARP_IMAGE} ${ROT_OUTPUT_PREFIX}log.txt ${ROT_OUTPUT_PREFIX}metric.nii.gz 35.4473 0.5) ADD_TEST(ANTS_ROT_EXP_WARP2 WarpImageMultiTransform 3 ${ROT_MOV_IMAGE} ${ROT_WARP_IMAGE} -R ${ROT_REF_IMAGE} --ANTS-prefix ${ROT_OUTPUT_PREFIX} ) ADD_TEST(ANTS_ROT_EXP_WARP2_METRIC_0_2 MeasureImageSimilarity 3 0 ${ROT_REF_IMAGE} ${ROT_WARP_IMAGE} ${ROT_OUTPUT_PREFIX}log.txt ${ROT_OUTPUT_PREFIX}metric.nii.gz 35.4473 0.5) ADD_TEST(ANTS_ROT_EXP_INVERSEWARP WarpImageMultiTransform 3 ${ROT_REF_IMAGE} ${ROT_INVERSEWARP_IMAGE} -R ${ROT_MOV_IMAGE} ${ROT_INVERSEWARP}) ADD_TEST(ANTS_ROT_EXP_INVERSEWARP_METRIC_0 MeasureImageSimilarity 3 0 ${ROT_MOV_IMAGE} ${ROT_INVERSEWARP_IMAGE} ${ROT_OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 34.8184 0.5) ADD_TEST(ANTS_ROT_EXP_INVERSEWARP2 WarpImageMultiTransform 3 ${ROT_REF_IMAGE} ${ROT_INVERSEWARP_IMAGE} -R ${ROT_MOV_IMAGE} --ANTS-prefix-invert ${ROT_OUTPUT_PREFIX}) ADD_TEST(ANTS_ROT_EXP_INVERSEWARP2_METRIC_0_2 MeasureImageSimilarity 3 0 ${ROT_MOV_IMAGE} ${ROT_INVERSEWARP_IMAGE} ${ROT_OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 34.8184 0.5) #ADD_TEST(ANTS_ROT_EXP_CLEAN rm ${ROT_WARP_FILES}) ### # Test SyN with time ### SET(CHALF_IMAGE ${DATA_DIR}/chalf.nii.gz) SET(C_IMAGE ${DATA_DIR}/c.nii.gz) ADD_TEST(ANTS_SYN_WITH_TIME ANTS 2 -m MSQ[${CHALF_IMAGE},${C_IMAGE},1,0.] -t SyN[1,10,0.05] -i 150x100x2x2 -r Gauss[0.5,0.1] -o ${OUTPUT_PREFIX} --geodesic 1 --number-of-affine-iterations 0) ADD_TEST(ANTS_SYN_WITH_TIME_WARP WarpImageMultiTransform 2 ${C_IMAGE} ${OUTPUT_PREFIX}.nii.gz ${OUTPUT_PREFIX}Warp.nii.gz -R ${CHALF_IMAGE} ) ADD_TEST(ANTS_SYN_WITH_TIME_METRIC MeasureImageSimilarity 2 0 ${CHALF_IMAGE} ${OUTPUT_PREFIX}.nii.gz ${OUTPUT_PREFIX}log.txt ${OUTPUT_PREFIX}metric.nii.gz 0.0943736 0.1) ## # Apocrita tests ## SET(R16_MASK ${DATA_DIR}/r16mask.nii.gz) SET(R16_PRIORS ${DATA_DIR}/r16priors.nii.gz) #ADD_TEST(APOC_OTSU_INIT Apocrita 2 -i otsu[${R16_IMAGE},3] -x ${R16_MASK} -n 10 -m [0.3,1x1,0.2,0.1] -o ${OUTPUT_PREFIX}.nii.gz ) #ADD_TEST(APOC_OTSU_INIT_RADIUS_2x2 Apocrita 2 -i otsu[${R16_IMAGE},3] -x ${R16_MASK} -n 10 -m [0.3,2,0.2,0.1] -o ${OUTPUT_PREFIX}.nii.gz ) #ADD_TEST(APOC_KMEANS_INIT Apocrita 2 -i kmeans[${R16_IMAGE},3] -x ${R16_MASK} -n 10 -m [0.3,1x1,0.2,0.1] -o [${OUTPUT_PREFIX}.nii.gz,${OUTPUT_PREFIX}_posteriors%d.nii.gz]) #ADD_TEST(APOC_PRIORLABELIMAGE_INIT Apocrita 2 -i priorlabelimage[${R16_IMAGE},5,${R16_PRIORS},0.5] -x ${R16_MASK} -n 10 -m [0.3,1x1,0.2,0.1] -o [${OUTPUT_PREFIX}.nii.gz,${OUTPUT_PREFIX}_posteriors%d.nii.gz] -l 1[1,0.75] -l 2[1,1.0] -l 3[0.5,0.5] -l 4[1,1]) ENDIF(RUN_LONG_TESTS) ants-1.9.2+svn680.dfsg/Examples/CTestConfig.cmake000066400000000000000000000010261147325206600214070ustar00rootroot00000000000000## This file should be placed in the root directory of your project. ## Then modify the CMakeLists.txt file in the root directory of your ## project to incorporate the testing dashboard. ## # The following are required to uses Dart and the Cdash dashboard ## ENABLE_TESTING() ## INCLUDE(CTest) set(CTEST_PROJECT_NAME "ANTS") set(CTEST_NIGHTLY_START_TIME "00:00:00 EST") set(CTEST_DROP_METHOD "http") set(CTEST_DROP_SITE "picsl.upenn.edu") set(CTEST_DROP_LOCATION "/cdash/submit.php?project=ANTS") set(CTEST_DROP_SITE_CDASH TRUE) ants-1.9.2+svn680.dfsg/Examples/CheckTopology.cxx000066400000000000000000000343611147325206600215430ustar00rootroot00000000000000 /*========================================================================= Program: Insight Segmentation & Registration Toolkit Module: $RCSfile: CheckTopology.cxx,v $ Language: C++ $Date: 2008/12/07 03:06:27 $ $Revision: 1.8 $ Copyright (c) 2002 Insight Consortium. All rights reserved. See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include #include #include #include #include "itkDiscreteGaussianImageFilter.h" #include "itkImage.h" #include "itkExceptionObject.h" #include "ReadWriteImage.h" #include "itkVector.h" #include "itkBinaryThresholdImageFilter.h" #include "itkRandomImageSource.h" #include "itkImageRandomConstIteratorWithIndex.h" #include "itkImageLinearIteratorWithIndex.h" #include "itkShapedNeighborhoodIterator.h" #include "BinaryImageToMeshFilter.h" #include "itkBinaryErodeImageFilter.h" #include "itkBinaryDilateImageFilter.h" #include "itkBinaryBallStructuringElement.h" #include "vtkCallbackCommand.h" #include "vtkPointPicker.h" #include "vtkCellPicker.h" #include "vtkPolyDataWriter.h" #include "vtkPolyDataReader.h" #include "vtkExtractEdges.h" #include "itkMinimumMaximumImageFilter.h" #include "itkConnectedComponentImageFilter.h" #include "itkRelabelComponentImageFilter.h" #include "itkBinaryThresholdImageFilter.h" #include "itkLabelStatisticsImageFilter.h" float random_range(float lowest_number, float highest_number) { float range = highest_number - lowest_number; return lowest_number + (float)(range * (float)rand()/(float)(RAND_MAX)); } float ComputeGenus(vtkPolyData* pd1) { vtkExtractEdges* edgeex=vtkExtractEdges::New(); edgeex->SetInput(pd1); edgeex->Update(); vtkPolyData* edg1=edgeex->GetOutput(); vtkIdType nedg=edg1->GetNumberOfCells(); vtkIdType vers = pd1->GetNumberOfPoints(); int nfac = pd1->GetNumberOfPolys(); float g = 0.5 * (2.0 - vers + nedg - nfac); std::cout << " Genus " << g << std::endl; std::cout << " face " << nfac << " edg " << nedg << " vert " << vers << std::endl; //edg1->Delete(); //caused malloc err edgeex->Delete(); // should be deleted b/c of New() above !! return g; } float vtkComputeTopology(vtkPolyData* pd) { vtkPolyDataConnectivityFilter* con = vtkPolyDataConnectivityFilter::New(); con->SetExtractionModeToLargestRegion(); con->SetInput(pd); float g = ComputeGenus(con->GetOutput()); con->Delete(); // should be deleted b/c of New() above !! return g; } template float GetImageTopology(typename TImage::Pointer image) { typedef TImage ImageType; typedef vtkPolyData MeshType; double aaParm = 0.024; typedef BinaryImageToMeshFilter FilterType; typename FilterType::Pointer fltMesh = FilterType::New(); fltMesh->SetInput(image); fltMesh->SetAntiAliasMaxRMSError(aaParm); fltMesh->SetAntiAliasMaxRMSError( -1000.0 ); // to do nothing fltMesh->SetSmoothingIterations(0); fltMesh->Update(); vtkPolyData* vtkmesh =fltMesh->GetMesh(); std::cout << " start topo " << std::endl; float genus = vtkComputeTopology(vtkmesh); std::cout << " Genus " << genus << std::endl; // vtkmesh->Delete(); return genus; } template void NormalizeImage(typename TImage::Pointer image) { typedef itk::ImageRegionIteratorWithIndex Iterator; float max=0; Iterator vfIter2( image, image->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) if (vfIter2.Get() > max) max=vfIter2.Get(); if (max == 0) max=1; for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) vfIter2.Set( vfIter2.Get()/max); } template typename TImage::Pointer SmoothImage( typename TImage::Pointer image, float sig ) { typedef float PixelType; typedef TImage ImageType; enum { ImageDimension = ImageType::ImageDimension }; typedef itk::DiscreteGaussianImageFilter dgf; typename dgf::Pointer filter = dgf::New(); filter->SetVariance(sig); filter->SetUseImageSpacingOff(); filter->SetMaximumError(.01f); filter->SetInput(image); filter->Update(); return filter->GetOutput(); } template typename TImage::Pointer BinaryThreshold( typename TImage::PixelType low, typename TImage::PixelType high, typename TImage::PixelType replaceval, typename TImage::Pointer input) { typedef typename TImage::PixelType PixelType; // Begin Threshold Image typedef itk::BinaryThresholdImageFilter InputThresholderType; typename InputThresholderType::Pointer inputThresholder = InputThresholderType::New(); inputThresholder->SetInput( input ); inputThresholder->SetInsideValue( replaceval ); int outval=0; if ((float) replaceval == (float) -1) outval=1; inputThresholder->SetOutsideValue( outval ); if (high < low) high=255; inputThresholder->SetLowerThreshold((PixelType) low ); inputThresholder->SetUpperThreshold((PixelType) high); inputThresholder->Update(); return inputThresholder->GetOutput(); } template //std::vector typename TImage::Pointer GetLargestComponent(typename TImage::Pointer image) { enum { ImageDimension = TImage::ImageDimension }; typedef int InternalPixelType; typedef itk::Image InternalImageType; typedef TImage OutputImageType; typedef itk::BinaryThresholdImageFilter< TImage, InternalImageType > ThresholdFilterType; typedef itk::ConnectedComponentImageFilter< InternalImageType, InternalImageType > FilterType; typedef itk::RelabelComponentImageFilter< InternalImageType, InternalImageType > RelabelType; typename ThresholdFilterType::Pointer threshold = ThresholdFilterType::New(); typename FilterType::Pointer filter = FilterType::New(); typename RelabelType::Pointer relabel = RelabelType::New(); threshold->SetInput (image); threshold->SetInsideValue(itk::NumericTraits::One); threshold->SetOutsideValue(itk::NumericTraits::Zero); threshold->SetLowerThreshold(0.499); threshold->SetUpperThreshold(1.001); threshold->Update(); filter->SetInput (threshold->GetOutput()); // if (argc > 5) { int fullyConnected = 1;//atoi( argv[5] ); filter->SetFullyConnected( fullyConnected ); } relabel->SetInput( filter->GetOutput() ); unsigned int minSize=50; std::cout << " min Size " << minSize << std::endl; relabel->SetMinimumObjectSize( minSize ); // relabel->SetUseHistograms(true); try { relabel->Update(); } catch( itk::ExceptionObject & excep ) { std::cerr << "Relabel: exception caught !" << std::endl; std::cerr << excep << std::endl; } typename TImage::Pointer Clusters=TImage::New(); //typename TImage::Pointer Clusters=relabel->GetOutput(); Clusters->SetLargestPossibleRegion( image->GetLargestPossibleRegion() ); Clusters->SetBufferedRegion( image->GetLargestPossibleRegion().GetSize() ); Clusters->SetSpacing(image->GetSpacing()); Clusters->SetDirection( image->GetDirection() ); Clusters->SetOrigin(image->GetOrigin()); Clusters->Allocate(); Clusters->FillBuffer(0); typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter( relabel->GetOutput(), relabel->GetOutput()->GetLargestPossibleRegion() ); float maximum=relabel->GetNumberOfObjects(); float maxtstat=0; std::vector histogram((int)maximum+1); std::vector clustersum((int)maximum+1); for (int i=0; i<=maximum; i++) { histogram[i]=0; clustersum[i]=0; } for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) { if (vfIter.Get() > 0 ) { float vox=image->GetPixel(vfIter.GetIndex()); histogram[(unsigned int)vfIter.Get()]=histogram[(unsigned int)vfIter.Get()]+1; clustersum[(unsigned int)vfIter.Get()]+=vox; if (vox > maxtstat) maxtstat=vox; } } for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) { if (vfIter.Get() > 0 ) { Clusters->SetPixel( vfIter.GetIndex(), histogram[(unsigned int)vfIter.Get()] ); // if ( Clusters->GetPixel( vfIter.GetIndex() ) > maximgval ) // maximgval=Clusters->GetPixel( vfIter.GetIndex()); } else Clusters->SetPixel(vfIter.GetIndex(),0); } float maximgval=0; for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) if ( Clusters->GetPixel( vfIter.GetIndex() ) > maximgval ) maximgval=Clusters->GetPixel( vfIter.GetIndex()); std::cout << " max size " << maximgval << std::endl; for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) if ( Clusters->GetPixel( vfIter.GetIndex() ) >= maximgval ) Clusters->SetPixel( vfIter.GetIndex(), 1); else Clusters->SetPixel( vfIter.GetIndex(), 0); // for (int i=0; i<=maximum; i++) // std::cout << " label " << i << " ct is: " << histogram[i] << std::endl; return Clusters; } template typename TImage::Pointer Morphological( typename TImage::Pointer input,float rad, bool option) { typedef TImage ImageType; enum { ImageDimension = TImage::ImageDimension }; typedef typename TImage::PixelType PixelType; if (!option) std::cout << " eroding the image " << std::endl; else std::cout << " dilating the image " << std::endl; typedef itk::BinaryBallStructuringElement< PixelType, ImageDimension > StructuringElementType; // Software Guide : BeginCodeSnippet typedef itk::BinaryErodeImageFilter< TImage, TImage, StructuringElementType > ErodeFilterType; typedef itk::BinaryDilateImageFilter< TImage, TImage, StructuringElementType > DilateFilterType; typename ErodeFilterType::Pointer binaryErode = ErodeFilterType::New(); typename DilateFilterType::Pointer binaryDilate = DilateFilterType::New(); StructuringElementType structuringElement; structuringElement.SetRadius((unsigned long) rad ); // 3x3x3 structuring element structuringElement.CreateStructuringElement(); binaryErode->SetKernel( structuringElement ); binaryDilate->SetKernel( structuringElement ); // It is necessary to define what could be considered objects on the binary // images. This is specified with the methods \code{SetErodeValue()} and // \code{SetDilateValue()}. The value passed to these methods will be // considered the value over which the dilation and erosion rules will apply binaryErode->SetErodeValue( 1 ); binaryDilate->SetDilateValue( 1 ); typename TImage::Pointer temp; if (option) { binaryDilate->SetInput( input ); binaryDilate->Update(); temp = binaryDilate->GetOutput(); } else { binaryErode->SetInput( input );//binaryDilate->GetOutput() ); binaryErode->Update(); temp = binaryErode->GetOutput(); typedef itk::ImageRegionIteratorWithIndex< ImageType > ImageIteratorType ; ImageIteratorType o_iter( temp, temp->GetLargestPossibleRegion() ); o_iter.GoToBegin() ; while ( !o_iter.IsAtEnd() ) { if (o_iter.Get() > 0.5 && input->GetPixel(o_iter.GetIndex()) > 0.5) o_iter.Set(1); else o_iter.Set(0); ++o_iter; } } return temp; } int main(int argc, char *argv[]) { if ( argc < 2 ) { std::cout << "Parameter missing" << std::endl; std::cout << std::endl; std::cout << "Usage:" << argv[0] << " image.nii {g0image.nii} {threshold}" << std::endl; std::cout << " If you put an arg for g0image then image will be smoothed and thresholded \n until it has genus zero or the smoothing kernel gets too large " << std::endl; return 1; } float thresh=-1;//0.0001; if (argc > 3) thresh=atof(argv[3]); typedef float PixelType; const unsigned int ImageDimension=3;//AvantsImageDimension; typedef itk::Image ImageType; typedef float FieldValueType; ImageType::Pointer image; ReadImage(image,argv[1]); image=BinaryThreshold(0.5,1.e9,1,image); float initG = GetImageTopology(image); if (initG < 0 && argc > 2) { std::cout << "smoothing into a Genus Zero image with thresh " << thresh << std::endl; float G=1; float smooth=1; ImageType::Pointer simage; while ( G != 0 && smooth < 20 ) { simage=SmoothImage(image,smooth); NormalizeImage(simage); simage=BinaryThreshold(thresh,1.e9,1,simage); ImageType::Pointer bigimage = GetLargestComponent(simage); G = GetImageTopology(bigimage); smooth=smooth+1; simage=bigimage; std::cout << " G " << G << " at smoothing " << smooth << std::endl; } std::cout << " Final G " << G << " at smoothing " << smooth << std::endl; float G2=0; unsigned int mct=0; float err=1.e9,lasterr=1.e10,derr=1.e9; derr=lasterr-err; while ( G2 == 0 && derr > 0 ) { lasterr=err; err=0; ImageType::Pointer out = Morphological(simage,3,0); ImageType::Pointer bigimage = GetLargestComponent(out); G2 = GetImageTopology(bigimage); typedef itk::ImageRegionIteratorWithIndex< ImageType > ImageIteratorType ; ImageIteratorType iter( bigimage, bigimage->GetLargestPossibleRegion() ); iter.GoToBegin() ; while ( !iter.IsAtEnd() ) { err+=fabs(iter.Get() - image->GetPixel(iter.GetIndex())); ++iter; } mct++; derr=lasterr-err; std::cout << " G2 " << G2 << " at morph " << mct << " err " << err << std::endl; if (G2==0 && derr > 0) { simage=GetLargestComponent(out); } } WriteImage(simage ,argv[2]); } else if (argc > 2 ) { WriteImage(image ,argv[2]); } return 0; } ants-1.9.2+svn680.dfsg/Examples/ClusterImageStatistics.cxx000066400000000000000000000262141147325206600234260ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: ClusterImageStatistics.cxx,v $ Language: C++ Date: $Date: 2008/12/15 16:35:13 $ Version: $Revision: 1.20 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include "itkDiscreteGaussianImageFilter.h" // RecursiveAverageImages img1 img2 weightonimg2 outputname // We divide the 2nd input image by its mean and add it to the first // input image with weight 1/n. //The output overwrites the 1st img with the sum. #include #include #include #include "vnl/vnl_vector.h" #include "itkMinimumMaximumImageFilter.h" #include "itkConnectedComponentImageFilter.h" #include "itkRelabelComponentImageFilter.h" #include "itkBinaryThresholdImageFilter.h" #include "itkLabelStatisticsImageFilter.h" #include "ReadWriteImage.h" template int ClusterStatistics(unsigned int argc, char *argv[]) { typedef float PixelType; // const unsigned int ImageDimension = AvantsImageDimension; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; // typedef itk::ImageRegionIteratorWithIndex Iterator; typedef float InternalPixelType; typedef unsigned long ULPixelType; typedef itk::Image labelimagetype; typedef ImageType InternalImageType; typedef ImageType OutputImageType; typedef itk::ConnectedComponentImageFilter< ImageType, labelimagetype > FilterType; typedef itk::RelabelComponentImageFilter< labelimagetype, labelimagetype > RelabelType; // want the average value in each cluster as defined by the mask and the value thresh and the clust thresh std::string roimaskfn = std::string(argv[2]); std::string labelimagefn = std::string(argv[3]); std::string outname = std::string(argv[4]); float clusterthresh = atof(argv[5]); float minSize=clusterthresh; float valuethresh = atof(argv[6]); // std::cout << " Cth " << clusterthresh << " Vth " << valuethresh << std::endl; typename ImageType::Pointer valimage = NULL; typename ImageType::Pointer roiimage = NULL; typename ImageType::Pointer labelimage= NULL; ReadImage(roiimage,roimaskfn.c_str()); ReadImage(labelimage,labelimagefn.c_str()); typedef itk::MinimumMaximumImageFilter MinMaxFilterType; typename MinMaxFilterType::Pointer minMaxFilter = MinMaxFilterType::New(); minMaxFilter->SetInput( labelimage ); minMaxFilter->Update(); double min = minMaxFilter->GetMinimum(); double max = minMaxFilter->GetMaximum(); double range=max-min; for (unsigned int filecount=7; filecount < argc; filecount++) { // std::cout <<" doing " << std::string(argv[filecount]) << std::endl; ReadImage(valimage,argv[filecount]); // first, threshold the value image then get the clusters of min size typedef itk::BinaryThresholdImageFilter< ImageType,ImageType > ThresholdFilterType; typename ThresholdFilterType::Pointer threshold = ThresholdFilterType::New(); threshold->SetInput (valimage); threshold->SetInsideValue(1); threshold->SetOutsideValue(0); threshold->SetLowerThreshold(valuethresh); threshold->SetUpperThreshold(1.e9); threshold->Update(); typename ImageType::Pointer thresh=threshold->GetOutput(); typedef itk::ImageRegionIteratorWithIndex fIterator; typedef itk::ImageRegionIteratorWithIndex Iterator; fIterator tIter( thresh, thresh->GetLargestPossibleRegion() ); for( tIter.GoToBegin(); !tIter.IsAtEnd(); ++tIter ) if (roiimage->GetPixel(tIter.GetIndex()) < 0.5) tIter.Set(0); // typename typename FilterType::Pointer filter = FilterType::New(); //typename typename RelabelType::Pointer relabel = RelabelType::New(); filter->SetInput( thresh ); int fullyConnected = 0;//atoi( argv[5] ); filter->SetFullyConnected( fullyConnected ); relabel->SetInput( filter->GetOutput() ); relabel->SetMinimumObjectSize( (unsigned int) minSize ); try { relabel->Update(); } catch( itk::ExceptionObject & excep ) { std::cerr << "Relabel: exception caught !" << std::endl; std::cerr << excep << std::endl; } typename ImageType::Pointer Clusters=MakeNewImage(valimage,0); typename ImageType::Pointer Values=MakeNewImage(valimage,0); typename ImageType::Pointer Labels=MakeNewImage(valimage,0); Iterator vfIter( relabel->GetOutput(), relabel->GetOutput()->GetLargestPossibleRegion() ); float maximum=relabel->GetNumberOfObjects(); // std::cout << " #object " << maximum << std::endl; // float maxtstat=0; std::vector histogram((int)maximum+1); std::vector maxlabel((int)maximum+1); std::vector suminlabel((unsigned long) range+1); std::vector countinlabel((unsigned long) range+1); std::vector sumofvalues((int)maximum+1); std::vector maxvalue((int)maximum+1); for (int i=0; i<=maximum; i++) { histogram[i]=0; sumofvalues[i]=0; maxvalue[i]=0; maxlabel[i]=0; } for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) { if (vfIter.Get() > 0 ) { float vox=valimage->GetPixel(vfIter.GetIndex()); if (vox >= valuethresh) { histogram[(unsigned long)vfIter.Get()]=histogram[(unsigned long)vfIter.Get()]+1; sumofvalues[(unsigned long)vfIter.Get()]=sumofvalues[(unsigned long)vfIter.Get()]+vox; if ( maxvalue[(unsigned long)vfIter.Get()] < vox ) { maxvalue[(unsigned long)vfIter.Get()]=vox; maxlabel[(unsigned long)vfIter.Get()]=(long int)labelimage->GetPixel(vfIter.GetIndex()); } suminlabel[(unsigned long)(labelimage->GetPixel(vfIter.GetIndex()) - min)]+=vox; countinlabel[(unsigned long)(labelimage->GetPixel(vfIter.GetIndex()) - min)]+=1; } } } for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) { if (vfIter.Get() > 0 ) { Clusters->SetPixel( vfIter.GetIndex(), histogram[(unsigned long)vfIter.Get()] ); Values->SetPixel( vfIter.GetIndex(), sumofvalues[(unsigned long)vfIter.Get()]/(float)histogram[(unsigned int)vfIter.Get()] ); Labels->SetPixel( vfIter.GetIndex(),labelimage->GetPixel(vfIter.GetIndex())); } else { Clusters->SetPixel(vfIter.GetIndex(),0); Labels->SetPixel(vfIter.GetIndex(),0); Values->SetPixel(vfIter.GetIndex(),0); } } // WriteImage(Values,std::string("temp.nii.gz").c_str()); // WriteImage(Clusters,std::string("temp2.nii.gz").c_str()); float maximgval=0; for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) if ( Clusters->GetPixel( vfIter.GetIndex() ) > maximgval ) maximgval=Clusters->GetPixel( vfIter.GetIndex()); // std::cout << " max size " << maximgval << std::endl; for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) if ( Clusters->GetPixel( vfIter.GetIndex() ) < minSize ) { Clusters->SetPixel( vfIter.GetIndex(), 0); Values->SetPixel( vfIter.GetIndex(), 0); Labels->SetPixel( vfIter.GetIndex(), 0); } // WriteImage(Values,(outname+"values.nii.gz").c_str()); // WriteImage(Labels,(outname+"labels.nii.gz").c_str()); WriteImage(Clusters,(outname+"sizes.nii.gz").c_str()); // now begin output // std::cout << " Writing Text File " << outname << std::endl; std::string outname2=outname+std::string(".csv"); std::ofstream outf ((outname2).c_str(), std::ofstream::app); if (outf.good()) { // outf << std::string(argv[filecount]) << std::endl; for (int i=0; i < maximum+1; i++) { if ( histogram[i] >= minSize) { // outf << " Cluster " << i << " size " << histogram[i] << " average " << sumofvalues[i]/(float)histogram[i] << " max " << maxvalue[i] << " label " << maxlabel[i] << std::endl; std::cout << " Cluster " << i << " size " << histogram[i] << " average " << sumofvalues[i]/(float)histogram[i] << " max " << maxvalue[i] << " label " << maxlabel[i] << std::endl; } } for (unsigned int i=0; i <= range; i++) { // if ( countinlabel[i] > 0) { if (countinlabel[i] == 0) countinlabel[i]=1; // outf << " Label " << i+min << " average " << suminlabel[i]/(float)countinlabel[i] << std::endl; std::cout << " Label " << i+min << " average " << suminlabel[i]/(float)countinlabel[i] << std::endl; if ( i >= 0 && i < range ) { outf << suminlabel[i]/(float)countinlabel[i] << ","; } else { outf << suminlabel[i]/(float)countinlabel[i] << std::endl; } } } } else std::cout <<" File No Good! " << outname << std::endl; outf.close(); } return 0; } int main(int argc, char *argv[]) { if ( argc < 4 ) { std::cout << " Given an ROI and Label Image, find the max and average value \n in a value image where the value > some user-defined threshold \n and the cluster size is larger than some min size. \n " << std::endl; std::cout << "Usage: \n "<< std::endl; std::cout << argv[0] << " ImageDimension ROIMask.ext LabelImage.ext OutPrefix MinimumClusterSize ValueImageThreshold Image1WithValuesOfInterest.ext ... ImageNWithValuesOfInterest.ext \n \n " << std::endl; std::cout << " ROIMask.ext -- overall region of interest \n \n LabelImage.ext -- labels for the sub-regions, e.g. Brodmann or just unique labels (see LabelClustersUniquely ) \n \n OutputPrefix -- all output has this prefix \n \n MinimumClusterSize -- the minimum size of clusters of interest \n \n ValueImageThreshold -- minimum value of interest \n \n Image*WithValuesOfInterest.ext --- image(s) that define the values you want to measure \n "; return 1; } switch ( atoi(argv[1]) ) { case 2: ClusterStatistics<2>(argc,argv); break; case 3: ClusterStatistics<3>(argc,argv); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } return 0; } ants-1.9.2+svn680.dfsg/Examples/ComposeMultiTransform.cxx000066400000000000000000000400311147325206600232740ustar00rootroot00000000000000#include #include #include "itkImageFileReader.h" #include "itkVector.h" #include "itkVectorImageFileReader.h" #include "itkVectorImageFileWriter.h" #include "itkImageFileWriter.h" #include "itkMatrixOffsetTransformBase.h" #include "itkTransformFactory.h" //#include "itkWarpImageMultiTransformFilter.h" #include "itkDeformationFieldFromMultiTransformFilter.h" #include "itkTransformFileReader.h" #include "itkTransformFileWriter.h" typedef enum { INVALID_FILE = 1, AFFINE_FILE, DEFORMATION_FILE } TRAN_FILE_TYPE; typedef struct { char * filename; TRAN_FILE_TYPE file_type; bool do_affine_inv; } TRAN_OPT; typedef std::vector TRAN_OPT_QUEUE; TRAN_FILE_TYPE CheckFileType(char *str) { std::string filename = str; std::string::size_type pos = filename.rfind("."); std::string filepre = std::string(filename, 0, pos); if (pos != std::string::npos) { std::string extension = std::string(filename, pos, filename.length() - 1); if (extension == std::string(".gz")) { pos = filepre.rfind("."); extension = std::string(filepre, pos, filepre.length() - 1); } if (extension == ".txt") return AFFINE_FILE; else return DEFORMATION_FILE; } else { return INVALID_FILE; } return AFFINE_FILE; } bool ParseInput(int argc, char **argv, char *&output_image_filename, char *&reference_image_filename, TRAN_OPT_QUEUE &opt_queue) { opt_queue.clear(); opt_queue.reserve(argc - 2); output_image_filename = argv[0]; reference_image_filename = NULL; int ind = 1; while (ind < argc) { if (strcmp(argv[ind], "-R") == 0) { ind++; if (ind >= argc) return false; reference_image_filename = argv[ind]; } else if (strcmp(argv[ind], "-i") == 0) { ind++; if (ind >= argc) return false; TRAN_OPT opt; opt.filename = argv[ind]; if (CheckFileType(opt.filename) != AFFINE_FILE) { std::cout << "file: " << opt.filename << " is not an affine .txt file. Invalid to use '-i' " << std::endl; return false; } opt.file_type = AFFINE_FILE; opt.do_affine_inv = true; opt_queue.push_back(opt); } else { TRAN_OPT opt; opt.filename = argv[ind]; opt.file_type = CheckFileType(opt.filename); opt.do_affine_inv = false; opt_queue.push_back(opt); } ind++; } // if (reference_image_filename == NULL) { // std::cout << "the reference image file (-R) must be given!!!" // << std::endl; // return false; // } return true; } void DisplayOptQueue(const TRAN_OPT_QUEUE &opt_queue) { const int kQueueSize = opt_queue.size(); for (int i = 0; i < kQueueSize; i++) { std::cout << "[" << i << "/" << kQueueSize << "]: "; switch (opt_queue[i].file_type) { case AFFINE_FILE: std::cout << "AFFINE"; if (opt_queue[i].do_affine_inv) std::cout << "-INV"; break; case DEFORMATION_FILE: std::cout << "FIELD"; break; default: std::cout << "Invalid Format!!!"; break; } std::cout << ": " << opt_queue[i].filename << std::endl; } } template void ComposeMultiTransform(char *output_image_filename, char *reference_image_filename, TRAN_OPT_QUEUE &opt_queue) { typedef itk::Image ImageType; typedef itk::Vector VectorType; typedef itk::Image DeformationFieldType; typedef itk::MatrixOffsetTransformBase AffineTransformType; // typedef itk::WarpImageMultiTransformFilter WarperType; typedef itk::DeformationFieldFromMultiTransformFilter WarperType; itk::TransformFactory::RegisterTransform(); typedef itk::ImageFileReader ImageFileReaderType; typename ImageFileReaderType::Pointer reader_img = ImageFileReaderType::New(); typename ImageType::Pointer img_ref = ImageType::New(); typename ImageFileReaderType::Pointer reader_img_ref = ImageFileReaderType::New(); if (reference_image_filename) { reader_img_ref->SetFileName(reference_image_filename); reader_img_ref->Update(); img_ref = reader_img_ref->GetOutput(); } else { std::cout << "the reference image file (-R) must be given!!!" << std::endl; return; } typename WarperType::Pointer warper = WarperType::New(); // warper->SetInput(img_mov); // warper->SetEdgePaddingValue( 0); VectorType pad; pad.Fill(0); // warper->SetEdgePaddingValue(pad); typedef itk::TransformFileReader TranReaderType; typedef itk::VectorImageFileReader FieldReaderType; const int kOptQueueSize = opt_queue.size(); for (int i = 0; i < kOptQueueSize; i++) { const TRAN_OPT &opt = opt_queue[i]; switch (opt_queue[i].file_type) { case AFFINE_FILE: { typename TranReaderType::Pointer tran_reader = TranReaderType::New(); tran_reader->SetFileName(opt.filename); tran_reader->Update(); typename AffineTransformType::Pointer aff = dynamic_cast ((tran_reader->GetTransformList())->front().GetPointer()); if (opt_queue[i].do_affine_inv) { aff->GetInverse(aff); } // std::cout << aff << std::endl; warper->PushBackAffineTransform(aff); break; } case DEFORMATION_FILE: { typename FieldReaderType::Pointer field_reader = FieldReaderType::New(); field_reader->SetFileName(opt.filename); field_reader->Update(); typename DeformationFieldType::Pointer field = field_reader->GetOutput(); // std::cout << field << std::endl; warper->PushBackDeformationFieldTransform(field); break; } default: std::cout << "Unknown file type!" << std::endl; } } warper->SetOutputSize(img_ref->GetLargestPossibleRegion().GetSize()); warper->SetOutputSpacing(img_ref->GetSpacing()); warper->SetOutputOrigin(img_ref->GetOrigin()); warper->SetOutputDirection(img_ref->GetDirection()); std::cout << "output size: " << warper->GetOutputSize() << std::endl; std::cout << "output spacing: " << warper->GetOutputSpacing() << std::endl; // warper->PrintTransformList(); warper->DetermineFirstDeformNoInterp(); warper->Update(); typename DeformationFieldType::Pointer field_output = DeformationFieldType::New(); field_output = warper->GetOutput(); std::string filePrefix = output_image_filename; std::string::size_type pos = filePrefix.rfind("."); std::string extension = std::string(filePrefix, pos, filePrefix.length() - 1); filePrefix = std::string(filePrefix, 0, pos); std::cout << "output extension is: " << extension << std::endl; if (extension != std::string(".mha")) { typedef itk::VectorImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetFileName(output_image_filename); writer->SetUseAvantsNamingConvention(true); writer->SetInput(field_output); writer->Update(); } else { typedef itk::ImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetFileName(output_image_filename); writer->SetInput(field_output); writer->Update(); } } template void ComposeMultiAffine(char *output_affine_txt, char *reference_affine_txt, TRAN_OPT_QUEUE &opt_queue) { typedef itk::Image ImageType; typedef itk::Vector VectorType; typedef itk::Image DeformationFieldType; typedef itk::MatrixOffsetTransformBase AffineTransformType; typedef itk::WarpImageMultiTransformFilter WarperType; // typedef itk::DeformationFieldFromMultiTransformFilter WarperType; itk::TransformFactory::RegisterTransform(); // typedef itk::ImageFileReader ImageFileReaderType; // typename ImageFileReaderType::Pointer reader_img = ImageFileReaderType::New(); // typename ImageType::Pointer img_ref = ImageType::New(); // typename ImageFileReaderType::Pointer reader_img_ref = ImageFileReaderType::New(); typename WarperType::Pointer warper = WarperType::New(); // warper->SetInput(img_mov); // warper->SetEdgePaddingValue( 0); VectorType pad; pad.Fill(0); // warper->SetEdgePaddingValue(pad); typedef itk::TransformFileReader TranReaderType; typedef itk::VectorImageFileReader FieldReaderType; int cnt_affine = 0; const int kOptQueueSize = opt_queue.size(); for (int i = 0; i < kOptQueueSize; i++) { const TRAN_OPT &opt = opt_queue[i]; switch (opt_queue[i].file_type) { case AFFINE_FILE: { typename TranReaderType::Pointer tran_reader = TranReaderType::New(); tran_reader->SetFileName(opt.filename); tran_reader->Update(); typename AffineTransformType::Pointer aff = dynamic_cast ((tran_reader->GetTransformList())->front().GetPointer()); if (opt_queue[i].do_affine_inv) { aff->GetInverse(aff); } // std::cout << aff << std::endl; warper->PushBackAffineTransform(aff); cnt_affine++; break; } case DEFORMATION_FILE: { std::cout << "Compose affine only files: ignore " << opt.filename << std::endl; break; } default: std::cout << "Unknown file type!" << std::endl; } } typedef typename AffineTransformType::CenterType PointType; PointType aff_center; typename AffineTransformType::Pointer aff_ref_tmp; if (reference_affine_txt) { typename TranReaderType::Pointer tran_reader = TranReaderType::New(); tran_reader->SetFileName(reference_affine_txt); tran_reader->Update(); aff_ref_tmp = dynamic_cast ((tran_reader->GetTransformList())->front().GetPointer()); } else { if (cnt_affine > 0) { std::cout << "the reference affine file for center is selected as the first affine!" << std::endl; aff_ref_tmp = ((warper->GetTransformList()).begin())->second.aex.aff; } else { std::cout << "No affine input is given. nothing to do ......" << std::endl; return; } } aff_center = aff_ref_tmp->GetCenter(); std::cout << "new center is : " << aff_center << std::endl; // warper->PrintTransformList(); // typename AffineTransformType::Pointer aff_output = warper->ComposeAffineOnlySequence(aff_center); typename AffineTransformType::Pointer aff_output = AffineTransformType::New(); warper->ComposeAffineOnlySequence(aff_center, aff_output); typedef itk::TransformFileWriter TranWriterType; typename TranWriterType::Pointer tran_writer = TranWriterType::New(); tran_writer->SetFileName(output_affine_txt); tran_writer->SetInput(aff_output); tran_writer->Update(); std::cout << "wrote file to : " << output_affine_txt << std::endl; } int main(int argc, char **argv) { if (argc <= 3) { std::cout << "ComposeMultiTransform ImageDimension output_field [-R reference_image] " << "{[deformation_field | [-i] affine_transform_txt ]}" << std::endl; std::cout <<" Usage has the same form as WarpImageMultiTransform " << std::endl; std::cout <<" For Example: " << std::endl; std::cout << std::endl; std::cout << argv[0] << " Dimension outwarp.nii -R template.nii ExistingWarp.nii ExistingAffine.nii " << std::endl; std::cout << " or for an inverse mapping : " << std::endl; std::cout << argv[0] << " Dimension outwarp.nii -R template.nii -i ExistingAffine.nii ExistingInverseWarp.nii " << std::endl; std::cout <<" recalling that the -i option takes the inverse of the affine mapping " << std::endl; std::cout << std::endl; std::cout << "Or: to compose multiple affine text file into one: " << std::endl; std::cout << "ComposeMultiTransform ImageDimension output_affine_txt [-R reference_affine_txt] " << "{[-i] affine_transform_txt}" << std::endl << "This will be evoked if a text file is given as the second parameter. In this case " << "reference_affine_txt is used to define the center of the output affine. " << "The default reference is the first given affine text file. " << "This ignores all non-txt files among the following parameters." << std::endl; exit(0); } TRAN_OPT_QUEUE opt_queue; // char *moving_image_filename = NULL; char *output_image_filename = NULL; char *reference_image_filename = NULL; bool is_parsing_ok = false; int kImageDim = atoi(argv[1]); is_parsing_ok = ParseInput(argc - 2, argv + 2, output_image_filename, reference_image_filename, opt_queue); if (is_parsing_ok) { switch (CheckFileType(output_image_filename)) { case DEFORMATION_FILE: { if (reference_image_filename == NULL) { std::cout << "the reference image file (-R) must be given!!!" << std::endl; return false; } std::cout << "output_image_filename: " << output_image_filename << std::endl; std::cout << "reference_image_filename: "; if (reference_image_filename) std::cout << reference_image_filename << std::endl; else std::cout << "NULL" << std::endl; DisplayOptQueue(opt_queue); switch (kImageDim) { case 2: { ComposeMultiTransform<2> (output_image_filename, reference_image_filename, opt_queue); break; } case 3: { ComposeMultiTransform<3> (output_image_filename, reference_image_filename, opt_queue); break; } } break; } case AFFINE_FILE: { std::cout << "output_affine_txt: " << output_image_filename << std::endl; std::cout << "reference_affine_txt: "; if (reference_image_filename) std::cout << reference_image_filename << std::endl; else std::cout << "NULL" << std::endl; DisplayOptQueue(opt_queue); switch (kImageDim) { case 2: { ComposeMultiAffine<2> (output_image_filename, reference_image_filename, opt_queue); break; } case 3: { ComposeMultiAffine<3> (output_image_filename, reference_image_filename, opt_queue); break; } } break; } default: std::cout << "Unknow output file format: " << output_image_filename << std::endl; break; } } else { std::cout << "Input error!" << std::endl; } exit(0); } ants-1.9.2+svn680.dfsg/Examples/ComputeSimilarityMetric.cxx000066400000000000000000000175701147325206600236230ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: ComputeSimilarityMetric.cxx,v $ Language: C++ Date: $Date: 2008/09/10 19:53:13 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include "ReadWriteImage.h" #include "itkDiscreteGaussianImageFilter.h" #include "itkAvantsMutualInformationRegistrationFunction.h" #include "itkProbabilisticRegistrationFunction.h" #include "itkCrossCorrelationRegistrationFunction.h" // #include "itkLandmarkCrossCorrelationRegistrationFunction.h" template int ComputeSimilarityMetric(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::ImageRegionIteratorWithIndex Iterator; typedef itk::Image JointHistType; typedef itk::ImageFileWriter jhwritertype; // get command line params unsigned int argct=2; unsigned int whichmetric = atoi(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; std::string fn2 = std::string(argv[argct]); argct++; std::string logfilename=""; if (argc > argct) logfilename = std::string(argv[argct]); argct++; std::string imgfilename=""; if (argc > argct) imgfilename = std::string(argv[argct]); argct++; typename ImageType::Pointer image1 = NULL; ReadImage(image1, fn1.c_str()); typename ImageType::Pointer image2 = NULL; ReadImage(image2, fn2.c_str()); /* typedef itk::ImageRegionIteratorWithIndex VIterator; typename FieldType::Pointer field=FieldType::New(); field->SetLargestPossibleRegion( image1->GetLargestPossibleRegion() ); field->SetBufferedRegion( image1->GetLargestPossibleRegion() ); field->SetLargestPossibleRegion( image1->GetLargestPossibleRegion() ); field->Allocate(); field->SetSpacing(image1->GetSpacing()); field->SetOrigin(image1->GetOrigin()); VectorType zero; zero.Fill(0); VIterator vfIter2( field, field->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { IndexType index=vfIter2.GetIndex(); vfIter2.Set(zero); } */ typename ImageType::Pointer metricimg=ImageType::New(); metricimg->SetLargestPossibleRegion( image1->GetLargestPossibleRegion() ); metricimg->SetBufferedRegion( image1->GetLargestPossibleRegion() ); metricimg->SetLargestPossibleRegion( image1->GetLargestPossibleRegion() ); metricimg->Allocate(); metricimg->SetSpacing(image1->GetSpacing()); metricimg->SetOrigin(image1->GetOrigin()); Iterator iter( metricimg, metricimg->GetLargestPossibleRegion() ); for( iter.GoToBegin(); !iter.IsAtEnd(); ++iter ) iter.Set(0); typedef ImageType FixedImageType; typedef ImageType MovingImageType; typedef FieldType DeformationFieldType; // Choose the similarity metric typedef itk::AvantsMutualInformationRegistrationFunction MIMetricType; typedef itk::CrossCorrelationRegistrationFunction CCMetricType; //typedef itk::LandmarkCrossCorrelationRegistrationFunction MetricType; //typename typename MIMetricType::Pointer mimet=MIMetricType::New(); typename CCMetricType::Pointer ccmet=CCMetricType::New(); int nbins=32; typename CCMetricType::RadiusType hradius; typename CCMetricType::RadiusType ccradius; ccradius.Fill(4); typename MIMetricType::RadiusType miradius; miradius.Fill(0); // mimet->SetDeformationField(field); mimet->SetFixedImage(image1); mimet->SetMovingImage(image2); mimet->SetRadius(miradius); mimet->SetGradientStep(1.e2); mimet->SetNormalizeGradient(false); // ccmet->SetDeformationField(field); ccmet->SetFixedImage(image1); ccmet->SetMovingImage(image2); ccmet->SetRadius(ccradius); ccmet->SetGradientStep(1.e2); ccmet->SetNormalizeGradient(false); double metricvalue=0; std::string metricname=""; if (whichmetric == 0) { hradius=miradius; unsigned long ct = 0; for( iter.GoToBegin(); !iter.IsAtEnd(); ++iter ) { IndexType index=iter.GetIndex(); double fval=image1->GetPixel(index); double mval=image2->GetPixel(index); metricvalue+=fabs(fval-mval); ct++; } metricvalue/=(float)ct; metricname="MSQ "; } else if (whichmetric == 1 ) // imagedifference { hradius=ccradius; ccmet->InitializeIteration(); metricvalue=ccmet->ComputeCrossCorrelation(); metricname="CC "; } else { hradius=miradius; mimet->InitializeIteration(); metricvalue=mimet->ComputeMutualInformation(); metricname="MI "; } std::cout << fn1 << " : " << fn2 << " => " << metricname << metricvalue << std::endl; if (logfilename.length() > 3 ){ std::ofstream logfile; logfile.open(logfilename.c_str(), std::ofstream::app); if (logfile.good()) { logfile << fn1 << " : " << fn2 << " => " << metricname << metricvalue << std::endl; } else std::cout << " cant open file "; logfile.close(); } if (imgfilename.length() > 3 ){ /* typename MetricType::NeighborhoodType asamIt( hradius, field,field->GetLargestPossibleRegion()); unsigned long ct = 0; double totval = 0; for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { IndexType index=vfIter2.GetIndex(); double val=0; asamIt.SetLocation(index); // met->ComputeUpdate( asamIt, gd); met->ComputeMetricAtPairB(index, zero); metricimg->SetPixel(index, val); //if (ct % 10000 == 0) // std::cout << val << " index " << index << std::endl; // asamIt.SetLocation(index); // totval+=met->localProbabilistic; ct++; } std::cout << " AvantsMI : " << totval/(double)ct << " E " << met->GetEnergy() << std::endl; std::cout << " write begin " << std::endl; typedef itk::ImageFileWriter writertype; writertype::Pointer w= writertype::New(); w->SetInput(metricimg); w->SetFileName(outname.c_str()); w->Write(); // met->WriteImages(); std::cout << " write end " << std::endl; */ } return 0; } int main(int argc, char *argv[]) { if ( argc < 3 ) { std::cout << "Basic useage ex: " << std::endl; std::cout << argv[0] << " ImageDimension whichmetric image1.ext image2.ext {logfile} {outimage.ext} " << std::endl; std::cout << " outimage and logfile are optional " << std::endl; return 1; } // Get the image dimension switch( atoi(argv[1])) { case 2: ComputeSimilarityMetric<2>(argc,argv); break; case 3: ComputeSimilarityMetric<3>(argc,argv); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } return 0; } ants-1.9.2+svn680.dfsg/Examples/ConformalMapping.cxx000066400000000000000000001047161147325206600222270ustar00rootroot00000000000000 /*========================================================================= Program: Insight Segmentation & Registration Toolkit Module: $RCSfile: ConformalMapping.cxx,v $ Language: C++ Date: $Date: 2008/03/12 03:23:04 $ Version: $Revision: 1.15 $ Copyright (c) 2002 Insight Consortium. All rights reserved. See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include #include #include #include "itkImage.h" #include "itkBinaryThresholdImageFilter.h" #include "itkImageFileReader.h" #include "itkImageFileWriter.h" #include "itkImageRegionIterator.h" #include "itkMesh.h" #include "itkSphereMeshSource.h" #include "itkBinaryMask3DMeshSource.h" #include "itkNearestNeighborInterpolateImageFunction.h" #include "itkFEMConformalMap.h" #include "itkFEMDiscConformalMap.h" #include "BinaryImageToMeshFilter.h" #include "vtkCallbackCommand.h" #include "vtkPointPicker.h" #include "vtkCellPicker.h" #include "vtkPolyDataWriter.h" #include "vtkPolyDataReader.h" #include "ReadWriteImage.h" #include "itkRescaleIntensityImageFilter.h" #include "itkSurfaceImageCurvature.h" //#include "../BSpline/itkBSplineScatteredDataPointSetToImageFilter.h" #include "itkImageRegionConstIterator.h" #include "itkImageRegionConstIteratorWithIndex.h" #include "itkImageRegionIterator.h" #include "itkPointSet.h" #include #include /* OPEN QUESTIONS: LOCAL FROM GLOBAL FOR TRIANGLE IN 3D (MARCELO) USEFUL FOR INTERPOLATING STEREOGRAPHIC COORDS INTO IMAGE. CURRENTLY JUST DENSELY INTERPOLATE ACROSS EACH TRIANGLE. -- Not necessary for triangle TRIANGLE AREA IN 3D (D0NE : USING CROSS PRODUCT ) - need to insure we use the hypotneuse as the AB edge : so theta is <= 1.0; - try to apply bcs to even out the map - what is the effect of material param E? */ template typename TImage::Pointer BinaryThreshold( typename TImage::PixelType bkg, typename TImage::PixelType foreground, typename TImage::PixelType replaceval, typename TImage::Pointer input) { typedef typename TImage::PixelType PixelType; // Begin Threshold Image typedef itk::BinaryThresholdImageFilter InputThresholderType; typename InputThresholderType::Pointer inputThresholder = InputThresholderType::New(); inputThresholder->SetInput( input ); inputThresholder->SetInsideValue( replaceval ); int outval=0; if ((float) replaceval == (float) -1) outval=1; inputThresholder->SetOutsideValue( outval ); float low=bkg; float high=foreground; if (high < low) high=255; inputThresholder->SetLowerThreshold((PixelType) low ); inputThresholder->SetUpperThreshold((PixelType) high); inputThresholder->Update(); return inputThresholder->GetOutput(); } void Display(vtkUnstructuredGrid* vtkgrid, bool secondwin=false, bool delinter=true) { // Create the renderer and window stuff std::cout <<" second win " << secondwin << std::endl; vtkRenderer* ren1 = vtkRenderer::New(); vtkRenderer* ren2 = vtkRenderer::New(); vtkRenderWindow* renWin = vtkRenderWindow::New(); renWin->AddRenderer(ren1); if (secondwin) renWin->AddRenderer(ren2); vtkRenderWindowInteractor* inter = vtkRenderWindowInteractor::New(); inter->SetRenderWindow(renWin); // vtkCellPicker* cpicker = vtkCellPicker::New(); vtkCallbackCommand *cbc = vtkCallbackCommand::New(); /*------------------------------------------------------------------------- void vtkCompositeManagerExitInteractor(vtkObject *vtkNotUsed(o), unsigned long vtkNotUsed(event), void *clientData, void *) { vtkCompositeManager *self = (vtkCompositeManager *)clientData; self->ExitInteractor(); }*/ // cbc=vectraEventHandler; //-> SetCallback(vectraEventHandler); ren1 -> AddObserver(vtkCommand::KeyPressEvent, cbc); vtkDataSetMapper* mapper = vtkDataSetMapper::New(); mapper->SetInput(vtkgrid); mapper->SetScalarRange(0,255); vtkActor* actor = vtkActor::New(); actor->SetMapper(mapper); vtkDataSetMapper* mapper2 = vtkDataSetMapper::New(); if (secondwin) mapper2->SetInput(vtkgrid); if (secondwin) mapper2->SetScalarRange(0,255); vtkActor* actor2 = vtkActor::New(); if (secondwin) actor2->SetMapper(mapper2); if (secondwin) { ren1->SetViewport(0.0, 0.0, 0.5, 1.0); ren2->SetViewport(0.5, 0.0, 1.0, 1.0); }else ren1->SetViewport(0.0, 0.0, 1.0, 1.0); if (secondwin) ren2->AddActor(actor2); // add the actor and start the render loop ren1->AddActor(actor); // this->InteractionPicker->Pick(x, y, 0.0, this->CurrentRenderer); // this->InteractionPicker->GetPickPosition(this->DownPt); renWin->Render(); inter->Start(); /* int X, Y; char keypressed = *(inter -> GetKeySym()); inter -> GetMousePosition(&X, &Y); std::cout <<" X Y " << X << " " << Y << std::endl; renWin->Render(); inter->Start();*/ /* vtkPointPicker* picker=vtkPointPicker::New(); // vtkPoints *GetPickedPositions() {return this->PickedPositions;}; float x = inter->GetEventPosition()[0]; float y = inter->GetEventPosition()[1]; float z = 0.0; picker->Pick(x,y,z,ren1); std::cout <<" picked " << (*picker->GetPickedPositions()) << std::endl;*/ mapper->Delete(); actor->Delete(); ren1->Delete(); mapper2->Delete(); actor2->Delete(); ren2->Delete(); renWin->Delete(); if (delinter) inter->Delete(); } template void MapToSphere(typename TImage::Pointer image,int fixdir, float e) { typedef TImage ImageType; typedef unsigned char PixelType; typedef itk::Mesh MeshType; typedef itk::BinaryMask3DMeshSource MeshSourceType; PixelType backgroundValue = 0; PixelType internalValue = 1; typename MeshSourceType::Pointer meshSource = MeshSourceType::New(); meshSource->SetBinaryImage( image ); meshSource->SetObjectValue( internalValue ); try { meshSource->Update(); } catch( itk::ExceptionObject & exp ) { std::cerr << "Exception thrown during Update() " << std::endl; std::cerr << exp << std::endl; return; } meshSource->GetOutput(); std::cout << meshSource->GetNumberOfNodes() << std::endl; std::cout << meshSource->GetNumberOfCells() << std::endl; typedef itk::FEMConformalMap ParamType; typename ParamType::Pointer Parameterizer=ParamType::New(); Parameterizer->SetDebug(false); // Parameterizer->SetDebug(true); Parameterizer->SetReadFromFile(false); Parameterizer->SetParameterFileName(""); Parameterizer->SetImage(image); std::cout << " fixdir " << fixdir << " e " << e << " best e ~ 3.e-3 "<SetNorthPole(fixdir); Parameterizer->SetSigma(e); Parameterizer->SetSurfaceMesh(meshSource->GetOutput()); // Parameterizer->GenerateSystemFromSurfaceMesh(); std::cout << std::endl; Parameterizer->ConformalMap(); Parameterizer->ComputeStereographicCoordinates(); int irad=50; if (Parameterizer->GetImage()) { std::cout << " writing param images " << std::endl; { Parameterizer->MapCheckerboardToImage(0.05); typename itk::ImageFileWriter::Pointer writer; writer = itk::ImageFileWriter::New(); std::string fn="checker.nii"; writer->SetFileName(fn.c_str()); writer->SetInput(Parameterizer->GetImage()); writer->Write(); } } } template typename TImage::Pointer SmoothImage( typename TImage::Pointer input,double var) { typedef TImage ImageType; typedef typename ImageType::PixelType PixelType; enum { ImageDimension = ImageType::ImageDimension }; typedef itk::Image realImageType; typedef itk::DiscreteGaussianImageFilter dgf; typename dgf::Pointer filter = dgf::New(); filter->SetVariance(var); filter->SetMaximumError(.01f); filter->SetUseImageSpacingOn(); filter->SetInput(input); filter->Update(); typedef itk::CastImageFilter CasterType1; typename CasterType1::Pointer caster1 = CasterType1::New(); caster1->SetInput(filter->GetOutput()); caster1->Update(); return caster1->GetOutput(); } float ComputeGenus(vtkPolyData* pd1) { vtkExtractEdges* edgeex=vtkExtractEdges::New(); edgeex->SetInput(pd1); edgeex->Update(); vtkPolyData* edg1=edgeex->GetOutput(); vtkIdType nedg=edg1->GetNumberOfCells(); vtkIdType vers = pd1->GetNumberOfPoints(); int nfac = pd1->GetNumberOfPolys(); float g = 0.5 * (2.0 - vers + nedg - nfac); std::cout << " Genus " << g << std::endl; std::cout << " face " << nfac << " edg " << nedg << " vert " << vers << std::endl; return g; } float vtkComputeTopology(vtkPolyData* pd) { // Marching cubes // std::cout << " Marching Cubes "; // vtkMarchingCubes *marchingCubes = vtkMarchingCubes::New(); // vtkContourFilter *marchingCubes = vtkContourFilter::New(); // vtkKitwareContourFilter *marchingCubes = vtkKitwareContourFilter::New(); // marchingCubes->SetInput((vtkDataSet*) vds); // marchingCubes->SetValue(0, hithresh); // int nc; // std::cout << " Input # conts "; std::cin >> nc; // marchingCubes->SetNumberOfContours(2); // marchingCubes->SetComputeScalars(false); // marchingCubes->SetComputeGradients(false); // marchingCubes->SetComputeNormals(false); vtkPolyDataConnectivityFilter* con = vtkPolyDataConnectivityFilter::New(); con->SetExtractionModeToLargestRegion(); // con->SetInput(marchingCubes->GetOutput()); con->SetInput(pd); // vtkUnstructuredGridToPolyDataFilter* gp = vtkUnstructuredGridToPolyDataFilter::New(); // gp->SetInput(con->GetOutput()); float g = ComputeGenus(con->GetOutput()); // marchingCubes->Delete(); return g; } template void GetMeshAndCurvature(typename TImage::Pointer image,float e, const char* filename) { typedef TImage ImageType; typedef vtkPolyData MeshType; double aaParm = 0.024; typedef BinaryImageToMeshFilter FilterType; typename FilterType::Pointer fltMesh = FilterType::New(); fltMesh->SetInput(image); fltMesh->SetAntiAliasMaxRMSError(aaParm); fltMesh->SetAntiAliasMaxRMSError( -1000.0 ); // to do nothing fltMesh->Update(); vtkPolyData* vtkmesh =fltMesh->GetMesh(); // assign scalars to the original surface mesh // Display((vtkUnstructuredGrid*)vtkmesh); std::cout << " Genus " << vtkComputeTopology(vtkmesh) << std::endl; typedef itk::SurfaceImageCurvature surfktype; typename surfktype::Pointer surfk=surfktype::New(); // kappa=; typedef itk::Image FloatImageType; surfk->SetInput(image); //SmoothImage(image,1.0)); surfk->SetNeighborhoodRadius( 1.5 ); surfk->SetSigma(1.); surfk->SetUseLabel(false); surfk->SetUseGeodesicNeighborhood(false); surfk->SetkSign(1.0); surfk->ComputeFrameOverDomain( 3 ); typename FloatImageType::Pointer kappa=surfk->GetFunctionImage(); typedef itk::Image itype; typedef itk::RescaleIntensityImageFilter< FloatImageType, itype > CastFilterType; typename CastFilterType::Pointer caster=CastFilterType::New(); caster->SetInput( image ); caster->SetOutputMinimum( 0 ); caster->SetOutputMaximum( 255 ); std::string fn=std::string(filename); fn=fn.substr(0,fn.length()-4)+"kappa.nii"; typedef itk::ImageFileWriter writertype; typename writertype::Pointer w= writertype::New(); typename itype::Pointer kimg=caster->GetOutput(); w->SetInput(kimg); w->SetFileName(fn.c_str()); w->Write(); typename itype::SpacingType spacing=image->GetSpacing(); vtkPoints* vtkpoints =vtkmesh->GetPoints(); int numPoints = vtkpoints->GetNumberOfPoints(); float mx=0,mn=9.e9,meank=0; for(int i =0; i < numPoints; i++) { typename ImageType::IndexType index; for (int j=0;j<3;j++) index[j]=(int)(vtkpoints->GetPoint(i)[j]/spacing[j]+0.5); float temp=kimg->GetPixel(index); // float temp=image->GetPixel(index); if (fabs(temp)>mx) mx=fabs(temp); if (fabs(temp) 0) mn=fabs(temp); meank+=fabs(temp); } std::cout <<" max kap " << mx <<" mn k " << mn << std::endl; meank/=numPoints; // mx=1.3; // mx=2.0; vtkFloatArray* param; // bool done=false; //while (!done) { param= vtkFloatArray::New(); param->SetName("angle"); float dif=(mx-mn)*0.25; float mx2=meank+dif; float mn2=meank-dif; dif=mx2-mn2; for(int i =0; i < numPoints; i++) { typename ImageType::IndexType index; for (int j=0;j<3;j++) index[j]=(int)(vtkpoints->GetPoint(i)[j]/spacing[j]+0.5); float temp=kimg->GetPixel(index); // float temp=surfk->CurvatureAtIndex(index); if (i % 1000 == 0) std::cout << " kappa " << temp << std::endl; //=fabs(manifoldIntegrator->GetGraphNode(i)->GetTotalCost()); temp=fabs(temp); param->InsertNextValue((temp-mn2)*255./dif); } vtkmesh->GetPointData()->SetScalars(param); // Display((vtkUnstructuredGrid*)vtkmesh); // std::cout<<"DOne? "; std::cin >> done; } std::cout <<" done with curvature map "; vtkPolyDataWriter *writer = vtkPolyDataWriter::New(); writer->SetInput(vtkmesh); std::string outnm=std::string(filename); outnm=outnm.substr(0,outnm.length()-4)+".vtk"; std::cout << " writing " << outnm << std::endl; // outnm="C:\\temp\\mesh.vtk"; writer->SetFileName(outnm.c_str()); writer->SetFileTypeToBinary(); writer->Update(); std::cout << " done writing "; return; } template float GetImageTopology(typename TImage::Pointer image,float e, const char* filename) { typedef TImage ImageType; typedef vtkPolyData MeshType; double aaParm = 0.024; typedef BinaryImageToMeshFilter FilterType; typename FilterType::Pointer fltMesh = FilterType::New(); fltMesh->SetInput(image); fltMesh->SetAntiAliasMaxRMSError(aaParm); fltMesh->SetAntiAliasMaxRMSError( -1000.0 ); // to do nothing fltMesh->Update(); vtkPolyData* vtkmesh =fltMesh->GetMesh(); // assign scalars to the original surface mesh // Display((vtkUnstructuredGrid*)vtkmesh); float genus = vtkComputeTopology(vtkmesh); std::cout << " Genus " << genus << std::endl; return genus; } template void MapToDisc(vtkPolyData* vtkmesh, float e, std::string outfn) { typedef TImage ImageType; typedef vtkPolyData MeshType; typedef itk::FEMDiscConformalMap ParamType; typename ParamType::Pointer Parameterizer=ParamType::New(); Parameterizer->SetDebug(false); // Parameterizer->SetDebug(true); Parameterizer->SetReadFromFile(false); Parameterizer->SetParameterFileName(""); Parameterizer->SetSigma(e); Parameterizer->SetSurfaceMesh(vtkmesh); ComputeGenus((vtkPolyData*)Parameterizer->GetSurfaceMesh()); Parameterizer->ExtractSurfaceDisc(); ComputeGenus((vtkPolyData*)Parameterizer->GetSurfaceMesh()); // Display((vtkUnstructuredGrid*)Parameterizer->GetSurfaceMesh()); std::cout << " begin conformal mapping "; Parameterizer->ConformalMap(); std::cout << " display patch "; // ComputeGenus((vtkPolyData*)Parameterizer->m_ExtractedSurfaceMesh); //Display((vtkUnstructuredGrid*)Parameterizer->m_ExtractedSurfaceMesh); // std::cout << " display flattened patch "; int segct = 0; // float step = 0.1; // float maxt=0.0; // for (float tt = 0.0; tt<=maxt; tt=tt+step) { // std::cout <<" Building at : " << tt << std::endl; /// Parameterizer->BuildOutputMeshes(tt); // if (tt == 0.) { vtkPolyDataWriter *writer = vtkPolyDataWriter::New(); writer->SetInput(Parameterizer->m_ExtractedSurfaceMesh); std::string outnm;//=std::string(filename); //outnm=outnm.substr(0,outnm.length()-4)+".vtk"; outnm=outfn+"mapspace.vtk"; std::cout << " writing " << outnm << std::endl; writer->SetFileName(outnm.c_str()); writer->SetFileTypeToBinary(); writer->Update(); } { vtkPolyDataWriter *writer = vtkPolyDataWriter::New(); writer->SetInput(Parameterizer->m_DiskSurfaceMesh); std::string outnm; std::ostringstream buf; buf<<( segct+10 ); // outnm=outfn+std::string(buf.str().c_str())+"mapflat.vtk"; outnm=outfn+"mapflat.vtk"; std::cout << " writing " << outnm << std::endl; writer->SetFileName(outnm.c_str()); writer->SetFileTypeToBinary(); writer->Update(); segct++; } } /* for (float sig=0.4; sig<=1.0; sig=sig+0.1) { Parameterizer->SetSmooth(sig); Parameterizer->ConjugateHarmonic(); // ComputeGenus((vtkPolyData*)Parameterizer->m_DiskSurfaceMesh); Display((vtkUnstructuredGrid*)Parameterizer->m_DiskSurfaceMesh); } */ typedef typename ParamType::FlatImageType imtype; typename itk::ImageFileWriter::Pointer writer; writer = itk::ImageFileWriter::New(); std::string fn=outfn+"flat.nii"; writer->SetFileName(fn.c_str()); writer->SetInput(Parameterizer->m_FlatImage); if (Parameterizer->m_FlatImage ) writer->Write(); std::cout << " done writing "; } /* template void MeshToImage(vtkPolyData* vtkmesh, int imagesize, std::string outfn) { const unsigned int Dimension = 2; const int splineOrder = 3; const int component = 0; const char* dataname = "scalars"; typedef TImage ImageType; typedef vtkPolyData MeshType; typedef typename ImageType::PixelType PixelType; typedef typename itk::Vector FunctionalDataType; typedef typename itk::Image FunctionalImageType; int numVertices = vtkmesh->GetNumberOfPoints(); vtkmesh->GetPointData()->SetActiveScalars( dataname ); std::cout << "# of vertices " << numVertices << " Fitting variable " << vtkmesh->GetPointData()->GetScalars()->GetName( ) << std::endl; typedef itk::PointSet FunctionalMapType; typedef itk::BSplineScatteredDataPointSetToImageFilter BSplineFilterType; typename FunctionalMapType::Pointer funcdataPoints = FunctionalMapType::New(); typename BSplineFilterType::Pointer bspliner = BSplineFilterType::New(); FunctionalDataType data, data1; typename FunctionalMapType::PointType point; double bounds[6]; vtkPoints *meshpoints = vtkmesh->GetPoints(); meshpoints->ComputeBounds(); meshpoints->GetBounds( bounds ); for (int ID=0; ID < numVertices; ++ID) { data[0] = vtkmesh->GetPointData()->GetScalars()->GetComponent(ID, component); for (int dir=0; dir < Dimension; ++dir) point[dir] = meshpoints->GetPoint(ID)[dir]; //Debug //data[0] = 1*point[0] + 0*point[1]; funcdataPoints->SetPointData( ID, data); funcdataPoints->SetPoint( ID, point); //std::cout << ID << " " << point[0] << " " << point[1] << " " << data[0] << std::endl; } std::cout << "Read all mesh data" << std::endl; typename FunctionalImageType::PointType origin; typename FunctionalImageType::SpacingType spacing; typename FunctionalImageType::SizeType size; float maxX, minX, maxY, minY; maxX = static_cast(bounds[1]); minX = static_cast(bounds[0]); maxY = static_cast(bounds[3]); minY = static_cast(bounds[2]); std::cout << "minX " << minX << " maxX " << maxX << " minY " << minY << " maxY " << maxY << std::endl; size[0] = imagesize; size[1] = imagesize; origin[0] = (minX + maxX)/2; origin[1] = (minY + maxY)/2; origin[0] = minX - (maxX-minX)*0.1; origin[1] = minY - (maxY-minY)*0.1; origin[0] = minX; origin[1] = minY; spacing[0] = 1.1*((((maxX-minX)>(maxY-minY))?(maxX-minX):(maxY-minY)))/imagesize; spacing[1] = spacing[0]; std::cout << "size " << size << " origin " << origin << " spacing " << spacing << std::endl; typename BSplineFilterType::ArrayType ncps; ncps.Fill( splineOrder + 1 ); //const int numLevels = round((1/static_cast(Dimension))*log(static_cast(numVertices))/log(2.0)); const int numLevels = 13; typename BSplineFilterType::ArrayType close; close.Fill( false ); std::cout << "No. of levels " << numLevels << " splineOrder " << splineOrder << " # control points " << ncps << std::endl; bspliner->SetOrigin( origin ); bspliner->SetSpacing( spacing ); bspliner->SetSize( size ); bspliner->SetGenerateOutputImage( true ); bspliner->SetNumberOfLevels( numLevels ); bspliner->SetSplineOrder( splineOrder ); bspliner->SetNumberOfControlPoints( ncps ); bspliner->SetCloseDimension( close ); bspliner->SetInput( funcdataPoints ); bspliner->DebugOn(); std::cout << "Entering BSpline" << std::endl; bspliner->Update(); std::cout << "BSpline fitting done" << std::endl; typename ImageType::Pointer outimage = ImageType::New(); outimage->SetSpacing( bspliner->GetOutput()->GetSpacing() ); outimage->SetOrigin( bspliner->GetOutput()->GetOrigin() ); outimage->SetRegions( bspliner->GetOutput()->GetLargestPossibleRegion() ); outimage->Allocate(); typename itk::ImageRegionIterator ItO( outimage, outimage->GetRequestedRegion() ); typename itk::ImageRegionConstIterator ItB( bspliner->GetOutput(), bspliner->GetOutput()->GetRequestedRegion() ); for ( ItB.GoToBegin(), ItO.GoToBegin(); !ItB.IsAtEnd(); ++ItB, ++ItO ) { ItO.Set( static_cast( ItB.Get()[0] ) ); } typename itk::ImageFileWriter::Pointer writer; writer = itk::ImageFileWriter::New(); std::string fn=outfn+"flat.nii"; writer->SetFileName(fn.c_str()); writer->SetInput( outimage ); writer->Write(); */ /*** Evaluate quality of fit ***/ /* typedef typename itk::LinearInterpolateImageFunction< ImageType, float > InterpolatorType; typename InterpolatorType::PointType testpoint; typename InterpolatorType::Pointer interp = InterpolatorType::New(); interp->SetInputImage( outimage ); for (int ID=0; ID < numVertices; ++ID) { data[0] = vtkmesh->GetPointData()->GetScalars()->GetComponent(ID, component); for (int dir=0; dir < Dimension; ++dir) { point[dir] = meshpoints->GetPoint(ID)[dir]; testpoint[dir] = point[dir] - origin[dir]; } vtkmesh->GetPointData()->GetScalars()->SetComponent(ID, component, static_cast(data[0]) - static_cast(interp->Evaluate( testpoint ))); //vtkmesh->GetPointData()->GetScalars()->SetComponent(ID, component, interp->Evaluate( testpoint )); data1[0] = vtkmesh->GetPointData()->GetScalars()->GetComponent(ID, component); cout << "error " << data1[0] << " original " << data[0] << " at " << point << " interpolated " << interp->Evaluate( testpoint ) << " at " << testpoint << endl; } vtkPolyDataWriter *vtkwriter = vtkPolyDataWriter::New(); vtkwriter->SetInput( vtkmesh ); vtkwriter->SetFileName( "newmesh3.vtk" ); vtkwriter->Write(); */ //} template typename TImage::Pointer RemoveNaNs(typename TImage::Pointer image, float replaceval ) { typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter(image,image->GetLargestPossibleRegion() ); for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter) { typename TImage::PixelType v1=vfIter.Get(); if (vnl_math_isnan(v1)) vfIter.Set(replaceval); } return image; } /* template void ImageToMesh(vtkPolyData* vtkmesh, typename TImage::Pointer image, std::string outfn) { const unsigned int Dimension = 2; const int splineOrder = 3; const int imagesize = 512; const int component = 0; const char* dataname = "points"; typedef TImage ImageType; typedef vtkPolyData MeshType; typedef typename ImageType::PixelType PixelType; typedef typename itk::Vector FunctionalDataType; typedef typename itk::Image FunctionalImageType; float replaceval = 5; image = RemoveNaNs(image, replaceval); int numVertices = vtkmesh->GetNumberOfPoints(); vtkmesh->GetPointData()->SetActiveScalars( dataname ); std::cout << "# of vertices " << numVertices << " Fitting variable " << vtkmesh->GetPointData()->GetScalars()->GetName( ) << std::endl; typedef itk::PointSet FunctionalMapType; typedef itk::BSplineScatteredDataPointSetToImageFilter BSplineFilterType; typename FunctionalMapType::Pointer funcdataPoints = FunctionalMapType::New(); typename BSplineFilterType::Pointer bspliner = BSplineFilterType::New(); FunctionalDataType data; typename FunctionalMapType::PointType point; double bounds[6]; vtkPoints *meshpoints = vtkmesh->GetPoints(); meshpoints->ComputeBounds(); meshpoints->GetBounds( bounds ); for (int ID=0; ID < numVertices; ++ID) { data[0] = vtkmesh->GetPointData()->GetScalars()->GetComponent(ID, component); for (int dir=0; dir < Dimension; ++dir) point[dir] = meshpoints->GetPoint(ID)[dir]; //Debug //data[0] = 1*point[0] + 0*point[1]; funcdataPoints->SetPointData( ID, data); funcdataPoints->SetPoint( ID, point); //std::cout << ID << " " << point[0] << " " << point[1] << " " << data[0] << std::endl; } std::cout << "Read all mesh data" << std::endl; // Create new data array vtkFloatArray *newdata; newdata = vtkFloatArray::New(); newdata->SetName("atlas"); typename FunctionalImageType::PointType origin; typename FunctionalImageType::SpacingType spacing; typename FunctionalImageType::SizeType size; float maxX, minX, maxY, minY; maxX = static_cast(bounds[1]); minX = static_cast(bounds[0]); maxY = static_cast(bounds[3]); minY = static_cast(bounds[2]); std::cout << "minX " << minX << " maxX " << maxX << " minY " << minY << " maxY " << maxY << std::endl; size[0] = imagesize; size[1] = imagesize; origin[0] = (minX + maxX)/2; origin[1] = (minY + maxY)/2; origin[0] = minX - (maxX-minX)*0.1; origin[1] = minY - (maxY-minY)*0.1; origin[0] = minX; origin[1] = minY; spacing[0] = 1.1*((((maxX-minX)>(maxY-minY))?(maxX-minX):(maxY-minY)))/imagesize; spacing[1] = spacing[0]; std::cout << "size " << size << " origin " << origin << " spacing " << spacing << std::endl; */ /* bspline code -- not doing b-spline now typename BSplineFilterType::ArrayType ncps; ncps.Fill( splineOrder + 1 ); //const int numLevels = round((1/static_cast(Dimension))*log(static_cast(numVertices))/log(2.0)); const int numLevels = 13; typename BSplineFilterType::ArrayType close; close.Fill( false ); std::cout << "No. of levels " << numLevels << " splineOrder " << splineOrder << " # control points " << ncps << std::endl; bspliner->SetOrigin( origin ); bspliner->SetSpacing( spacing ); bspliner->SetSize( size ); bspliner->SetGenerateOutputImage( true ); bspliner->SetNumberOfLevels( numLevels ); bspliner->SetSplineOrder( splineOrder ); bspliner->SetNumberOfControlPoints( ncps ); bspliner->SetCloseDimension( close ); bspliner->SetInput( funcdataPoints ); bspliner->DebugOn(); std::cout << "Entering BSpline" << std::endl; bspliner->Update(); std::cout << "BSpline fitting done" << std::endl; typename ImageType::Pointer outimage = ImageType::New(); outimage->SetSpacing( bspliner->GetOutput()->GetSpacing() ); outimage->SetOrigin( bspliner->GetOutput()->GetOrigin() ); outimage->SetRegions( bspliner->GetOutput()->GetLargestPossibleRegion() ); outimage->Allocate(); */ /* typename itk::ImageRegionIterator ItO( image, image->GetRequestedRegion() ); // Evaluate quality of fit //typedef typename itk::LinearInterpolateImageFunction< ImageType, float > InterpolatorType; typedef typename itk::NearestNeighborInterpolateImageFunction< ImageType, float > InterpolatorType; typename InterpolatorType::PointType testpoint; typedef typename itk::ContinuousIndex< float, Dimension > ContinuousIndexType; typename InterpolatorType::Pointer interp = InterpolatorType::New(); interp->SetInputImage( image ); ContinuousIndexType contind; for (int ID=0; ID < numVertices; ++ID) { for (int dir=0; dir < Dimension; ++dir) testpoint[dir] = meshpoints->GetPoint(ID)[dir] - origin[dir]; newdata->InsertNextValue( interp->Evaluate( testpoint ) ); //vtkmesh->GetPointData()->GetScalars()->SetComponent(ID, component, (interp->Evaluate( testpoint ))); //vtkmesh->GetPointData()->GetScalars()->SetComponent(ID, component, interp->Evaluate( testpoint )); // cout << vtkmesh->GetPointData()->GetScalars()->GetComponent(ID, component) << " " << data[0] << " " << interp->Evaluate( testpoint ) << endl; } vtkmesh->GetPointData()->AddArray( newdata ); vtkPolyDataWriter *vtkwriter = vtkPolyDataWriter::New(); vtkwriter->SetInput( vtkmesh ); std::string fn=outfn+".vtk"; vtkwriter->SetFileName(fn.c_str()); vtkwriter->Write(); } */ int main(int argc, char *argv[]) { // Define the dimension of the images const unsigned int Dimension = 3; typedef float PixelType; // Declare the types of the output images typedef itk::Image ImageType; typedef itk::Image Image2DType; // Declare the type of the index,size and region to initialize images typedef itk::Index IndexType; typedef itk::Size SizeType; typedef itk::ImageRegion RegionType; typedef itk::ImageRegionIterator IteratorType; // Declare the type of the Mesh char* filename; char* refmeshname; PixelType loth=1; PixelType hith=256; int fixdir=1; float param=2.e-4; std::string outfn; std::cout << "to get mesh: ConformalMapping image.nii 1 255 1.e-3 0 outname " << std::endl; std::cout << "to flatten mesh: ConformalMapping mesh.vtk 1 2 3 4 outname " << std::endl; std::cout << "to view mesh: ConformalMapping mesh.vtk 1 2 3 1 outname " << std::endl; std::cout << "to get image topology: ConformalMapping image.nii 1 255 1.e-3 5 outname " << std::endl; std::cout << "to convert flattened mesh to image : ConformalMapping mesh.vtk 1 2 3 6 outname " << std::endl; std::cout << "to interpolate data in flattened image domain to original mesh: ConformalMapping image.nii 1 2 3 7 outname originalflatmesh.vtk" << std::endl; std::cout << " to smooth a mesh --- ConformalMapping mesh.vtk 1 2 NumSmoothIts 8 outname " <=2) { filename=argv[1]; loth=atof(argv[2]); hith=atof(argv[3]); param=atof(argv[4]); fixdir=atoi(argv[5]); outfn=std::string(argv[6]); } else { filename= (char *)"image.nii"; return 0; } if (fixdir == 0) { typedef itk::ImageFileReader< ImageType > FileSourceType; typedef ImageType::PixelType PixType; FileSourceType::Pointer readfilter = FileSourceType::New(); readfilter->SetFileName( filename ); try { readfilter->Update(); } catch( itk::ExceptionObject & e ) { std::cerr << "Exception caught during reference file reading " << std::endl; std::cerr << e << std::endl; return -1; } ImageType::Pointer image = readfilter->GetOutput(); // PixelType backgroundValue = 0; // PixelType internalValue = 255; ImageType::Pointer thresh=BinaryThreshold(loth,hith,hith,image ); // Save the mesh GetMeshAndCurvature(thresh,param,filename); // MapToSphere(thresh,fixdir,e); } else if (fixdir == 5 ) { typedef itk::ImageFileReader< ImageType > FileSourceType; typedef ImageType::PixelType PixType; FileSourceType::Pointer readfilter = FileSourceType::New(); readfilter->SetFileName( filename ); try { readfilter->Update(); } catch( itk::ExceptionObject & e ) { std::cerr << "Exception caught during reference file reading " << std::endl; std::cerr << e << std::endl; return -1; } ImageType::Pointer image = readfilter->GetOutput(); // PixelType backgroundValue = 0; // PixelType internalValue = 255; ImageType::Pointer thresh=BinaryThreshold(loth,hith,hith,image ); // Save the mesh GetImageTopology(thresh,param,filename); // MapToSphere(thresh,fixdir,e); } /*************** flat mesh to image ****************/ else if (fixdir == 6) { vtkPolyDataReader *fltReader = vtkPolyDataReader::New(); fltReader->SetFileName(filename); fltReader->Update(); //MeshToImage(fltReader->GetOutput(), 512, outfn); } /*************** flat mesh to image ****************/ else if (fixdir == 7) /*************** flat map to mesh *****************/ { refmeshname = argv[7]; vtkPolyDataReader *fltReader = vtkPolyDataReader::New(); fltReader->SetFileName(refmeshname); fltReader->Update(); typedef itk::ImageFileReader< Image2DType > FileSourceType; FileSourceType::Pointer readfilter = FileSourceType::New(); readfilter->SetFileName( filename ); readfilter->Update(); Image2DType::Pointer image = readfilter->GetOutput(); //ImageToMesh(fltReader->GetOutput(), image, outfn); } else if (fixdir == 8) /*************** flat map to mesh *****************/ { std::cout << " read " << std::string(filename) << " write " << outfn << std::endl; vtkPolyDataReader *fltReader = vtkPolyDataReader::New(); fltReader->SetFileName(filename); fltReader->Update(); vtkSmartPointer smoother = vtkSmartPointer::New(); smoother->SetInput(fltReader->GetOutput()); smoother->SetNumberOfIterations( (int) param ); smoother->BoundarySmoothingOn(); smoother->FeatureEdgeSmoothingOff(); smoother->SetFeatureAngle(180.0); smoother->SetEdgeAngle(180.0); smoother->SetPassBand(1.e-3); // smaller values increase smoothing smoother->NonManifoldSmoothingOn(); smoother->NormalizeCoordinatesOff(); smoother->Update(); vtkPolyDataWriter *writer = vtkPolyDataWriter::New(); writer->SetInput( smoother->GetOutput() ); writer->SetFileName(outfn.c_str()); writer->SetFileTypeToBinary(); writer->Update(); std::cout << " done writing "; } /*************** flat map to mesh ****************/ else { vtkPolyDataReader *fltReader = vtkPolyDataReader::New(); fltReader->SetFileName(filename); fltReader->Update(); vtkPolyData* polydata = fltReader->GetOutput(); vtkSmartPointer normalGenerator = vtkSmartPointer::New(); normalGenerator->SetInput(polydata); normalGenerator->Update(); polydata = normalGenerator->GetOutput(); if ( fixdir == 1 ) Display((vtkUnstructuredGrid*)polydata); // if ( fixdir == 1 ) Display((vtkUnstructuredGrid*)fltReader->GetOutput()); std::cout << " m_Smooth " << param << std::endl; MapToDisc(fltReader->GetOutput(),param,outfn); } return 0; } ants-1.9.2+svn680.dfsg/Examples/ConvertImagePixelType.cxx000066400000000000000000000152711147325206600232170ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: ConvertToJpg.cxx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.19 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include #include #include #include #include "itkImage.h" #include "itkImageFileWriter.h" #include "itkImageFileReader.h" #include "itkCastImageFilter.h" #include "itkRescaleIntensityImageFilter.h" template int ConvertType(int argc, char *argv[] , double MINVAL, double MAXVAL) { typedef TPIXELTYPE outPixelType; typedef float floatPixelType; typedef float inPixelType; typedef itk::Image ImageType; typedef itk::Image IntermediateType; typedef itk::Image OutImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typename readertype::Pointer reader = readertype::New(); reader->SetFileName(argv[1]); reader->Update(); std::cout << " Updated reader " << std::endl; typedef itk::CastImageFilter castertype; typename castertype::Pointer caster=castertype::New(); caster->SetInput(reader->GetOutput()); caster->Update(); // Rescale the image intensities so that they fall between 0 and 255 typedef itk::RescaleIntensityImageFilter FilterType; typename FilterType::Pointer fixedrescalefilter = FilterType::New(); fixedrescalefilter->SetInput(caster->GetOutput()); const double desiredMinimum = MINVAL; double desiredMaximum = MAXVAL; fixedrescalefilter->SetOutputMinimum( desiredMinimum ); fixedrescalefilter->SetOutputMaximum( desiredMaximum ); fixedrescalefilter->UpdateLargestPossibleRegion(); typedef itk::CastImageFilter castertype2; typename castertype2::Pointer caster2=castertype2::New(); caster2->SetInput(fixedrescalefilter->GetOutput()); caster2->Update(); typename OutImageType::Pointer outim = caster2->GetOutput(); typename OutImageType::SpacingType spc = outim->GetSpacing(); outim->SetSpacing(spc); std::cout << " Dire in " << reader->GetOutput()->GetDirection() << std::endl; std::cout << " Dire out " << outim->GetDirection() << std::endl; typename writertype::Pointer writer = writertype::New(); writer->SetFileName(argv[2]); writer->SetInput(outim); writer->Update(); writer->Write(); return 0; } int main(int argc, char *argv[]) { if ( argc < 3 ) { std::cout << "Usage: " << argv[0] << " infile.nii out.ext TYPE-OPTION " << std::endl; std::cout <<" ext is the extension you want, e.g. tif. " << std::endl; std::cout << " TYPE-OPTION : TYPE " << std::endl; std::cout << " 0 : char " << std::endl; std::cout << " 1 : unsigned char " << std::endl; std::cout << " 2 : short " << std::endl; std::cout << " 3 : unsigned short " << std::endl; std::cout << " 4 : int " << std::endl; std::cout << " 5 : unsigned int " << std::endl; std::cout << " Note that some pixel types are not supported by some image formats. e.g. int is not supported by jpg. " << std::endl; std::cout << " You can easily extend this for other pixel types with a few lines of code and adding usage info. " << std::endl; std::cout <<" The image intensity will be scaled to the dynamic range of the pixel type. E.g. uchar => 0 (min), 255 (max). " << std::endl; return 1; } unsigned int typeoption=0; if ( argc > 3 ) { typeoption=atoi(argv[3]); } // Get the image dimension std::string fn = std::string(argv[1]); itk::ImageIOBase::Pointer imageIO = itk::ImageIOFactory::CreateImageIO( fn.c_str(), itk::ImageIOFactory::ReadMode); imageIO->SetFileName(fn.c_str()); imageIO->ReadImageInformation(); if ( typeoption == 0 ) { switch ( imageIO->GetNumberOfDimensions() ) { case 2: ConvertType<2,char>(argc,argv, SCHAR_MIN, SCHAR_MAX ); break; case 3: ConvertType<3,char>(argc,argv, SCHAR_MIN, SCHAR_MAX ); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } } else if ( typeoption == 1 ) { switch ( imageIO->GetNumberOfDimensions() ) { case 2: ConvertType<2,unsigned char>(argc,argv, 0, UCHAR_MAX ); break; case 3: ConvertType<3,unsigned char>(argc,argv, 0, UCHAR_MAX ); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } } else if ( typeoption == 2 ) { switch ( imageIO->GetNumberOfDimensions() ) { case 2: ConvertType<2, short>(argc,argv, SHRT_MIN, SHRT_MAX); break; case 3: ConvertType<3, short>(argc,argv, SHRT_MIN, SHRT_MAX); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } } else if ( typeoption == 3 ) { switch ( imageIO->GetNumberOfDimensions() ) { case 2: ConvertType<2,unsigned short>(argc,argv, 0, USHRT_MAX ); break; case 3: ConvertType<3,unsigned short>(argc,argv, 0, USHRT_MAX ); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } } else if ( typeoption == 4 ) { switch ( imageIO->GetNumberOfDimensions() ) { case 2: ConvertType<2,int>(argc,argv, INT_MIN, INT_MAX); break; case 3: ConvertType<3,int>(argc,argv, INT_MIN, INT_MAX); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } } else if ( typeoption == 5 ) { switch ( imageIO->GetNumberOfDimensions() ) { case 2: ConvertType<2,unsigned int>(argc,argv, 0, UINT_MAX ); break; case 3: ConvertType<3,unsigned int>(argc,argv, 0, UINT_MAX ); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } } return 0; } ants-1.9.2+svn680.dfsg/Examples/ConvertScalarImageToRGB.cxx000066400000000000000000000221371147325206600233360ustar00rootroot00000000000000 #include #include #include #include "itkImage.h" #include "itkImageFileReader.h" #include "itkImageFileWriter.h" #include "itkImageRegionIterator.h" #include "itkRGBPixel.h" #include "itkRGBAPixel.h" #include "itkRedColormapFunctor.h" #include "itkGreenColormapFunctor.h" #include "itkBlueColormapFunctor.h" #include "itkGreyColormapFunctor.h" #include "itkHotColormapFunctor.h" #include "itkCoolColormapFunctor.h" #include "itkSpringColormapFunctor.h" #include "itkSummerColormapFunctor.h" #include "itkAutumnColormapFunctor.h" #include "itkWinterColormapFunctor.h" #include "itkCopperColormapFunctor.h" #include "itkHSVColormapFunctor.h" #include "itkJetColormapFunctor.h" #include "itkCustomColormapFunctor.h" #include "itkOverUnderColormapFunctor.h" #include "itkScalarToRGBColormapImageFilter.h" template int ConvertScalarImageToRGB( int argc, char *argv[] ) { typedef unsigned int PixelType; typedef itk::RGBPixel RGBPixelType; // typedef itk::RGBAPixel RGBPixelType; typedef float RealType; typedef itk::Image ImageType; typedef itk::Image RealImageType; typedef itk::Image RGBImageType; typedef itk::ImageFileReader ReaderType; typename ReaderType::Pointer reader = ReaderType::New(); reader->SetFileName( argv[2] ); reader->Update(); typedef itk::Image MaskImageType; typename MaskImageType::Pointer maskImage = MaskImageType::New(); typedef itk::ImageFileReader MaskReaderType; typename MaskReaderType::Pointer maskreader = MaskReaderType::New(); maskreader->SetFileName( argv[4] ); try { maskreader->Update(); maskImage = maskreader->GetOutput(); } catch(...) { maskImage = NULL; }; std::string colormapString( argv[5] ); typedef itk::ScalarToRGBColormapImageFilter RGBFilterType; typename RGBFilterType::Pointer rgbfilter = RGBFilterType::New(); rgbfilter->SetInput( reader->GetOutput() ); if ( colormapString == "red" ) { rgbfilter->SetColormap( RGBFilterType::Red ); } else if ( colormapString == "green" ) { rgbfilter->SetColormap( RGBFilterType::Green ); } else if ( colormapString == "blue" ) { rgbfilter->SetColormap( RGBFilterType::Blue ); } else if ( colormapString == "grey" ) { rgbfilter->SetColormap( RGBFilterType::Grey ); } else if ( colormapString == "cool" ) { rgbfilter->SetColormap( RGBFilterType::Cool ); } else if ( colormapString == "hot" ) { rgbfilter->SetColormap( RGBFilterType::Hot ); } else if ( colormapString == "spring" ) { rgbfilter->SetColormap( RGBFilterType::Spring ); } else if ( colormapString == "autumn" ) { rgbfilter->SetColormap( RGBFilterType::Autumn ); } else if ( colormapString == "winter" ) { rgbfilter->SetColormap( RGBFilterType::Winter ); } else if ( colormapString == "copper" ) { rgbfilter->SetColormap( RGBFilterType::Copper ); } else if ( colormapString == "summer" ) { rgbfilter->SetColormap( RGBFilterType::Summer ); } else if ( colormapString == "jet" ) { // rgbfilter->SetColormap( RGBFilterType::Jet ); typedef itk::Functor::JetColormapFunctor ColormapType; typename ColormapType::Pointer colormap = ColormapType::New(); rgbfilter->SetColormap( colormap ); } else if ( colormapString == "hsv" ) { // rgbfilter->SetColormap( RGBFilterType::HSV ); typedef itk::Functor::HSVColormapFunctor ColormapType; typename ColormapType::Pointer colormap = ColormapType::New(); rgbfilter->SetColormap( colormap ); } else if ( colormapString == "overunder" ) { rgbfilter->SetColormap( RGBFilterType::OverUnder ); } else if ( colormapString == "custom" ) { typedef itk::Functor::CustomColormapFunctor ColormapType; typename ColormapType::Pointer colormap = ColormapType::New(); std::ifstream str( argv[6] ); std::string line; // Get red values { std::getline( str, line ); std::istringstream iss( line ); float value; typename ColormapType::ChannelType channel; while ( iss >> value ) { channel.push_back( value ); } colormap->SetRedChannel( channel ); } // Get green values { std::getline( str, line ); std::istringstream iss( line ); float value; typename ColormapType::ChannelType channel; while ( iss >> value ) { channel.push_back( value ); } colormap->SetGreenChannel( channel ); } // Get blue values { std::getline( str, line ); std::istringstream iss( line ); float value; typename ColormapType::ChannelType channel; while ( iss >> value ) { channel.push_back( value ); } colormap->SetBlueChannel( channel ); } rgbfilter->SetColormap( colormap ); } if( maskImage ) { RealType maskMinimumValue = itk::NumericTraits::max(); RealType maskMaximumValue = itk::NumericTraits::NonpositiveMin(); itk::ImageRegionIterator ItM( maskImage, maskImage->GetLargestPossibleRegion() ); itk::ImageRegionIterator ItS( reader->GetOutput(), reader->GetOutput()->GetLargestPossibleRegion() ); for( ItM.GoToBegin(), ItS.GoToBegin(); !ItM.IsAtEnd(); ++ItM, ++ItS ) { if( ItM.Get() != 0 ) { if( maskMinimumValue >= ItS.Get() ) { maskMinimumValue = ItS.Get(); } if( maskMaximumValue <= ItS.Get() ) { maskMaximumValue = ItS.Get(); } } } rgbfilter->SetUseInputImageExtremaForScaling( false ); rgbfilter->GetColormap()->SetMinimumInputValue( maskMinimumValue ); rgbfilter->GetColormap()->SetMaximumInputValue( maskMaximumValue ); } rgbfilter->GetColormap()->SetMinimumRGBComponentValue( ( argc > 9 ) ? static_cast< typename RGBPixelType::ComponentType>( atof( argv[9] ) ) : 0 ); rgbfilter->GetColormap()->SetMaximumRGBComponentValue( ( argc > 10 ) ? static_cast< typename RGBPixelType::ComponentType>( atof( argv[10] ) ) : 255 ); if( argc > 8 ) { rgbfilter->SetUseInputImageExtremaForScaling( false ); rgbfilter->GetColormap()->SetMinimumInputValue( static_cast( atof( argv[7] ) ) ); rgbfilter->GetColormap()->SetMaximumInputValue( static_cast( atof( argv[8] ) ) ); } try { rgbfilter->Update(); } catch (...) { return EXIT_FAILURE; } if( maskImage ) { itk::ImageRegionIterator ItM( maskImage, maskImage->GetLargestPossibleRegion() ); itk::ImageRegionIterator ItC( rgbfilter->GetOutput(), rgbfilter->GetOutput()->GetLargestPossibleRegion() ); itk::ImageRegionIterator ItS( reader->GetOutput(), reader->GetOutput()->GetLargestPossibleRegion() ); ItM.GoToBegin(); ItC.GoToBegin(); ItS.GoToBegin(); while( !ItM.IsAtEnd() ) { if( ItM.Get() == 0 ) { RGBPixelType rgbpixel; // RealType minimumValue = rgbfilter->GetColormap()->GetMinimumInputValue(); // RealType maximumValue = rgbfilter->GetColormap()->GetMaximumInputValue(); // // RealType minimumRGBValue // = rgbfilter->GetColormap()->GetMinimumRGBComponentValue(); // RealType maximumRGBValue // = rgbfilter->GetColormap()->GetMaximumRGBComponentValue(); // // RealType ratio = ( ItS.Get() - minimumValue ) / ( maximumValue - minimumValue ); // // rgbpixel.Fill( ratio * ( maximumRGBValue - minimumRGBValue ) // + minimumRGBValue ); rgbpixel.Fill( itk::NumericTraits::Zero ); ItC.Set( rgbpixel ); } ++ItM; ++ItC; ++ItS; } } typedef itk::ImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetInput( rgbfilter->GetOutput() ); writer->SetFileName( argv[3] ); writer->Update(); return 0; } int main( int argc, char *argv[] ) { if ( argc < 6 ) { std::cout << "Usage: " << argv[0] << " imageDimension inputImage outputImage " << "mask colormap [customColormapFile] [minimumInput] [maximumInput] " << "[minimumRGBOutput] [maximumRGBOutput]" << std::endl; std::cout << " Possible colormaps: grey, red, green, blue, copper, jet, hsv, "; std::cout << "spring, summer, autumn, winter, hot, cool, overunder, custom" << std::endl; exit( 1 ); } switch( atoi( argv[1] ) ) { case 2: ConvertScalarImageToRGB<2>( argc, argv ); break; case 3: ConvertScalarImageToRGB<3>( argc, argv ); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } } ants-1.9.2+svn680.dfsg/Examples/ConvertToJpg.cxx000066400000000000000000000075111147325206600213520ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: ConvertToJpg.cxx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.19 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include #include #include #include "itkImage.h" #include "itkImageFileWriter.h" #include "itkImageFileReader.h" #include "itkCastImageFilter.h" #include "itkRescaleIntensityImageFilter.h" template int ConvertType(int argc, char *argv[]) { typedef unsigned char outPixelType; typedef float floatPixelType; typedef float inPixelType; typedef itk::Image ImageType; typedef itk::Image IntermediateType; typedef itk::Image OutImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typename readertype::Pointer reader = readertype::New(); reader->SetFileName(argv[1]); reader->Update(); std::cout << " Updated reader " << std::endl; typedef itk::CastImageFilter castertype; typename castertype::Pointer caster=castertype::New(); caster->SetInput(reader->GetOutput()); caster->Update(); // Rescale the image intensities so that they fall between 0 and 255 typedef itk::RescaleIntensityImageFilter FilterType; typename FilterType::Pointer fixedrescalefilter = FilterType::New(); fixedrescalefilter->SetInput(caster->GetOutput()); const double desiredMinimum = 0.0; const double desiredMaximum = 255.0; fixedrescalefilter->SetOutputMinimum( desiredMinimum ); fixedrescalefilter->SetOutputMaximum( desiredMaximum ); fixedrescalefilter->UpdateLargestPossibleRegion(); typedef itk::CastImageFilter castertype2; typename castertype2::Pointer caster2=castertype2::New(); caster2->SetInput(fixedrescalefilter->GetOutput()); caster2->Update(); typename OutImageType::Pointer outim = caster2->GetOutput(); typename OutImageType::SpacingType spc = outim->GetSpacing(); outim->SetSpacing(spc); std::cout << " Dire in " << reader->GetOutput()->GetDirection() << std::endl; std::cout << " Dire out " << outim->GetDirection() << std::endl; typename writertype::Pointer writer = writertype::New(); writer->SetFileName(argv[2]); writer->SetInput(outim); writer->Update(); writer->Write(); return 0; } int main(int argc, char *argv[]) { if ( argc < 3 ) { std::cout << "Usage: ConvertToJpg infile.nii out.jpg " << std::endl; return 1; } // Get the image dimension std::string fn = std::string(argv[1]); itk::ImageIOBase::Pointer imageIO = itk::ImageIOFactory::CreateImageIO( fn.c_str(), itk::ImageIOFactory::ReadMode); imageIO->SetFileName(fn.c_str()); imageIO->ReadImageInformation(); switch ( imageIO->GetNumberOfDimensions() ) { case 2: ConvertType<2>(argc,argv); break; case 3: ConvertType<3>(argc,argv); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } return 0; } ants-1.9.2+svn680.dfsg/Examples/ConvertVectorFieldToVTK.cxx000066400000000000000000000104551147325206600234260ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: ConvertVectorFieldToVTK.cxx,v $ Language: C++ Date: $Date: 2009/01/27 23:25:24 $ Version: $Revision: 1.00 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include "itkImageFileReader.h" #include "itkVectorImageFileReader.h" #include "itkVector.h" #include "itkImageRegionIteratorWithIndex.h" #include "vtkUnstructuredGrid.h" #include "vtkUnstructuredGridWriter.h" #include "vtkFloatArray.h" #include "vtkPoints.h" #include "vtkPointData.h" int main( int argc, char *argv[] ) { if ( argc < 3 ) { std::cout << "Usage: " << argv[0] << " inputDeformationField outputVTKFile maskImage(optional) slice(optional) whichAxis(optional)" << std::endl; exit( 1 ); } typedef float PixelType; const unsigned int ImageDimension = 3; typedef itk::Image ImageType; typedef itk::Image MaskImageType; typedef double RealType; typedef itk::Vector VectorType; typedef itk::Image DeformationFieldType; typedef itk::VectorImageFileReader ReaderType; ReaderType::Pointer reader = ReaderType::New(); reader->SetFileName( argv[1] ); reader->SetUseAvantsNamingConvention( true ); reader->Update(); MaskImageType::Pointer mask = MaskImageType::New(); if ( argc >= 4 ) { typedef itk::ImageFileReader MaskReaderType; MaskReaderType::Pointer maskreader = MaskReaderType::New(); maskreader->SetFileName( argv[3] ); maskreader->Update(); mask = maskreader->GetOutput(); } else { mask->SetOrigin( reader->GetOutput()->GetOrigin() ); mask->SetSpacing( reader->GetOutput()->GetSpacing() ); mask->SetRegions( reader->GetOutput()->GetLargestPossibleRegion() ); mask->Allocate(); mask->FillBuffer( 1 ); } double origin[ImageDimension]; double spacing[ImageDimension]; int size[ImageDimension]; int totalsize = 1; for ( unsigned int i = 0; i < ImageDimension; i++ ) { origin[i] = reader->GetOutput()->GetOrigin()[i]; spacing[i] = reader->GetOutput()->GetSpacing()[i]; size[i] = reader->GetOutput()->GetLargestPossibleRegion().GetSize()[i]; if ( argc > 4 && atoi( argv[5] ) == (int) i ) { size[i] = 1; } totalsize *= size[i]; } int totalPoints = totalsize; vtkUnstructuredGrid *field = vtkUnstructuredGrid::New(); vtkPoints *points = vtkPoints::New(); points->Allocate( totalPoints ); vtkFloatArray *vectors = vtkFloatArray::New(); vectors->SetNumberOfComponents( 3 ); vectors->SetNumberOfTuples( totalPoints ); float x[3], v[3]; int offset = 0; itk::ImageRegionIteratorWithIndex It ( mask, mask->GetLargestPossibleRegion() ); for ( It.GoToBegin(); !It.IsAtEnd(); ++It ) { DeformationFieldType::IndexType idx = It.GetIndex(); if ( ( argc > 4 && idx[atoi( argv[5] )] != atoi( argv[4] ) ) || It.Get() == 0 ) { continue; } DeformationFieldType::PointType point; reader->GetOutput()->TransformIndexToPhysicalPoint( idx, point ); VectorType V = reader->GetOutput()->GetPixel( idx ); for ( unsigned int i = 0; i < ImageDimension; i++ ) { x[i] = point[i]; v[i] = V[i]; } // offset = idx[0] + idx[1]*(size[1]+1) + idx[2]*(size[1]+1)*(size[3]+1); points->InsertPoint( offset, x ); vectors->InsertTuple( offset++, v ); } field->SetPoints( points ); field->GetPointData()->SetVectors( vectors ); points->Delete(); vectors->Delete(); vtkUnstructuredGridWriter *writer = vtkUnstructuredGridWriter::New(); writer->SetInput( field ); // writer->SetFileTypeToBinary(); writer->SetFileName( argv[2] ); writer->Write(); return 0; } ants-1.9.2+svn680.dfsg/Examples/CopyImageHeaderInformation.cxx000066400000000000000000000107651147325206600241670ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: CopyImageHeaderInformation.cxx,v $ Language: C++ Date: $Date: 2009/04/30 18:32:36 $ Version: $Revision: 1.19 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include #include #include #include "itkImage.h" #include "itkImageFileWriter.h" #include "itkImageFileReader.h" #include "itkCastImageFilter.h" #include "itkRescaleIntensityImageFilter.h" #include "ReadWriteImage.h" #include "TensorFunctions.h" template int CopyImageHeaderInformation(int argc, char *argv[]) { typedef float outPixelType; typedef float floatPixelType; typedef float inPixelType; typedef itk::Image ImageType; typedef itk::Image IntermediateType; typedef itk::Image OutImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typename readertype::Pointer reader = readertype::New(); reader->SetFileName(argv[1]); reader->Update(); // std::cout << " Spacing " << reader->GetOutput()->GetSpacing() << std::endl; //std::cout << " Origin " << reader->GetOutput()->GetOrigin() << std::endl; //std::cout << " Direction " << std::endl << reader->GetOutput()->GetDirection() << std::endl; //std::cout << " Size " << std::endl << reader->GetOutput()->GetLargestPossibleRegion().GetSize() << std::endl; bool istensor=false; if (argc >7) if (atoi(argv[7])) istensor=true; if (istensor) { typedef itk::Vector TensorType; typedef itk::Image TensorFieldType; typename TensorFieldType::Pointer timage; ReadTensorImage(timage,argv[2],false); // std::cout<< " tim dir " << timage->GetDirection() << std::endl; if (argc >6) if (atoi(argv[6])) timage->SetSpacing( reader->GetOutput()->GetSpacing() ); if (argc > 5) if (atoi(argv[5])) timage->SetOrigin( reader->GetOutput()->GetOrigin() ); if (argc > 4) if (atoi(argv[4])) timage->SetDirection( reader->GetOutput()->GetDirection() ); // std::cout<< " tim dir " << timage->GetDirection() << std::endl; WriteTensorImage( timage ,argv[3],false); return 0; } typename readertype::Pointer reader2 = readertype::New(); reader2->SetFileName(argv[2]); reader2->Update(); //MakeNewImage(typename TImage::Pointer image1, typename TImage::PixelType initval) typename ImageType::Pointer newimage = MakeNewImage(reader2->GetOutput(), -1); if (argc >6) if (atoi(argv[6])) newimage->SetSpacing( reader->GetOutput()->GetSpacing() ); if (argc > 5) if (atoi(argv[5])) newimage->SetOrigin( reader->GetOutput()->GetOrigin() ); if (argc > 4) if (atoi(argv[4])) newimage->SetDirection( reader->GetOutput()->GetDirection() ); WriteImage(newimage,argv[3]); return 1; } int main(int argc, char *argv[]) { if ( argc < 4 ) { std::cout << "Usage: " << argv[0] << " refimage.ext imagetocopyrefimageinfoto.ext imageout.ext boolcopydirection boolcopyorigin boolcopyspacing {bool-Image2-IsTensor}" << std::endl; return 1; } // Get the image dimension std::string fn = std::string(argv[1]); itk::ImageIOBase::Pointer imageIO = itk::ImageIOFactory::CreateImageIO( fn.c_str(), itk::ImageIOFactory::ReadMode); imageIO->SetFileName(fn.c_str()); imageIO->ReadImageInformation(); unsigned int dim=imageIO->GetNumberOfDimensions(); switch ( dim ) { case 2: CopyImageHeaderInformation<2>(argc,argv); break; case 3: CopyImageHeaderInformation<3>(argc,argv); break; default: std::cerr << "Unsupported dimension : " << dim<< std::endl; exit( EXIT_FAILURE ); } return 0; } ants-1.9.2+svn680.dfsg/Examples/CreateJacobianDeterminantImage.cxx000066400000000000000000000134551147325206600247620ustar00rootroot00000000000000#include "itkConstNeighborhoodIterator.h" #include "itkDecomposeTensorFunction.h" #include "itkImageRegionIteratorWithIndex.h" //#include "itkVectorFieldGradientImageFunction.h" #include "itkImageFileReader.h" #include "itkImageFileWriter.h" #include "itkNeighborhoodAlgorithm.h" #include "itkTimeProbe.h" #include "itkVariableSizeMatrix.h" #include "itkVectorImageFileReader.h" #include "itkVector.h" #include "itkZeroFluxNeumannBoundaryCondition.h" #include "itkANTSImageRegistrationOptimizer.h" template typename TDeformationField::PixelType TransformVector(TDeformationField* field, typename TImage::IndexType index ) { enum { ImageDimension = TImage::ImageDimension }; typename TDeformationField::PixelType vec=field->GetPixel(index); typename TDeformationField::PixelType newvec; newvec.Fill(0); for (unsigned int row=0; rowGetDirection()[row][col]; return newvec; } template int CreateJacobianDeterminantImage( int argc, char *argv[] ) { typedef float RealType; typedef itk::Image ImageType; typedef itk::Vector VectorType; typedef itk::Image VectorImageType; /** * Read in vector field */ typedef itk::VectorImageFileReader ReaderType; typename ReaderType::Pointer reader = ReaderType::New(); reader->SetFileName( argv[2] ); reader->SetUseAvantsNamingConvention( true ); reader->Update(); typename VectorImageType::Pointer vecimg=reader->GetOutput(); /** smooth before finite differencing */ typedef itk::ANTSImageRegistrationOptimizer RegistrationOptimizerType; typedef typename RegistrationOptimizerType::Pointer RegistrationOptimizerPointer; RegistrationOptimizerPointer reg=RegistrationOptimizerType::New(); reg->SmoothDeformationFieldGauss(vecimg,3); typename VectorImageType::SpacingType spacing = vecimg->GetSpacing(); typename ImageType::Pointer jacobian = ImageType::New(); jacobian->SetOrigin( vecimg->GetOrigin() ); jacobian->SetSpacing( vecimg->GetSpacing() ); jacobian->SetRegions( vecimg->GetLargestPossibleRegion() ); jacobian->SetDirection( vecimg->GetDirection()); jacobian->Allocate(); itk::TimeProbe timer; timer.Start(); bool calculateLogJacobian = false; if ( argc > 4 ) { calculateLogJacobian = static_cast( atoi( argv[4] ) ); } typedef itk::ConstNeighborhoodIterator ConstNeighborhoodIteratorType; typename ConstNeighborhoodIteratorType::RadiusType radius; radius.Fill( 2 ); itk::ZeroFluxNeumannBoundaryCondition nbc; ConstNeighborhoodIteratorType bit; itk::ImageRegionIterator It; // Find the data-set boundary "faces" typename itk::NeighborhoodAlgorithm ::ImageBoundaryFacesCalculator::FaceListType faceList; typename itk::NeighborhoodAlgorithm ::ImageBoundaryFacesCalculator bC; faceList = bC( vecimg, vecimg->GetLargestPossibleRegion(), radius ); typedef itk::VariableSizeMatrix MatrixType; typedef itk::DecomposeTensorFunction DecomposerType; typename DecomposerType::Pointer decomposer = DecomposerType::New(); typename itk::NeighborhoodAlgorithm::ImageBoundaryFacesCalculator ::FaceListType::iterator fit; for ( fit = faceList.begin(); fit != faceList.end(); ++fit ) { bit = ConstNeighborhoodIteratorType( radius, vecimg, *fit ); bit.OverrideBoundaryCondition( &nbc ); bit.GoToBegin(); It = itk::ImageRegionIterator( jacobian, *fit ); It.GoToBegin(); while ( !bit.IsAtEnd() ) { MatrixType J; J.SetSize( ImageDimension, ImageDimension ); for( unsigned int i = 0; i < ImageDimension; i++ ) { for( unsigned int j = 0; j < ImageDimension; j++ ) { RealType x = bit.GetCenterPixel()[j]; RealType xp1 = bit.GetNext( i )[j]; RealType xp2 = bit.GetNext( i, 2 )[j]; RealType xm1 = bit.GetPrevious( i )[j]; RealType xm2 = bit.GetPrevious( i, 2 )[j]; RealType h = 0.5; xp1 = xp1*h + x*(1.0-h); xm1 = xm1*h + x*(1.0-h); xp2 = xp2*h + xp1*(1.0-h); xp2 = xm2*h + xm1*(1.0-h); J[i][j] = ( -xp2 + 8.0*xp1 - 8.0*xm1 + xm2 ) / ( 12.0*spacing[i] ); } J[i][i] += 1.0; } try { RealType jacDet = decomposer->EvaluateDeterminant( J ); if (jacDet < 1.e-4 && calculateLogJacobian ) jacDet=1.e-4; if (vnl_math_isnan(jacDet)) jacDet=1; It.Set( ( calculateLogJacobian ? vcl_log( jacDet ) : jacDet ) ); } catch(...) { It.Set( itk::NumericTraits::max() ); } ++bit; ++It; } } timer.Stop(); // std::cout << "Elapsed time: " << timer.GetMeanTime() << std::endl; typedef itk::ImageFileWriter RealImageWriterType; typename RealImageWriterType::Pointer realwriter = RealImageWriterType::New(); realwriter->SetFileName( argv[3] ); realwriter->SetInput( jacobian ); realwriter->Update(); return 0; } int main( int argc, char *argv[] ) { if ( argc < 3 ) { std::cout << "Usage: " << argv[0] << " ImageDimension deformationField outputImage log-jac?(default-false)" << std::endl; exit( 1 ); } switch( atoi( argv[1] ) ) { case 2: CreateJacobianDeterminantImage<2>( argc, argv ); break; case 3: CreateJacobianDeterminantImage<3>( argc, argv ); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } } ants-1.9.2+svn680.dfsg/Examples/CreateWarpedGridImage.cxx000066400000000000000000000137771147325206600231200ustar00rootroot00000000000000#include "itkImageFileReader.h" #include "itkImageFileWriter.h" #include "itkVectorImageFileReader.h" #include "itkVector.h" #include "itkMatrixOffsetTransformBase.h" #include "itkWarpImageMultiTransformFilter.h" #include "itkGridImageSource.h" template TValue Convert( std::string optionString ) { TValue value; std::istringstream iss( optionString ); iss >> value; return value; } template std::vector ConvertVector( std::string optionString ) { std::vector values; std::string::size_type crosspos = optionString.find( 'x', 0 ); if ( crosspos == std::string::npos ) { values.push_back( Convert( optionString ) ); } else { std::string element = optionString.substr( 0, crosspos ) ; TValue value; std::istringstream iss( element ); iss >> value; values.push_back( value ); while ( crosspos != std::string::npos ) { std::string::size_type crossposfrom = crosspos; crosspos = optionString.find( 'x', crossposfrom + 1 ); if ( crosspos == std::string::npos ) { element = optionString.substr( crossposfrom + 1, optionString.length() ); } else { element = optionString.substr( crossposfrom + 1, crosspos ) ; } std::istringstream iss( element ); iss >> value; values.push_back( value ); } } return values; } template int CreateWarpedGridImage( int argc, char *argv[] ) { typedef float RealType; typedef itk::Image RealImageType; typedef itk::Vector VectorType; typedef itk::Image VectorImageType; /** * Read in vector field */ typedef itk::VectorImageFileReader ReaderType; typename ReaderType::Pointer reader = ReaderType::New(); reader->SetFileName( argv[2] ); reader->Update(); typedef itk::GridImageSource GridSourceType; typename GridSourceType::Pointer gridder = GridSourceType::New(); gridder->SetSpacing( reader->GetOutput()->GetSpacing() ); gridder->SetOrigin( reader->GetOutput()->GetOrigin() ); gridder->SetSize( reader->GetOutput()->GetLargestPossibleRegion().GetSize() ); typename GridSourceType::ArrayType gridSpacing; typename GridSourceType::ArrayType gridSigma; typename GridSourceType::BoolArrayType which; which.Fill( false ); for( unsigned int i = 0; i < 2; i++ ) { which[i] = true; } if( argc > 4 ) { std::vector directions = ConvertVector( std::string( argv[4] ) ); if( directions.size() != ImageDimension ) { std::cerr << "Incorrect direction size." << std::endl; return EXIT_FAILURE; } else { for ( unsigned int i = 0; i < ImageDimension; i++ ) { which[i] = static_cast( directions[i] ); } } } for ( unsigned int i = 0; i < ImageDimension; i++ ) { gridSpacing[i] = reader->GetOutput()->GetLargestPossibleRegion().GetSize()[i] *reader->GetOutput()->GetSpacing()[i]/25.0; gridSigma[i] = gridSpacing[i]/10.0; } if( argc > 5 ) { std::vector spacing = ConvertVector( std::string( argv[5] ) ); if( spacing.size() != ImageDimension ) { std::cerr << "Incorrect spacing size." << std::endl; return EXIT_FAILURE; } else { for ( unsigned int i = 0; i < ImageDimension; i++ ) { gridSpacing[i] = spacing[i]; gridSigma[i] = gridSpacing[i]/10.0; } } } if( argc > 6 ) { std::vector sigma = ConvertVector( std::string( argv[6] ) ); if( sigma.size() != ImageDimension ) { std::cerr << "Incorrect sigma size." << std::endl; return EXIT_FAILURE; } else { for ( unsigned int i = 0; i < ImageDimension; i++ ) { gridSigma[i] = sigma[i]/10.0; } } } gridder->SetGridSpacing( gridSpacing ); gridder->SetSigma( gridSigma ); gridder->SetWhichDimensions( which ); gridder->Update(); typename RealImageType::Pointer grid=gridder->GetOutput(); grid->SetDirection(reader->GetOutput()->GetDirection()); grid->SetOrigin(reader->GetOutput()->GetOrigin()); grid->SetSpacing(reader->GetOutput()->GetSpacing()); typedef itk::MatrixOffsetTransformBase< double, ImageDimension, ImageDimension > TransformType; typedef itk::WarpImageMultiTransformFilter WarperType; typename WarperType::Pointer warper = WarperType::New(); warper->SetInput(grid); warper->SetEdgePaddingValue( 0); warper->SetSmoothScale(1); warper->PushBackDeformationFieldTransform(reader->GetOutput()); warper->SetOutputOrigin(reader->GetOutput()->GetOrigin()); warper->SetOutputSize(reader->GetOutput()->GetLargestPossibleRegion().GetSize()); warper->SetOutputSpacing(reader->GetOutput()->GetSpacing()); warper->SetOutputDirection(reader->GetOutput()->GetDirection()); warper->Update(); std::string file = std::string( argv[3] ); typedef itk::ImageFileWriter ImageWriterType; typename ImageWriterType::Pointer gridWriter = ImageWriterType::New(); gridWriter->SetFileName( file.c_str() ); gridWriter->SetInput( warper->GetOutput() ); gridWriter->Update(); return 0; } int main( int argc, char 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typedef itk::Image ImageType; typedef itk::Image SliceType; typedef itk::ImageFileReader ReaderType; typename ReaderType::Pointer reader = ReaderType::New(); reader->SetFileName( argv[2] ); reader->Update(); typename ImageType::RegionType region; typename ImageType::RegionType::SizeType size = reader->GetOutput()->GetLargestPossibleRegion().GetSize(); size[atoi( argv[4] )] = 0; typename ImageType::IndexType index; index.Fill( 0 ); index[atoi( argv[4] )] = atoi( argv[5] ); region.SetIndex( index ); region.SetSize( size ); typedef itk::ExtractImageFilter ExtracterType; typename ExtracterType::Pointer extracter = ExtracterType::New(); extracter->SetInput( reader->GetOutput() ); extracter->SetExtractionRegion( region ); extracter->Update(); typedef itk::ImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetFileName( argv[3] ); writer->SetInput( extracter->GetOutput() ); writer->Update(); return 0; } int main( int argc, char *argv[] ) { if ( argc != 6 ) { std::cout << "Usage: " << argv[0] << " imageDimension inputImage outputSlice direction(e.g. 0, 1, 2) slice_number" << std::endl; exit( 1 ); } switch( atoi( argv[1] ) ) { case 2: ExtractSliceFromImage<2>( argc, argv ); break; case 3: ExtractSliceFromImage<3>( argc, argv ); break; case 4: ExtractSliceFromImage<4>( argc, argv ); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } } ants-1.9.2+svn680.dfsg/Examples/GetMeshAndTopology.cxx000066400000000000000000000273221147325206600225040ustar00rootroot00000000000000 #include #include #include #include "itkImage.h" #include "itkBinaryThresholdImageFilter.h" #include "itkImageFileReader.h" #include "itkImageFileWriter.h" #include "itkImageRegionIterator.h" #include "itkMesh.h" #include "itkSphereMeshSource.h" #include "itkBinaryMask3DMeshSource.h" #include "itkNearestNeighborInterpolateImageFunction.h" #include "vtkPolyData.h" #include "vtkPolyDataConnectivityFilter.h" #include "vtkExtractEdges.h" #include "vtkPolyDataReader.h" // #include "itkFEMConformalMap.h" // #include "itkFEMDiscConformalMap.h" #include "BinaryImageToMeshFilter.h" #include "vtkCallbackCommand.h" #include "vtkPointPicker.h" #include "vtkCellPicker.h" #include "vtkPolyDataWriter.h" #include "vtkPolyDataReader.h" #include "ReadWriteImage.h" #include "itkRescaleIntensityImageFilter.h" #include "vtkDelaunay2D.h" #include "vtkFloatArray.h" #include #include #include "vtkVolume16Reader.h" #include "vtkImageReader2.h" #include "vtkPolyDataMapper.h" #include "vtkActor.h" #include "vtkOutlineFilter.h" #include "vtkCamera.h" #include "vtkProperty.h" #include "vtkPolyData.h" #include "vtkPolyVertex.h" #include "vtkPointData.h" #include "vtkExtractEdges.h" #include "vtkPolyDataNormals.h" #include "vtkMarchingCubes.h" #include "vtkImageGaussianSmooth.h" #include "vtkDecimatePro.h" #include "vtkContourFilter.h" #include "vtkPolyDataConnectivityFilter.h" //#include "vtkKitwareContourFilter.h" #include "vtkSmoothPolyDataFilter.h" #include "vtkSTLWriter.h" #include "vtkUnstructuredGridToPolyDataFilter.h" #include "itkSurfaceImageCurvature.h" #include "itkImageRegionConstIterator.h" #include "itkImageRegionConstIteratorWithIndex.h" #include "itkImageRegionIterator.h" #include "itkPointSet.h" template typename TImage::Pointer BinaryThreshold( typename TImage::PixelType bkg, typename TImage::PixelType foreground, typename TImage::PixelType replaceval, typename TImage::Pointer input) { typedef typename TImage::PixelType PixelType; // Begin Threshold Image typedef itk::BinaryThresholdImageFilter InputThresholderType; typename InputThresholderType::Pointer inputThresholder = InputThresholderType::New(); inputThresholder->SetInput( input ); inputThresholder->SetInsideValue( replaceval ); int outval=0; if ((float) replaceval == (float) -1) outval=1; inputThresholder->SetOutsideValue( outval ); float low=bkg; float high=foreground; if (high < low) high=255; inputThresholder->SetLowerThreshold((PixelType) low ); inputThresholder->SetUpperThreshold((PixelType) high); inputThresholder->Update(); return inputThresholder->GetOutput(); } float ComputeGenus(vtkPolyData* pd1) { vtkExtractEdges* edgeex=vtkExtractEdges::New(); edgeex->SetInput(pd1); edgeex->Update(); vtkPolyData* edg1=edgeex->GetOutput(); vtkIdType nedg=edg1->GetNumberOfCells(); vtkIdType vers = pd1->GetNumberOfPoints(); int nfac = pd1->GetNumberOfPolys(); float g = 0.5 * (2.0 - vers + nedg - nfac); std::cout << " Genus " << g << std::endl; std::cout << " face " << nfac << " edg " << nedg << " vert " << vers << std::endl; return g; } float vtkComputeTopology(vtkPolyData* pd) { // Marching cubes // std::cout << " Marching Cubes "; // vtkMarchingCubes *marchingCubes = vtkMarchingCubes::New(); // vtkContourFilter *marchingCubes = vtkContourFilter::New(); // vtkKitwareContourFilter *marchingCubes = vtkKitwareContourFilter::New(); // marchingCubes->SetInput((vtkDataSet*) vds); // marchingCubes->SetValue(0, hithresh); // int nc; // std::cout << " Input # conts "; std::cin >> nc; // marchingCubes->SetNumberOfContours(2); // marchingCubes->SetComputeScalars(false); // marchingCubes->SetComputeGradients(false); // marchingCubes->SetComputeNormals(false); vtkPolyDataConnectivityFilter* con = vtkPolyDataConnectivityFilter::New(); con->SetExtractionModeToLargestRegion(); // con->SetInput(marchingCubes->GetOutput()); con->SetInput(pd); con->Update(); float g = ComputeGenus(con->GetOutput()); return g; // vtkUnstructuredGridToPolyDataFilter* gp = vtkUnstructuredGridToPolyDataFilter::New(); // gp->SetInput(con->GetOutput()); // marchingCubes->Delete(); int inputNumberOfPoints = con->GetOutput()->GetNumberOfPoints(); int inputNumberOfPolys = con->GetOutput()->GetNumberOfPolys(); vtkPolyDataConnectivityFilter *polyDataConnectivityFilter = vtkPolyDataConnectivityFilter::New(); polyDataConnectivityFilter->SetInput( con->GetOutput() ); polyDataConnectivityFilter->SetExtractionModeToAllRegions(); polyDataConnectivityFilter->SetExtractionModeToLargestRegion(); polyDataConnectivityFilter->Update(); int connectivityNumberOfExtractedRegions = polyDataConnectivityFilter-> GetNumberOfExtractedRegions(); polyDataConnectivityFilter->Delete(); vtkExtractEdges *extractEdges = vtkExtractEdges::New(); extractEdges->SetInput( con->GetOutput() ); extractEdges->Update(); int extractNumberOfLines = extractEdges->GetOutput()->GetNumberOfLines(); extractEdges->Delete(); int EulerCharacteristic = inputNumberOfPoints - extractNumberOfLines + inputNumberOfPolys; double genus = 0.5 * ( 2 * connectivityNumberOfExtractedRegions - EulerCharacteristic ); std::cout << "EulerCharacteristic " << EulerCharacteristic << std::endl; std::cout << "genus " << genus << std::endl; return genus; } template void GetValueMesh(typename TImage::Pointer image, typename TImage::Pointer image2, std::string outfn, const char* paramname, float scaledata, float aaParm ) { // std::cout << " parname " << std::string(paramname) << std::endl; typedef TImage ImageType; typedef ImageType itype; typedef vtkPolyData MeshType; typedef itk::DiscreteGaussianImageFilter dgf; typename dgf::Pointer filter = dgf::New(); filter->SetVariance(0.8); filter->SetMaximumError(.01f); filter->SetUseImageSpacingOn(); filter->SetInput(image); filter->Update(); typedef BinaryImageToMeshFilter FilterType; typename FilterType::Pointer fltMesh = FilterType::New(); fltMesh->SetInput( image ); fltMesh->SetAntiAliasMaxRMSError( aaParm ); // to do nothing, set negative fltMesh->SetSmoothingIterations( 0 ); fltMesh->Update(); vtkPolyData* vtkmesh =fltMesh->GetMesh(); // assign scalars to the original surface mesh // Display((vtkUnstructuredGrid*)vtkmesh); vtkSmartPointer smoother = vtkSmartPointer::New(); smoother->SetInput(vtkmesh); smoother->SetNumberOfIterations(25); smoother->BoundarySmoothingOff(); smoother->FeatureEdgeSmoothingOff(); smoother->SetFeatureAngle(120.0); smoother->SetPassBand(.01); smoother->NonManifoldSmoothingOn(); smoother->NormalizeCoordinatesOn(); smoother->Update(); vtkmesh=smoother->GetOutput(); std::cout << " Genus " << vtkComputeTopology(vtkmesh) << std::endl; typename itype::SpacingType spacing=image->GetSpacing(); vtkPoints* vtkpoints =vtkmesh->GetPoints(); int numPoints = vtkpoints->GetNumberOfPoints(); float mx=0,mn=9.e9,meank=0; for(int i =0; i < numPoints; i++) { typename ImageType::IndexType index; typename ImageType::PointType point; for (int j=0;j<3;j++) point[j]=(vtkpoints->GetPoint(i)[j]); image2->TransformPhysicalPointToIndex(point,index); float temp=image2->GetPixel(index); if (fabs(temp)>mx) mx=fabs(temp); if (fabs(temp) 0) mn=fabs(temp); meank+=fabs(temp); } std::cout <<" max kap " << mx <<" mn k " << mn << std::endl; meank/=numPoints; // mx=1.3; // mx=2.0; vtkFloatArray* param; //while (!done) { param= vtkFloatArray::New(); param->SetName(paramname); float dif=(mx-mn)*scaledata; float mx2=meank+dif; float mn2=meank-dif; dif=mx2-mn2; for(int i =0; i < numPoints; i++) { typename ImageType::IndexType index; typename ImageType::PointType point; for (int j=0;j<3;j++) point[j]=(vtkpoints->GetPoint(i)[j]); image2->TransformPhysicalPointToIndex(point,index); float temp=image2->GetPixel(index); // param->InsertNextValue(temp); // float temp=surfk->CurvatureAtIndex(index); if (i % 1000 == 0) std::cout << " kappa " << temp << std::endl; //=fabs(manifoldIntegrator->GetGraphNode(i)->GetTotalCost()); temp=fabs(temp); float vvv=(temp-mn2)*255./dif; /* if (vvv > 128) { float dif=255-vvv; vvv = 128 - dif; } else { float dif=128-vvv; vvv = 128 + dif; }*/ param->InsertNextValue(vvv); } vtkmesh->GetPointData()->SetScalars(param); // Display((vtkUnstructuredGrid*)vtkmesh); // std::cout<<"DOne? "; std::cin >> done; } std::cout <<" done with mesh map "; vtkPolyDataWriter *writer = vtkPolyDataWriter::New(); writer->SetInput(vtkmesh); std::cout << " writing " << outfn << std::endl; // outnm="C:\\temp\\mesh.vtk"; writer->SetFileName(outfn.c_str()); writer->SetFileTypeToBinary(); writer->Update(); std::cout << " done writing "; return; } template float GetImageTopology(typename TImage::Pointer image) { typedef TImage ImageType; typedef vtkPolyData MeshType; double aaParm = 0.024; typedef BinaryImageToMeshFilter FilterType; typename FilterType::Pointer fltMesh = FilterType::New(); fltMesh->SetInput(image); fltMesh->SetAntiAliasMaxRMSError(aaParm); fltMesh->SetAntiAliasMaxRMSError( -1000.0 ); // to do nothing fltMesh->Update(); vtkPolyData* vtkmesh =fltMesh->GetMesh(); // assign scalars to the original surface mesh // Display((vtkUnstructuredGrid*)vtkmesh); float genus = vtkComputeTopology(vtkmesh); std::cout << " Genus " << genus << std::endl; return genus; } int main(int argc, char *argv[]) { if (argc < 2) { std::cout << argv[0] << " binaryimage valueimage out paramname ValueScale AntiaAliasParm=0.001" << std::endl; std::cout << " outputs vtk version of input image -- assumes object is defined by non-zero values " << std::endl; std::cout << " mesh is colored by the value of the image voxel " << std::endl; std::cout << " the AA-Param could cause topo problems but makes nicer meshes " << std::endl; std::cout << " ValueScale controls contrast in image appearance - lower increaseses -- should be <= 1 " << std::endl; exit(0); } // Define the dimension of the images const unsigned int Dimension = 3; typedef float PixelType; // Declare the types of the output images typedef itk::Image ImageType; typedef itk::Image Image2DType; // Declare the type of the index,size and region to initialize images typedef itk::Index IndexType; typedef itk::Size SizeType; typedef itk::ImageRegion RegionType; typedef itk::ImageRegionIterator IteratorType; // Declare the type of the Mesh std::string outfn=std::string(argv[3]); ImageType::Pointer image2; ImageType::Pointer image; ReadImage(image,argv[1]); ReadImage(image2,argv[2]); ImageType::DirectionType fmat=image->GetDirection(); fmat.SetIdentity(); image->SetDirection(fmat); image2->SetDirection(fmat); // Save the mesh float aaParm = 0.03; const char* paramname=std::string("f(x)").c_str(); if (argc > 4) paramname =(argv[4]); float scaledata=0.5; if (argc > 5) scaledata=atof(argv[5]); if (argc > 6) aaParm=atof(argv[6]); std::cout << "aaParm " << aaParm<(image,image2,outfn,paramname,scaledata, aaParm); // GetImageTopology(image); return 0; } ants-1.9.2+svn680.dfsg/Examples/ImageCompare.cxx000066400000000000000000000177441147325206600213300ustar00rootroot00000000000000#include "itkWin32Header.h" #include #include #include "itkNumericTraits.h" #include "itkImage.h" #include "itkImageFileReader.h" #include "itkImageFileWriter.h" #include "itkRescaleIntensityImageFilter.h" #include "itkExtractImageFilter.h" #include "itkDifferenceImageFilter.h" using namespace std; #define ITK_TEST_DIMENSION_MAX 6 int RegressionTestImage (const char *, const char *, int, bool); int main(int argc, char **argv) { if(argc < 3) { cerr << "Usage:" << endl; cerr << "testImage, baselineImage1, [baselineImage2, baselineImage3, ...]" << endl; cerr << "Note that if you supply more than one baselineImage, this test will pass if any" << endl; cerr << "of them match the testImage" << endl; return -1; } int bestBaselineStatus = 2001; int bestBaseline = 2; try { if(argc == 3) { bestBaselineStatus = RegressionTestImage(argv[1], argv[2], 0, false); } else { int currentStatus = 2001; for(int i=2;i ImageType; typedef itk::Image OutputType; typedef itk::Image DiffOutputType; typedef itk::ImageFileReader ReaderType; // Read the baseline file ReaderType::Pointer baselineReader = ReaderType::New(); baselineReader->SetFileName(baselineImageFilename); try { baselineReader->UpdateLargestPossibleRegion(); } catch (itk::ExceptionObject& e) { std::cerr << "Exception detected while reading " << baselineImageFilename << " : " << e.GetDescription(); return 1000; } // Read the file generated by the test ReaderType::Pointer testReader = ReaderType::New(); testReader->SetFileName(testImageFilename); try { testReader->UpdateLargestPossibleRegion(); } catch (itk::ExceptionObject& e) { std::cerr << "Exception detected while reading " << testImageFilename << " : " << e.GetDescription() << std::endl; return 1000; } // The sizes of the baseline and test image must match ImageType::SizeType baselineSize; baselineSize = baselineReader->GetOutput()->GetLargestPossibleRegion().GetSize(); ImageType::SizeType testSize; testSize = testReader->GetOutput()->GetLargestPossibleRegion().GetSize(); if (baselineSize != testSize) { std::cerr << "The size of the Baseline image and Test image do not match!" << std::endl; std::cerr << "Baseline image: " << baselineImageFilename << " has size " << baselineSize << std::endl; std::cerr << "Test image: " << testImageFilename << " has size " << testSize << std::endl; return 1; } // Now compare the two images typedef itk::DifferenceImageFilter DiffType; DiffType::Pointer diff = DiffType::New(); diff->SetValidInput(baselineReader->GetOutput()); diff->SetTestInput(testReader->GetOutput()); diff->SetDifferenceThreshold(2.0); diff->UpdateLargestPossibleRegion(); double status = diff->GetTotalDifference(); if (reportErrors) { typedef itk::RescaleIntensityImageFilter RescaleType; typedef itk::ExtractImageFilter ExtractType; typedef itk::ImageFileWriter WriterType; typedef itk::ImageRegion RegionType; OutputType::IndexType index; index.Fill(0); OutputType::SizeType size; size.Fill(0); RescaleType::Pointer rescale = RescaleType::New(); rescale->SetOutputMinimum(itk::NumericTraits::NonpositiveMin()); rescale->SetOutputMaximum(itk::NumericTraits::max()); rescale->SetInput(diff->GetOutput()); rescale->UpdateLargestPossibleRegion(); RegionType region; region.SetIndex(index); size = rescale->GetOutput()->GetLargestPossibleRegion().GetSize(); for (unsigned int i = 2; i < ITK_TEST_DIMENSION_MAX; i++) { size[i] = 0; } region.SetSize(size); ExtractType::Pointer extract = ExtractType::New(); extract->SetInput(rescale->GetOutput()); extract->SetExtractionRegion(region); WriterType::Pointer writer = WriterType::New(); writer->SetInput(extract->GetOutput()); if(differences) { // if there are discrepencies, create an diff image std::cout << ""; std::cout << status; std::cout << "" << std::endl; ::itk::OStringStream diffName; diffName << testImageFilename << ".diff.png"; try { rescale->SetInput(diff->GetOutput()); rescale->Update(); } catch (...) { std::cerr << "Error during rescale of " << diffName.str() << std::endl; } writer->SetFileName(diffName.str().c_str()); try { writer->Update(); } catch (...) { std::cerr << "Error during write of " << diffName.str() << std::endl; } std::cout << ""; std::cout << diffName.str(); std::cout << "" << std::endl; } ::itk::OStringStream baseName; baseName << testImageFilename << ".base.png"; try { rescale->SetInput(baselineReader->GetOutput()); rescale->Update(); } catch (...) { std::cerr << "Error during rescale of " << baseName.str() << std::endl; } try { writer->SetFileName(baseName.str().c_str()); writer->Update(); } catch (...) { std::cerr << "Error during write of " << baseName.str() << std::endl; } std::cout << ""; std::cout << baseName.str(); std::cout << "" << std::endl; ::itk::OStringStream testName; testName << testImageFilename << ".test.png"; try { rescale->SetInput(testReader->GetOutput()); rescale->Update(); } catch (...) { std::cerr << "Error during rescale of " << testName.str() << std::endl; } try { writer->SetFileName(testName.str().c_str()); writer->Update(); } catch (...) { std::cerr << "Error during write of " << testName.str() << std::endl; } std::cout << ""; std::cout << testName.str(); std::cout << "" << std::endl; } return (status != 0) ? 1 : 0; } ants-1.9.2+svn680.dfsg/Examples/ImageMath.cxx000066400000000000000000007430711147325206600206320ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RSfile: ImageMath.cxx,v $ Language: C++ Date: $Date: 2009/06/02 21:51:08 $ Version: $Revision: 1.103 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include // Here I'm using a map but you could choose even other containers #include #include #include #include #include "itkTDistribution.h" #include "itkTimeProbe.h" #include "itkMedianImageFilter.h" #include "itkVariableSizeMatrix.h" #include "itkVectorImageFileReader.h" #include "itkVector.h" #include "itkVectorLinearInterpolateImageFunction.h" #include "itkHessianRecursiveGaussianImageFilter.h" #include "itkLaplacianRecursiveGaussianImageFilter.h" #include "itkBilateralImageFilter.h" #include "itkDiscreteGaussianImageFilter.h" #include "itkDanielssonDistanceMapImageFilter.h" #include "itkBinaryErodeImageFilter.h" #include "itkBinaryDilateImageFilter.h" #include "itkLabeledPointSetFileReader.h" #include "itkLabeledPointSetFileWriter.h" //#include "itkBinaryMorphologicalClosingImageFilter.h" //#include "itkBinaryMorphologicalOpeningImageFilter.h" #include "itkGrayscaleErodeImageFilter.h" #include "itkGrayscaleDilateImageFilter.h" #include "itkBinaryBallStructuringElement.h" #include "itkGradientMagnitudeRecursiveGaussianImageFilter.h" #include "itkRescaleIntensityImageFilter.h" #include "itkGradientAnisotropicDiffusionImageFilter.h" #include "itkBayesianClassifierImageFilter.h" #include "itkGradientAnisotropicDiffusionImageFilter.h" #include "itkRescaleIntensityImageFilter.h" #include "itkBayesianClassifierInitializationImageFilter.h" #include "itkScalarImageKmeansImageFilter.h" #include "itkRelabelComponentImageFilter.h" #include "itkTranslationTransform.h" #include "itkImageMomentsCalculator.h" #include "itkBinaryThresholdImageFilter.h" #include "ReadWriteImage.h" #include "itkBSplineControlPointImageFilter.h" #include "itkExpImageFilter.h" #include "itkOtsuThresholdImageFilter.h" #include "itkShrinkImageFilter.h" #include "itkKdTree.h" #include "itkKdTreeBasedKmeansEstimator.h" #include "itkWeightedCentroidKdTreeGenerator.h" #include "../Temporary/itkFastMarchingImageFilter.h" // #include "itkMinimumDecisionRule.h" // #include "itkEuclideanDistance.h" // #include "itkSampleClassifier.h" #include "itkCastImageFilter.h" // #include "itkScalarImageToListAdaptor.h" #include "itkConnectedComponentImageFilter.h" #include "itkRescaleIntensityImageFilter.h" #include "itkHistogramMatchingImageFilter.h" #include "itkLabelStatisticsImageFilter.h" #include "itkExtractImageFilter.h" #include "itkMRIBiasFieldCorrectionFilter.h" #include "itkImage.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkGaussianImageSource.h" #include "itkMultivariateLegendrePolynomial.h" #include "itkCompositeValleyFunction.h" #include "itkNormalVariateGenerator.h" #include "itkArray.h" #include "itkImageFileWriter.h" #include "itkSphereSpatialFunction.h" #include "itkLabelContourImageFilter.h" #include "itkMaskImageFilter.h" // #include "itkDecisionRuleBase.h" // #include "itkMinimumDecisionRule.h" // #include "itkImageClassifierBase.h" // #include "itkWellComposedImageFilter.h" #include "itkBinaryErodeImageFilter.h" #include "itkBinaryDilateImageFilter.h" #include "itkBinaryBallStructuringElement.h" #include "itkImageKmeansModelEstimator.h" #include "itkDistanceToCentroidMembershipFunction.h" #include "itkMRFImageFilter.h" #include "itkImageClassifierBase.h" #include "itkImageGaussianModelEstimator.h" // #include "itkMahalanobisDistanceMembershipFunction.h" // #include "itkMinimumDecisionRule.h" #include "itkSize.h" #include "itkImage.h" #include "itkVector.h" #include "vnl/vnl_matrix_fixed.h" #include "itkImageRegionIterator.h" #include "itkConstNeighborhoodIterator.h" #include "itkNeighborhoodIterator.h" #include "itkNeighborhoodAlgorithm.h" #include "itkNeighborhood.h" #include "itkRGBPixel.h" #include "ReadWriteImage.h" #include "TensorFunctions.h" std::string ANTSGetFilePrefix(const char *str){ std::string filename = str; std::string::size_type pos = filename.rfind( "." ); std::string filepre = std::string( filename, 0, pos ); if ( pos != std::string::npos ){ std::string extension = std::string( filename, pos, filename.length()-1); if (extension==std::string(".gz")){ pos = filepre.rfind( "." ); extension = std::string( filepre, pos, filepre.length()-1 ); } // if (extension==".txt") return AFFINE_FILE; // else return DEFORMATION_FILE; } // else{ // return INVALID_FILE; //} return filepre; } template int GetLargestComponent(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; std::string fn2=""; unsigned long smallest=50; if (argc > argct) { smallest=atoi(argv[argct]); argct++; } typename ImageType::Pointer image1 = NULL; typename readertype::Pointer reader1 = readertype::New(); reader1->SetFileName(fn1.c_str()); reader1->UpdateLargestPossibleRegion(); try { image1 = reader1->GetOutput(); } catch(...) { std::cout << " read 1 error "; } // compute the voxel volume typename ImageType::SpacingType spacing=image1->GetSpacing(); float volumeelement=1.0; for (unsigned int i=0; i labelimagetype; typedef itk::Image labelimagetype; typedef ImageType InternalImageType; typedef ImageType OutputImageType; typedef itk::BinaryThresholdImageFilter< InternalImageType, labelimagetype > ThresholdFilterType; typedef itk::ConnectedComponentImageFilter< labelimagetype, labelimagetype > FilterType; typedef itk::RelabelComponentImageFilter< labelimagetype, ImageType > RelabelType; typename ThresholdFilterType::Pointer threshold = ThresholdFilterType::New(); typename FilterType::Pointer filter = FilterType::New(); typename RelabelType::Pointer relabel = RelabelType::New(); // InternalPixelType threshold_low, threshold_hi; threshold->SetInput (image1); threshold->SetInsideValue(1); threshold->SetOutsideValue(0); threshold->SetLowerThreshold(0.25); threshold->SetUpperThreshold(1.e9); threshold->Update(); filter->SetInput (threshold->GetOutput()); filter->SetFullyConnected( 0 ); filter->Update(); relabel->SetInput( filter->GetOutput() ); relabel->SetMinimumObjectSize( smallest ); // relabel->SetUseHistograms(true); try { relabel->Update(); } catch( itk::ExceptionObject & excep ) { std::cerr << "Relabel: exception caught !" << std::endl; std::cerr << excep << std::endl; } // WriteImage(relabel->GetOutput(),outname.c_str()); // return 0; typename ImageType::Pointer Clusters=MakeNewImage(relabel->GetOutput(),0); //typename ImageType::Pointer Clusters=relabel->GetOutput(); typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter( relabel->GetOutput(), relabel->GetOutput()->GetLargestPossibleRegion() ); float maximum=relabel->GetNumberOfObjects(); std::cout <<" # ob " << maximum << std::endl; float maxtstat=0; std::vector histogram((int)maximum+1); std::vector clustersum((int)maximum+1); for (int i=0; i<=maximum; i++) { histogram[i]=0; clustersum[i]=0; } for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) { if (vfIter.Get() > 0 ) { float vox=image1->GetPixel(vfIter.GetIndex()); histogram[(unsigned int)vfIter.Get()]=histogram[(unsigned int)vfIter.Get()]+1; clustersum[(unsigned int)vfIter.Get()]+=vox; if (vox > maxtstat) maxtstat=vox; } } for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) { if (vfIter.Get() > 0 ) { Clusters->SetPixel( vfIter.GetIndex(), histogram[(unsigned int)vfIter.Get()] ); // if ( Clusters->GetPixel( vfIter.GetIndex() ) > maximgval ) // maximgval=Clusters->GetPixel( vfIter.GetIndex()); } else Clusters->SetPixel(vfIter.GetIndex(),0); } float maximgval=0; for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) if ( Clusters->GetPixel( vfIter.GetIndex() ) > maximgval ) maximgval=Clusters->GetPixel( vfIter.GetIndex()); std::cout << " max float size " << (maximgval*volumeelement) << " long-size: " << (unsigned long) (maximgval*volumeelement) << std::endl; for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) if ( Clusters->GetPixel( vfIter.GetIndex() ) >= maximgval ) image1->SetPixel( vfIter.GetIndex(), 1); else image1->SetPixel( vfIter.GetIndex(), 0); if (outname.length() > 3) WriteImage(image1,outname.c_str()); return 0; } template int ExtractSlice(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::Image OutImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; unsigned int slice = atoi(argv[argct]); argct++; std::cout << " Extract slice " << slice << " from dimension" << ImageDimension << std::endl; typename ImageType::Pointer image1 = NULL; typename OutImageType::Pointer outimage = NULL; typedef itk::ExtractImageFilter ExtractFilterType; typedef itk::ImageRegionIteratorWithIndex ImageIt; typedef itk::ImageRegionIteratorWithIndex SliceIt; if (fn1.length() > 3) ReadImage(image1, fn1.c_str()); else return 1; unsigned int timedims=image1->GetLargestPossibleRegion().GetSize()[ImageDimension-1]; if ( slice >= timedims ) { std::cout << " max slice number is " << timedims << std::endl; return 1; } typename ImageType::RegionType extractRegion = image1->GetLargestPossibleRegion(); extractRegion.SetSize(ImageDimension-1, 0); extractRegion.SetIndex(ImageDimension-1, slice ); typename ExtractFilterType::Pointer extractFilter = ExtractFilterType::New(); extractFilter->SetInput( image1 ); extractFilter->SetExtractionRegion( extractRegion ); extractFilter->Update(); outimage=extractFilter->GetOutput(); /* typename ImageType::SpacingType qspc=warpthisimage->GetSpacing(); typename ImageType::PointType qorg=warpthisimage->GetOrigin(); typename ImageType::DirectionType qdir=warpthisimage->GetDirection(); qdir.Fill(0); for (unsigned int qq=0; qqGetDirection()[qq][pp]; } qspc[qq]=img_mov->GetSpacing()[qq]; qorg[qq]=img_mov->GetOrigin()[qq]; } warpthisimage->SetSpacing(qspc); warpthisimage->SetOrigin(qorg); warpthisimage->SetDirection(qdir); */ if (outname.length() > 3) WriteImage(outimage,outname.c_str()); else return 1; return 0; } template int ThresholdAtMean(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; float percentofmean=1.0; if (argc > argct) { percentofmean=atof(argv[argct]); argct++; } typename ImageType::Pointer image1 = NULL; typename readertype::Pointer reader1 = readertype::New(); reader1->SetFileName(fn1.c_str()); reader1->UpdateLargestPossibleRegion(); try { image1 = reader1->GetOutput(); } catch(...) { std::cout << " read 1 error "; } typedef itk::ImageRegionIteratorWithIndex Iterator; double mean=0,max=-1.e9,min=1.e9; unsigned long ct = 0; Iterator vfIter2( image1, image1->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { double val=vfIter2.Get(); mean+=val; if (val >max) max=val; else if (val < min) min=val; ct++; } if (ct > 0) mean/= (float)ct; typedef itk::BinaryThresholdImageFilter< ImageType, ImageType > ThresholdFilterType; typename ThresholdFilterType::Pointer threshold = ThresholdFilterType::New(); threshold->SetInput (image1); threshold->SetInsideValue(1); threshold->SetOutsideValue(0); threshold->SetLowerThreshold(mean*percentofmean); threshold->SetUpperThreshold(max); threshold->Update(); WriteImage(threshold->GetOutput(),outname.c_str()); return 0; } template int FlattenImage(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; float percentofmax=1.0; if (argc > argct) { percentofmax=atof(argv[argct]); argct++; } typename ImageType::Pointer image1 = NULL; typename readertype::Pointer reader1 = readertype::New(); reader1->SetFileName(fn1.c_str()); reader1->UpdateLargestPossibleRegion(); try { image1 = reader1->GetOutput(); } catch(...) { std::cout << " read 1 error "; } typedef itk::ImageRegionIteratorWithIndex Iterator; double mean=0,max=-1.e9,min=1.e9; unsigned long ct = 0; Iterator vfIter2( image1, image1->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { double val=vfIter2.Get(); mean+=val; if (val >max) max=val; else if (val < min) min=val; ct++; } if (ct > 0) mean/= (float)ct; typename ImageType::Pointer out=MakeNewImage(image1,0); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { double val=vfIter2.Get(); if (val > max*percentofmax) val=(max*percentofmax); out->SetPixel(vfIter2.GetIndex(),val); ct++; } std::cout << " Flattening to : " << percentofmax << std::endl; WriteImage(out,outname.c_str()); return 0; } template int TruncateImageIntensity( unsigned int argc, char *argv[] ) { typedef int PixelType; typedef float RealType; // usage ImageMath 3 out.nii.gz TrunateImageIntensity InImage.nii.gz FractionLo(e.g.0.025) FractionHi(e.g.0.975) Bins Mask if (argc < 4 ) { std::cout <<" need more args -- see usage " << std::endl << " ImageMath 3 outimage.nii.gz TruncateImageIntensity inputImage {lowerQuantile=0.025} {upperQuantile=0.975} {numberOfBins=65} {binary-maskImage} " << std::endl; exit(0); } unsigned int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1=std::string(argv[argct]); argct++; float lo = 0.025; if ( argc > argct ) lo=atof(argv[argct]); argct++; float hi = 0.975; if ( argc > argct ) hi=atof(argv[argct]); else hi=1.0-lo ; argct++; unsigned int numberOfBins = 64; if ( argc > argct ) numberOfBins=atoi(argv[argct]); argct++; // std::cout << " bin " << numberOfBins << " lo " << lo << " Hi " << hi << std::endl; typedef itk::Image ImageType; typedef itk::Image RealImageType; typedef itk::ImageFileReader ReaderType; typename ReaderType::Pointer imageReader = ReaderType::New(); imageReader->SetFileName( fn1.c_str() ); imageReader->Update(); typename ImageType::Pointer mask=NULL; /* if ( argc > argct ) { mask = ImageType::New(); try { typedef itk::ImageFileReader ReaderType; typename ReaderType::Pointer labelImageReader = ReaderType::New(); labelImageReader->SetFileName( argv[argct] ); labelImageReader->Update(); mask = labelImageReader->GetOutput(); } catch(...) { std::cout << " can't read mask " << std::endl; mask=NULL; }; } */ // std::cout << " Mask " << std::endl; if( !mask ) { mask = ImageType::New(); mask->SetOrigin( imageReader->GetOutput()->GetOrigin() ); mask->SetSpacing( imageReader->GetOutput()->GetSpacing() ); mask->SetRegions( imageReader->GetOutput()->GetLargestPossibleRegion() ); mask->SetDirection( imageReader->GetOutput()->GetDirection() ); mask->Allocate(); mask->FillBuffer( itk::NumericTraits::One ); } // std::cout << " iterate " << std::endl; itk::ImageRegionIterator ItI( imageReader->GetOutput(), imageReader->GetOutput()->GetLargestPossibleRegion() ); itk::ImageRegionIterator ItM( mask, mask->GetLargestPossibleRegion() ); RealType maxValue = itk::NumericTraits::NonpositiveMin(); RealType minValue = itk::NumericTraits::max(); ItM.GoToBegin(); for (ItI.GoToBegin(); !ItI.IsAtEnd(); ++ItI ) { // std::cout << " ind " << ItI.GetIndex() << std::endl; if ( ItI.Get() > 0 && ItM.Get() >= 0.5 ) { if ( ItI.Get() < minValue ) { minValue = ItI.Get(); } else if ( ItI.Get() > maxValue ) { maxValue = ItI.Get(); } ItM.Set( itk::NumericTraits::One ); } else { ItM.Set( itk::NumericTraits::Zero ); } if( vnl_math_isnan( ItI.Get() ) || vnl_math_isinf( ItI.Get() ) ) { ItM.Set( itk::NumericTraits::Zero ); } ++ItM; } // std::cout << " label " << std::endl; typedef itk::LabelStatisticsImageFilter HistogramGeneratorType; typename HistogramGeneratorType::Pointer stats = HistogramGeneratorType::New(); stats->SetInput( imageReader->GetOutput() ); stats->SetLabelInput( mask ); stats->SetUseHistograms( true ); stats->SetHistogramParameters( numberOfBins, minValue, maxValue ); stats->Update(); // std::cout << " labeld " << std::endl; typedef typename HistogramGeneratorType::HistogramType HistogramType; const HistogramType *histogram = stats->GetHistogram( 1 ); double lowerQuantile = histogram->Quantile( 0, lo ); double upperQuantile = histogram->Quantile( 0, hi ); std::cout << "Lower quantile: " << lowerQuantile << std::endl; std::cout << "Upper quantile: " << upperQuantile << std::endl; for ( ItI.GoToBegin(); !ItI.IsAtEnd(); ++ItI ) { if ( ItI.Get() < lowerQuantile ) { ItI.Set( lowerQuantile ); } if ( ItI.Get() > upperQuantile ) { ItI.Set( upperQuantile ); } } typedef itk::ImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetFileName( argv[2] ); writer->SetInput( imageReader->GetOutput() ); writer->Update(); return EXIT_SUCCESS; } template int TileImages(unsigned int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; unsigned int nx = atoi(argv[argct]); argct++; unsigned int numberofimages=0; typename ImageType::Pointer averageimage = NULL; typename ImageType::Pointer image2 = NULL; typename ImageType::SizeType size; double meanval=1; size.Fill(0); unsigned int bigimage=0; for (unsigned int j=argct; j< argc; j++) { numberofimages++; // Get the image dimension std::string fn = std::string(argv[j]); typename itk::ImageIOBase::Pointer imageIO = itk::ImageIOFactory::CreateImageIO(fn.c_str(), itk::ImageIOFactory::ReadMode); imageIO->SetFileName(fn.c_str()); imageIO->ReadImageInformation(); for (unsigned int i=0; iGetNumberOfDimensions(); i++) { if ( imageIO->GetDimensions(i) > size[i] ) { size[i]=imageIO->GetDimensions(i); bigimage=j; std::cout << " bigimage " << j << " size " << size << std::endl; } } } ReadImage(image2,argv[bigimage]); std:: cout << " largest image " << size << std::endl; /** declare the tiled image */ unsigned int xsize=size[0]; unsigned int ysize=size[1]; typename ImageType::SizeType tilesize; unsigned int ny=(unsigned int)((float)numberofimages/(float)nx+0.5); if ( nx*ny < numberofimages) ny++; std::cout << " nx " << nx << " ny " << ny << std::endl; tilesize[0]=xsize*nx; tilesize[1]=ysize*ny; typename ImageType::RegionType region; region.SetSize( tilesize ); bool normalizei=false; typename ImageType::Pointer tiledimage=ImageType::New(); tiledimage->SetLargestPossibleRegion( region ); tiledimage->SetBufferedRegion( region ); tiledimage->SetSpacing( image2->GetSpacing() ); tiledimage->SetDirection( image2->GetDirection() ); tiledimage->SetOrigin( image2->GetOrigin() ); tiledimage->Allocate(); unsigned int imagecount=0,imagexct=0,imageyct=0; for (unsigned int j=argct; j< argc; j++) { // Get the image dimension std::string fn = std::string(argv[j]); ReadImage(image2,fn.c_str()); unsigned long ct = 0; if (normalizei) { meanval = 0.0; Iterator vfIter2( image2, image2->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { meanval+=vfIter2.Get(); ct++; } if (ct > 0) meanval /= (float)ct; if (meanval <= 0) meanval=1.0; } imagexct=imagecount % nx; imageyct=imagecount/nx; std::cout << "doing " << fn << " " << imagecount << " x " << imagexct << " y " << imageyct << std::endl; imagecount++; Iterator vfIter( image2, image2->GetLargestPossibleRegion() ); for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) { typename ImageType::IndexType locind=vfIter.GetIndex(); typename ImageType::IndexType globind; globind[0]=size[0]*imagexct+locind[0]; globind[1]=size[1]*imageyct+locind[1]; double val = vfIter.Get()/meanval; tiledimage->SetPixel(globind, val ); } } WriteImage(tiledimage,outname.c_str()); return 0; typedef itk::Image ByteImageType; typedef itk::RescaleIntensityImageFilter RescaleFilterType; typename RescaleFilterType::Pointer rescaler = RescaleFilterType::New(); rescaler->SetOutputMinimum( 0 ); rescaler->SetOutputMaximum( 255 ); rescaler->SetInput( tiledimage ); std::cout << " writing output "; typedef itk::ImageFileWriter writertype; typename writertype::Pointer writer = writertype::New(); writer->SetFileName(outname.c_str()); writer->SetInput( rescaler->GetOutput() ); writer->Update(); return 0; } template int ConvertLandmarkFile(unsigned int argc, char *argv[]) { unsigned int argct=2; if (argc < 5 ) { std::cout <<" need more args -- see usage " << std::endl; exit(0); } std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string infn=std::string(argv[argct]); argct++; float pointp=1; typedef itk::PointSet PointSetType; typedef itk::LabeledPointSetFileReader ReaderType; typename ReaderType::Pointer reader = ReaderType::New(); reader->SetFileName( infn.c_str() ); reader->SetRandomPercentage( 1 ); if ( pointp > 0 && pointp < 1 ) { reader->SetRandomPercentage( pointp ); } reader->Update(); std::cout << "Number of labels: " << reader->GetNumberOfLabels() << std::endl; std::cout << "Labels: "; for ( unsigned int i = 0; i < reader->GetNumberOfLabels(); i++ ) { std::cout << reader->GetLabelSet()->operator[](i) << " "; } std::cout << std::endl; typedef itk::LabeledPointSetFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetFileName( outname.c_str() ); writer->SetInput( reader->GetOutput() ); writer->Update(); return 0; } template int TriPlanarView(unsigned int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::Image MatrixImageType; typedef itk::ImageFileReader readertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; unsigned int argct=2; if (argc < 5 ) { std::cout <<" need more args -- see usage " << std::endl; exit(0); } std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string maskfn=std::string(argv[argct]); argct++; std::cout << " file name " << maskfn << std::endl; typename ImageType::Pointer mask = NULL; typename readertype::Pointer reader2 = readertype::New(); reader2->SetFileName(maskfn.c_str()); try { reader2->UpdateLargestPossibleRegion(); } catch(...) { std::cout << " Error reading " << maskfn << std::endl; } mask=reader2->GetOutput(); //ReadImage(mask,maskfn.c_str()); // WriteImage(mask,"temp.nii"); float clamppercent1 = 0.1; if (argc > argct) clamppercent1=atof(argv[argct]); argct++; if (clamppercent1 > 1) clamppercent1=1; float clamppercent2=0.1; if (argc > argct) clamppercent2=atof(argv[argct]); argct++; if (clamppercent2 > 1) clamppercent2=1; typename ImageType::SizeType size=mask->GetLargestPossibleRegion().GetSize(); unsigned int xslice = size[0]/2; if (argc > argct) xslice=atoi(argv[argct]); argct++; unsigned int yslice = size[1]/2; if (argc > argct) yslice=atoi(argv[argct]); argct++; unsigned int zslice = size[2]/2; if (argc > argct) zslice=atoi(argv[argct]); argct++; typedef itk::RescaleIntensityImageFilter RescaleFilterType; typename RescaleFilterType::Pointer rescaler = RescaleFilterType::New(); rescaler->SetOutputMinimum( 0 ); rescaler->SetOutputMaximum( 255 ); rescaler->SetInput( mask ); rescaler->Update(); mask=rescaler->GetOutput(); // typedef itk::IntensityWindowingImageFilter wFilterType; // typename wFilterType::Pointer wer = wFilterType::New(); //wer->SetInput(mask); /** declare the tiled image */ unsigned long xsize=size[0]; unsigned long ysize=size[1]; unsigned long zsize=size[2]; typename MatrixImageType::SizeType ztilesize; ztilesize[0]=xsize; ztilesize[1]=ysize; typename MatrixImageType::SizeType ytilesize; ytilesize[0]=xsize; ytilesize[1]=zsize; typename MatrixImageType::SizeType xtilesize; xtilesize[0]=ysize; xtilesize[1]=zsize; typename MatrixImageType::SizeType tilesize; tilesize[0]=xtilesize[0]+ytilesize[0]+ztilesize[0]; tilesize[1]=xtilesize[1]; if ( ytilesize[1] > tilesize[1] ) tilesize[1]=ytilesize[1]; if ( ztilesize[1] > tilesize[1] ) tilesize[1]=ztilesize[1]; std::cout << " allocate matrix " << tilesize << std::endl; typename MatrixImageType::RegionType region; region.SetSize( tilesize ); typename MatrixImageType::Pointer matimage=MatrixImageType::New(); matimage->SetLargestPossibleRegion( region ); matimage->SetBufferedRegion( region ); typename MatrixImageType::DirectionType mdir; mdir.Fill(0); mdir[0][0]=1; mdir[1][1]=1; typename MatrixImageType::SpacingType mspc; mspc.Fill(1); typename MatrixImageType::PointType morg; morg.Fill(0); matimage->SetSpacing( mspc ); matimage->SetDirection(mdir); matimage->SetOrigin( morg ); matimage->Allocate(); unsigned int lowgetridof=(unsigned int) (clamppercent1*256); unsigned int higetridof=(unsigned int) (256 - clamppercent2*256); // std::cout << " get rid of " << getridof << std::endl; matimage->FillBuffer(lowgetridof); // now loop over each slice and put the pixels in the right place in matimage typename MatrixImageType::IndexType index2d; typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter2( mask, mask->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { double val1=vfIter2.Get(); if ( val1 > higetridof ) vfIter2.Set(higetridof ); if ( val1 < lowgetridof ) vfIter2.Set(lowgetridof ); // first do z-slice if ( vfIter2.GetIndex()[2] == (long) zslice) { double val=vfIter2.Get(); typename ImageType::IndexType index=vfIter2.GetIndex(); index2d[0]=index[0]+xtilesize[0]+ytilesize[0]; index2d[1]=index[1]; index2d[1]=tilesize[1]-index2d[1]-1; matimage->SetPixel(index2d,val); } if ( vfIter2.GetIndex()[1] == (long)yslice) { double val=vfIter2.Get(); typename ImageType::IndexType index=vfIter2.GetIndex(); index2d[0]=index[0]+xtilesize[0]; index2d[1]=index[2]; index2d[1]=tilesize[1]-index2d[1]-1; matimage->SetPixel(index2d,val); } if ( vfIter2.GetIndex()[0] == (long)xslice) { double val=vfIter2.Get(); typename ImageType::IndexType index=vfIter2.GetIndex(); index2d[0]=index[1]; index2d[1]=index[2]; index2d[1]=tilesize[1]-index2d[1]-1; matimage->SetPixel(index2d,val); } } typedef itk::Image ByteImageType; typedef itk::RescaleIntensityImageFilter RescaleFilterType2; typename RescaleFilterType2::Pointer rescaler2 = RescaleFilterType2::New(); rescaler2->SetOutputMinimum( 0 ); rescaler2->SetOutputMaximum( 255 ); rescaler2->SetInput( matimage ); rescaler2->Update(); std::cout << " writing output "; typedef itk::ImageFileWriter writertype; typename writertype::Pointer writer = writertype::New(); writer->SetFileName(outname.c_str()); writer->SetInput( rescaler2->GetOutput() ); writer->Update(); return 0; } template int ConvertVectorToImage(unsigned int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::Image MatrixImageType; typedef itk::ImageFileReader readertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; typedef itk::ImageRegionIteratorWithIndex vIterator; int argct=2; if (argc < 5 ) { std::cout <<" need more args -- see usage " << std::endl; exit(0); } std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string maskfn=std::string(argv[argct]); argct++; std::string vecfn=std::string(argv[argct]); argct++; typename ImageType::Pointer mask = NULL; ReadImage(mask,maskfn.c_str()); typename MatrixImageType::Pointer vecimg = NULL; ReadImage(vecimg,vecfn.c_str()); unsigned long voxct=0,mct=0; Iterator mIter( mask,mask->GetLargestPossibleRegion() ); for( mIter.GoToBegin(); !mIter.IsAtEnd(); ++mIter ) if (mIter.Get() >= 0.5) mct++; vIterator vIter(vecimg,vecimg->GetLargestPossibleRegion() ); for( vIter.GoToBegin(); !vIter.IsAtEnd(); ++vIter ) voxct++; std::cout << " vct " << voxct << " mct " << mct << std::endl; typename ImageType::Pointer outimage = NULL; ReadImage(outimage,maskfn.c_str()); outimage->FillBuffer(0); vIter.GoToBegin(); for( mIter.GoToBegin(); !mIter.IsAtEnd(); ++mIter ) if (mIter.Get() >= 0.5) { outimage->SetPixel(mIter.GetIndex(),vIter.Get()); ++vIter; } WriteImage(outimage,outname.c_str()); return 0; } template int CorruptImage(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; float noiselevel=1.0; if (argc > argct) { noiselevel=atof(argv[argct]); argct++; } float smoothlevel=1.0; if (argc > argct) { smoothlevel=atof(argv[argct]); argct++; } typename ImageType::Pointer image1 = NULL; typename readertype::Pointer reader1 = readertype::New(); reader1->SetFileName(fn1.c_str()); reader1->UpdateLargestPossibleRegion(); try { image1 = reader1->GetOutput(); } catch(...) { std::cout << " read 1 error "; } typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator iter( image1, image1->GetLargestPossibleRegion() ); for( iter.GoToBegin(); !iter.IsAtEnd(); ++iter ) { double r = ( (double)rand() / ((double)(RAND_MAX) +(double)(1)) )-0.5; iter.Set(iter.Get()+r*noiselevel); } if (smoothlevel > 0) { { typedef itk::DiscreteGaussianImageFilter dgf; typename dgf::Pointer filter = dgf::New(); filter->SetVariance(smoothlevel); filter->SetUseImageSpacingOff(); filter->SetMaximumError(.01f); filter->SetInput(image1); filter->Update(); image1=filter->GetOutput(); } } WriteImage(image1,outname.c_str()); return 0; } template int Where(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; float value=atof(argv[argct]); argct++; std::string fn2 = ""; if (argc > argct) {fn2=std::string(argv[argct]); argct++; } float tol=0.0; if (argc > argct) { tol=atof(argv[argct]); argct++; } typename ImageType::Pointer image1 = NULL; typename ImageType::Pointer image2 = NULL; if (fn1.length() > 3) ReadImage(image1, fn1.c_str()); if (fn2.length() > 3) ReadImage(image2, fn2.c_str()); unsigned long ct=0; typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator iter( image1, image1->GetLargestPossibleRegion() ); for( iter.GoToBegin(); !iter.IsAtEnd(); ++iter ) { if (!image2) { if ( fabs(iter.Get() - value) < tol ) { std::cout << iter.GetIndex() << std::endl; ct++; } } else if (image2->GetPixel(iter.GetIndex()) > 0 && fabs(iter.Get() - value) < tol ) { std::cout << iter.GetIndex() << std::endl; ct++; } } std::cout << ct << " voxels have the value " << value << std::endl; return 0; } template int SetOrGetPixel(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; float value=atof(argv[argct]); std::string Get=std::string(argv[argct]); argct++; float indx=atof(argv[argct]); argct++; float indy=0; if (ImageDimension >= 2) { indy=atof(argv[argct]); argct++ ;} float indz=0; if (ImageDimension >= 3) { indz=atof(argv[argct]); argct++ ;} bool usephyspace=false; if (argc > argct ) { usephyspace=atoi(argv[argct]); argct++; } bool get=false; if (strcmp(Get.c_str(),"Get") == 0) get=true; typename ImageType::Pointer image1 = NULL; typename ImageType::Pointer image2 = NULL; if (fn1.length() > 3) { ReadImage(image1, fn1.c_str()); ReadImage(image2, fn1.c_str()); } if (!image1) {std::cout <<" no image ! " << std::endl; exit(0);} typename ImageType::IndexType index; index.Fill(0); if (usephyspace==false) { index[0]=(long int)indx; index[1]=(long int)indy; if ( ImageDimension == 3) index[2]=(long int)indz; } else { typename ImageType::PointType porig; porig[0]=indx; porig[1]=indy; if (ImageDimension==3) porig[2]=indz; image1->TransformPhysicalPointToIndex(porig, index); } std::cout << " use phy " << usephyspace << " " << indx << " " << indy << " " << indz << std::endl; std::cout << " Ind " << index << std::endl; bool isinside=true; for (unsigned int i=0; i image1->GetLargestPossibleRegion().GetSize()[i]-1 ) isinside=false; } if ( isinside == true ) { if ( get ) { std::cout <<" GetValue at " << index << " is " << image1->GetPixel(index) << std::endl; } else { std::cout <<" SetValue at " << index << " value " << value << " replaces " << image1->GetPixel(index) << std::endl; image2->SetPixel(index,value); WriteImage(image2,outname.c_str()); } } else std::cout <<" not in image " << index << std::endl; return 0; } template int HistogramMatching(int argc, char * argv[]) { typedef float PixelType; typedef itk::Image ImageType; typedef itk::HistogramMatchingImageFilter MatchingFilterType; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; std::string fn2 = std::string(argv[argct]); argct++; long bins = 255; if (argc > argct) bins =atoi(argv[argct]); argct++; long points = 64; if (argc > argct) points =atoi(argv[argct]); argct++; typename ImageType::Pointer source; ReadImage(source,fn1.c_str()); typename ImageType::Pointer reference; ReadImage(reference,fn2.c_str()); typename MatchingFilterType::Pointer match = MatchingFilterType::New(); match->SetSourceImage(source); match->SetReferenceImage(reference); match->SetNumberOfHistogramLevels(bins); match->SetNumberOfMatchPoints(points); match->Update(); WriteImage(match->GetOutput(), outname.c_str()); return 0; } template int PadImage(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; float padvalue=atof(argv[argct]); argct++; std::string fn2 = ""; if (argc > argct) fn2=std::string(argv[argct]); argct++; typename ImageType::Pointer image1 = NULL; if (fn1.length() > 3) ReadImage(image1, fn1.c_str()); typename ImageType::PointType origin=image1->GetOrigin(); typename ImageType::PointType origin2=image1->GetOrigin(); typename ImageType::SizeType size=image1->GetLargestPossibleRegion().GetSize(); typename ImageType::SizeType newsize=image1->GetLargestPossibleRegion().GetSize(); typename ImageType::RegionType newregion; // determine new image size for (unsigned int i=0; iGetLargestPossibleRegion().GetIndex()); typename ImageType::Pointer padimage = ImageType::New(); padimage->SetSpacing(image1->GetSpacing()); padimage->SetOrigin(origin2); padimage->SetDirection(image1->GetDirection()); padimage->SetRegions(newregion ); padimage->Allocate(); padimage->FillBuffer( 0 ); typename ImageType::IndexType index; typename ImageType::IndexType index2; if (padvalue > 0) { index.Fill(0); index2.Fill((unsigned int)fabs(padvalue)); } else { index2.Fill(0); index.Fill((unsigned int)fabs(padvalue)); } typename ImageType::PointType point1,pointpad; image1->TransformIndexToPhysicalPoint(index, point1); padimage->TransformIndexToPhysicalPoint(index2, pointpad); std::cout << " pre " << point1 << " pad " << pointpad << std::endl; for (unsigned int i=0; iSetOrigin(origin2); Iterator iter( image1, image1->GetLargestPossibleRegion() ); for( iter.GoToBegin(); !iter.IsAtEnd(); ++iter ) { typename ImageType::IndexType oindex=iter.GetIndex(); typename ImageType::IndexType padindex=iter.GetIndex(); bool isinside=true; for (unsigned int i=0; i newsize[i]-1 ) isinside=false; // if (shifted < 0) shifted=0; //padindex[i]= } if (isinside) { for (unsigned int i=0; iSetPixel(padindex,iter.Get()); } } WriteImage(padimage,outname.c_str()); return 0; } template int StackImage(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; std::string fn2 = ""; if (argc > argct) fn2=std::string(argv[argct]); argct++; typename ImageType::Pointer image1 = NULL; if (fn1.length() > 3) ReadImage(image1, fn1.c_str()); typename ImageType::Pointer image2 = NULL; if (fn2.length() > 3) ReadImage(image2, fn2.c_str()); typename ImageType::PointType origin=image1->GetOrigin(); typename ImageType::PointType origin2=image1->GetOrigin(); typename ImageType::SizeType size=image1->GetLargestPossibleRegion().GetSize(); typename ImageType::SizeType newsize=image1->GetLargestPossibleRegion().GetSize(); typename ImageType::RegionType newregion; // determine new image size newsize[ImageDimension-1]=(unsigned int)newsize[ImageDimension-1]+image2->GetLargestPossibleRegion().GetSize()[ImageDimension-1]; std::cout << " oldsize " << size << " newsize " << newsize << std::endl; newregion.SetSize(newsize); newregion.SetIndex(image1->GetLargestPossibleRegion().GetIndex()); typename ImageType::Pointer padimage = ImageType::New(); padimage->SetSpacing(image1->GetSpacing()); padimage->SetOrigin(origin2); padimage->SetDirection(image1->GetDirection()); padimage->SetRegions(newregion ); padimage->Allocate(); padimage->FillBuffer( 0 ); typename ImageType::IndexType index; index.Fill(0); typename ImageType::IndexType index2; index2.Fill(0); typename ImageType::PointType point1,pointpad; image1->TransformIndexToPhysicalPoint(index, point1); padimage->TransformIndexToPhysicalPoint(index2, pointpad); // std::cout << " pre " << point1 << " pad " << pointpad << std::endl; for (unsigned int i=0; iSetOrigin(origin2); float padvalue=image1->GetLargestPossibleRegion().GetSize()[ImageDimension-1]; Iterator iter( padimage, padimage->GetLargestPossibleRegion() ); for( iter.GoToBegin(); !iter.IsAtEnd(); ++iter ) { typename ImageType::IndexType oindex=iter.GetIndex(); typename ImageType::IndexType padindex=iter.GetIndex(); bool isinside=false; if ( oindex[ImageDimension-1] >= padvalue ) isinside=true; if (isinside) { float shifted=((float)oindex[ImageDimension-1]-padvalue); padindex[ImageDimension-1]=(unsigned int)shifted; padimage->SetPixel(oindex,image2->GetPixel(padindex)); } else padimage->SetPixel(oindex,image1->GetPixel(oindex)); } WriteImage(padimage,outname.c_str()); return 0; } template int MakeImage(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; unsigned int sizevalx = atoi(argv[argct]); argct++; unsigned int sizevaly=0; if (argc >argct) { sizevaly=atoi(argv[argct]); argct++; } unsigned int sizevalz=0; if (argc >argct && ImageDimension == 3) { sizevalz=atoi(argv[argct]); argct++; } typename ImageType::SizeType size; size[0]=sizevalx; size[1]=sizevaly; if ( ImageDimension == 3) size[2]=sizevalz; typename ImageType::RegionType newregion; std::cout << " size " << size << std::endl; newregion.SetSize(size); typename ImageType::Pointer padimage = ImageType::New(); typename ImageType::SpacingType spacing; spacing.Fill(1); typename ImageType::PointType origin; origin.Fill(0); padimage->SetSpacing(spacing); padimage->SetOrigin(origin); padimage->SetRegions(newregion ); padimage->Allocate(); padimage->FillBuffer( 0 ); WriteImage(padimage,outname.c_str()); return 0; } template typename TImage::Pointer LabelSurface(typename TImage::Pointer input, typename TImage::Pointer input2 ) { std::cout << " Label Surf " << std::endl; typedef TImage ImageType; enum { ImageDimension = ImageType::ImageDimension }; typename ImageType::Pointer Image = ImageType::New(); Image->SetLargestPossibleRegion(input->GetLargestPossibleRegion() ); Image->SetBufferedRegion(input->GetLargestPossibleRegion()); Image->Allocate(); Image->SetSpacing(input->GetSpacing()); Image->SetOrigin(input->GetOrigin()); typedef itk::NeighborhoodIterator iteratorType; typename iteratorType::RadiusType rad; for (int j=0; jGetLargestPossibleRegion()); GHood.GoToBegin(); while (!GHood.IsAtEnd()) { typename TImage::PixelType p = GHood.GetCenterPixel(); typename TImage::IndexType ind = GHood.GetIndex(); typename TImage::IndexType ind2; if ( p >= 0.5 ) { bool atedge=false; for (unsigned int i = 0; i < GHood.Size(); i++) { ind2=GHood.GetIndex(i); float dist=0.0; for (int j=0; jGetPixel(ind2) >= 0.5 ) secondval=true; if (GHood.GetPixel(i) < 0.5 && dist < 2. && secondval ) { atedge=true; } } if (atedge && p >= 0.5) Image->SetPixel(ind,1); else Image->SetPixel(ind,0); } ++GHood; } return Image; } template int FitSphere(int argc, char *argv[]) { /* typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; std::string fn2 = ""; if (argc > argct) fn2=std::string(argv[argct]); argct++; float MaxRad=5; if (argc > argct) MaxRad = atof(argv[argct]); argct++; typename ImageType::Pointer image1 = NULL; typename ImageType::Pointer radimage = NULL; typename ImageType::Pointer radimage2 = NULL; typename ImageType::Pointer priorimage = NULL; typename ImageType::Pointer wmimage = NULL; if (fn2.length() > 3) ReadImage(wmimage, fn2.c_str()); std::cout <<" read " << fn1 << " MXR " << MaxRad << std::endl; ReadImage(image1, fn1.c_str()); ReadImage(radimage, fn1.c_str()); ReadImage(radimage2, fn1.c_str()); ReadImage(priorimage, fn1.c_str()); radimage->FillBuffer(0); radimage2->FillBuffer(0); priorimage->FillBuffer(0); typename ImageType::SpacingType spacing=image1->GetSpacing(); typename ImageType::Pointer surf = LabelSurface(image1,wmimage); typedef itk::LinearInterpolateImageFunction ScalarInterpolatorType; typename ScalarInterpolatorType::Pointer ginterp = ScalarInterpolatorType::New(); ginterp->SetInputImage(image1); typename ScalarInterpolatorType::ContinuousIndexType Y1; typename ScalarInterpolatorType::ContinuousIndexType Y2; typename ScalarInterpolatorType::ContinuousIndexType GMx; typename ScalarInterpolatorType::ContinuousIndexType WMx; typename ScalarInterpolatorType::Pointer winterp=NULL; if (wmimage) { winterp=ScalarInterpolatorType::New(); winterp->SetInputImage(wmimage); } // float x=0,y=0,z=0; // float xc=0,yc=0,zc=0; float globalbestrad=0; typename ImageType::IndexType bestind; bestind.Fill(0); typename ImageType::IndexType ind2; ind2.Fill(0); typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator iter( image1, image1->GetLargestPossibleRegion() ); std::cout <<" Begin " << std::endl; unsigned long npx=0; // float pi=3.141; unsigned long numpx=image1->GetBufferedRegion().GetNumberOfPixels(); //unsigned int prog=0; // float gmtotal,wmtotal,gvol,wvol,warea,garea; for( iter.GoToBegin(); !iter.IsAtEnd(); ++iter ) { npx++; typename ImageType::IndexType ind=iter.GetIndex(); // float val=surf->GetPixel(ind);//iter.Get(); // float minrad=1.e9; */ /* if (val > 0.5 && fabs((float)ind[2]-80.) < 6 ) { //float bestrad=0; //parameterize sphere at this index float epspi=pi*0.1; float epsrad=0.2; gvol=0;wvol=0;warea=0;garea=0; float svol=0;//4./3.*pi*MaxRad*MaxRad*MaxRad; // float sarea=0;//4.*pi*MaxRad*MaxRad; //float bestvol=0; // float wdiff=0; // for (float theta=0; theta<=pi; theta+=epspi) float theta=0; gmtotal=0; wmtotal=0; float glength=0,wlength=0; while ( theta < pi ) { garea=0;warea=0; float psi=0; while ( psi < pi ) { glength=0;wlength=0; float rr=0; bool raddone=false; GMx.Fill(0); WMx.Fill(0); while ( rr <= MaxRad && !raddone) { // integrate the wm/gm probability along this radius, at these angles Y1[0]=(float)ind[0]/spacing[0]+rr*cos(psi)*sin(theta); Y1[1]=(float)ind[1]/spacing[1]+rr*sin(psi)*sin(theta); Y1[2]=(float)ind[2]/spacing[2]+rr*cos(theta); Y2[0]=(float)ind[0]/spacing[0]+rr*cos(psi+pi)*sin(theta); Y2[1]=(float)ind[1]/spacing[1]+rr*sin(psi+pi)*sin(theta); Y2[2]=(float)ind[2]/spacing[2]+rr*cos(theta); float gval1 = ginterp->EvaluateAtContinuousIndex( Y1 ); float gval2 = ginterp->EvaluateAtContinuousIndex( Y2 ); float wval1=0,wval2=0; if (wmimage) wval1 = winterp->EvaluateAtContinuousIndex( Y1 ); if (wmimage) wval2 = winterp->EvaluateAtContinuousIndex( Y2 ); glength+=(gval1+gval2)*epsrad*2; wlength+=(wval2+wval2)*epsrad*2; gmtotal+=(gval1+gval2); wmtotal+=(wval1+wval2); for (unsigned int dd=0; dd 0) gwrat=garea/warea; if (wvol > 0) gvrat=gvol/wvol; priorimage->SetPixel(ind,gmtotal/(wmtotal+gmtotal)); radimage->SetPixel(ind,cmdist); } */ /* // radimage2->SetPixel(ind,gvrat); if (image1->GetPixel(ind) >= 0.5) { bool okfit=true; float dorad=1; float bestrad=1; while (okfit && dorad <= MaxRad ) { typedef itk::NeighborhoodIterator iteratorType; typename iteratorType::RadiusType rad,rad2; rad2.Fill(0); for (unsigned int j=0; jGetLargestPossibleRegion()); GHood.SetLocation(ind); typename ImageType::PixelType p = GHood.GetCenterPixel(); unsigned int goodct=0; unsigned int possct=0; if ( p > 0 && radimage->GetPixel(ind) <= 0 ) { for (unsigned int i = 0; i < GHood.Size(); i++) { ind2=GHood.GetIndex(i); float dist=sqrt(((float)ind2[0]-(float)ind[0])*((float)ind2[0]-(float)ind[0])+ ((float)ind2[1]-(float)ind[1])*((float)ind2[1]-(float)ind[1])+ ((float)ind2[2]-(float)ind[2])*((float)ind2[2]-(float)ind[2])); if ( GHood.GetPixel(i) == p && dist <= tardist) { goodct++; possct++; } else if ( dist <= tardist ) possct++; // std::cout << " Ind " << ind << " : " << bestrad << " tardist " << tardist << " gct " << goodct <<" pos " << possct << " dist " << dist << " ind2 " << ind2 << std::endl; } if (goodct==possct) { bestrad=dorad; radimage->SetPixel(ind,bestrad*(-1.0)); } else { okfit=false; radimage->SetPixel(ind,radimage->GetPixel(ind)*(-1.0)); } } dorad=dorad+1; } if (bestrad >= globalbestrad) { globalbestrad=bestrad; bestind=ind; } if (npx % 10000 == 0) { std::cout <<" prog " << (float)npx/(float)numpx << std::endl; // WriteImage(radimage,outname.c_str()); // WriteImage(radimage2,(std::string("Sphere")+outname).c_str()); //WriteImage(priorimage,(std::string("Prior")+outname).c_str()); } } } for( iter.GoToBegin(); !iter.IsAtEnd(); ++iter ) { float val=iter.Get(); typename ImageType::IndexType ind=iter.GetIndex(); if (val > 0) { unsigned int dorad=(unsigned int)fabs(radimage->GetPixel(ind)); typedef itk::NeighborhoodIterator iteratorType; typename iteratorType::RadiusType rad; for (unsigned int j=0; jGetLargestPossibleRegion()); GHood.SetLocation(ind); typename ImageType::PixelType p = GHood.GetCenterPixel(); for (unsigned int i = 0; i < GHood.Size(); i++) { ind2=GHood.GetIndex(i); float dist=sqrt(((float)ind2[0]-(float)ind[0])*((float)ind2[0]-(float)ind[0])+ ((float)ind2[1]-(float)ind[1])*((float)ind2[1]-(float)ind[1])+ ((float)ind2[2]-(float)ind[2])*((float)ind2[2]-(float)ind[2])); if ( GHood.GetPixel(i) == p && dist <= tardist && diameter > priorimage->GetPixel(ind2)) { priorimage->SetPixel(ind2,diameter); } } } } // now, make rad image std::cout << " Best " << bestind << " gbr " << globalbestrad << std::endl; typedef itk::NeighborhoodIterator iteratorType; typename iteratorType::RadiusType rad; for (unsigned int j=0; jGetLargestPossibleRegion()); GHood.SetLocation(bestind); typename ImageType::PixelType p = GHood.GetCenterPixel(); for (unsigned int i = 0; i < GHood.Size(); i++) { ind2=GHood.GetIndex(i); float dist=0; dist+=sqrt((float)(ind2[0]-(float)bestind[0])*(float)(ind2[0]-(float)bestind[0])+ (float)(ind2[1]-(float)bestind[1])*(float)(ind2[1]-(float)bestind[1])+ (float)(ind2[2]-(float)bestind[2])*(float)(ind2[2]-(float)bestind[2])); if ( dist <= (globalbestrad*sqrt((double)2))) { radimage2->SetPixel(ind2,p); } } // WriteImage(radimage,outname.c_str()); WriteImage(radimage2,outname.c_str()); //WriteImage(priorimage,(std::string("Prior")+outname).c_str()); */ return 0; } template int ImageMath(int argc, char *argv[]) { typedef float PixelType; // const unsigned int ImageDimension = AvantsImageDimension; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; std::string fn2=""; if (argc > argct) { fn2=std::string(argv[argct]); } typename ImageType::Pointer image1 = NULL; typename ImageType::Pointer image2 = NULL; typename ImageType::Pointer varimage = NULL; typename readertype::Pointer reader2 = readertype::New(); typename readertype::Pointer reader1 = readertype::New(); reader2->SetFileName(fn2.c_str()); bool isfloat=false; try { reader2->UpdateLargestPossibleRegion(); } catch(...) { std::cout << " Error reading " << fn2 << " as a file -- will treat " << argv[argct] << " as a float value " << std::endl; isfloat=true; } float floatval=1.0; if (isfloat) floatval=atof(argv[argct]); else image2 = reader2->GetOutput(); reader1->SetFileName(fn1.c_str()); try { reader1->UpdateLargestPossibleRegion(); image1 = reader1->GetOutput(); } catch(...) { std::cout << " read 1 error "; } varimage=ImageType::New(); varimage->SetLargestPossibleRegion( image1->GetLargestPossibleRegion() ); varimage->SetBufferedRegion( image1->GetLargestPossibleRegion() ); varimage->SetLargestPossibleRegion( image1->GetLargestPossibleRegion() ); varimage->Allocate(); varimage->SetSpacing(image1->GetSpacing()); varimage->SetOrigin(image1->GetOrigin()); varimage->SetDirection(image1->GetDirection()); if (strcmp(operation.c_str(),"mresample") == 0 && !isfloat ) { typename ImageType::SpacingType spc=image2->GetSpacing(); typedef itk::TranslationTransform< double, ImageDimension> TransformType0; typename TransformType0::Pointer m_Transform0=TransformType0::New(); typename TransformType0::ParametersType trans=m_Transform0->GetParameters(); for( unsigned int i=0; iGetSpacing()[i]*image1->GetLargestPossibleRegion().GetSize()[i]/image2->GetLargestPossibleRegion().GetSize()[i]; } image2->SetSpacing(spc); image2->SetOrigin(image1->GetOrigin()); image2->SetDirection(image1->GetDirection()); m_Transform0->SetParameters(trans); std::cout <<" trans " << m_Transform0->GetParameters() << " Nspc " << image2->GetSpacing() << std::endl; typedef itk::ResampleImageFilter< ImageType, ImageType > ResampleFilterType; typename ResampleFilterType::Pointer resample = ResampleFilterType::New(); resample->SetTransform( m_Transform0 ); resample->SetInput( image2 ); resample->SetSize( image1->GetLargestPossibleRegion().GetSize() ); resample->SetOutputOrigin( image1->GetOrigin() ); resample->SetOutputSpacing( image1->GetSpacing() ); typename ImageType::IndexType zeroind; zeroind.Fill(0); resample->SetDefaultPixelValue( image1->GetPixel(zeroind) ); resample->UpdateLargestPossibleRegion(); image2=resample->GetOutput(); WriteImage(image2,outname.c_str()); return 0; } float volumeelement=1.0; for (unsigned int i=0; iGetSpacing()[i]; float result=0; Iterator vfIter2( varimage, varimage->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { IndexType ind=vfIter2.GetIndex(); float pix2; if (isfloat) pix2= floatval; else pix2=image2->GetPixel(ind); float pix1 = image1->GetPixel(ind); if (strcmp(operation.c_str(),"m") == 0) result=pix1*pix2; else if (strcmp(operation.c_str(),"+") == 0) result=pix1+pix2; else if (strcmp(operation.c_str(),"-") == 0) result=pix1-pix2; else if (strcmp(operation.c_str(),"/") == 0) { if (pix2 > 0) result=pix1/pix2; } else if (strcmp(operation.c_str(),"^") == 0) { result=pow(pix1,pix2); } else if (strcmp(operation.c_str(),"exp") == 0) { result=exp(pix1*pix2); } else if (strcmp(operation.c_str(),"abs") == 0) { result=fabs(pix1); } else if (strcmp(operation.c_str(),"addtozero") == 0 && pix1 == 0) { result=pix1+pix2; } else if (strcmp(operation.c_str(),"addtozero") == 0 && pix1 != 0) { result=pix1; } else if (strcmp(operation.c_str(),"overadd") == 0 && pix2 != 0) { result=pix2; } else if (strcmp(operation.c_str(),"overadd") == 0 ) { result=pix1; } else if (strcmp(operation.c_str(),"Decision") == 0 ) { result=1./(1.+exp(-1.0*( pix1-0.25)/pix2));} else if (strcmp(operation.c_str(),"total") == 0 ) { result+=pix1*pix2 ;} vfIter2.Set(result); } if (strcmp(operation.c_str(),"total") == 0 ) std::cout << "total: " << result << " total-volume: " << result*volumeelement << std::endl; else std::cout << "operation " << operation << std::endl; if ( outname.length() > 3 ) WriteImage(varimage,outname.c_str()); return 0; } template int TensorFunctions(int argc, char *argv[]) { typedef float PixelType; // typedef itk::Vector TensorType; typedef itk::SymmetricSecondRankTensor< float, 3 > TensorType; typedef typename itk::RGBPixel RGBType; typedef itk::Image TensorImageType; typedef typename TensorImageType::IndexType IndexType; typedef itk::Image ImageType; typedef itk::Image ColorImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef itk::ImageFileWriter ColorWriterType; typedef itk::ImageRegionIteratorWithIndex Iterator; typedef itk::Vector VectorType; typedef itk::Image VectorImageType; typedef itk::ImageRegionIteratorWithIndex vecIterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; unsigned int whichvec=ImageDimension-1; if (argc > argct) { whichvec=atoi(argv[argct]); } argct++; std::cout << " whichvec " << whichvec << std::endl; typename TensorImageType::Pointer timage = NULL; // input tensor image typename ImageType::Pointer vimage = NULL; // output scalar image typename ColorImageType::Pointer cimage = NULL; // output color image typename VectorImageType::Pointer vecimage = NULL; // output vector image ReadTensorImage(timage,fn1.c_str(),false); if (strcmp(operation.c_str(), "TensorIOTest") == 0) { std::cout <<" test function for tensor I/O " << std::endl; WriteTensorImage(timage, outname.c_str() ,false); return 0; } std::cout << " imagedir " << timage->GetDirection() << std::endl; if (strcmp(operation.c_str(), "TensorColor") == 0) { cimage = ColorImageType::New(); cimage->SetLargestPossibleRegion( timage->GetLargestPossibleRegion() ); cimage->SetBufferedRegion( timage->GetLargestPossibleRegion() ); cimage->SetLargestPossibleRegion( timage->GetLargestPossibleRegion() ); cimage->Allocate(); cimage->SetSpacing(timage->GetSpacing()); cimage->SetOrigin(timage->GetOrigin()); cimage->SetDirection(timage->GetDirection()); } else if (strcmp(operation.c_str(), "TensorToVector") == 0) { vecimage = VectorImageType::New(); vecimage->SetLargestPossibleRegion( timage->GetLargestPossibleRegion() ); vecimage->SetBufferedRegion( timage->GetLargestPossibleRegion() ); vecimage->SetLargestPossibleRegion( timage->GetLargestPossibleRegion() ); vecimage->Allocate(); vecimage->SetSpacing(timage->GetSpacing()); vecimage->SetOrigin(timage->GetOrigin()); vecimage->SetDirection(timage->GetDirection()); VectorType zero; zero.Fill(0); vecimage->FillBuffer(zero); } else { vimage=ImageType::New(); vimage->SetLargestPossibleRegion( timage->GetLargestPossibleRegion() ); vimage->SetBufferedRegion( timage->GetLargestPossibleRegion() ); vimage->SetLargestPossibleRegion( timage->GetLargestPossibleRegion() ); vimage->Allocate(); vimage->SetSpacing(timage->GetSpacing()); vimage->SetOrigin(timage->GetOrigin()); vimage->SetDirection(timage->GetDirection()); } Iterator tIter(timage, timage->GetLargestPossibleRegion() ); for( tIter.GoToBegin(); !tIter.IsAtEnd(); ++tIter ) { IndexType ind=tIter.GetIndex(); float result=0; if (strcmp(operation.c_str(),"TensorFA") == 0) { result=GetTensorFA(tIter.Value()); if (vnl_math_isnan(result)) result=0; vimage->SetPixel(ind,result); } else if (strcmp(operation.c_str(),"TensorMeanDiffusion") == 0) { result=GetTensorADC(tIter.Value(),0); if (vnl_math_isnan(result)) result=0; vimage->SetPixel(ind,result); } else if (strcmp(operation.c_str(),"TensorColor") == 0) { RGBType rgb = GetTensorRGB(tIter.Value()); cimage->SetPixel(ind,rgb); } else if (strcmp(operation.c_str(),"TensorToVector") == 0) { VectorType vv = GetTensorPrincipalEigenvector(tIter.Value(),whichvec); vecimage->SetPixel(ind,vv); } else if (strcmp(operation.c_str(),"TensorToVectorComponent") == 0) { if ( whichvec <= 2 ){ VectorType vv = GetTensorPrincipalEigenvector(tIter.Value(),whichvec); vimage->SetPixel(ind,vv[whichvec]); } else if ( whichvec > 2 && whichvec < 9){ vimage->SetPixel(ind,tIter.Value()[whichvec]); } } } if (strcmp(operation.c_str(), "TensorColor") == 0) { typename ColorWriterType::Pointer cwrite = ColorWriterType::New(); cwrite->SetInput(cimage); cwrite->SetFileName(outname.c_str()); cwrite->Update(); } else if (strcmp(operation.c_str(), "TensorToVector") == 0) { WriteImage(vecimage,outname.c_str()); } else { WriteImage(vimage,outname.c_str()); } return 0; } template int CompareHeadersAndImages(int argc, char *argv[]) { typedef float PixelType; // const unsigned int ImageDimension = AvantsImageDimension; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; std::string fn2=""; if (argc > argct) { fn2=std::string(argv[argct]); } typename ImageType::Pointer image1 = NULL; typename ImageType::Pointer image2 = NULL; typename readertype::Pointer reader2 = readertype::New(); typename readertype::Pointer reader1 = readertype::New(); reader2->SetFileName(fn2.c_str()); bool isfloat=false; try { reader2->UpdateLargestPossibleRegion(); } catch(...) { std::cout << " Error reading " << fn2 << std::endl; isfloat=true; } float floatval=1.0; if (isfloat) floatval=atof(argv[argct]); else image2 = reader2->GetOutput(); reader1->SetFileName(fn1.c_str()); try { reader1->UpdateLargestPossibleRegion(); image1 = reader1->GetOutput(); } catch(...) { std::cout << " read 1 error "; } // compute error in spacing, in orientation and in offset unsigned int failure=0; float sperr=0,merr=0,operr=0,orsignerr=0; typename ImageType::SpacingType sp1,sp2; sp1=image1->GetSpacing(); sp2=image2->GetSpacing(); for (unsigned int i=0;iGetOrigin(); op2=image2->GetOrigin(); for (unsigned int i=0;i 0 && op2[i] <= 0) orsignerr+=1; else if ( op1[i] < 0 && op2[i] >= 0) orsignerr+=1; } std::cout << " OriginError: " << sqrt(operr) << std::endl; std::cout << " OriginSignError: " << orsignerr << std::endl; for (unsigned int i=0;iGetDirection()[i][j]-image2->GetDirection()[i][j]; merr+=temp*temp; } std::cout << " OrientError: " << sqrt(merr) << std::endl; bool samesize=true; for (unsigned int i=0; iGetLargestPossibleRegion().GetSize()[i] != image2->GetLargestPossibleRegion().GetSize()[i] ) samesize=false; if (samesize ) { // run a quick registration if (false){ typedef typename itk::ImageMomentsCalculator< ImageType > ImageCalculatorType; typename ImageCalculatorType::Pointer calculator = ImageCalculatorType::New(); calculator->SetImage( image1 ); typename ImageCalculatorType::VectorType fixed_center; fixed_center.Fill(0); typename ImageCalculatorType::VectorType moving_center; moving_center.Fill(0); try { calculator->Compute(); fixed_center = calculator->GetCenterOfGravity(); calculator->SetImage( image2 ); try { calculator->Compute(); moving_center = calculator->GetCenterOfGravity(); } catch(...) { std::cout << " zero image2 error "; fixed_center.Fill(0); } } catch(...) { std::cout << " zero image1 error "; } typedef itk::TranslationTransform< double, ImageDimension> TransformType0; typename TransformType0::Pointer m_Transform0=TransformType0::New(); typename TransformType0::ParametersType trans=m_Transform0->GetParameters(); for( unsigned int i=0; iSetParameters(trans); std::cout <<" trans " << m_Transform0->GetParameters() << std::endl; typedef itk::ResampleImageFilter< ImageType, ImageType > ResampleFilterType; typename ResampleFilterType::Pointer resample = ResampleFilterType::New(); resample->SetTransform( m_Transform0 ); resample->SetInput( image2 ); resample->SetSize( image1->GetLargestPossibleRegion().GetSize() ); resample->SetOutputOrigin( image1->GetOrigin() ); resample->SetOutputSpacing( image1->GetSpacing() ); typename ImageType::IndexType zeroind; zeroind.Fill(0); resample->SetDefaultPixelValue( image1->GetPixel(zeroind) ); resample->UpdateLargestPossibleRegion(); typename ImageType::Pointer varimage=resample->GetOutput(); } float i1norm=0,i2norm=0,i1i2norm=0; //i3norm=0,i1i3norm=0; unsigned long ct1=1,ct2=1,ct12=1; //ct3=1,ct13=1; Iterator vfIter2( image1, image1->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { IndexType ind=vfIter2.GetIndex(); float pix2; if (isfloat) pix2= floatval; else pix2=image2->GetPixel(ind); if ( vnl_math_isnan(pix2) || vnl_math_isinf(pix2) ) { pix2=0; image2->SetPixel(ind,0);} // float pix3=varimage->GetPixel(ind); float pix1 = image1->GetPixel(ind); if (pix1 > 0 ) { i1norm+=fabs(pix1); ct1++; } if (pix2 > 0 ) { i2norm+=fabs(pix2); ct2++; } //if (pix3 > 0 ) { i3norm+=fabs(pix3); ct3++; } } float mean1=i1norm/ct1; if (mean1 == 0) mean1=1; float mean2=i2norm/ct2; if (mean2 == 0) mean2=1; for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { IndexType ind=vfIter2.GetIndex(); float pix2; if (isfloat) pix2= floatval; else pix2=image2->GetPixel(ind)/mean2; // float pix3=vfIter2.Get()/mean2; float pix1 = image1->GetPixel(ind)/mean1; if ( pix1 > 0 || pix2 > 0) { i1i2norm+=fabs(pix1-pix2); ct12++; } // if ( pix1 > 0 || pix3 > 0) //{ // i1i3norm+=fabs(pix1-pix3); // ct13++; //} } float idice0 = 1.0 - 2.0*i1i2norm/ct12 / ( i1norm/ct1 + i2norm / ct2 ); // float idice1 = 1.0 - 2.0*i1i3norm/ct13 / ( i1norm/ct1 + i3norm / ct3 ); std::cout << " DiceImageDifference: " << idice0 << " IntensityDifference: " << i1i2norm << std::endl; //std::cout << " CenterOfMassTransImageDifference: " << idice1 << " and " << i1i3norm << std::endl; } typename ImageType::PointType fixedorig=image2->GetOrigin(); if ( orsignerr > 0 ) { failure=1; // now fix the error for (unsigned int i=0;i 0 && op2[i] < 0 ) || (op1[i] < 0 && op2[i] > 0) ) fixedorig[i]*=(-1.0); else if ( (op1[i] > 0 && op2[i] == 0 ) || (op1[i] < 0 && op2[i] == 0) ) fixedorig[i]=image1->GetOrigin()[i]; } } image2->SetOrigin(fixedorig); // if ( sqrt(operr) > 100) failure= if (sqrt(merr) >= 0.7 && failure==0 ) failure=2; if (sqrt(merr) >= 0.7 && failure != 0 ) failure=3; if (failure > 1) { image2->SetDirection(image1->GetDirection()); } // write repaired images typename writertype::Pointer writer = writertype::New(); writer->SetFileName(outname.c_str()); writer->SetInput( image2 ); writer->Write(); std::cout << " FailureState: " << failure << " for " << fn2 << std::endl; return failure; } template typename TImage::Pointer BinaryThreshold(typename TImage::PixelType low, typename TImage::PixelType high,typename TImage::PixelType replaceval, typename TImage::Pointer input) { //std::cout << " Binary Thresh " << std::endl; typedef typename TImage::PixelType PixelType; // Begin Threshold Image typedef itk::BinaryThresholdImageFilter InputThresholderType; typename InputThresholderType::Pointer inputThresholder = InputThresholderType::New(); inputThresholder->SetInput( input ); inputThresholder->SetInsideValue( replaceval ); int outval=0; if ((float) replaceval == (float) -1) outval=1; inputThresholder->SetOutsideValue( outval ); if (high < low) high=255; inputThresholder->SetLowerThreshold((PixelType) low ); inputThresholder->SetUpperThreshold((PixelType) high); inputThresholder->Update(); return inputThresholder->GetOutput(); } // template // typename TImage::Pointer // SegmentKMeans(typename TImage::Pointer image , unsigned int nclasses) // { // // typedef TImage ImageType; // typedef typename TImage::PixelType PixelType; // enum { ImageDimension = ImageType::ImageDimension }; // typedef itk::ImageRegionIteratorWithIndex Iterator; // // typedef itk::Statistics::ScalarImageToListAdaptor< ImageType > AdaptorType; // // typename AdaptorType::Pointer adaptor = AdaptorType::New(); // // adaptor->SetImage( image ); // // // Define the Measurement vector type from the AdaptorType // typedef typename AdaptorType::MeasurementVectorType MeasurementVectorType; // // // Create the K-d tree structure // typedef itk::Statistics::WeightedCentroidKdTreeGenerator< // AdaptorType > // TreeGeneratorType; // // typename TreeGeneratorType::Pointer treeGenerator = TreeGeneratorType::New(); // // treeGenerator->SetSample( adaptor ); // treeGenerator->SetBucketSize( 16 ); // treeGenerator->Update(); // // typedef typename TreeGeneratorType::KdTreeType TreeType; // typedef itk::Statistics::KdTreeBasedKmeansEstimator EstimatorType; // // typename EstimatorType::Pointer estimator = EstimatorType::New(); // // typename EstimatorType::ParametersType initialMeans( nclasses ); // // Iterator vfIter2( image, image->GetLargestPossibleRegion() ); // double mx =-1.e12, mn=1.e12; // for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) // { // double px = vfIter2.Get(); // if (px > mx) mx=px; // else if (px < mn) mn=px; // } // float range=(mx-mn); // // float bins=1.0/((float)nclasses+1.); // for (unsigned int i=0; iSetParameters( initialMeans ); // // estimator->SetKdTree( treeGenerator->GetOutput() ); // estimator->SetMaximumIteration( 200 ); // estimator->SetCentroidPositionChangesThreshold(0.0); // estimator->StartOptimization(); // // typename EstimatorType::ParametersType estimatedMeans = estimator->GetParameters(); // // // typename ImageType::Pointer varimage=ImageType::New(); // varimage->SetLargestPossibleRegion( image->GetLargestPossibleRegion() ); // varimage->SetBufferedRegion( image->GetLargestPossibleRegion() ); // varimage->SetLargestPossibleRegion( image->GetLargestPossibleRegion() ); // varimage->Allocate(); // varimage->SetSpacing(image->GetSpacing()); // varimage->SetOrigin(image->GetOrigin()); // varimage->SetDirection(image->GetDirection()); // // // float var=sqrt(range); // for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) // { // double px = vfIter2.Get(); // unsigned int best=0; // float mindist=1.e9; // for ( unsigned int i = 0 ; i < nclasses ; ++i ) // { // float dist=fabs(px-estimatedMeans[i]); // if (dist < mindist) { mindist=dist; best=i; } // //vec[i]=exp(-1.0*dist*dist/var); // } // varimage->SetPixel(vfIter2.GetIndex(),best+1); // // vecimage->SetPixel(vfIter2.GetIndex(),vec); // } // // return varimage; // // // } template typename TImage::Pointer Morphological( typename TImage::Pointer input,float rad, unsigned int option, float dilateval) { typedef TImage ImageType; enum { ImageDimension = TImage::ImageDimension }; typedef typename TImage::PixelType PixelType; if (option == 0) std::cout << " binary eroding the image " << std::endl; else if (option == 1) std::cout << " binary dilating the image " << std::endl; else if (option == 2) std::cout << " binary opening the image " << std::endl; else if (option == 3) std::cout << " binary closing the image " << std::endl; else if (option == 4) std::cout << " grayscale eroding the image " << std::endl; else if (option == 5) std::cout << " grayscale dilating the image " << std::endl; else if (option == 6) std::cout << " grayscale opening the image " << std::endl; else if (option == 7) std::cout << " grayscale closing the image " << std::endl; typedef itk::BinaryBallStructuringElement< PixelType, ImageDimension > StructuringElementType; // Software Guide : BeginCodeSnippet typedef itk::BinaryErodeImageFilter< TImage, TImage, StructuringElementType > ErodeFilterType; typedef itk::BinaryDilateImageFilter< TImage, TImage, StructuringElementType > DilateFilterType; //typedef itk::BinaryMorphologicalOpeningImageFilter< // TImage, // TImage, // StructuringElementType > OpeningFilterType; //typedef itk::BinaryMorphologicalClosingImageFilter< // TImage, // TImage, // StructuringElementType > ClosingFilterType; typedef itk::GrayscaleErodeImageFilter< TImage, TImage, StructuringElementType > GrayscaleErodeFilterType; typedef itk::GrayscaleDilateImageFilter< TImage, TImage, StructuringElementType > GrayscaleDilateFilterType; typename ErodeFilterType::Pointer binaryErode = ErodeFilterType::New(); typename DilateFilterType::Pointer binaryDilate = DilateFilterType::New(); //typename OpeningFilterType::Pointer binaryOpen = OpeningFilterType::New(); //typename ClosingFilterType::Pointer binaryClose = ClosingFilterType::New(); typename GrayscaleErodeFilterType::Pointer grayscaleErode = GrayscaleErodeFilterType::New(); typename GrayscaleDilateFilterType::Pointer grayscaleDilate = GrayscaleDilateFilterType::New(); StructuringElementType structuringElement; structuringElement.SetRadius((unsigned long) rad ); // 3x3x3 structuring element structuringElement.CreateStructuringElement(); binaryErode->SetKernel( structuringElement ); binaryDilate->SetKernel( structuringElement ); //binaryOpen->SetKernal( structuringElement ); //binaryClose->SetKernel( structuringElement ); grayscaleErode->SetKernel( structuringElement ); grayscaleDilate->SetKernel( structuringElement ); // It is necessary to define what could be considered objects on the binary // images. This is specified with the methods \code{SetErodeValue()} and // \code{SetDilateValue()}. The value passed to these methods will be // considered the value over which the dilation and erosion rules will apply binaryErode->SetErodeValue( 1 ); binaryDilate->SetDilateValue( 1 ); typename TImage::Pointer temp; if (option == 1) { std::cout << " Dilate " << rad << std::endl; binaryDilate->SetInput( input ); binaryDilate->Update(); temp = binaryDilate->GetOutput(); } else if (option == 0) { std::cout << " Erode " << rad << std::endl; binaryErode->SetInput( input );//binaryDilate->GetOutput() ); binaryErode->Update(); temp = binaryErode->GetOutput(); } else if (option == 2) { // dilate(erode(img)) std::cout << " Binary Open " << rad << std::endl; //binaryOpen->SetInput( input );//binaryDilate->GetOutput() ); //binaryOpen->Update(); binaryErode->SetInput( input ); binaryDilate->SetInput( binaryErode->GetOutput() ); binaryDilate->Update(); temp = binaryDilate->GetOutput(); } else if (option == 3) { std::cout << " Binary Close " << rad << std::endl; //binaryClose->SetInput( input );//binaryDilate->GetOutput() ); //binaryClose->Update(); binaryDilate->SetInput( input ); binaryErode->SetInput( binaryDilate->GetOutput() ); binaryErode->Update(); temp = binaryErode->GetOutput(); } else if (option == 4) { std::cout << " Grayscale Erode " << rad << std::endl; grayscaleErode->SetInput( input );//binaryDilate->GetOutput() ); grayscaleErode->Update(); temp = binaryErode->GetOutput(); } else if (option == 5) { std::cout << " Grayscale Dilate " << rad << std::endl; grayscaleDilate->SetInput( input );//binaryDilate->GetOutput() ); grayscaleDilate->Update(); temp = binaryDilate->GetOutput(); } else if (option == 6) { std::cout << " Grayscale Open " << rad << std::endl; grayscaleErode->SetInput( input );//binaryDilate->GetOutput() ); grayscaleErode->Update(); grayscaleDilate->SetInput( grayscaleErode->GetOutput() ); grayscaleDilate->Update(); temp = grayscaleDilate->GetOutput(); } else if (option == 7) { std::cout << " Grayscale Close " << rad << std::endl; grayscaleDilate->SetInput( input );//binaryDilate->GetOutput() ); grayscaleDilate->Update(); grayscaleErode->SetInput( grayscaleDilate->GetOutput() ); grayscaleErode->Update(); temp = grayscaleErode->GetOutput(); } if (option == 0 ) { // FIXME - replace with threshold filter? typedef itk::ImageRegionIteratorWithIndex< ImageType > ImageIteratorType ; ImageIteratorType o_iter( temp, temp->GetLargestPossibleRegion() ); o_iter.GoToBegin() ; while ( !o_iter.IsAtEnd() ) { if (o_iter.Get() > 0.5 && input->GetPixel(o_iter.GetIndex()) > 0.5) o_iter.Set(1); else o_iter.Set(0); ++o_iter; } } return temp; } // template // typename TImage::Pointer // BayesianSegmentation(typename TImage::Pointer image , unsigned int nclasses, std::string priorfn , unsigned int nsmooth = 2 ) // { // // typedef TImage ImageType; // typedef typename TImage::PixelType PixelType; // enum { ImageDimension = ImageType::ImageDimension }; // typedef itk::ImageRegionIteratorWithIndex Iterator; // // // const unsigned int ImageDimension = AvantsImageDimension; // typedef itk::Vector VectorType; // typedef itk::Image FieldType; // typedef itk::ImageFileReader readertype; // typedef itk::ImageFileWriter writertype; // typedef typename ImageType::IndexType IndexType; // typedef typename ImageType::SizeType SizeType; // typedef typename ImageType::SpacingType SpacingType; // typedef itk::AffineTransform AffineTransformType; // typedef itk::LinearInterpolateImageFunction InterpolatorType1; // typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; // typedef itk::ImageRegionIteratorWithIndex Iterator; // // // // typedef itk::Statistics::ScalarImageToListAdaptor< ImageType > AdaptorType; // typename AdaptorType::Pointer adaptor = AdaptorType::New(); // adaptor->SetImage( image ); // // Define the Measurement vector type from the AdaptorType // typedef typename AdaptorType::MeasurementVectorType MeasurementVectorType; // // Create the K-d tree structure // typedef itk::Statistics::WeightedCentroidKdTreeGenerator< // AdaptorType > // TreeGeneratorType; // typename TreeGeneratorType::Pointer treeGenerator = TreeGeneratorType::New(); // treeGenerator->SetSample( adaptor ); // treeGenerator->SetBucketSize( 16 ); // treeGenerator->Update(); // typedef typename TreeGeneratorType::KdTreeType TreeType; // typedef itk::Statistics::KdTreeBasedKmeansEstimator EstimatorType; // typename EstimatorType::Pointer estimator = EstimatorType::New(); // typename EstimatorType::ParametersType initialMeans( nclasses ); // Iterator vfIter2( image, image->GetLargestPossibleRegion() ); // double mx =-1.e12, mn=1.e12; // for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) // { // double px = vfIter2.Get(); // if (px > mx) mx=px; // else if (px < mn) mn=px; // } // float range=(mx-mn); // float bins=1.0/((float)nclasses+1.); // for (unsigned int i=0; iSetParameters( initialMeans ); // estimator->SetKdTree( treeGenerator->GetOutput() ); // estimator->SetMaximumIteration( 200 ); // estimator->SetCentroidPositionChangesThreshold(0.0); // estimator->StartOptimization(); // typename EstimatorType::ParametersType estimatedMeans = estimator->GetParameters(); // // typename ImageType::Pointer varimage=ImageType::New(); // varimage->SetLargestPossibleRegion( image->GetLargestPossibleRegion() ); // varimage->SetBufferedRegion( image->GetLargestPossibleRegion() ); // varimage->SetLargestPossibleRegion( image->GetLargestPossibleRegion() ); // varimage->SetSpacing(image->GetSpacing()); // varimage->SetOrigin(image->GetOrigin()); // varimage->SetDirection(image->GetDirection()); // varimage->Allocate(); // // std::vector estimatedVar(nclasses,0); // std::vector estimatedCounts(nclasses,0); // // // float var=sqrt(range); // for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) // { // double px = vfIter2.Get(); // unsigned int best=0; // float mindist=1.e9; // for ( unsigned int i = 0 ; i < nclasses ; ++i ) // { // float dist=fabs(px-estimatedMeans[i]); // if (dist < mindist) { mindist=dist; best=i; } // //vec[i]=exp(-1.0*dist*dist/var); // } // varimage->SetPixel(vfIter2.GetIndex(),best); // } // for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) // { // double px = vfIter2.Get(); // unsigned int i = (unsigned int) varimage->GetPixel(vfIter2.GetIndex()); // estimatedCounts[i]=estimatedCounts[i]+1; // float dist=(px -estimatedMeans[i]); // estimatedVar[i]+=dist*dist; // } // // for (unsigned int i=0; i 0) estimatedVar[i]=(estimatedVar[i])/ct; // else estimatedVar[i]=0; // std::cout << " Sample SD Ests " << sqrt(estimatedVar[i]) << " Mean " << estimatedMeans[i] << std::endl; // } // // typedef float InputPixelType; // typedef itk::VectorImage< InputPixelType, ImageDimension > InputImageType; // typedef float LabelType; // typedef float PriorType; // typedef float PosteriorType; // typedef itk::BayesianClassifierImageFilter< // InputImageType,LabelType, // PosteriorType,PriorType > ClassifierFilterType; // // typename InputImageType::Pointer vecImage = InputImageType::New(); // typedef typename InputImageType::PixelType VecPixelType; // vecImage->SetSpacing(image->GetSpacing()); // vecImage->SetOrigin(image->GetOrigin()); // vecImage->SetRegions( image->GetLargestPossibleRegion() ); // vecImage->SetVectorLength(nclasses); // vecImage->Allocate(); // VecPixelType vvv(nclasses); // vvv.Fill(0); // vecImage->FillBuffer(vvv); // // for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { // double px = vfIter2.Get(); // VecPixelType probs(nclasses); // float total=0; // for (unsigned int i=0; i0) probs[i]/=total; // } // vecImage->SetPixel( vfIter2.GetIndex(), probs); // } // // typename ClassifierFilterType::Pointer filter = ClassifierFilterType::New(); // // // typedef itk::ImageFileReader< InputImageType > ReaderType; // typename ReaderType::Pointer reader = ReaderType::New(); // typename InputImageType::Pointer priors=NULL; // // if (priorfn.length() > 3 ) // { // std::cout << " Setting Priors " << priorfn << std::endl; // bool geometric=false; // if ( strcmp(priorfn.c_str(),"Geometric") == 0) geometric=true; // if (geometric) // { // std::cout <<" Using a geometric thickness prior to aid cortical segmentation " << std::endl; // typename ImageType::Pointer outbrainmask = BinaryThreshold(0,nclasses-3,1,varimage); // typename ImageType::Pointer inwmask = BinaryThreshold(nclasses-1,nclasses,1,varimage); // typename ImageType::Pointer outwmask = BinaryThreshold(0,nclasses-2,1,varimage); // typedef itk::DanielssonDistanceMapImageFilter FilterType; // typename FilterType::Pointer distmap = FilterType::New(); // distmap->InputIsBinaryOn(); // distmap->SetUseImageSpacing(true); // distmap->SetInput(outbrainmask); // distmap->Update(); // typename ImageType::Pointer distcortex=distmap->GetOutput(); // // typename FilterType::Pointer distmap2 = FilterType::New(); // distmap2->InputIsBinaryOn(); // distmap2->SetUseImageSpacing(true); // distmap2->SetInput(inwmask); // distmap2->Update(); // typename ImageType::Pointer distwm=distmap2->GetOutput(); // // typedef itk::LaplacianRecursiveGaussianImageFilter dgf; // typename dgf::Pointer lfilter = dgf::New(); // lfilter->SetSigma(1.3); // lfilter->SetInput(distwm); // lfilter->Update(); // typename ImageType::Pointer image2=lfilter->GetOutput(); // typedef itk::RescaleIntensityImageFilter RescaleFilterType; // typename RescaleFilterType::Pointer rescaler = RescaleFilterType::New(); // rescaler->SetOutputMinimum( 0 ); // rescaler->SetOutputMaximum( 1 ); // rescaler->SetInput( image2 ); // rescaler->Update(); // typename ImageType::Pointer sulci= rescaler->GetOutput(); // // priors= InputImageType::New(); // typedef typename InputImageType::PixelType VecPixelType; // priors->SetSpacing(image->GetSpacing()); // priors->SetOrigin(image->GetOrigin()); // priors->SetDirection(image->GetDirection()); // priors->SetRegions( image->GetLargestPossibleRegion() ); // priors->SetVectorLength(nclasses); // priors->Allocate(); // std::cout <<" Allocated " << std::endl; // // for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) // { // // std::cout <<" ind " <GetPixel( vfIter2.GetIndex()); // // float inw = inwmask->GetPixel( vfIter2.GetIndex()); // float distance = distcortex->GetPixel( vfIter2.GetIndex()); // float wdistance = distwm->GetPixel( vfIter2.GetIndex()); // VecPixelType probs(nclasses); // probs.Fill(1.0/(float)nclasses); // VecPixelType posteriors=vecImage->GetPixel(vfIter2.GetIndex()); // if (nclasses > 2) // { // float dvar=2; // float basedist=4; // float distmag=basedist-distance; // // if (distmag > 0) distmag=0; // distmag*=distmag; // float gdistprob=1.0-exp(-1.0*distmag/dvar); // // float wdistprob=1.0/(1.0+exp(-1.0*distance/5)); // // float wdistmag=basedist-wdistance; // if (wdistmag > 0) wdistmag=0; // wdistmag*=wdistmag; // float gdistprob2=exp(-1.0*wdistmag/dvar); // // float sulcprob=sulci->GetPixel( vfIter2.GetIndex()); // float sdiff=(0.25-sulcprob); // if (sdiff > 0) sdiff=0; // sdiff*=sdiff; // sulcprob=exp(-1.0*sdiff/0.5); // // std::cout << " Sulc " << sulcprob << std::endl; // // bool test = (outbrain < 1 && inw > 1); // if ( true ) // { // for (unsigned int i=0; i 0) probs[i]=sulcprob; // } // } // else // { // for (unsigned int i=0; i0) probs[i]/=prtotal; else probs[i]=1.0/(float)nclasses; // if (pototal>0) posteriors[i]/=pototal; else posteriors[i]=1.0/(float)nclasses; // } // priors->SetPixel( vfIter2.GetIndex(), probs); // vecImage->SetPixel( vfIter2.GetIndex(), posteriors); // } // std::cout << " ok " << std::endl; // // // } // else // { // reader->SetFileName( priorfn.c_str() ); // reader->Update(); // priors=reader->GetOutput(); // // for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) // { // VecPixelType posteriors=vecImage->GetPixel(vfIter2.GetIndex()); // VecPixelType probs=priors->GetPixel(vfIter2.GetIndex()); // for (unsigned int i=0; i0) probs[i]/=prtotal; else probs[i]=1.0/(float)nclasses; // if (pototal>0) posteriors[i]/=pototal; else posteriors[i]=1.0/(float)nclasses; // } // vecImage->SetPixel( vfIter2.GetIndex(), posteriors); // } // } // // if (priors) filter->SetInput( 1, priors ); // Bug -- // // classification filter does not actually use priors // } else std::cout << " No Priors " << std::endl; // // filter->SetInput( vecImage ); // // // if( nsmooth >= 1 ) // { // std::cout << " Smoothing Iterations: " << nsmooth << std::endl; // filter->SetNumberOfSmoothingIterations( nsmooth ); // typedef typename ClassifierFilterType::ExtractedComponentImageType ExtractedComponentImageType; // typedef itk::DiscreteGaussianImageFilter< // ExtractedComponentImageType, ExtractedComponentImageType > SmoothingFilterType; // typedef itk::BilateralImageFilter< // ExtractedComponentImageType, ExtractedComponentImageType > SmoothingFilterType2; // typename SmoothingFilterType::Pointer smoother = SmoothingFilterType::New(); // smoother->SetVariance(1.0); // smoother->SetUseImageSpacingOff(); // //smoother->SetDomainSigma(1); // //smoother->SetRangeSigma(1); // filter->SetSmoothingFilter( smoother ); // } // // // SET FILTER'S PRIOR PARAMETERS // // do nothing here to default to uniform priors // // otherwise set the priors to some user provided values // // // // // Setup writer.. Rescale the label map to the dynamic range of the // // datatype and write it // // // filter->Update(); // // return filter->GetOutput(); // // } template int NegativeImage(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; std::string fn2=""; if (argc > argct) { fn2=std::string(argv[argct]); } typename ImageType::Pointer image1 = NULL; typename readertype::Pointer reader1 = readertype::New(); reader1->SetFileName(fn1.c_str()); reader1->UpdateLargestPossibleRegion(); try { image1 = reader1->GetOutput(); } catch(...) { std::cout << " read 1 error "; } Iterator vfIter2( image1, image1->GetLargestPossibleRegion() ); double mx =-1.e12, mn=1.e12; for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { double px = vfIter2.Get(); if (px > mx) mx=px; else if (px < mn) mn=px; } if (mx == mn) { mx=1; mn=0; } for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { IndexType ind=vfIter2.GetIndex(); double pix = image1->GetPixel(ind); pix = (pix - mn)/(mx-mn); pix = (1.0 - pix)*(mx-mn); vfIter2.Set(pix); } typename writertype::Pointer writer = writertype::New(); writer->SetFileName(outname.c_str()); writer->SetInput( image1 ); writer->Write(); return 0; } // template // typename TImage::Pointer // //void // SegmentMRF(typename TImage::Pointer image , // typename TImage::Pointer labelimage, unsigned int nclasses, float smf, unsigned int maxits) // { // // typedef TImage ImageType; // typedef typename TImage::PixelType PixelType; // enum { ImageDimension = ImageType::ImageDimension }; // enum { Dimension = ImageType::ImageDimension }; // // // // const unsigned int NUMBANDS=1; // typedef itk::Image,ImageDimension> VecImageType; // typedef typename VecImageType::PixelType VecPixelType; // // /** copy the input image into this vector image. stupid. */ // // typename VecImageType::Pointer vecImage = VecImageType::New(); // vecImage->SetSpacing(image->GetSpacing()); // vecImage->SetOrigin(image->GetOrigin()); // vecImage->SetLargestPossibleRegion( image->GetLargestPossibleRegion() ); // vecImage->SetBufferedRegion( image->GetLargestPossibleRegion() ); // vecImage->Allocate(); // VecPixelType vvv; // vvv.Fill(0); // vecImage->FillBuffer(vvv); // // // setup the iterators // // typedef VecImageType::PixelType::VectorType VecPixelType; // // enum { VecImageDimension = VecImageType::ImageDimension }; // typedef itk::ImageRegionIterator< VecImageType > VecIterator; // // VecIterator outIt( vecImage, vecImage->GetBufferedRegion() ); // for (outIt.GoToBegin(); !outIt.IsAtEnd(); ++outIt) // { // vvv[0]=image->GetPixel(outIt.GetIndex()); // outIt.Set(vvv); // } // // // namespace stat = itk::Statistics; // typedef itk::Image ClassImageType; // typedef stat::MahalanobisDistanceMembershipFunction< VecPixelType > // MembershipFunctionType ; // typedef typename MembershipFunctionType::Pointer MembershipFunctionPointer ; // typedef std::vector< MembershipFunctionPointer > MembershipFunctionPointerVector; // // typedef itk::ImageGaussianModelEstimator // ImageGaussianModelEstimatorType; // // typename ImageGaussianModelEstimatorType::Pointer applyEstimateModel = // ImageGaussianModelEstimatorType::New(); // /* // typedef itk::Statistics::WeightedCentroidKdTreeGenerator< // AdaptorType > // TreeGeneratorType; // typedef typename TreeGeneratorType::KdTreeType TreeType; // typedef itk::Statistics::KdTreeBasedKmeansEstimator EstimatorType; // // */ // applyEstimateModel->SetNumberOfModels(nclasses); // applyEstimateModel->SetInputImage(vecImage); // applyEstimateModel->SetTrainingImage(labelimage); // // //Run the gaussian classifier algorithm // applyEstimateModel->Update(); // applyEstimateModel->Print(std::cout); // // MembershipFunctionPointerVector membershipFunctions = // applyEstimateModel->GetMembershipFunctions(); // // //---------------------------------------------------------------------- // //Set the decision rule // //---------------------------------------------------------------------- // typedef typename itk::DecisionRuleBase::Pointer DecisionRuleBasePointer; // // typedef itk::MinimumDecisionRule DecisionRuleType; // typename DecisionRuleType::Pointer // myDecisionRule = DecisionRuleType::New(); // // //---------------------------------------------------------------------- // // Set the classifier to be used and assigne the parameters for the // // supervised classifier algorithm except the input image which is // // grabbed from the MRF application pipeline. // //---------------------------------------------------------------------- // //--------------------------------------------------------------------- // typedef PixelType MeasurementVectorType; // // typedef itk::ImageClassifierBase< VecImageType, // ClassImageType > ClassifierType; // // typedef typename itk::ClassifierBase::Pointer // ClassifierBasePointer; // // typedef typename ClassifierType::Pointer ClassifierPointer; // ClassifierPointer myClassifier = ClassifierType::New(); // // Set the Classifier parameters // myClassifier->SetNumberOfClasses(nclasses); // // // Set the decison rule // myClassifier-> // SetDecisionRule((DecisionRuleBasePointer) myDecisionRule ); // // //Add the membership functions // double meanDistance=0; // for( unsigned int i=0; iAddMembershipFunction( membershipFunctions[i] ); // meanDistance+=membershipFunctions[i]->GetMean()[0]; // } // meanDistance/=(float)nclasses; // std::cout << " mean dist " << meanDistance << std::endl; // // // //---------------------------------------------------------------------- // // Set the MRF labeller and populate the parameters // //---------------------------------------------------------------------- // // //Set the MRF labeller // typedef itk::MRFImageFilter MRFImageFilterType; // typename MRFImageFilterType::Pointer applyMRFImageFilter = MRFImageFilterType::New(); // // // Set the MRF labeller parameters // applyMRFImageFilter->SetNumberOfClasses( nclasses ); // applyMRFImageFilter->SetMaximumNumberOfIterations( maxits ); // applyMRFImageFilter->SetErrorTolerance( 1.e-4 ); // applyMRFImageFilter->SetSmoothingFactor( smf ); // applyMRFImageFilter->SetInput(vecImage); // applyMRFImageFilter->SetClassifier( myClassifier ); // // //For setting up a square/cubic or hypercubic neighborhood // applyMRFImageFilter->SetNeighborhoodRadius( 1 ); // std::vector weights = // applyMRFImageFilter->GetMRFNeighborhoodWeight(); // std::vector testNewNeighborhoodWeight( weights.size(), 1); // double totalWeight = 0; // for(std::vector< double >::const_iterator wcIt = weights.begin(); // wcIt != weights.end(); ++wcIt ) // { // totalWeight += *wcIt; // } // unsigned int jj = 0; // for(std::vector< double >::iterator wIt = weights.begin(); // wIt != weights.end(); wIt++ ) // { // testNewNeighborhoodWeight[jj] = static_cast< double > ( (*wIt) * meanDistance / (2 * totalWeight)); // // std::cout << " ow " << weights[jj] << " nw " << testNewNeighborhoodWeight[jj] << std::endl; // jj++; // } // // // applyMRFImageFilter->SetMRFNeighborhoodWeight( testNewNeighborhoodWeight ); // // applyMRFImageFilter->SetMRFNeighborhoodWeight( weights ); // // //Kick off the MRF labeller function // applyMRFImageFilter->Update(); // // applyMRFImageFilter->Print(std::cout); // std::cout << "Number of Iterations : " << applyMRFImageFilter->GetNumberOfIterations() // << std::endl; // std::cout << "Stop condition: (1) Maximum number of iterations (2) Error tolerance: " // << applyMRFImageFilter->GetStopCondition() << std::endl; // // typename ClassImageType::Pointer outClassImage = applyMRFImageFilter->GetOutput(); // // //Testing of different parameter access functions in the filter // std::cout << "The number of classes labelled was: " << // applyMRFImageFilter->GetNumberOfClasses() << std::endl; // std::cout << "The maximum number of iterations were: " << // applyMRFImageFilter->GetMaximumNumberOfIterations() << std::endl; // std::cout << "The error tolerace threshold was: " << // applyMRFImageFilter->GetErrorTolerance() << std::endl; // std::cout << "The smoothing MRF parameter used was: " << // applyMRFImageFilter->GetSmoothingFactor() << std::endl; // std::cout << "The MRF neighborhood weights are: " << std::endl; // // // return outClassImage; // // } template typename TImage::Pointer //void itkMRIBiasFieldCorrectionFilter(typename TImage::Pointer image , typename TImage::Pointer labelimage, unsigned int sd=2) { std::cout << "doing Bias corr " << std::endl; typedef TImage ImageType; enum { ImageDimension = ImageType::ImageDimension }; typedef itk::ImageRegionIteratorWithIndex< ImageType > ImageIteratorType ; // bool SaveImages = false; // WriteImage(image,"temp1.nii"); // WriteImage(labelimage,"temp2.nii"); // class statistics for two classes: a bright sphere and background // 3 or 1 classes ? SD //unsigned int numclasses=1; unsigned int numclasses=4; itk::Array classMeans(numclasses); itk::Array classSigmas(numclasses); itk::Array classCounts(numclasses); classMeans.Fill(0.0); classSigmas.Fill(0.0); classCounts.Fill(0); //******** get output mask from image SD typename ImageType::Pointer outputmask = BinaryThreshold(0.1,1.e9,1,image); //******** get output mask from image SD // WriteImage(mask,"outMK1.nii"); // SD play with mask to see if we can move the artifact line // mask=MorphologicalErosion(1.5, mask); //mask=MorphologicalErosion(3.5, mask); //WriteImage(mask,"outMK2.nii"); ImageIteratorType o_iter( labelimage,labelimage->GetLargestPossibleRegion() ); o_iter.GoToBegin() ; while ( !o_iter.IsAtEnd() ) { unsigned int label = (unsigned int) o_iter.Get(); float pix = image->GetPixel(o_iter.GetIndex()); // uncomment for using one tissue class only for input mask SD /* if ( mask->GetPixel(o_iter.GetIndex()) == 1 ) { label=label-1; unsigned int ind=0; classCounts[ind] +=1; float n = classCounts[ind]; classMeans[ind] = (n-1.0)/( n )* classMeans[ind] + 1.0 / n * pix; double sqrdf = ( pix - classMeans[ind])*( pix - classMeans[ind]); if (n > 1)classSigmas[ind]= (n-1)/n * classSigmas[ind] + 1.0 / (n-1) * sqrdf; } */ // uncomment for using 3 tissue classes SD if ( outputmask->GetPixel(o_iter.GetIndex()) == 1 ) { //label=label-1; classCounts[label] +=1; float n = classCounts[label]; classMeans[label] = (n-1.0)/( n )* classMeans[label] + 1.0 / n * pix; double sqrdf = ( pix - classMeans[label])*( pix - classMeans[label]); if (n > 1)classSigmas[label]= (n-1)/n * classSigmas[label] + 1.0 / (n-1) * sqrdf; } ++o_iter ; } for (unsigned int k=0; k < numclasses; k++) { classSigmas[k]=sqrt(classSigmas[k]); std::cout << " Initial Means pre-bias " << classMeans[k] << " sig " << classSigmas[k] << std::endl; } // creats a normal random variate generator //itk::Statistics::NormalVariateGenerator::Pointer randomGenerator = // itk::Statistics::NormalVariateGenerator::New() ; // creates a bias correction filter and run it. typedef itk::MRIBiasFieldCorrectionFilter FilterType; std::cout << "before new filter" << std::endl; typename FilterType::Pointer filter = FilterType::New() ; std::cout << "after new filter" << std::endl; // typename FilterType::BiasFieldType::CoefficientArrayType filter->SetInput( image.GetPointer() ) ; //filter->SetInput( image ) ; filter->IsBiasFieldMultiplicative( true ) ; // correct with multiplicative bias unsigned int biasDegree=4; filter->SetBiasFieldDegree( biasDegree ) ; // default value = 3 filter->SetTissueClassStatistics( classMeans, classSigmas ) ; //filter->SetOptimizerGrowthFactor( 1.01 ) ; // default value //filter->SetOptimizerInitialRadius( 0.02 ) ; // default value // SD debug don't do interslice correction //filter->SetUsingInterSliceIntensityCorrection( true ) ; // default value filter->SetUsingInterSliceIntensityCorrection( false ) ; // default value filter->SetVolumeCorrectionMaximumIteration( 200) ; // default value = 100 filter->SetInterSliceCorrectionMaximumIteration( 100 ) ; // default value = 100 filter->SetUsingSlabIdentification( false ) ; // default value = false //filter->SetSlabBackgroundMinimumThreshold( 0 ) ; // default value //filter->SetSlabNumberOfSamples( 10 ) ; // default value //filter->SetSlabTolerance(0.0) ; // default value filter->SetSlicingDirection(sd) ; // default value filter->SetUsingBiasFieldCorrection( true ) ; // default value filter->SetGeneratingOutput( true ) ; // default value filter->SetInputMask( outputmask ) ; //******** Try different output mask SD // filter->SetOutputMask( labelimage ) ; filter->SetOutputMask( outputmask ) ; // filter->SetDebug( true ); // WriteImage(mask,"mask.nii"); // WriteImage(outputmask,"outputmask.nii"); // WriteImage(image,"image.nii"); // WriteImage(labelimage,"labelimage.nii"); //******** Try different output mask SD // default schedule is 2 2 2 - 1 1 1, let's change this bool setnewsched=false; if (setnewsched) { unsigned int nlev=1; typename FilterType::ScheduleType schedule ( nlev, ImageDimension) ; schedule.Fill( 4 ); // for (unsigned int jj=0; jjSetNumberOfLevels( nlev ) ; // Important to set this first, otherwise the filter rejects the new schedule filter->SetSchedule( schedule ) ; } // filter->SetInitialBiasFieldCoefficients(initCoefficients); filter->SetVolumeCorrectionMaximumIteration( 200 ) ; // default value = 100 filter->SetInterSliceCorrectionMaximumIteration( 100 ) ; // default value = 100 //filter->SetOptimizerInitialRadius( 0.02 ) ; // default value //timing long int t1 = time(NULL); filter->Update() ; long int t2 = time(NULL); std::cout << "Run time (in s)" << t2-t1 << std::endl ; return filter->GetOutput(); } // template // typename TImage::Pointer // //void // SegmentMRFKM(typename TImage::Pointer image , // typename TImage::Pointer labelimage, unsigned int nclasses, float smf, unsigned int maxit) // { // // typedef TImage ImageType; // typedef typename TImage::PixelType PixelType; // enum { ImageDimension = ImageType::ImageDimension }; // enum { Dimension = ImageType::ImageDimension }; // // // // const unsigned int NUMBANDS=1; // typedef itk::Image,ImageDimension> VecImageType; // typedef typename VecImageType::PixelType VecPixelType; // // /** copy the input image into this vector image. stupid. */ // // typename VecImageType::Pointer vecImage = VecImageType::New(); // vecImage->SetSpacing(image->GetSpacing()); // vecImage->SetOrigin(image->GetOrigin()); // vecImage->SetLargestPossibleRegion( image->GetLargestPossibleRegion() ); // vecImage->SetBufferedRegion( image->GetLargestPossibleRegion() ); // vecImage->Allocate(); // VecPixelType vvv; // vvv.Fill(0); // vecImage->FillBuffer(vvv); // // // setup the iterators // // typedef VecImageType::PixelType::VectorType VecPixelType; // // enum { VecImageDimension = VecImageType::ImageDimension }; // typedef itk::ImageRegionIterator< VecImageType > VecIterator; // // VecIterator outIt( vecImage, vecImage->GetBufferedRegion() ); // for (outIt.GoToBegin(); !outIt.IsAtEnd(); ++outIt) // { // vvv[0]=image->GetPixel(outIt.GetIndex()); // outIt.Set(vvv); // } // // // namespace stat = itk::Statistics; // // typedef itk::Image ClassImageType; // // // //---------------------------------------------------------------------- // //Set membership function (Using the statistics objects) // //---------------------------------------------------------------------- // // typedef itk::Statistics::DistanceToCentroidMembershipFunction< VecPixelType > // MembershipFunctionType ; // // typedef typename MembershipFunctionType::Pointer MembershipFunctionPointer ; // // typedef std::vector< MembershipFunctionPointer > // MembershipFunctionPointerVector; // // //---------------------------------------------------------------------- // //Set the image model estimator // //---------------------------------------------------------------------- // typedef itk::ImageKmeansModelEstimator< VecImageType, // MembershipFunctionType> ImageKmeansModelEstimatorType; // // typename ImageKmeansModelEstimatorType::Pointer // applyKmeansModelEstimator = ImageKmeansModelEstimatorType::New(); // // //---------------------------------------------------------------------- // //Set the parameters of the clusterer // //---------------------------------------------------------------------- // // std::cout << "Starting to build the K-means model ....." << std::endl; // // applyKmeansModelEstimator->SetInputImage( vecImage ); // applyKmeansModelEstimator->SetNumberOfModels(nclasses); // applyKmeansModelEstimator->SetThreshold(0.0001); // applyKmeansModelEstimator->Update(); // // MembershipFunctionPointerVector membershipFunctions = // applyKmeansModelEstimator->GetMembershipFunctions(); // // typedef std::vector TempVectorType; // typedef TempVectorType::iterator TempVectorIterator; // TempVectorIterator start, end; // // std::vector kmeansResultForClass(membershipFunctions.size()); // // // std::cout << "Result of K-Means clustering" << std::endl; // // double meanDistance=0; // for(unsigned int classIndex=0; classIndex < membershipFunctions.size(); // classIndex++ ) // { // kmeansResultForClass[classIndex] = // (double) (membershipFunctions[classIndex]->GetCentroid())[0]; // meanDistance+=kmeansResultForClass[classIndex];//membershipFunctions[i]->GetMean()[0]; // } // meanDistance/=(float)nclasses; // std::cout << " mean dist " << meanDistance << std::endl; // // // start = kmeansResultForClass.begin(); // end = kmeansResultForClass.end(); // // std::sort( start, end ); // // vnl_vector temp = membershipFunctions[0]->GetCentroid(); // for(unsigned int classIndex=0; classIndex < membershipFunctions.size(); // classIndex++ ) // { // temp[0] = (double) kmeansResultForClass[classIndex]; // membershipFunctions[classIndex]->SetCentroid(temp); // } // // for(unsigned int classIndex=0; classIndex < membershipFunctions.size(); // classIndex++ ) // { // std::cout << (membershipFunctions[classIndex]->GetCentroid())[0] << std::endl; // } // // //---------------------------------------------------------------------- // //Set the decision rule // //---------------------------------------------------------------------- // typedef itk::DecisionRuleBase::Pointer DecisionRuleBasePointer; // // typedef itk::MinimumDecisionRule DecisionRuleType; // DecisionRuleType::Pointer // classifierDecisionRule = DecisionRuleType::New(); // // //------------------------------------------------------ // //Instantiate the classifier model (as the input image is in right format) // //------------------------------------------------------ // // //Assign a class label image type // // typedef itk::Image ClassImageType; // // typedef itk::ImageClassifierBase< VecImageType,ClassImageType > // SupervisedClassifierType; // // typename SupervisedClassifierType::Pointer // classifierPointer = SupervisedClassifierType::New(); // // // //------------------------------------------------------ // // Set the Classifier parameters // //------------------------------------------------------ // classifierPointer->SetNumberOfClasses( nclasses ); // classifierPointer->SetInputImage( vecImage ); // // // Set the decison rule // classifierPointer-> // SetDecisionRule( (DecisionRuleBasePointer) classifierDecisionRule ); // // MembershipFunctionPointer membershipFunction; // //------------------------------------------------------ // //Set the classifier membership functions // //------------------------------------------------------ // for( unsigned int i=0; iAddMembershipFunction( membershipFunctions[i] ); // } // // //Do the classification // //Run the kmeans classifier algorithm // classifierPointer->Update(); // // //Get the classified image // typedef typename ClassImageType::Pointer ClassifiedImagePointer; // ClassifiedImagePointer outClassImage = // classifierPointer->GetClassifiedImage(); // // //------------------------------------------------------ // //Mask the output of the classifier // //------------------------------------------------------ // // // Declare the type for the MaskInput filter // // typedef itk::MaskImageFilter< ClassImageType, // ClassImageType, // ClassImageType > MaskFilterType; // // typedef typename ClassImageType::Pointer MaskedOutputImagePointer; // typedef typename MaskFilterType::Pointer MaskFilterTypePointer; // // // Create an ADD Filter // MaskFilterTypePointer maskfilter = MaskFilterType::New(); // // // Connect the input images // maskfilter->SetInput1( outClassImage ); // maskfilter->SetInput2( labelimage ); // // // Execute the filter // maskfilter->Update(); // // // Get the Smart Pointer to the Filter Output // MaskedOutputImagePointer maskedOutputImage = maskfilter->GetOutput(); // // // this->SetClassifiedImage( maskedOutputImage ); // // //------------------------------------------------------ // //Set the MRF labeller and populate the parameters // //------------------------------------------------------ // //Set the MRF labeller // typedef itk::MRFImageFilter // MRFFilterType; // // typename MRFFilterType::Pointer applyMRFFilter = MRFFilterType::New(); // // // Set the MRF labeller parameters // applyMRFFilter->SetNumberOfClasses(nclasses); // unsigned int m_MaximumNumberOfIterations=maxit; // applyMRFFilter->SetMaximumNumberOfIterations(m_MaximumNumberOfIterations); // float m_ErrorTolerance=1.e-5; // applyMRFFilter->SetErrorTolerance(m_ErrorTolerance); // float m_SmoothingFactor=smf; // applyMRFFilter->SetSmoothingFactor( m_SmoothingFactor ); // // //For setting up a square/cubic or hypercubic neighborhood // applyMRFFilter->SetNeighborhoodRadius( 1 ); // std::vector weights = // applyMRFFilter->GetMRFNeighborhoodWeight(); // std::vector testNewNeighborhoodWeight( weights.size(), 1); // double totalWeight = 0; // for(std::vector< double >::const_iterator wcIt = weights.begin(); // wcIt != weights.end(); ++wcIt ) // { // totalWeight += *wcIt; // } // unsigned int jj = 0; // for(std::vector< double >::iterator wIt = weights.begin(); // wIt != weights.end(); wIt++ ) // { // testNewNeighborhoodWeight[jj] = static_cast< double > ( (*wIt) * meanDistance / (2 * totalWeight)); // //std::cout << " ow " << weights[jj] << " nw " << testNewNeighborhoodWeight[jj] << std::endl; // jj++; // } // // applyMRFFilter->SetMRFNeighborhoodWeight( testNewNeighborhoodWeight ); // // applyMRFFilter->SetInput(vecImage); // applyMRFFilter->SetClassifier( classifierPointer ); // // //Kick off the MRF labeller function // applyMRFFilter->Update(); // // applyMRFFilter->Print(std::cout); // outClassImage = applyMRFFilter->GetOutput(); // // //------------------------------------------------------ // //Mask the output of the classifier // //------------------------------------------------------ // // // Declare the type for the MaskInput filter // // // Create an ADD Filter // MaskFilterTypePointer maskfilter2 = MaskFilterType::New(); // // // Connect the input images // maskfilter2->SetInput1( outClassImage ); // maskfilter2->SetInput2( labelimage ); // // // Execute the filter // maskfilter2->Update(); // // // Get the Smart Pointer to the Filter Output // maskedOutputImage = maskfilter2->GetOutput(); // // // this->SetClassifiedImage( maskedOutputImage ); // // return outClassImage;//maskedOutputImage; // // } template int SmoothImage(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; float sigma = 1.0; if (argc > argct) sigma=atof(argv[argct]); typename ImageType::Pointer image1 = NULL; typename ImageType::Pointer varimage = NULL; ReadImage(image1, fn1.c_str()); typedef itk::DiscreteGaussianImageFilter dgf; typename dgf::Pointer filter = dgf::New(); filter->SetVariance(sigma*sigma); bool usespacing=true; if (!usespacing) filter->SetUseImageSpacingOff(); else filter->SetUseImageSpacingOn(); filter->SetMaximumError(.01f); filter->SetInput(image1); filter->Update(); varimage=filter->GetOutput(); typename writertype::Pointer writer = writertype::New(); writer->SetFileName(outname.c_str()); writer->SetInput( varimage ); writer->Write(); return 0; } template int MorphImage(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; float sigma = 1.0; if (argc > argct) sigma=atof(argv[argct]); unsigned int morphopt=1; if ( strcmp(operation.c_str(),"ME") == 0) morphopt=0; else if ( strcmp(operation.c_str(),"MO") == 0) morphopt=2; else if ( strcmp(operation.c_str(),"MC") == 0) morphopt=3; else if ( strcmp(operation.c_str(),"GE") == 0) morphopt=4; else if ( strcmp(operation.c_str(),"GD") == 0) morphopt=5; else if ( strcmp(operation.c_str(),"GO") == 0) morphopt=6; else if ( strcmp(operation.c_str(),"GC") == 0) morphopt=7; typename ImageType::Pointer image1 = NULL; ReadImage(image1, fn1.c_str()); // SRD set dilatevalue float dilateval = 1.0; // if (argc > argct + 1) dilateval=atof(argv[argct+1]); image1=Morphological(image1, sigma, morphopt , dilateval); typename writertype::Pointer writer = writertype::New(); writer->SetFileName(outname.c_str()); writer->SetInput( image1 ); writer->Write(); return 0; } template int FastMarchingSegmentation( unsigned int argc, char *argv[] ) { typedef float InternalPixelType; typedef itk::Image InternalImageType; typedef itk::Image ImageType; typedef unsigned char OutputPixelType; typedef itk::Image OutputImageType; unsigned int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; std::string fn2 = ""; float stoppingValue=100.0; if ( argc > argct) { fn2=std::string(argv[argct]); argct++; } else { std::cout << " not enough parameters -- need label image " << std::endl; return 0; } if ( argc > argct) { stoppingValue=atof(argv[argct]); argct++; } typename ImageType::Pointer speedimage = NULL; ReadImage(speedimage, fn1.c_str()); typename ImageType::Pointer labimage = NULL; ReadImage(labimage, fn2.c_str()); typename ImageType::Pointer fastimage = NULL; ReadImage(fastimage, fn1.c_str()); typename ImageType::Pointer outlabimage = NULL; ReadImage(outlabimage, fn2.c_str()); /* typedef itk::WellComposedImageFilter WCFilterType; typename WCFilterType::Pointer filter3D = WCFilterType::New(); filter3D->SetInput( labimage ); filter3D->SetTotalNumberOfLabels( 1 ); filter3D->Update(); labimage=filter3D->GetOutput(); WriteImage(labimage,"temp.nii.gz"); */ typedef itk::FastMarchingImageFilter FastMarchingFilterType; typename FastMarchingFilterType::Pointer fastMarching = FastMarchingFilterType::New(); fastMarching->SetInput( speedimage ); typedef typename FastMarchingFilterType::NodeContainer NodeContainer; typedef typename FastMarchingFilterType::NodeType NodeType; // typedef typename FastMarchingFilterType::LabelImageType LabelImageType; typedef ImageType LabelImageType; typedef itk::LabelContourImageFilter ContourFilterType; typename ContourFilterType::Pointer contour = ContourFilterType::New(); contour->SetInput( labimage ); contour->FullyConnectedOff(); contour->SetBackgroundValue( itk::NumericTraits::Zero ); contour->Update(); // WriteImage(contour->GetOutput(),"temp2.nii.gz"); typename NodeContainer::Pointer alivePoints = NodeContainer::New(); alivePoints->Initialize(); unsigned long aliveCount = 0; typename NodeContainer::Pointer trialPoints = NodeContainer::New(); trialPoints->Initialize(); unsigned long trialCount = 0; itk::ImageRegionIteratorWithIndex ItL( labimage , labimage->GetLargestPossibleRegion() ); itk::ImageRegionIteratorWithIndex ItC( contour->GetOutput(), contour->GetOutput()->GetLargestPossibleRegion() ); for( ItL.GoToBegin(), ItC.GoToBegin(); !ItL.IsAtEnd(); ++ItL, ++ItC ) { if( ItC.Get() != itk::NumericTraits::Zero ) { typename LabelImageType::IndexType position = ItC.GetIndex(); NodeType node; const double value = 0.0; node.SetValue( value ); node.SetIndex( position ); trialPoints->InsertElement( trialCount++, node ); } if( ItL.Get() != itk::NumericTraits::Zero ) { typename LabelImageType::IndexType position = ItL.GetIndex(); NodeType node; const double value = 0.0; node.SetValue( value ); node.SetIndex( position ); alivePoints->InsertElement( aliveCount++, node ); } } fastMarching->SetTrialPoints( trialPoints ); fastMarching->SetAlivePoints( alivePoints ); fastMarching->SetStoppingValue( stoppingValue ); fastMarching->SetTopologyCheck( FastMarchingFilterType::None ); if( argc > 7 && atoi( argv[7] ) == 1 ) { std::cout << "Strict." << std::endl; fastMarching->SetTopologyCheck( FastMarchingFilterType::Strict ); } if( argc > 7 && atoi( argv[7] ) == 2 ) { std::cout << "No handles." << std::endl; fastMarching->SetTopologyCheck( FastMarchingFilterType::NoHandles ); } try { fastMarching->Update(); } catch( itk::ExceptionObject & excep ) { std::cerr << "Exception caught !" << std::endl; std::cerr << excep << std::endl; } typedef itk::BinaryThresholdImageFilter ThresholdingFilterType; typename ThresholdingFilterType::Pointer thresholder = ThresholdingFilterType::New(); thresholder->SetLowerThreshold( 0.0 ); thresholder->SetUpperThreshold( stoppingValue ); thresholder->SetOutsideValue( 0 ); thresholder->SetInsideValue( 1 ); thresholder->SetInput( fastMarching->GetOutput() ); typedef itk::ImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetInput( thresholder->GetOutput() ); writer->SetFileName( outname.c_str() ); writer->Update(); return 0; } template int PropagateLabelsThroughMask(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; float thresh=0.5; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; std::string fn2 = ""; float stopval=100.0; if ( argc > argct) { fn2=std::string(argv[argct]); argct++; } else { std::cout << " not enough parameters -- need label image " << std::endl; return 0; } if ( argc > argct) { stopval=atof(argv[argct]); argct++; } typename ImageType::Pointer speedimage = NULL; ReadImage(speedimage, fn1.c_str()); typename ImageType::Pointer labimage = NULL; ReadImage(labimage, fn2.c_str()); typename ImageType::Pointer fastimage = NULL; ReadImage(fastimage, fn1.c_str()); typename ImageType::Pointer outlabimage = NULL; ReadImage(outlabimage, fn2.c_str()); fastimage->FillBuffer(1.e9); // compute max label double maxlabel=0; Iterator vfIter2( labimage, labimage->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { bool isinside=true; double speedval=speedimage->GetPixel(vfIter2.GetIndex()); double labval=labimage->GetPixel(vfIter2.GetIndex()); if ( speedval < thresh ) isinside=false; if (isinside ) { if ( labval > maxlabel ) maxlabel=labval; } } for (unsigned int lab=1; lab<=(unsigned int)maxlabel; lab++) { typedef itk::FastMarchingImageFilter< ImageType, ImageType > FastMarchingFilterType; typename FastMarchingFilterType::Pointer fastMarching = FastMarchingFilterType::New(); fastMarching->SetInput( speedimage ); fastMarching->SetTopologyCheck( FastMarchingFilterType::None ); typedef typename FastMarchingFilterType::NodeContainer NodeContainer; typedef typename FastMarchingFilterType::NodeType NodeType; typename NodeContainer::Pointer seeds = NodeContainer::New(); seeds->Initialize(); unsigned long ct=0; for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { bool isinside=true; double speedval=speedimage->GetPixel(vfIter2.GetIndex()); double labval=labimage->GetPixel(vfIter2.GetIndex()); if ( speedval < thresh ) isinside=false; if (isinside && (unsigned int) labval == lab ) { NodeType node; const double seedValue = 0.0; node.SetValue( seedValue ); node.SetIndex( vfIter2.GetIndex() ); seeds->InsertElement( ct, node ); ct++; } } fastMarching->SetTrialPoints( seeds ); fastMarching->SetStoppingValue( stopval ); fastMarching->Update(); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { bool isinside=true; double speedval=speedimage->GetPixel(vfIter2.GetIndex()); double labval=labimage->GetPixel(vfIter2.GetIndex()); if ( speedval < thresh ) isinside=false; if (isinside && labval == 0 ) { double fmarrivaltime=fastMarching->GetOutput()->GetPixel( vfIter2.GetIndex() ); double mmm= fastimage->GetPixel(vfIter2.GetIndex()); if ( fmarrivaltime < mmm ) { fastimage->SetPixel(vfIter2.GetIndex() , fmarrivaltime ); outlabimage->SetPixel(vfIter2.GetIndex() , lab ); } } else if (!isinside) outlabimage->SetPixel(vfIter2.GetIndex() , 0 ); } } std::string::size_type idx; idx = outname.find_first_of('.'); std::string tempname = outname.substr(0,idx); std::string extension = outname.substr(idx,outname.length()); std::string kname=tempname+std::string("_speed")+extension; WriteImage(fastimage,kname.c_str()); WriteImage(outlabimage,outname.c_str()); return 0; } template int DistanceMap(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; float sigma = 1.0; if (argc > argct) sigma=atof(argv[argct]); typename ImageType::Pointer image1 = NULL; ReadImage(image1, fn1.c_str()); typedef itk::DanielssonDistanceMapImageFilter FilterType; typename FilterType::Pointer filter = FilterType::New(); filter->InputIsBinaryOff(); filter->SetUseImageSpacing(true); filter->SetInput(image1); filter->Update(); typename writertype::Pointer writer = writertype::New(); writer->SetFileName(outname.c_str()); writer->SetInput( filter->GetOutput() ); writer->Write(); return 0; } template int FillHoles(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::Image LabelImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; typedef itk::CastImageFilter CastFilterType; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; float holeparam = 2.0; if (argc > argct) holeparam=atof(argv[argct]); typename ImageType::Pointer image1 = NULL; ReadImage(image1, fn1.c_str()); typename ImageType::Pointer imageout = NULL; ReadImage(imageout, fn1.c_str()); image1 = BinaryThreshold(0.5,1.e9,1,image1); typedef itk::DanielssonDistanceMapImageFilter FilterType; typename FilterType::Pointer filter = FilterType::New(); filter->InputIsBinaryOff(); filter->SetUseImageSpacing(false); filter->SetInput(image1); filter->Update(); // algorithm : // 1. get distance map of object // 2. threshold // 3. label connected components // 4. label surface // 5. if everywhere on surface is next to object then it's a hole // 6. make sure it's not the background typedef itk::Image labelimagetype; typename ImageType::Pointer dist = filter->GetOutput(); typename ImageType::Pointer regions = BinaryThreshold(0.001,1.e9,1,dist); typedef itk::ConnectedComponentImageFilter< labelimagetype, labelimagetype > ccFilterType; typedef itk::RelabelComponentImageFilter< labelimagetype, ImageType > RelabelType; typename RelabelType::Pointer relabel = RelabelType::New(); typename ccFilterType::Pointer ccfilter = ccFilterType::New(); typename CastFilterType::Pointer castRegions = CastFilterType::New(); castRegions->SetInput( regions ); ccfilter->SetInput(castRegions->GetOutput()); ccfilter->SetFullyConnected( 0 ); ccfilter->Update(); relabel->SetInput( ccfilter->GetOutput() ); relabel->SetMinimumObjectSize( 0 ); // relabel->SetUseHistograms(true); try { relabel->Update(); } catch( itk::ExceptionObject & excep ) { std::cerr << "Relabel: exception caught !" << std::endl; std::cerr << excep << std::endl; } // WriteImage(relabel->GetOutput(),"test.nii"); if (holeparam == 2) { std::cout <<" Filling all holes "<< std::endl; typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter( relabel->GetOutput(), relabel->GetOutput()->GetLargestPossibleRegion() ); for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) { if (vfIter.Get() > 1 ) imageout->SetPixel(vfIter.GetIndex(),1); } WriteImage(imageout,outname.c_str()); return 0; } typedef itk::NeighborhoodIterator iteratorType; typename iteratorType::RadiusType rad; for (unsigned int j=0; jGetOutput(),relabel->GetOutput()->GetLargestPossibleRegion()); float maximum=relabel->GetNumberOfObjects(); //now we have the exact number of objects labeled independently for (unsigned int lab=2; lab<=maximum; lab++) { float erat=2; if (holeparam <= 1 ) { GHood.GoToBegin(); unsigned long objectedge=0; unsigned long backgroundedge=0; unsigned long totaledge=0; unsigned long volume=0; while (!GHood.IsAtEnd()) { typename ImageType::PixelType p = GHood.GetCenterPixel(); typename ImageType::IndexType ind = GHood.GetIndex(); typename ImageType::IndexType ind2; if ( p == lab ) { volume++; for (unsigned int i = 0; i < GHood.Size(); i++) { ind2=GHood.GetIndex(i); float dist=0.0; for (unsigned int j=0; jGetPixel(ind2); if (val2 >= 0.5 && GHood.GetPixel(i) != lab ) { objectedge++; totaledge++; } else if (val2 < 0.5 && GHood.GetPixel(i) != lab ) { backgroundedge++; totaledge++; } } } ++GHood; } float vrat=(float)totaledge/(float)volume; erat=(float)objectedge/(float)totaledge; std::cout <<" Lab " << lab << " volume " << volume << " v-rat " << vrat << " edge " << erat << std::endl; } if (erat > holeparam)// fill the hole { std::cout <<" Filling " << lab << " of " << maximum << std::endl; typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter( relabel->GetOutput(), relabel->GetOutput()->GetLargestPossibleRegion() ); for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) { if (vfIter.Get() == lab ) imageout->SetPixel(vfIter.GetIndex(),1); } } } WriteImage(imageout,outname.c_str()); return 0; } template int NormalizeImage(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; float option = 0; if (argc > argct) option=atof(argv[argct]); typename ImageType::Pointer image = NULL; ReadImage(image, fn1.c_str()); float max=0; float min=1.e9; float mean = 0.0; unsigned long ct = 0; typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator iter( image, image->GetLargestPossibleRegion() ); for( iter.GoToBegin(); !iter.IsAtEnd(); ++iter ) { float pix=iter.Get(); // if (option == 0) if (pix < 0) pix=0; mean+=pix; ct++; if (pix > max ) max=pix; if (pix < min) min=pix; } mean/=(float)ct; for( iter.GoToBegin(); !iter.IsAtEnd(); ++iter ) { float pix=iter.Get(); if (option == 0) { pix= (pix - min)/(max-min); iter.Set(pix); } else iter.Set(pix/mean); } typename writertype::Pointer writer = writertype::New(); writer->SetFileName(outname.c_str()); writer->SetInput( image ); writer->Write(); return 0; } template int PrintHeader(int argc, char *argv[]) { typedef float outPixelType; typedef float floatPixelType; typedef float inPixelType; typedef itk::Image ImageType; typedef itk::Image IntermediateType; typedef itk::Image OutImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; typename readertype::Pointer reader = readertype::New(); reader->SetFileName(fn1.c_str()); reader->Update(); std::cout << " Spacing " << reader->GetOutput()->GetSpacing() << std::endl; std::cout << " Origin " << reader->GetOutput()->GetOrigin() << std::endl; std::cout << " Direction " << std::endl << reader->GetOutput()->GetDirection() << std::endl; std::cout << " Size " << std::endl << reader->GetOutput()->GetLargestPossibleRegion().GetSize() << std::endl; return 1; } template int GradientImage( int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; float sig = 1; if (argc > argct) sig=atof(argv[argct]); argct++; if (sig <= 0) sig=0.5; bool normalize=false; if (argc > argct) normalize=atoi(argv[argct]); typename ImageType::Pointer image = NULL; typename ImageType::Pointer image2 = NULL; ReadImage(image, fn1.c_str()); typedef itk::GradientMagnitudeRecursiveGaussianImageFilter dgf; typename dgf::Pointer filter = dgf::New(); filter->SetSigma(sig); filter->SetInput(image); filter->Update(); image2=filter->GetOutput(); if ( !normalize ) { WriteImage( image2,outname.c_str()); return 0; } typedef itk::RescaleIntensityImageFilter RescaleFilterType; typename RescaleFilterType::Pointer rescaler = RescaleFilterType::New(); rescaler->SetOutputMinimum( 0 ); rescaler->SetOutputMaximum( 1 ); rescaler->SetInput( image2 ); WriteImage( rescaler->GetOutput() ,outname.c_str()); return 0; } template int LaplacianImage( int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; float sig = 1; if (argc > argct) sig=atof(argv[argct]); if (sig <= 0) sig=0.5; bool normalize=false; if (argc > argct) normalize=atoi(argv[argct]); typename ImageType::Pointer image = NULL; typename ImageType::Pointer image2 = NULL; ReadImage(image, fn1.c_str()); typedef itk::LaplacianRecursiveGaussianImageFilter dgf; typename dgf::Pointer filter = dgf::New(); filter->SetSigma(sig); filter->SetInput(image); filter->Update(); image2=filter->GetOutput(); if ( !normalize ) { WriteImage( image2,outname.c_str()); return 0; } typedef itk::RescaleIntensityImageFilter RescaleFilterType; typename RescaleFilterType::Pointer rescaler = RescaleFilterType::New(); rescaler->SetOutputMinimum( 0 ); rescaler->SetOutputMaximum( 1 ); rescaler->SetInput( image2 ); WriteImage( rescaler->GetOutput() ,outname.c_str()); return 0; } template void RemoveLabelInterfaces(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; typename ImageType::Pointer input = NULL; ReadImage(input , fn1.c_str()); typedef itk::NeighborhoodIterator iteratorType; typename iteratorType::RadiusType rad; for (unsigned int j=0; jGetLargestPossibleRegion()); GHood.GoToBegin(); // std::cout << " foreg " << (int) foreground; while (!GHood.IsAtEnd()) { typename ImageType::PixelType p = GHood.GetCenterPixel(); typename ImageType::IndexType ind = GHood.GetIndex(); typename ImageType::IndexType ind2; if ( p > 0 ) { bool atedge=false; for (unsigned int i = 0; i < GHood.Size(); i++) { ind2=GHood.GetIndex(i); if (GHood.GetPixel(i) > 0 && GHood.GetPixel(i) != p ) { atedge=true; } } if (atedge) input->SetPixel(ind,0); } ++GHood; } WriteImage(input,outname.c_str()); return; } template void EnumerateLabelInterfaces(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; std::string outname2 = ""; if (argc > argct) outname2=std::string(argv[argct]); argct++; float nfrac = 0.1; if (argc > argct) nfrac=atof(argv[argct]); argct++; typename ImageType::Pointer input = NULL; ReadImage(input , fn1.c_str()); typename ImageType::Pointer output = NULL; ReadImage(output , fn1.c_str()); output->FillBuffer(0); typename ImageType::Pointer colored = NULL; ReadImage(colored , fn1.c_str()); colored->FillBuffer(0); unsigned int max=0; Iterator o_iter( input,input->GetLargestPossibleRegion() ); o_iter.GoToBegin() ; while ( !o_iter.IsAtEnd() ) { unsigned int label = (unsigned int) o_iter.Get(); if ( label > max ) max=label; ++o_iter; } std::cout << " Max Label " << max << std::endl; typedef itk::Image myInterfaceImageType; typename myInterfaceImageType::SizeType size; size[0]=max+1; size[1]=max+1; typename myInterfaceImageType::RegionType region; typename myInterfaceImageType::SpacingType spacing; spacing.Fill(1); typename myInterfaceImageType::PointType origin; origin.Fill(max/2); region.SetSize(size); typename myInterfaceImageType::Pointer faceimage = myInterfaceImageType::New(); faceimage->SetSpacing(spacing); faceimage->SetOrigin(origin); faceimage->SetRegions(region ); faceimage->Allocate(); faceimage->FillBuffer( 0 ); typename myInterfaceImageType::Pointer colorimage = myInterfaceImageType::New(); colorimage->SetSpacing(spacing); colorimage->SetOrigin(origin); colorimage->SetRegions(region ); colorimage->Allocate(); colorimage->FillBuffer( 0 ); typedef itk::ImageRegionIteratorWithIndex FIterator; // we can use this to compute a 4-coloring of the brain typedef itk::NeighborhoodIterator iteratorType; typename iteratorType::RadiusType rad; for (unsigned int j=0; jGetLargestPossibleRegion()); GHood.GoToBegin(); // std::cout << " foreg " << (int) foreground; while (!GHood.IsAtEnd()) { typename ImageType::PixelType p = GHood.GetCenterPixel(); typename ImageType::IndexType ind = GHood.GetIndex(); typename ImageType::IndexType ind2; if ( p > 0 ) { bool atedge=false; unsigned long linearinda=0,linearindb=0,linearind=0; for (unsigned int i = 0; i < GHood.Size(); i++) { ind2=GHood.GetIndex(i); if (GHood.GetPixel(i) > 0 && GHood.GetPixel(i) != p ) { atedge=true; typename myInterfaceImageType::IndexType inda,indb; inda[0]=(long int)p; inda[1]=(long int)GHood.GetPixel(i); indb[1]=(long int)p; indb[0]=(long int)GHood.GetPixel(i); faceimage->SetPixel(inda,faceimage->GetPixel(inda)+1); faceimage->SetPixel(indb,faceimage->GetPixel(indb)+1); linearinda=inda[1]*size[0]+inda[0]; linearindb=indb[1]*size[0]+indb[0]; if (linearinda < linearindb) linearind=linearinda; else linearind=linearindb; } } if (atedge) output->SetPixel(ind,linearind); } ++GHood; } // first normalize the interfaces typename myInterfaceImageType::IndexType find; typename myInterfaceImageType::IndexType find2; find2.Fill(0); for (unsigned int j=0; j<=max; j++) { find[0]=j; float total=0; for (unsigned int i=0; i<=max; i++) { find[1]=i; total+= faceimage->GetPixel(find) ; // if ( faceimage->GetPixel(find) > 50 ) //std::cout << i <<" :: " << faceimage->GetPixel(find) << std::endl; } std::cout << " total interfaces for label : " << j << " are " << total << std::endl; for (unsigned int i=0; i<=max; i++) { find[1]=i; if (total > 0) faceimage->SetPixel(find,faceimage->GetPixel(find)/total); if ( faceimage->GetPixel(find) > 0.01 ) std::cout << i <<" :: " << faceimage->GetPixel(find) << std::endl; } } // colors 1 , 2 , 3 , 4 // set all to color 1. typedef std::vector ColorSetType; colorimage->FillBuffer(0); // for (unsigned int j=1; j<=max; j++) for (unsigned int j=max; j>=1; j--) { ColorSetType myColorSet1; ColorSetType myColorSet2; ColorSetType myColorSet3; ColorSetType myColorSet4; ColorSetType myColorSet5; find[0]=j; // list all indices that have an interface with j // for (unsigned int i=1; i<=max; i++) for (unsigned int i=max; i>=1; i--) { if ( i != j) { find[1]=i; find2[0]=i; unsigned int color=(unsigned int) colorimage->GetPixel(find2); if ( faceimage->GetPixel(find) > nfrac ) // then color { if ( color == 1) myColorSet1.push_back( (unsigned int) i ); if ( color == 2) myColorSet2.push_back( (unsigned int) i ); if ( color == 3) myColorSet3.push_back( (unsigned int) i ); if ( color == 4) myColorSet4.push_back( (unsigned int) i ); if ( color == 5) myColorSet5.push_back( (unsigned int) i ); // now you know all of j's neighbors and their colors } } } //we have to recolor j so that his color is different //than any in the label set unsigned int okcolor=6; find[0]=j; find[1]=0; if( myColorSet1.empty() ) okcolor=1; else if( myColorSet2.empty() ) okcolor=2; else if( myColorSet3.empty() ) okcolor=3; else if( myColorSet4.empty() ) okcolor=4; else if( myColorSet5.empty() ) okcolor=5; colorimage->SetPixel(find,okcolor); std::cout <<" Label " << j << " color " << okcolor << std::endl; } o_iter.GoToBegin() ; colored->FillBuffer(0); while ( !o_iter.IsAtEnd() ) { unsigned int label = (unsigned int) o_iter.Get(); if (label > 0) { find[0]=label; find[1]=0; unsigned int color=(unsigned int)colorimage->GetPixel(find); colored->SetPixel(o_iter.GetIndex(),color); } ++o_iter; } WriteImage(output,outname.c_str()); WriteImage(faceimage,(std::string("face")+outname).c_str()); if (outname2.length() > 3) WriteImage(colored,outname2.c_str()); return; } template int CountVoxelDifference( int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::Image ByteImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; std::string fn2 = ""; if ( argc > argct) { fn2=std::string(argv[argct]); argct++; } std::string maskfn = ""; if ( argc > argct) { maskfn=std::string(argv[argct]); argct++; } typename ImageType::Pointer image1 = NULL; typename ImageType::Pointer image2 = NULL; typename ImageType::Pointer mask = NULL; ReadImage(image1, fn1.c_str()); ReadImage(image2, fn2.c_str()); ReadImage(mask, maskfn.c_str()); unsigned long maskct=0; float err=0,poserr=0,negerr=0; Iterator It ( mask,mask->GetLargestPossibleRegion() ); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { if( It.Get() > 0 ) { float p1=image1->GetPixel(It.GetIndex()); float p2=image2->GetPixel(It.GetIndex()); float locerr=p1-p2; err+=fabs(locerr); if (locerr > 0) poserr+=locerr; if (locerr < 0) negerr+=locerr; maskct++; } } if (maskct ==0) maskct=1; std::ofstream logfile; logfile.open(outname.c_str() ); if (logfile.good() ) { logfile << " Err " << " : " << err << " %ER " << " : " << err/(maskct)*100. << " NER " << " : " << negerr/maskct*100.0 < int DiceAndMinDistSum( int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::Image ByteImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; std::string fn2 = ""; if ( argc > argct) { fn2=std::string(argv[argct]); argct++; } std::string outdistfn = ""; if ( argc > argct) { outdistfn=std::string(argv[argct]); argct++; } std::string diceimagename=ANTSGetFilePrefix(outname.c_str())+std::string("dice.nii.gz"); std::string mdsimagename=ANTSGetFilePrefix(outname.c_str())+std::string("mds.nii.gz"); typename ImageType::Pointer image1 = NULL; typename ImageType::Pointer image2 = NULL; typename ImageType::Pointer outdist = NULL; ReadImage(image1, fn1.c_str()); if ( fn2.length() > 3) ReadImage(image2, fn2.c_str()); if ( outdistfn.length() > 3) { ReadImage(outdist, fn1.c_str()); outdist->FillBuffer(0); } // 1: for each label, and each image, compute the distance map // 2: sum min-dist(i) over every point in each image // 3: take average over all points typedef float PixelType; typedef std::vector LabelSetType; LabelSetType myLabelSet1; { /** count the labels in the image */ Iterator It ( image1,image1->GetLargestPossibleRegion() ); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { PixelType label = It.Get(); if( fabs(label) > 0 ) { if( find( myLabelSet1.begin(), myLabelSet1.end(), label ) == myLabelSet1.end() ) { myLabelSet1.push_back( label ); } } }} LabelSetType myLabelSet2; unsigned int labct=0; { Iterator It ( image2,image2->GetLargestPossibleRegion() ); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { PixelType label = It.Get(); if( fabs(label) > 0 ) { if( find( myLabelSet2.begin(), myLabelSet2.end(), label ) == myLabelSet2.end() && find( myLabelSet1.begin(), myLabelSet1.end(), label ) != myLabelSet1.end() ) { myLabelSet2.push_back( label ); labct++; } } }} vnl_vector distances(labct,0.0); vnl_vector dicevals(labct,0.0); vnl_vector rovals(labct,0.0); vnl_vector tpvals(labct,0.0); vnl_vector tpvals2(labct,0.0); /** now we have the common labels */ std::ofstream logfile; logfile.open(outname.c_str() ); labct=0; typename LabelSetType::const_iterator it; for( it = myLabelSet2.begin(); it != myLabelSet2.end(); ++it ) { typename ImageType::Pointer mask1 = BinaryThreshold(*it,*it,1,image1); typename ImageType::Pointer surf = NULL; typename ImageType::Pointer d1 = NULL; typename ImageType::Pointer d2 = NULL; float count1=0;// count vox in label 1 float count2=0;// count vox in label 2 float counti=0;// count vox intersection in label 1 and label 2 float countu=0;// count vox union label 1 and label 2 float surfdist=0,surfct=0; // float truepos=0; if (outdist) { surf=LabelSurface(mask1,mask1); // WriteImage(surf,outdistfn.c_str()); //exit(0); typedef itk::DanielssonDistanceMapImageFilter FilterType; typename FilterType::Pointer dfilter1 = FilterType::New(); dfilter1->InputIsBinaryOn(); dfilter1->SetUseImageSpacing(true); dfilter1->SetInput(mask1); typename ImageType::Pointer mask2 = BinaryThreshold(*it,*it,1,image2); typename FilterType::Pointer dfilter2 = FilterType::New(); dfilter2->InputIsBinaryOn(); dfilter2->SetUseImageSpacing(true); dfilter2->SetInput(mask2); dfilter1->Update(); d1=dfilter1->GetOutput(); dfilter2->Update(); d2=dfilter2->GetOutput(); } float dist1=0; float dist2=0; Iterator It2 ( image2,image2->GetLargestPossibleRegion() ); for( It2.GoToBegin(); !It2.IsAtEnd(); ++It2 ) { if (It2.Get() == *it || image1->GetPixel(It2.GetIndex()) == *it) countu++; if (It2.Get() == *it && image1->GetPixel(It2.GetIndex()) == *it) counti++; if (It2.Get() == *it) { count2++; if (d1) dist2+=d1->GetPixel(It2.GetIndex()); } if ( image1->GetPixel(It2.GetIndex()) == *it) { count1++; if (d2) dist1+=d2->GetPixel(It2.GetIndex()); } if (outdist) if ( surf->GetPixel(It2.GetIndex()) > 0 ) { float sdist=d2->GetPixel(It2.GetIndex()); outdist->SetPixel(It2.GetIndex(),sdist); surfdist+=sdist; surfct+=1; } } // std::cout << " sdist " << surfdist << " sct " << surfct << std::endl if (outdist) WriteImage(outdist,outdistfn.c_str()); if (count2+count1 > 0) { distances[labct]+=(dist2+dist1)/(count2+count1); dicevals[labct]=2.0*counti/(count2+count1); rovals[labct]=counti/(countu); tpvals[labct]=counti/count1; tpvals2[labct]=counti/count2; } labct++; } labct=0; float sum=0,sumdice=0,sumro=0; // std::sort(myLabelSet2.begin(),myLabelSet2.end()); unsigned long labelcount=0; for( it = myLabelSet2.begin(); it != myLabelSet2.end(); ++it ) labelcount++; float temp=sqrt((float)labelcount); unsigned int squareimagesize=(unsigned int)(temp+1); typedef itk::Image TwoDImageType; typename TwoDImageType::RegionType newregion; typename TwoDImageType::SizeType size; size[0]=size[1]=squareimagesize; newregion.SetSize(size); typename TwoDImageType::Pointer squareimage = TwoDImageType::New(); typename TwoDImageType::SpacingType spacingb; spacingb.Fill(1); typename TwoDImageType::PointType origin; origin.Fill(0); squareimage->SetSpacing(spacingb); squareimage->SetOrigin(origin); squareimage->SetRegions(newregion ); squareimage->Allocate(); squareimage->FillBuffer( 0 ); typename TwoDImageType::Pointer squareimage2 = TwoDImageType::New(); squareimage2->SetSpacing(spacingb); squareimage2->SetOrigin(origin); squareimage2->SetRegions(newregion ); squareimage2->Allocate(); squareimage2->FillBuffer( 0 ); labelcount=0; for( it = myLabelSet2.begin(); it != myLabelSet2.end(); ++it ) { if (logfile.good() ) { if (outdist) logfile <<" Label " << *it << " MD " << distances[labct] << " DICE " << dicevals[labct] << std::endl; else logfile <<" Label " << *it << " DICE " << dicevals[labct] << " RO " << rovals[labct] << " TP1 " << tpvals[labct] << " TP2 " << tpvals2[labct] << std::endl; } sum+=distances[labct]; sumdice+=dicevals[labct]; sumro+=rovals[labct]; // square image squareimage->GetBufferPointer()[labct]=distances[labct]; squareimage2->GetBufferPointer()[labct]= dicevals[labct]; labct++; } if (labct ==0) labct=1; if (logfile.good() ) { if (outdist) logfile <<" AvgMinDist " << " : " << sum/(float)labct << " AvgDice " << " : " << sumdice/(float)labct << " AvgRO " << " : " << sumro/(float)labct << std::endl; else logfile << " AvgDice " << " : " << sumdice/(float)labct << std::endl; } WriteImage(squareimage,mdsimagename.c_str()); WriteImage(squareimage2,diceimagename.c_str()); return 0; } template int Lipschitz( int argc, char *argv[] ) { std::cout << " Compute Lipschitz continuity of the mapping " << std::endl; typedef float RealType; typedef itk::Image RealImageType; typedef itk::Vector VectorType; typedef itk::Image VectorImageType; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string vecname = std::string(argv[argct]); argct++; /** * Read in vector field */ typedef itk::VectorImageFileReader ReaderType; typename ReaderType::Pointer reader = ReaderType::New(); reader->SetFileName( vecname.c_str() ); reader->SetUseAvantsNamingConvention( true ); reader->Update(); typename VectorImageType::Pointer vecimage=reader->GetOutput(); typename RealImageType::Pointer lipcon = RealImageType::New(); lipcon->SetOrigin( vecimage->GetOrigin() ); lipcon->SetSpacing( vecimage->GetSpacing() ); lipcon->SetRegions( vecimage->GetLargestPossibleRegion() ); lipcon->SetDirection( vecimage->GetDirection()); lipcon->Allocate(); lipcon->FillBuffer(0); itk::TimeProbe timer; timer.Start(); /** global Lipschitz points */ typename VectorImageType::PointType gx; gx.Fill(0); typename VectorImageType::PointType gxt; gxt.Fill(0); typename VectorImageType::PointType gy; gy.Fill(0); typename VectorImageType::PointType gyt; gyt.Fill(0); typename VectorImageType::PointType lx; lx.Fill(0); typename VectorImageType::PointType lxt; lxt.Fill(0); typename VectorImageType::PointType ly; ly.Fill(0); typename VectorImageType::PointType lyt; lyt.Fill(0); float globalmaxval=0; unsigned long ct1=0; // unsigned long numpx=vecimage->GetBufferedRegion().GetNumberOfPixels(); Iterator It1 ( vecimage,vecimage->GetLargestPossibleRegion() ); for( It1.GoToBegin(); !It1.IsAtEnd(); ++It1 ) { ct1++; typename VectorImageType::PointType x; typename VectorImageType::PointType xt; vecimage->TransformIndexToPhysicalPoint( It1.GetIndex(), x); typename VectorImageType::PixelType vecx=It1.Get(); for (unsigned int i=0; iGetLargestPossibleRegion() ); for( It2.GoToBegin(); !It2.IsAtEnd(); ++It2 ) { ct2++; if (ct2 != ct1) { typename VectorImageType::PointType y; typename VectorImageType::PointType yt; vecimage->TransformIndexToPhysicalPoint( It2.GetIndex(), y); typename VectorImageType::PixelType vecy=It2.Get(); float numer=0; float denom=0; for (unsigned int i=0; i localmaxval) { lx=x; ly=y; lxt=xt; lyt=yt; localmaxval=localval; } } } if (localmaxval > globalmaxval) { gx=lx; gy=ly; gxt=lxt; gyt=lyt; globalmaxval = localmaxval; } lipcon->SetPixel(It1.GetIndex(),localmaxval); // if (ct1 % 1000 == 0) std::cout << " Progress : " << (float ) ct1 / (float) numpx *100.0 << " val " << localmaxval << std::endl; } std::cout << " Lipschitz continuity related to: " << globalmaxval << std::endl; std::cout << " Tx : " << gxt << " Ty: " << gyt << std::endl; std::cout << " x : " << gx << " y: " << gy << std::endl; timer.Stop(); // std::cout << "Elapsed time: " << timer.GetMeanTime() << std::endl; typedef itk::ImageFileWriter RealImageWriterType; typename RealImageWriterType::Pointer realwriter = RealImageWriterType::New(); realwriter->SetFileName( outname.c_str() ); realwriter->SetInput( lipcon ); realwriter->Update(); return 0; } template int InvId( int argc, char *argv[] ) { std::cout << " Compute phi( phi^{-1}(x)) " << std::endl; typedef float RealType; typedef itk::Image RealImageType; typedef itk::Vector VectorType; typedef itk::Image VectorImageType; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string vecname1 = std::string(argv[argct]); argct++; std::string vecname2 = std::string(argv[argct]); argct++; /** * Read in vector field */ typedef itk::VectorImageFileReader ReaderType; typename ReaderType::Pointer reader1 = ReaderType::New(); reader1->SetFileName( vecname1.c_str() ); reader1->SetUseAvantsNamingConvention( true ); reader1->Update(); typename VectorImageType::Pointer vecimage1=reader1->GetOutput(); typename ReaderType::Pointer reader2 = ReaderType::New(); reader2->SetFileName( vecname2.c_str() ); reader2->SetUseAvantsNamingConvention( true ); reader2->Update(); typename VectorImageType::Pointer vecimage2=reader2->GetOutput(); typename RealImageType::Pointer invid = RealImageType::New(); invid->SetOrigin( vecimage1->GetOrigin() ); invid->SetSpacing( vecimage1->GetSpacing() ); invid->SetRegions( vecimage1->GetLargestPossibleRegion() ); invid->SetDirection( vecimage1->GetDirection()); invid->Allocate(); invid->FillBuffer(0); itk::TimeProbe timer; timer.Start(); typedef itk::VectorLinearInterpolateImageFunction DefaultInterpolatorType; typename DefaultInterpolatorType::Pointer vinterp = DefaultInterpolatorType::New(); vinterp->SetInputImage(vecimage2); /** global Lipschitz points */ typename VectorImageType::PointType gx; gx.Fill(0); float globalmaxval=0; Iterator It1 ( vecimage1,vecimage1->GetLargestPossibleRegion() ); for( It1.GoToBegin(); !It1.IsAtEnd(); ++It1 ) { typename VectorImageType::PointType x; typename DefaultInterpolatorType::PointType xt,yt; vecimage1->TransformIndexToPhysicalPoint( It1.GetIndex(), x); typename VectorImageType::PixelType vecx=It1.Get(); for (unsigned int i=0; iIsInsideBuffer(xt)) disp = vinterp->Evaluate(xt); else disp.Fill(0); for (unsigned int jj=0; jj globalmaxval) { globalmaxval=error; gx=x; } invid->SetPixel(It1.GetIndex(),error); } std::cout << " Max error " << globalmaxval << " at " << gx << std::endl; timer.Stop(); // std::cout << "Elapsed time: " << timer.GetMeanTime() << std::endl; typedef itk::ImageFileWriter RealImageWriterType; typename RealImageWriterType::Pointer realwriter = RealImageWriterType::New(); realwriter->SetFileName( outname.c_str() ); realwriter->SetInput( invid ); realwriter->Update(); return 0; } template int LabelStats( int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::Image ByteImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string imagename=ANTSGetFilePrefix(outname.c_str())+std::string(".nii.gz"); std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; std::string fn2 = ""; if ( argc > argct) { fn2=std::string(argv[argct]); argct++; } typename ImageType::Pointer image = NULL; typename ImageType::Pointer valimage = NULL; ReadImage(image, fn1.c_str()); if ( fn2.length() > 3) ReadImage(valimage, fn2.c_str()); typedef float PixelType; typedef std::vector LabelSetType; LabelSetType myLabelSet; /** count the labels in the image */ Iterator It ( image,image->GetLargestPossibleRegion() ); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { PixelType label = It.Get(); if( fabs(label) > 0 ) { if( find( myLabelSet.begin(), myLabelSet.end(), label ) == myLabelSet.end() ) { myLabelSet.push_back( label ); } } } // compute the voxel volume typename ImageType::SpacingType spacing=image->GetSpacing(); float volumeelement=1.0; for (unsigned int i=0; i TwoDImageType; typename TwoDImageType::RegionType newregion; typename TwoDImageType::SizeType size; size[0]=size[1]=squareimagesize; newregion.SetSize(size); typename TwoDImageType::Pointer squareimage = TwoDImageType::New(); typename TwoDImageType::SpacingType spacingb; spacingb.Fill(1); typename TwoDImageType::PointType origin; origin.Fill(0); squareimage->SetSpacing(spacingb); squareimage->SetOrigin(origin); squareimage->SetRegions(newregion ); squareimage->Allocate(); squareimage->FillBuffer( 0 ); labelcount=0; for( it = myLabelSet.begin(); it != myLabelSet.end(); ++it ) { float currentlabel= *it ; float totalvolume=0; float totalmass=0; float totalct=0; typename ImageType::PointType myCenterOfMass; myCenterOfMass.Fill(0); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { PixelType label = It.Get(); if( label == currentlabel ) { totalvolume+=volumeelement; totalct+=1; if ( valimage) totalmass+=valimage->GetPixel(It.GetIndex()); // compute center of mass typename ImageType::PointType point; image->TransformIndexToPhysicalPoint(It.GetIndex(), point); for (unsigned int i=0; i 500 && totalmass/totalct > 1/500 ) { { std::cout << " Volume Of Label " << *it << " is " << totalvolume << " Avg-Location " << myCenterOfMass <<" mass is " << totalmass << " average-val is " << totalmass/totalct << std::endl; // std::cout << *it << " " << totalvolume << " & " << totalmass/totalct << " \ " << std::endl; } // square image squareimage->GetBufferPointer()[labelcount]=totalmass/totalct; labelcount++; } logfile.close(); WriteImage(squareimage,imagename.c_str()); return 0; } //int is the key, string the return value std::map RoiList(std::string file) { unsigned int wordindex=1; std::string tempstring=""; std::map RoiList; char str[2000]; std::fstream file_op(file.c_str(),std::ios::in); while(file_op >> str) { tempstring=std::string(str); RoiList[wordindex]=tempstring; wordindex++; std::cout << tempstring << std::endl; } return RoiList; //returns the maximum index } // now words can be accessed like this WordList[n]; where 'n' is the index template int ROIStatistics( int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::Image ByteImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; // first create a label image => ROI value map std::map cortroimap; cortroimap[1]=std::string("L. Occipital Lobe"); cortroimap[2]=std::string("R. Occipital Lobe"); cortroimap[3]=std::string("L. Cingulate Gyrus"); cortroimap[4]=std::string("R. Cingulate Gyrus"); cortroimap[5]=std::string("L. Insula"); cortroimap[1]=std::string("CSF"); cortroimap[2]=std::string("CGM"); cortroimap[3]=std::string("WM"); cortroimap[4]=std::string("DGM"); cortroimap[5]=std::string("Cerebellum"); cortroimap[6]=std::string("R. Insula"); cortroimap[7]=std::string("L. Temporal Pole"); cortroimap[8]=std::string("R. Temporal Pole"); cortroimap[9]=std::string("L. Sup. Temp. Gyrus"); cortroimap[10]=std::string("R. Sup. Temp. Gyrus "); cortroimap[11]=std::string("L. Infero. Temp. Gyrus"); cortroimap[12]=std::string("R. Infero. Temp. Gyrus"); cortroimap[13]=std::string("L. Parahippocampal Gyrus"); cortroimap[14]=std::string("R. Parahippocampal Gyrus"); cortroimap[15]=std::string("L. Frontal Pole"); cortroimap[16]=std::string("R. Frontal Pole"); cortroimap[17]=std::string("L. Sup. Frontal Gyrus"); cortroimap[18]=std::string("R. Sup. Frontal Gyrus"); cortroimap[19]=std::string("L. Mid. Frontal Gyrus"); cortroimap[20]=std::string("R. Mid. Frontal Gyrus"); cortroimap[21]=std::string("L. Inf. Frontal Gyrus"); cortroimap[22]=std::string("R. Inf. Frontal Gyrus"); cortroimap[23]=std::string("L. Orbital Frontal Gyrus"); cortroimap[24]=std::string("R. Orbital Frontal Gyrus"); cortroimap[25]=std::string("L. Precentral Gyrus"); cortroimap[26]=std::string("R. Precentral Gyrus"); cortroimap[27]=std::string("L. Sup. Parietal Lobe"); cortroimap[28]=std::string("R. Sup. Parietal Lobe"); cortroimap[29]=std::string("L. Inf. Parietal Lobe"); cortroimap[30]=std::string("R. Inf. Parietal Lobe"); cortroimap[31]=std::string("L. Postcentral Gyrus"); cortroimap[32]=std::string("R. Postcentral Gyrus"); cortroimap[33]=std::string("L. Hipp. Head"); cortroimap[34]=std::string("R. Hipp Head"); cortroimap[35]=std::string("L. Hipp Midbody"); cortroimap[36]=std::string("R. Hipp Midbody"); cortroimap[37]=std::string("L. Hipp Tail"); cortroimap[38]=std::string("R. Hipp Tail"); cortroimap[39]=std::string("L. Caudate"); cortroimap[40]=std::string("R. Caudate"); cortroimap[41]=std::string("L. Putamen"); cortroimap[42]=std::string("R. Putamen"); cortroimap[43]=std::string("L. Thalamus"); cortroimap[44]=std::string("R. Thalamus"); cortroimap[45]=std::string("White Matter"); std::map wmroimap; wmroimap[1]=std::string("R. corticospinal Tract"); wmroimap[2]=std::string("R. inferior fronto-occipital fasciculus"); wmroimap[3]=std::string("R. inferior longitudinal fasciculus"); wmroimap[4]=std::string("R. superior longitudinal fasciculus"); wmroimap[5]=std::string("R. uncinate fasciculus"); wmroimap[6]=std::string("L. corticospinal Tract"); wmroimap[7]=std::string("L. inferior fronto-occipital fasciculus"); wmroimap[8]=std::string("L. inferior longitudinal fasciculus"); wmroimap[9]=std::string("L. superior longitudinal fasciculus"); wmroimap[10]=std::string("L. uncinate fasciculus"); wmroimap[11]=std::string("Anterior corpus callosum"); wmroimap[12]=std::string("Posterior corpus callosum"); wmroimap[13]=std::string("Mid-body corpus callosum"); // if(grade_list.find("Tim") == grade_list.end()) { std::cout<<"Tim is not in the map!"<second int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string imagename=ANTSGetFilePrefix(outname.c_str())+std::string(".nii.gz"); std::string operation = std::string(argv[argct]); argct++; std::string fn0 = std::string(argv[argct]); argct++; std::cout <<" fn0 " << fn0 << std::endl; std::map roimap=RoiList(fn0); std::string fn1 = std::string(argv[argct]); argct++; std::string fn2 = ""; if ( argc > argct) { fn2=std::string(argv[argct]); argct++; } std::string fn3 = ""; if ( argc > argct) { fn3=std::string(argv[argct]); argct++; } std::string fn4 = ""; if ( argc > argct) { fn4=std::string(argv[argct]); argct++; } std::string fn5 = ""; if ( argc > argct) { fn5=std::string(argv[argct]); argct++; } typename ImageType::Pointer image = NULL; typename ImageType::Pointer valimage = NULL; typename ImageType::Pointer valimage3 = NULL; typename ImageType::Pointer valimage4 = NULL; typename ImageType::Pointer valimage5 = NULL; ReadImage(image, fn1.c_str()); if ( fn2.length() > 3) ReadImage(valimage, fn2.c_str()); if ( fn3.length() > 3) ReadImage(valimage3, fn3.c_str()); if ( fn4.length() > 3) ReadImage(valimage4, fn4.c_str()); if ( fn5.length() > 3) ReadImage(valimage5, fn5.c_str()); typedef float PixelType; typedef std::vector LabelSetType; LabelSetType myLabelSet; /** count the labels in the image */ unsigned long maxlab=0; Iterator It ( image,image->GetLargestPossibleRegion() ); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { PixelType label = It.Get(); if( fabs(label) > 0 ) { if( find( myLabelSet.begin(), myLabelSet.end(), label ) == myLabelSet.end() ) { myLabelSet.push_back( label ); } if (label > maxlab) maxlab=(unsigned long)label; } } // maxlab=32; // for cortical analysis // compute the voxel volume typename ImageType::SpacingType spacing=image->GetSpacing(); float volumeelement=1.0; for (unsigned int i=0; i pvals(maxlab+1,1.0); vnl_vector pvals3(maxlab+1,1.0); vnl_vector pvals4(maxlab+1,1.0); vnl_vector pvals5(maxlab+1,1.0); vnl_vector clusters(maxlab+1,0.0); vnl_vector masses(maxlab+1,0.0); typename ImageType::PointType mycomlist[33]; std::cout <<" csv b " << std::endl; std::ofstream logfile; logfile.open(outname.c_str() ); std::sort(myLabelSet.begin(),myLabelSet.end()); typename LabelSetType::const_iterator it; unsigned long labelcount=0; for( it = myLabelSet.begin(); it != myLabelSet.end(); ++it ) labelcount++; float temp=sqrt((float)labelcount); unsigned int squareimagesize=(unsigned int)(temp+1); typedef itk::Image TwoDImageType; typename TwoDImageType::RegionType newregion; typename TwoDImageType::SizeType size; size[0]=size[1]=squareimagesize; newregion.SetSize(size); typename TwoDImageType::Pointer squareimage = TwoDImageType::New(); typename TwoDImageType::SpacingType spacingb; spacingb.Fill(1); typename TwoDImageType::PointType origin; origin.Fill(0); squareimage->SetSpacing(spacingb); squareimage->SetOrigin(origin); squareimage->SetRegions(newregion ); squareimage->Allocate(); squareimage->FillBuffer( 0 ); labelcount=0; typename ImageType::PointType myCenterOfMass; myCenterOfMass.Fill(0); for (unsigned int i=0; i<=maxlab; i++) mycomlist[i]=myCenterOfMass; for( it = myLabelSet.begin(); it != myLabelSet.end(); ++it ) { float currentlabel= *it ; float totalvolume=0; float totalmass=0; float totalct=0; float maxoneminuspval=0.0; float maxoneminuspval3=0.0; float maxoneminuspval4=0.0; float maxoneminuspval5=0.0; myCenterOfMass.Fill(0); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { PixelType label = It.Get(); if( label == currentlabel ) { totalvolume+=volumeelement; totalct+=1; float vv=valimage->GetPixel(It.GetIndex()); if ( valimage) totalmass+=vv; if ( vv > maxoneminuspval) maxoneminuspval=vv; if ( valimage3 ){ vv=valimage3->GetPixel(It.GetIndex()); if ( fabs(vv) > fabs(maxoneminuspval3)) maxoneminuspval3=vv; } if ( valimage4 ){ vv=valimage4->GetPixel(It.GetIndex()); if ( vv > maxoneminuspval4) maxoneminuspval4=vv; } if ( valimage5 ){ vv=valimage5->GetPixel(It.GetIndex()); if ( vv > maxoneminuspval5) maxoneminuspval5=vv; } // compute center of mass typename ImageType::PointType point; image->TransformIndexToPhysicalPoint(It.GetIndex(), point); for (unsigned int i=0; iGetBufferPointer()[labelcount]=totalmass/totalct; labelcount++; } bool iswm=false; // if (maxlab > 13 ) iswm=false; // unsigned int roi=(unsigned int) *it; for (unsigned int roi=1; roi <=maxlab ; roi++) { if (roi < maxlab ) logfile << roimap.find(roi)->second << ","; else logfile << roimap.find(roi)->second << ","; //<< std::endl; } for (unsigned int roi=maxlab+1; roi <=maxlab*2 ; roi++) { if (roi < maxlab*2 ) logfile << roimap.find(roi-maxlab)->second+std::string("mass") << ","; else logfile << roimap.find(roi-maxlab)->second+std::string("mass") << std::endl; } for (unsigned int roi=1; roi <=maxlab ; roi++) { if (roi < maxlab ) logfile << clusters[roi] << ","; else logfile << clusters[roi] << ","; //<< std::endl; } for (unsigned int roi=maxlab+1; roi <=maxlab*2 ; roi++) { if (roi < maxlab*2 ) logfile << masses[roi-maxlab] << ","; else logfile << masses[roi-maxlab] << std::endl; } logfile.close(); return 0; for (unsigned int roi=1; roi <=maxlab ; roi++) { //unsigned int resol=5000; //unsigned int intpvalue=resol*totalmass/totalct; // float pvalue=1.0-(float)intpvalue/(float)resol;// average pvalue myCenterOfMass=mycomlist[roi]; if( roimap.find(roi) != roimap.end() && iswm ) std::cout << roimap.find(roi)->second << " & " << clusters[roi] << " , " << pvals[roi] << " & xy & yz \\ " << std::endl; else if( roimap.find(roi) != roimap.end() && ! iswm ) std::cout << roimap.find(roi)->second << " & " << clusters[roi] << " , " << pvals[roi] << " & " << pvals3[roi] << " & " << pvals4[roi] << " & " << (float)((int)(myCenterOfMass[0]*10))/10. << " " << (float)((int)(myCenterOfMass[1]*10))/10. << " " << (float)((int)(myCenterOfMass[2]*10))/10. << " \\ " << std::endl; } // WriteImage(squareimage,imagename.c_str()); return 0; } template int ByteImage( int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::Image ByteImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; float sig = 1; if (argc > argct) sig=atof(argv[argct]); if (sig <= 0) sig=0.5; typename ImageType::Pointer image = NULL; typename ByteImageType::Pointer image2 = NULL; ReadImage(image, fn1.c_str()); typedef itk::RescaleIntensityImageFilter RescaleFilterType; typename RescaleFilterType::Pointer rescaler = RescaleFilterType::New(); rescaler->SetOutputMinimum( 0 ); rescaler->SetOutputMaximum( 255 ); rescaler->SetInput( image ); typename writertype::Pointer writer = writertype::New(); writer->SetFileName(outname.c_str()); writer->SetInput( rescaler->GetOutput() ); writer->Update(); return 0; } template int PValueImage( int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::Image ByteImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; unsigned int dof = 1; if (argc > argct) dof=atoi(argv[argct]); argct++; typename ImageType::Pointer image = NULL; ReadImage(image, fn1.c_str()); std::cout <<" read Image" << fn1 << " dof " << dof << std::endl; typedef itk::Statistics::TDistribution DistributionType; typename DistributionType::Pointer distributionFunction = DistributionType::New(); distributionFunction->SetDegreesOfFreedom( dof ); typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter( image, image->GetLargestPossibleRegion() ); for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) { float val=image->GetPixel(vfIter.GetIndex()); if ( ! vnl_math_isnan(val) && ! vnl_math_isinf(val) ) { if ( fabs( val ) > 0 ) { float pval=0; if (val >= 1) pval = 1.0-distributionFunction->EvaluateCDF( val , dof ); else if (val < 1) pval = distributionFunction->EvaluateCDF( val , dof ); image->SetPixel(vfIter.GetIndex() , pval ); } } else image->SetPixel(vfIter.GetIndex() , 0); } WriteImage(image,outname.c_str()); return 0; } template int ConvertImageSetToMatrix(unsigned int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::Image MatrixImageType; typedef itk::ImageFileReader readertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; if (argc < 5 ) { std::cout <<" need more args -- see usage " << std::endl; exit(0); } std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; unsigned int rowcoloption = atoi(argv[argct]); argct++; std::string maskfn=std::string(argv[argct]); argct++; unsigned int numberofimages=0; typename ImageType::Pointer mask = NULL; ReadImage(mask,maskfn.c_str()); unsigned long voxct=0; Iterator mIter( mask,mask->GetLargestPossibleRegion() ); for( mIter.GoToBegin(); !mIter.IsAtEnd(); ++mIter ) if (mIter.Get() >= 0.5) voxct++; typename ImageType::Pointer image2 = NULL; typename ImageType::SizeType size; size.Fill(0); unsigned int bigimage=0; for (unsigned int j=argct; j< argc; j++) { numberofimages++; // Get the image dimension std::string fn = std::string(argv[j]); typename itk::ImageIOBase::Pointer imageIO = itk::ImageIOFactory::CreateImageIO(fn.c_str(), itk::ImageIOFactory::ReadMode); imageIO->SetFileName(fn.c_str()); imageIO->ReadImageInformation(); for (unsigned int i=0; iGetNumberOfDimensions(); i++) { if ( imageIO->GetDimensions(i) > size[i] ) { size[i]=imageIO->GetDimensions(i); bigimage=j; std::cout << " bigimage " << j << " size " << size << std::endl; } } } std:: cout << " largest image " << size << " num images " << numberofimages << " voxct " << voxct << std::endl; /** declare the tiled image */ unsigned long xx=0,yy=0; if (rowcoloption==0) { std::cout << " row option " << std::endl; xx=voxct; yy=numberofimages; } if (rowcoloption==1) { std::cout << " col option " << std::endl; yy=voxct; xx=numberofimages; } unsigned long xsize=xx; unsigned long ysize=yy; typename MatrixImageType::SizeType tilesize; tilesize[0]=xsize; tilesize[1]=ysize; std::cout << " allocate matrix " << tilesize << std::endl; typename MatrixImageType::RegionType region; region.SetSize( tilesize ); typename MatrixImageType::Pointer matimage=MatrixImageType::New(); matimage->SetLargestPossibleRegion( region ); matimage->SetBufferedRegion( region ); typename MatrixImageType::DirectionType mdir; mdir.Fill(0); mdir[0][0]=1; mdir[1][1]=1; typename MatrixImageType::SpacingType mspc; mspc.Fill(1); typename MatrixImageType::PointType morg; morg.Fill(0); matimage->SetSpacing( mspc ); matimage->SetDirection(mdir); matimage->SetOrigin( morg ); matimage->Allocate(); unsigned int imagecount=0; for (unsigned int j=argct; j< argc; j++) { std::string fn = std::string(argv[j]); ReadImage(image2,fn.c_str()); std::cout << " image " << j << " is " << fn << std::endl; unsigned long xx=0,yy=0,tvoxct=0; if (rowcoloption==0) { yy=imagecount; } if (rowcoloption==1) { xx=imagecount; } typename MatrixImageType::IndexType mind; for( mIter.GoToBegin(); !mIter.IsAtEnd(); ++mIter ) { if (mIter.Get() >= 0.5) { if (rowcoloption==0) { xx=tvoxct; } if (rowcoloption==1) { yy=tvoxct; } mind[0]=xx; mind[1]=yy; // std::cout << " Mind " << mind << std::endl; matimage->SetPixel(mind,image2->GetPixel(mIter.GetIndex())); tvoxct++; } } imagecount++; } std::cout << " mat size " << matimage->GetLargestPossibleRegion().GetSize() << std::endl; WriteImage(matimage,outname.c_str()); return 0; } template int ConvertImageToFile( int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::Image ByteImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; std::string fn2=""; if (argc > argct) fn2=std::string(argv[argct]); argct++; typename ImageType::Pointer image = NULL; ReadImage(image, fn1.c_str()); typename ImageType::Pointer mask = NULL; if (fn2.length() > 3) ReadImage(mask, fn2.c_str()); std::cout <<" read Image" << fn1 << " mask? " << fn2 << std::endl; std::ofstream logfile; logfile.open(outname.c_str() ); if (logfile.good()) { typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter( image, image->GetLargestPossibleRegion() ); for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) { bool getval=true; if ( mask->GetPixel(vfIter.GetIndex()) < 0.5) getval=false; if (getval) { float val=image->GetPixel(vfIter.GetIndex()); logfile << " " << val ; } } logfile << std::endl; } logfile.close(); return 0; } template int CorrelationUpdate( int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::Image ByteImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; int argct=2; std::string outname=std::string(argv[argct]); argct++; std::string operation = std::string(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; std::string fn2 =""; if (argc > argct) { fn2=std::string(argv[argct]); argct++; }else { std::cout <<" Not enough inputs "<< std::endl; return 1; } unsigned int radius=2; if (argc > argct) radius=atoi(argv[argct]); argct++; typename ImageType::Pointer image1 = NULL; ReadImage(image1, fn1.c_str()); typename ImageType::Pointer imageout = NULL; ReadImage(imageout, fn1.c_str()); imageout->FillBuffer(0); typename ImageType::Pointer image2 = NULL; ReadImage(image2, fn2.c_str()); typedef itk::NeighborhoodIterator iteratorType; typename iteratorType::RadiusType rad; for (unsigned int j=0; jGetLargestPossibleRegion()); float Gsz=(float)GHood.Size(); GHood.GoToBegin(); while (!GHood.IsAtEnd()) { typename ImageType::IndexType ind = GHood.GetIndex(); bool isinside=true; for (unsigned int j=0; j image1->GetLargestPossibleRegion().GetSize()[j]-radius-1 ) isinside=false; } if (isinside) { if ( image1->GetPixel(ind) > 0 || image2->GetPixel(ind) > 0) { typename ImageType::IndexType ind2; // compute mean difference float diff=0.0; for (unsigned int i = 0; i < GHood.Size(); i++) { ind2=GHood.GetIndex(i); diff+=(image1->GetPixel(ind2)-image2->GetPixel(ind2)); } diff/=Gsz; float upd=(image1->GetPixel(ind)-image2->GetPixel(ind))-diff; imageout->SetPixel(ind,upd); } } ++GHood; } WriteImage(imageout,outname.c_str()); return 0; } int main(int argc, char *argv[]) { if ( argc < 5 ) { std::cout << "Usage: "<< std::endl; std::cout << argv[0] << " ImageDimension OutputImage.ext Operator Image1.ext Image2.extOrFloat " << std::endl; std::cout <<" some options output text files " << std::endl; std::cout << " The last two arguments can be an image or float value " << std::endl; std::cout << " Valid Operators : \n m (multiply) , \n + (add) , \n - (subtract) , \n / (divide) , \n ^ (power) , \n exp -- take exponent exp(imagevalue*value) \n addtozero \n overadd \n abs \n total -- sums up values in an image or in image1*image2 (img2 is the probability mask) \n Decision -- computes result=1./(1.+exp(-1.0*( pix1-0.25)/pix2)) " << std::endl; std::cout << " Neg (Produce Image Negative ) , \n G Image1.ext s (Smooth with Gaussian of sigma = s ) " << std::endl; std::cout << " MD Image1.ext s ( Morphological Dilation with radius s ) , \n \n ME Image1.ext s ( Morphological Erosion with radius s ) , \n \n MO Image1.ext s ( Morphological Opening with radius s ) \n \n MC Image1.ext ( Morphological Closing with radius s ) \n \n GD Image1.ext s ( Grayscale Dilation with radius s ) , \n \n GE Image1.ext s ( Grayscale Erosion with radius s ) , \n \n GO Image1.ext s ( Grayscale Opening with radius s ) \n \n GC Image1.ext ( Grayscale Closing with radius s ) \n" << std::endl; std::cout << " D (DistanceTransform) , \n \n Segment Image1.ext N-Classes LocalityVsGlobalityWeight-In-ZeroToOneRange OptionalPriorImages ( Segment an Image with option of Priors , weight 1 => maximally local/prior-based ) \n " << std::endl; std::cout << " Grad Image.ext S ( Gradient magnitude with sigma s -- if normalize, then output in range [0, 1] ) , \n " << std::endl; std::cout << " Laplacian Image.ext S normalize? ( laplacian computed with sigma s -- if normalize, then output in range [0, 1] ) , \n " << std::endl; std::cout << " Normalize image.ext opt ( Normalize to [0,1] option instead divides by average value ) \n " << std::endl; std::cout << " PH (Print Header) , \n Byte ( Convert to Byte image in [0,255] ) \n " << std::endl; std::cout << " LabelStats labelimage.ext valueimage.nii ( compute volumes / masses of objects in a label image -- write to text file ) \n" << std::endl; std::cout << " ROIStatistics LabelNames.txt labelimage.ext valueimage.nii ( see the code ) \n" << std::endl; std::cout << " DiceAndMinDistSum LabelImage1.ext LabelImage2.ext OptionalDistImage -- outputs DiceAndMinDistSum and Dice Overlap to text log file + optional distance image \n " << std::endl; std::cout << " Lipschitz VectorFieldName -- prints to cout & writes to image \n " << std::endl; std::cout << " InvId VectorFieldName VectorFieldName -- prints to cout & writes to image \n " << std::endl; std::cout << " GetLargestComponent InputImage {MinObjectSize} -- get largest object in image \n " << std::endl; std::cout << " ThresholdAtMean Image %ofMean \n " << std::endl; std::cout << " FlattenImage Image %ofMax -- replaces values greater than %ofMax*Max to the value %ofMax*Max \n " << std::endl; std::cout << " stack Image1.nii.gz Image2.nii.gz --- will put these 2 images in the same volume " << std::endl; std::cout << " CorruptImage Image NoiseLevel Smoothing " << std::endl; std::cout << " TileImages NumColumns ImageList* " << std::endl; std::cout << " RemoveLabelInterfaces ImageIn " << std::endl; std::cout << " EnumerateLabelInterfaces ImageIn ColoredImageOutname NeighborFractionToIgnore " << std::endl; std::cout << " FitSphere GM-ImageIn {WM-Image} {MaxRad-Default=5}" << std::endl; std::cout << " HistogramMatch SourceImage ReferenceImage {NumberBins-Default=255} {NumberPoints-Default=64}" << std::endl; std::cout << " PadImage ImageIn Pad-Number ( if Pad-Number is negative, de-Padding occurs ) " << std::endl; std::cout << " Where Image ValueToLookFor maskImage-option tolerance --- the where function from IDL " << std::endl; std::cout << " TensorFA DTImage " << std::endl; std::cout << " TensorColor DTImage --- produces RGB values identifying principal directions " << std::endl; std::cout << " TensorToVector DTImage WhichVec --- produces vector field identifying one of the principal directions, 2 = largest eigenvalue " << std::endl; std::cout << " TensorToVectorComponent DTImage WhichVec --- 0 => 2 produces component of the principal vector field , i.e. largest eigenvalue. 3 = 8 => gets values from the tensor " << std::endl; std::cout << " TensorIOTest DTImage --- will write the DT image back out ... tests I/O processes for consistency. " << std::endl; std::cout << " MakeImage SizeX SizeY {SizeZ} " << std::endl; std::cout << " SetOrGetPixel ImageIn Get/Set-Value IndexX IndexY {IndexZ} -- for example \n ImageMath 2 outimage.nii SetOrGetPixel Image Get 24 34 -- gets the value at 24, 34 \n ImageMath 2 outimage.nii SetOrGetPixel Image 1.e9 24 34 -- this sets 1.e9 as the value at 23 34 " << std::endl << " you can also pass a boolean at the end to force the physical space to be used " << std::endl; std::cout << " TensorMeanDiffusion DTImage " << std::endl; std::cout << " CompareHeadersAndImages Image1 Image2 --- tries to find and fix header error! output is the repaired image with new header. never use this if you trust your header information. " << std::endl; std::cout << " CountVoxelDifference Image1 Image2 Mask --- the where function from IDL " << std::endl; std::cout << " stack image1 image2 --- stack image2 onto image1 " << std::endl; std::cout << " CorrelationUpdate Image1 Image2 RegionRadius --- in voxels , Compute update that makes Image2 more like Image1 " << std::endl; std::cout << " ConvertImageToFile imagevalues.nii {Optional-ImageMask.nii} -- will write voxel values to a file " << std::endl; std::cout << " PValueImage TValueImage dof " << std::endl; std::cout << " ConvertToGaussian TValueImage sigma-float " << std::endl; std::cout << " ConvertImageSetToMatrix rowcoloption Mask.nii *images.nii -- each row/column contains image content extracted from mask applied to images in *img.nii " << std::endl; std::cout << " ConvertVectorToImage Mask.nii vector.nii -- the vector contains image content extracted from a mask - here we return the vector to its spatial origins as image content " << std::endl; std::cout << " TriPlanarView ImageIn.nii.gz PercentageToClampLowIntensity PercentageToClampHiIntensity x-slice y-slice z-slice " << std::endl; std::cout << " TruncateImageIntensity inputImage {lowerQuantile=0.05} {upperQuantile=0.95} {numberOfBins=65} {binary-maskImage} " << std::endl; std::cout << " FillHoles Image parameter : parameter = ratio of edge at object to edge at background = 1 is a definite hole bounded by object only, 0.99 is close -- default of parameter > 1 will fill all holes " << std::endl; std::cout << " PropagateLabelsThroughMask speed/binaryimagemask.nii.gz initiallabelimage.nii.gz Optional-Stopping-Value -- final output is the propagated label image " << std::endl << " optional stopping value -- higher values allow more distant propagation " << std::endl; std::cout << " FastMarchingSegmentation speed/binaryimagemask.nii.gz initiallabelimage.nii.gz Optional-Stopping-Value -- final output is the propagated label image " << std::endl << " optional stopping value -- higher values allow more distant propagation " << std::endl; std::cout << " ExtractSlice volume.nii.gz slicetoextract --- will extract slice number from last dimension of volume (2,3,4) dimensions " << std::endl; std::cout << " ConvertLandmarkFile InFile.txt ---- will convert landmark file between formats. see ants.pdf for description of formats. e.g. ImageMath 3 outfile.vtk ConvertLandmarkFile infile.txt " << std::endl; return 1; } std::string operation=std::string(argv[3]); switch ( atoi(argv[1]) ) { case 2: if (strcmp(operation.c_str(),"m") == 0) ImageMath<2>(argc,argv); else if (strcmp(operation.c_str(),"mresample") == 0) ImageMath<2>(argc,argv); else if (strcmp(operation.c_str(),"+") == 0) ImageMath<2>(argc,argv); else if (strcmp(operation.c_str(),"-") == 0) ImageMath<2>(argc,argv); else if (strcmp(operation.c_str(),"/") == 0) ImageMath<2>(argc,argv); else if (strcmp(operation.c_str(),"^") == 0) ImageMath<2>(argc,argv); else if (strcmp(operation.c_str(),"exp") == 0) ImageMath<2>(argc,argv); else if (strcmp(operation.c_str(),"abs") == 0) ImageMath<2>(argc,argv); else if (strcmp(operation.c_str(),"addtozero") == 0) ImageMath<2>(argc,argv); else if (strcmp(operation.c_str(),"overadd") == 0) ImageMath<2>(argc,argv); else if (strcmp(operation.c_str(),"total") == 0) ImageMath<2>(argc,argv); else if (strcmp(operation.c_str(),"Decision") == 0) ImageMath<2>(argc,argv); else if (strcmp(operation.c_str(),"Neg") == 0) NegativeImage<2>(argc,argv); else if (strcmp(operation.c_str(),"G") == 0) SmoothImage<2>(argc,argv); else if (strcmp(operation.c_str(),"MD") == 0 || strcmp(operation.c_str(),"ME") == 0) MorphImage<2>(argc,argv); else if (strcmp(operation.c_str(),"MO") == 0 || strcmp(operation.c_str(),"MC") == 0) MorphImage<2>(argc,argv); else if (strcmp(operation.c_str(),"GD") == 0 || strcmp(operation.c_str(),"GE") == 0) MorphImage<2>(argc,argv); else if (strcmp(operation.c_str(),"GO") == 0 || strcmp(operation.c_str(),"GC") == 0) MorphImage<2>(argc,argv); else if (strcmp(operation.c_str(),"D") == 0 ) DistanceMap<2>(argc,argv); else if (strcmp(operation.c_str(),"Normalize") == 0 ) NormalizeImage<2>(argc,argv); else if (strcmp(operation.c_str(),"Grad") == 0 ) GradientImage<2>(argc,argv); else if (strcmp(operation.c_str(),"Laplacian") == 0 ) LaplacianImage<2>(argc,argv); else if (strcmp(operation.c_str(),"PH") == 0 ) PrintHeader<2>(argc,argv); else if (strcmp(operation.c_str(),"Byte") == 0 ) ByteImage<2>(argc,argv); else if (strcmp(operation.c_str(),"LabelStats") == 0 ) LabelStats<2>(argc,argv); else if (strcmp(operation.c_str(),"ROIStatistics") == 0 ) ROIStatistics<2>(argc,argv); else if (strcmp(operation.c_str(),"DiceAndMinDistSum") == 0 ) DiceAndMinDistSum<2>(argc,argv); else if (strcmp(operation.c_str(),"Lipschitz") == 0 ) Lipschitz<2>(argc,argv); else if (strcmp(operation.c_str(),"InvId") == 0 ) InvId<2>(argc,argv); else if (strcmp(operation.c_str(),"GetLargestComponent") == 0 ) GetLargestComponent<2>(argc,argv); else if (strcmp(operation.c_str(),"ThresholdAtMean") == 0 ) ThresholdAtMean<2>(argc,argv); else if (strcmp(operation.c_str(),"FlattenImage") == 0 ) FlattenImage<2>(argc,argv); else if (strcmp(operation.c_str(),"CorruptImage") == 0 ) CorruptImage<2>(argc,argv); else if (strcmp(operation.c_str(),"TileImages") == 0 ) TileImages<2>(argc,argv); else if (strcmp(operation.c_str(),"Where") == 0 ) Where<2>(argc,argv); else if (strcmp(operation.c_str(),"FillHoles") == 0 ) FillHoles<2>(argc,argv); else if (strcmp(operation.c_str(),"HistogramMatch") == 0) HistogramMatching<2>(argc,argv); else if (strcmp(operation.c_str(),"PadImage") == 0 ) PadImage<2>(argc,argv); else if (strcmp(operation.c_str(),"SetOrGetPixel") == 0 ) SetOrGetPixel<2>(argc,argv); else if (strcmp(operation.c_str(),"MakeImage") == 0 ) MakeImage<2>(argc,argv); else if (strcmp(operation.c_str(),"stack") == 0 ) StackImage<2>(argc,argv); else if (strcmp(operation.c_str(),"CompareHeadersAndImages") == 0 ) CompareHeadersAndImages<2>(argc,argv); else if (strcmp(operation.c_str(),"CountVoxelDifference") == 0 ) CountVoxelDifference<2>(argc,argv); // else if (strcmp(operation.c_str(),"AddToZero") == 0 ) AddToZero<2>(argc,argv); else if (strcmp(operation.c_str(),"RemoveLabelInterfaces") == 0 ) RemoveLabelInterfaces<2>(argc,argv); else if (strcmp(operation.c_str(),"EnumerateLabelInterfaces") == 0 ) EnumerateLabelInterfaces<2>(argc,argv); else if (strcmp(operation.c_str(),"ConvertImageToFile") == 0 ) ConvertImageToFile<2>(argc,argv); else if (strcmp(operation.c_str(),"PValueImage") == 0 ) PValueImage<2>(argc,argv); else if (strcmp(operation.c_str(),"CorrelationUpdate") == 0 ) CorrelationUpdate<2>(argc,argv); else if (strcmp(operation.c_str(),"ConvertImageSetToMatrix") == 0 ) ConvertImageSetToMatrix<2>(argc,argv); else if (strcmp(operation.c_str(),"ConvertVectorToImage") == 0 ) ConvertVectorToImage<2>(argc,argv); else if (strcmp(operation.c_str(),"PropagateLabelsThroughMask") == 0 ) PropagateLabelsThroughMask<2>(argc,argv); else if (strcmp(operation.c_str(),"FastMarchingSegmentation") == 0 ) FastMarchingSegmentation<2>(argc,argv); else if (strcmp(operation.c_str(),"TruncateImageIntensity") == 0 ) TruncateImageIntensity<2>(argc,argv); else if (strcmp(operation.c_str(),"ExtractSlice") == 0) ExtractSlice<2>(argc,argv); // else if (strcmp(operation.c_str(),"ConvertLandmarkFile") == 0) ConvertLandmarkFile<2>(argc,argv); else std::cout << " cannot find operation : " << operation << std::endl; break; case 3: if (strcmp(operation.c_str(),"m") == 0) ImageMath<3>(argc,argv); else if (strcmp(operation.c_str(),"mresample") == 0) ImageMath<3>(argc,argv); else if (strcmp(operation.c_str(),"+") == 0) ImageMath<3>(argc,argv); else if (strcmp(operation.c_str(),"-") == 0) ImageMath<3>(argc,argv); else if (strcmp(operation.c_str(),"/") == 0) ImageMath<3>(argc,argv); else if (strcmp(operation.c_str(),"^") == 0) ImageMath<3>(argc,argv); else if (strcmp(operation.c_str(),"exp") == 0) ImageMath<3>(argc,argv); else if (strcmp(operation.c_str(),"abs") == 0) ImageMath<3>(argc,argv); else if (strcmp(operation.c_str(),"addtozero") == 0) ImageMath<3>(argc,argv); else if (strcmp(operation.c_str(),"overadd") == 0) ImageMath<3>(argc,argv); else if (strcmp(operation.c_str(),"total") == 0) ImageMath<3>(argc,argv); else if (strcmp(operation.c_str(),"Decision") == 0) ImageMath<3>(argc,argv); else if (strcmp(operation.c_str(),"Neg") == 0) NegativeImage<3>(argc,argv); else if (strcmp(operation.c_str(),"G") == 0) SmoothImage<3>(argc,argv); else if (strcmp(operation.c_str(),"MD") == 0 || strcmp(operation.c_str(),"ME") == 0) MorphImage<3>(argc,argv); else if (strcmp(operation.c_str(),"MO") == 0 || strcmp(operation.c_str(),"MC") == 0) MorphImage<3>(argc,argv); else if (strcmp(operation.c_str(),"GD") == 0 || strcmp(operation.c_str(),"GE") == 0) MorphImage<3>(argc,argv); else if (strcmp(operation.c_str(),"GO") == 0 || strcmp(operation.c_str(),"GC") == 0) MorphImage<3>(argc,argv); else if (strcmp(operation.c_str(),"D") == 0 ) DistanceMap<3>(argc,argv); else if (strcmp(operation.c_str(),"Normalize") == 0 ) NormalizeImage<3>(argc,argv); else if (strcmp(operation.c_str(),"Grad") == 0 ) GradientImage<3>(argc,argv); else if (strcmp(operation.c_str(),"Laplacian") == 0 ) LaplacianImage<3>(argc,argv); else if (strcmp(operation.c_str(),"PH") == 0 ) PrintHeader<3>(argc,argv); else if (strcmp(operation.c_str(),"Byte") == 0 ) ByteImage<3>(argc,argv); else if (strcmp(operation.c_str(),"LabelStats") == 0 ) LabelStats<3>(argc,argv); else if (strcmp(operation.c_str(),"ROIStatistics") == 0 ) ROIStatistics<3>(argc,argv); else if (strcmp(operation.c_str(),"DiceAndMinDistSum") == 0 ) DiceAndMinDistSum<3>(argc,argv); else if (strcmp(operation.c_str(),"Lipschitz") == 0 ) Lipschitz<3>(argc,argv); else if (strcmp(operation.c_str(),"InvId") == 0 ) InvId<3>(argc,argv); else if (strcmp(operation.c_str(),"GetLargestComponent") == 0 ) GetLargestComponent<3>(argc,argv); else if (strcmp(operation.c_str(),"ThresholdAtMean") == 0 ) ThresholdAtMean<3>(argc,argv); else if (strcmp(operation.c_str(),"FlattenImage") == 0 ) FlattenImage<3>(argc,argv); else if (strcmp(operation.c_str(),"CorruptImage") == 0 ) CorruptImage<3>(argc,argv); else if (strcmp(operation.c_str(),"RemoveLabelInterfaces") == 0 ) RemoveLabelInterfaces<3>(argc,argv); else if (strcmp(operation.c_str(),"EnumerateLabelInterfaces") == 0 ) EnumerateLabelInterfaces<3>(argc,argv); else if (strcmp(operation.c_str(),"FitSphere") == 0 ) FitSphere<3>(argc,argv); else if (strcmp(operation.c_str(),"Where") == 0 ) Where<3>(argc,argv); else if (strcmp(operation.c_str(),"TensorFA") == 0 ) TensorFunctions<3>(argc,argv); else if (strcmp(operation.c_str(),"TensorIOTest") == 0 ) TensorFunctions<3>(argc,argv); else if (strcmp(operation.c_str(),"TensorMeanDiffusion") == 0 ) TensorFunctions<3>(argc,argv); else if (strcmp(operation.c_str(),"TensorColor") == 0) TensorFunctions<3>(argc,argv); else if (strcmp(operation.c_str(),"TensorToVector") == 0) TensorFunctions<3>(argc,argv); else if (strcmp(operation.c_str(),"TensorToVectorComponent") == 0) TensorFunctions<3>(argc,argv); else if (strcmp(operation.c_str(),"FillHoles") == 0 ) FillHoles<3>(argc,argv); else if (strcmp(operation.c_str(),"HistogramMatch") == 0) HistogramMatching<3>(argc,argv); else if (strcmp(operation.c_str(),"PadImage") == 0 ) PadImage<3>(argc,argv); else if (strcmp(operation.c_str(),"SetOrGetPixel") == 0 ) SetOrGetPixel<3>(argc,argv); else if (strcmp(operation.c_str(),"MakeImage") == 0 ) MakeImage<3>(argc,argv); else if (strcmp(operation.c_str(),"stack") == 0 ) StackImage<3>(argc,argv); else if (strcmp(operation.c_str(),"CompareHeadersAndImages") == 0 ) CompareHeadersAndImages<3>(argc,argv); else if (strcmp(operation.c_str(),"CountVoxelDifference") == 0 ) CountVoxelDifference<3>(argc,argv); else if (strcmp(operation.c_str(),"ConvertImageToFile") == 0 ) ConvertImageToFile<3>(argc,argv); else if (strcmp(operation.c_str(),"PValueImage") == 0 ) PValueImage<3>(argc,argv); else if (strcmp(operation.c_str(),"CorrelationUpdate") == 0 ) CorrelationUpdate<3>(argc,argv); else if (strcmp(operation.c_str(),"ConvertImageSetToMatrix") == 0 ) ConvertImageSetToMatrix<3>(argc,argv); else if (strcmp(operation.c_str(),"ConvertVectorToImage") == 0 ) ConvertVectorToImage<3>(argc,argv); else if (strcmp(operation.c_str(),"PropagateLabelsThroughMask") == 0 ) PropagateLabelsThroughMask<3>(argc,argv); else if (strcmp(operation.c_str(),"FastMarchingSegmentation") == 0 ) FastMarchingSegmentation<3>(argc,argv); else if (strcmp(operation.c_str(),"TriPlanarView") == 0 ) TriPlanarView<3>(argc,argv); else if (strcmp(operation.c_str(),"TruncateImageIntensity") == 0 ) TruncateImageIntensity<3>(argc,argv); else if (strcmp(operation.c_str(),"ExtractSlice") == 0) ExtractSlice<3>(argc,argv); else if (strcmp(operation.c_str(),"ConvertLandmarkFile") == 0) ConvertLandmarkFile<3>(argc,argv); else std::cout << " cannot find operation : " << operation << std::endl; break; case 4: if (strcmp(operation.c_str(),"m") == 0) ImageMath<4>(argc,argv); else if (strcmp(operation.c_str(),"mresample") == 0) ImageMath<4>(argc,argv); else if (strcmp(operation.c_str(),"+") == 0) ImageMath<4>(argc,argv); else if (strcmp(operation.c_str(),"-") == 0) ImageMath<4>(argc,argv); else if (strcmp(operation.c_str(),"/") == 0) ImageMath<4>(argc,argv); else if (strcmp(operation.c_str(),"^") == 0) ImageMath<4>(argc,argv); else if (strcmp(operation.c_str(),"exp") == 0) ImageMath<4>(argc,argv); else if (strcmp(operation.c_str(),"abs") == 0) ImageMath<4>(argc,argv); else if (strcmp(operation.c_str(),"addtozero") == 0) ImageMath<4>(argc,argv); else if (strcmp(operation.c_str(),"overadd") == 0) ImageMath<4>(argc,argv); else if (strcmp(operation.c_str(),"total") == 0) ImageMath<4>(argc,argv); else if (strcmp(operation.c_str(),"Decision") == 0) ImageMath<4>(argc,argv); else if (strcmp(operation.c_str(),"Neg") == 0) NegativeImage<4>(argc,argv); else if (strcmp(operation.c_str(),"G") == 0) SmoothImage<4>(argc,argv); else if (strcmp(operation.c_str(),"MD") == 0 || strcmp(operation.c_str(),"ME") == 0) MorphImage<4>(argc,argv); else if (strcmp(operation.c_str(),"MO") == 0 || strcmp(operation.c_str(),"MC") == 0) MorphImage<4>(argc,argv); else if (strcmp(operation.c_str(),"GD") == 0 || strcmp(operation.c_str(),"GE") == 0) MorphImage<4>(argc,argv); else if (strcmp(operation.c_str(),"GO") == 0 || strcmp(operation.c_str(),"GC") == 0) MorphImage<4>(argc,argv); else if (strcmp(operation.c_str(),"D") == 0 ) DistanceMap<4>(argc,argv); else if (strcmp(operation.c_str(),"Normalize") == 0 ) NormalizeImage<4>(argc,argv); else if (strcmp(operation.c_str(),"Grad") == 0 ) GradientImage<4>(argc,argv); else if (strcmp(operation.c_str(),"Laplacian") == 0 ) LaplacianImage<4>(argc,argv); else if (strcmp(operation.c_str(),"PH") == 0 ) PrintHeader<4>(argc,argv); else if (strcmp(operation.c_str(),"Byte") == 0 ) ByteImage<4>(argc,argv); else if (strcmp(operation.c_str(),"LabelStats") == 0 ) LabelStats<4>(argc,argv); else if (strcmp(operation.c_str(),"ROIStatistics") == 0 ) ROIStatistics<4>(argc,argv); else if (strcmp(operation.c_str(),"DiceAndMinDistSum") == 0 ) DiceAndMinDistSum<4>(argc,argv); else if (strcmp(operation.c_str(),"Lipschitz") == 0 ) Lipschitz<4>(argc,argv); else if (strcmp(operation.c_str(),"InvId") == 0 ) InvId<4>(argc,argv); else if (strcmp(operation.c_str(),"GetLargestComponent") == 0 ) GetLargestComponent<4>(argc,argv); else if (strcmp(operation.c_str(),"ThresholdAtMean") == 0 ) ThresholdAtMean<4>(argc,argv); else if (strcmp(operation.c_str(),"FlattenImage") == 0 ) FlattenImage<4>(argc,argv); else if (strcmp(operation.c_str(),"CorruptImage") == 0 ) CorruptImage<4>(argc,argv); else if (strcmp(operation.c_str(),"RemoveLabelInterfaces") == 0 ) RemoveLabelInterfaces<4>(argc,argv); else if (strcmp(operation.c_str(),"EnumerateLabelInterfaces") == 0 ) EnumerateLabelInterfaces<4>(argc,argv); else if (strcmp(operation.c_str(),"FitSphere") == 0 ) FitSphere<4>(argc,argv); else if (strcmp(operation.c_str(),"Where") == 0 ) Where<4>(argc,argv); // else if (strcmp(operation.c_str(),"TensorFA") == 0 ) TensorFunctions<4>(argc,argv); // else if (strcmp(operation.c_str(),"TensorIOTest") == 0 ) TensorFunctions<4>(argc,argv); // else if (strcmp(operation.c_str(),"TensorMeanDiffusion") == 0 ) TensorFunctions<4>(argc,argv); // else if (strcmp(operation.c_str(),"TensorColor") == 0) TensorFunctions<4>(argc,argv); // else if (strcmp(operation.c_str(),"TensorToVector") == 0) TensorFunctions<4>(argc,argv); // else if (strcmp(operation.c_str(),"TensorToVectorComponent") == 0) TensorFunctions<4>(argc,argv); else if (strcmp(operation.c_str(),"FillHoles") == 0 ) FillHoles<4>(argc,argv); else if (strcmp(operation.c_str(),"HistogramMatch") == 0) HistogramMatching<4>(argc,argv); else if (strcmp(operation.c_str(),"PadImage") == 0 ) PadImage<4>(argc,argv); // else if (strcmp(operation.c_str(),"SetOrGetPixel") == 0 ) SetOrGetPixel<4>(argc,argv); else if (strcmp(operation.c_str(),"MakeImage") == 0 ) MakeImage<4>(argc,argv); else if (strcmp(operation.c_str(),"stack") == 0 ) StackImage<4>(argc,argv); else if (strcmp(operation.c_str(),"CompareHeadersAndImages") == 0 ) CompareHeadersAndImages<4>(argc,argv); else if (strcmp(operation.c_str(),"CountVoxelDifference") == 0 ) CountVoxelDifference<4>(argc,argv); else if (strcmp(operation.c_str(),"ConvertImageToFile") == 0 ) ConvertImageToFile<4>(argc,argv); else if (strcmp(operation.c_str(),"PValueImage") == 0 ) PValueImage<4>(argc,argv); else if (strcmp(operation.c_str(),"CorrelationUpdate") == 0 ) CorrelationUpdate<4>(argc,argv); else if (strcmp(operation.c_str(),"ConvertImageSetToMatrix") == 0 ) ConvertImageSetToMatrix<4>(argc,argv); else if (strcmp(operation.c_str(),"ConvertVectorToImage") == 0 ) ConvertVectorToImage<4>(argc,argv); else if (strcmp(operation.c_str(),"PropagateLabelsThroughMask") == 0 ) PropagateLabelsThroughMask<4>(argc,argv); else if (strcmp(operation.c_str(),"FastMarchingSegmentation") == 0 ) FastMarchingSegmentation<4>(argc,argv); else if (strcmp(operation.c_str(),"TriPlanarView") == 0 ) TriPlanarView<4>(argc,argv); else if (strcmp(operation.c_str(),"TruncateImageIntensity") == 0 ) TruncateImageIntensity<4>(argc,argv); else if (strcmp(operation.c_str(),"ExtractSlice") == 0) ExtractSlice<4>(argc,argv); else if (strcmp(operation.c_str(),"ConvertLandmarkFile") == 0) ConvertLandmarkFile<4>(argc,argv); else std::cout << " cannot find operation : " << operation << std::endl; break; default: std::cerr << " Dimension Not supported " << atoi(argv[1]) << std::endl; exit( 1 ); } return 0; } ants-1.9.2+svn680.dfsg/Examples/ImageSetStatistics.cxx000066400000000000000000000632521147325206600225430ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: ImageSetStatistics.cxx,v $ Language: C++ Date: $Date: 2009/05/15 13:23:53 $ Version: $Revision: 1.2 $ Copriyght (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include #include #include #include #include "ReadWriteImage.h" //#include "DoSomethingToImage.cxx" #include "itkMersenneTwisterRandomVariateGenerator.h" #include "itkHistogramMatchingImageFilter.h" #include "itkMinimumMaximumImageFilter.h" #include "itkConnectedComponentImageFilter.h" #include "itkRelabelComponentImageFilter.h" #include "itkBinaryThresholdImageFilter.h" #include "itkLabelStatisticsImageFilter.h" #include "itkNeighborhoodIterator.h" // RecursiveAverageImages img1 img2 weight // We divide the 2nd input image by its mean and add it to the first // input image with weight 1/n. //The output overwrites the 1st img with the sum. //Note: could easily add variance computation //http://people.revoledu.com/kardi/tutorial/RecursiveStatistic/Time-Variance.htm #include "itkDiscreteGaussianImageFilter.h" template void ReadImage(itk::SmartPointer &target, const char *file, bool copy) { // std::cout << " reading b " << std::string(file) << std::endl; typedef itk::ImageFileReader readertype; typename readertype::Pointer reader = readertype::New(); reader->SetFileName(file); reader->Update(); if (!copy) target=(reader->GetOutput() ); else { typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter2( target, target->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { vfIter2.Set( reader->GetOutput()->GetPixel(vfIter2.GetIndex() )); } } } double TProb(double t, int df) { if (t == 0) return 0; double a = 0.36338023; double w = atan(t / sqrt((double)df)); double s = sin(w); double c = cos(w); double t1, t2; int j1, j2, k2; if (df % 2 == 0) // even { t1 = s; if (df == 2) // special case df=2 return (0.5 * (1 + t1)); t2 = s; j1 = -1; j2 = 0; k2 = (df - 2) / 2; } else { t1 = w; if (df == 1) // special case df=1 return 1 - (0.5 * (1 + (t1 * (1 - a)))); t2 = s * c; t1 = t1 + t2; if (df == 3) // special case df=3 return 1 - (0.5 * (1 + (t1 * (1 - a)))); j1 = 0; j2 = 1; k2 = (df - 3)/2; } for (int i=1; i >= k2; i++) { j1 = j1 + 2; j2 = j2 + 2; t2 = t2 * c * c * j1/j2; t1 = t1 + t2; } return 1 - (0.5 * (1 + (t1 * (1 - a * (df % 2))))); } template typename TImage::Pointer SmoothImage(typename TImage::Pointer image, float sig) { typedef itk::DiscreteGaussianImageFilter dgf; typename dgf::Pointer filter = dgf::New(); filter->SetVariance(sig); filter->SetUseImageSpacingOn(); filter->SetMaximumError(.01f); filter->SetInput(image); filter->Update(); return filter->GetOutput(); } template //typename TInputImage::Pointer void HistogramMatch(typename TInputImage::Pointer m_InputFixedImage, typename TInputImage::Pointer m_InputMovingImage)// typename TInputImage::Pointer m_OutputMovingImage ) { std::cout << " MATCHING INTENSITIES " << std::endl; typedef itk::HistogramMatchingImageFilter FilterType; typename FilterType::Pointer filter = FilterType::New(); filter->SetInput( m_InputMovingImage ); filter->SetReferenceImage( m_InputFixedImage ); filter->SetNumberOfHistogramLevels( 256 ); filter->SetNumberOfMatchPoints( 10 ); filter->ThresholdAtMeanIntensityOn(); filter->ThresholdAtMeanIntensityOff(); filter->Update(); typename TInputImage::Pointer img = filter->GetOutput(); typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter( img, img->GetLargestPossibleRegion() ); for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) { m_InputMovingImage->SetPixel(vfIter.GetIndex(), vfIter.Get() ); } return; } template void LocalMean(typename TImage::Pointer image, unsigned int nhood, typename TImage::Pointer meanimage ) { typename TImage::Pointer localmean=MakeNewImage(image,0); typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator outIter(image, image->GetLargestPossibleRegion() ); typename TImage::SizeType imagesize=image->GetLargestPossibleRegion().GetSize(); const unsigned int ImageDimension = 3; typedef itk::NeighborhoodIterator iteratorType; typename iteratorType::RadiusType rad; for( unsigned int j = 0; j < ImageDimension; j++ ) rad[j] = nhood; for( outIter.GoToBegin(); !outIter.IsAtEnd(); ++outIter ) { itk::NeighborhoodIterator hoodIt( rad,image ,image->GetLargestPossibleRegion()); typename TImage::IndexType oindex = outIter.GetIndex(); hoodIt.SetLocation(oindex); double fixedMean=0; //double movingMean=0; unsigned int indct; unsigned int hoodlen=hoodIt.Size(); //unsigned int inct=0; bool takesample = true; //double sumj=0; //double sumi=0; if (takesample) { //double sumj=0; double sumi=0; unsigned int cter=0; for(indct=0; indct static_cast(imagesize[dd]-1) ) inimage=false; } if (inimage) { sumi+=image->GetPixel(index); cter++; } } if (cter > 0) fixedMean=sumi/(float)cter; } float val = image->GetPixel(oindex) - fixedMean; meanimage->SetPixel( oindex, meanimage->GetPixel(oindex) +fixedMean); localmean->SetPixel( oindex, val ); } typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter( image, image->GetLargestPossibleRegion() ); for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) { vfIter.Set( localmean->GetPixel( vfIter.GetIndex() )); } return;// localmean; } template //std::vector float GetClusterStat(typename TImage::Pointer image, float Tthreshold, unsigned int minSize, unsigned int whichstat, std::string outfn, bool TRUTH) { typedef float InternalPixelType; typedef TImage InternalImageType; typedef TImage OutputImageType; typedef itk::BinaryThresholdImageFilter< InternalImageType, InternalImageType > ThresholdFilterType; typedef itk::ConnectedComponentImageFilter< InternalImageType, OutputImageType > FilterType; typedef itk::RelabelComponentImageFilter< OutputImageType, OutputImageType > RelabelType; typename ThresholdFilterType::Pointer threshold = ThresholdFilterType::New(); typename FilterType::Pointer filter = FilterType::New(); typename RelabelType::Pointer relabel = RelabelType::New(); InternalPixelType threshold_low, threshold_hi; threshold_low = Tthreshold; threshold_hi = 1.e9; threshold->SetInput (image); threshold->SetInsideValue(itk::NumericTraits::One); threshold->SetOutsideValue(itk::NumericTraits::Zero); threshold->SetLowerThreshold(threshold_low); threshold->SetUpperThreshold(threshold_hi); threshold->Update(); filter->SetInput (threshold->GetOutput()); // if (argc > 5) { int fullyConnected = 1;//atoi( argv[5] ); filter->SetFullyConnected( fullyConnected ); } relabel->SetInput( filter->GetOutput() ); relabel->SetMinimumObjectSize( minSize ); // relabel->SetUseHistograms(true); try { relabel->Update(); } catch( itk::ExceptionObject & excep ) { std::cerr << "Relabel: exception caught !" << std::endl; std::cerr << excep << std::endl; } typename TImage::Pointer Clusters=MakeNewImage(relabel->GetOutput(),0); //typename TImage::Pointer Clusters=relabel->GetOutput(); typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter( relabel->GetOutput(), relabel->GetOutput()->GetLargestPossibleRegion() ); /* typename itk::LabelStatisticsImageFilter::Pointer labstat= itk::LabelStatisticsImageFilter::New(); labstat->SetInput(image); labstat->SetLabelImage(Clusters); labstat->SetUseHistograms(true); labstat->Update(); typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter( Clusters, Clusters->GetLargestPossibleRegion() ); float maximum=0; // Relabel the Clusters image with the right statistic for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) { if (relabel->GetOutput()->GetPixel(vfIter.GetIndex()) > 0 ) { float pix = relabel->GetOutput()->GetPixel(vfIter.GetIndex()); float val; if (whichstat == 0) val=pix; else if (whichstat == 1) val = labstat->GetSum(pix); else if (whichstat == 2) val = labstat->GetMean(pix); else if (whichstat == 3) val = labstat->GetMaximum(pix); if (val > maximum) maximum=val; vfIter.Set(val); } } */ float maximum=relabel->GetNumberOfObjects(); float maxtstat=0; std::vector histogram((int)maximum+1); std::vector clustersum((int)maximum+1); for (int i=0; i<=maximum; i++) { histogram[i]=0; clustersum[i]=0; } for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) { if (vfIter.Get() > 0 ) { float vox=image->GetPixel(vfIter.GetIndex()); histogram[(unsigned int)vfIter.Get()]=histogram[(unsigned int)vfIter.Get()]+1; clustersum[(unsigned int)vfIter.Get()]+=vox; if (vox > maxtstat) maxtstat=vox; } } for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) { if (vfIter.Get() > 0 ) { if (whichstat == 0) //size { Clusters->SetPixel( vfIter.GetIndex(), histogram[(unsigned int)vfIter.Get()] ); } if (whichstat == 1) //sum { Clusters->SetPixel( vfIter.GetIndex(), clustersum[(unsigned int)vfIter.Get()] ); } if (whichstat == 2) //mean { Clusters->SetPixel( vfIter.GetIndex(), clustersum[(unsigned int)vfIter.Get()]/(float)histogram[(unsigned int)vfIter.Get()] ); } if (whichstat == 3) //max { Clusters->SetPixel( vfIter.GetIndex(), histogram[(unsigned int)vfIter.Get()] ); } } else Clusters->SetPixel(vfIter.GetIndex(),0); } // for (int i=0; i<=maximum; i++) // std::cout << " label " << i << " ct is: " << histogram[i] << std::endl; if (TRUTH) { typedef itk::ImageFileWriter writertype; typename writertype::Pointer writer = writertype::New(); writer->SetFileName( (outfn+std::string("Clusters.nii")).c_str()); writer->SetInput( Clusters ); writer->Write(); } if (whichstat == 0) return histogram[1]; else if (whichstat == 1) { float mx=0; for (int i=1; i<=maximum; i++) if (clustersum[i] > mx) mx=clustersum[i]; return mx; } else if (whichstat == 2) { float mx=0; for (int i=1; i<=maximum; i++) if (clustersum[i]/(float)histogram[i] > mx) mx=clustersum[i]/(float)histogram[i]*1000.0; return mx; } else if (whichstat == 3) return maxtstat*1000.0; else return histogram[1]; } float median(std::vector vec) { typedef std::vector::size_type vec_sz; vec_sz size = vec.size(); if (size == 0) return 0; // throw domain_error("median of an empty vector"); sort(vec.begin(),vec.end()); vec_sz mid = size/2; return size % 2 == 0 ? (vec[mid] + vec[mid-1]) / 2 : vec[mid]; } float npdf(std::vector vec, bool opt, float www) { typedef std::vector::size_type vec_sz; vec_sz size = vec.size(); if (size == 0) return 0; // throw domain_error("median of an empty vector"); float mean=0,var=0; float max=-1.e9,min=1.e9; for (unsigned int i=0; i max) max=val; else if (val < min) min=val; float n = (float) (i+1); float wt1 = 1.0/(float)n; float wt2 = 1.0-wt1; mean = mean*wt2+val*wt1; if ( i > 0) { float wt3 = 1.0/( (float) n - 1.0 ); var = var*wt2 + ( val - mean )*( val - mean)*wt3; } } if (var == 0) return mean; // else std::cout << " Mean " << mean << " var " << var << std::endl; // eval parzen probability std::vector prob(size); float maxprob=0; // float maxprobval=0; float weightedmean=0; float weighttotal=0; unsigned int maxprobind=0; // float sample=0.0; float width; if (www > 0 ) width = www; else width = sqrt(var)/2.0; // std::cout << " using width " << width << std::endl; // float N=(float)size; for (unsigned int j=0; j maxprob){ maxprob=total; maxprobind=j; } weightedmean+=sample*total; weighttotal+=total; // for (unsigned int i=0; i vec) { typedef std::vector::size_type vec_sz; vec_sz size = vec.size(); if (size == 0) return 0; // throw domain_error("median of an empty vector"); sort(vec.begin(),vec.end()); vec_sz mid = size/2; float lo=mid-(0.1*size+1); float hi=mid+(0.1*size+1); lo=0; hi=size; float ct=hi-lo; float total=0; for (unsigned int i=(unsigned int)lo; i vec) { typedef std::vector::size_type vec_sz; vec_sz size = vec.size(); if (size == 0) return 0; float max=-1.e9; for (unsigned int i=0; i max) max=val; } return max; } float myantssimilaritymaxlabel(std::vector labelvec, std::vector similarityvec, bool opt) { typedef std::vector::size_type vec_sz; vec_sz size = labelvec.size(); if (size == 0) return 0; unsigned int max=0; float maxsim=-1.e9; float totalsim=0; float estapp=0; for (unsigned int i=0; i maxsim) { maxsim=simval; max=i; } // std::cout << " simval " << simval << " i " << i << " appval " << appval << " maxsim " << maxsim << " max " << max << std::endl; } // std::cout <<" estapp " << estapp << " max " << max << std::endl; //return estapp; if ( opt == true ) return labelvec[max]; else return max; } template int ImageSetStatistics(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; unsigned int mch=0; int argct=2; std::string fn1 = std::string(argv[argct]); argct++; std::string outfn = std::string(argv[argct]);argct++; unsigned int whichstat = atoi(argv[argct]);argct++; std::string roifn=""; if (argc > argct){ roifn=std::string(argv[argct]); argct++; } std::string simimagelist=std::string(""); if (argc > argct){ simimagelist=std::string(argv[argct]); argct++; } float www = 0; // if (argc > argct) { www=atof(argv[argct]);argct++;} // unsigned int mchmax= 0; // if (argc > argct) { mchmax=atoi(argv[argct]); argct++;} unsigned int localmeanrad= 0; // if (argc > argct) { localmeanrad=atoi(argv[argct]);argct++;} // std::cout <<" roifn " << roifn << " fn1 " << fn1 << " whichstat " << whichstat << std::endl; typename ImageType::Pointer outimage = NULL; typename ImageType::Pointer ROIimg = NULL; if (roifn.length() > 4) { std::cout <<" reading roi image " << roifn << std::endl; typename readertype::Pointer reader2 = readertype::New(); reader2->SetFileName(roifn.c_str()); reader2->UpdateLargestPossibleRegion(); try { ROIimg = reader2->GetOutput(); } catch(...) { ROIimg=NULL; std::cout << " Error reading ROI image " << std::endl; // return 0; } } // now do the recursive average const unsigned int maxChar = 512; char lineBuffer[maxChar]; char filenm[maxChar]; unsigned int filecount1=0; { std::ifstream inputStreamA( fn1.c_str(), std::ios::in ); if ( !inputStreamA.is_open() ) { std::cout << "Can't open parameter file: " << fn1 << std::endl; return -1; } while ( !inputStreamA.eof() ) { inputStreamA.getline( lineBuffer, maxChar, '\n' ); if ( sscanf( lineBuffer, "%s ",filenm) != 1 ) { // std::cout << "Done. read " << lineBuffer << " n " << ct1 << " files " << std::endl; //std::cout << std::endl; continue; } else { filecount1++; } } inputStreamA.close(); } std::cout << " NFiles1 " << filecount1 << std::endl; unsigned int filecount2=0; if ( simimagelist.length() > 2 && ( whichstat == 5 || whichstat == 6 ) ) { std::ifstream inputStreamA( simimagelist.c_str(), std::ios::in ); if ( !inputStreamA.is_open() ) { std::cout << "Can't open parameter file: " << fn1 << std::endl; return -1; } while ( !inputStreamA.eof() ) { inputStreamA.getline( lineBuffer, maxChar, '\n' ); if ( sscanf( lineBuffer, "%s ",filenm) != 1 ) { // std::cout << "Done. read " << lineBuffer << " n " << ct1 << " files " << std::endl; //std::cout << std::endl; continue; } else { filecount2++; } } inputStreamA.close(); if ( filecount1 != filecount2 ) { std::cout << " the number of similarity images does not match the number of label images --- thus, we have to get out of here !! i.e. something's wrong. " << std::endl; return 1; } } // fi simimagelist std::cout << " NFiles2 " << filecount2 << std::endl; typename ImageType::Pointer meanimage; std::vector imagestack; imagestack.resize(filecount1); // imagestack.fill(NULL); std::vector filenames(filecount1); typename ImageType::Pointer StatImage; unsigned int ct = 0; std::ifstream inputStreamA( fn1.c_str(), std::ios::in ); if ( !inputStreamA.is_open() ) { std::cout << "Can't open parameter file: " << fn1 << std::endl; return -1; } while ( !inputStreamA.eof() ) { inputStreamA.getline( lineBuffer, maxChar, '\n' ); if ( sscanf( lineBuffer, "%s ",filenm) != 1 ) { // std::cout << "Done. read " << lineBuffer << " n " << ct1 << " files " << std::endl; //std::cout << std::endl; continue; } else { filenames[ct]=std::string(filenm); ReadImage(imagestack[ct] , filenm ,false); if (ct ==0) meanimage=MakeNewImage(imagestack[ct],0); if (localmeanrad > 0) LocalMean(imagestack[ct],localmeanrad,meanimage); std::cout << " done reading " << (float) ct / (float ) filecount1 << std::endl; ct++; } } inputStreamA.close(); // read similarity images, if needed std::vector simimagestack; simimagestack.resize(filecount2); std::vector simfilenames(filecount2); ct = 0; if ( simimagelist.length() > 2 && ( whichstat == 5 || whichstat == 6 ) ) { std::ifstream inputStreamA( simimagelist.c_str(), std::ios::in ); if ( !inputStreamA.is_open() ) { std::cout << "Can't open parameter file: " << fn1 << std::endl; return -1; } while ( !inputStreamA.eof() ) { inputStreamA.getline( lineBuffer, maxChar, '\n' ); if ( sscanf( lineBuffer, "%s ",filenm) != 1 ) { continue; } else { simfilenames[ct]=std::string(filenm); ReadImage(simimagestack[ct] , filenm ,false); ct++; } } inputStreamA.close(); } // fi read similarity images ReadImage( StatImage , filenames[0].c_str() , false); Iterator vfIter(StatImage , StatImage->GetLargestPossibleRegion() ); std::vector voxels(filecount1); std::vector similarities(filecount2); unsigned long nvox=1; for (unsigned int i=0; iGetLargestPossibleRegion().GetSize()[i]; ct=0; unsigned long prog = nvox/15; for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) { if (ct % prog == 0) std::cout << " % " << (float) ct / (float) nvox << std::endl; ct++; IndexType ind=vfIter.GetIndex(); unsigned int maxval=0; bool takesample=true; if ( ROIimg ) { if ( ROIimg->GetPixel(ind) < 0.5 ) takesample=false; else maxval=(unsigned int)(ROIimg->GetPixel(ind)-1); } if ( takesample ) { if (mch == 0) meanimage->SetPixel(ind, meanimage->GetPixel(ind)/filecount1 ); for (unsigned int j=0; jGetPixel(ind); } for (unsigned int j=0; jGetPixel(ind); } float stat = 0; switch( whichstat ) { case 1: stat=npdf(voxels,true,www); if (ct == 1) std::cout << "the max prob appearance \n"; break; case 2: stat=npdf(voxels,false,www); if (ct == 1) std::cout << "the probabilistically weighted appearance " << www << " \n"; break; case 3: stat=trimmean(voxels); if (ct == 1) std::cout << "the trimmed mean appearance \n"; break; case 4: stat=myantsmax(voxels); if (ct == 1) std::cout << "the maximum appearance \n"; break; case 5: stat=myantssimilaritymaxlabel(voxels,similarities,true); if (ct == 1) std::cout << "the maximum similarity-based label \n"; break; case 6: stat=myantssimilaritymaxlabel(voxels,similarities,false); if (ct == 1) std::cout << "which image provides the maximum similarity-based label \n"; break; case 7: stat=voxels[maxval]; if (ct == 1) std::cout << "which image provides the maximum similarity-based label \n"; break; default: stat=median(voxels); if (ct == 1) std::cout << "the median appearance \n"; break; } float sval=stat; if ( localmeanrad > 0 ) sval+= meanimage->GetPixel(ind); StatImage->SetPixel(ind, sval); } else StatImage->SetPixel(ind, 0); } WriteImage(StatImage, outfn.c_str() ); std::cout << " Done " << std::endl; return 0; } int main( int argc, char * argv[] ) { if ( argc < 4 ) { std::cout << "Usage: "<< std::endl; std::cout << argv[0] << " ImageDimension controlslist.txt outimage.nii whichstat {roi.nii} {imagelist2forsimilarityweightedstats.txt}" << std::endl; std::cout << " whichstat = 0: median, 1: max prob appearance , 2: weighted mean appearance , 3: trimmed mean , 4 : max value , option 5 : similarity-weighted (must pass imagelist2 as well) else median , option 6 : same as similarity-weighted option 5 but the label corresponds to the image that provides the best local match ... useful if you want to MRF smooth these indices , option 7 : similar to 5 but expects the max-value to be stored in the ROI image and uses it to get the intensity ... " << std::endl; std::cout << " example: ImageSetStatistics 3 imagelist.txt maxvalueimage.nii.gz 4 " << std::endl; std::cout << " similarity weighted --- pass in a list of similarity images here which will be used to select the best label --- thus, number of similarity images must match the number of label images . " << std::endl; return 1; } // Get the image dimension switch ( atoi(argv[1])) { case 2: ImageSetStatistics<2>(argc,argv); break; case 3: ImageSetStatistics<3>(argc,argv); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } return 0; } ants-1.9.2+svn680.dfsg/Examples/KellySlater.cxx000066400000000000000000001236621147325206600212270ustar00rootroot00000000000000//#include "DoSomethingToImage.cxx" #include "itkVectorIndexSelectionCastImageFilter.h" #include "itkImageRegionIteratorWithIndex.h" #include "vnl/algo/vnl_determinant.h" #include "itkWarpImageFilter.h" #include "itkWarpImageMultiTransformFilter.h" #include "itkDeformationFieldFromMultiTransformFilter.h" #include "itkImageFileWriter.h" #include "itkANTSImageRegistrationOptimizer.h" #include "vnl/algo/vnl_determinant.h" #include "itkDiscreteGaussianImageFilter.h" #include "itkVectorLinearInterpolateImageFunction.h" #include "itkBinaryThresholdImageFilter.h" #include "itkCovariantVector.h" #include "itkGradientRecursiveGaussianImageFilter.h" #include "itkVectorCurvatureAnisotropicDiffusionImageFilter.h" #include "itkBinaryErodeImageFilter.h" #include "itkBinaryDilateImageFilter.h" #include "itkBinaryBallStructuringElement.h" #include "ReadWriteImage.h" #include "itkSignedMaurerDistanceMapImageFilter.h" #include "itkGradientRecursiveGaussianImageFilter.h" #include "itkCentralDifferenceImageFunction.h" #include "itkSurfaceCurvatureBase.h" #include "itkSurfaceImageCurvature.h" template typename TImage::Pointer GetVectorComponent(typename TField::Pointer field, unsigned int index) { // Initialize the Moving to the displacement field typedef TField FieldType; typedef TImage ImageType; typename ImageType::Pointer sfield=ImageType::New(); sfield->SetSpacing( field->GetSpacing() ); sfield->SetOrigin( field->GetOrigin() ); sfield->SetDirection( field->GetDirection() ); sfield->SetLargestPossibleRegion(field->GetLargestPossibleRegion() ); sfield->SetRequestedRegion(field->GetRequestedRegion() ); sfield->SetBufferedRegion( field->GetBufferedRegion() ); sfield->Allocate(); typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter( field, field->GetLargestPossibleRegion() ); for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter) { typename TField::PixelType v1=vfIter.Get(); sfield->SetPixel(vfIter.GetIndex(),v1[index]); } return sfield; } template typename TImage::Pointer BinaryThreshold( typename TImage::PixelType low, typename TImage::PixelType high, typename TImage::PixelType replaceval, typename TImage::Pointer input) { //std::cout << " Binary Thresh " << std::endl; typedef typename TImage::PixelType PixelType; // Begin Threshold Image typedef itk::BinaryThresholdImageFilter InputThresholderType; typename InputThresholderType::Pointer inputThresholder = InputThresholderType::New(); inputThresholder->SetInput( input ); inputThresholder->SetInsideValue( replaceval ); int outval=0; if ((float) replaceval == (float) -1) outval=1; inputThresholder->SetOutsideValue( outval ); if (high < low) high=255; inputThresholder->SetLowerThreshold((PixelType) low ); inputThresholder->SetUpperThreshold((PixelType) high); inputThresholder->Update(); return inputThresholder->GetOutput(); } template typename TImage::Pointer MaurerDistanceMap( typename TImage::PixelType pixlo, typename TImage::PixelType pixhi, typename TImage::Pointer input) { //std::cout << " DDMap " << std::endl; typedef TImage ImageType; typedef itk::SignedMaurerDistanceMapImageFilter< ImageType, ImageType > FilterType; typename FilterType::Pointer filter = FilterType::New(); filter->SetSquaredDistance(false); // filter->InputIsBinaryOn(); filter->SetUseImageSpacing(true); filter->SetBackgroundValue(0); filter->SetInput(BinaryThreshold(pixlo,pixhi,pixhi,input)); filter->Update(); // WriteImage(filter->GetOutput(),"temp1.nii"); return filter->GetOutput(); } template typename TImage::Pointer SmoothImage(typename TImage::Pointer image, float sig) { // find min value typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter(image,image->GetLargestPossibleRegion() ); for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter) { typename TImage::PixelType v1=vfIter.Get(); if (vnl_math_isnan(v1)) vfIter.Set(0); } typedef itk::DiscreteGaussianImageFilter dgf; typename dgf::Pointer filter = dgf::New(); filter->SetVariance(sig); filter->SetUseImageSpacingOn(); filter->SetMaximumError(.01f); filter->SetInput(image); filter->Update(); typename TImage::Pointer out= filter->GetOutput(); return out; } template void SmoothDeformation(typename TImage::Pointer vectorimage, float sig) { enum { ImageDimension = TImage::ImageDimension }; typedef itk::Vector VectorType; typedef itk::Image ImageType; typename ImageType::Pointer subimgx=GetVectorComponent(vectorimage,0); subimgx=SmoothImage(subimgx,sig); typename ImageType::Pointer subimgy=GetVectorComponent(vectorimage,1); subimgy=SmoothImage(subimgy,sig); typename ImageType::Pointer subimgz=GetVectorComponent(vectorimage,2); subimgz=SmoothImage(subimgz,sig); typedef itk::ImageRegionIteratorWithIndex IteratorType; IteratorType Iterator( vectorimage, vectorimage->GetLargestPossibleRegion().GetSize() ); Iterator.GoToBegin(); while( !Iterator.IsAtEnd() ) { VectorType vec; vec[0]=subimgx->GetPixel(Iterator.GetIndex()); vec[1]=subimgy->GetPixel(Iterator.GetIndex()); vec[2]=subimgz->GetPixel(Iterator.GetIndex()); Iterator.Set(vec); ++Iterator; } return; } template typename TImage::Pointer ComputeJacobian(TDeformationField* field ) { typedef TImage ImageType; enum { ImageDimension = TImage::ImageDimension }; // unsigned int row=0; //unsigned int col=0; typedef itk::Image FloatImageType; typename FloatImageType::RegionType m_JacobianRegion; typename TImage::SizeType s= field->GetLargestPossibleRegion().GetSize(); typename TImage::SpacingType sp= field->GetSpacing(); typename FloatImageType::Pointer m_FloatImage=NULL; m_FloatImage = FloatImageType::New(); m_FloatImage->SetLargestPossibleRegion( field->GetLargestPossibleRegion() ); m_FloatImage->SetBufferedRegion( field->GetLargestPossibleRegion().GetSize() ); m_FloatImage->SetSpacing(field->GetSpacing()); m_FloatImage->SetDirection( field->GetDirection() ); m_FloatImage->SetOrigin(field->GetOrigin()); m_FloatImage->Allocate(); m_FloatImage->FillBuffer(0); typename FloatImageType::SizeType m_FieldSize = field->GetLargestPossibleRegion().GetSize(); typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator wimIter( m_FloatImage, m_FloatImage->GetLargestPossibleRegion() ); wimIter.GoToBegin(); for( ; !wimIter.IsAtEnd(); ++wimIter ) wimIter.Set(1.0); typedef vnl_matrix MatrixType; MatrixType jMatrix,idMatrix,avgMatrix; jMatrix.set_size(ImageDimension,ImageDimension); avgMatrix.set_size(ImageDimension,ImageDimension); avgMatrix.fill(0); itk::ImageRegionIteratorWithIndex m_FieldIter( field, field->GetLargestPossibleRegion() ); typename TImage::IndexType rindex; typename TImage::IndexType ddrindex; typename TImage::IndexType ddlindex; typename TImage::IndexType difIndex[ImageDimension][2]; double det=0.0; unsigned int posoff=1; float difspace=1.0; float space=1.0; if (posoff == 0) difspace=1.0; typedef itk::Vector VectorType; typedef itk::Image FieldType; typename FieldType::PixelType dPix; typename FieldType::PixelType lpix; typename FieldType::PixelType llpix; typename FieldType::PixelType rpix; typename FieldType::PixelType rrpix; typename FieldType::PixelType cpix; float volumeelt=1.0; for (int j=0;jGetPixel(rindex); for(unsigned int row=0; row< ImageDimension;row++) { difIndex[row][0]=rindex; difIndex[row][1]=rindex; ddrindex=rindex; ddlindex=rindex; if ((int) rindex[row] < (int)(m_FieldSize[row]-2) ) { difIndex[row][0][row]=rindex[row]+posoff; ddrindex[row]=rindex[row]+posoff*2; } if (rindex[row] > 1 ) { difIndex[row][1][row]=rindex[row]-1; ddlindex[row]=rindex[row]-2; } float h=0.5; space=1.0; // should use image spacing here? rpix = field->GetPixel(difIndex[row][1]); rpix = rpix*h+cpix*(1.-h); lpix = field->GetPixel(difIndex[row][0]); lpix = lpix*h+cpix*(1.-h); // dPix = ( rpix - lpix)*(1.0)/(2.0); rrpix = field->GetPixel(ddrindex); rrpix = rrpix*h+rpix*(1.-h); llpix = field->GetPixel(ddlindex); llpix = llpix*h+lpix*(1.-h); dPix=( lpix*(-8.0) + rpix*8.0 - rrpix + llpix )*(-1.0)*space/(12.0); //4th order centered difference //dPix=( lpix - rpix )*(1.0)*space/(2.0*h); //4th order centered difference for(unsigned int col=0; col< ImageDimension;col++){ float val; if (row == col) val=dPix[col]/sp[col]+1.0; else val = dPix[col]/sp[col]; // std::cout << " row " << row << " col " << col << " val " << val << std::endl; jMatrix.put(col,row,val); avgMatrix.put(col,row,avgMatrix.get(col,row)+val); } } //the determinant of the jacobian matrix // std::cout << " get det " << std::endl; det = vnl_determinant(jMatrix); // float prodval = m_FloatImage->GetPixel(rindex); if (det < 0.0) det = 0; m_FloatImage->SetPixel(rindex, det ); //totaljac+=det; }//oktosample if } return m_FloatImage; } template typename TImage::Pointer LabelSurface(typename TImage::PixelType foreground, typename TImage::PixelType newval, typename TImage::Pointer input, float distthresh ) { std::cout << " Label Surf " << std::endl; typedef TImage ImageType; enum { ImageDimension = ImageType::ImageDimension }; typename ImageType::Pointer Image = ImageType::New(); Image->SetLargestPossibleRegion(input->GetLargestPossibleRegion() ); Image->SetBufferedRegion(input->GetLargestPossibleRegion()); Image->Allocate(); Image->SetSpacing(input->GetSpacing()); Image->SetOrigin(input->GetOrigin()); typedef itk::NeighborhoodIterator iteratorType; typename iteratorType::RadiusType rad; for (int j=0; jGetLargestPossibleRegion()); GHood.GoToBegin(); // std::cout << " foreg " << (int) foreground; while (!GHood.IsAtEnd()) { typename TImage::PixelType p = GHood.GetCenterPixel(); typename TImage::IndexType ind = GHood.GetIndex(); typename TImage::IndexType ind2; if ( p == foreground ) { bool atedge=false; for (unsigned int i = 0; i < GHood.Size(); i++) { ind2=GHood.GetIndex(i); float dist=0.0; for (int j=0; jSetPixel(ind,newval); else Image->SetPixel(ind,0); } ++GHood; } return Image; } template typename TImage::Pointer SpeedPrior(typename TImage::Pointer image1 , typename TImage::Pointer wmimage, typename TImage::Pointer surf ) { typedef TImage ImageType; enum { ImageDimension = ImageType::ImageDimension }; typedef itk::SurfaceImageCurvature ParamType; typename ParamType::Pointer Parameterizer=ParamType::New(); float sig=1.5; Parameterizer->SetInput(wmimage); Parameterizer->SetNeighborhoodRadius( 1. ); Parameterizer->SetSigma(sig); Parameterizer->SetUseLabel(false); Parameterizer->SetUseGeodesicNeighborhood(false); float sign=1.0; Parameterizer->SetkSign(sign); Parameterizer->SetThreshold(0); Parameterizer->ComputeFrameOverDomain( 3 ); typename ImageType::Pointer outimage=Parameterizer->GetFunctionImage(); float max=0; float min=1.e9; float mean = 0.0; unsigned long ct = 0; typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator iter( outimage, outimage->GetLargestPossibleRegion() ); for( iter.GoToBegin(); !iter.IsAtEnd(); ++iter ) { float pix=iter.Get(); mean+=pix; ct++; if (pix > max ) max=pix; if (pix < min) min=pix; } mean/=(float)ct; for( iter.GoToBegin(); !iter.IsAtEnd(); ++iter ) { float pix=iter.Get(); pix= (pix - min)/(max-min); iter.Set(pix); } return outimage; } template typename TImage::Pointer Morphological( typename TImage::Pointer input,float rad, bool option) { typedef TImage ImageType; enum { ImageDimension = TImage::ImageDimension }; typedef typename TImage::PixelType PixelType; if (!option) std::cout << " eroding the image " << std::endl; else std::cout << " dilating the image " << std::endl; typedef itk::BinaryBallStructuringElement< PixelType, ImageDimension > StructuringElementType; // Software Guide : BeginCodeSnippet typedef itk::BinaryErodeImageFilter< TImage, TImage, StructuringElementType > ErodeFilterType; typedef itk::BinaryDilateImageFilter< TImage, TImage, StructuringElementType > DilateFilterType; typename ErodeFilterType::Pointer binaryErode = ErodeFilterType::New(); typename DilateFilterType::Pointer binaryDilate = DilateFilterType::New(); StructuringElementType structuringElement; structuringElement.SetRadius((unsigned long) rad ); // 3x3x3 structuring element structuringElement.CreateStructuringElement(); binaryErode->SetKernel( structuringElement ); binaryDilate->SetKernel( structuringElement ); // It is necessary to define what could be considered objects on the binary // images. This is specified with the methods \code{SetErodeValue()} and // \code{SetDilateValue()}. The value passed to these methods will be // considered the value over which the dilation and erosion rules will apply binaryErode->SetErodeValue( 1 ); binaryDilate->SetDilateValue( 1 ); typename TImage::Pointer temp; if (option) { binaryDilate->SetInput( input ); binaryDilate->Update(); temp = binaryDilate->GetOutput(); } else { binaryErode->SetInput( input );//binaryDilate->GetOutput() ); binaryErode->Update(); temp = binaryErode->GetOutput(); typedef itk::ImageRegionIteratorWithIndex< ImageType > ImageIteratorType ; ImageIteratorType o_iter( temp, temp->GetLargestPossibleRegion() ); o_iter.GoToBegin() ; while ( !o_iter.IsAtEnd() ) { if (o_iter.Get() > 0.5 && input->GetPixel(o_iter.GetIndex()) > 0.5) o_iter.Set(1); else o_iter.Set(0); ++o_iter; } } return temp; } template typename TField::Pointer LaplacianGrad(typename TImage::Pointer wm,typename TImage::Pointer gm, float sig) { typedef typename TImage::IndexType IndexType; IndexType ind; typedef TImage ImageType; typedef TField GradientImageType; typedef itk::GradientRecursiveGaussianImageFilter< ImageType,GradientImageType > GradientImageFilterType; typedef typename GradientImageFilterType::Pointer GradientImageFilterPointer; typename TField::Pointer sfield=TField::New(); sfield->SetSpacing( wm->GetSpacing() ); sfield->SetOrigin( wm->GetOrigin() ); sfield->SetDirection( wm->GetDirection() ); sfield->SetLargestPossibleRegion(wm->GetLargestPossibleRegion() ); sfield->SetRequestedRegion(wm->GetRequestedRegion() ); sfield->SetBufferedRegion( wm->GetBufferedRegion() ); sfield->Allocate(); typename TImage::Pointer laplacian=SmoothImage(wm,3); laplacian->FillBuffer(0); typedef itk::ImageRegionIteratorWithIndex IteratorType; IteratorType Iterator( wm, wm->GetLargestPossibleRegion().GetSize() ); Iterator.GoToBegin(); //initialize L(wm)=1, L(gm)=0.5, else 0 while( !Iterator.IsAtEnd() ) { ind=Iterator.GetIndex(); if (wm->GetPixel(ind) ) laplacian->SetPixel(ind,1); else if (gm->GetPixel(ind) && wm->GetPixel(ind) == 0 ) laplacian->SetPixel(ind,0.); else laplacian->SetPixel(ind,0.); ++Iterator; } //smooth and then reset the values for(unsigned int iterations=0; iterations<100; iterations++) { laplacian=SmoothImage(laplacian,sqrt(sig)); Iterator.GoToBegin(); while( !Iterator.IsAtEnd() ) { ind=Iterator.GetIndex(); if (wm->GetPixel(ind) ) laplacian->SetPixel(ind,1); else if (gm->GetPixel(ind) ==0 && wm->GetPixel(ind) == 0 ) laplacian->SetPixel(ind,0.); ++Iterator; } } GradientImageFilterPointer filter=GradientImageFilterType::New(); filter->SetInput( laplacian ); filter->SetSigma(sig); filter->Update(); return filter->GetOutput(); } template typename TField::Pointer ExpDiffMap(typename TField::Pointer velofield, typename TImage::Pointer wm, float sign, unsigned int numtimepoints ) { typedef TImage ImageType; typedef TField DeformationFieldType; typename TField::PixelType zero,disp; enum { ImageDimension = TImage::ImageDimension }; disp.Fill(0); zero.Fill(0); typename DeformationFieldType::Pointer incrfield=DeformationFieldType::New(); incrfield->SetSpacing( velofield->GetSpacing() ); incrfield->SetOrigin( velofield->GetOrigin() ); incrfield->SetDirection( velofield->GetDirection() ); incrfield->SetLargestPossibleRegion(velofield->GetLargestPossibleRegion() ); incrfield->SetRequestedRegion(velofield->GetRequestedRegion() ); incrfield->SetBufferedRegion( velofield->GetBufferedRegion() ); incrfield->Allocate(); incrfield->FillBuffer(zero); typedef itk::ImageRegionIteratorWithIndex IteratorType; IteratorType Iterator( wm, wm->GetLargestPossibleRegion().GetSize() ); Iterator.GoToBegin(); while( !Iterator.IsAtEnd() ) { incrfield->SetPixel(Iterator.GetIndex(),velofield->GetPixel(Iterator.GetIndex())*sign); ++Iterator; } // generate phi typedef itk::MatrixOffsetTransformBase AffineTransformType; typedef itk::DeformationFieldFromMultiTransformFilter WarperType; typename WarperType::Pointer warper = WarperType::New(); warper->SetOutputSize(velofield->GetLargestPossibleRegion().GetSize()); warper->SetOutputSpacing(velofield->GetSpacing()); warper->SetOutputOrigin(velofield->GetOrigin()); warper->SetOutputDirection(velofield->GetDirection()); warper->DetermineFirstDeformNoInterp(); unsigned int ttiter=0; while( ttiter < numtimepoints ) // 10 time integration points { ttiter++; warper->PushBackDeformationFieldTransform(incrfield); } warper->Update(); return warper->GetOutput(); } template typename TField::Pointer DiReCTCompose(typename TField::Pointer velofield, typename TField::Pointer diffmap ) { typedef TImage ImageType; typedef TField DeformationFieldType; typename TField::PixelType zero,disp; enum { ImageDimension = TImage::ImageDimension }; disp.Fill(0); zero.Fill(0); typedef itk::MatrixOffsetTransformBase AffineTransformType; typedef itk::DeformationFieldFromMultiTransformFilter WarperType; typename WarperType::Pointer warper = WarperType::New(); warper->SetOutputSize(velofield->GetLargestPossibleRegion().GetSize()); warper->SetOutputSpacing(velofield->GetSpacing()); warper->SetOutputOrigin(velofield->GetOrigin()); warper->SetOutputDirection(velofield->GetDirection()); warper->DetermineFirstDeformNoInterp(); warper->PushBackDeformationFieldTransform(diffmap); warper->PushBackDeformationFieldTransform(velofield); warper->Update(); return warper->GetOutput(); } template int LaplacianThicknessExpDiff2(int argc, char *argv[]) { int argct=2; std::string segfn = std::string(argv[argct]); argct++; std::string wfn = std::string(argv[argct]); argct++; std::string gfn = std::string(argv[argct]); argct++; std::string outname = std::string(argv[argct]); argct++; unsigned int numtimepoints=10; float gradstep=(float)(-1.0)*0.5;//(ImageDimension-1); if (argc > argct) gradstep=atof(argv[argct])*(-1.0); gradstep*=1.0/(float)numtimepoints*10; argct++; unsigned int alltheits=50; if (argc > argct) alltheits=atoi(argv[argct]); argct++; float thickprior=6.0; if (argc > argct ) thickprior=atof(argv[argct]); argct++; bool useCurvaturePrior=false; if (argc > argct) useCurvaturePrior=atoi(argv[argct]); argct++; float smoothingsigma=1.5; if (argc > argct ) smoothingsigma=atof(argv[argct]); argct++; bool useEuclidean=true; if (argc > argct) useEuclidean=atoi(argv[argct]); argct++; std::cout <<" smooth " << smoothingsigma << " thp " << thickprior << " gs " << gradstep << std::endl; typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image DeformationFieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::Image tvt; typedef itk::ANTSImageRegistrationOptimizer ROType; typename ROType::Pointer m_MFR=ROType::New(); typename ImageType::Pointer segmentationimage; ReadImage(segmentationimage,segfn.c_str()); typename ImageType::Pointer wm; ReadImage(wm,wfn.c_str()); typename ImageType::DirectionType omat=wm->GetDirection(); typename ImageType::DirectionType fmat=wm->GetDirection(); fmat.SetIdentity(); std::cout <<" Setting Identity Direction " << fmat << std::endl; wm->SetDirection(fmat); typename ImageType::Pointer totalimage; ReadImage(totalimage,wfn.c_str()); totalimage->SetDirection(fmat); typename ImageType::Pointer hitimage; ReadImage(hitimage,wfn.c_str()); hitimage->SetDirection(fmat); typename ImageType::Pointer gm; ReadImage(gm,gfn.c_str()); gm->SetDirection(fmat); wm->SetDirection(fmat); segmentationimage->SetDirection(fmat); SpacingType spacing=wm->GetSpacing(); typename DeformationFieldType::Pointer lapgrad; typename ImageType::Pointer gmb=BinaryThreshold(2,2,1,segmentationimage); // fixme typename ImageType::Pointer wmb=BinaryThreshold(3,3,1,segmentationimage); // fixme typename ImageType::Pointer laplacian= SmoothImage(wm,smoothingsigma); lapgrad=LaplacianGrad(wmb,gmb,1); typename DeformationFieldType::Pointer corrfield=DeformationFieldType::New(); corrfield->SetSpacing( wm->GetSpacing() ); corrfield->SetOrigin( wm->GetOrigin() ); corrfield->SetDirection( wm->GetDirection() ); corrfield->SetLargestPossibleRegion(wm->GetLargestPossibleRegion() ); corrfield->SetRequestedRegion(wm->GetRequestedRegion() ); corrfield->SetBufferedRegion( wm->GetBufferedRegion() ); corrfield->Allocate(); VectorType zero; zero.Fill(0); corrfield->FillBuffer(zero); typename DeformationFieldType::Pointer incrfield=DeformationFieldType::New(); incrfield->SetSpacing( wm->GetSpacing() ); incrfield->SetOrigin( wm->GetOrigin() ); incrfield->SetDirection( wm->GetDirection() ); incrfield->SetLargestPossibleRegion(wm->GetLargestPossibleRegion() ); incrfield->SetRequestedRegion(wm->GetRequestedRegion() ); incrfield->SetBufferedRegion( wm->GetBufferedRegion() ); incrfield->Allocate(); incrfield->FillBuffer(zero); typename DeformationFieldType::Pointer invfield=DeformationFieldType::New(); invfield->SetSpacing( wm->GetSpacing() ); invfield->SetOrigin( wm->GetOrigin() ); invfield->SetDirection( wm->GetDirection() ); invfield->SetLargestPossibleRegion(wm->GetLargestPossibleRegion() ); invfield->SetRequestedRegion(wm->GetRequestedRegion() ); invfield->SetBufferedRegion( wm->GetBufferedRegion() ); invfield->Allocate(); invfield->FillBuffer(zero); typename DeformationFieldType::Pointer incrinvfield=DeformationFieldType::New(); incrinvfield->SetSpacing( wm->GetSpacing() ); incrinvfield->SetOrigin( wm->GetOrigin() ); incrinvfield->SetDirection( wm->GetDirection() ); incrinvfield->SetLargestPossibleRegion(wm->GetLargestPossibleRegion() ); incrinvfield->SetRequestedRegion(wm->GetRequestedRegion() ); incrinvfield->SetBufferedRegion( wm->GetBufferedRegion() ); incrinvfield->Allocate(); incrinvfield->FillBuffer(zero); typename DeformationFieldType::Pointer velofield=DeformationFieldType::New(); velofield->SetSpacing( wm->GetSpacing() ); velofield->SetOrigin( wm->GetOrigin() ); velofield->SetDirection( wm->GetDirection() ); velofield->SetLargestPossibleRegion(wm->GetLargestPossibleRegion() ); velofield->SetRequestedRegion(wm->GetRequestedRegion() ); velofield->SetBufferedRegion( wm->GetBufferedRegion() ); velofield->Allocate(); velofield->FillBuffer(zero); // LabelSurface(typename TImage::PixelType foreground, // typename TImage::PixelType newval, typename TImage::Pointer input, float distthresh ) float distthresh = 1.1; typename ImageType::Pointer wmgrow = Morphological(wmb,1,true); typename ImageType::Pointer bsurf = LabelSurface(1,1,wmgrow , distthresh); // or wmb ? typename ImageType::Pointer speedprior=NULL; if ( useCurvaturePrior ) speedprior=SpeedPrior(gm,wm,bsurf); //WriteImage(bsurf,"surf.hdr"); // typename DoubleImageType::Pointer distfromboundary = // typename ImageType::Pointer surf=MaurerDistanceMap(0.5,1.e9,bsurf); //surf= SmoothImage(surf,3); typename ImageType::Pointer finalthickimage=BinaryThreshold(3,3,1,segmentationimage); // fixme //typename ImageType::Pointer gmsurf = LabelSurface(1,1,gmb, distthresh); // gmsurf=MaurerDistanceMap(0.5,1.e9,gmsurf); // gmsurf= SmoothImage(gmsurf,3); typename ImageType::SizeType s= wm->GetLargestPossibleRegion().GetSize(); typename DeformationFieldType::IndexType velind; velind.Fill(0); typedef DeformationFieldType TimeVaryingVelocityFieldType; typedef itk::ImageRegionIteratorWithIndex FieldIterator; typedef typename DeformationFieldType::IndexType DIndexType; typedef typename DeformationFieldType::PointType DPointType; typedef typename TimeVaryingVelocityFieldType::IndexType VIndexType; typedef typename TimeVaryingVelocityFieldType::PointType VPointType; typedef itk::VectorLinearInterpolateImageFunction DefaultInterpolatorType; typedef itk::VectorLinearInterpolateImageFunction DefaultInterpolatorType2; typename DefaultInterpolatorType::Pointer vinterp = DefaultInterpolatorType::New(); vinterp->SetInputImage(lapgrad); typedef itk::LinearInterpolateImageFunction ScalarInterpolatorType; typename ScalarInterpolatorType::Pointer ginterp = ScalarInterpolatorType::New(); typename ScalarInterpolatorType::Pointer winterp = ScalarInterpolatorType::New(); winterp->SetInputImage(wm); ginterp->SetInputImage(gm); DPointType pointIn1; DPointType pointIn2; typename DefaultInterpolatorType::ContinuousIndexType vcontind; DPointType pointIn3; typename ImageType::Pointer surfdef; typedef itk::ImageRegionIteratorWithIndex IteratorType; typedef itk::ImageRegionIteratorWithIndex VIteratorType; VIteratorType VIterator( lapgrad, lapgrad->GetLargestPossibleRegion().GetSize() ); VIterator.GoToBegin(); while( !VIterator.IsAtEnd() ) { VectorType vec=VIterator.Get(); float mag=0; for (unsigned dd=0; dd 0) vec=vec/mag; VIterator.Set( (vec)*gradstep); ++VIterator; } // m_MFR->SmoothDeformationFieldGauss(lapgrad,1.7); std::cout << " Scaling done " << std::endl; // float thislength=0; unsigned long ct = 1; bool timedone = false; typename ImageType::Pointer thickimage=laplacian; VectorType disp; VectorType incdisp; disp.Fill(0.0); incdisp.Fill(0.0); IteratorType Iterator( wm, wm->GetLargestPossibleRegion().GetSize() ); timedone=false; float totalerr=1.e8, lasterr=1.e10; unsigned its=0; wmgrow->FillBuffer(0); float dmag=0; float thicknesserror=0; unsigned long thickerrct=0; unsigned int badct=0; float thickoffset=0; bool checknans=true; while ( its < alltheits && badct < 4) { its++; if (totalerr > lasterr) { badct++; std::cout << " badct " << badct << std::endl; } else badct=0; lasterr=totalerr; // Optimization Error initialized for this iteration totalerr=0; ct=0; incrfield->FillBuffer(zero); incrfield->FillBuffer(zero); incrinvfield->FillBuffer(zero); // generate phi corrfield->FillBuffer(zero); invfield->FillBuffer(zero); unsigned int ttiter=0; thickimage->FillBuffer(0); hitimage->FillBuffer(0); totalimage->FillBuffer(0); thicknesserror=0; thickerrct=1; bool debug=false; bool spatprior=false; typename ImageType::Pointer priorim=NULL; if (speedprior){ spatprior=true; priorim=speedprior; } typename ImageType::Pointer wpriorim=NULL; float origthickprior=thickprior; while( ttiter < numtimepoints ) // N time integration points { // m_MFR->Compose(incrinvfield,invfield,NULL); m_MFR->ComposeDiffs(invfield,incrinvfield,invfield,1); if (debug) std::cout <<" exp " << std::endl; // Integrate the negative velocity field to generate diffeomorphism corrfield step 3(a) // ExpDiffMap( velofield, corrfield, wm, -1, numtimepoints-ttiter); corrfield=ExpDiffMap( velofield, wm, -1, numtimepoints-ttiter); // why integrate velofield, but only compose incrinvfield ??? // technically, we should warp the gm image by corrfield but this can be avoided if (debug) std::cout <<" gmdef " << std::endl; typename ImageType::Pointer gmdef =m_MFR->WarpImageBackward(gm,corrfield); totalerr=0; typename ImageType::Pointer surfdef=m_MFR->WarpImageBackward(wm,invfield); if (debug) std::cout <<" thkdef " << std::endl; typename ImageType::Pointer thkdef =m_MFR->WarpImageBackward(thickimage,invfield); if (debug) std::cout <<" thindef " << std::endl; typename ImageType::Pointer thindef =m_MFR->WarpImageBackward(bsurf,invfield); if (spatprior) wpriorim=m_MFR->WarpImageBackward(priorim,invfield); typedef DeformationFieldType GradientImageType; typedef itk::GradientRecursiveGaussianImageFilter< ImageType,GradientImageType > GradientImageFilterType; typedef typename GradientImageFilterType::Pointer GradientImageFilterPointer; GradientImageFilterPointer gfilter=GradientImageFilterType::New(); gfilter->SetInput( surfdef ); gfilter->SetSigma( smoothingsigma ); gfilter->Update(); typename DeformationFieldType::Pointer lapgrad2=gfilter->GetOutput(); GradientImageFilterPointer gfilter2=GradientImageFilterType::New(); gfilter2->SetInput( gm ); gfilter2->SetSigma( smoothingsigma ); gfilter2->Update(); typename DeformationFieldType::Pointer lapgrad3=gfilter2->GetOutput(); typename ImageType::Pointer lapjac=ComputeJacobian(invfield); IteratorType xxIterator( lapjac, lapjac->GetLargestPossibleRegion().GetSize() ); xxIterator.GoToBegin(); float maxlapgrad2mag=0; while( !xxIterator.IsAtEnd() ) { typename ImageType::IndexType speedindex=xxIterator.GetIndex(); if ( segmentationimage->GetPixel(speedindex) == 2 ) // fixme { // float thkval=thkdef->GetPixel(speedindex); float thkval=finalthickimage->GetPixel(speedindex); float prior=1; if (spatprior){ float prval=wpriorim->GetPixel(speedindex); float partialvol=surfdef->GetPixel(speedindex) ; if ( partialvol >= 0.1 ) prior=prval/partialvol;//7;//0.5*origthickprior;// prval; if (prior > 10 ) prior=10; /** Potential cause of problem 1 -- this line added */ } //else thickprior = origthickprior; //} else thickprior=origthickprior; VectorType wgradval=lapgrad2->GetPixel(speedindex);//velofield->GetPixel(speedindex);// // VectorType ggradval=lapgrad3->GetPixel(speedindex); double dp=0; double gmag=0,wmag=0; for (unsigned kq=0;kqSetPixel(speedindex,wgradval); wmag=0; } // if (gmag > 0 && wmag > 0) // { // for (unsigned kq=0;kqSetPixel(speedindex,dp); double fval=(thickprior-thkval); double sigmoidf=1; // double sigmoidf=1.0- thkval/thickprior; if (fval >=0) sigmoidf=1.0/(1.0+exp((-1.0)*fval*0.01)); if (fval < 0) sigmoidf=-1.0*(1.0-thickprior/thkval); // else sigmoidf=1.0- thkval/thickprior; float thkscale=thickprior/thkval; if (thkscale < 0.99) thkscale=0.99;//*+thkscale*0.1; if (fval < 0 ) velofield->SetPixel(speedindex,velofield->GetPixel(speedindex)*thkscale); float dd=surfdef->GetPixel(speedindex) - gmdef->GetPixel(speedindex); // float gmd=gmdef->GetPixel(speedindex); totalerr+=fabs(dd); if ( segmentationimage->GetPixel(speedindex) != 2 /* && bsurf->GetPixel(speedindex) < 0.5 */ ) dd=0; // fixme float stopval=gm->GetPixel(speedindex); // if (wm->GetPixel(speedindex) > stopval) stopval=1; float jwt=xxIterator.Get(); if (jwt < 1) jwt=1; else jwt=1.0/xxIterator.Get(); // dd*=stopval*jwt*thindef->GetPixel(speedindex)*sigmoidf*gradstep*dp*gmd*jwt; dd*=stopval*sigmoidf*gradstep*jwt*prior;// speed function here IMPORTANT!! if (checknans) if ( vnl_math_isnan(dd) || vnl_math_isinf(dd) ) dd=0; if ( wmag*dd > 1 ) dd=stopval*(surfdef->GetPixel(speedindex) - gmdef->GetPixel(speedindex))*gradstep; lapjac->SetPixel(speedindex, dd); // std::cout <<" dd " << dd << " prior " << prior << " wmag " << wmag << std::endl; if ( wmag*dd > maxlapgrad2mag ) maxlapgrad2mag=wmag*dd; } else lapjac->SetPixel(speedindex, 0); ++xxIterator; } if ( maxlapgrad2mag < 1.e-4) maxlapgrad2mag=1.e9; lapjac=SmoothImage(lapjac,1); // if (ImageDimension==2) WriteJpg(surfdef,"surfdef.jpg"); //if (ImageDimension==2) WriteJpg(thindef,"thindef.jpg"); //if (ImageDimension==2) WriteJpg(gmdef,"gmdef.jpg"); //if (ImageDimension==2) WriteJpg(lapjac,"diff.jpg"); //if (ImageDimension==2) WriteJpg(wpriorim,"prior.jpg"); //if (ImageDimension==2) WriteJpg(thkdef,"thick2.jpg"); // if (ImageDimension==2) WriteJpg(tempim,"dotp.jpg"); //exit(0); /* Now that we have the gradient image, we need to visit each voxel and compute objective function */ // std::cout << " maxlapgrad2mag " << maxlapgrad2mag << std::endl; Iterator.GoToBegin(); while( !Iterator.IsAtEnd() ) { velind=Iterator.GetIndex(); // float currentthickvalue=finalthickimage->GetPixel(velind); VectorType wgradval=lapgrad2->GetPixel(velind); //*5.0/(maxlapgrad2mag*(float)numtimepoints); disp=wgradval*lapjac->GetPixel(velind); incrfield->SetPixel(velind,incrfield->GetPixel(velind)+disp); if (ttiter == 0)// make euclidean distance image { dmag=0; disp=corrfield->GetPixel(velind); for (unsigned int jj=0; jjGetPixel(velind); if (checknans) { if ( vnl_math_isnan(dmag) || vnl_math_isinf(dmag) ) dmag=0; if ( vnl_math_isnan(bval) || vnl_math_isinf(bval) ) bval=0; } /** Change 2-26-2010 = incoporate gm prob in length ... */ dmag=sqrt(dmag)*bval*gm->GetPixel(velind); thickimage->SetPixel(velind,dmag); totalimage->SetPixel(velind,dmag); hitimage->SetPixel(velind,bval); } else if ( segmentationimage->GetPixel(velind) == 2 ) // fixme { float thkval=thkdef->GetPixel(velind); float putval=thindef->GetPixel(velind); // float getval=hitimage->GetPixel(velind); hitimage->SetPixel(velind,hitimage->GetPixel(velind)+putval); totalimage->SetPixel(velind,totalimage->GetPixel(velind)+thkval); } // std::cout << "disp " << incrfield->GetPixel(velind) << " hit " << hitimage->GetPixel(velind) << " thk " << totalimage->GetPixel(velind) << std::endl; ++Iterator; } // if (ttiter ==0) { //WriteImage(totalimage,"Ztotalimage.nii.gz"); //WriteImage(hitimage,"Zhitimage.nii.gz"); //WriteImage(lapjac,"Zlapjac.nii.gz"); } Iterator.GoToBegin(); while( !Iterator.IsAtEnd() ) { incrinvfield->SetPixel(Iterator.GetIndex(),velofield->GetPixel(Iterator.GetIndex())); ++Iterator; } ttiter++; } Iterator.GoToBegin(); float maxth=0; while( !Iterator.IsAtEnd() ) { velind=Iterator.GetIndex(); // increment velocity field at every voxel v = v + u, step 4 velofield->SetPixel(Iterator.GetIndex(),velofield->GetPixel(Iterator.GetIndex()) + incrfield->GetPixel(Iterator.GetIndex()) ); float hitval=hitimage->GetPixel(velind); float thkval=0; if ( hitval >= 0.001 ) /** potential source of problem 2 -- this value could be smaller ... */ thkval=totalimage->GetPixel(velind)/hitval - thickoffset; if (thkval < 0) thkval=0; finalthickimage->SetPixel(velind ,thkval); if (thkval > maxth) maxth=thkval; ++Iterator; } if (debug) std::cout << " now smooth " << std::endl; m_MFR->SmoothDeformationFieldGauss(velofield,smoothingsigma); // std::string velofieldname = outname + "velofield"; //WriteDisplacementField(velofield,velofieldname.c_str()); //std::string incrfieldname = outname + "incrfield"; //WriteDisplacementField(incrfield,incrfieldname.c_str()); //std::string tname = outname + "dork1.nii.gz"; //WriteImage(hitimage,tname.c_str()); //tname = outname + "dork2.nii.gz"; //WriteImage(totalimage,tname.c_str()); if (thickerrct == 0) thickerrct=1; std::cout << " error " << totalerr << " at it " << its << " th-err " << thicknesserror/(float)thickerrct << " max thick " << maxth << std::endl; // std::string sulcthickname =outname + "sulcthick.nii"; // if (ImageDimension==2) WriteJpg(finalthickimage,"thick.jpg"); // std::string velofieldname = outname + "velofield"; //WriteDisplacementField(velofield,velofieldname.c_str()); if (debug)std::cout << "outside it " << its << std::endl; //std::cin.get(); } finalthickimage->SetDirection(omat); WriteImage(finalthickimage,outname.c_str()); finalthickimage->SetDirection(fmat); return 0; thickimage->FillBuffer(0); typename ImageType::Pointer thkdef =m_MFR->WarpImageBackward(finalthickimage,invfield); Iterator.GoToBegin(); while( !Iterator.IsAtEnd() ) { float tt1=finalthickimage->GetPixel(Iterator.GetIndex()); float tt=thkdef->GetPixel(Iterator.GetIndex()); if (tt1 > tt) tt=tt1; thickimage->SetPixel(Iterator.GetIndex(),tt); ++Iterator; } std::string thickname = outname; thickimage->SetDirection(omat); WriteImage(thickimage,thickname.c_str()); return 0; } int main(int argc, char *argv[]) { if ( argc < 6) { std::cout << "Usage: " << argv[0] << " ImageDimension Segmentation.nii.gz WMProb.nii.gz GMProb.nii.gz Out.nii {GradStep-1-2D,2-3D} {#Its-~50} {ThickPriorValue-6} {Bool-use-curvature-prior} {smoothing} {BoolUseEuclidean?}" << std::endl; std::cout << " this is a kind of binary image registration thing with diffeomorphisms " << std::endl; std::cout << " Segmentation.nii.gz -- should contain the value 3 where WM exists and the value 2 where GM exists " << std::endl; return 1; } unsigned int dim = atoi(argv[1]); std::cout << " dim " << dim << std::endl; switch ( dim ) { case 2: LaplacianThicknessExpDiff2<2>(argc,argv); break; case 3: LaplacianThicknessExpDiff2<3>(argc,argv); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } return EXIT_SUCCESS; return 1; } ants-1.9.2+svn680.dfsg/Examples/LabelClustersUniquely.cxx000066400000000000000000000106431147325206600232660ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: LabelClustersUniquely.cxx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include "itkDiscreteGaussianImageFilter.h" // RecursiveAverageImages img1 img2 weightonimg2 outputname // We divide the 2nd input image by its mean and add it to the first // input image with weight 1/n. //The output overwrites the 1st img with the sum. #include #include #include #include "vnl/vnl_vector.h" #include "itkMinimumMaximumImageFilter.h" #include "itkConnectedComponentImageFilter.h" #include "itkRelabelComponentImageFilter.h" #include "itkBinaryThresholdImageFilter.h" #include "itkLabelStatisticsImageFilter.h" #include "itkCastImageFilter.h" #include "ReadWriteImage.h" template int LabelUniquely(int argc, char *argv[]) { typedef float PixelType; // const unsigned int ImageDimension = AvantsImageDimension; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; // typedef itk::ImageRegionIteratorWithIndex Iterator; typedef float InternalPixelType; typedef int ULPixelType; typedef itk::Image labelimagetype; typedef itk::CastImageFilter CastFilterType; typedef ImageType InternalImageType; typedef ImageType OutputImageType; typedef itk::ConnectedComponentImageFilter< labelimagetype, labelimagetype > FilterType; typedef itk::RelabelComponentImageFilter< labelimagetype, labelimagetype > RelabelType; // want the average value in each cluster as defined by the mask and the value thresh and the clust thresh std::string fn1 = std::string(argv[1]); std::string fn2 = std::string(argv[2]); float clusterthresh = atof(argv[3]); typename ImageType::Pointer image1 = NULL; ReadImage(image1,fn1.c_str()); // typename typename FilterType::Pointer filter = FilterType::New(); //typename typename RelabelType::Pointer relabel = RelabelType::New(); typename CastFilterType::Pointer castInput = CastFilterType::New(); castInput->SetInput(image1); filter->SetInput( castInput->GetOutput() ); int fullyConnected = 0;//atoi( argv[5] ); filter->SetFullyConnected( fullyConnected ); relabel->SetInput( filter->GetOutput() ); relabel->SetMinimumObjectSize( (unsigned int) clusterthresh ); try { relabel->Update(); } catch( itk::ExceptionObject & excep ) { std::cerr << "Relabel: exception caught !" << std::endl; std::cerr << excep << std::endl; } // float maximum=relabel->GetNumberOfObjects(); WriteImage( relabel->GetOutput() , argv[2]); return 0; } int main(int argc, char *argv[]) { if ( argc < 3) { std::cout << "Usage: "<< std::endl; std::cout << argv[0] << " ImageDimension clustersin.hdr labeledclustersout.hdr sizethresh " << std::endl; return 1; } switch ( atoi(argv[1])) { case 2: LabelUniquely<2>(argc,argv+1); break; case 3: LabelUniquely<3>(argc,argv+1); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } return 0; } ants-1.9.2+svn680.dfsg/Examples/LabelOverlapMeasures.cxx000066400000000000000000000161151147325206600230430ustar00rootroot00000000000000#include "itkBinaryThresholdImageFilter.h" #include "itkHausdorffDistanceImageFilter.h" #include "itkImage.h" #include "itkImageFileReader.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkLabelOverlapMeasuresImageFilter.h" #include "itkSignedMaurerDistanceMapImageFilter.h" #include #include template int LabelOverlapMeasures( int argc, char * argv[] ) { typedef unsigned int PixelType; typedef itk::Image ImageType; typedef itk::ImageFileReader ReaderType; typename ReaderType::Pointer reader1 = ReaderType::New(); reader1->SetFileName( argv[2] ); typename ReaderType::Pointer reader2 = ReaderType::New(); reader2->SetFileName( argv[3] ); typedef itk::LabelOverlapMeasuresImageFilter FilterType; typename FilterType::Pointer filter = FilterType::New(); filter->SetSourceImage( reader1->GetOutput() ); filter->SetTargetImage( reader2->GetOutput() ); filter->Update(); std::cout << " " << "************ All Labels *************" << std::endl; std::cout << std::setw( 10 ) << " " << std::setw( 17 ) << "Total" << std::setw( 17 ) << "Union (jaccard)" << std::setw( 17 ) << "Mean (dice)" << std::setw( 17 ) << "Volume sim." << std::setw( 17 ) << "False negative" << std::setw( 17 ) << "False positive" << std::endl; std::cout << std::setw( 10 ) << " "; std::cout << std::setw( 17 ) << filter->GetTotalOverlap(); std::cout << std::setw( 17 ) << filter->GetUnionOverlap(); std::cout << std::setw( 17 ) << filter->GetMeanOverlap(); std::cout << std::setw( 17 ) << filter->GetVolumeSimilarity(); std::cout << std::setw( 17 ) << filter->GetFalseNegativeError(); std::cout << std::setw( 17 ) << filter->GetFalsePositiveError(); std::cout << std::endl; std::cout << " " << "************ Individual Labels *************" << std::endl; std::cout << std::setw( 10 ) << "Label" << std::setw( 17 ) << "Target" << std::setw( 17 ) << "Union (jaccard)" << std::setw( 17 ) << "Mean (dice)" << std::setw( 17 ) << "Volume sim." << std::setw( 17 ) << "False negative" << std::setw( 17 ) << "False positive" << std::setw( 17 ) << "Hausdorff" << std::setw( 17 ) << "Avg. Hausdorff" << std::setw( 17 ) << "Min. dist. sum" << std::endl; typename FilterType::MapType labelMap = filter->GetLabelSetMeasures(); typename FilterType::MapType::const_iterator it; for( it = labelMap.begin(); it != labelMap.end(); ++it ) { if( (*it).first == 0 ) { continue; } int label = (*it).first; std::cout << std::setw( 10 ) << label; std::cout << std::setw( 17 ) << filter->GetTargetOverlap( label ); std::cout << std::setw( 17 ) << filter->GetUnionOverlap( label ); std::cout << std::setw( 17 ) << filter->GetMeanOverlap( label ); std::cout << std::setw( 17 ) << filter->GetVolumeSimilarity( label ); std::cout << std::setw( 17 ) << filter->GetFalseNegativeError( label ); std::cout << std::setw( 17 ) << filter->GetFalsePositiveError( label ); /** * Calculate distance-related measures which, perhaps, aren't considered * "label overlap measures" in a precise sense but are still used to determine * segmentation/registration accuracy. These measurements include * 1. Hausdorff distance * 2. Min distance sum */ typedef itk::BinaryThresholdImageFilter ThresholderType; typename ThresholderType::Pointer source = ThresholderType::New(); source->SetInput( filter->GetSourceImage() ); source->SetLowerThreshold( label ); source->SetUpperThreshold( label ); source->SetInsideValue( static_cast( 1 ) ); source->SetOutsideValue( static_cast( 0 ) ); source->Update(); typename ThresholderType::Pointer target = ThresholderType::New(); target->SetInput( filter->GetTargetImage() ); target->SetLowerThreshold( label ); target->SetUpperThreshold( label ); target->SetInsideValue( static_cast( 1 ) ); target->SetOutsideValue( static_cast( 0 ) ); target->Update(); // Calculate Hausdorff distances typedef itk::HausdorffDistanceImageFilter HausdorffType; typename HausdorffType::Pointer hausdorff = HausdorffType::New(); hausdorff->SetInput1( source->GetOutput() ); hausdorff->SetInput2( target->GetOutput() ); hausdorff->Update(); std::cout << std::setw( 17 ) << hausdorff->GetHausdorffDistance(); std::cout << std::setw( 17 ) << hausdorff->GetAverageHausdorffDistance(); // Calculate min sum distance typedef itk::SignedMaurerDistanceMapImageFilter DistancerType; typename DistancerType::Pointer sourceDistance = DistancerType::New(); sourceDistance->SetInput( source->GetOutput() ); sourceDistance->SetSquaredDistance( false ); sourceDistance->SetUseImageSpacing( true ); sourceDistance->SetInsideIsPositive( false ); sourceDistance->Update(); typename DistancerType::Pointer targetDistance = DistancerType::New(); targetDistance->SetInput( target->GetOutput() ); targetDistance->SetSquaredDistance( false ); targetDistance->SetUseImageSpacing( true ); targetDistance->SetInsideIsPositive( false ); targetDistance->Update(); float distanceToSource = 0.0; float NS = 0.0; float distanceToTarget = 0.0; float NT = 0.0; itk::ImageRegionIteratorWithIndex ItS( sourceDistance->GetOutput(), sourceDistance->GetOutput()->GetLargestPossibleRegion() ); itk::ImageRegionIteratorWithIndex ItT( targetDistance->GetOutput(), targetDistance->GetOutput()->GetLargestPossibleRegion() ); for( ItS.GoToBegin(), ItT.GoToBegin(); !ItS.IsAtEnd(); ++ItS, ++ItT ) { // on the boundary or inside the source object? if( ItS.Get() <= 0.0 ) { // outside the target object? if( ItT.Get() > 0.0 ) { distanceToTarget += ItT.Get(); NS += 1.0; } } // on the boundary or inside the target object? if( ItT.Get() <= 0.0 ) { // outside the source object? if( ItS.Get() > 0.0 ) { distanceToSource += ItS.Get(); NT += 1.0; } } } float minDistanceSum = ( distanceToSource + distanceToTarget ) / ( NS + NT ); std::cout << std::setw( 17 ) << minDistanceSum; std::cout << std::endl; } return EXIT_SUCCESS; } int main( int argc, char *argv[] ) { if( argc < 4 ) { std::cerr << "Usage: " << argv[0] << " imageDimension sourceImage " << "targetImage" << std::endl; return EXIT_FAILURE; } switch( atoi( argv[1] ) ) { case 2: LabelOverlapMeasures<2>( argc, argv ); break; case 3: LabelOverlapMeasures<3>( argc, argv ); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } } ants-1.9.2+svn680.dfsg/Examples/LaplacianThickness.cxx000066400000000000000000000727221147325206600225340ustar00rootroot00000000000000 //#include "DoSomethingToImage.cxx" #include "itkVectorIndexSelectionCastImageFilter.h" #include "itkImageRegionIteratorWithIndex.h" #include "vnl/algo/vnl_determinant.h" #include "itkWarpImageFilter.h" #include "itkImageFileWriter.h" #include "itkRescaleIntensityImageFilter.h" #include "vnl/algo/vnl_determinant.h" #include "itkDiscreteGaussianImageFilter.h" #include "itkVectorLinearInterpolateImageFunction.h" #include "itkBinaryThresholdImageFilter.h" #include "itkDanielssonDistanceMapImageFilter.h" #include "itkGradientRecursiveGaussianImageFilter.h" #include "itkVectorCurvatureAnisotropicDiffusionImageFilter.h" #include "itkBinaryErodeImageFilter.h" #include "itkBinaryDilateImageFilter.h" #include "itkBinaryBallStructuringElement.h" #include "itkLaplacianRecursiveGaussianImageFilter.h" #include "ReadWriteImage.h" #include "itkGradientRecursiveGaussianImageFilter.h" template typename TImage::Pointer BinaryThreshold( typename TImage::PixelType low, typename TImage::PixelType high, typename TImage::PixelType replaceval, typename TImage::Pointer input) { //std::cout << " Binary Thresh " << std::endl; typedef typename TImage::PixelType PixelType; // Begin Threshold Image typedef itk::BinaryThresholdImageFilter InputThresholderType; typename InputThresholderType::Pointer inputThresholder = InputThresholderType::New(); inputThresholder->SetInput( input ); inputThresholder->SetInsideValue( replaceval ); int outval=0; if ((float) replaceval == (float) -1) outval=1; inputThresholder->SetOutsideValue( outval ); if (high < low) high=255; inputThresholder->SetLowerThreshold((PixelType) low ); inputThresholder->SetUpperThreshold((PixelType) high); inputThresholder->Update(); return inputThresholder->GetOutput(); } template typename TImage::Pointer GetVectorComponent(typename TField::Pointer field, unsigned int index) { // Initialize the Moving to the displacement field typedef TField FieldType; typedef TImage ImageType; typename ImageType::Pointer sfield=ImageType::New(); sfield->SetSpacing( field->GetSpacing() ); sfield->SetOrigin( field->GetOrigin() ); sfield->SetDirection( field->GetDirection() ); sfield->SetLargestPossibleRegion(field->GetLargestPossibleRegion() ); sfield->SetRequestedRegion(field->GetRequestedRegion() ); sfield->SetBufferedRegion( field->GetBufferedRegion() ); sfield->Allocate(); typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter( field, field->GetLargestPossibleRegion() ); for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter) { typename TField::PixelType v1=vfIter.Get(); sfield->SetPixel(vfIter.GetIndex(),v1[index]); } return sfield; } template typename TImage::Pointer SmoothImage(typename TImage::Pointer image, float sig) { // find min value typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter(image,image->GetLargestPossibleRegion() ); for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter) { typename TImage::PixelType v1=vfIter.Get(); if (vnl_math_isnan(v1)) vfIter.Set(0); } typedef itk::DiscreteGaussianImageFilter dgf; typename dgf::Pointer filter = dgf::New(); filter->SetVariance(sig); filter->SetUseImageSpacingOn(); filter->SetMaximumError(.01f); filter->SetInput(image); filter->Update(); typename TImage::Pointer out= filter->GetOutput(); return out; } template void SmoothDeformation(typename TImage::Pointer vectorimage, float sig) { typedef itk::Vector VectorType; typedef itk::Image ImageType; typename ImageType::Pointer subimgx=GetVectorComponent(vectorimage,0); subimgx=SmoothImage(subimgx,sig); typename ImageType::Pointer subimgy=GetVectorComponent(vectorimage,1); subimgy=SmoothImage(subimgy,sig); typename ImageType::Pointer subimgz=GetVectorComponent(vectorimage,2); subimgz=SmoothImage(subimgz,sig); typedef itk::ImageRegionIteratorWithIndex IteratorType; IteratorType Iterator( vectorimage, vectorimage->GetLargestPossibleRegion().GetSize() ); Iterator.GoToBegin(); while( !Iterator.IsAtEnd() ) { VectorType vec; vec[0]=subimgx->GetPixel(Iterator.GetIndex()); vec[1]=subimgy->GetPixel(Iterator.GetIndex()); vec[2]=subimgz->GetPixel(Iterator.GetIndex()); Iterator.Set(vec); ++Iterator; } return; } template typename TImage::Pointer LabelSurface(typename TImage::PixelType foreground, typename TImage::PixelType newval, typename TImage::Pointer input, float distthresh ) { std::cout << " Label Surf " << std::endl; typedef TImage ImageType; enum { ImageDimension = ImageType::ImageDimension }; typename ImageType::Pointer Image = ImageType::New(); Image->SetLargestPossibleRegion(input->GetLargestPossibleRegion() ); Image->SetBufferedRegion(input->GetLargestPossibleRegion()); Image->Allocate(); Image->SetSpacing(input->GetSpacing()); Image->SetOrigin(input->GetOrigin()); typedef itk::NeighborhoodIterator iteratorType; typename iteratorType::RadiusType rad; for (int j=0; jGetLargestPossibleRegion()); GHood.GoToBegin(); // std::cout << " foreg " << (int) foreground; while (!GHood.IsAtEnd()) { typename TImage::PixelType p = GHood.GetCenterPixel(); typename TImage::IndexType ind = GHood.GetIndex(); typename TImage::IndexType ind2; if ( p == foreground ) { bool atedge=false; for (unsigned int i = 0; i < GHood.Size(); i++) { ind2=GHood.GetIndex(i); float dist=0.0; for (int j=0; jSetPixel(ind,newval); else Image->SetPixel(ind,0); } ++GHood; } return Image; } template typename TImage::Pointer Morphological( typename TImage::Pointer input,float rad, bool option) { typedef TImage ImageType; enum { ImageDimension = TImage::ImageDimension }; typedef typename TImage::PixelType PixelType; if (!option) std::cout << " eroding the image " << std::endl; else std::cout << " dilating the image " << std::endl; typedef itk::BinaryBallStructuringElement< PixelType, ImageDimension > StructuringElementType; // Software Guide : BeginCodeSnippet typedef itk::BinaryErodeImageFilter< TImage, TImage, StructuringElementType > ErodeFilterType; typedef itk::BinaryDilateImageFilter< TImage, TImage, StructuringElementType > DilateFilterType; typename ErodeFilterType::Pointer binaryErode = ErodeFilterType::New(); typename DilateFilterType::Pointer binaryDilate = DilateFilterType::New(); StructuringElementType structuringElement; structuringElement.SetRadius((unsigned long) rad ); // 3x3x3 structuring element structuringElement.CreateStructuringElement(); binaryErode->SetKernel( structuringElement ); binaryDilate->SetKernel( structuringElement ); // It is necessary to define what could be considered objects on the binary // images. This is specified with the methods \code{SetErodeValue()} and // \code{SetDilateValue()}. The value passed to these methods will be // considered the value over which the dilation and erosion rules will apply binaryErode->SetErodeValue( 1 ); binaryDilate->SetDilateValue( 1 ); typename TImage::Pointer temp; if (option) { binaryDilate->SetInput( input ); binaryDilate->Update(); temp = binaryDilate->GetOutput(); } else { binaryErode->SetInput( input );//binaryDilate->GetOutput() ); binaryErode->Update(); temp = binaryErode->GetOutput(); typedef itk::ImageRegionIteratorWithIndex< ImageType > ImageIteratorType ; ImageIteratorType o_iter( temp, temp->GetLargestPossibleRegion() ); o_iter.GoToBegin() ; while ( !o_iter.IsAtEnd() ) { if (o_iter.Get() > 0.5 && input->GetPixel(o_iter.GetIndex()) > 0.5) o_iter.Set(1); else o_iter.Set(0); ++o_iter; } } return temp; } template typename TField::Pointer LaplacianGrad(typename TImage::Pointer wm,typename TImage::Pointer gm, float sig , unsigned int numits, float tolerance) { typedef typename TImage::IndexType IndexType; IndexType ind; typedef TImage ImageType; typedef TField GradientImageType; typedef itk::GradientRecursiveGaussianImageFilter< ImageType,GradientImageType > GradientImageFilterType; typedef typename GradientImageFilterType::Pointer GradientImageFilterPointer; typename TField::Pointer sfield=TField::New(); sfield->SetSpacing( wm->GetSpacing() ); sfield->SetOrigin( wm->GetOrigin() ); sfield->SetDirection( wm->GetDirection() ); sfield->SetLargestPossibleRegion(wm->GetLargestPossibleRegion() ); sfield->SetRequestedRegion(wm->GetRequestedRegion() ); sfield->SetBufferedRegion( wm->GetBufferedRegion() ); sfield->Allocate(); typename TImage::Pointer laplacian=SmoothImage(wm,1); laplacian->FillBuffer(0); typedef itk::ImageRegionIteratorWithIndex IteratorType; IteratorType Iterator( wm, wm->GetLargestPossibleRegion().GetSize() ); Iterator.GoToBegin(); //initialize L(wm)=1, L(gm)=0.5, else 0 while( !Iterator.IsAtEnd() ) { ind=Iterator.GetIndex(); if (wm->GetPixel(ind) >= 0.5 ) laplacian->SetPixel(ind,1); else if (gm->GetPixel(ind) >= 0.5 && wm->GetPixel(ind) < 0.5 ) laplacian->SetPixel(ind,2.); else laplacian->SetPixel(ind,2.); ++Iterator; } //smooth and then reset the values float meanvalue=0,lastmean=1; unsigned int iterations=0; while ( fabs(meanvalue-lastmean) > tolerance && iterations < numits) { iterations++; std::cout << " % " << (float) iterations/(float)(numits+1) << " delta-mean " << fabs(meanvalue-lastmean) << std::endl; laplacian=SmoothImage(laplacian,sqrt(sig)); Iterator.GoToBegin(); unsigned int ct=0; lastmean=meanvalue; while( !Iterator.IsAtEnd() ) { ind=Iterator.GetIndex(); if (wm->GetPixel(ind) >= 0.5 ) laplacian->SetPixel(ind,1); else if (gm->GetPixel(ind) < 0.5 && wm->GetPixel(ind) < 0.5 ) laplacian->SetPixel(ind,2.); else { meanvalue+=laplacian->GetPixel(ind); ct++;} ++Iterator; } meanvalue/=(float)ct; } /// WriteImage(laplacian, "laplacian.hdr"); GradientImageFilterPointer filter=GradientImageFilterType::New(); filter->SetInput( laplacian ); filter->SetSigma(sig*0.5); filter->Update(); return filter->GetOutput(); } template float IntegrateLength( typename TImage::Pointer gmsurf, typename TImage::Pointer thickimage, typename TImage::IndexType velind, typename TField::Pointer lapgrad, float itime, float starttime, float finishtime, bool timedone, float deltaTime, typename TInterp::Pointer vinterp, typename TInterp2::Pointer sinterp, unsigned int task, bool propagate,bool domeasure, unsigned int m_NumberOfTimePoints, typename TImage::SpacingType spacing, float vecsign, float timesign, float gradsign, unsigned int ct, typename TImage::Pointer wm, typename TImage::Pointer gm, float priorthickval , typename TImage::Pointer smooththick , bool printprobability, typename TImage::Pointer sulci ) { typedef TField TimeVaryingVelocityFieldType; typedef typename TField::PixelType VectorType; typedef itk::ImageRegionIteratorWithIndex FieldIterator; typedef typename TField::IndexType DIndexType; typedef typename TField::PointType DPointType; typedef itk::VectorLinearInterpolateImageFunction DefaultInterpolatorType; VectorType zero; zero.Fill(0); VectorType disp; disp.Fill(0); ct=0; DPointType pointIn1; DPointType pointIn2; typename DefaultInterpolatorType::ContinuousIndexType vcontind; DPointType pointIn3; enum { ImageDimension = TImage::ImageDimension }; typedef typename TImage::IndexType IndexType; float startprob=gm->GetPixel(velind); if (sulci) startprob=sulci->GetPixel(velind); bool printout=false; if ( gmsurf->GetPixel(velind) > 0) printout=true; IndexType index; for (unsigned int jj=0; jjGetSpacing()[jj]; } if (task ==0) { propagate=false; } else propagate=true; itime=starttime; timedone=false; float totalmag=0; if ( domeasure ) { while ( !timedone ) { float scale = 1;//*m_DT[timeind]/m_DS[timeind]; // std::cout << " scale " << scale << std::endl; double itimetn1 = itime - timesign*deltaTime*scale; double itimetn1h = itime - timesign*deltaTime*0.5*scale; if (itimetn1h < 0 ) itimetn1h=0; if (itimetn1h > m_NumberOfTimePoints-1 ) itimetn1h=m_NumberOfTimePoints-1; if (itimetn1 < 0 ) itimetn1=0; if (itimetn1 > m_NumberOfTimePoints-1 ) itimetn1=m_NumberOfTimePoints-1; // first get current position of particle IndexType index; for (unsigned int jj=0; jjGetSpacing()[jj]; } // std::cout << " ind " << index << std::endl; // now index the time varying field at that position. typename DefaultInterpolatorType::OutputType f1; f1.Fill(0); typename DefaultInterpolatorType::OutputType f2; f2.Fill(0); typename DefaultInterpolatorType::OutputType f3; f3.Fill(0); typename DefaultInterpolatorType::OutputType f4; f4.Fill(0); typename DefaultInterpolatorType::ContinuousIndexType Y1; typename DefaultInterpolatorType::ContinuousIndexType Y2; typename DefaultInterpolatorType::ContinuousIndexType Y3; typename DefaultInterpolatorType::ContinuousIndexType Y4; for (unsigned int jj=0; jjGetSpacing()[jj]; Y1[jj]=vcontind[jj]; Y2[jj]=vcontind[jj]; Y3[jj]=vcontind[jj]; Y4[jj]=vcontind[jj]; } //Y1[ImageDimension]=itimetn1; //Y2[ImageDimension]=itimetn1h; //Y3[ImageDimension]=itimetn1h; // Y4[ImageDimension]=itime; f1 = vinterp->EvaluateAtContinuousIndex( Y1 ); for (unsigned int jj=0; jj lapgrad->GetLargestPossibleRegion().GetSize()[jj] -2 ) isinside=false; if (isinside) f2 = vinterp->EvaluateAtContinuousIndex( Y2 ); for (unsigned int jj=0; jj lapgrad->GetLargestPossibleRegion().GetSize()[jj] -2 ) isinside=false; if (isinside) f3 = vinterp->EvaluateAtContinuousIndex( Y3 ); for (unsigned int jj=0; jj lapgrad->GetLargestPossibleRegion().GetSize()[jj] -2 ) isinside=false; if (isinside) f4 = vinterp->EvaluateAtContinuousIndex( Y4 ); for (unsigned int jj=0; jjGetPixel(myind) < 0.5 && wm->GetPixel(myind) < 0.5 || wm->GetPixel(myind) >= 0.5 && gm->GetPixel(myind) < 0.5 || mag < 1.e-1*deltaTime) { timedone=true; } if ( gm->GetPixel(myind) < 0.5 ) { timedone=true; } if ( ct > 2.0/deltaTime ) { timedone=true;} if ( totalmag > priorthickval ) timedone=true; if (smooththick) if ( (totalmag - smooththick->GetPixel(velind)) > 1 ) timedone=true; if (printprobability) std::cout << " ind " << Y1 << " prob " << sinterp->EvaluateAtContinuousIndex(Y1) << " t " << itime << std::endl; } } return totalmag; } template int LaplacianThickness(int argc, char *argv[]) { float gradstep= -50.0;//atof(argv[3])*(-1.0); unsigned int nsmooth=2; float smoothparam=1; float priorthickval=500; double dT=0.01; std::string wfn = std::string(argv[1]); std::string gfn = std::string(argv[2]); int argct=3; std::string outname=std::string(argv[argct]); argct++; if (argc > argct) smoothparam=atof(argv[argct]); argct++; if (argc > argct) priorthickval=atof(argv[argct]); argct++; if (argc > argct) dT=atof(argv[argct]); argct++; float dosulc=0; if (argc > argct ) dosulc=atof(argv[argct]); argct++; unsigned int totalits=500; float tolerance=0.001; if (argc > argct ) tolerance=atof(argv[argct]); argct++; std::cout << " using tolerance " << tolerance << std::endl; typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image DeformationFieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::Image tvt; // typename tvt::Pointer gWarp; //ReadImage( gWarp, ifn.c_str() ); typename ImageType::Pointer thickimage; ReadImage(thickimage,wfn.c_str()); thickimage->FillBuffer(0); typename ImageType::Pointer thickimage2; ReadImage(thickimage2,wfn.c_str()); thickimage2->FillBuffer(0); typename ImageType::Pointer wm; ReadImage(wm,wfn.c_str()); typename ImageType::Pointer gm; ReadImage(gm,gfn.c_str()); SpacingType spacing=wm->GetSpacing(); typedef itk::ImageRegionIteratorWithIndex IteratorType; IteratorType Iterator( wm, wm->GetLargestPossibleRegion().GetSize() ); typename ImageType::Pointer wmb=BinaryThreshold(0.5,1.e9,1,wm); typename DeformationFieldType::Pointer lapgrad=NULL; typename DeformationFieldType::Pointer lapgrad2=NULL; typename ImageType::Pointer gmb=BinaryThreshold(0.5,1.e9,1,gm); /** get sulcal priors */ typename ImageType::Pointer sulci=NULL; if (dosulc > 0 ){ std::cout <<" using sulcal prior " << std::endl; typedef itk::DanielssonDistanceMapImageFilter FilterType; typename FilterType::Pointer distmap = FilterType::New(); distmap->InputIsBinaryOn(); distmap->SetUseImageSpacing(true); distmap->SetInput(wmb); distmap->Update(); typename ImageType::Pointer distwm=distmap->GetOutput(); typedef itk::LaplacianRecursiveGaussianImageFilter dgf; typename dgf::Pointer lfilter = dgf::New(); lfilter->SetSigma(smoothparam); lfilter->SetInput(distwm); lfilter->Update(); typename ImageType::Pointer image2=lfilter->GetOutput(); typedef itk::RescaleIntensityImageFilter RescaleFilterType; typename RescaleFilterType::Pointer rescaler = RescaleFilterType::New(); rescaler->SetOutputMinimum( 0 ); rescaler->SetOutputMaximum( 1 ); rescaler->SetInput( image2 ); rescaler->Update(); sulci= rescaler->GetOutput(); WriteImage(sulci,"sulci.nii"); Iterator.GoToBegin(); while( !Iterator.IsAtEnd() ) { // std::cout << " a good value for use sulcus prior is 0.002 -- in a function : 1/(1.+exp(-0.1*(sulcprob-0.275)/use-sulcus-prior)) " << std::endl; // float gmprob=gm->GetPixel(Iterator.GetIndex()); if (gmprob == 0) gmprob=0.05; float sprob=sulci->GetPixel(Iterator.GetIndex()); sprob=1/(1.+exp(-0.1*(sprob-0.5)/dosulc)); sulci->SetPixel(Iterator.GetIndex(),sprob ); // if (gmprob > 0) std::cout << " gmp " << gmprob << std::endl; ++Iterator; } std::cout << " modified gm prior by sulcus prior " << std::endl; WriteImage(sulci,"sulcigm.nii"); typedef itk::GradientRecursiveGaussianImageFilter< ImageType,DeformationFieldType > GradientImageFilterType; typedef typename GradientImageFilterType::Pointer GradientImageFilterPointer; GradientImageFilterPointer filter=GradientImageFilterType::New(); filter->SetInput( distwm ); filter->SetSigma(smoothparam); filter->Update(); lapgrad2 = filter->GetOutput(); // return 0; /** sulc priors done */ } lapgrad=LaplacianGrad(wmb,gmb,smoothparam,totalits,tolerance); // LabelSurface(typename TImage::PixelType foreground, // typename TImage::PixelType newval, typename TImage::Pointer input, float distthresh ) float distthresh = 1.9; typename ImageType::Pointer wmgrow = Morphological(wmb,1,true); typename ImageType::Pointer surf = LabelSurface(1,1,wmgrow, distthresh); typename ImageType::Pointer gmsurf = LabelSurface(1,1,gmb, distthresh); // now integrate // double timezero=0; //1 typename ImageType::SizeType s= wm->GetLargestPossibleRegion().GetSize(); double timeone=1;//(s[ImageDimension]-1-timezero); // unsigned int m_NumberOfTimePoints = s[ImageDimension]; float starttime=timezero;//timezero; float finishtime=timeone;//s[ImageDimension]-1;//timeone; //std::cout << " MUCKING WITH START FINISH TIME " << finishtime << std::endl; typename DeformationFieldType::IndexType velind; typename ImageType::Pointer smooththick=NULL; float timesign=1.0; if (starttime > finishtime ) timesign= -1.0; unsigned int m_NumberOfTimePoints=2; typedef DeformationFieldType TimeVaryingVelocityFieldType; typedef itk::ImageRegionIteratorWithIndex FieldIterator; typedef typename DeformationFieldType::IndexType DIndexType; typedef typename DeformationFieldType::PointType DPointType; typedef typename TimeVaryingVelocityFieldType::IndexType VIndexType; typedef typename TimeVaryingVelocityFieldType::PointType VPointType; typedef itk::VectorLinearInterpolateImageFunction DefaultInterpolatorType; typedef itk::VectorLinearInterpolateImageFunction DefaultInterpolatorType2; typename DefaultInterpolatorType::Pointer vinterp = DefaultInterpolatorType::New(); typedef itk::LinearInterpolateImageFunction ScalarInterpolatorType; typename ScalarInterpolatorType::Pointer sinterp = ScalarInterpolatorType::New(); sinterp->SetInputImage(gm); if (sulci) sinterp->SetInputImage(sulci); VectorType zero; zero.Fill(0); DPointType pointIn1; DPointType pointIn2; typename DefaultInterpolatorType::ContinuousIndexType vcontind; DPointType pointIn3; typedef itk::ImageRegionIteratorWithIndex VIteratorType; VIteratorType VIterator( lapgrad, lapgrad->GetLargestPossibleRegion().GetSize() ); VIterator.GoToBegin(); while( !VIterator.IsAtEnd() ) { VectorType vec=VIterator.Get(); float mag=0; for (unsigned int qq=0; qq 0) vec=vec/mag; VIterator.Set(vec*gradstep); if (lapgrad2) { vec=lapgrad2->GetPixel(VIterator.GetIndex()); mag=0; for (unsigned int qq=0; qq 0) vec=vec/mag; lapgrad2->SetPixel(VIterator.GetIndex(), vec*gradstep); } ++VIterator; } bool propagate=false; for (unsigned int smoothit=0; smoothitGetPixel(velind) << " gm " << gm->GetPixel(velind) << std::endl; // if (surf->GetPixel(velind) != 0) printprobability=true; if ( gm->GetPixel(velind) > 0.25 )//&& wmb->GetPixel(velind) < 1 ) { cter++; domeasure=true; } vinterp->SetInputImage(lapgrad); gradsign=-1.0; vecsign=-1.0; float len1=IntegrateLength (gmsurf, thickimage, velind, lapgrad, itime, starttime, finishtime, timedone, deltaTime, vinterp, sinterp,task,propagate,domeasure,m_NumberOfTimePoints,spacing,vecsign, gradsign, timesign, ct, wm,gm, priorthickval , smooththick , printprobability , sulci ); gradsign=1.0; vecsign=1; float len2=IntegrateLength (gmsurf, thickimage, velind, lapgrad, itime, starttime, finishtime, timedone, deltaTime, vinterp, sinterp,task,propagate,domeasure,m_NumberOfTimePoints,spacing,vecsign, gradsign, timesign, ct,wm,gm , priorthickval-len1 , smooththick , printprobability , sulci ); float len3=1.e9,len4=1.e9; if (lapgrad2) { vinterp->SetInputImage(lapgrad2); gradsign=-1.0; vecsign=-1.0; len3=IntegrateLength (gmsurf, thickimage, velind, lapgrad2, itime, starttime, finishtime, timedone, deltaTime, vinterp, sinterp,task,propagate,domeasure,m_NumberOfTimePoints,spacing,vecsign, gradsign, timesign, ct, wm,gm, priorthickval , smooththick , printprobability , sulci ); gradsign=1.0; vecsign=1; len4=IntegrateLength (gmsurf, thickimage, velind, lapgrad2, itime, starttime, finishtime, timedone, deltaTime, vinterp, sinterp,task,propagate,domeasure,m_NumberOfTimePoints,spacing,vecsign, gradsign, timesign, ct,wm,gm , priorthickval-len3, smooththick , printprobability , sulci ); } float totalength=len1+len2; // if (totalength > 5 && totalength < 8) std::cout<< " t1 " << len3+len4 << " t2 " << len1+len2 << std::endl; if (len3+len4 < totalength) totalength=len3+len4; if (smoothit==0) { if ( thickimage2->GetPixel(velind) == 0 ) { thickimage2->SetPixel(velind,totalength); } else if ( (totalength) > 0 && thickimage2->GetPixel(velind) < (totalength) ) { thickimage2->SetPixel(velind,totalength); } } if ( smoothit > 0 && smooththick ) { thickimage2->SetPixel(velind, (totalength)*0.5 + smooththick->GetPixel(velind)*0.5 ); } if (domeasure && (totalength)> 0 && cter % 10000 == 0) std::cout << " len1 " << len1 << " len2 " << len2 << " ind " << velind << std::endl; } ++Iterator; } smooththick=SmoothImage(thickimage2,1.0); // set non-gm voxels to zero IteratorType gIterator( gm, gm->GetLargestPossibleRegion().GetSize() ); gIterator.GoToBegin(); while( !gIterator.IsAtEnd() ) { if (gm->GetPixel(gIterator.GetIndex()) < 0.25 ) thickimage2->SetPixel(gIterator.GetIndex(),0); ++gIterator; } std::cout << " writing " << outname << std::endl; WriteImage(thickimage2,outname.c_str()); } // WriteImage(thickimage,"turd.hdr"); return 0; } int main(int argc, char *argv[]) { if ( argc < 4) { std::cout << "Usage: " << argv[0] << " WM.nii GM.nii Out.nii {smoothparam=3} {priorthickval=5} {dT=0.01} use-sulcus-prior optional-laplacian-tolerance=0.001" << std::endl; std::cout << " a good value for use sulcus prior is 0.15 -- in a function : 1/(1.+exp(-0.1*(laplacian-img-value-sulcprob)/0.01)) " << std::endl; return 1; } std::string ifn = std::string(argv[1]); // std::cout << " image " << ifn << std::endl; // Get the image dimension itk::ImageIOBase::Pointer imageIO = itk::ImageIOFactory::CreateImageIO(ifn.c_str(), itk::ImageIOFactory::ReadMode); imageIO->SetFileName(ifn.c_str()); imageIO->ReadImageInformation(); unsigned int dim = imageIO->GetNumberOfDimensions(); // std::cout << " dim " << dim << std::endl; switch ( dim ) { case 2: LaplacianThickness<2>(argc,argv); break; case 3: LaplacianThickness<3>(argc,argv); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } return EXIT_SUCCESS; return 1; } ants-1.9.2+svn680.dfsg/Examples/MeasureImageSimilarity.cxx000066400000000000000000000226071147325206600234040ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: MeasureImageSimilarity.cxx,v $ Language: C++ Date: $Date: 2009/01/05 20:09:47 $ Version: $Revision: 1.19 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include "ReadWriteImage.h" #include "itkDiscreteGaussianImageFilter.h" #include "itkAvantsMutualInformationRegistrationFunction.h" #include "itkSpatialMutualInformationRegistrationFunction.h" #include "itkProbabilisticRegistrationFunction.h" #include "itkCrossCorrelationRegistrationFunction.h" // #include "itkLandmarkCrossCorrelationRegistrationFunction.h" template int MeasureImageSimilarity(unsigned int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::ImageRegionIteratorWithIndex Iterator; typedef itk::Image JointHistType; typedef itk::ImageFileWriter jhwritertype; // get command line params unsigned int argct=2; unsigned int whichmetric = atoi(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; std::string fn2 = std::string(argv[argct]); argct++; std::string logfilename=""; if (argc > argct) logfilename = std::string(argv[argct]); argct++; std::string imgfilename=""; if (argc > argct) imgfilename = std::string(argv[argct]); argct++; double targetvalue=0; if (argc > argct) targetvalue = atof(argv[argct]); argct++; double epsilontolerance=1.e20; if (argc > argct) epsilontolerance = atof(argv[argct]); argct++; typename ImageType::Pointer image1 = NULL; ReadImage(image1, fn1.c_str()); typename ImageType::Pointer image2 = NULL; ReadImage(image2, fn2.c_str()); /* typedef itk::ImageRegionIteratorWithIndex VIterator; typename FieldType::Pointer field=FieldType::New(); field->SetLargestPossibleRegion( image1->GetLargestPossibleRegion() ); field->SetBufferedRegion( image1->GetLargestPossibleRegion() ); field->SetLargestPossibleRegion( image1->GetLargestPossibleRegion() ); field->Allocate(); field->SetSpacing(image1->GetSpacing()); field->SetOrigin(image1->GetOrigin()); VectorType zero; zero.Fill(0); VIterator vfIter2( field, field->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { IndexType index=vfIter2.GetIndex(); vfIter2.Set(zero); } */ typename ImageType::Pointer metricimg=ImageType::New(); metricimg->SetRegions( image1->GetLargestPossibleRegion() ); metricimg->Allocate(); metricimg->SetSpacing(image1->GetSpacing()); metricimg->SetOrigin(image1->GetOrigin()); metricimg->SetDirection(image1->GetDirection()); Iterator iter( metricimg, metricimg->GetLargestPossibleRegion() ); for( iter.GoToBegin(); !iter.IsAtEnd(); ++iter ) iter.Set(0); typedef ImageType FixedImageType; typedef ImageType MovingImageType; typedef FieldType DeformationFieldType; // Choose the similarity metric typedef itk::AvantsMutualInformationRegistrationFunction MIMetricType; typedef itk::SpatialMutualInformationRegistrationFunction SMIMetricType; typedef itk::CrossCorrelationRegistrationFunction CCMetricType; //typedef itk::LandmarkCrossCorrelationRegistrationFunction MetricType; //typename typename MIMetricType::Pointer mimet=MIMetricType::New(); typename SMIMetricType::Pointer smimet=SMIMetricType::New(); typename CCMetricType::Pointer ccmet=CCMetricType::New(); // int nbins=32; typename CCMetricType::RadiusType hradius; typename CCMetricType::RadiusType ccradius; ccradius.Fill(4); typename MIMetricType::RadiusType miradius; miradius.Fill(0); // mimet->SetDeformationField(field); mimet->SetFixedImage(image1); mimet->SetMovingImage(image2); mimet->SetRadius(miradius); mimet->SetGradientStep(1.e2); mimet->SetNormalizeGradient(false); // ccmet->SetDeformationField(field); ccmet->SetFixedImage(image1); ccmet->SetMovingImage(image2); ccmet->SetRadius(ccradius); ccmet->SetGradientStep(1.e2); ccmet->SetNormalizeGradient(false); double metricvalue=0; std::string metricname=""; if (whichmetric == 0) { hradius=miradius; unsigned long ct = 0; for( iter.GoToBegin(); !iter.IsAtEnd(); ++iter ) { IndexType index=iter.GetIndex(); double fval=image1->GetPixel(index); double mval=image2->GetPixel(index); metricvalue+=fabs(fval-mval); metricimg->SetPixel(index,fabs(fval-mval)); ct++; } metricvalue/=(float)ct; metricname="MSQ "; } else if (whichmetric == 1 ) // imagedifference { hradius=ccradius; metricname="CC "; std::cout << metricname << std::endl; ccmet->InitializeIteration(); metricimg=ccmet->MakeImage(); metricvalue=ccmet->ComputeCrossCorrelation()*(1.0); } else if (whichmetric == 2 ) { hradius=miradius; mimet->InitializeIteration(); metricvalue=mimet->ComputeMutualInformation(); metricname="MI "; } else if (whichmetric == 3 ) { hradius=miradius; smimet->InitializeIteration(); metricvalue=smimet->ComputeSpatialMutualInformation(); metricname="SMI "; } std::cout << fn1 << " : " << fn2 << " => " << metricname << metricvalue << std::endl; if (logfilename.length() > 3 ){ std::ofstream logfile; logfile.open(logfilename.c_str(), std::ofstream::app); if (logfile.good()) { logfile << fn1 << " : " << fn2 << " => " << metricname << metricvalue << std::endl; } else std::cout << " cant open file "; logfile.close(); } if (imgfilename.length() > 3 ){ std::cout << "Only Implemented for MSQ and CC " << std::endl; typedef itk::ImageFileWriter writertype; typename writertype::Pointer w= writertype::New(); w->SetInput(metricimg); w->SetFileName(imgfilename.c_str()); w->Write(); // met->WriteImages(); /* typename MetricType::NeighborhoodType asamIt( hradius, field,field->GetLargestPossibleRegion()); unsigned long ct = 0; double totval = 0; for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { IndexType index=vfIter2.GetIndex(); double val=0; asamIt.SetLocation(index); // met->ComputeUpdate( asamIt, gd); met->ComputeMetricAtPairB(index, zero); metricimg->SetPixel(index, val); //if (ct % 10000 == 0) // std::cout << val << " index " << index << std::endl; // asamIt.SetLocation(index); // totval+=met->localProbabilistic; ct++; } std::cout << " AvantsMI : " << totval/(double)ct << " E " << met->GetEnergy() << std::endl; std::cout << " write begin " << std::endl; std::cout << " write end " << std::endl; */ } double diff = ((double)metricvalue - (double) targetvalue); std::cout << " targetvalue " << targetvalue << " metricvalue " << metricvalue << " diff " << diff << " toler " << epsilontolerance << std::endl; if ( diff < epsilontolerance) return EXIT_SUCCESS; else return EXIT_FAILURE; } int main(int argc, char *argv[]) { if ( argc < 3 ) { std::cout << "Basic useage ex: " << std::endl; std::cout << argv[0] << " ImageDimension whichmetric image1.ext image2.ext {logfile} {outimage.ext} {target-value} {epsilon-tolerance}" << std::endl; std::cout << " outimage (Not Implemented for MI yet) and logfile are optional " << std::endl; std::cout <<" target-value and epsilon-tolerance set goals for the metric value -- if the metric value is within epsilon-tolerance of the target-value, then the test succeeds " << std::endl; std::cout << " Metric 0 - MeanSquareDifference, 1 - Cross-Correlation, 2-Mutual Information , 3-SMI " << std::endl; return 1; } int metricsuccess=EXIT_FAILURE; // Get the image dimension switch( atoi(argv[1])) { case 2: metricsuccess=MeasureImageSimilarity<2>(argc,argv); break; case 3: metricsuccess=MeasureImageSimilarity<3>(argc,argv); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } std::cout << " Failure? " << metricsuccess << std::endl; return metricsuccess; } ants-1.9.2+svn680.dfsg/Examples/MeasureMinMaxMean.cxx000066400000000000000000000105451147325206600223030ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: MeasureMinMaxMean.cxx,v $ Language: C++ Date: $Date: 2008/12/16 17:56:34 $ Version: $Revision: 1.20 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ //#include "DoSomethingToImage.cxx" // RecursiveAverageImages img1 img2 weight // We divide the 2nd input image by its mean and add it to the first // input image with weight 1/n. //The output overwrites the 1st img with the sum. //Note: could easily add variance computation //http://people.revoledu.com/kardi/tutorial/RecursiveStatistic/Time-Variance.htm #include "ReadWriteImage.h" template int MeasureMinMaxMean(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; typename ImageType::Pointer image = NULL; typename ImageType::Pointer mask = NULL; // typename ImageType::SizeType size; double mean=0,max=-1.e9,min=1.e9; unsigned long ct = 0; ReadImage(image,argv[2]); bool takeabsval=false; if ( argc > 4 ) takeabsval=atoi(argv[4]); if ( argc > 5 ) { ReadImage(mask,argv[5]);} Iterator vfIter2( image, image->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { bool isinside=true; if (mask) if (mask->GetPixel(vfIter2.GetIndex()) < 0.5) isinside=false; if (isinside) { double val=vfIter2.Get(); if (takeabsval) val=fabs(val); mean+=val; if (val >max) max=val; else if (val < min) min=val; ct++; } } if (ct > 0) mean/= (float)ct; float variance=0; for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { bool isinside=true; if (mask) if (mask->GetPixel(vfIter2.GetIndex()) < 0.5) isinside=false; if (isinside) { double val=vfIter2.Get(); if (takeabsval) val=fabs(val); variance+=vnl_math_sqr(val-mean); } } if (ct > 0) variance/= (float)ct; std::cout << argv[2] <<" Max : " << max << " Min : " << min << " Mean : " << mean << " Var : " << variance << " SD : " << sqrt(variance) << std::endl; if (argc > 3){ std::ofstream logfile; logfile.open(argv[3], std::ofstream::app); if (logfile.good()) { logfile << argv[2] << " Max : " << max << " Min : " << min << " Mean : " << mean << std::endl; } else std::cout << " cant open file " << argv[3] << std::endl; logfile.close(); } return 0; } int main(int argc, char *argv[]) { if ( argc < 3 ) { std::cout << "Basic useage ex: " << std::endl; std::cout << argv[0] << " ImageDimension image.nii {log.txt} {take-absolute-value} {mask-name} " << std::endl; std::cout << " log.txt is optional - take-abs-val reports min-max-mean of abs val image " << std::endl; return 1; } // Get the image dimension switch( atoi(argv[1])) { case 2: MeasureMinMaxMean<2>(argc,argv); break; case 3: MeasureMinMaxMean<3>(argc,argv); break; case 4: MeasureMinMaxMean<4>(argc,argv); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } return 0; } ants-1.9.2+svn680.dfsg/Examples/MemoryTest.cxx000066400000000000000000000151651147325206600211020ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: MemoryTest.cxx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include "ReadWriteImage.h" #include "itkDiscreteGaussianImageFilter.h" #include "itkAvantsMutualInformationRegistrationFunction.h" #include "itkProbabilisticRegistrationFunction.h" #include "itkCrossCorrelationRegistrationFunction.h" // #include "itkLandmarkCrossCorrelationRegistrationFunction.h" template int MemoryTest(unsigned int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::ImageRegionIteratorWithIndex Iterator; typedef itk::Image JointHistType; typedef itk::ImageFileWriter jhwritertype; // get command line params unsigned int argct=2; unsigned int whichmetric = atoi(argv[argct]); argct++; std::string fn1 = std::string(argv[argct]); argct++; std::string fn2 = std::string(argv[argct]); argct++; unsigned int numberoffields=11; if (argc > argct) numberoffields=atoi(argv[argct]); argct++; typename ImageType::Pointer image1 = NULL; ReadImage(image1, fn1.c_str()); typename ImageType::Pointer image2 = NULL; ReadImage(image2, fn2.c_str()); typedef itk::ImageRegionIteratorWithIndex VIterator; std::vector fieldvec; for (unsigned int i=0; i < numberoffields; i++) { std::cout <<" NFields " << i << " of " << numberoffields << std::endl; typename FieldType::Pointer field=FieldType::New(); field->SetLargestPossibleRegion( image1->GetLargestPossibleRegion() ); field->SetBufferedRegion( image1->GetLargestPossibleRegion() ); field->SetLargestPossibleRegion( image1->GetLargestPossibleRegion() ); field->Allocate(); field->SetSpacing(image1->GetSpacing()); field->SetOrigin(image1->GetOrigin()); VectorType zero; zero.Fill(0); VIterator vfIter2( field, field->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { IndexType index=vfIter2.GetIndex(); vfIter2.Set(zero); } fieldvec.push_back(field); } typename ImageType::Pointer metricimg=ImageType::New(); metricimg->SetLargestPossibleRegion( image1->GetLargestPossibleRegion() ); metricimg->SetBufferedRegion( image1->GetLargestPossibleRegion() ); metricimg->SetLargestPossibleRegion( image1->GetLargestPossibleRegion() ); metricimg->Allocate(); metricimg->SetSpacing(image1->GetSpacing()); metricimg->SetOrigin(image1->GetOrigin()); Iterator iter( metricimg, metricimg->GetLargestPossibleRegion() ); for( iter.GoToBegin(); !iter.IsAtEnd(); ++iter ) iter.Set(0); typedef ImageType FixedImageType; typedef ImageType MovingImageType; typedef FieldType DeformationFieldType; // Choose the similarity metric typedef itk::AvantsMutualInformationRegistrationFunction MIMetricType; typedef itk::CrossCorrelationRegistrationFunction CCMetricType; //typedef itk::LandmarkCrossCorrelationRegistrationFunction MetricType; //typename typename MIMetricType::Pointer mimet=MIMetricType::New(); typename CCMetricType::Pointer ccmet=CCMetricType::New(); // int nbins=32; typename CCMetricType::RadiusType hradius; typename CCMetricType::RadiusType ccradius; ccradius.Fill(4); typename MIMetricType::RadiusType miradius; miradius.Fill(0); // mimet->SetDeformationField(field); mimet->SetFixedImage(image1); mimet->SetMovingImage(image2); mimet->SetRadius(miradius); mimet->SetGradientStep(1.e2); mimet->SetNormalizeGradient(false); // ccmet->SetDeformationField(field); ccmet->SetFixedImage(image1); ccmet->SetMovingImage(image2); ccmet->SetRadius(ccradius); ccmet->SetGradientStep(1.e2); ccmet->SetNormalizeGradient(false); double metricvalue=0; std::string metricname=""; if (whichmetric == 0) { hradius=miradius; unsigned long ct = 0; for( iter.GoToBegin(); !iter.IsAtEnd(); ++iter ) { IndexType index=iter.GetIndex(); double fval=image1->GetPixel(index); double mval=image2->GetPixel(index); metricvalue+=fabs(fval-mval); ct++; } metricvalue/=(float)ct; metricname="MSQ "; } else if (whichmetric == 1 ) // imagedifference { hradius=ccradius; ccmet->InitializeIteration(); metricvalue=ccmet->ComputeCrossCorrelation(); metricname="CC "; } else { hradius=miradius; mimet->InitializeIteration(); metricvalue=mimet->ComputeMutualInformation(); metricname="MI "; } return 0; } int main(int argc, char *argv[]) { if ( argc < 3 ) { std::cout << "Basic useage ex: " << std::endl; std::cout << argv[0] << " ImageDimension whichmetric image1.ext image2.ext NumberOfFieldsToAllocate " << std::endl; std::cout << " outimage and logfile are optional " << std::endl; std::cout << " Metric 0 - MeanSquareDifference, 1 - Cross-Correlation, 2-Mutual Information " << std::endl; return 1; } // Get the image dimension switch( atoi(argv[1])) { case 2: MemoryTest<2>(argc,argv); break; case 3: MemoryTest<3>(argc,argv); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } return 0; } ants-1.9.2+svn680.dfsg/Examples/MultiplyImages.cxx000066400000000000000000000111371147325206600217320ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: MultiplyImages.cxx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include "itkDiscreteGaussianImageFilter.h" // RecursiveAverageImages img1 img2 weightonimg2 outputname // We divide the 2nd input image by its mean and add it to the first // input image with weight 1/n. //The output overwrites the 1st img with the sum. #include "ReadWriteImage.h" template int MultiplyImages(int argc, char *argv[]) { typedef float PixelType; // const unsigned int ImageDimension = AvantsImageDimension; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; std::string fn1 = std::string(argv[2]); std::string fn2 = std::string(argv[3]); std::string outname=std::string(argv[4]); typename ImageType::Pointer image1 = NULL; typename ImageType::Pointer image2 = NULL; typename ImageType::Pointer varimage = NULL; typename readertype::Pointer reader2 = readertype::New(); typename readertype::Pointer reader1 = readertype::New(); reader2->SetFileName(fn2.c_str()); bool isfloat=false; try { reader2->UpdateLargestPossibleRegion(); } catch(...) { std::cout << " Rather than opening " << fn2 << " as an image file, this program has decided, in its great wisdom, to consider it to be a floating point numerical value, and has acted accordingly -- i.e. read this as a number. " << std::endl; isfloat=true; } float floatval=1.0; if (isfloat) floatval=atof(argv[3]); else image2 = reader2->GetOutput(); reader1->SetFileName(fn1.c_str()); try { reader1->UpdateLargestPossibleRegion(); image1 = reader1->GetOutput(); } catch(...) { std::cout << " read 1 error "; } varimage=ImageType::New(); varimage->SetLargestPossibleRegion( image1->GetLargestPossibleRegion() ); varimage->SetBufferedRegion( image1->GetLargestPossibleRegion() ); varimage->SetLargestPossibleRegion( image1->GetLargestPossibleRegion() ); varimage->Allocate(); varimage->SetSpacing(image1->GetSpacing()); varimage->SetOrigin(image1->GetOrigin()); varimage->SetDirection(image1->GetDirection()); Iterator vfIter2( varimage, varimage->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { IndexType ind=vfIter2.GetIndex(); float pix2; if (isfloat) pix2= floatval; else pix2=image2->GetPixel(ind); float pix1 = image1->GetPixel(ind); //std::cout << " p1 " << pix1 << " p2 " << pix2 << std::endl; float div=pix1*pix2; vfIter2.Set(div); } typename writertype::Pointer writer = writertype::New(); writer->SetFileName(argv[4]); writer->SetInput( varimage ); writer->Write(); return 0; } int main(int argc, char *argv[]) { if ( argc < 4 ) { std::cout << "Usage: "<< std::endl; std::cout << argv[0] << " ImageDimension img1.nii img2.nii product.nii {smoothing}" << std::endl; return 1; } switch ( atoi(argv[1]) ) { case 2: MultiplyImages<2>(argc,argv); break; case 3: MultiplyImages<3>(argc,argv); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } return 0; } ants-1.9.2+svn680.dfsg/Examples/N3BiasFieldCorrection.cxx000066400000000000000000000157671147325206600230550ustar00rootroot00000000000000#include "itkBSplineControlPointImageFilter.h" #include "itkExpImageFilter.h" #include "itkImageFileReader.h" #include "itkImageFileWriter.h" #include "itkImageRegionIterator.h" #include "itkN3MRIBiasFieldCorrectionImageFilter.h" #include "itkOtsuThresholdImageFilter.h" #include "itkShrinkImageFilter.h" template class CommandIterationUpdate : public itk::Command { public: typedef CommandIterationUpdate Self; typedef itk::Command Superclass; typedef itk::SmartPointer Pointer; itkNewMacro( Self ); protected: CommandIterationUpdate() {}; public: void Execute(itk::Object *caller, const itk::EventObject & event) { Execute( (const itk::Object *) caller, event); } void Execute(const itk::Object * object, const itk::EventObject & event) { const TFilter * filter = dynamic_cast< const TFilter * >( object ); if( typeid( event ) != typeid( itk::IterationEvent ) ) { return; } std::cout << "Iteration " << filter->GetElapsedIterations() << " (of " << filter->GetMaximumNumberOfIterations() << "). "; std::cout << " Current convergence value = " << filter->GetCurrentConvergenceMeasurement() << " (threshold = " << filter->GetConvergenceThreshold() << ")" << std::endl; } }; template int N3BiasFieldCorrection( int argc, char *argv[] ) { typedef float RealType; typedef itk::Image ImageType; typedef itk::Image MaskImageType; typedef itk::ImageFileReader ReaderType; typename ReaderType::Pointer reader = ReaderType::New(); reader->SetFileName( argv[2] ); reader->Update(); typedef itk::ShrinkImageFilter ShrinkerType; typename ShrinkerType::Pointer shrinker = ShrinkerType::New(); shrinker->SetInput( reader->GetOutput() ); shrinker->SetShrinkFactors( 1 ); typename MaskImageType::Pointer maskImage = NULL; if( argc > 5 ) { typedef itk::ImageFileReader MaskReaderType; typename MaskReaderType::Pointer maskreader = MaskReaderType::New(); maskreader->SetFileName( argv[5] ); try { maskreader->Update(); maskImage = maskreader->GetOutput(); } catch(...) { std::cout << "Mask file not read. Generating mask file using otsu" << " thresholding." << std::endl; } } if( !maskImage ) { typedef itk::OtsuThresholdImageFilter ThresholderType; typename ThresholderType::Pointer otsu = ThresholderType::New(); otsu->SetInput( reader->GetOutput() ); otsu->SetNumberOfHistogramBins( 200 ); otsu->SetInsideValue( 0 ); otsu->SetOutsideValue( 1 ); otsu->Update(); maskImage = otsu->GetOutput(); } typedef itk::ShrinkImageFilter MaskShrinkerType; typename MaskShrinkerType::Pointer maskshrinker = MaskShrinkerType::New(); maskshrinker->SetInput( maskImage ); maskshrinker->SetShrinkFactors( 1 ); if( argc > 4 ) { shrinker->SetShrinkFactors( atoi( argv[4] ) ); maskshrinker->SetShrinkFactors( atoi( argv[4] ) ); } shrinker->Update(); maskshrinker->Update(); typedef itk::N3MRIBiasFieldCorrectionImageFilter CorrecterType; typename CorrecterType::Pointer correcter = CorrecterType::New(); correcter->SetInput( shrinker->GetOutput() ); correcter->SetMaskImage( maskshrinker->GetOutput() ); if( argc > 6 ) { correcter->SetMaximumNumberOfIterations( atoi( argv[6] ) ); } if( argc > 7 ) { correcter->SetNumberOfFittingLevels( atoi( argv[7] ) ); } typedef CommandIterationUpdate CommandType; typename CommandType::Pointer observer = CommandType::New(); correcter->AddObserver( itk::IterationEvent(), observer ); try { correcter->Update(); } catch(...) { std::cerr << "Exception caught." << std::endl; return EXIT_FAILURE; } // correcter->Print( std::cout, 3 ); /** * Reconstruct the bias field at full image resolution. Divide * the original input image by the bias field to get the final * corrected image. */ typedef itk::BSplineControlPointImageFilter BSplinerType; typename BSplinerType::Pointer bspliner = BSplinerType::New(); bspliner->SetInput( correcter->GetLogBiasFieldControlPointLattice() ); bspliner->SetSplineOrder( correcter->GetSplineOrder() ); bspliner->SetSize( reader->GetOutput()->GetLargestPossibleRegion().GetSize() ); bspliner->SetOrigin( reader->GetOutput()->GetOrigin() ); bspliner->SetDirection( reader->GetOutput()->GetDirection() ); bspliner->SetSpacing( reader->GetOutput()->GetSpacing() ); bspliner->Update(); typename ImageType::Pointer logField = ImageType::New(); logField->SetOrigin( bspliner->GetOutput()->GetOrigin() ); logField->SetSpacing( bspliner->GetOutput()->GetSpacing() ); logField->SetRegions( bspliner->GetOutput()->GetLargestPossibleRegion().GetSize() ); logField->SetDirection( bspliner->GetOutput()->GetDirection() ); logField->Allocate(); itk::ImageRegionIterator ItB( bspliner->GetOutput(), bspliner->GetOutput()->GetLargestPossibleRegion() ); itk::ImageRegionIterator ItF( logField, logField->GetLargestPossibleRegion() ); for( ItB.GoToBegin(), ItF.GoToBegin(); !ItB.IsAtEnd(); ++ItB, ++ItF ) { ItF.Set( ItB.Get()[0] ); } typedef itk::ExpImageFilter ExpFilterType; typename ExpFilterType::Pointer expFilter = ExpFilterType::New(); expFilter->SetInput( logField ); expFilter->Update(); typedef itk::DivideImageFilter DividerType; typename DividerType::Pointer divider = DividerType::New(); divider->SetInput1( reader->GetOutput() ); divider->SetInput2( expFilter->GetOutput() ); divider->Update(); typedef itk::ImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetFileName( argv[3] ); writer->SetInput( divider->GetOutput() ); writer->Update(); if( argc > 8 ) { typedef itk::ImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetFileName( argv[8] ); writer->SetInput( expFilter->GetOutput() ); writer->Update(); } return EXIT_SUCCESS; } int main( int argc, char *argv[] ) { if ( argc < 4 ) { std::cerr << "Usage: " << argv[0] << " imageDimension inputImage " << "outputImage [shrinkFactor] [maskImage] [numberOfIterations] " << "[numberOfFittingLevels] [outputBiasField] " << std::endl; exit( EXIT_FAILURE ); } switch( atoi( argv[1] ) ) { case 2: N3BiasFieldCorrection<2>( argc, argv ); break; case 3: N3BiasFieldCorrection<3>( argc, argv ); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } } ants-1.9.2+svn680.dfsg/Examples/N4BiasFieldCorrection.cxx000066400000000000000000000664641147325206600230560ustar00rootroot00000000000000#include "itkBSplineControlPointImageFilter.h" #include "antsCommandLineParser.h" #include "itkConstantPadImageFilter.h" #include "itkExpImageFilter.h" #include "itkExtractImageFilter.h" #include "itkImageFileReader.h" #include "itkImageFileWriter.h" #include "itkImageRegionIterator.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkN4MRIBiasFieldCorrectionImageFilter.h" #include "itkOtsuThresholdImageFilter.h" #include "itkShrinkImageFilter.h" #include "itkTimeProbe.h" #include #include #include template class CommandIterationUpdate : public itk::Command { public: typedef CommandIterationUpdate Self; typedef itk::Command Superclass; typedef itk::SmartPointer Pointer; itkNewMacro( Self ); protected: CommandIterationUpdate() {}; public: void Execute(itk::Object *caller, const itk::EventObject & event) { Execute( (const itk::Object *) caller, event); } void Execute(const itk::Object * object, const itk::EventObject & event) { const TFilter * filter = dynamic_cast< const TFilter * >( object ); if( typeid( event ) != typeid( itk::IterationEvent ) ) { return; } if( filter->GetElapsedIterations() == 1 ) { std::cout << "Current level = " << filter->GetCurrentLevel() + 1 << std::endl; } std::cout << " Iteration " << filter->GetElapsedIterations() << " (of " << filter->GetMaximumNumberOfIterations()[filter->GetCurrentLevel()] << "). "; std::cout << " Current convergence value = " << filter->GetCurrentConvergenceMeasurement() << " (threshold = " << filter->GetConvergenceThreshold() << ")" << std::endl; } }; template int N4( itk::ants::CommandLineParser *parser ) { typedef float RealType; typedef itk::Image ImageType; typename ImageType::Pointer inputImage = NULL; typedef itk::Image MaskImageType; typename MaskImageType::Pointer maskImage = NULL; typedef itk::N4MRIBiasFieldCorrectionImageFilter CorrecterType; typename CorrecterType::Pointer correcter = CorrecterType::New(); typedef itk::ImageFileReader ReaderType; typename ReaderType::Pointer reader = ReaderType::New(); typename itk::ants::CommandLineParser::OptionType::Pointer inputImageOption = parser->GetOption( "input-image" ); if( inputImageOption ) { std::string inputFile = inputImageOption->GetValue(); reader->SetFileName( inputFile.c_str() ); reader->Update(); inputImage = reader->GetOutput(); } else { std::cerr << "Input image not specified." << std::endl; return EXIT_FAILURE; } /** * handle the mask image */ typename itk::ants::CommandLineParser::OptionType::Pointer maskImageOption = parser->GetOption( "mask-image" ); if( maskImageOption && maskImageOption->GetNumberOfValues() ) { std::string inputFile = maskImageOption->GetValue(); typedef itk::ImageFileReader ReaderType; typename ReaderType::Pointer maskreader = ReaderType::New(); maskreader->SetFileName( inputFile.c_str() ); try { maskreader->Update(); maskImage = maskreader->GetOutput(); } catch(...) {} } if( !maskImage ) { std::cout << "Mask not read. Creating Otsu mask." << std::endl; typedef itk::OtsuThresholdImageFilter ThresholderType; typename ThresholderType::Pointer otsu = ThresholderType::New(); otsu->SetInput( inputImage ); otsu->SetNumberOfHistogramBins( 200 ); otsu->SetInsideValue( 0 ); otsu->SetOutsideValue( 1 ); otsu->Update(); maskImage = otsu->GetOutput(); } typename ImageType::Pointer weightImage = NULL; typename itk::ants::CommandLineParser::OptionType::Pointer weightImageOption = parser->GetOption( "weight-image" ); if( weightImageOption && weightImageOption->GetNumberOfValues() ) { std::string inputFile = weightImageOption->GetValue(); typedef itk::ImageFileReader ReaderType; typename ReaderType::Pointer weightreader = ReaderType::New(); weightreader->SetFileName( inputFile.c_str() ); weightreader->Update(); weightImage = weightreader->GetOutput(); } /** * convergence options */ typename itk::ants::CommandLineParser::OptionType::Pointer convergenceOption = parser->GetOption( "convergence" ); if( convergenceOption ) { if( convergenceOption->GetNumberOfParameters() > 0 ) { std::vector numIters = parser->ConvertVector( convergenceOption->GetParameter( 0 ) ); typename CorrecterType::VariableSizeArrayType maximumNumberOfIterations( numIters.size() ); for( unsigned int d = 0; d < numIters.size(); d++ ) { maximumNumberOfIterations[d] = numIters[d]; } correcter->SetMaximumNumberOfIterations( maximumNumberOfIterations ); typename CorrecterType::ArrayType numberOfFittingLevels; numberOfFittingLevels.Fill( numIters.size() ); correcter->SetNumberOfFittingLevels( numberOfFittingLevels ); } if( convergenceOption->GetNumberOfParameters() > 1 ) { correcter->SetConvergenceThreshold( parser->Convert( convergenceOption->GetParameter( 1 ) ) ); } } /** * B-spline options -- we place this here to take care of the case where * the user wants to specify things in terms of the spline distance. */ bool useSplineDistance = false; typename ImageType::IndexType inputImageIndex = inputImage->GetLargestPossibleRegion().GetIndex(); typename ImageType::SizeType inputImageSize = inputImage->GetLargestPossibleRegion().GetSize(); typename ImageType::IndexType maskImageIndex = maskImage->GetLargestPossibleRegion().GetIndex(); typename ImageType::SizeType maskImageSize = maskImage->GetLargestPossibleRegion().GetSize(); typename ImageType::PointType newOrigin = inputImage->GetOrigin(); typename itk::ants::CommandLineParser::OptionType::Pointer bsplineOption = parser->GetOption( "bspline-fitting" ); if( bsplineOption ) { if( bsplineOption->GetNumberOfParameters() > 1 ) { correcter->SetSplineOrder( parser->Convert( bsplineOption->GetParameter( 1 ) ) ); } if( bsplineOption->GetNumberOfParameters() > 2 ) { correcter->SetSigmoidNormalizedAlpha( parser->Convert( bsplineOption->GetParameter( 2 ) ) ); } if( bsplineOption->GetNumberOfParameters() > 3 ) { correcter->SetSigmoidNormalizedBeta( parser->Convert( bsplineOption->GetParameter( 3 ) ) ); } if( bsplineOption->GetNumberOfParameters() > 0 ) { std::vector array = parser->ConvertVector( bsplineOption->GetParameter( 0 ) ); typename CorrecterType::ArrayType numberOfControlPoints; if( array.size() == 1 ) { // the user wants to specify things in terms of spline distance. // 1. need to pad the images to get as close to possible to the // requested domain size. useSplineDistance = true; float splineDistance = array[0]; unsigned long lowerBound[ImageDimension]; unsigned long upperBound[ImageDimension]; for( unsigned int d = 0; d < ImageDimension; d++ ) { float domain = static_cast( inputImage-> GetLargestPossibleRegion().GetSize()[d] - 1 ) * inputImage->GetSpacing()[d]; unsigned int numberOfSpans = static_cast( vcl_ceil( domain / splineDistance ) ); unsigned long extraPadding = static_cast( ( numberOfSpans * splineDistance - domain ) / inputImage->GetSpacing()[d] + 0.5 ); lowerBound[d] = static_cast( 0.5 * extraPadding ); upperBound[d] = extraPadding - lowerBound[d]; newOrigin[d] -= ( static_cast( lowerBound[d] ) * inputImage->GetSpacing()[d] ); numberOfControlPoints[d] = numberOfSpans + correcter->GetSplineOrder(); } typedef itk::ConstantPadImageFilter PadderType; typename PadderType::Pointer padder = PadderType::New(); padder->SetInput( inputImage ); padder->SetPadLowerBound( lowerBound ); padder->SetPadUpperBound( upperBound ); padder->SetConstant( 0 ); padder->Update(); inputImage = padder->GetOutput(); typedef itk::ConstantPadImageFilter MaskPadderType; typename MaskPadderType::Pointer maskPadder = MaskPadderType::New(); maskPadder->SetInput( maskImage ); maskPadder->SetPadLowerBound( lowerBound ); maskPadder->SetPadUpperBound( upperBound ); maskPadder->SetConstant( 0 ); maskPadder->Update(); maskImage = maskPadder->GetOutput(); if( weightImage ) { typename PadderType::Pointer weightPadder = PadderType::New(); weightPadder->SetInput( weightImage ); weightPadder->SetPadLowerBound( lowerBound ); weightPadder->SetPadUpperBound( upperBound ); weightPadder->SetConstant( 0 ); weightPadder->Update(); weightImage = weightPadder->GetOutput(); } } else if( array.size() == ImageDimension ) { for( unsigned int d = 0; d < ImageDimension; d++ ) { numberOfControlPoints[d] = static_cast( array[d] ) + correcter->GetSplineOrder(); } } else { std::cerr << "Incorrect mesh resolution" << std::endl; return EXIT_FAILURE; } correcter->SetNumberOfControlPoints( numberOfControlPoints ); } } typedef itk::ShrinkImageFilter ShrinkerType; typename ShrinkerType::Pointer shrinker = ShrinkerType::New(); shrinker->SetInput( inputImage ); shrinker->SetShrinkFactors( 1 ); typedef itk::ShrinkImageFilter MaskShrinkerType; typename MaskShrinkerType::Pointer maskshrinker = MaskShrinkerType::New(); maskshrinker->SetInput( maskImage ); maskshrinker->SetShrinkFactors( 1 ); typename itk::ants::CommandLineParser::OptionType::Pointer shrinkFactorOption = parser->GetOption( "shrink-factor" ); int shrinkFactor = 4; if( shrinkFactorOption ) { shrinkFactor = parser->Convert( shrinkFactorOption->GetValue() ); } shrinker->SetShrinkFactors( shrinkFactor ); maskshrinker->SetShrinkFactors( shrinkFactor ); shrinker->Update(); maskshrinker->Update(); itk::TimeProbe timer; timer.Start(); correcter->SetInput( shrinker->GetOutput() ); correcter->SetMaskImage( maskshrinker->GetOutput() ); if( weightImage ) { typedef itk::ShrinkImageFilter WeightShrinkerType; typename WeightShrinkerType::Pointer weightshrinker = WeightShrinkerType::New(); weightshrinker->SetInput( weightImage ); weightshrinker->SetShrinkFactors( 1 ); if( shrinkFactorOption ) { int shrinkFactor = parser->Convert( shrinkFactorOption->GetValue() ); weightshrinker->SetShrinkFactors( shrinkFactor ); } weightshrinker->Update(); correcter->SetConfidenceImage( weightshrinker->GetOutput() ); } typedef CommandIterationUpdate CommandType; typename CommandType::Pointer observer = CommandType::New(); correcter->AddObserver( itk::IterationEvent(), observer ); /** * histogram sharpening options */ typename itk::ants::CommandLineParser::OptionType::Pointer histOption = parser->GetOption( "histogram-sharpening" ); if( histOption ) { if( histOption->GetNumberOfParameters() > 0 ) { correcter->SetBiasFieldFullWidthAtHalfMaximum( parser->Convert( histOption->GetParameter( 0 ) ) ); } if( histOption->GetNumberOfParameters() > 1 ) { correcter->SetWeinerFilterNoise( parser->Convert( histOption->GetParameter( 1 ) ) ); } if( histOption->GetNumberOfParameters() > 2 ) { correcter->SetNumberOfHistogramBins( parser->Convert( histOption->GetParameter( 2 ) ) ); } } try { correcter->Update(); } catch(...) { std::cerr << "Exception caught." << std::endl; return EXIT_FAILURE; } correcter->Print( std::cout, 3 ); timer.Stop(); std::cout << "Elapsed time: " << timer.GetMeanTime() << std::endl; /** * output */ typename itk::ants::CommandLineParser::OptionType::Pointer outputOption = parser->GetOption( "output" ); if( outputOption ) { /** * Reconstruct the bias field at full image resolution. Divide * the original input image by the bias field to get the final * corrected image. */ typedef itk::BSplineControlPointImageFilter BSplinerType; typename BSplinerType::Pointer bspliner = BSplinerType::New(); bspliner->SetInput( correcter->GetLogBiasFieldControlPointLattice() ); bspliner->SetSplineOrder( correcter->GetSplineOrder() ); bspliner->SetSize( inputImage->GetLargestPossibleRegion().GetSize() ); bspliner->SetOrigin( newOrigin ); bspliner->SetDirection( inputImage->GetDirection() ); bspliner->SetSpacing( inputImage->GetSpacing() ); bspliner->Update(); typename ImageType::Pointer logField = ImageType::New(); logField->SetOrigin( inputImage->GetOrigin() ); logField->SetSpacing( inputImage->GetSpacing() ); logField->SetRegions( inputImage->GetLargestPossibleRegion() ); logField->SetDirection( inputImage->GetDirection() ); logField->Allocate(); itk::ImageRegionIterator ItB( bspliner->GetOutput(), bspliner->GetOutput()->GetLargestPossibleRegion() ); itk::ImageRegionIterator ItF( logField, logField->GetLargestPossibleRegion() ); for( ItB.GoToBegin(), ItF.GoToBegin(); !ItB.IsAtEnd(); ++ItB, ++ItF ) { ItF.Set( ItB.Get()[0] ); } typedef itk::ExpImageFilter ExpFilterType; typename ExpFilterType::Pointer expFilter = ExpFilterType::New(); expFilter->SetInput( logField ); expFilter->Update(); typedef itk::DivideImageFilter DividerType; typename DividerType::Pointer divider = DividerType::New(); divider->SetInput1( inputImage ); divider->SetInput2( expFilter->GetOutput() ); divider->Update(); if( weightImage && ( maskImageOption && maskImageOption->GetNumberOfValues() > 0 ) ) { itk::ImageRegionIteratorWithIndex ItD( divider->GetOutput(), divider->GetOutput()->GetLargestPossibleRegion() ); for( ItD.GoToBegin(); !ItD.IsAtEnd(); ++ItD ) { if( maskImage->GetPixel( ItD.GetIndex() ) != correcter->GetMaskLabel() ) { ItD.Set( inputImage->GetPixel( ItD.GetIndex() ) ); } } } typename ImageType::RegionType inputRegion; inputRegion.SetIndex( inputImageIndex ); inputRegion.SetSize( inputImageSize ); typedef itk::ExtractImageFilter CropperType; typename CropperType::Pointer cropper = CropperType::New(); cropper->SetInput( divider->GetOutput() ); cropper->SetExtractionRegion( inputRegion ); cropper->Update(); typename CropperType::Pointer biasFieldCropper = CropperType::New(); biasFieldCropper->SetInput( expFilter->GetOutput() ); biasFieldCropper->SetExtractionRegion( inputRegion ); biasFieldCropper->Update(); if( outputOption->GetNumberOfParameters() == 0 ) { typedef itk::ImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetInput( cropper->GetOutput() ); writer->SetFileName( ( outputOption->GetValue() ).c_str() ); writer->Update(); } if( outputOption->GetNumberOfParameters() > 0 ) { typedef itk::ImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetInput( cropper->GetOutput() ); writer->SetFileName( ( outputOption->GetParameter( 0 ) ).c_str() ); writer->Update(); } if( outputOption->GetNumberOfParameters() > 1 ) { typedef itk::ImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetFileName( ( outputOption->GetParameter( 1 ) ).c_str() ); writer->SetInput( biasFieldCropper->GetOutput() ); writer->Update(); } } return EXIT_SUCCESS; } void InitializeCommandLineOptions( itk::ants::CommandLineParser *parser ) { typedef itk::ants::CommandLineParser::OptionType OptionType; { std::string description = std::string( "This option forces the image to be treated as a specified-" ) + std::string( "dimensional image. If not specified, N4 tries to " ) + std::string( "infer the dimensionality from the input image." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "image-dimensionality" ); option->SetShortName( 'd' ); option->SetUsageOption( 0, "2/3/4" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "A scalar image is expected as input for bias correction. " ) + std::string( "Since N4 log transforms the intensities, negative values " ) + std::string( "or values close to zero should be processed prior to " ) + std::string( "correction." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "input-image" ); option->SetShortName( 'i' ); option->SetUsageOption( 0, "inputImageFilename" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "If a mask image is specified, the final bias correction is " ) + std::string( "only performed in the mask region. If a weight image is not " ) + std::string( "specified, only intensity values inside the masked region are " ) + std::string( "used during the execution of the algorithm. If a weight " ) + std::string( "image is specified, only the non-zero weights are used in the " ) + std::string( "execution of the algorithm although the mask region defines " ) + std::string( "where bias correction is performed in the final output. " ) + std::string( "Otherwise bias correction occurs over the entire image domain. " ) + std::string( "See also the option description for the weight image." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "mask-image" ); option->SetShortName( 'x' ); option->SetUsageOption( 0, "maskImageFilename" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "The weight image allows the user to perform a relative " ) + std::string( "weighting of specific voxels during the B-spline fitting. " ) + std::string( "For example, some studies have shown that N3 performed on " ) + std::string( "white matter segmentations improves performance. If one " ) + std::string( "has a spatial probability map of the white matter, one can " ) + std::string( "use this map to weight the b-spline fitting towards those " ) + std::string( "voxels which are more probabilistically classified as white " ) + std::string( "matter. See also the option description for the mask image." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "weight-image" ); option->SetUsageOption( 0, "weightImageFilename" ); option->SetShortName( 'w' ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "Running N4 on large images can be time consuming. " ) + std::string( "To lessen computation time, the input image can be resampled. " ) + std::string( "The shrink factor, specified as a single integer, describes " ) + std::string( "this resampling. Shrink factors <= 4 are commonly used." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "shrink-factor" ); option->SetShortName( 's' ); option->SetUsageOption( 0, "1/2/3/4/..." ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "Convergence is determined by calculating the coefficient of " ) + std::string( "variation between subsequent iterations. When this value " ) + std::string( "is less than the specified threshold " ) + std::string( "from the previous iteration or the maximum number of " ) + std::string( "iterations is exceeded the program terminates. Multiple " ) + std::string( "resolutions can be specified by using 'x' between the number " ) + std::string( "of iterations at each resolution, e.g. 100x50x50." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "convergence" ); option->SetShortName( 'c' ); option->SetUsageOption( 0, "[,]" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "These options describe the b-spline fitting parameters. " ) + std::string( "The initial b-spline mesh at the coarsest resolution is " ) + std::string( "specified either as the number of elements in each dimension, " ) + std::string( "e.g. 2x2x3 for 3-D images, or it can be specified as a " ) + std::string( "single scalar parameter which describes the isotropic sizing " ) + std::string( "of the mesh elements. The latter option is typically preferred. " ) + std::string( "For each subsequent level, the spline distance decreases in " ) + std::string( "half, or equivalently, the number of mesh elements doubles " ) + std::string( "Cubic splines (order = 3) are typically used. The final " ) + std::string( "two parameters are experimental and really do not need to " ) + std::string( "be used for good performance." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "bspline-fitting" ); option->SetShortName( 'b' ); option->SetUsageOption( 0, "[splineDistance,,,]" ); option->SetUsageOption( 1, "[initialMeshResolution,,,]" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "These options describe the histogram sharpening parameters, " ) + std::string( "i.e. the deconvolution step parameters described in the " ) + std::string( "original N3 algorithm. The default values have been shown " ) + std::string( "to work fairly well." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "histogram-sharpening" ); option->SetShortName( 't' ); option->SetUsageOption( 0, "[,,]" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "The output consists of the bias corrected version of the " ) + std::string( "input image. Optionally, one can also output the estimated " ) + std::string( "bias field." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "output" ); option->SetShortName( 'o' ); option->SetUsageOption( 0, "[correctedImage,]" ); option->SetDescription( description ); parser->AddOption( option ); } { std::string description = std::string( "Print the help menu (short version)." ); OptionType::Pointer option = OptionType::New(); option->SetShortName( 'h' ); option->SetDescription( description ); option->AddValue( std::string( "0" ) ); parser->AddOption( option ); } { std::string description = std::string( "Print the help menu." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "help" ); option->SetDescription( description ); option->AddValue( std::string( "0" ) ); parser->AddOption( option ); } } int main( int argc, char *argv[] ) { itk::ants::CommandLineParser::Pointer parser = itk::ants::CommandLineParser::New(); parser->SetCommand( argv[0] ); std::string commandDescription = std::string( "N4 is a variant of the popular N3 (nonparameteric nonuniform " ) + std::string( "normalization) retrospective bias correction algorithm. Based " ) + std::string( "on the assumption that the corruption of the low frequency bias " ) + std::string( "field can be modeled as a convolution of the intensity histogram " ) + std::string( "by a Gaussian, the basic algorithmic protocol is to iterate " ) + std::string( "between deconvolving the intensity histogram by a Gaussian, " ) + std::string( "remapping the intensities, and then spatially smoothing this " ) + std::string( "result by a B-spline modeling of the bias field itself. " ) + std::string( "The modifications from and improvements obtained over " ) + std::string( "the original N3 algorithm are described in the following paper: " ) + std::string( "N. Tustison et al., N4ITK: Improved N3 Bias Correction, " ) + std::string( "IEEE Transactions on Medical Imaging, 29(6):1310-1320, June 2010." ); parser->SetCommandDescription( commandDescription ); InitializeCommandLineOptions( parser ); parser->Parse( argc, argv ); if( argc < 2 || parser->Convert( parser->GetOption( "help" )->GetValue() ) ) { parser->PrintMenu( std::cout, 5, false ); exit( EXIT_FAILURE ); } else if( parser->Convert( parser->GetOption( 'h' )->GetValue() ) ) { parser->PrintMenu( std::cout, 5, true ); exit( EXIT_FAILURE ); } // Get dimensionality unsigned int dimension = 3; itk::ants::CommandLineParser::OptionType::Pointer dimOption = parser->GetOption( "image-dimensionality" ); if( dimOption && dimOption->GetNumberOfValues() > 0 ) { dimension = parser->Convert( dimOption->GetValue() ); } else { // Read in the first intensity image to get the image dimension. std::string filename; itk::ants::CommandLineParser::OptionType::Pointer imageOption = parser->GetOption( "input-image" ); if( imageOption && imageOption->GetNumberOfValues() > 0 ) { if( imageOption->GetNumberOfParameters( 0 ) > 0 ) { filename = imageOption->GetParameter( 0, 0 ); } else { filename = imageOption->GetValue( 0 ); } } else { std::cerr << "No input images were specified. Specify an input image" << " with the -i option" << std::endl; return( EXIT_FAILURE ); } itk::ImageIOBase::Pointer imageIO = itk::ImageIOFactory::CreateImageIO( filename.c_str(), itk::ImageIOFactory::ReadMode ); dimension = imageIO->GetNumberOfDimensions(); } std::cout << std::endl << "Running N4 for " << dimension << "-dimensional images." << std::endl << std::endl; switch( dimension ) { case 2: N4<2>( parser ); break; case 3: N4<3>( parser ); break; case 4: N4<4>( parser ); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } } ants-1.9.2+svn680.dfsg/Examples/PermuteFlipImageOrientationAxes.cxx000066400000000000000000000107021147325206600252160ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: PermuteFlipImageOrientationAxes.cxx,v $ Language: C++ Date: $Date: 2009/01/30 16:40:33 $ Version: $Revision: 1.19 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include "itkImage.h" #include "itkConstantPadImageFilter.h" #include "itkIdentityTransform.h" #include "itkLinearInterpolateImageFunction.h" #include "itkRecursiveGaussianImageFilter.h" #include "itkPermuteAxesImageFilter.h" #include "itkFlipImageFilter.h" #include "ReadWriteImage.h" template int PermuteFlipImageOrientationAxes( int argc, char * argv[] ) { typedef float InputPixelType; typedef float InternalPixelType; typedef float OutputPixelType; typedef itk::Image< InputPixelType, Dimension > InputImageType; typedef itk::Image< InternalPixelType, Dimension > InternalImageType; typedef itk::Image< OutputPixelType, Dimension > OutputImageType; typename InputImageType::Pointer inputImage = NULL; ReadImage(inputImage,argv[1]); typename InputImageType::SpacingType inputSpacing = inputImage->GetSpacing(); // Create a filter typedef OutputImageType ShortImage; typename itk::PermuteAxesImageFilter< ShortImage >::Pointer permute; permute = itk::PermuteAxesImageFilter< ShortImage >::New(); permute->SetInput( inputImage ); unsigned int upperFactors[Dimension]; unsigned int lowerFactors[Dimension]; bool flipaboutorigin=false; if (Dimension == 2) { if (argc > 3) upperFactors[0] = atoi(argv[3]); if (argc > 4) upperFactors[1] = atoi(argv[4]); if (argc > 5) lowerFactors[0] = atoi(argv[5]); if (argc > 6) lowerFactors[1] = atoi(argv[6]); if (argc > 7) flipaboutorigin=atoi(argv[7]); } else if (Dimension == 3) { if (argc > 3) upperFactors[0] = atoi(argv[3]); if (argc > 4) upperFactors[1] = atoi(argv[4]); if (argc > 5 ) upperFactors[2] = atoi(argv[5]); if (argc > 6) lowerFactors[0] = atoi(argv[6]); if (argc > 7) lowerFactors[1] = atoi(argv[7]); if (argc > 8 ) lowerFactors[2] = atoi(argv[8]); if (argc > 9) flipaboutorigin=atoi(argv[9]); } permute->SetOrder( upperFactors ); permute->Update(); typedef itk::FlipImageFilter FlipType; typename FlipType::FlipAxesArrayType flip; for (unsigned int i=0; iSetFlipAboutOrigin(flipaboutorigin); flipper->SetFlipAxes(flip); flipper->SetInput( permute->GetOutput()); flipper->Update(); typename InputImageType::Pointer image = flipper->GetOutput(); WriteImage(image,argv[2]); return 0; } int main(int argc, char *argv[]) { if( argc < 3 ) { std::cerr << "Usage: " << std::endl; std::cerr << argv[0] << " ImageDimension inputImageFile outputImageFile xperm yperm {zperm} xflip yflip {zflip} {FlipAboutOrigin}" << std::endl; std::cout << " for 3D: " << argv[0] << " 3 in.nii out.nii 2 0 1 1 1 1 \n would map z=>x, x=>y, y=>z and flip each " << std::endl; std::cout << " for 2D: " << argv[0] << " 2 in.nii out.nii 1 0 1 0 \n would map x=>y, y=>x and flip x " << std::endl; std::cout << std::endl; std::cout << " 0 1 2 for permute factors gives no axis permutation " << std::endl; std::cout << " 1 2 0 maps y to x, z to y and x to z " << std::endl; std::cout <<" the flip values are boolean - 0 1 0 would flip the y-axis only " << std::endl; std::cout << std::endl << " The FlipAboutOrigin boolean lets you flip about the coordinate set in the origin " << std::endl; return 1; } // Get the image dimension switch( atoi(argv[1])) { case 2: PermuteFlipImageOrientationAxes<2>(argc-1,argv+1); break; case 3: PermuteFlipImageOrientationAxes<3>(argc-1,argv+1); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } return 0; } ants-1.9.2+svn680.dfsg/Examples/PrintHeader.cxx000066400000000000000000000317341147325206600211770ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: PrintHeader.cxx,v $ Language: C++ Date: $Date: 2009/01/05 20:09:47 $ Version: $Revision: 1.4 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include #include #include #include #include "itkImage.h" #include "itkImageFileWriter.h" #include "itkImageFileReader.h" #include "itkCastImageFilter.h" #include "itkRescaleIntensityImageFilter.h" #include "itkMetaDataDictionary.h" #include "itkMetaDataObject.h" #include "itkSpatialOrientation.h" using namespace std; /** below code from Paul Yushkevich's c3d */ template bool try_print_metadata(itk::MetaDataDictionary &mdd, std::string key) { AnyType value=0; if(itk::ExposeMetaData(mdd, key, value)) { cout << " " << key << " = " << value << endl; return true; } else return false; } string get_rai_code(itk::SpatialOrientation::ValidCoordinateOrientationFlags code) { std::map m_CodeToString; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_RIP] = "RIP"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_LIP] = "LIP"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_RSP] = "RSP"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_LSP] = "LSP"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_RIA] = "RIA"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_LIA] = "LIA"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_RSA] = "RSA"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_LSA] = "LSA"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_IRP] = "IRP"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_ILP] = "ILP"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_SRP] = "SRP"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_SLP] = "SLP"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_IRA] = "IRA"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_ILA] = "ILA"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_SRA] = "SRA"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_SLA] = "SLA"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_RPI] = "RPI"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_LPI] = "LPI"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_RAI] = "RAI"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_LAI] = "LAI"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_RPS] = "RPS"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_LPS] = "LPS"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_RAS] = "RAS"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_LAS] = "LAS"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_PRI] = "PRI"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_PLI] = "PLI"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_ARI] = "ARI"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_ALI] = "ALI"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_PRS] = "PRS"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_PLS] = "PLS"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_ARS] = "ARS"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_ALS] = "ALS"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_IPR] = "IPR"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_SPR] = "SPR"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_IAR] = "IAR"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_SAR] = "SAR"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_IPL] = "IPL"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_SPL] = "SPL"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_IAL] = "IAL"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_SAL] = "SAL"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_PIR] = "PIR"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_PSR] = "PSR"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_AIR] = "AIR"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_ASR] = "ASR"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_PIL] = "PIL"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_PSL] = "PSL"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_AIL] = "AIL"; m_CodeToString[itk::SpatialOrientation::ITK_COORDINATE_ORIENTATION_ASL] = "ASL"; return m_CodeToString[code]; } template int PrintHeader(int argc, char *argv[]) { typedef float outPixelType; typedef float floatPixelType; typedef float inPixelType; typedef itk::Image ImageType; typedef itk::Image IntermediateType; typedef itk::Image OutImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typename readertype::Pointer reader = readertype::New(); reader->SetFileName(argv[1]); reader->Update(); std::cout << " Spacing " << reader->GetOutput()->GetSpacing() << std::endl; std::cout << " Origin " << reader->GetOutput()->GetOrigin() << std::endl; std::cout << " Direction " << std::endl << reader->GetOutput()->GetDirection() << std::endl; if ( ImageDimension == 1 ) std::cout << " Size : " << reader->GetOutput()->GetLargestPossibleRegion().GetSize()[0] << " " << std::endl; else if ( ImageDimension == 2 ) std::cout << " Size : " << reader->GetOutput()->GetLargestPossibleRegion().GetSize()[0] << " " << reader->GetOutput()->GetLargestPossibleRegion().GetSize()[1] << " " << std::endl; else if ( ImageDimension == 3 ) std::cout << " Size : " << reader->GetOutput()->GetLargestPossibleRegion().GetSize()[0] << " " << reader->GetOutput()->GetLargestPossibleRegion().GetSize()[1] << " " << " " << reader->GetOutput()->GetLargestPossibleRegion().GetSize()[2] << std::endl; else std::cout << " Size : " << reader->GetOutput()->GetLargestPossibleRegion().GetSize() << std::endl; // std::cout << " Orientation " << reader->GetOutput()->GetOrientation() << std::endl; unsigned int VDim=ImageDimension; // Get the input image typename ImageType::Pointer image = reader->GetOutput(); // Compute the bounding box vnl_vector bb0, bb1, ospm; bb0.set_size(VDim); bb1.set_size(VDim); ospm.set_size(VDim); for(size_t i = 0; i < VDim; i++) { bb0[i] = image->GetOrigin()[i]; bb1[i] = bb0[i] + image->GetSpacing()[i] * image->GetBufferedRegion().GetSize()[i]; ospm[i] = -image->GetOrigin()[i] / image->GetSpacing()[i]; } // Compute the intensity range of the image size_t n = image->GetBufferedRegion().GetNumberOfPixels(); float *vox = image->GetBufferPointer(); double iMax = vox[0], iMin = vox[0], iMean = vox[0]; for(size_t i = 1; i < n; i++) { iMax = (iMax > vox[i]) ? iMax : vox[i]; iMin = (iMin < vox[i]) ? iMin : vox[i]; iMean += vox[i]; } iMean /= n; // Short or long? bool full=true; if(!full) { cout << " dim = " << image->GetBufferedRegion().GetSize() << "; "; cout << " bb = {[" << bb0 << "], [" << bb1 << "]}; "; cout << " vox = " << image->GetSpacing() << "; "; cout << " range = [" << iMin << ", " << iMax << "]; "; cout << endl; } else { cout << endl; cout << " Image Dimensions : " << image->GetBufferedRegion().GetSize() << endl; cout << " Bounding Box : " << "{[" << bb0 << "], [" << bb1 << "]}" << endl; cout << " Voxel Spacing : " << image->GetSpacing() << endl; cout << " Intensity Range : [" << iMin << ", " << iMax << "]" << endl; cout << " Mean Intensity : " << iMean << endl; cout << " Direction Cos Mtx. : " << endl; std::cout << image->GetDirection().GetVnlMatrix() << std::endl; // Print NIFTI s-form matrix (check against freesurfer's MRIinfo) cout << " Voxel->RAS x-form : " << endl; // image->GetVoxelSpaceToRASPhysicalSpaceMatrix().GetVnlMatrix(); // std::cout << image->GetVoxelSpaceToRASPhysicalSpaceMatrix().GetVnlMatrix() << std::endl; // // Print metadata cout << " Image Metadata: " << endl; itk::MetaDataDictionary &mdd = image->GetMetaDataDictionary(); itk::MetaDataDictionary::ConstIterator itMeta; for(itMeta = mdd.Begin(); itMeta != mdd.End(); ++itMeta) { // Get the metadata as a generic object string key = itMeta->first, v_string; itk::SpatialOrientation::ValidCoordinateOrientationFlags v_oflags; if(itk::ExposeMetaData(mdd, key, v_string)) { // For some weird reason, some of the strings returned by this method // contain '\0' characters. We will replace them by spaces std::ostringstream sout(""); for(unsigned int i=0;i= ' ') sout << v_string[i]; v_string = sout.str(); // Make sure the value has more than blanks if(v_string.find_first_not_of(" ") != v_string.npos) cout << " " << key << " = " << v_string << endl; } else if(itk::ExposeMetaData(mdd, key, v_oflags)) { cout << " " << key << " = " << get_rai_code(v_oflags) << endl; } else { bool rc = false; if(!rc) rc |= try_print_metadata(mdd, key); if(!rc) rc |= try_print_metadata(mdd, key); if(!rc) rc |= try_print_metadata(mdd, key); if(!rc) rc |= try_print_metadata(mdd, key); if(!rc) rc |= try_print_metadata(mdd, key); if(!rc) rc |= try_print_metadata(mdd, key); if(!rc) rc |= try_print_metadata(mdd, key); if(!rc) rc |= try_print_metadata(mdd, key); if(!rc) rc |= try_print_metadata(mdd, key); if(!rc) rc |= try_print_metadata(mdd, key); if(!rc) { cout << " " << key << " of unsupported type " << itMeta->second->GetMetaDataObjectTypeName() << endl; } } } } return 1; } bool FileExists(string strFilename) { struct stat stFileInfo; bool blnReturn; int intStat; // Attempt to get the file attributes intStat = stat(strFilename.c_str(),&stFileInfo); if(intStat == 0) { // We were able to get the file attributes // so the file obviously exists. blnReturn = true; } else { // We were not able to get the file attributes. // This may mean that we don't have permission to // access the folder which contains this file. If you // need to do that level of checking, lookup the // return values of stat which will give you // more details on why stat failed. blnReturn = false; } return(blnReturn); } int main(int argc, char *argv[]) { if ( argc < 2 || ((argc == 2) && strcmp(argv[1], "--help") == 0)) { std::cout << "Usage: " << argv[0] << " image.ext " << std::endl; return 1; } // Get the image dimension std::string fn = std::string(argv[1]); if ( ! FileExists(fn) ) { std::cout << " file " << fn << " does not exist . " << std::endl; return 1; } itk::ImageIOBase::Pointer imageIO = itk::ImageIOFactory::CreateImageIO( fn.c_str(), itk::ImageIOFactory::ReadMode); imageIO->SetFileName(fn.c_str()); try { imageIO->ReadImageInformation(); } catch ( ... ) { std::cout << " cant read " << fn << std::endl; return 1 ; } switch ( imageIO->GetNumberOfDimensions() ) { case 2: PrintHeader<2>(argc,argv); break; case 3: PrintHeader<3>(argc,argv); break; case 4: PrintHeader<4>(argc,argv); break; default: std::cerr << "Unsupported dimension " << imageIO->GetNumberOfDimensions() << std::endl; exit( EXIT_FAILURE ); } return 0; } ants-1.9.2+svn680.dfsg/Examples/ReorientTensorImage.cxx000066400000000000000000000517501147325206600227170ustar00rootroot00000000000000/*=========================================================================1 Program: Advanced Normalization Tools Module: $RCSfile: ReorientTensorImage.cxx,v $ Language: C++ Date: $Date: 2009/03/17 18:55:26 $ Version: $Revision: 1.2 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include "ReadWriteImage.h" #include "TensorFunctions.h" #include "itkPreservationOfPrincipalDirectionTensorReorientationImageFilter.h" #include "itkVectorImageFileReader.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkVectorResampleImageFilter.h" #include "itkWarpTensorImageMultiTransformFilter.h" #include "itkTransformFileReader.h" #include "itkTransformFactory.h" #include "itkVectorNearestNeighborInterpolateImageFunction.h" typedef enum{INVALID_FILE=1, AFFINE_FILE, DEFORMATION_FILE, IMAGE_AFFINE_HEADER, IDENTITY_TRANSFORM} TRAN_FILE_TYPE; typedef struct{ std::string filename; TRAN_FILE_TYPE file_type; bool do_affine_inv; } TRAN_OPT; typedef std::vector TRAN_OPT_QUEUE; typedef struct{ bool use_NN_interpolator; bool use_TightestBoundingBox; char * reference_image_filename; bool use_RotationHeader; } MISC_OPT; void DisplayOptQueue(const TRAN_OPT_QUEUE &opt_queue); void DisplayOpt(const TRAN_OPT &opt); TRAN_FILE_TYPE CheckFileType(const char *str){ std::string filename = str; std::string::size_type pos = filename.rfind( "." ); std::string filepre = std::string( filename, 0, pos ); if ( pos != std::string::npos ){ std::string extension = std::string( filename, pos, filename.length()-1); if (extension==std::string(".gz")){ pos = filepre.rfind( "." ); extension = std::string( filepre, pos, filepre.length()-1 ); } if (extension==".txt") return AFFINE_FILE; else return DEFORMATION_FILE; } else{ return INVALID_FILE; } return AFFINE_FILE; } void FilePartsWithgz(const std::string &filename, std::string &path, std::string &name, std::string &ext){ std::string extension; std::string::size_type pos = filename.rfind( "." ); std::string filepre = std::string( filename, 0, pos ); if ( pos != std::string::npos ){ extension = std::string( filename, pos, filename.length()-1); if (extension==std::string(".gz")){ pos = filepre.rfind( "." ); if (pos != std::string::npos){ extension = std::string( filepre, pos, filepre.length()-1 ) + ".gz"; filepre = std::string(filepre, 0, pos); } } } else { extension = std::string(""); } ext = extension; pos = filepre.rfind('/'); if ( pos != std::string::npos ){ path = std::string(filepre, 0, pos+1); name = std::string(filepre, pos+1, filepre.length()-1); } else { path = std::string(""); name = filepre; } // std::cout << "filename: " << filename << std::endl // << "path: " << path << std::endl // << "name: " << name << std::endl // << "ext: " << ext << std::endl; } bool CheckFileExistence(const char *str){ std::ifstream myfile(str); bool b = myfile.is_open(); myfile.close(); return b; } void SetAffineInvFlag(TRAN_OPT &opt, bool &set_current_affine_inv){ opt.do_affine_inv = set_current_affine_inv; if (set_current_affine_inv) set_current_affine_inv = false; } bool ParseInput(int argc, char **argv, char *&moving_image_filename, char *&output_image_filename, TRAN_OPT_QUEUE &opt_queue, MISC_OPT &misc_opt){ opt_queue.clear(); opt_queue.reserve(argc-2); misc_opt.reference_image_filename = NULL; misc_opt.use_NN_interpolator = false; misc_opt.use_TightestBoundingBox = false; misc_opt.use_RotationHeader = false; moving_image_filename = argv[0]; output_image_filename = argv[1]; int ind = 2; bool set_current_affine_inv = false; while(ind < argc){ if (strcmp(argv[ind], "--use-NN")==0) { misc_opt.use_NN_interpolator = true; } else if (strcmp(argv[ind], "-R")==0) { ind++; if(ind >= argc) return false; misc_opt.reference_image_filename = argv[ind]; } else if ((strcmp(argv[ind], "--tightest-bounding-box")==0) && (strcmp(argv[ind], "-R")!=0) ) { misc_opt.use_TightestBoundingBox = true; } else if (strcmp(argv[ind], "--reslice-by-header")==0) { misc_opt.use_RotationHeader = true; TRAN_OPT opt; opt.file_type = IMAGE_AFFINE_HEADER; opt.do_affine_inv = false; opt_queue.push_back(opt); } else if (strcmp(argv[ind], "--Id")==0) { TRAN_OPT opt; opt.filename = "--Id"; opt.do_affine_inv = false; opt.file_type = IDENTITY_TRANSFORM; opt_queue.push_back(opt); } else if (strcmp(argv[ind], "--moving-image-header")==0 || strcmp(argv[ind], "-mh") ==0){ TRAN_OPT opt; opt.file_type = IMAGE_AFFINE_HEADER; opt.filename = moving_image_filename; // opt.do_affine_inv = false; SetAffineInvFlag(opt, set_current_affine_inv); opt_queue.push_back(opt); } else if (strcmp(argv[ind], "--reference-image-header")==0 || strcmp(argv[ind], "-rh") ==0){ if (misc_opt.reference_image_filename==NULL){ std::cout << "reference image filename is not given yet. Specify it with -R before --reference-image-header / -rh." << std::endl; return false; } TRAN_OPT opt; opt.file_type = IMAGE_AFFINE_HEADER; opt.filename = misc_opt.reference_image_filename; // opt.do_affine_inv = false; SetAffineInvFlag(opt, set_current_affine_inv); opt_queue.push_back(opt); } else if (strcmp(argv[ind], "-i")==0) { set_current_affine_inv = true; } else if (strcmp(argv[ind], "--ANTS-prefix")==0){ ind++; std::string prefix = argv[ind]; std::string path, name, ext; FilePartsWithgz(prefix, path, name, ext); if (ext=="") ext=".nii.gz"; std::string deform_file_name, x_deform_name; deform_file_name = path+name+std::string("Warp")+ext; x_deform_name = path+name+std::string("Warpxvec")+ext; if (CheckFileExistence(x_deform_name.c_str())){ TRAN_OPT opt; opt.filename = deform_file_name.c_str(); opt.file_type = CheckFileType(opt.filename.c_str()); opt.do_affine_inv = false; opt_queue.push_back(opt); std::cout << "found deformation file: " << opt.filename << std::endl; DisplayOpt(opt); } std::string affine_file_name; affine_file_name = path+name+std::string("Affine.txt"); if (CheckFileExistence(affine_file_name.c_str())){ TRAN_OPT opt; opt.filename = affine_file_name.c_str(); opt.file_type = CheckFileType(opt.filename.c_str()); opt.do_affine_inv = false; opt_queue.push_back(opt); std::cout << "found affine file: " << opt.filename << std::endl; DisplayOpt(opt); } } else if (strcmp(argv[ind], "--ANTS-prefix-invert")==0){ ind++; std::string prefix = argv[ind]; std::string path, name, ext; FilePartsWithgz(prefix, path, name, ext); if (ext=="") ext=".nii.gz"; std::string affine_file_name; affine_file_name = path+name+std::string("Affine.txt"); if (CheckFileExistence(affine_file_name.c_str())){ TRAN_OPT opt; opt.filename = affine_file_name.c_str(); opt.file_type = CheckFileType(opt.filename.c_str()); opt.do_affine_inv = true; opt_queue.push_back(opt); std::cout << "found affine file: " << opt.filename << std::endl; DisplayOpt(opt); } std::string deform_file_name, x_deform_name; deform_file_name = path+name+std::string("InverseWarp.nii.gz"); x_deform_name = path+name+std::string("InverseWarpxvec.nii.gz"); if (CheckFileExistence(x_deform_name.c_str())){ TRAN_OPT opt; opt.filename = deform_file_name.c_str(); opt.file_type = CheckFileType(opt.filename.c_str()); opt.do_affine_inv = false; opt_queue.push_back(opt); std::cout << "found deformation file: " << opt.filename << std::endl; DisplayOpt(opt); } } else{ TRAN_OPT opt; opt.filename = argv[ind]; opt.file_type = CheckFileType(opt.filename.c_str()); opt.do_affine_inv = false; if (opt.file_type == AFFINE_FILE) SetAffineInvFlag(opt, set_current_affine_inv); else if (opt.file_type == DEFORMATION_FILE && set_current_affine_inv){ std::cout << "Ignore inversion of non-affine file type! " << std::endl; std::cout << "opt.do_affine_inv:" << opt.do_affine_inv << std::endl; } opt_queue.push_back(opt); DisplayOpt(opt); } ind++; } if (misc_opt.use_RotationHeader) { // if (misc_opt.reference_image_filename) { // opt_queue[0].filename = misc_opt.reference_image_filename; // } else { opt_queue[0].filename = "--Id"; opt_queue[0].file_type = IDENTITY_TRANSFORM; opt_queue[0].do_affine_inv = false; // } // TRAN_OPT opt; // opt.file_type = IMAGE_AFFINE_HEADER; // opt.filename = moving_image_filename; // opt.do_affine_inv = true; // opt_queue.push_back(opt); // // std::cout << "Use Rotation Header!" << std::endl; } return true; } void DisplayOptQueue(const TRAN_OPT_QUEUE &opt_queue){ const int kQueueSize = opt_queue.size(); for(int i=0; i void GetIdentityTransform(AffineTransformPointer &aff){ typedef typename AffineTransformPointer::ObjectType AffineTransform; aff = AffineTransform::New(); aff->SetIdentity(); } template void GetAffineTransformFromImage(const ImageTypePointer& img, AffineTransformPointer &aff){ typedef typename ImageTypePointer::ObjectType ImageType; typedef typename ImageType::DirectionType DirectionType; typedef typename ImageType::PointType PointType; typedef typename ImageType::SpacingType SpacingType; typedef typename AffineTransformPointer::ObjectType::TranslationType VectorType; DirectionType direction = img->GetDirection(); SpacingType spacing = img->GetSpacing(); VectorType translation; // translation.Fill(0); for(unsigned int i=0; iGetOrigin()[i]; aff->SetMatrix(direction); // aff->SetCenter(pt); PointType pt; pt.Fill(0); aff->SetOffset(translation); aff->SetCenter(pt); std::cout << "aff from image:" << aff << std::endl; } template void GetLaregstSizeAfterWarp(WarperPointerType &warper, ImagePointerType &img, SizeType &largest_size, PointType &origin_warped){ typedef typename ImagePointerType::ObjectType ImageType; const int ImageDimension = ImageType::GetImageDimension(); // typedef typename ImageType::PointType PointType; typedef typename std::vector PointList; typedef typename ImageType::IndexType IndexType; // PointList pts_orig; PointList pts_warped; typename ImageType::SizeType imgsz; imgsz = img->GetLargestPossibleRegion().GetSize(); typename ImageType::SpacingType spacing; spacing = img->GetSpacing(); pts_warped.clear(); if (ImageDimension == 3){ for(int i=0; i<8; i++){ IndexType ind; switch (i){ case 0: ind[0]=0; ind[1]=0; ind[2]=0; break; case 1: ind[0]=imgsz[0]-1; ind[1]=0; ind[2]=0; break; case 2: ind[0]=0; ind[1]=imgsz[1]-1; ind[2]=0; break; case 3: ind[0]=imgsz[0]-1; ind[1]=imgsz[1]-1; ind[2]=0; break; case 4: ind[0]=0; ind[1]=0; ind[2]=imgsz[2]-1; break; case 5: ind[0]=imgsz[0]-1; ind[1]=0; ind[2]=imgsz[2]-1; break; case 6: ind[0]=0; ind[1]=imgsz[1]-1; ind[2]=imgsz[2]-1; break; case 7: ind[0]=imgsz[0]-1; ind[1]=imgsz[1]-1; ind[2]=imgsz[2]-1; break; } PointType pt_orig, pt_warped; img->TransformIndexToPhysicalPoint(ind, pt_orig); if (warper->MultiInverseAffineOnlySinglePoint(pt_orig, pt_warped)==false){ std::cout << "ERROR: outside of numeric boundary with affine transform." << std::endl; exit(-1); } pts_warped.push_back(pt_warped); std::cout << '[' << i << ']' << ind << ',' << pt_orig << "->" << pt_warped << std::endl; } } else if (ImageDimension == 2) { for(int i=0; i<4; i++){ IndexType ind; switch (i){ case 0: ind[0]=0; ind[1]=0; break; case 1: ind[0]=imgsz[0]-1; ind[1]=0; break; case 2: ind[0]=0; ind[1]=imgsz[1]-1; break; case 3: ind[0]=imgsz[0]-1; ind[1]=imgsz[1]-1; break; } PointType pt_orig, pt_warped; img->TransformIndexToPhysicalPoint(ind, pt_orig); if (warper->MultiInverseAffineOnlySinglePoint(pt_orig, pt_warped)==false){ std::cout << "ERROR: outside of numeric boundary with affine transform." << std::endl; exit(-1); } pts_warped.push_back(pt_warped); std::cout << '[' << i << ']' << ind << ',' << pt_orig << "->" << pt_warped << std::endl; } } else { std::cout << "could not determine the dimension after warping for non 2D/3D volumes" << std::endl; exit(-1); } PointType pt_min, pt_max; pt_min = pts_warped[0]; pt_max = pts_warped[0]; for(unsigned int k=0; kpts_warped[k][i])?(pt_max[i]):(pts_warped[k][i]); } } for(int i=0; i void ReorientTensorImage(char *moving_image_filename, char *output_image_filename, TRAN_OPT_QUEUE &opt_queue, MISC_OPT &misc_opt){ typedef itk::SymmetricSecondRankTensor< float, 3 > TensorType; typedef itk::SymmetricSecondRankTensor< float, 3 > PixelType; typedef itk::Image TensorImageType; typedef itk::Image ImageType; typedef itk::Vector VectorType; typedef itk::Image DeformationFieldType; typedef itk::MatrixOffsetTransformBase< double, ImageDimension, ImageDimension > AffineTransformType; itk::TransformFactory::RegisterTransform(); typedef itk::ImageFileReader ImageFileReaderType; typename TensorImageType::Pointer img_mov; ReadTensorImage(img_mov,moving_image_filename,true); typename ImageType::Pointer img_ref; typename ImageFileReaderType::Pointer reader_img_ref = ImageFileReaderType::New(); if (misc_opt.reference_image_filename){ reader_img_ref->SetFileName(misc_opt.reference_image_filename); reader_img_ref->Update(); img_ref = reader_img_ref->GetOutput(); } // else // img_ref = NULL; typedef itk::TransformFileReader TranReaderType; typedef itk::VectorImageFileReader FieldReaderType; typename DeformationFieldType::Pointer field; unsigned int transcount=0; const int kOptQueueSize = opt_queue.size(); for(int i=0; iSetFileName( opt.filename ); field_reader->Update(); field = field_reader->GetOutput(); transcount++; break; } default: std::cout << "Unknown file type!" << std::endl; } } // warper->PrintTransformList(); typedef itk::PreservationOfPrincipalDirectionTensorReorientationImageFilter ReorientType; typename ReorientType::Pointer reo = ReorientType::New(); reo->SetDeformationField( field ); reo->SetInput( img_mov ); reo->Update(); typename TensorImageType::Pointer img_output = reo->GetOutput(); WriteTensorImage(img_output, output_image_filename,true); } int main(int argc, char *argv[]) { std::cout << " Does not take into account reorientation needed when orientations change only in the header!! " << std::endl; std::cout << " consider the same DT image in 2 different orientations under an applied identity transform. " << std::endl; std::cout << " the components will not be rotated correctly, but should be, b/c the header rotation is not accounted for in the reorientation filter." << std::endl; std::cout << " this is a bug we need to fix." << std::endl; std::cout << " ... " << std::endl; if ( argc < 4 ) { std::cout << "Usage: " << argv[0] << " dimension infile.nii outfile.nii warp.nii " << std::endl; return 1; } TRAN_OPT_QUEUE opt_queue; char *moving_image_filename = NULL; char *output_image_filename = NULL; MISC_OPT misc_opt; bool is_parsing_ok = false; int kImageDim = atoi(argv[1]); is_parsing_ok = ParseInput(argc-2, argv+2, moving_image_filename, output_image_filename, opt_queue, misc_opt); if (is_parsing_ok){ std::cout << "moving_image_filename: " << moving_image_filename << std::endl; std::cout << "output_image_filename: " << output_image_filename << std::endl; std::cout << "reference_image_filename: "; if (misc_opt.reference_image_filename) std::cout << misc_opt.reference_image_filename << std::endl; else std::cout << "NULL" << std::endl; DisplayOptQueue(opt_queue); switch (kImageDim){ case 2:{ //WarpImageMultiTransform<2>(moving_image_filename, output_image_filename, opt_queue, misc_opt); break; } case 3:{ ReorientTensorImage<3>(moving_image_filename, output_image_filename, opt_queue, misc_opt); break; } } } else{ std::cout << "Input error!" << std::endl; } exit(0); //ReorientTensorImage<3>(argc,argv); // WarpImageForward(argc,argv); return 0; } ants-1.9.2+svn680.dfsg/Examples/ResampleImage.cxx000077500000000000000000000233511147325206600215040ustar00rootroot00000000000000#include #include "itkImage.h" #include "itkImageFileReader.h" #include "itkImageFileWriter.h" #include "itkResampleImageFilter.h" #include "itkConstantBoundaryCondition.h" #include "itkIdentityTransform.h" #include "itkBSplineInterpolateImageFunction.h" #include "itkLinearInterpolateImageFunction.h" #include "itkGaussianInterpolateImageFunction.h" #include "itkNearestNeighborInterpolateImageFunction.h" #include "itkWindowedSincInterpolateImageFunction.h" #include #include template TValue Convert( std::string optionString ) { TValue value; std::istringstream iss( optionString ); iss >> value; return value; } template std::vector ConvertVector( std::string optionString ) { std::vector values; std::string::size_type crosspos = optionString.find( 'x', 0 ); if ( crosspos == std::string::npos ) { values.push_back( Convert( optionString ) ); } else { std::string element = optionString.substr( 0, crosspos ) ; TValue value; std::istringstream iss( element ); iss >> value; values.push_back( value ); while ( crosspos != std::string::npos ) { std::string::size_type crossposfrom = crosspos; crosspos = optionString.find( 'x', crossposfrom + 1 ); if ( crosspos == std::string::npos ) { element = optionString.substr( crossposfrom + 1, optionString.length() ); } else { element = optionString.substr( crossposfrom + 1, crosspos ) ; } std::istringstream iss( element ); iss >> value; values.push_back( value ); } } return values; } template int ResampleImage( int argc, char *argv[] ) { typedef double RealType; typedef double PixelType; typedef itk::Image ImageType; typedef itk::ImageFileReader ReaderType; typename ReaderType::Pointer reader = ReaderType::New(); reader->SetFileName( argv[2] ); reader->Update(); typedef itk::IdentityTransform TransformType; typename TransformType::Pointer transform = TransformType::New(); transform->SetIdentity(); typedef itk::LinearInterpolateImageFunction LinearInterpolatorType; typename LinearInterpolatorType::Pointer interpolator = LinearInterpolatorType::New(); interpolator->SetInputImage( reader->GetOutput() ); typedef itk::NearestNeighborInterpolateImageFunction NearestNeighborInterpolatorType; typename NearestNeighborInterpolatorType::Pointer nn_interpolator = NearestNeighborInterpolatorType::New(); nn_interpolator->SetInputImage( reader->GetOutput() ); typedef itk::BSplineInterpolateImageFunction BSplineInterpolatorType; typename BSplineInterpolatorType::Pointer bs_interpolator = BSplineInterpolatorType::New(); bs_interpolator->SetInputImage( reader->GetOutput() ); typedef itk::GaussianInterpolateImageFunction GaussianInterpolatorType; typename GaussianInterpolatorType::Pointer g_interpolator = GaussianInterpolatorType::New(); g_interpolator->SetInputImage( reader->GetOutput() ); typedef itk::WindowedSincInterpolateImageFunction HammingInterpolatorType; typename HammingInterpolatorType::Pointer sh_interpolator = HammingInterpolatorType::New(); sh_interpolator->SetInputImage( reader->GetOutput() ); typedef itk::WindowedSincInterpolateImageFunction > Sinc1InterpolatorType; typename Sinc1InterpolatorType::Pointer sc_interpolator = Sinc1InterpolatorType::New(); sc_interpolator->SetInputImage( reader->GetOutput() ); typedef itk::WindowedSincInterpolateImageFunction > Sinc2InterpolatorType; typename Sinc2InterpolatorType::Pointer sw_interpolator = Sinc2InterpolatorType::New(); sw_interpolator->SetInputImage( reader->GetOutput() ); typedef itk::WindowedSincInterpolateImageFunction > Sinc3InterpolatorType; typename Sinc3InterpolatorType::Pointer sl_interpolator = Sinc3InterpolatorType::New(); sl_interpolator->SetInputImage( reader->GetOutput() ); typedef itk::WindowedSincInterpolateImageFunction > Sinc4InterpolatorType; typename Sinc3InterpolatorType::Pointer sb_interpolator = Sinc3InterpolatorType::New(); sb_interpolator->SetInputImage( reader->GetOutput() ); typedef itk::ResampleImageFilter ResamplerType; typename ResamplerType::Pointer resampler = ResamplerType::New(); typename ResamplerType::SpacingType spacing; typename ResamplerType::SizeType size; std::vector sp = ConvertVector( std::string( argv[4] ) ); if( argc <= 5 || atoi( argv[5] ) == 0 ) { if ( sp.size() == 1 ) { spacing.Fill( sp[0] ); } else if ( sp.size() == ImageDimension ) { for ( unsigned int d = 0; d < ImageDimension; d++ ) { spacing[d] = sp[d]; } } else { std::cerr << "Invalid spacing." << std::endl; } for ( unsigned int i = 0; i < ImageDimension; i++ ) { RealType spacing_old = reader->GetOutput()->GetSpacing()[i]; RealType size_old = reader->GetOutput()->GetLargestPossibleRegion().GetSize()[i]; size[i] = static_cast( ( spacing_old * size_old ) / spacing[i] + 0.5 ); } } else { if ( sp.size() == 1 ) { size.Fill( static_cast( sp[0] ) ); } else if ( sp.size() == ImageDimension ) { for ( unsigned int d = 0; d < ImageDimension; d++ ) { size[d] = static_cast( sp[d] ); } } else { std::cerr << "Invalid size." << std::endl; } for ( unsigned int i = 0; i < ImageDimension; i++ ) { RealType spacing_old = reader->GetOutput()->GetSpacing()[i]; RealType size_old = reader->GetOutput()->GetLargestPossibleRegion().GetSize()[i]; spacing[i] = spacing_old * static_cast( size_old - 1.0 ) / static_cast( size[i] - 1.0 ); } } char arg7 = '\0'; if( argc > 7 ) { arg7 = *argv[7]; } resampler->SetTransform( transform ); resampler->SetInterpolator( interpolator ); if( argc > 6 && atoi( argv[6] ) ) { switch( atoi( argv[6] ) ) { case 0: default: resampler->SetInterpolator( interpolator ); break; case 1: resampler->SetInterpolator( nn_interpolator ); break; case 2: { double sigma[ImageDimension]; for( unsigned int d = 0; d < ImageDimension; d++ ) { sigma[d] = reader->GetOutput()->GetSpacing()[d]; } double alpha = 1.0; if( argc > 7 ) { std::vector sg = ConvertVector( std::string( argv[7] ) ); for( unsigned int d = 0; d < ImageDimension; d++ ) { sigma[d] = sg[d]; } } if( argc > 8 ) { alpha = static_cast( atof( argv[8] ) ); } g_interpolator->SetParameters( sigma, alpha ); resampler->SetInterpolator( g_interpolator ); } break; case 3: { switch( arg7 ) { case 'h': default: resampler->SetInterpolator( sh_interpolator ); break; case 'c': resampler->SetInterpolator( sc_interpolator ); break; case 'l': resampler->SetInterpolator( sl_interpolator ); break; case 'w': resampler->SetInterpolator( sw_interpolator ); break; case 'b': resampler->SetInterpolator( sb_interpolator ); break; } } case 4: { if( argc > 7 && atoi( argv[7] ) >= 0 && atoi( argv[7] ) <= 5 ) { bs_interpolator->SetSplineOrder( atoi( argv[7] ) ); } else { bs_interpolator->SetSplineOrder( 3 ); } resampler->SetInterpolator( bs_interpolator ); break; } } } resampler->SetInput( reader->GetOutput() ); resampler->SetOutputSpacing( spacing ); resampler->SetOutputOrigin( reader->GetOutput()->GetOrigin() ); resampler->SetSize( size ); resampler->SetOutputDirection( reader->GetOutput()->GetDirection() ); resampler->Update(); typedef itk::ImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetFileName( argv[3] ); writer->SetInput( resampler->GetOutput() ); writer->Update(); return 0; } int main( int argc, char *argv[] ) { if ( argc < 5 ) { std::cout << "Usage: " << argv[0] << " imageDimension inputImage " << "outputImage MxNxO [size=1,spacing=0] [interpolate type]" << std::endl; std::cout << " Interpolation type: " << std::endl; std::cout << " 0. linear (default)" << std::endl; std::cout << " 1. nn " << std::endl; std::cout << " 2. gaussian [sigma=imageSpacing] [alpha=1.0]" << std::endl; std::cout << " 3. windowedSinc [type = 'c'osine, 'w'elch, 'b'lackman, 'l'anczos, 'h'amming]" << std::endl; std::cout << " 4. B-Spline [order=3]" << std::endl; exit( 1 ); } switch( atoi( argv[1] ) ) { case 2: ResampleImage<2>( argc, argv ); break; case 3: ResampleImage<3>( argc, argv ); break; case 4: ResampleImage<4>( argc, argv ); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } } ants-1.9.2+svn680.dfsg/Examples/ResampleImageBySpacing.cxx000066400000000000000000000256401147325206600233040ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: ResampleImageBySpacing.cxx,v $ Language: C++ Date: $Date: 2009/03/31 21:22:00 $ Version: $Revision: 1.17 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include "itkImage.h" #include "itkImageFileReader.h" #include "itkImageFileWriter.h" #include "itkResampleImageFilter.h" #include "itkIdentityTransform.h" #include "itkLinearInterpolateImageFunction.h" #include "itkNearestNeighborInterpolateImageFunction.h" #include "itkRecursiveGaussianImageFilter.h" #include "itkIntensityWindowingImageFilter.h" #include "itkDiscreteGaussianImageFilter.h" template typename TImage::Pointer SmoothImage(typename TImage::Pointer image, float sig) { typedef itk::DiscreteGaussianImageFilter dgf; typename dgf::Pointer filter = dgf::New(); filter->SetVariance(sig); filter->SetUseImageSpacingOn(); filter->SetMaximumError(.01f); filter->SetInput(image); filter->Update(); return filter->GetOutput(); } int main( int argc, char * argv[] ) { if( argc < 5 ) { std::cerr << "Usage: " << std::endl; std::cerr << argv[0] << " ImageDimension inputImageFile outputImageFile outxspc outyspc {outzspacing} {dosmooth?} {addvox} {nn-interp?}" << std::endl; std::cout <<" addvox pads each dimension by addvox " << std::endl; std::cerr << " " << std::endl; // std::cout << " interp 0 = linear, 1 = nn " << std::endl; return 1; } unsigned int Dimension = atoi(argv[1]); if (Dimension == 2) { typedef float InputPixelType; typedef float InternalPixelType; typedef float OutputPixelType; typedef itk::Image< InputPixelType, 2 > InputImageType; typedef itk::Image< InternalPixelType, 2 > InternalImageType; typedef itk::Image< OutputPixelType, 2 > OutputImageType; typedef itk::ImageFileReader< InputImageType > ReaderType; typedef itk::ImageFileWriter< OutputImageType > WriterType; ReaderType::Pointer reader = ReaderType::New(); WriterType::Pointer writer = WriterType::New(); reader->SetFileName( argv[2] ); writer->SetFileName( argv[3] ); try { reader->Update(); } catch( itk::ExceptionObject & excep ) { std::cerr << "Exception caught!" << std::endl; std::cerr << excep << std::endl; } InputImageType::ConstPointer inputImage = reader->GetOutput(); const InputImageType::SpacingType& inputSpacing = inputImage->GetSpacing(); OutputImageType::SpacingType spacing; for (int i=0; i<2; i++) spacing[i]=inputSpacing[i]; std::cout << " spacing " << spacing << " dim " << 2 << std::endl; bool dosmooth=1; if (argc > 4) spacing[0] = atof(argv[4]); if (argc > 5) spacing[1] = atof(argv[5]); if (argc > 6) dosmooth = atoi(argv[6]); int addvox=0; if (argc > 7) addvox = atoi(argv[7]); bool nn=false; if (argc > 8) nn = atoi(argv[7]); std::cout << " spacing2 " << spacing << std::endl; InternalImageType::ConstPointer smoothedImage = reader->GetOutput(); if( dosmooth ) { for (int sm=0; sm<2; sm++) { typedef itk::RecursiveGaussianImageFilter< OutputImageType, OutputImageType > GaussianFilterType; GaussianFilterType::Pointer smootherX = GaussianFilterType::New(); smootherX->SetInput( smoothedImage ); float sig = 0; sig = atof(argv[4+sm])/inputSpacing[sm]-1.0; std::cout << " smoothing by : " << sig << " dir " << sm << std::endl; smootherX->SetSigma( sig ); smootherX->SetDirection( sm ); smootherX->SetNormalizeAcrossScale( false ); if (sig > 0 && dosmooth) { try { smootherX->Update(); } catch( itk::ExceptionObject & excep ) { std::cerr << "Exception catched !" << std::endl; std::cerr << excep << std::endl; } smoothedImage = smootherX->GetOutput(); } } } // InternalImageType::ConstPointer smoothedImage = smootherY->GetOutput(); // InternalImageType::ConstPointer smoothedImage = reader->GetOutput(); //smoothedImage =SmoothImage(reader->GetOutput() , ); typedef itk::ResampleImageFilter< InternalImageType, OutputImageType > ResampleFilterType; ResampleFilterType::Pointer resampler = ResampleFilterType::New(); typedef itk::IdentityTransform< double, 2 > TransformType; typedef itk::LinearInterpolateImageFunction< InternalImageType, double > InterpolatorType; typedef itk::NearestNeighborInterpolateImageFunction< InternalImageType, double > InterpolatorType2; InterpolatorType::Pointer interpolator = InterpolatorType::New(); InterpolatorType2::Pointer interpolator2 = InterpolatorType2::New(); resampler->SetInterpolator( interpolator ); if (nn == 1 ) resampler->SetInterpolator( interpolator2 ); InternalImageType::IndexType ind; ind.Fill(1); resampler->SetDefaultPixelValue( inputImage->GetPixel(ind) ); // zero regions without source std::cout << " out space " << spacing << std::endl; resampler->SetOutputSpacing( spacing ); // Use the same origin resampler->SetOutputOrigin( inputImage->GetOrigin() ); // Use the same origin resampler->SetOutputDirection( inputImage->GetDirection() ); InputImageType::SizeType inputSize = inputImage->GetLargestPossibleRegion().GetSize(); typedef InputImageType::SizeType::SizeValueType SizeValueType; InputImageType::SizeType size; for (int i=0; i<2; i++) size[i] = static_cast(inputSize[i] * inputSpacing[i] / spacing[i]+addvox); std::cout << " output size " << size << " spc " << spacing << std::endl; resampler->SetSize( size ); resampler->SetInput( smoothedImage ); writer->SetInput( resampler->GetOutput() ); TransformType::Pointer transform = TransformType::New(); transform->SetIdentity(); resampler->SetTransform( transform ); try { writer->Update(); } catch( itk::ExceptionObject & excep ) { std::cerr << "Exception catched !" << std::endl; std::cerr << excep << std::endl; } } if (Dimension == 3) { typedef float InputPixelType; typedef float InternalPixelType; typedef float OutputPixelType; typedef itk::Image< InputPixelType, 3 > InputImageType; typedef itk::Image< InternalPixelType, 3 > InternalImageType; typedef itk::Image< OutputPixelType, 3 > OutputImageType; typedef itk::ImageFileReader< InputImageType > ReaderType; typedef itk::ImageFileWriter< OutputImageType > WriterType; ReaderType::Pointer reader = ReaderType::New(); WriterType::Pointer writer = WriterType::New(); reader->SetFileName( argv[2] ); writer->SetFileName( argv[3] ); try { reader->Update(); } catch( itk::ExceptionObject & excep ) { std::cerr << "Exception caught!" << std::endl; std::cerr << excep << std::endl; } InputImageType::ConstPointer inputImage = reader->GetOutput(); const InputImageType::SpacingType& inputSpacing = inputImage->GetSpacing(); OutputImageType::SpacingType spacing; for (int i=0; i<3; i++) spacing[i]=inputSpacing[i]; std::cout << " spacing " << spacing << " dim " << 3 << std::endl; bool dosmooth=1; if (argc > 4) spacing[0] = atof(argv[4]); if (argc > 5) spacing[1] = atof(argv[5]); if (argc > 6) spacing[2] = atof(argv[6]); if (argc > 7) dosmooth = atoi(argv[7]); int addvox=0; if (argc > 8) addvox = atoi(argv[8]); bool nn=false; if (argc > 9) nn = atoi(argv[9]); std::cout << " spacing2 " << spacing << std::endl; InternalImageType::ConstPointer smoothedImage = reader->GetOutput(); if( dosmooth ) { for (int sm=0; sm<3; sm++) { typedef itk::RecursiveGaussianImageFilter< OutputImageType, OutputImageType > GaussianFilterType; GaussianFilterType::Pointer smootherX = GaussianFilterType::New(); smootherX->SetInput( smoothedImage ); float sig = 0; sig = atof(argv[4+sm])/inputSpacing[sm]-1.0; std::cout << " smoothing by : " << sig << " dir " << sm << std::endl; smootherX->SetSigma( sig ); smootherX->SetDirection( sm ); smootherX->SetNormalizeAcrossScale( false ); if (sig > 0 && dosmooth) { try { smootherX->Update(); } catch( itk::ExceptionObject & excep ) { std::cerr << "Exception catched !" << std::endl; std::cerr << excep << std::endl; } smoothedImage = smootherX->GetOutput(); } } } // InternalImageType::ConstPointer smoothedImage = smootherY->GetOutput(); // InternalImageType::ConstPointer smoothedImage = reader->GetOutput(); //smoothedImage =SmoothImage(reader->GetOutput() , ); typedef itk::ResampleImageFilter< InternalImageType, OutputImageType > ResampleFilterType; ResampleFilterType::Pointer resampler = ResampleFilterType::New(); typedef itk::IdentityTransform< double, 3 > TransformType; typedef itk::LinearInterpolateImageFunction< InternalImageType, double > InterpolatorType; typedef itk::NearestNeighborInterpolateImageFunction< InternalImageType, double > InterpolatorType2; InterpolatorType::Pointer interpolator = InterpolatorType::New(); InterpolatorType2::Pointer interpolator2 = InterpolatorType2::New(); resampler->SetInterpolator( interpolator ); if (nn == 1 ) resampler->SetInterpolator( interpolator2 ); InternalImageType::IndexType ind; ind.Fill(1); resampler->SetDefaultPixelValue( inputImage->GetPixel(ind) ); // zero regions without source std::cout << " out space " << spacing << std::endl; resampler->SetOutputSpacing( spacing ); // Use the same origin resampler->SetOutputOrigin( inputImage->GetOrigin() ); // Use the same origin resampler->SetOutputDirection( inputImage->GetDirection() ); InputImageType::SizeType inputSize = inputImage->GetLargestPossibleRegion().GetSize(); typedef InputImageType::SizeType::SizeValueType SizeValueType; InputImageType::SizeType size; for (int i=0; i<3; i++) size[i] = static_cast(inputSize[i] * inputSpacing[i] / spacing[i]+addvox); std::cout << " output size " << size << " spc " << spacing << std::endl; resampler->SetSize( size ); resampler->SetInput( smoothedImage ); writer->SetInput( resampler->GetOutput() ); TransformType::Pointer transform = TransformType::New(); transform->SetIdentity(); resampler->SetTransform( transform ); try { writer->Update(); } catch( itk::ExceptionObject & excep ) { std::cerr << "Exception catched !" << std::endl; std::cerr << excep << std::endl; } } return 0; } ants-1.9.2+svn680.dfsg/Examples/ResetDirection.cxx000066400000000000000000000067351147325206600217200ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: ResetDirection.cxx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.3 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include #include #include #include "itkImage.h" #include "itkImageFileWriter.h" #include "itkImageFileReader.h" #include "itkCastImageFilter.h" #include "itkRescaleIntensityImageFilter.h" // #include "../itkAvants.DefineDimension" template int ResetDirection(int argc, char *argv[]) { if ( argc < 3 ) { std::cout << "Usage: " << argv[0] << " infile.nii outfile.nii " << std::endl; return 1; } typedef float outPixelType; typedef float floatPixelType; typedef float inPixelType; typedef itk::Image ImageType; typedef itk::Image IntermediateType; typedef itk::Image OutImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typename readertype::Pointer reader = readertype::New(); reader->SetFileName(argv[1]); reader->Update(); typename OutImageType::Pointer outim = reader->GetOutput(); typename OutImageType::DirectionType direction=outim->GetDirection(); //direction->SetIdentity(); direction.Fill(0); for (unsigned int i=0;i Iterator; typename ImageType::Pointer varimage=ImageType::New(); varimage->SetLargestPossibleRegion( outim->GetLargestPossibleRegion() ); varimage->SetBufferedRegion( outim->GetLargestPossibleRegion() ); varimage->SetLargestPossibleRegion( outim->GetLargestPossibleRegion() ); varimage->Allocate(); varimage->SetSpacing(outim->GetSpacing()); varimage->SetOrigin(outim->GetOrigin()); varimage->SetDirection( direction ); Iterator vfIter2( varimage, varimage->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) vfIter2.Set(outim->GetPixel(vfIter2.GetIndex())); typename writertype::Pointer writer = writertype::New(); writer->SetFileName(argv[2]); writer->SetInput( varimage ); writer->Update(); writer->Write(); return 0; } int main(int argc, char *argv[]) { if ( argc < 3 ) { std::cout << "Usage: " << argv[0] << " infile.nii outfile.nii " << std::endl; return 1; } // Get the image dimension std::string fn = std::string(argv[1]); itk::ImageIOBase::Pointer imageIO = itk::ImageIOFactory::CreateImageIO( fn.c_str(), itk::ImageIOFactory::ReadMode); imageIO->SetFileName(fn.c_str()); imageIO->ReadImageInformation(); switch ( imageIO->GetNumberOfDimensions() ) { case 2: ResetDirection<2>(argc,argv); break; case 3: ResetDirection<3>(argc,argv); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } return 0; } ants-1.9.2+svn680.dfsg/Examples/SetDirectionByMatrix.cxx000066400000000000000000000076161147325206600230500ustar00rootroot00000000000000/* * ResetDirection2.cxx * * Created on: Nov 14, 2008 * Author: songgang */ /*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: ResetDirection2.cxx,v $ Language: C++ Date: $Date: 2008/11/14 20:47:46 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include #include #include #include "itkImage.h" #include "itkImageFileWriter.h" #include "itkImageFileReader.h" #include "itkCastImageFilter.h" #include "itkRescaleIntensityImageFilter.h" // #include "../itkAvants.DefineDimension" template int ResetDirection(int argc, char *argv[]) { if ( argc < 3 ) { std::cout << "Usage: " << argv[0] << " infile.nii outfile.nii direction matrix in a row " << std::endl; return 1; } typedef float outPixelType; typedef float floatPixelType; typedef float inPixelType; typedef itk::Image ImageType; typedef itk::Image IntermediateType; typedef itk::Image OutImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typename readertype::Pointer reader = readertype::New(); reader->SetFileName(argv[1]); reader->Update(); typename OutImageType::Pointer outim = reader->GetOutput(); typename OutImageType::DirectionType direction=outim->GetDirection(); //direction->SetIdentity(); // direction.Fill(0); // for (unsigned int i=0;iSetFileName(fn.c_str()); imageIO->ReadImageInformation(); switch ( imageIO->GetNumberOfDimensions() ) { case 2: ResetDirection<2>(argc,argv); break; case 3: ResetDirection<3>(argc,argv); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } return 0; } ants-1.9.2+svn680.dfsg/Examples/SetOrigin.cxx000066400000000000000000000065611147325206600206750ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: SetOrigin.cxx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.19 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include #include #include #include "itkImage.h" #include "itkImageFileWriter.h" #include "itkImageFileReader.h" #include "itkCastImageFilter.h" #include "itkRescaleIntensityImageFilter.h" template int SetOrigin(int argc, char *argv[]) { typedef float outPixelType; typedef float floatPixelType; typedef float inPixelType; typedef itk::Image ImageType; typedef itk::Image IntermediateType; typedef itk::Image OutImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typename readertype::Pointer reader = readertype::New(); reader->SetFileName(argv[1]); reader->Update(); typename OutImageType::Pointer outim = reader->GetOutput(); typename OutImageType::PointType orig = outim->GetOrigin(); std::cout << " Old Orig " << outim->GetOrigin(); if (argc > 3) orig[0]=atof(argv[3]); if (argc > 4) orig[1]=atof(argv[4]); if (argc > 5) orig[2]=atof(argv[5]); std::cout <<" New Orig " << orig << std::endl; outim->SetOrigin(orig); typedef itk::ImageRegionIteratorWithIndex Iterator; typename ImageType::Pointer varimage=ImageType::New(); varimage->SetLargestPossibleRegion( outim->GetLargestPossibleRegion() ); varimage->SetBufferedRegion( outim->GetLargestPossibleRegion() ); varimage->SetLargestPossibleRegion( outim->GetLargestPossibleRegion() ); varimage->Allocate(); varimage->SetSpacing(outim->GetSpacing()); varimage->SetOrigin(orig); varimage->SetDirection( outim->GetDirection()); Iterator vfIter2( varimage, varimage->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) vfIter2.Set(outim->GetPixel(vfIter2.GetIndex())); typename writertype::Pointer writer = writertype::New(); writer->SetFileName(argv[2]); writer->SetInput( varimage ); writer->Update(); writer->Write(); return 0; } int main(int argc, char *argv[]) { if ( argc < 3 ) { std::cout << "Usage: " << argv[0] << " Dimension infile.hdr outfile.nii OriginX OriginY {OriginZ} " << std::endl; return 1; } // Get the image dimension switch( atoi(argv[1])) { case 2: SetOrigin<2>(argc-1,argv+1); break; case 3: SetOrigin<3>(argc-1,argv+1); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } return 0; } ants-1.9.2+svn680.dfsg/Examples/SetSpacing.cxx000066400000000000000000000064121147325206600210250ustar00rootroot00000000000000/* * SetSpacing.cxx * * Created on: Mar 5, 2009 * Author: songgang */ /*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: SetSpacing.cxx,v $ Language: C++ Date: $Date: 2009/03/06 03:57:48 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include #include #include #include "itkImage.h" #include "itkImageFileWriter.h" #include "itkImageFileReader.h" #include "itkCastImageFilter.h" #include "itkRescaleIntensityImageFilter.h" template int SetSpacing(int argc, char *argv[]) { typedef float outPixelType; typedef float floatPixelType; typedef float inPixelType; typedef itk::Image ImageType; typedef itk::Image IntermediateType; typedef itk::Image OutImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typename readertype::Pointer reader = readertype::New(); reader->SetFileName(argv[1]); reader->Update(); typename OutImageType::Pointer outim = reader->GetOutput(); typename OutImageType::SpacingType spacing = outim->GetSpacing(); std::cout << " Old Spacing " << outim->GetSpacing(); if (argc > 3) spacing[0]=atof(argv[3]); if (argc > 4) spacing[1]=atof(argv[4]); if (argc > 5) spacing[2]=atof(argv[5]); std::cout <<" New Spacing " << spacing << std::endl; typedef itk::ImageRegionIteratorWithIndex Iterator; typename ImageType::Pointer varimage=ImageType::New(); varimage->SetLargestPossibleRegion( outim->GetLargestPossibleRegion() ); varimage->SetBufferedRegion( outim->GetLargestPossibleRegion() ); varimage->SetLargestPossibleRegion( outim->GetLargestPossibleRegion() ); varimage->Allocate(); varimage->SetSpacing(spacing); varimage->SetOrigin(outim->GetOrigin()); varimage->SetDirection( outim->GetDirection()); Iterator vfIter2( varimage, varimage->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) vfIter2.Set(outim->GetPixel(vfIter2.GetIndex())); typename writertype::Pointer writer = writertype::New(); writer->SetFileName(argv[2]); writer->SetInput( varimage ); writer->Update(); writer->Write(); return 0; } int main(int argc, char *argv[]) { if ( argc < 3 ) { std::cout << "Usage: " << argv[0] << " Dimension infile.hdr outfile.nii SpacingX SpacingY {SpacingZ} " << std::endl; return 1; } // Get the image dimension switch( atoi(argv[1])) { case 2: SetSpacing<2>(argc-1,argv+1); break; case 3: SetSpacing<3>(argc-1,argv+1); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } return 0; } ants-1.9.2+svn680.dfsg/Examples/SmoothImage.cxx000066400000000000000000000070121147325206600211760ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: SmoothImage.cxx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include "itkMedianImageFilter.h" #include "itkDiscreteGaussianImageFilter.h" #include "ReadWriteImage.h" template int SmoothImage(int argc, char *argv[]) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef itk::AffineTransform AffineTransformType; typedef itk::LinearInterpolateImageFunction InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::ImageRegionIteratorWithIndex Iterator; std::string fn1 = std::string(argv[2]); float sigma=atof(argv[3]); typename ImageType::Pointer image1 = NULL; typename ImageType::Pointer varimage = NULL; ReadImage(image1, argv[2]); typedef itk::DiscreteGaussianImageFilter dgf; typedef itk::MedianImageFilter medf; typename dgf::Pointer filter = dgf::New(); typename medf::Pointer filter2 = medf::New(); bool usespacing=false; if (argc > 5) usespacing=atoi(argv[5]); bool usemedian=false; if (argc > 6) usemedian=atoi(argv[6]); if (!usespacing) filter->SetUseImageSpacingOff(); else filter->SetUseImageSpacingOn(); if ( !usemedian) { filter->SetVariance(sigma*sigma); filter->SetMaximumError(.01f); filter->SetInput(image1); filter->Update(); varimage=filter->GetOutput(); } else { typename ImageType::SizeType rad; rad.Fill((long unsigned int) sigma); filter2->SetRadius(rad); filter2->SetInput(image1); filter2->Update(); varimage=filter2->GetOutput(); } typename writertype::Pointer writer = writertype::New(); writer->SetFileName(argv[4]); writer->SetInput( varimage ); writer->Write(); return 0; } int main(int argc, char *argv[]) { if ( argc < 4 ) { std::cout << "Usage: "<< std::endl; std::cout << argv[0] << " ImageDimension image.ext smoothingsigma outimage.ext {sigma-is-in-spacing-coordinates-0/1} {medianfilter-0/1}" << std::endl; std::cout <<" if median, then sigma means radius of filtering " << std::endl; return 1; } switch ( atoi(argv[1]) ) { case 2: SmoothImage<2>(argc,argv); break; case 3: SmoothImage<3>(argc,argv); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } return 0; } ants-1.9.2+svn680.dfsg/Examples/StackSlices.cxx000066400000000000000000000161451147325206600212010ustar00rootroot00000000000000/*========================================================================= Program: ITK General Module: $RCSfile: StackSlices.cxx,v $ Language: C++ Date: $Date: 2009/04/02 19:55:26 $ Version: $Revision: 1.22 $ Author: Jeffrey T. Duda (jtduda@seas.upenn.edu) Institution: PICSL This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. =========================================================================*/ #include #include "itkImage.h" #include "itkImageFileReader.h" #include "itkImageFileWriter.h" #include "itkExtractImageFilter.h" #include "itkImageRegionIteratorWithIndex.h" #include "ReadWriteImage.h" /* FlipScalarVolume * This program takes a volume and flips it along the * indicated axes */ int main( int argc, char *argv[] ) { // Pixel and Image typedefs typedef float PixelType; typedef itk::Image ImageType; typedef itk::Image SliceType; typedef itk::ImageFileReader ReaderType; typedef itk::ImageFileWriter WriterType; typedef itk::ExtractImageFilter ExtractFilterType; typedef itk::ImageRegionIteratorWithIndex ImageIt; typedef itk::ImageRegionIteratorWithIndex SliceIt; // Check for valid input paramters if (argc < 5) { std::cout << "Usage: " << argv[0] << " outputvolume x y z inputvolumes" << std::endl; return 1; } char * stackName = argv[1]; int dimVars[3]; dimVars[0] = atoi(argv[2]); dimVars[1] = atoi(argv[3]); dimVars[2] = atoi(argv[4]); int dim = -1; int slice = -1; // which dim to extract slice from for (unsigned int i=0; i<3; i++) { if (dimVars[i] > -1) { if ( (dim > -1) || (slice > -1) ) { std::cerr << "Can only choose slice from 1 dimension" << std::endl; return EXIT_FAILURE; } dim = i; slice = dimVars[i]; } } unsigned long nSlices = argc-5; //std::cout << nSlices << std::endl; ReaderType::Pointer firstReader = ReaderType::New(); firstReader->SetFileName( argv[5] ); firstReader->Update(); std::cout <<" Slice 0 :: " << std::string(argv[5]) << std::endl; ImageType::Pointer stack = ImageType::New(); ImageType::RegionType region = firstReader->GetOutput()->GetLargestPossibleRegion(); region.SetSize(dim, nSlices); stack->SetRegions(region); stack->Allocate(); SliceType::SizeType size; size.Fill(0); SliceType::IndexType index2d; if (dim == 0){ size[0]=firstReader->GetOutput()->GetLargestPossibleRegion().GetSize()[1]; size[1]=firstReader->GetOutput()->GetLargestPossibleRegion().GetSize()[2]; } if (dim == 1){ size[0]=firstReader->GetOutput()->GetLargestPossibleRegion().GetSize()[0]; size[1]=firstReader->GetOutput()->GetLargestPossibleRegion().GetSize()[2]; } if (dim == 2){ size[0]=firstReader->GetOutput()->GetLargestPossibleRegion().GetSize()[0]; size[1]=firstReader->GetOutput()->GetLargestPossibleRegion().GetSize()[1]; } SliceType::Pointer stack2 = SliceType::New(); SliceType::RegionType region2; region2.SetSize(size); stack2->SetRegions(region2); stack2->Allocate(); // std::cout << region << std::endl; ImageType::RegionType extractRegion = stack->GetLargestPossibleRegion(); extractRegion.SetSize(dim, 0); extractRegion.SetIndex(dim, slice); ExtractFilterType::Pointer extractFilter = ExtractFilterType::New(); extractFilter->SetInput( firstReader->GetOutput() ); extractFilter->SetExtractionRegion( extractRegion ); extractFilter->Update(); SliceIt it1(extractFilter->GetOutput(), extractFilter->GetOutput()->GetLargestPossibleRegion()); float mean = 0.0, ct=1; while (!it1.IsAtEnd()) { float val=it1.Value(); if (val > 0) { mean+=it1.Value(); ct++; } ++it1; } mean/=ct; std::cout << " Mean " << mean << std::endl; mean=1; it1.GoToBegin(); while (!it1.IsAtEnd()) { ImageType::IndexType index; index.Fill(0); if (dim == 0) { index[0] = 0; index[1] = it1.GetIndex()[0]; index[2] = it1.GetIndex()[1]; } if (dim == 1) { index[0] = it1.GetIndex()[0]; index[1] = 0; index[2] = it1.GetIndex()[1]; } if (dim == 2) { index[0] = it1.GetIndex()[0]; index[1] = it1.GetIndex()[1]; index[2] = 0; } index2d[0]=it1.GetIndex()[0]; index2d[1]=it1.GetIndex()[1]; stack->SetPixel(index, it1.Value()/mean); stack2->SetPixel(index2d,it1.Value()/mean); ++it1; } if (nSlices == 1 ) { std::cout << " write slice " << std::endl; WriteImage(stack2,stackName); return 0; } //std::cout << "Stacking." << std::flush; for (unsigned int i=1; iSetFileName( argv[5+i] ); reader->Update(); std::cout <<" Slice "<< i << " :: " << std::string(argv[5+i]) << std::endl; // std::cout << reader->GetOutput()->GetLargestPossibleRegion().GetSize() << std::endl; ExtractFilterType::Pointer extract = ExtractFilterType::New(); extract->SetInput( reader->GetOutput() ); extract->SetExtractionRegion( extractRegion ); extract->Update(); SliceIt it(extract->GetOutput(), extract->GetOutput()->GetLargestPossibleRegion()); float mean = 0.0, ct=1; while (!it.IsAtEnd()) { mean+=it.Value(); ct++; ++it; } mean/=ct; std::cout << " Mean " << mean << std::endl; mean=1; it.GoToBegin(); while (!it.IsAtEnd()) { ImageType::IndexType index; index.Fill(0); if (dim == 0) { index[0] = i; index[1] = it.GetIndex()[0]; index[2] = it.GetIndex()[1]; } if (dim == 1) { index[0] = it.GetIndex()[0]; index[1] = i; index[2] = it.GetIndex()[1]; } if (dim == 2) { index[0] = it.GetIndex()[0]; index[1] = it.GetIndex()[1]; index[2] = i; } stack->SetPixel(index, it.Value()/mean); ++it; } //std::cout << i << "." << std::flush; } //std::cout << "Done" << std::endl; WriterType::Pointer writer = WriterType::New(); writer->SetFileName( stackName ); writer->SetInput( stack ); writer->Update(); // Input parameters // char * inputName = argv[1]; // unsigned int flip_x = atoi( argv[2] ); // unsigned int flip_y = atoi( argv[3] ); // unsigned int flip_z = atoi( argv[4] ); // char * outputName = argv[5]; // ReaderType::Pointer reader = ReaderType::New(); // reader->SetFileName( inputName ); // reader->Update(); // // flip in desired directions for correct display // FlipFilterType::Pointer flip = FlipFilterType::New(); // FlipFilterType::FlipAxesArrayType flipOver; // flipOver[0] = flip_x; // flipOver[1] = flip_y; // flipOver[2] = flip_z; // flip->SetInput( reader->GetOutput() ); // flip->SetFlipAxes( flipOver ); // flip->Update(); // // write output // WriterType::Pointer writer = WriterType::New(); // writer->SetInput( flip->GetOutput() ); // writer->SetFileName( outputName ); // writer->Update(); return 0; } ants-1.9.2+svn680.dfsg/Examples/StudentsTestOnImages.cxx000066400000000000000000000353071147325206600230660ustar00rootroot00000000000000 #include #include #include #include #include #include #include #include #include #include #include #include "ReadWriteImage.h" //#include "itkSampleFalseDiscoveryRateCorrectionFilter.h" #include #include #include #include #include "itkTDistribution.h" #include "vnl/vnl_math.h" #include "vnl/vnl_erf.h" // for computing F distribution extern "C" double dbetai_(double *x, double *pin, double *qin); extern "C" double dgamma_(double *x); typedef struct { double statVal; int index; } StatElement; int smallerStatElem(StatElement * elem1, StatElement * elem2) // comparison function for sorting { if (elem1->statVal > elem2->statVal) return 1; else if (elem1->statVal < elem2->statVal) return -1; else return 0; } template void ReadImage(itk::SmartPointer &target, const char *file, bool copy) { // std::cout << " reading b " << std::string(file) << std::endl; typedef itk::ImageFileReader readertype; typename readertype::Pointer reader = readertype::New(); reader->SetFileName(file); reader->Update(); if (!copy) target=(reader->GetOutput() ); else { typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter2( target, target->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { vfIter2.Set( reader->GetOutput()->GetPixel(vfIter2.GetIndex() )); } } } #define GROUPALABEL 0 #define GROUPBLABEL 1 typedef struct { int randNum; int index; } PermElement; void generatePerm(int length, int * genPerm); // generate a permutation of numbers from 0 to length-1 void generatePermGroup(int * groupID, int lengthGroupA, int lengthGroupB, int * genGroupID); // generate a permutation of group assignments int smallerPermElem(PermElement * elem1, PermElement * elem2); static int first = 0; void generatePermGroup(int * groupID, int lengthGroupA, int lengthGroupB, int * genGroupID) // generate a permutation of group assignments { int numSubjects = lengthGroupA + lengthGroupB; if (!first) { first = 1; srand(time(NULL)); //cout << "generatePermGroup called" << endl; } int * newPerm = new int [numSubjects]; generatePerm(numSubjects, newPerm); for (int i=0; irandNum > elem2->randNum) return 1; else if (elem1->randNum < elem2->randNum) return -1; else return 0; } double computeQuantile(int numObs, double * stat, double quantile) // computes the value in the provided statistic for the given quantile value // // numObs = Number of Observations // stat = double array[numObs ] contains the test statisticss { static int first = 0; if (!first) { first = 1; srand(time(NULL)); } StatElement * sortStat= new StatElement[numObs]; for (int perm = 0; perm < numObs; perm++) { sortStat[perm].statVal = stat[perm]; sortStat[perm].index = perm; } // sort, smallest first qsort(sortStat, numObs, sizeof(StatElement), (int (*) (const void *, const void *)) smallerStatElem); // index at value double quantindex = (double) numObs * quantile; if (quantile == 1.0 ) quantindex = numObs; double retval = stat[sortStat[(int) quantindex].index]; delete sortStat; return retval; } void computePermStatPval(int numFeatures, int numPerms, double * permStat, double * permStatPval) // computes the Pval for all permutation statistics // the p-val is computed as the percentual ordered rank over all permutations // // numFeatures = Number of scalar features per Subject // numPerms = Number of Permutations // permStat = double array[numPerms * numFeatures] contains the test statistics // permStatPval = double array [numPerms * numFeatures] returns the p-val of the statistics { static int first = 0; if (!first) { first = 1; srand(time(NULL)); } int feat; int perm; StatElement * sortPermStat= new StatElement[numPerms]; for (feat = 0; feat < numFeatures; feat++) { for (perm = 0; perm < numPerms; perm++) { sortPermStat[perm].statVal = permStat[perm * numFeatures + feat]; sortPermStat[perm].index = perm; } // sort, smallest first qsort(sortPermStat, numPerms, sizeof(StatElement), (int (*) (const void *, const void *)) smallerStatElem); double prevPval = 0; double curPval = 0; for (perm = 0 ; perm < numPerms; perm++) { // percentual rank 0..1 -> cumulative probability -> p-val double nextPval = 1.0 - (double) (perm+1) / (double) numPerms; int curIndex = sortPermStat[perm].index; if ((perm == 0) || (sortPermStat[perm].statVal != sortPermStat[perm-1].statVal)) { // current value is different from previous value (or first value), // thus step up p-value prevPval = curPval; curPval = nextPval; } permStatPval[curIndex * numFeatures + feat] = curPval; } } delete sortPermStat; } double decode_ieee_single( unsigned char *v, int natural_order) { unsigned char *data = v; int s, e; unsigned long src; long f; double value = 0.0; if (natural_order) { src = ((unsigned long)data[0] << 24) | ((unsigned long)data[1] << 16) | ((unsigned long)data[2] << 8) | ((unsigned long)data[3]); } else { src = ((unsigned long)data[3] << 24) | ((unsigned long)data[2] << 16) | ((unsigned long)data[1] << 8) | ((unsigned long)data[0]); } s = (src & 0x80000000UL) >> 31; e = (src & 0x7F800000UL) >> 23; f = (src & 0x007FFFFFUL); if (e == 255 && f != 0) { /* NaN (Not a Number) */ value = DBL_MAX; } else if (e == 255 && f == 0 && s == 1) { /* Negative infinity */ value = -DBL_MAX; } else if (e == 255 && f == 0 && s == 0) { /* Positive infinity */ value = DBL_MAX; } else if (e > 0 && e < 255) { /* Normal number */ f += 0x00800000UL; if (s) f = -f; value = ldexp(f, e - 150); } else if (e == 0 && f != 0) { /* Denormal number */ if (s) f = -f; value = ldexp(f, -149); } else if (e == 0 && f == 0 && s == 1) { /* Negative zero */ value = 0; } else if (e == 0 && f == 0 && s == 0) { /* Positive zero */ value = 0; } else { /* Never happens */ printf("s = %d, e = %d, f = %lu\n", s, e, f); assert(!"Woops, unhandled case in decode_ieee_single()"); } return value; } double factorial( double x) { if ( x <= 1) return 1; double fac=x; double n=fac-1; while ( n >= 1) { fac*=n; n=n-1; } return fac; } double betadist( double a , double b ) { double numer = factorial( a -1)*factorial(b-1); double denom =factorial( a+b-1); return (numer/denom); } double TTest(int numSubjects, int* groupLabel, double * featureValue ) { int numSubjA = 0; int numSubjB = 0; double meanA=0,meanB=0; // unsigned int GROUPALABEL=0; //unsigned int GROUPBLABEL=1; for (int subj = 0; subj < numSubjects; subj++) { if (groupLabel[subj] == GROUPALABEL) { numSubjA++; meanA += featureValue[subj]; } else if (groupLabel[subj] == GROUPBLABEL) { numSubjB++; meanB += featureValue[subj]; } else { std::cerr << " group label " << groupLabel[subj] << " does not exist" << std::endl; } } meanA/=(float)numSubjA; meanB/=(float)numSubjB; double varA=0,varB=0; for (int subj = 0; subj < numSubjects; subj++) { if (groupLabel[subj] == GROUPALABEL) { varA += (featureValue[subj] - meanA)*(featureValue[subj] - meanA); } else if (groupLabel[subj] == GROUPBLABEL) { varB += (featureValue[subj] - meanB)*(featureValue[subj] - meanB); } } float n1 = (float) numSubjA; float n2 = (float) numSubjB; varA/=(n1); // use n1 -1 for unbiased estimator, here assume normal distribution varB/=(n2); // use n2 - 1 " ... " // float sdA=sqrt(varA); // float sdB=sqrt(varB); // float df = n1 + n2 - 2; // unequal vars float denom = varA/n1 + varB/n2; // for equal vars // float var = ( (n1-1.)*newvar1 + (n2-1.)*newvar2 ) / df; // denom = var*(1.0/n1+1.0/n2); double tt=0; if ( denom > 0) tt = (meanA - meanB)/sqrt(denom); return tt; } template int StudentsTestOnImages(int argc, char *argv[]) { typedef float PixelType; typedef itk::Image ImageType; typename ImageType::Pointer mask=NULL; // ReadImage(mask, argv[1], false); unsigned int numSubjectsA=atoi(argv[3]); unsigned int numSubjectsB=atoi(argv[4]); unsigned int numSubjects= numSubjectsA+numSubjectsB; std::string outname=std::string(argv[2]); unsigned int numvals=numSubjects; int* groupLabel = new int [numSubjects]; for (unsigned int i=0; i < numSubjectsA; i++) groupLabel[i]=0; for (unsigned int i=numSubjectsA; i < numSubjects; i++) groupLabel[i]=1; double* feature = new double [numvals]; for (unsigned int i=0; i < numvals; i++) feature[i]=0; std::cout << " Numvals " << numvals << std::endl; // Get the image dimension std::string fn = std::string(argv[5]); itk::ImageIOBase::Pointer imageIO = itk::ImageIOFactory::CreateImageIO(fn.c_str(), itk::ImageIOFactory::ReadMode); imageIO->SetFileName(fn.c_str()); imageIO->ReadImageInformation(); typename ImageType::SizeType size; typename ImageType::SpacingType spacing; typename ImageType::PointType origin; typename ImageType::DirectionType direction; std::vector axis; for(unsigned int i=0; iGetDimensions(i); // if (size[i] != mask->GetLargestPossibleRegion().GetSize()[i]) // { // std::cout << " mask not same size as data !! " << std::endl; // exit(0); //} spacing[i] = imageIO->GetSpacing(i); origin[i] = imageIO->GetOrigin(i); axis= imageIO->GetDirection(i); for (unsigned j=0; jGetNumberOfDimensions()) { direction[j][i] = axis[j]; } else { direction[j][i] = 0.0; } } } std::cout << " size " << size << std::endl; typename ImageType::RegionType region; region.SetSize(size ); typename ImageType::Pointer StatImage=ImageType::New(); StatImage->SetSpacing( spacing ); StatImage->SetOrigin( origin ); StatImage->SetLargestPossibleRegion(region ); StatImage->SetRequestedRegion( region ); StatImage->SetBufferedRegion( region ); StatImage->Allocate(); StatImage->FillBuffer(0); typename ImageType::Pointer PImage=ImageType::New(); PImage->SetSpacing( spacing ); PImage->SetOrigin( origin ); PImage->SetLargestPossibleRegion(region ); PImage->SetRequestedRegion( region ); PImage->SetBufferedRegion( region ); PImage->Allocate(); PImage->FillBuffer(0); // unsigned int sizeofpixel=sizeof(PixelType); std::vector imagestack; imagestack.resize(numvals); for (unsigned int j=0; j(imagestack[j] , ifn.c_str() ,false); } typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter(PImage , PImage->GetLargestPossibleRegion() ); unsigned long nvox=1; for (unsigned int i=0; iGetLargestPossibleRegion().GetSize()[i]; unsigned long ct=0; unsigned long prog = nvox/20; std::cout << " NVals " << numvals << " NSub " << numSubjects << std::endl; for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) { typename ImageType::IndexType index=vfIter.GetIndex(); for (unsigned int subj=0; subjGetPixel(index); } if (ct % prog == 0) std::cout << " % " << (float) ct / (float) nvox << std::endl; double stat=0; // if (mask->GetPixel(index) >= 0.5) if (true) stat=TTest(numSubjects,groupLabel,feature); ct++; StatImage->SetPixel( index , stat); } typedef itk::Statistics::TDistribution DistributionType; typename DistributionType::Pointer distributionFunction = DistributionType::New(); // unsigned long dof=numSubjects-2; WriteImage(StatImage,outname.c_str()); std::string soutname=std::string("PVAL")+outname; //WriteImage(PImage,soutname.c_str()); return 1; } int main(int argc, char *argv[]) { std::cout << " df P = 0.05 P = 0.01 P = 0.001 " << std::endl; std::cout <<" 1 12.71 63.66 636.61 " << std::endl; std::cout << " 2 4.30 9.92 31.60 " << std::endl; std::cout << " 3 3.18 5.84 12.92" << std::endl; std::cout << " 4 2.78 4.60 8.61" << std::endl; std::cout << " 5 2.57 4.03 6.87" << std::endl; std::cout << " 6 2.45 3.71 5.96" << std::endl; std::cout << " 7 2.36 3.50 5.41" << std::endl; std::cout << " 8 2.31 3.36 5.04" << std::endl; std::cout << " 9 2.26 3.25 4.78" << std::endl; std::cout << " 10 2.23 3.17 4.59" << std::endl; std::cout << " 15 2.13 2.95 4.07" << std::endl; std::cout << " 20 2.09 2.85 3.85" << std::endl; std::cout << " 30 2.04 2.75 3.65" << std::endl; std::cout << " 50 2.01 2.68 3.50" << std::endl; std::cout << " 100 1.98 2.63 3.39 " << std::endl; if ( argc < 6 ) { std::cout << "Usage: " << argv[0] << " ImageDimension OutName NGroup1 NGroup2 ControlV1* SubjectV1* " << std::endl; std::cout << " Assume all images the same size " << std::endl; std::cout <<" Writes out an F-Statistic image " << std::endl; std::cout << " \n example call \n \n "; std::cout << argv[0] << " 2 TEST.nii.gz 4 8 FawtJandADCcon/*SUB.nii FawtJandADCsub/*SUB.nii \n "; return 1; } switch ( atoi(argv[1]) ) { case 2: StudentsTestOnImages<2>(argc,argv); break; case 3: StudentsTestOnImages<3>(argc,argv); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } return 0; } ants-1.9.2+svn680.dfsg/Examples/SurfaceBasedSmoothing.cxx000066400000000000000000000056741147325206600232150ustar00rootroot00000000000000 #include "itkBinaryThresholdImageFilter.h" #include "itkBinaryThresholdImageFilter.h" #include "itkBinaryErodeImageFilter.h" #include "itkBinaryDilateImageFilter.h" #include "itkBinaryBallStructuringElement.h" #include "ReadWriteImage.h" #include "itkSurfaceImageCurvature.h" int main(int argc, char *argv[]) { if (argc < 3) { std::cout << " usage : " << argv[0] << " ImageToSmooth sigma SurfaceImage outname {numrepeatsofsmoothing}" << std::endl; std::cout << " We assume the SurfaceImage has a label == 1 that defines the surface " << std::endl; std::cout << " sigma defines the geodesic n-hood radius --- numrepeats allows one to use " << std::endl; std::cout << " a small geodesic n-hood repeatedly applied many times -- faster computation, same effect " << std::endl; return 0; } typedef itk::Image ImageType; enum { ImageDimension = ImageType::ImageDimension }; typedef itk::Image floatImageType; typedef itk::SurfaceImageCurvature ParamType; ParamType::Pointer Parameterizer=ParamType::New(); typedef itk::ImageFileReader< ImageType > ReaderType; typedef ImageType::PixelType PixType; // std::string fn="C://Data//brain15labelimage.img"; std::string ext=".nii"; float opt = 0; float sig=1.0; // float thresh=0.0; if (argc > 2) sig = atof( argv[2]); unsigned int numrepeats=0; if (argc > 5) numrepeats=atoi(argv[5]); ImageType::Pointer input; ReadImage(input, argv[1]); ImageType::Pointer surflabel; ReadImage(surflabel,argv[3]); Parameterizer->SetInput(surflabel); Parameterizer->SetFunctionImage(input); Parameterizer->SetNeighborhoodRadius( sig ); if (sig <= 0) sig=1.0; std::cout << " sigma " << sig << " thresh " << opt << std::endl; Parameterizer->SetSigma(sig); Parameterizer->SetUseGeodesicNeighborhood(true); Parameterizer->SetUseLabel(true); Parameterizer->SetThreshold(0.5); // Parameterizer->ComputeSurfaceArea(); // Parameterizer->IntegrateFunctionOverSurface(); Parameterizer->SetNeighborhoodRadius( sig ); std::cout <<" begin integration NOW " << std::endl; Parameterizer->IntegrateFunctionOverSurface(true); for (unsigned int i=0; iIntegrateFunctionOverSurface(true); std::cout <<" end integration " << std::endl; // Parameterizer->PostProcessGeometry(); // double mn=0.0; ImageType::Pointer output=NULL; floatImageType::Pointer smooth=NULL; smooth=Parameterizer->GetFunctionImage(); std::string fnname = std::string(argv[1]).substr(0,std::string(argv[1]).length()-4); std::string ofn = std::string(argv[4]); std::cout << " writing result " << ofn << std::endl; //writer->SetFileName(ofn.c_str()); // writer->SetInput( smooth ); WriteImage(smooth,ofn.c_str()); std::cout << " done writing "; return 1; } ants-1.9.2+svn680.dfsg/Examples/SurfaceCurvature.cxx000066400000000000000000000111461147325206600222560ustar00rootroot00000000000000 //#include "curvatureapp.h" #include "itkSurfaceCurvatureBase.h" #include "itkSurfaceImageCurvature.h" #include "ReadWriteImage.h" /* void test1() { typedef itk::SurfaceCurvatureBase ParamType; ParamType::Pointer Parameterizer=ParamType::New(); // Parameterizer->TestEstimateTangentPlane(p); Parameterizer->FindNeighborhood(); // Parameterizer->WeightedEstimateTangentPlane( Parameterizer->GetOrigin() ); Parameterizer->EstimateTangentPlane( Parameterizer->GetAveragePoint()); Parameterizer->PrintFrame(); // Parameterizer->SetOrigin(Parameterizer->GetAveragePoint()); for(int i=0; i<3; i++){ Parameterizer->ComputeWeightsAndDirectionalKappaAndAngles (Parameterizer->GetOrigin()); Parameterizer->ComputeFrame(Parameterizer->GetOrigin()); Parameterizer->EstimateCurvature(); Parameterizer->PrintFrame(); } Parameterizer->ComputeJoshiFrame(Parameterizer->GetOrigin()); Parameterizer->PrintFrame(); std::cout << " err 1 " << Parameterizer->ErrorEstimate(Parameterizer->GetOrigin()) << " err 2 " << Parameterizer->ErrorEstimate(Parameterizer->GetOrigin(),-1) << std::endl; } */ int main(int argc, char *argv[]) { if (argc < 3) { std::cout << " usage : SurfaceCurvature FileNameIn FileNameOut sigma option " << std::endl; std::cout << " e.g : SurfaceCurvature BrainIn.nii BrainOut.nii 3 0 " << std::endl; std::cout << " option 0 means just compute mean curvature from intensity " << std::endl; std::cout << " option 5 means characterize surface from intensity " << std::endl; return 0; } typedef itk::Image ImageType; typedef itk::Image floatImageType; enum { ImageDimension = ImageType::ImageDimension }; typedef itk::SurfaceImageCurvature ParamType; ParamType::Pointer Parameterizer=ParamType::New(); typedef ImageType::PixelType PixType; int opt = 0; float sig=1.0; if (argc > 3) sig = atof( argv[3]); std::cout << " sigma " << sig << std::endl; if (argc > 4) opt = (int) atoi(argv[4]); if ( opt < 0) { std::cout << " error " << std::endl; return 0; } ImageType::Pointer input; ReadImage(input,argv[1]); std::cout << " done reading " << std::string(argv[1]) << std::endl; // float ballradius = 2.0; // if (argc >= 6) ballradius = (float) atof(argv[5]); // if (ballradius > 0 && thresh > 0) input = SegmentImage(input, thresh, ballradius); Parameterizer->SetInput(input); // Parameterizer->ProcessLabelImage(); Parameterizer->SetNeighborhoodRadius( 1. ); // std::cout << " set sig " ; std::cin >> sig; if (sig <= 0.5) sig=1.66; std::cout << " sigma " << sig << " option " << opt << std::endl; Parameterizer->SetSigma(sig); if (opt == 1) { Parameterizer->SetUseLabel(true); Parameterizer->SetUseGeodesicNeighborhood(false); } else { Parameterizer->SetUseLabel(false); Parameterizer->SetUseGeodesicNeighborhood(false); float sign=1.0; if (opt == 3) sign=-1.0; std::cout << " setting outward direction as " << sign; Parameterizer->SetkSign(sign); Parameterizer->SetThreshold(0); } // Parameterizer->ComputeSurfaceArea(); // Parameterizer->IntegrateFunctionOverSurface(); // Parameterizer->IntegrateFunctionOverSurface(true); std::cout << " computing frame " << std::endl; if (opt != 5) Parameterizer->ComputeFrameOverDomain( 3 ); else Parameterizer->ComputeFrameOverDomain( opt ); // Parameterizer->SetNeighborhoodRadius( 2 ); // Parameterizer->LevelSetMeanCurvature(); // Parameterizer->SetNeighborhoodRadius( 2.9 ); // Parameterizer->IntegrateFunctionOverSurface(false); // Parameterizer->SetNeighborhoodRadius( 1.5 ); // Parameterizer->IntegrateFunctionOverSurface(true); // for (int i=0; i<1; i++) Parameterizer->PostProcessGeometry(); ImageType::Pointer output=NULL; // Parameterizer->GetFunctionImage()->SetSpacing( input->GetSpacing() ); // Parameterizer->GetFunctionImage()->SetDirection( input->GetDirection() ); // Parameterizer->GetFunctionImage()->SetOrigin( input->GetOrigin() ); // smooth->SetSpacing(reader->GetOutput()->GetSpacing()); // SmoothImage(Parameterizer->GetFunctionImage(),smooth,3); // NormalizeImage(smooth,output,mn); // NormalizeImage(Parameterizer->GetFunctionImage(),output,mn); WriteImage(Parameterizer->GetFunctionImage() , argv[2]); std::cout << " done writing "; return 1; } ants-1.9.2+svn680.dfsg/Examples/TensorDerivedImage.cxx000066400000000000000000000136021147325206600225040ustar00rootroot00000000000000/*========================================================================= Program: ITK General Module: $RCSfile: TensorDerivedImage.cxx,v $ Language: C++ Date: $Date: 2009/03/17 18:58:59 $ Version: $Revision: 1.2 $ Author: Jeffrey T. Duda (jtduda@seas.upenn.edu) Institution: PICSL This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. =========================================================================*/ #include #include "itkImage.h" #include "itkImageFileReader.h" #include "itkImageFileWriter.h" //#include "itkDiffusionTensor3D.h" #include "itkVector.h" //#include "itkTensorFractionalAnisotropyImageFilter.h" #include "itkImageRegionIteratorWithIndex.h" #include #include "TensorFunctions.h" #include "ReadWriteImage.h" #include "itkRGBPixel.h" int main( int argc, char *argv[] ) { // Pixel and Image typedefs typedef float PixelType; typedef itk::Image ScalarImageType; typedef itk::ImageFileWriter WriterType; //typedef itk::Vector TensorType; typedef itk::SymmetricSecondRankTensor< float, 3 > TensorType; typedef itk::Image TensorImageType; typedef itk::ImageFileReader ReaderType; typedef itk::RGBPixel< float > ColorPixelType; typedef itk::Image ColorImageType; typedef itk::ImageFileWriter ColorWriterType; // Check for valid input paramters if (argc < 3) { std::cout << "Usage: " << argv[0] << " tensorvolume outputvolume outputtype" << std::endl; return 1; } // Input parameters char * inputName = argv[1]; char * outputName = argv[2]; std::string outType = argv[3]; TensorImageType::Pointer dtimg; ReadTensorImage(dtimg, inputName, false); std::cout << "tensor_image: " << inputName << std::endl; std::cout << "output_image: " << outputName << std::endl; ScalarImageType::Pointer outImage; ColorImageType::Pointer colorImage; if (outType == "DEC") { colorImage = ColorImageType::New(); colorImage->SetRegions(dtimg->GetLargestPossibleRegion() ); colorImage->SetSpacing(dtimg->GetSpacing()); colorImage->SetOrigin(dtimg->GetOrigin()); colorImage->SetDirection(dtimg->GetDirection()); colorImage->Allocate(); } else { outImage = ScalarImageType::New(); outImage->SetRegions(dtimg->GetLargestPossibleRegion() ); outImage->SetSpacing(dtimg->GetSpacing()); outImage->SetOrigin(dtimg->GetOrigin()); outImage->SetDirection(dtimg->GetDirection()); outImage->Allocate(); } itk::ImageRegionIteratorWithIndex inputIt(dtimg, dtimg->GetLargestPossibleRegion()); if ( (outType == "XX") || (outType == "xx") ) outType = "0"; if ( (outType == "XY") || (outType == "YX") || (outType == "xy") || (outType == "yx") ) outType = "1"; if ( (outType == "XZ") || (outType == "ZX") || (outType == "xz") || (outType == "zx") ) outType = "2"; if ( (outType == "YY") || (outType == "yy") ) outType = "3"; if ( (outType == "YZ") || (outType == "ZY") || (outType == "yz") || (outType == "zy") ) outType = "4"; if ( (outType == "ZZ") || (outType == "zz") ) outType = "5"; std::cout << "Calculating output..." << std::flush; while (!inputIt.IsAtEnd()) { if ((outType == "0") || (outType == "1") || (outType == "2") || (outType == "3") || (outType == "4") || (outType == "5")) { int idx = atoi(outType.c_str()); outImage->SetPixel(inputIt.GetIndex(),inputIt.Value()[idx]); } else if ((outType == "TR") || (outType == "MD")) { ScalarImageType::PixelType tr; tr = inputIt.Value()[0] + inputIt.Value()[2] + inputIt.Value()[5]; if (tr < 0) tr = 0; if (tr != tr) tr = 0; if (outType == "TR") { outImage->SetPixel(inputIt.GetIndex(),tr); } else { outImage->SetPixel(inputIt.GetIndex(),tr/3.0); } } else if (outType == "V") { //unsigned int invalids = 0; bool success=true; TensorType t = TensorLogAndExp(inputIt.Value(), true, success); int current=1; if (!success) current=0; outImage->SetPixel(inputIt.GetIndex(),current); //std::cout << "Found " << invalids << " invalid tensors" << std::endl; } else if (outType == "FA") { float fa = GetTensorFA(inputIt.Value()); outImage->SetPixel(inputIt.GetIndex(),fa); } else if (outType == "DEC") { colorImage->SetPixel(inputIt.GetIndex(), GetTensorRGB(inputIt.Value())); ++inputIt; } } std::cout << "Done. " << std::endl; if (outType == "DEC") { std::cout << "output origin: " << colorImage->GetOrigin() << std::endl; std::cout << "output size: " << colorImage->GetLargestPossibleRegion().GetSize() << std::endl; std::cout << "output spacing: " << colorImage->GetSpacing() << std::endl; std::cout << "output direction: " << colorImage->GetDirection() << std::endl; } else { std::cout << "output origin: " << outImage->GetOrigin() << std::endl; std::cout << "output size: " << outImage->GetLargestPossibleRegion().GetSize() << std::endl; std::cout << "output spacing: " << outImage->GetSpacing() << std::endl; std::cout << "output direction: " << outImage->GetDirection() << std::endl; } if (outType == "DEC") { ColorWriterType::Pointer writer = ColorWriterType::New(); writer->SetInput( colorImage ); writer->SetFileName( outputName ); writer->Update(); } else { WriterType::Pointer writer = WriterType::New(); writer->SetInput( outImage ); writer->SetFileName( outputName ); writer->Update(); } return 0; } ants-1.9.2+svn680.dfsg/Examples/ThresholdImage.cxx000066400000000000000000000206271147325206600216700ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: ThresholdImage.cxx,v $ Language: C++ Date: $Date: 2009/01/27 23:45:44 $ Version: $Revision: 1.20 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ // Software Guide : BeginLatex // // This example illustrates how to deform an image using a BSplineTransform. // // \index{BSplineDeformableTransform} // // Software Guide : EndLatex #include #include #include // Software Guide : BeginCodeSnippet #include "itkImageFileReader.h" #include "itkImageFileWriter.h" #include "itkNeighborhoodIterator.h" #include "itkImage.h" #include "itkResampleImageFilter.h" #include "itkLinearInterpolateImageFunction.h" #include "itkBSplineDeformableTransform.h" #include "itkExtractImageFilter.h" #include "itkBinaryThresholdImageFilter.h" #include "itkDanielssonDistanceMapImageFilter.h" #include "itkMultiplyImageFilter.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkOtsuMultipleThresholdsImageFilter.h" #include template typename TImage::Pointer MultiplyImage(typename TImage::Pointer image1, typename TImage::Pointer image2) { std::cout << " Multiply " << std::endl; // Begin Multiply Images typedef TImage tImageType; // output will be the speed image for FMM typedef itk::MultiplyImageFilter MultFilterType; typename MultFilterType::Pointer filter = MultFilterType::New(); filter->SetInput1( image1 ); filter->SetInput2( image2 ); filter->Update(); return filter->GetOutput(); // this is the speed image // write a function to threshold the speedimage so // if the dist is g.t. D then speed = 1 } template typename TImage::Pointer BinaryThreshold( typename TImage::PixelType low, typename TImage::PixelType high, typename TImage::PixelType replaceval, typename TImage::Pointer input) { std::cout << " Binary Thresh " << std::endl; typedef typename TImage::PixelType PixelType; // Begin Threshold Image typedef itk::BinaryThresholdImageFilter InputThresholderType; typename InputThresholderType::Pointer inputThresholder = InputThresholderType::New(); inputThresholder->SetInput( input ); inputThresholder->SetInsideValue( replaceval ); int outval=0; if ((float) replaceval == (float) -1) outval=1; inputThresholder->SetOutsideValue( outval ); if (high < low) high=255; float eps=1.e-6*low; inputThresholder->SetLowerThreshold((PixelType) low-eps ); inputThresholder->SetUpperThreshold((PixelType) high+eps); inputThresholder->Update(); return inputThresholder->GetOutput(); } template typename TImage::Pointer LabelSurface(typename TImage::PixelType foreground, typename TImage::PixelType newval, typename TImage::Pointer input) { std::cout << " Label Surf " << std::endl; typedef TImage ImageType; enum { ImageDimension = ImageType::ImageDimension }; typename ImageType::Pointer Image = ImageType::New(); Image->SetLargestPossibleRegion(input->GetLargestPossibleRegion() ); Image->SetBufferedRegion(input->GetLargestPossibleRegion()); Image->Allocate(); Image->SetSpacing(input->GetSpacing()); Image->SetOrigin(input->GetOrigin()); typedef itk::NeighborhoodIterator iteratorType; typename iteratorType::RadiusType rad; for (int j=0; jGetLargestPossibleRegion()); GHood.GoToBegin(); // std::cout << " foreg " << (int) foreground; while (!GHood.IsAtEnd()) { typename TImage::PixelType p = GHood.GetCenterPixel(); typename TImage::IndexType ind = GHood.GetIndex(); typename TImage::IndexType ind2; if ( p == foreground ) { bool atedge=false; for (int i = 0; i < GHood.Size(); i++) { ind2=GHood.GetIndex(i); float dist=0.0; for (int j=0; jSetPixel(ind,newval); else if ( p == foreground) Image->SetPixel(ind,0); } ++GHood; } return Image; } template typename TImage::Pointer DanielssonDistanceMap( typename TImage::PixelType pixlo, typename TImage::PixelType pixhi, typename TImage::Pointer input) { std::cout << " DDMap " << std::endl; typedef TImage ImageType; typedef itk::DanielssonDistanceMapImageFilter< ImageType, ImageType > FilterType; typename FilterType::Pointer filter = FilterType::New(); filter->InputIsBinaryOn(); filter->SetUseImageSpacing(true); filter->SetInput(BinaryThreshold(pixlo,pixhi,pixhi,input)); filter->Update(); // std::string fn="C:\\Data\\temp.img"; // WriteImage(filter->GetOutput(),fn.c_str()); // fn="C:\\Data\\temp2.img"; // WriteImage(filter->GetVoronoiMap(),fn.c_str()); return filter->GetOutput(); } template typename TImage::Pointer OtsuThreshold( int NumberOfThresholds, typename TImage::Pointer input) { std::cout << " Otsu Thresh with " << NumberOfThresholds << " thresholds" << std::endl; typedef typename TImage::PixelType PixelType; // Begin Threshold Image typedef itk::OtsuMultipleThresholdsImageFilter InputThresholderType; typename InputThresholderType::Pointer inputThresholder = InputThresholderType::New(); inputThresholder->SetInput( input ); /* inputThresholder->SetInsideValue( replaceval ); int outval=0; if ((float) replaceval == (float) -1) outval=1; inputThresholder->SetOutsideValue( outval ); */ inputThresholder->SetNumberOfThresholds ( NumberOfThresholds ); inputThresholder->Update(); return inputThresholder->GetOutput(); } template int ThresholdImage( int argc, char * argv[] ) { // const unsigned int InImageDimension = AvantsImageDimension; typedef float PixelType; typedef itk::Image< PixelType, InImageDimension > FixedImageType; typedef itk::ImageFileReader< FixedImageType > FixedReaderType; typename FixedReaderType::Pointer fixedReader = FixedReaderType::New(); fixedReader->SetFileName( argv[2] ); typedef itk::ImageFileWriter< FixedImageType > MovingWriterType; typename MovingWriterType::Pointer movingWriter = MovingWriterType::New(); typename MovingWriterType::Pointer movingWriter2 = MovingWriterType::New(); movingWriter->SetFileName( argv[3] ); try { fixedReader->Update(); } catch( itk::ExceptionObject & excp ) { std::cerr << "Exception thrown " << std::endl; std::cerr << excp << std::endl; return EXIT_FAILURE; } // Label the surface of the image typename FixedImageType::Pointer thresh; std::string threshtype=std::string(argv[4]); if (strcmp(threshtype.c_str(),"Otsu") == 0 ) { thresh = OtsuThreshold(atoi(argv[5]),fixedReader->GetOutput()); } else thresh = BinaryThreshold(atof(argv[4]),atof(argv[5]),1.,fixedReader->GetOutput()); movingWriter->SetInput(thresh); movingWriter->Write( ); return EXIT_SUCCESS; } int main( int argc, char * argv[] ) { if( argc < 3 ) { std::cerr << "Usage: " << argv[0]; std::cerr << " ImageDimension ImageIn.ext outImage.ext threshlo threshhi " << std::endl; std::cerr << " ImageDimension ImageIn.ext outImage.ext Otsu NumberofThresholds " << std::endl; std::cout << " Inclusive thresholds " << std::endl; return 1; } // Get the image dimension switch ( atoi(argv[1])) { case 2: ThresholdImage<2>(argc,argv); break; case 3: ThresholdImage<3>(argc,argv); break; case 4: ThresholdImage<4>(argc,argv); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } return 0; } ants-1.9.2+svn680.dfsg/Examples/TileImages.cxx000077500000000000000000000056071147325206600210200ustar00rootroot00000000000000#include "itkImageFileReader.h" #include "itkImageFileWriter.h" #include "itkTileImageFilter.h" #include #include template TValue Convert( std::string optionString ) { TValue value; std::istringstream iss( optionString ); iss >> value; return value; } template std::vector ConvertVector( std::string optionString ) { std::vector values; std::string::size_type crosspos = optionString.find( 'x', 0 ); if ( crosspos == std::string::npos ) { values.push_back( Convert( optionString ) ); } else { std::string element = optionString.substr( 0, crosspos ) ; TValue value; std::istringstream iss( element ); iss >> value; values.push_back( value ); while ( crosspos != std::string::npos ) { std::string::size_type crossposfrom = crosspos; crosspos = optionString.find( 'x', crossposfrom + 1 ); if ( crosspos == std::string::npos ) { element = optionString.substr( crossposfrom + 1, optionString.length() ); } else { element = optionString.substr( crossposfrom + 1, crosspos ) ; } std::istringstream iss( element ); iss >> value; values.push_back( value ); } } return values; } template int TileImages( unsigned int argc, char *argv[] ) { typedef float PixelType; typedef itk::Image ImageType; typedef itk::TileImageFilter FilterType; typename FilterType::Pointer filter = FilterType::New(); typename FilterType::LayoutArrayType array; std::vector layout = ConvertVector( std::string( argv[3] ) ); for ( unsigned int d = 0; d < ImageDimension; d++ ) { array[d] = layout[d]; } filter->SetLayout( array ); for ( unsigned int n = 4; n < argc; n++ ) { typedef itk::ImageFileReader ReaderType; typename ReaderType::Pointer reader = ReaderType::New(); reader->SetFileName( argv[n] ); reader->Update(); filter->SetInput( n-4, reader->GetOutput() ); } filter->Update(); typedef itk::ImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetFileName( argv[2] ); writer->SetInput( filter->GetOutput() ); writer->Update(); return 0; } int main( int argc, char *argv[] ) { if ( argc < 4 ) { std::cout << argv[0] << " imageDimension outputImage layout inputImage1 ... inputImageN" << std::endl; exit( 1 ); } switch( atoi( argv[1] ) ) { case 2: TileImages<2>( argc, argv ); break; case 3: TileImages<3>( argc, argv ); break; case 4: TileImages<4>( argc, argv ); break; default: std::cerr << "Unsupported dimension" << std::endl; exit( EXIT_FAILURE ); } } ants-1.9.2+svn680.dfsg/Examples/WarpImageMultiTransform.cxx000066400000000000000000001027501147325206600235520ustar00rootroot00000000000000#include #include #include "itkImageFileReader.h" #include "itkVector.h" #include "itkVectorImageFileReader.h" #include "itkImageFileWriter.h" #include "itkMatrixOffsetTransformBase.h" #include "itkTransformFactory.h" #include "itkWarpImageMultiTransformFilter.h" #include "itkTransformFileReader.h" #include "itkNearestNeighborInterpolateImageFunction.h" #include "itkBSplineInterpolateImageFunction.h" #include "itkLinearInterpolateImageFunction.h" typedef enum{INVALID_FILE=1, AFFINE_FILE, DEFORMATION_FILE, IMAGE_AFFINE_HEADER, IDENTITY_TRANSFORM} TRAN_FILE_TYPE; typedef struct{ // char *filename; std::string filename; TRAN_FILE_TYPE file_type; bool do_affine_inv; // void SetValue(char *filename, TRAN_FILE_TYPE file_type, bool do_affine_inv){ // this.filename = filename; // this.file_type = file_type; // this.do_affine_inv = do_affine_inv; // }; } TRAN_OPT; typedef std::vector TRAN_OPT_QUEUE; typedef struct{ bool use_NN_interpolator; bool use_BSpline_interpolator; bool use_TightestBoundingBox; char * reference_image_filename; bool use_RotationHeader; } MISC_OPT; void DisplayOptQueue(const TRAN_OPT_QUEUE &opt_queue); void DisplayOpt(const TRAN_OPT &opt); TRAN_FILE_TYPE CheckFileType(const char *str){ std::string filename = str; std::string::size_type pos = filename.rfind( "." ); std::string filepre = std::string( filename, 0, pos ); if ( pos != std::string::npos ){ std::string extension = std::string( filename, pos, filename.length()-1); if (extension==std::string(".gz")){ pos = filepre.rfind( "." ); extension = std::string( filepre, pos, filepre.length()-1 ); } if (extension==".txt") return AFFINE_FILE; else return DEFORMATION_FILE; } else{ return INVALID_FILE; } return AFFINE_FILE; } bool IsInverseDeformation(const char *str){ std::string filename = str; std::string::size_type pos = filename.rfind( "Inverse" ); if ( pos == std::string::npos ) return false; else return true; } void FilePartsWithgz(const std::string &filename, std::string &path, std::string &name, std::string &ext){ std::string extension; std::string::size_type pos = filename.rfind( "." ); std::string filepre = std::string( filename, 0, pos ); if ( pos != std::string::npos ){ extension = std::string( filename, pos, filename.length()-1); if (extension==std::string(".gz")){ pos = filepre.rfind( "." ); if (pos != std::string::npos){ extension = std::string( filepre, pos, filepre.length()-1 ) + ".gz"; filepre = std::string(filepre, 0, pos); } } } else { extension = std::string(""); } ext = extension; pos = filepre.rfind('/'); if ( pos != std::string::npos ){ path = std::string(filepre, 0, pos+1); name = std::string(filepre, pos+1, filepre.length()-1); } else { path = std::string(""); name = filepre; } // std::cout << "filename: " << filename << std::endl // << "path: " << path << std::endl // << "name: " << name << std::endl // << "ext: " << ext << std::endl; } bool CheckFileExistence(const char *str){ std::ifstream myfile(str); bool b = myfile.is_open(); myfile.close(); return b; } void SetAffineInvFlag(TRAN_OPT &opt, bool &set_current_affine_inv){ opt.do_affine_inv = set_current_affine_inv; if (set_current_affine_inv) set_current_affine_inv = false; } bool ParseInput(int argc, char **argv, char *&moving_image_filename, char *&output_image_filename, TRAN_OPT_QUEUE &opt_queue, MISC_OPT &misc_opt){ opt_queue.clear(); opt_queue.reserve(argc-2); misc_opt.reference_image_filename = NULL; misc_opt.use_BSpline_interpolator = false; misc_opt.use_TightestBoundingBox = false; misc_opt.use_RotationHeader = false; misc_opt.use_NN_interpolator=false; misc_opt.use_BSpline_interpolator=false; moving_image_filename = argv[0]; output_image_filename = argv[1]; int ind = 2; bool set_current_affine_inv = false; while(ind < argc){ if (strcmp(argv[ind], "--use-NN")==0) { misc_opt.use_NN_interpolator = true; } else if (strcmp(argv[ind], "--use-BSpline")==0) { misc_opt.use_BSpline_interpolator = true; } else if (strcmp(argv[ind], "-R")==0) { ind++; if(ind >= argc) return false; misc_opt.reference_image_filename = argv[ind]; } else if ((strcmp(argv[ind], "--tightest-bounding-box")==0) && (strcmp(argv[ind], "-R")!=0) ) { misc_opt.use_TightestBoundingBox = true; } else if (strcmp(argv[ind], "--reslice-by-header")==0) { misc_opt.use_RotationHeader = true; TRAN_OPT opt; opt.file_type = IMAGE_AFFINE_HEADER; opt.do_affine_inv = false; opt_queue.push_back(opt); } else if (strcmp(argv[ind], "--Id")==0) { TRAN_OPT opt; opt.filename = "--Id"; opt.do_affine_inv = false; opt.file_type = IDENTITY_TRANSFORM; opt_queue.push_back(opt); } else if (strcmp(argv[ind], "--moving-image-header")==0 || strcmp(argv[ind], "-mh") ==0){ TRAN_OPT opt; opt.file_type = IMAGE_AFFINE_HEADER; opt.filename = moving_image_filename; // opt.do_affine_inv = false; SetAffineInvFlag(opt, set_current_affine_inv); opt_queue.push_back(opt); } else if (strcmp(argv[ind], "--reference-image-header")==0 || strcmp(argv[ind], "-rh") ==0){ if (misc_opt.reference_image_filename==NULL){ std::cout << "reference image filename is not given yet. Specify it with -R before --reference-image-header / -rh." << std::endl; return false; } TRAN_OPT opt; opt.file_type = IMAGE_AFFINE_HEADER; opt.filename = misc_opt.reference_image_filename; // opt.do_affine_inv = false; SetAffineInvFlag(opt, set_current_affine_inv); opt_queue.push_back(opt); } else if (strcmp(argv[ind], "-i")==0) { set_current_affine_inv = true; } else if (strcmp(argv[ind], "--ANTS-prefix")==0){ ind++; std::string prefix = argv[ind]; std::string path, name, ext; FilePartsWithgz(prefix, path, name, ext); if (ext=="") ext=".nii.gz"; std::string deform_file_name, x_deform_name; deform_file_name = path+name+std::string("Warp")+ext; x_deform_name = path+name+std::string("Warpxvec")+ext; if (CheckFileExistence(x_deform_name.c_str())){ TRAN_OPT opt; opt.filename = deform_file_name.c_str(); opt.file_type = CheckFileType(opt.filename.c_str()); opt.do_affine_inv = false; opt_queue.push_back(opt); std::cout << "found deformation file: " << opt.filename << std::endl; DisplayOpt(opt); } std::string affine_file_name; affine_file_name = path+name+std::string("Affine.txt"); if (CheckFileExistence(affine_file_name.c_str())){ TRAN_OPT opt; opt.filename = affine_file_name.c_str(); opt.file_type = CheckFileType(opt.filename.c_str()); opt.do_affine_inv = false; opt_queue.push_back(opt); std::cout << "found affine file: " << opt.filename << std::endl; DisplayOpt(opt); } } else if (strcmp(argv[ind], "--ANTS-prefix-invert")==0){ ind++; std::string prefix = argv[ind]; std::string path, name, ext; FilePartsWithgz(prefix, path, name, ext); if (ext=="") ext=".nii.gz"; std::string affine_file_name; affine_file_name = path+name+std::string("Affine.txt"); if (CheckFileExistence(affine_file_name.c_str())){ TRAN_OPT opt; opt.filename = affine_file_name.c_str(); opt.file_type = CheckFileType(opt.filename.c_str()); opt.do_affine_inv = true; opt_queue.push_back(opt); std::cout << "found affine file: " << opt.filename << std::endl; DisplayOpt(opt); } std::string deform_file_name, x_deform_name; deform_file_name = path+name+std::string("InverseWarp.nii.gz"); x_deform_name = path+name+std::string("InverseWarpxvec.nii.gz"); if (CheckFileExistence(x_deform_name.c_str())){ TRAN_OPT opt; opt.filename = deform_file_name.c_str(); opt.file_type = CheckFileType(opt.filename.c_str()); opt.do_affine_inv = false; opt_queue.push_back(opt); std::cout << "found deformation file: " << opt.filename << std::endl; DisplayOpt(opt); } } else{ TRAN_OPT opt; opt.filename = argv[ind]; opt.file_type = CheckFileType(opt.filename.c_str()); opt.do_affine_inv = false; if (opt.file_type == AFFINE_FILE) SetAffineInvFlag(opt, set_current_affine_inv); else if (opt.file_type == DEFORMATION_FILE && set_current_affine_inv){ std::cout << "Ignore inversion of non-affine file type! " << std::endl; std::cout << "opt.do_affine_inv:" << opt.do_affine_inv << std::endl; } opt_queue.push_back(opt); DisplayOpt(opt); } ind++; } if (misc_opt.use_RotationHeader) { // if (misc_opt.reference_image_filename) { // opt_queue[0].filename = misc_opt.reference_image_filename; // } else { opt_queue[0].filename = "--Id"; opt_queue[0].file_type = IDENTITY_TRANSFORM; opt_queue[0].do_affine_inv = false; // } // TRAN_OPT opt; // opt.file_type = IMAGE_AFFINE_HEADER; // opt.filename = moving_image_filename; // opt.do_affine_inv = true; // opt_queue.push_back(opt); // // std::cout << "Use Rotation Header!" << std::endl; } return true; } void DisplayOptQueue(const TRAN_OPT_QUEUE &opt_queue){ const int kQueueSize = opt_queue.size(); for(int i=0; i void GetIdentityTransform(AffineTransformPointer &aff){ typedef typename AffineTransformPointer::ObjectType AffineTransform; aff = AffineTransform::New(); aff->SetIdentity(); } template void GetAffineTransformFromImage(const ImageTypePointer& img, AffineTransformPointer &aff){ typedef typename ImageTypePointer::ObjectType ImageType; typedef typename ImageType::DirectionType DirectionType; typedef typename ImageType::PointType PointType; typedef typename ImageType::SpacingType SpacingType; typedef typename AffineTransformPointer::ObjectType::TranslationType VectorType; DirectionType direction = img->GetDirection(); SpacingType spacing = img->GetSpacing(); VectorType translation; // translation.Fill(0); for(unsigned int i=0; iGetOrigin()[i]; aff->SetMatrix(direction); // aff->SetCenter(pt); PointType pt; pt.Fill(0); aff->SetOffset(translation); aff->SetCenter(pt); std::cout << "aff from image:" << aff << std::endl; } template void GetLaregstSizeAfterWarp(WarperPointerType &warper, ImagePointerType &img, SizeType &largest_size, PointType &origin_warped){ typedef typename ImagePointerType::ObjectType ImageType; const int ImageDimension = ImageType::GetImageDimension(); // typedef typename ImageType::PointType PointType; typedef typename std::vector PointList; typedef typename ImageType::IndexType IndexType; // PointList pts_orig; PointList pts_warped; typename ImageType::SizeType imgsz; imgsz = img->GetLargestPossibleRegion().GetSize(); typename ImageType::SpacingType spacing; spacing = img->GetSpacing(); pts_warped.clear(); if (ImageDimension == 3){ for(int i=0; i<8; i++){ IndexType ind; switch (i){ case 0: ind[0]=0; ind[1]=0; ind[2]=0; break; case 1: ind[0]=imgsz[0]-1; ind[1]=0; ind[2]=0; break; case 2: ind[0]=0; ind[1]=imgsz[1]-1; ind[2]=0; break; case 3: ind[0]=imgsz[0]-1; ind[1]=imgsz[1]-1; ind[2]=0; break; case 4: ind[0]=0; ind[1]=0; ind[2]=imgsz[2]-1; break; case 5: ind[0]=imgsz[0]-1; ind[1]=0; ind[2]=imgsz[2]-1; break; case 6: ind[0]=0; ind[1]=imgsz[1]-1; ind[2]=imgsz[2]-1; break; case 7: ind[0]=imgsz[0]-1; ind[1]=imgsz[1]-1; ind[2]=imgsz[2]-1; break; } PointType pt_orig, pt_warped; img->TransformIndexToPhysicalPoint(ind, pt_orig); if (warper->MultiInverseAffineOnlySinglePoint(pt_orig, pt_warped)==false){ std::cout << "ERROR: outside of numeric boundary with affine transform." << std::endl; exit(-1); } pts_warped.push_back(pt_warped); std::cout << '[' << i << ']' << ind << ',' << pt_orig << "->" << pt_warped << std::endl; } } else if (ImageDimension == 2) { for(int i=0; i<4; i++){ IndexType ind; switch (i){ case 0: ind[0]=0; ind[1]=0; break; case 1: ind[0]=imgsz[0]-1; ind[1]=0; break; case 2: ind[0]=0; ind[1]=imgsz[1]-1; break; case 3: ind[0]=imgsz[0]-1; ind[1]=imgsz[1]-1; break; } PointType pt_orig, pt_warped; img->TransformIndexToPhysicalPoint(ind, pt_orig); if (warper->MultiInverseAffineOnlySinglePoint(pt_orig, pt_warped)==false){ std::cout << "ERROR: outside of numeric boundary with affine transform." << std::endl; exit(-1); } pts_warped.push_back(pt_warped); std::cout << '[' << i << ']' << ind << ',' << pt_orig << "->" << pt_warped << std::endl; } } else { std::cout << "could not determine the dimension after warping for non 2D/3D volumes" << std::endl; exit(-1); } PointType pt_min, pt_max; pt_min = pts_warped[0]; pt_max = pts_warped[0]; for(unsigned int k=0; kpts_warped[k][i])?(pt_max[i]):(pts_warped[k][i]); } } for(int i=0; i void WarpImageMultiTransform(char *moving_image_filename, char *output_image_filename, TRAN_OPT_QUEUE &opt_queue, MISC_OPT &misc_opt){ typedef itk::Image ImageType; typedef itk::Vector VectorType; typedef itk::Image DeformationFieldType; typedef itk::MatrixOffsetTransformBase< double, ImageDimension, ImageDimension > AffineTransformType; typedef itk::WarpImageMultiTransformFilter WarperType; itk::TransformFactory::RegisterTransform(); typedef itk::ImageFileReader ImageFileReaderType; typename ImageFileReaderType::Pointer reader_img = ImageFileReaderType::New(); reader_img->SetFileName(moving_image_filename); reader_img->Update(); typename ImageType::Pointer img_mov = ImageType::New(); img_mov = reader_img->GetOutput(); typename ImageType::Pointer img_ref; // = ImageType::New(); typename ImageFileReaderType::Pointer reader_img_ref = ImageFileReaderType::New(); if (misc_opt.reference_image_filename){ reader_img_ref->SetFileName(misc_opt.reference_image_filename); reader_img_ref->Update(); img_ref = reader_img_ref->GetOutput(); } // else // img_ref = NULL; typename WarperType::Pointer warper = WarperType::New(); warper->SetInput(img_mov); warper->SetEdgePaddingValue( 0); if (misc_opt.use_NN_interpolator){ typedef typename itk::NearestNeighborInterpolateImageFunction NNInterpolateType; typename NNInterpolateType::Pointer interpolator_NN = NNInterpolateType::New(); std::cout << "User nearest neighbor interpolation (was Haha) " << std::endl; warper->SetInterpolator(interpolator_NN); } else if (misc_opt.use_BSpline_interpolator){ typedef typename itk::BSplineInterpolateImageFunction BSInterpolateType; typename BSInterpolateType::Pointer interpolator_BS = BSInterpolateType::New(); interpolator_BS->SetSplineOrder(3); std::cout << "User B-spline interpolation " << std::endl; warper->SetInterpolator(interpolator_BS); } else { typedef typename itk::LinearInterpolateImageFunction LinInterpolateType; typename LinInterpolateType::Pointer interpolator_LN = LinInterpolateType::New(); std::cout << "User Linear interpolation " << std::endl; warper->SetInterpolator(interpolator_LN); } typedef itk::TransformFileReader TranReaderType; typedef itk::VectorImageFileReader FieldReaderType; bool takeaffinv=false; unsigned int transcount=0; const int kOptQueueSize = opt_queue.size(); for(int i=0; iSetFileName(opt.filename); tran_reader->Update(); typename AffineTransformType::Pointer aff = dynamic_cast< AffineTransformType* > ((tran_reader->GetTransformList())->front().GetPointer()); if (opt.do_affine_inv) { typename AffineTransformType::Pointer aff_inv = AffineTransformType::New(); aff->GetInverse(aff_inv); aff = aff_inv; takeaffinv=true; } // std::cout <<" aff " << transcount << std::endl; warper->PushBackAffineTransform(aff); if (transcount==0){ warper->SetOutputSize(img_mov->GetLargestPossibleRegion().GetSize()); warper->SetOutputSpacing(img_mov->GetSpacing()); warper->SetOutputOrigin(img_mov->GetOrigin()); warper->SetOutputDirection(img_mov->GetDirection()); } transcount++; break; } case IDENTITY_TRANSFORM:{ typename AffineTransformType::Pointer aff; GetIdentityTransform(aff); // std::cout << " aff id" << transcount << std::endl; warper->PushBackAffineTransform(aff); transcount++; break; } case IMAGE_AFFINE_HEADER:{ typename AffineTransformType::Pointer aff = AffineTransformType::New(); typename ImageType::Pointer img_affine = ImageType::New(); typename ImageFileReaderType::Pointer reader_image_affine = ImageFileReaderType::New(); reader_image_affine->SetFileName(opt.filename); reader_image_affine->Update(); img_affine = reader_image_affine->GetOutput(); GetAffineTransformFromImage(img_affine, aff); if (opt.do_affine_inv) { typename AffineTransformType::Pointer aff_inv = AffineTransformType::New(); aff->GetInverse(aff_inv); aff = aff_inv; takeaffinv=true; } // std::cout <<" aff from image header " << transcount << std::endl; warper->PushBackAffineTransform(aff); // if (transcount==0){ // warper->SetOutputSize(img_mov->GetLargestPossibleRegion().GetSize()); // warper->SetOutputSpacing(img_mov->GetSpacing()); // warper->SetOutputOrigin(img_mov->GetOrigin()); // warper->SetOutputDirection(img_mov->GetDirection()); // } transcount++; break; } case DEFORMATION_FILE:{ typename FieldReaderType::Pointer field_reader = FieldReaderType::New(); field_reader->SetFileName( opt.filename ); field_reader->Update(); typename DeformationFieldType::Pointer field = field_reader->GetOutput(); warper->PushBackDeformationFieldTransform(field); warper->SetOutputSize(field->GetLargestPossibleRegion().GetSize()); warper->SetOutputOrigin(field->GetOrigin()); warper->SetOutputSpacing(field->GetSpacing()); warper->SetOutputDirection(field->GetDirection()); transcount++; break; } default: std::cout << "Unknown file type!" << std::endl; } } //std::cout << " transcount " << transcount << std::endl; warper->PrintTransformList(); if ( transcount == 2 ) { std::cout << " We check the syntax of your call .... " << std::endl; const TRAN_OPT &opt1 = opt_queue[0]; const TRAN_OPT &opt2 = opt_queue[1]; if ( opt1.file_type == AFFINE_FILE && opt2.file_type == DEFORMATION_FILE ) { bool defisinv=IsInverseDeformation(opt2.filename.c_str()); if ( ! takeaffinv ) { std::cout << " Your 1st parameter should be an inverse affine map and the 2nd an InverseWarp --- exiting without applying warp. Check that , if using an inverse affine map, you pass the -i option before the Affine.txt." << std::endl; return ; } if ( ! defisinv ) { std::cout << " Your 2nd parameter should be an InverseWarp when your 1st parameter is an inverse affine map --- exiting without applying warp. " << std::endl; return ; } } if ( opt2.file_type == AFFINE_FILE && opt1.file_type == DEFORMATION_FILE ) { bool defisinv=IsInverseDeformation(opt1.filename.c_str()); if ( defisinv ) { std::cout << " Your 1st parameter should be a Warp (not Inverse) when your 2nd parameter is an affine map --- exiting without applying warp. " << std::endl; return ; } if ( takeaffinv ) { std::cout << " Your 2nd parameter should be a regular affine map (not inverted) if the 1st is a Warp --- exiting without applying warp. " << std::endl; return ; } } std::cout <<" syntax probably ok. " << std::endl; } else { std::cout << " You are doing something more complex -- we wont check syntax in this case " << std::endl; } if (img_ref.IsNotNull()){ warper->SetOutputSize(img_ref->GetLargestPossibleRegion().GetSize()); warper->SetOutputSpacing(img_ref->GetSpacing()); warper->SetOutputOrigin(img_ref->GetOrigin()); warper->SetOutputDirection(img_ref->GetDirection()); } else { if (misc_opt.use_TightestBoundingBox == true){ // compute the desired spacking after inputting all the transform files using the typename ImageType::SizeType largest_size; typename ImageType::PointType origin_warped; GetLaregstSizeAfterWarp(warper, img_mov, largest_size, origin_warped); warper->SetOutputSize(largest_size); warper->SetOutputSpacing(img_mov->GetSpacing()); warper->SetOutputOrigin(origin_warped); typename ImageType::DirectionType d; d.SetIdentity(); warper->SetOutputDirection(d); } } std::cout << "output origin: " << warper->GetOutputOrigin() << std::endl; std::cout << "output size: " << warper->GetOutputSize() << std::endl; std::cout << "output spacing: " << warper->GetOutputSpacing() << std::endl; std::cout << "output direction: " << warper->GetOutputDirection() << std::endl; // warper->PrintTransformList(); warper->DetermineFirstDeformNoInterp(); warper->Update(); // { // typename ImageType::IndexType ind_orig, ind_warped; // ind_orig[0] = 128; // ind_orig[1] = 128; // ind_orig[2] = 16; // typename ImageType::PointType pt_orig, pt_warped; // warper->GetOutput()->TransformIndexToPhysicalPoint(ind_orig, pt_orig); // warper->MultiTransformSinglePoint(pt_orig, pt_warped); // img_mov->TransformPhysicalPointToIndex(pt_warped, ind_warped); // std::cout << "Transform output index " << ind_orig << "("<GetOutputSize()[i] * 0.5; // } // warper->MultiTransformSinglePoint(pt_in, pt_out); // std::cout << "pt_in=" << pt_in << " pt_out=" <GetOutput(); typedef itk::ImageFileWriter ImageFileWriterType; typename ImageFileWriterType::Pointer writer_img = ImageFileWriterType::New(); if (img_ref) img_output->SetDirection(img_ref->GetDirection()); writer_img->SetFileName(output_image_filename); writer_img->SetInput(img_output); writer_img->Update(); } int main(int argc, char **argv){ if (argc<=3) { std::cout << " \n " << std::endl; std::cout << "Usage: \n " << std::endl; // std::cout << argv[0] << " ImageDimension moving_image output_image [-R reference_image | --tightest-bounding-box] (--reslice-by-header) [--use-NN]" //<< "[--ANTS-prefix prefix-name | --ANTS-prefix-invert prefix-name] {[deformation_field | [-i] InverseAffineTransform.txt | --Id | [-i] --moving-image-header / -mh | [-i] --reference-image-header / -rh]} \n" << std::endl; std::cout << argv[0] << " ImageDimension moving_image output_image -R reference_image --use-NN SeriesOfTransformations--(See Below) " << std::endl; std::cout <<" SeriesOfTransformations --- " << argv[0] << " can apply, via concatenation, an unlimited number of transformations to your data ." << std::endl; std::cout << " Thus, SeriesOfTransformations may be an Affine transform followed by a warp another affine and then another warp. " << std::endl; std::cout << " Inverse affine transformations are invoked by calling -i MyAffine.txt " << std::endl; std::cout << " InverseWarps are invoked by passing the InverseWarp.nii.gz filename (see below for a note about this). " << std::endl; std::cout << std::endl; std::cout << " Example 1: Mapping a warped image into the reference_image domain by applying abcdWarpxvec.nii.gz/abcdWarpyvec.nii.gz/abcdWarpzvec.nii.gz and then abcdAffine.txt\n" << std::endl; std::cout << argv[0] << " 3 moving_image output_image -R reference_image abcdWarp.nii.gz abcdAffine.txt\n" << std::endl; std::cout << " Example 2: To map the fixed/reference_image warped into the moving_image domain by applying the inversion of abcdAffine.txt and then abcdInverseWarpxvec.nii.gz/abcdInverseWarpyvec.nii.gz/abcdInverseWarpzvec.nii.gz .\n" << std::endl; std::cout << argv[0] << " 3 reference_image output_image -R moving_image -i abcdAffine.txt abcdInverseWarp.nii.gz \n \n" << std::endl; std::cout <<" Note that the inverse maps (Ex. 2) are passed to this program in the reverse order of the forward maps (Ex. 1). " << std::endl; std::cout <<" This makes sense, geometrically ... see ANTS.pdf for visualization of this syntax." << std::endl; std::cout << std::endl; std::cout << " Compulsory arguments:\n " << std::endl; std::cout << " ImageDimension: 2 or 3 (for 2 or 3 Dimensional registration)\n " << std::endl; std::cout << " moving_image: the image to apply the transformation to\n " << std::endl; std::cout << " output_image: the resulting image\n \n " << std::endl; std::cout << " Optional arguments:\n " << std::endl; std::cout << " -R: reference_image space that you wish to warp INTO." << std::endl; std::cout << " --tightest-bounding-box: Computes the tightest bounding box using all the affine transformations. It will be overrided by -R reference_image if given." << std::endl; std::cout << " --reslice-by-header: equivalient to -i -mh, or -fh -i -mh if used together with -R. It uses the orientation matrix and origin encoded in the image file header. " << std::endl; std::cout << " It can be used together with -R. This is typically not used together with any other transforms.\n " << std::endl; std::cout << " --use-NN: Use Nearest Neighbor Interpolation. \n " << std::endl; std::cout << " --use-BSpline: Use 3rd order B-Spline Interpolation. \n " << std::endl; // std::cout << " --ANTS-prefix prefix-name: followed by a deformation field filename. \n " << std::endl; // std::cout << " --ANTS-prefix-invert: . \n" << std::endl; std::cout << " -i: will use the inversion of the following affine transform. \n " << std::endl; // std::cout << " --Id: use an identity transform. \n " << std::endl; // std::cout << " --moving-image-header or -mh: will use the orientation header of the moving image file. This is typically not used with --reslice-by-header.\n " << std::endl; // std::cout << " --reference-image-header or -rh: use the orientation matrix and origin encoded in the image file header. It can be used together with -R.\n " << std::endl; std::cout << " \n " << std::endl; // std::cout << " For ANTS users:" << std::endl; std::cout << " Other Example Usages:" << std::endl; std::cout << " Reslice the image: WarpImageMultiTransform 3 Imov.nii.gz Iout.nii.gz --tightest-bounding-box --reslice-by-header" << std::endl; std::cout << " Reslice the image to a reference image: WarpImageMultiTransform 3 Imov.nii.gz Iout.nii.gz -R Iref.nii.gz --tightest-bounding-box --reslice-by-header\n" << std::endl; std::cout << " Important Notes: " << std::endl; std::cout << " Prefixname \"abcd\" without any extension will use \".nii.gz\" by default" << std::endl; std::cout << " The abcdWarp and abcdInverseWarp do not exist. They are formed on the basis of abcd(Inverse)Warpxvec/yvec/zvec.nii.gz when calling " << argv[0] <<", yet you have to use them as if they exist." << std::endl; exit(0); } TRAN_OPT_QUEUE opt_queue; char *moving_image_filename = NULL; char *output_image_filename = NULL; MISC_OPT misc_opt; bool is_parsing_ok = false; int kImageDim = atoi(argv[1]); is_parsing_ok = ParseInput(argc-2, argv+2, moving_image_filename, output_image_filename, opt_queue, misc_opt); if (is_parsing_ok){ std::cout << "moving_image_filename: " << moving_image_filename << std::endl; std::cout << "output_image_filename: " << output_image_filename << std::endl; std::cout << "reference_image_filename: "; if (misc_opt.reference_image_filename) std::cout << misc_opt.reference_image_filename << std::endl; else std::cout << "NULL" << std::endl; DisplayOptQueue(opt_queue); switch (kImageDim){ case 2:{ WarpImageMultiTransform<2>(moving_image_filename, output_image_filename, opt_queue, misc_opt); break; } case 3:{ WarpImageMultiTransform<3>(moving_image_filename, output_image_filename, opt_queue, misc_opt); break; } } } else{ std::cout << "Input error!" << std::endl; } exit(0); } ants-1.9.2+svn680.dfsg/Examples/WarpTensorImageMultiTransform.cxx000066400000000000000000000722011147325206600247420ustar00rootroot00000000000000#include #include #include "itkImageFileReader.h" #include "itkVector.h" #include "itkVariableLengthVector.h" #include "itkVectorImageFileReader.h" #include "itkImageFileWriter.h" #include "itkMatrixOffsetTransformBase.h" #include "itkTransformFactory.h" #include "itkWarpTensorImageMultiTransformFilter.h" #include "itkTransformFileReader.h" #include "itkVectorNearestNeighborInterpolateImageFunction.h" #include "ReadWriteImage.h" #include "itkWarpImageMultiTransformFilter.h" typedef enum{INVALID_FILE=1, AFFINE_FILE, DEFORMATION_FILE, IMAGE_AFFINE_HEADER, IDENTITY_TRANSFORM} TRAN_FILE_TYPE; typedef struct{ // char *filename; std::string filename; TRAN_FILE_TYPE file_type; bool do_affine_inv; // void SetValue(char *filename, TRAN_FILE_TYPE file_type, bool do_affine_inv){ // this.filename = filename; // this.file_type = file_type; // this.do_affine_inv = do_affine_inv; // }; } TRAN_OPT; typedef std::vector TRAN_OPT_QUEUE; typedef struct{ bool use_NN_interpolator; bool use_TightestBoundingBox; char * reference_image_filename; bool use_RotationHeader; } MISC_OPT; void DisplayOptQueue(const TRAN_OPT_QUEUE &opt_queue); void DisplayOpt(const TRAN_OPT &opt); TRAN_FILE_TYPE CheckFileType(const char *str){ std::string filename = str; std::string::size_type pos = filename.rfind( "." ); std::string filepre = std::string( filename, 0, pos ); if ( pos != std::string::npos ){ std::string extension = std::string( filename, pos, filename.length()-1); if (extension==std::string(".gz")){ pos = filepre.rfind( "." ); extension = std::string( filepre, pos, filepre.length()-1 ); } if (extension==".txt") return AFFINE_FILE; else return DEFORMATION_FILE; } else{ return INVALID_FILE; } return AFFINE_FILE; } void FilePartsWithgz(const std::string &filename, std::string &path, std::string &name, std::string &ext){ std::string extension; std::string::size_type pos = filename.rfind( "." ); std::string filepre = std::string( filename, 0, pos ); if ( pos != std::string::npos ){ extension = std::string( filename, pos, filename.length()-1); if (extension==std::string(".gz")){ pos = filepre.rfind( "." ); if (pos != std::string::npos){ extension = std::string( filepre, pos, filepre.length()-1 ) + ".gz"; filepre = std::string(filepre, 0, pos); } } } else { extension = std::string(""); } ext = extension; pos = filepre.rfind('/'); if ( pos != std::string::npos ){ path = std::string(filepre, 0, pos+1); name = std::string(filepre, pos+1, filepre.length()-1); } else { path = std::string(""); name = filepre; } // std::cout << "filename: " << filename << std::endl // << "path: " << path << std::endl // << "name: " << name << std::endl // << "ext: " << ext << std::endl; } bool CheckFileExistence(const char *str){ std::ifstream myfile(str); bool b = myfile.is_open(); myfile.close(); return b; } void SetAffineInvFlag(TRAN_OPT &opt, bool &set_current_affine_inv){ opt.do_affine_inv = set_current_affine_inv; if (set_current_affine_inv) set_current_affine_inv = false; } bool ParseInput(int argc, char **argv, char *&moving_image_filename, char *&output_image_filename, TRAN_OPT_QUEUE &opt_queue, MISC_OPT &misc_opt){ opt_queue.clear(); opt_queue.reserve(argc-2); misc_opt.reference_image_filename = NULL; misc_opt.use_NN_interpolator = false; misc_opt.use_TightestBoundingBox = false; misc_opt.use_RotationHeader = false; moving_image_filename = argv[0]; output_image_filename = argv[1]; int ind = 2; bool set_current_affine_inv = false; while(ind < argc){ if (strcmp(argv[ind], "--use-NN")==0) { misc_opt.use_NN_interpolator = true; } else if (strcmp(argv[ind], "-R")==0) { ind++; if(ind >= argc) return false; misc_opt.reference_image_filename = argv[ind]; } else if ((strcmp(argv[ind], "--tightest-bounding-box")==0) && (strcmp(argv[ind], "-R")!=0) ) { misc_opt.use_TightestBoundingBox = true; } else if (strcmp(argv[ind], "--reslice-by-header")==0) { misc_opt.use_RotationHeader = true; TRAN_OPT opt; opt.file_type = IMAGE_AFFINE_HEADER; opt.do_affine_inv = false; opt_queue.push_back(opt); } else if (strcmp(argv[ind], "--Id")==0) { TRAN_OPT opt; opt.filename = "--Id"; opt.do_affine_inv = false; opt.file_type = IDENTITY_TRANSFORM; opt_queue.push_back(opt); } else if (strcmp(argv[ind], "--moving-image-header")==0 || strcmp(argv[ind], "-mh") ==0){ TRAN_OPT opt; opt.file_type = IMAGE_AFFINE_HEADER; opt.filename = moving_image_filename; // opt.do_affine_inv = false; SetAffineInvFlag(opt, set_current_affine_inv); opt_queue.push_back(opt); } else if (strcmp(argv[ind], "--reference-image-header")==0 || strcmp(argv[ind], "-rh") ==0){ if (misc_opt.reference_image_filename==NULL){ std::cout << "reference image filename is not given yet. Specify it with -R before --reference-image-header / -rh." << std::endl; return false; } TRAN_OPT opt; opt.file_type = IMAGE_AFFINE_HEADER; opt.filename = misc_opt.reference_image_filename; // opt.do_affine_inv = false; SetAffineInvFlag(opt, set_current_affine_inv); opt_queue.push_back(opt); } else if (strcmp(argv[ind], "-i")==0) { set_current_affine_inv = true; } else if (strcmp(argv[ind], "--ANTS-prefix")==0){ ind++; std::string prefix = argv[ind]; std::string path, name, ext; FilePartsWithgz(prefix, path, name, ext); if (ext=="") ext=".nii.gz"; std::string deform_file_name, x_deform_name; deform_file_name = path+name+std::string("Warp")+ext; x_deform_name = path+name+std::string("Warpxvec")+ext; if (CheckFileExistence(x_deform_name.c_str())){ TRAN_OPT opt; opt.filename = deform_file_name.c_str(); opt.file_type = CheckFileType(opt.filename.c_str()); opt.do_affine_inv = false; opt_queue.push_back(opt); std::cout << "found deformation file: " << opt.filename << std::endl; DisplayOpt(opt); } std::string affine_file_name; affine_file_name = path+name+std::string("Affine.txt"); if (CheckFileExistence(affine_file_name.c_str())){ TRAN_OPT opt; opt.filename = affine_file_name.c_str(); opt.file_type = CheckFileType(opt.filename.c_str()); opt.do_affine_inv = false; opt_queue.push_back(opt); std::cout << "found affine file: " << opt.filename << std::endl; DisplayOpt(opt); } } else if (strcmp(argv[ind], "--ANTS-prefix-invert")==0){ ind++; std::string prefix = argv[ind]; std::string path, name, ext; FilePartsWithgz(prefix, path, name, ext); if (ext=="") ext=".nii.gz"; std::string affine_file_name; affine_file_name = path+name+std::string("Affine.txt"); if (CheckFileExistence(affine_file_name.c_str())){ TRAN_OPT opt; opt.filename = affine_file_name.c_str(); opt.file_type = CheckFileType(opt.filename.c_str()); opt.do_affine_inv = true; opt_queue.push_back(opt); std::cout << "found affine file: " << opt.filename << std::endl; DisplayOpt(opt); } std::string deform_file_name, x_deform_name; deform_file_name = path+name+std::string("InverseWarp.nii.gz"); x_deform_name = path+name+std::string("InverseWarpxvec.nii.gz"); if (CheckFileExistence(x_deform_name.c_str())){ TRAN_OPT opt; opt.filename = deform_file_name.c_str(); opt.file_type = CheckFileType(opt.filename.c_str()); opt.do_affine_inv = false; opt_queue.push_back(opt); std::cout << "found deformation file: " << opt.filename << std::endl; DisplayOpt(opt); } } else{ TRAN_OPT opt; opt.filename = argv[ind]; opt.file_type = CheckFileType(opt.filename.c_str()); opt.do_affine_inv = false; if (opt.file_type == AFFINE_FILE) SetAffineInvFlag(opt, set_current_affine_inv); else if (opt.file_type == DEFORMATION_FILE && set_current_affine_inv){ std::cout << "Ignore inversion of non-affine file type! " << std::endl; std::cout << "opt.do_affine_inv:" << opt.do_affine_inv << std::endl; } opt_queue.push_back(opt); DisplayOpt(opt); } ind++; } if (misc_opt.use_RotationHeader) { // if (misc_opt.reference_image_filename) { // opt_queue[0].filename = misc_opt.reference_image_filename; // } else { opt_queue[0].filename = "--Id"; opt_queue[0].file_type = IDENTITY_TRANSFORM; opt_queue[0].do_affine_inv = false; // } // TRAN_OPT opt; // opt.file_type = IMAGE_AFFINE_HEADER; // opt.filename = moving_image_filename; // opt.do_affine_inv = true; // opt_queue.push_back(opt); // // std::cout << "Use Rotation Header!" << std::endl; } return true; } void DisplayOptQueue(const TRAN_OPT_QUEUE &opt_queue){ const int kQueueSize = opt_queue.size(); for(int i=0; i void GetIdentityTransform(AffineTransformPointer &aff){ typedef typename AffineTransformPointer::ObjectType AffineTransform; aff = AffineTransform::New(); aff->SetIdentity(); } template void GetAffineTransformFromImage(const ImageTypePointer& img, AffineTransformPointer &aff){ typedef typename ImageTypePointer::ObjectType ImageType; typedef typename ImageType::DirectionType DirectionType; typedef typename ImageType::PointType PointType; typedef typename ImageType::SpacingType SpacingType; typedef typename AffineTransformPointer::ObjectType::TranslationType VectorType; DirectionType direction = img->GetDirection(); SpacingType spacing = img->GetSpacing(); VectorType translation; // translation.Fill(0); for(unsigned int i=0; iGetOrigin()[i]; aff->SetMatrix(direction); // aff->SetCenter(pt); PointType pt; pt.Fill(0); aff->SetOffset(translation); aff->SetCenter(pt); std::cout << "aff from image:" << aff << std::endl; } template void GetLaregstSizeAfterWarp(WarperPointerType &warper, ImagePointerType &img, SizeType &largest_size, PointType &origin_warped){ typedef typename ImagePointerType::ObjectType ImageType; const int ImageDimension = ImageType::GetImageDimension(); // typedef typename ImageType::PointType PointType; typedef typename std::vector PointList; typedef typename ImageType::IndexType IndexType; // PointList pts_orig; PointList pts_warped; typename ImageType::SizeType imgsz; imgsz = img->GetLargestPossibleRegion().GetSize(); typename ImageType::SpacingType spacing; spacing = img->GetSpacing(); pts_warped.clear(); if (ImageDimension == 3){ for(int i=0; i<8; i++){ IndexType ind; switch (i){ case 0: ind[0]=0; ind[1]=0; ind[2]=0; break; case 1: ind[0]=imgsz[0]-1; ind[1]=0; ind[2]=0; break; case 2: ind[0]=0; ind[1]=imgsz[1]-1; ind[2]=0; break; case 3: ind[0]=imgsz[0]-1; ind[1]=imgsz[1]-1; ind[2]=0; break; case 4: ind[0]=0; ind[1]=0; ind[2]=imgsz[2]-1; break; case 5: ind[0]=imgsz[0]-1; ind[1]=0; ind[2]=imgsz[2]-1; break; case 6: ind[0]=0; ind[1]=imgsz[1]-1; ind[2]=imgsz[2]-1; break; case 7: ind[0]=imgsz[0]-1; ind[1]=imgsz[1]-1; ind[2]=imgsz[2]-1; break; } PointType pt_orig, pt_warped; img->TransformIndexToPhysicalPoint(ind, pt_orig); if (warper->MultiInverseAffineOnlySinglePoint(pt_orig, pt_warped)==false){ std::cout << "ERROR: outside of numeric boundary with affine transform." << std::endl; exit(-1); } pts_warped.push_back(pt_warped); std::cout << '[' << i << ']' << ind << ',' << pt_orig << "->" << pt_warped << std::endl; } } else if (ImageDimension == 2) { for(int i=0; i<4; i++){ IndexType ind; switch (i){ case 0: ind[0]=0; ind[1]=0; break; case 1: ind[0]=imgsz[0]-1; ind[1]=0; break; case 2: ind[0]=0; ind[1]=imgsz[1]-1; break; case 3: ind[0]=imgsz[0]-1; ind[1]=imgsz[1]-1; break; } PointType pt_orig, pt_warped; img->TransformIndexToPhysicalPoint(ind, pt_orig); if (warper->MultiInverseAffineOnlySinglePoint(pt_orig, pt_warped)==false){ std::cout << "ERROR: outside of numeric boundary with affine transform." << std::endl; exit(-1); } pts_warped.push_back(pt_warped); std::cout << '[' << i << ']' << ind << ',' << pt_orig << "->" << pt_warped << std::endl; } } else { std::cout << "could not determine the dimension after warping for non 2D/3D volumes" << std::endl; exit(-1); } PointType pt_min, pt_max; pt_min = pts_warped[0]; pt_max = pts_warped[0]; for(unsigned int k=0; kpts_warped[k][i])?(pt_max[i]):(pts_warped[k][i]); } } for(int i=0; i void WarpImageMultiTransform(char *moving_image_filename, char *output_image_filename, TRAN_OPT_QUEUE &opt_queue, MISC_OPT &misc_opt){ //typedef itk::Vector PixelType; typedef itk::SymmetricSecondRankTensor< float, 3 > PixelType; typedef itk::Image TensorImageType; typedef itk::Image ImageType; typedef itk::Vector VectorType; typedef itk::Image DeformationFieldType; typedef itk::MatrixOffsetTransformBase< double, ImageDimension, ImageDimension > AffineTransformType; typedef itk::WarpImageMultiTransformFilter WarperType; itk::TransformFactory::RegisterTransform(); typedef itk::ImageFileReader ImageFileReaderType; //typename ImageFileReaderType::Pointer reader_img = ImageFileReaderType::New(); //reader_img->SetFileName(moving_image_filename); //reader_img->Update(); //typename ImageType::Pointer img_mov = ImageType::New(); //img_mov = reader_img->GetOutput(); typename TensorImageType::Pointer img_mov; ReadTensorImage(img_mov,moving_image_filename,true); typename ImageType::Pointer img_ref; // = ImageType::New(); typename ImageFileReaderType::Pointer reader_img_ref = ImageFileReaderType::New(); if (misc_opt.reference_image_filename){ reader_img_ref->SetFileName(misc_opt.reference_image_filename); reader_img_ref->Update(); img_ref = reader_img_ref->GetOutput(); } typename TensorImageType::Pointer img_output = TensorImageType::New(); img_output->SetLargestPossibleRegion( img_ref->GetLargestPossibleRegion() ); img_output->SetBufferedRegion( img_ref->GetLargestPossibleRegion() ); img_output->SetLargestPossibleRegion( img_ref->GetLargestPossibleRegion() ); img_output->Allocate(); img_output->SetSpacing(img_ref->GetSpacing()); img_output->SetOrigin(img_ref->GetOrigin()); img_output->SetDirection(img_ref->GetDirection()); // else // img_ref = NULL; for (unsigned int tensdim=0; tensdim < 6; tensdim++) { typedef itk::VectorIndexSelectionCastImageFilter IndexSelectCasterType; typename IndexSelectCasterType::Pointer fieldCaster = IndexSelectCasterType::New(); fieldCaster->SetInput( img_mov ); fieldCaster->SetIndex( tensdim ); fieldCaster->Update(); typename ImageType::Pointer tenscomponent=fieldCaster->GetOutput(); tenscomponent->SetSpacing(img_mov->GetSpacing()); tenscomponent->SetOrigin(img_mov->GetOrigin()); tenscomponent->SetDirection(img_mov->GetDirection()); typename WarperType::Pointer warper = WarperType::New(); warper->SetInput(tenscomponent); // PixelType nullPix; // nullPix.Fill(0); warper->SetEdgePaddingValue(0); if (misc_opt.use_NN_interpolator){ typedef typename itk::NearestNeighborInterpolateImageFunction NNInterpolateType; typename NNInterpolateType::Pointer interpolator_NN = NNInterpolateType::New(); std::cout << "Haha" << std::endl; warper->SetInterpolator(interpolator_NN); } typedef itk::TransformFileReader TranReaderType; typedef itk::VectorImageFileReader FieldReaderType; unsigned int transcount=0; const int kOptQueueSize = opt_queue.size(); for(int i=0; iSetFileName(opt.filename); tran_reader->Update(); typename AffineTransformType::Pointer aff = dynamic_cast< AffineTransformType* > ((tran_reader->GetTransformList())->front().GetPointer()); if (opt.do_affine_inv) { typename AffineTransformType::Pointer aff_inv = AffineTransformType::New(); aff->GetInverse(aff_inv); aff = aff_inv; } // std::cout <<" aff " << transcount << std::endl; warper->PushBackAffineTransform(aff); if (transcount==0){ warper->SetOutputSize(img_mov->GetLargestPossibleRegion().GetSize()); warper->SetOutputSpacing(img_mov->GetSpacing()); warper->SetOutputOrigin(img_mov->GetOrigin()); warper->SetOutputDirection(img_mov->GetDirection()); } transcount++; break; } case IDENTITY_TRANSFORM:{ typename AffineTransformType::Pointer aff; GetIdentityTransform(aff); // std::cout << " aff id" << transcount << std::endl; warper->PushBackAffineTransform(aff); transcount++; break; } case IMAGE_AFFINE_HEADER:{ typename AffineTransformType::Pointer aff = AffineTransformType::New(); typename ImageType::Pointer img_affine = ImageType::New(); typename ImageFileReaderType::Pointer reader_image_affine = ImageFileReaderType::New(); reader_image_affine->SetFileName(opt.filename); reader_image_affine->Update(); img_affine = reader_image_affine->GetOutput(); GetAffineTransformFromImage(img_affine, aff); if (opt.do_affine_inv) { typename AffineTransformType::Pointer aff_inv = AffineTransformType::New(); aff->GetInverse(aff_inv); aff = aff_inv; } // std::cout <<" aff from image header " << transcount << std::endl; warper->PushBackAffineTransform(aff); // if (transcount==0){ // warper->SetOutputSize(img_mov->GetLargestPossibleRegion().GetSize()); // warper->SetOutputSpacing(img_mov->GetSpacing()); // warper->SetOutputOrigin(img_mov->GetOrigin()); // warper->SetOutputDirection(img_mov->GetDirection()); // } transcount++; break; } case DEFORMATION_FILE:{ typename FieldReaderType::Pointer field_reader = FieldReaderType::New(); field_reader->SetFileName( opt.filename ); field_reader->Update(); typename DeformationFieldType::Pointer field = field_reader->GetOutput(); warper->PushBackDeformationFieldTransform(field); warper->SetOutputSize(field->GetLargestPossibleRegion().GetSize()); warper->SetOutputOrigin(field->GetOrigin()); warper->SetOutputSpacing(field->GetSpacing()); warper->SetOutputDirection(field->GetDirection()); transcount++; break; } default: std::cout << "Unknown file type!" << std::endl; } } // warper->PrintTransformList(); if (img_ref.IsNotNull()){ warper->SetOutputSize(img_ref->GetLargestPossibleRegion().GetSize()); warper->SetOutputSpacing(img_ref->GetSpacing()); warper->SetOutputOrigin(img_ref->GetOrigin()); warper->SetOutputDirection(img_ref->GetDirection()); } else { if (misc_opt.use_TightestBoundingBox == true){ // compute the desired spacking after inputting all the transform files using the typename ImageType::SizeType largest_size; typename ImageType::PointType origin_warped; GetLaregstSizeAfterWarp(warper, img_mov, largest_size, origin_warped); warper->SetOutputSize(largest_size); warper->SetOutputSpacing(img_mov->GetSpacing()); warper->SetOutputOrigin(origin_warped); typename ImageType::DirectionType d; d.SetIdentity(); warper->SetOutputDirection(d); } } //std::cout << "output origin: " << warper->GetOutputOrigin() << std::endl; //std::cout << "output size: " << warper->GetOutputSize() << std::endl; //std::cout << "output spacing: " << warper->GetOutputSpacing() << std::endl; // std::cout << "output direction: " << warper->GetOutputDirection() << std::endl; // warper->PrintTransformList(); warper->DetermineFirstDeformNoInterp(); warper->Update(); typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter2( img_output, img_output->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { PixelType tens=vfIter2.Get(); tens[tensdim]=warper->GetOutput()->GetPixel(vfIter2.GetIndex()); vfIter2.Set(tens); } } WriteTensorImage(img_output, output_image_filename,true); } int main(int argc, char **argv){ if (argc<=3){ std::cout << "WarpImageMultiTransform ImageDimension moving_image output_image [-R reference_image | --tightest-bounding-box] (--reslice-by-header) [--use-NN (use Nearest Neighbor Interpolator)]" << "[--ANTS-prefix prefix-name | --ANTS-prefix-invert prefix-name] {[deformation_field | [-i] affine_transform_txt | --Id | [-i] --moving-image-header / -mh | [-i] --reference-image-header / -rh]}" << std::endl << "Example:" << std::endl << "Reslice the image: WarpImageMultiTransform 3 Imov.nii Iout.nii --tightest-bounding-box --reslice-by-header" << std::endl << "Reslice the image to a reference image: WarpImageMultiTransform 3 Imov.nii Iout.nii -R Iref.nii --tightest-bounding-box --reslice-by-header" << std::endl << "Note:" << std::endl << "-i will use the inversion of the following affine transform." << std::endl << "--tightest-bounding-box will be overrided by -R reference_image if given. It computes the tightest bounding box using all the affine transformations." << std::endl << "--Id uses the identity transform." << std::endl << "--moving-image-header or -mh in short will use the orientation header of the moving image file. This is typically not used with --reslice-by-header." << std::endl << "--reference-image-header or -rh in short will use the orientation header of the fixed image file. This is typically not used with --reslice-by-header." << std::endl << "--reslice-by-header uses the orientation matrix and origin encoded in the image file header. It can be used together with -R. " << "This is typically not used together with any other transforms. " << "--reslice-by-header is equvalient to -i -mh, or -fh -i -mh if used together with -R. " << std::endl; std::cout << std::endl << "For ANTS users:" << std::endl << "To use with the deformation field and the affine transform files generated from ANTS:" << std::endl << "--ANTS-prefix prefix-name" << std::endl << "--ANTS-prefix-invert prefix-name" << std::endl << "Example:" << std::endl << "3 moving_image output_image -R reference_image --ANTS-prefix abcd.nii.gz" << std::endl << "Applies abcdWarpxvec.nii.gz/abcdWarpyvec.nii.gz/abcdWarpzvec.nii.gz and then abcdAffine.txt. Use this with ANTS to get the moving_image warped into the reference_image domain. " << std::endl << "3 reference_image output_image -R moving_image --ANTS-prefix-invert abcd.nii.gz --ANTS-invert" << std::endl << "Applies the inversion of abcdAffine.txt and then abcdInverseWarpxvec.nii.gz/abcdInverseWarpyvec.nii.gz/abcdInverseWarpzvec.nii.gz. Use this with ANTS to get the reference_image warped into the moving_image domain. " << std::endl << "Note: " << std::endl << "prefix name \"abcd\" without any extension will use \".nii.gz\" by default" << std::endl; exit(0); } TRAN_OPT_QUEUE opt_queue; char *moving_image_filename = NULL; char *output_image_filename = NULL; MISC_OPT misc_opt; bool is_parsing_ok = false; int kImageDim = atoi(argv[1]); is_parsing_ok = ParseInput(argc-2, argv+2, moving_image_filename, output_image_filename, opt_queue, misc_opt); if (is_parsing_ok){ std::cout << "moving_image_filename: " << moving_image_filename << std::endl; std::cout << "output_image_filename: " << output_image_filename << std::endl; std::cout << "reference_image_filename: "; if (misc_opt.reference_image_filename) std::cout << misc_opt.reference_image_filename << std::endl; else std::cout << "NULL" << std::endl; DisplayOptQueue(opt_queue); switch (kImageDim){ case 2:{ WarpImageMultiTransform<2>(moving_image_filename, output_image_filename, opt_queue, misc_opt); break; } case 3:{ WarpImageMultiTransform<3>(moving_image_filename, output_image_filename, opt_queue, misc_opt); break; } } } else{ std::cout << "Input error!" << std::endl; } exit(0); } ants-1.9.2+svn680.dfsg/Examples/WarpTimeSeriesImageMultiTransform.cxx000066400000000000000000001163071147325206600255470ustar00rootroot00000000000000#include #include #include "itkImageFileReader.h" #include "itkVector.h" #include "itkVariableLengthVector.h" #include "itkVectorImageFileReader.h" #include "itkImageFileWriter.h" #include "itkMatrixOffsetTransformBase.h" #include "itkTransformFactory.h" #include "itkTransformFileReader.h" #include "itkVectorNearestNeighborInterpolateImageFunction.h" #include "ReadWriteImage.h" #include "itkWarpImageMultiTransformFilter.h" #include "itkExtractImageFilter.h" typedef enum{INVALID_FILE=1, AFFINE_FILE, DEFORMATION_FILE, IMAGE_AFFINE_HEADER, IDENTITY_TRANSFORM} TRAN_FILE_TYPE; typedef struct{ // char *filename; std::string filename; TRAN_FILE_TYPE file_type; bool do_affine_inv; // void SetValue(char *filename, TRAN_FILE_TYPE file_type, bool do_affine_inv){ // this.filename = filename; // this.file_type = file_type; // this.do_affine_inv = do_affine_inv; // }; } TRAN_OPT; typedef std::vector TRAN_OPT_QUEUE; typedef struct{ bool use_NN_interpolator; bool use_TightestBoundingBox; char * reference_image_filename; bool use_RotationHeader; } MISC_OPT; void DisplayOptQueue(const TRAN_OPT_QUEUE &opt_queue); void DisplayOpt(const TRAN_OPT &opt); TRAN_FILE_TYPE CheckFileType(const char *str){ std::string filename = str; std::string::size_type pos = filename.rfind( "." ); std::string filepre = std::string( filename, 0, pos ); if ( pos != std::string::npos ){ std::string extension = std::string( filename, pos, filename.length()-1); if (extension==std::string(".gz")){ pos = filepre.rfind( "." ); extension = std::string( filepre, pos, filepre.length()-1 ); } if (extension==".txt") return AFFINE_FILE; else return DEFORMATION_FILE; } else{ return INVALID_FILE; } return AFFINE_FILE; } void FilePartsWithgz(const std::string &filename, std::string &path, std::string &name, std::string &ext){ std::string extension; std::string::size_type pos = filename.rfind( "." ); std::string filepre = std::string( filename, 0, pos ); if ( pos != std::string::npos ){ extension = std::string( filename, pos, filename.length()-1); if (extension==std::string(".gz")){ pos = filepre.rfind( "." ); if (pos != std::string::npos){ extension = std::string( filepre, pos, filepre.length()-1 ) + ".gz"; filepre = std::string(filepre, 0, pos); } } } else { extension = std::string(""); } ext = extension; pos = filepre.rfind('/'); if ( pos != std::string::npos ){ path = std::string(filepre, 0, pos+1); name = std::string(filepre, pos+1, filepre.length()-1); } else { path = std::string(""); name = filepre; } // std::cout << "filename: " << filename << std::endl // << "path: " << path << std::endl // << "name: " << name << std::endl // << "ext: " << ext << std::endl; } bool CheckFileExistence(const char *str){ std::ifstream myfile(str); bool b = myfile.is_open(); myfile.close(); return b; } void SetAffineInvFlag(TRAN_OPT &opt, bool &set_current_affine_inv){ opt.do_affine_inv = set_current_affine_inv; if (set_current_affine_inv) set_current_affine_inv = false; } bool ParseInput(int argc, char **argv, char *&moving_image_filename, char *&output_image_filename, TRAN_OPT_QUEUE &opt_queue, MISC_OPT &misc_opt){ opt_queue.clear(); opt_queue.reserve(argc-2); misc_opt.reference_image_filename = NULL; misc_opt.use_NN_interpolator = false; misc_opt.use_TightestBoundingBox = false; misc_opt.use_RotationHeader = false; moving_image_filename = argv[0]; output_image_filename = argv[1]; int ind = 2; bool set_current_affine_inv = false; while(ind < argc){ if (strcmp(argv[ind], "--use-NN")==0) { misc_opt.use_NN_interpolator = true; } else if (strcmp(argv[ind], "-R")==0) { ind++; if(ind >= argc) return false; misc_opt.reference_image_filename = argv[ind]; } else if ((strcmp(argv[ind], "--tightest-bounding-box")==0) && (strcmp(argv[ind], "-R")!=0) ) { misc_opt.use_TightestBoundingBox = true; } else if (strcmp(argv[ind], "--reslice-by-header")==0) { misc_opt.use_RotationHeader = true; TRAN_OPT opt; opt.file_type = IMAGE_AFFINE_HEADER; opt.do_affine_inv = false; opt_queue.push_back(opt); } else if (strcmp(argv[ind], "--Id")==0) { TRAN_OPT opt; opt.filename = "--Id"; opt.do_affine_inv = false; opt.file_type = IDENTITY_TRANSFORM; opt_queue.push_back(opt); } else if (strcmp(argv[ind], "--moving-image-header")==0 || strcmp(argv[ind], "-mh") ==0){ TRAN_OPT opt; opt.file_type = IMAGE_AFFINE_HEADER; opt.filename = moving_image_filename; // opt.do_affine_inv = false; SetAffineInvFlag(opt, set_current_affine_inv); opt_queue.push_back(opt); } else if (strcmp(argv[ind], "--reference-image-header")==0 || strcmp(argv[ind], "-rh") ==0){ if (misc_opt.reference_image_filename==NULL){ std::cout << "reference image filename is not given yet. Specify it with -R before --reference-image-header / -rh." << std::endl; return false; } TRAN_OPT opt; opt.file_type = IMAGE_AFFINE_HEADER; opt.filename = misc_opt.reference_image_filename; // opt.do_affine_inv = false; SetAffineInvFlag(opt, set_current_affine_inv); opt_queue.push_back(opt); } else if (strcmp(argv[ind], "-i")==0) { set_current_affine_inv = true; } else if (strcmp(argv[ind], "--ANTS-prefix")==0){ ind++; std::string prefix = argv[ind]; std::string path, name, ext; FilePartsWithgz(prefix, path, name, ext); if (ext=="") ext=".nii.gz"; std::string deform_file_name, x_deform_name; deform_file_name = path+name+std::string("Warp")+ext; x_deform_name = path+name+std::string("Warpxvec")+ext; if (CheckFileExistence(x_deform_name.c_str())){ TRAN_OPT opt; opt.filename = deform_file_name.c_str(); opt.file_type = CheckFileType(opt.filename.c_str()); opt.do_affine_inv = false; opt_queue.push_back(opt); std::cout << "found deformation file: " << opt.filename << std::endl; DisplayOpt(opt); } std::string affine_file_name; affine_file_name = path+name+std::string("Affine.txt"); if (CheckFileExistence(affine_file_name.c_str())){ TRAN_OPT opt; opt.filename = affine_file_name.c_str(); opt.file_type = CheckFileType(opt.filename.c_str()); opt.do_affine_inv = false; opt_queue.push_back(opt); std::cout << "found affine file: " << opt.filename << std::endl; DisplayOpt(opt); } } else if (strcmp(argv[ind], "--ANTS-prefix-invert")==0){ ind++; std::string prefix = argv[ind]; std::string path, name, ext; FilePartsWithgz(prefix, path, name, ext); if (ext=="") ext=".nii.gz"; std::string affine_file_name; affine_file_name = path+name+std::string("Affine.txt"); if (CheckFileExistence(affine_file_name.c_str())){ TRAN_OPT opt; opt.filename = affine_file_name.c_str(); opt.file_type = CheckFileType(opt.filename.c_str()); opt.do_affine_inv = true; opt_queue.push_back(opt); std::cout << "found affine file: " << opt.filename << std::endl; DisplayOpt(opt); } std::string deform_file_name, x_deform_name; deform_file_name = path+name+std::string("InverseWarp.nii.gz"); x_deform_name = path+name+std::string("InverseWarpxvec.nii.gz"); if (CheckFileExistence(x_deform_name.c_str())){ TRAN_OPT opt; opt.filename = deform_file_name.c_str(); opt.file_type = CheckFileType(opt.filename.c_str()); opt.do_affine_inv = false; opt_queue.push_back(opt); std::cout << "found deformation file: " << opt.filename << std::endl; DisplayOpt(opt); } } else{ TRAN_OPT opt; opt.filename = argv[ind]; opt.file_type = CheckFileType(opt.filename.c_str()); opt.do_affine_inv = false; if (opt.file_type == AFFINE_FILE) SetAffineInvFlag(opt, set_current_affine_inv); else if (opt.file_type == DEFORMATION_FILE && set_current_affine_inv){ std::cout << "Ignore inversion of non-affine file type! " << std::endl; std::cout << "opt.do_affine_inv:" << opt.do_affine_inv << std::endl; } opt_queue.push_back(opt); DisplayOpt(opt); } ind++; } if (misc_opt.use_RotationHeader) { // if (misc_opt.reference_image_filename) { // opt_queue[0].filename = misc_opt.reference_image_filename; // } else { opt_queue[0].filename = "--Id"; opt_queue[0].file_type = IDENTITY_TRANSFORM; opt_queue[0].do_affine_inv = false; // } // TRAN_OPT opt; // opt.file_type = IMAGE_AFFINE_HEADER; // opt.filename = moving_image_filename; // opt.do_affine_inv = true; // opt_queue.push_back(opt); // // std::cout << "Use Rotation Header!" << std::endl; } return true; } void DisplayOptQueue(const TRAN_OPT_QUEUE &opt_queue){ const int kQueueSize = opt_queue.size(); for(int i=0; i void GetIdentityTransform(AffineTransformPointer &aff){ typedef typename AffineTransformPointer::ObjectType AffineTransform; aff = AffineTransform::New(); aff->SetIdentity(); } template void GetAffineTransformFromImage(const ImageTypePointer& img, AffineTransformPointer &aff){ typedef typename ImageTypePointer::ObjectType ImageType; typedef typename ImageType::DirectionType DirectionType; typedef typename ImageType::PointType PointType; typedef typename ImageType::SpacingType SpacingType; typedef typename AffineTransformPointer::ObjectType::TranslationType VectorType; DirectionType direction = img->GetDirection(); SpacingType spacing = img->GetSpacing(); VectorType translation; // translation.Fill(0); for(unsigned int i=0; iGetOrigin()[i]; aff->SetMatrix(direction); // aff->SetCenter(pt); PointType pt; pt.Fill(0); aff->SetOffset(translation); aff->SetCenter(pt); std::cout << "aff from image:" << aff << std::endl; } template void GetLaregstSizeAfterWarp(WarperPointerType &warper, ImagePointerType &img, SizeType &largest_size, PointType &origin_warped){ typedef typename ImagePointerType::ObjectType ImageType; const int ImageDimension = ImageType::GetImageDimension(); // typedef typename ImageType::PointType PointType; typedef typename std::vector PointList; typedef typename ImageType::IndexType IndexType; // PointList pts_orig; PointList pts_warped; typename ImageType::SizeType imgsz; imgsz = img->GetLargestPossibleRegion().GetSize(); typename ImageType::SpacingType spacing; spacing = img->GetSpacing(); pts_warped.clear(); if (ImageDimension == 3){ for(int i=0; i<8; i++){ IndexType ind; switch (i){ case 0: ind[0]=0; ind[1]=0; ind[2]=0; break; case 1: ind[0]=imgsz[0]-1; ind[1]=0; ind[2]=0; break; case 2: ind[0]=0; ind[1]=imgsz[1]-1; ind[2]=0; break; case 3: ind[0]=imgsz[0]-1; ind[1]=imgsz[1]-1; ind[2]=0; break; case 4: ind[0]=0; ind[1]=0; ind[2]=imgsz[2]-1; break; case 5: ind[0]=imgsz[0]-1; ind[1]=0; ind[2]=imgsz[2]-1; break; case 6: ind[0]=0; ind[1]=imgsz[1]-1; ind[2]=imgsz[2]-1; break; case 7: ind[0]=imgsz[0]-1; ind[1]=imgsz[1]-1; ind[2]=imgsz[2]-1; break; } PointType pt_orig, pt_warped; img->TransformIndexToPhysicalPoint(ind, pt_orig); if (warper->MultiInverseAffineOnlySinglePoint(pt_orig, pt_warped)==false){ std::cout << "ERROR: outside of numeric boundary with affine transform." << std::endl; exit(-1); } pts_warped.push_back(pt_warped); std::cout << '[' << i << ']' << ind << ',' << pt_orig << "->" << pt_warped << std::endl; } } else if (ImageDimension == 2) { for(int i=0; i<4; i++){ IndexType ind; switch (i){ case 0: ind[0]=0; ind[1]=0; break; case 1: ind[0]=imgsz[0]-1; ind[1]=0; break; case 2: ind[0]=0; ind[1]=imgsz[1]-1; break; case 3: ind[0]=imgsz[0]-1; ind[1]=imgsz[1]-1; break; } PointType pt_orig, pt_warped; img->TransformIndexToPhysicalPoint(ind, pt_orig); if (warper->MultiInverseAffineOnlySinglePoint(pt_orig, pt_warped)==false){ std::cout << "ERROR: outside of numeric boundary with affine transform." << std::endl; exit(-1); } pts_warped.push_back(pt_warped); std::cout << '[' << i << ']' << ind << ',' << pt_orig << "->" << pt_warped << std::endl; } } else { std::cout << "could not determine the dimension after warping for non 2D/3D volumes" << std::endl; exit(-1); } PointType pt_min, pt_max; pt_min = pts_warped[0]; pt_max = pts_warped[0]; for(unsigned int k=0; kpts_warped[k][i])?(pt_max[i]):(pts_warped[k][i]); } } for(int i=0; i void WarpImageMultiTransformFourD(char *moving_image_filename, char *output_image_filename, TRAN_OPT_QUEUE &opt_queue, MISC_OPT &misc_opt) { typedef itk::Image VectorImageType; // 4D contains functional image typedef itk::Image ImageType; // 3D image domain -R option typedef itk::Vector VectorType; // 3D warp typedef itk::Image DeformationFieldType; // 3D Field typedef itk::MatrixOffsetTransformBase< double, ImageDimension-1, ImageDimension-1> AffineTransformType; typedef itk::WarpImageMultiTransformFilter WarperType; itk::TransformFactory::RegisterTransform(); typedef itk::ImageFileReader ImageFileReaderType; //typename ImageFileReaderType::Pointer reader_img = ImageFileReaderType::New(); //reader_img->SetFileName(moving_image_filename); //reader_img->Update(); //typename ImageType::Pointer img_mov = ImageType::New(); //img_mov = reader_img->GetOutput(); typename VectorImageType::Pointer img_mov; ReadImage(img_mov,moving_image_filename); std::cout << " Four-D image size: " << img_mov->GetLargestPossibleRegion().GetSize() << std::endl; typename ImageType::Pointer img_ref; // = ImageType::New(); typename ImageFileReaderType::Pointer reader_img_ref = ImageFileReaderType::New(); if (misc_opt.reference_image_filename){ reader_img_ref->SetFileName(misc_opt.reference_image_filename); reader_img_ref->Update(); img_ref = reader_img_ref->GetOutput(); } typedef itk::ExtractImageFilter ExtractFilterType; typedef itk::ImageRegionIteratorWithIndex ImageIt; typedef itk::ImageRegionIteratorWithIndex SliceIt; // allocate output image typename VectorImageType::Pointer transformedvecimage = VectorImageType::New(); typename VectorImageType::RegionType region =img_mov->GetLargestPossibleRegion(); for (unsigned int i=0; iGetLargestPossibleRegion().GetSize()[i] ); } transformedvecimage->SetRegions(region); // transformedvecimage->SetDirection( transformedvecimage->Allocate(); typename VectorImageType::DirectionType direction=transformedvecimage->GetDirection(); direction.Fill(0); typename VectorImageType::PointType origin=transformedvecimage->GetOrigin(); typename VectorImageType::SpacingType spc=transformedvecimage->GetSpacing(); for (unsigned int i=0; iGetDirection()[i][j]; } spc[i]=img_ref->GetSpacing()[i]; origin[i]=img_ref->GetOrigin()[i]; } direction[ImageDimension-1][ImageDimension-1]=1; origin[ImageDimension-1]=img_mov->GetOrigin()[ImageDimension-1]; spc[ImageDimension-1]=img_mov->GetSpacing()[ImageDimension-1]; transformedvecimage->SetDirection(direction); transformedvecimage->SetSpacing(spc); transformedvecimage->SetOrigin(origin); std::cout << " 4D-In-Spc " << img_mov->GetSpacing() << std::endl; std::cout << " 4D-In-Org " << img_mov->GetOrigin() << std::endl; std::cout << " 4D-In-Size " << img_mov->GetLargestPossibleRegion().GetSize() << std::endl; std::cout << " 4D-In-Dir " << img_mov->GetDirection() << std::endl; std::cout << " ...... " << std::endl; std::cout << " 4D-Out-Spc " << transformedvecimage->GetSpacing() << std::endl; std::cout << " 4D-Out-Org " << transformedvecimage->GetOrigin() << std::endl; std::cout << " 4D-Out-Size " << transformedvecimage->GetLargestPossibleRegion().GetSize() << std::endl; std::cout << " 4D-Out-Dir " << transformedvecimage->GetDirection() << std::endl; unsigned int timedims=img_mov->GetLargestPossibleRegion().GetSize()[ImageDimension-1]; for (unsigned int timedim=0; timedim < timedims ; timedim++ ) { typename WarperType::Pointer warper = WarperType::New(); warper->SetEdgePaddingValue(0); if (misc_opt.use_NN_interpolator){ typedef typename itk::NearestNeighborInterpolateImageFunction NNInterpolateType; typename NNInterpolateType::Pointer interpolator_NN = NNInterpolateType::New(); std::cout << " Use Nearest Neighbor interpolation " << std::endl; warper->SetInterpolator(interpolator_NN); } typedef itk::TransformFileReader TranReaderType; typedef itk::VectorImageFileReader FieldReaderType; unsigned int transcount=0; const int kOptQueueSize = opt_queue.size(); for(int i=0; iSetFileName(opt.filename); tran_reader->Update(); typename AffineTransformType::Pointer aff = dynamic_cast< AffineTransformType* > ((tran_reader->GetTransformList())->front().GetPointer()); if (opt.do_affine_inv) { typename AffineTransformType::Pointer aff_inv = AffineTransformType::New(); aff->GetInverse(aff_inv); aff = aff_inv; } // std::cout <<" aff " << transcount << std::endl; warper->PushBackAffineTransform(aff); if (transcount==0){ warper->SetOutputSize(img_ref->GetLargestPossibleRegion().GetSize()); warper->SetOutputSpacing(img_ref->GetSpacing()); warper->SetOutputOrigin(img_ref->GetOrigin()); warper->SetOutputDirection(img_ref->GetDirection()); } transcount++; break; } case IDENTITY_TRANSFORM:{ typename AffineTransformType::Pointer aff; GetIdentityTransform(aff); // std::cout << " aff id" << transcount << std::endl; warper->PushBackAffineTransform(aff); transcount++; break; } case IMAGE_AFFINE_HEADER:{ typename AffineTransformType::Pointer aff = AffineTransformType::New(); typename ImageType::Pointer img_affine = ImageType::New(); typename ImageFileReaderType::Pointer reader_image_affine = ImageFileReaderType::New(); reader_image_affine->SetFileName(opt.filename); reader_image_affine->Update(); img_affine = reader_image_affine->GetOutput(); GetAffineTransformFromImage(img_affine, aff); if (opt.do_affine_inv) { typename AffineTransformType::Pointer aff_inv = AffineTransformType::New(); aff->GetInverse(aff_inv); aff = aff_inv; } // std::cout <<" aff from image header " << transcount << std::endl; warper->PushBackAffineTransform(aff); // if (transcount==0){ // warper->SetOutputSize(img_mov->GetLargestPossibleRegion().GetSize()); // warper->SetOutputSpacing(img_mov->GetSpacing()); // warper->SetOutputOrigin(img_mov->GetOrigin()); // warper->SetOutputDirection(img_mov->GetDirection()); // } transcount++; break; } case DEFORMATION_FILE:{ typename FieldReaderType::Pointer field_reader = FieldReaderType::New(); field_reader->SetFileName( opt.filename ); field_reader->Update(); typename DeformationFieldType::Pointer field = field_reader->GetOutput(); warper->PushBackDeformationFieldTransform(field); warper->SetOutputSize(field->GetLargestPossibleRegion().GetSize()); warper->SetOutputOrigin(field->GetOrigin()); warper->SetOutputSpacing(field->GetSpacing()); warper->SetOutputDirection(field->GetDirection()); transcount++; break; } default: std::cout << "Unknown file type!" << std::endl; } } // warper->PrintTransformList(); if (img_ref.IsNotNull()){ warper->SetOutputSize(img_ref->GetLargestPossibleRegion().GetSize()); warper->SetOutputSpacing(img_ref->GetSpacing()); warper->SetOutputOrigin(img_ref->GetOrigin()); warper->SetOutputDirection(img_ref->GetDirection()); } else { if (misc_opt.use_TightestBoundingBox == true){ // compute the desired spacking after inputting all the transform files using the std::cout << " not implemented " << std::endl; /* typename ImageType::SizeType largest_size; typename ImageType::PointType origin_warped; GetLaregstSizeAfterWarp(warper, warpthisimage , largest_size, origin_warped); warper->SetOutputSize(largest_size); warper->SetOutputSpacing(warpthisimage->GetSpacing()); warper->SetOutputOrigin(origin_warped); typename ImageType::DirectionType d; d.SetIdentity(); warper->SetOutputDirection(d);*/ } } if ( timedim % vnl_math_max(timedims / 10, static_cast(1)) == 0 ) std::cout << (float) timedim/(float)timedims*100 << " % done ... " << std::flush; // << std::endl; typename VectorImageType::RegionType extractRegion = img_mov->GetLargestPossibleRegion(); extractRegion.SetSize(ImageDimension-1, 0); extractRegion.SetIndex(ImageDimension-1, timedim ); typename ExtractFilterType::Pointer extractFilter = ExtractFilterType::New(); extractFilter->SetInput( img_mov ); extractFilter->SetExtractionRegion( extractRegion ); extractFilter->Update(); typename ImageType::Pointer warpthisimage=extractFilter->GetOutput(); typename ImageType::SpacingType qspc=warpthisimage->GetSpacing(); typename ImageType::PointType qorg=warpthisimage->GetOrigin(); typename ImageType::DirectionType qdir=warpthisimage->GetDirection(); qdir.Fill(0); for (unsigned int qq=0; qqGetDirection()[qq][pp]; } qspc[qq]=img_mov->GetSpacing()[qq]; qorg[qq]=img_mov->GetOrigin()[qq]; } warpthisimage->SetSpacing(qspc); warpthisimage->SetOrigin(qorg); warpthisimage->SetDirection(qdir); warper->SetInput( warpthisimage ); warper->DetermineFirstDeformNoInterp(); warper->Update(); typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter2( warper->GetOutput(), warper->GetOutput()->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { typename ImageType::PixelType fval=vfIter2.Get(); typename VectorImageType::IndexType ind; for (unsigned int xx=0; xxSetPixel(ind,fval); // if ( ind[0] == 53 && ind[1] == 19 && ind[2] == 30 ) std::cout << " fval " << fval << " td " << timedim << std::endl; } if (timedim == 0) std::cout << warper->GetOutput()->GetDirection() << std::endl; } std::cout << " 100 % complete " << std::endl; WriteImage( transformedvecimage , output_image_filename); } template void WarpImageMultiTransform(char *moving_image_filename, char *output_image_filename, TRAN_OPT_QUEUE &opt_queue, MISC_OPT &misc_opt){ typedef itk::VectorImage VectorImageType; typedef itk::Image ImageType; typedef itk::Vector VectorType; typedef itk::Image DeformationFieldType; typedef itk::MatrixOffsetTransformBase< double, ImageDimension, ImageDimension > AffineTransformType; typedef itk::WarpImageMultiTransformFilter WarperType; itk::TransformFactory::RegisterTransform(); typedef itk::ImageFileReader ImageFileReaderType; //typename ImageFileReaderType::Pointer reader_img = ImageFileReaderType::New(); //reader_img->SetFileName(moving_image_filename); //reader_img->Update(); //typename ImageType::Pointer img_mov = ImageType::New(); //img_mov = reader_img->GetOutput(); typename VectorImageType::Pointer img_mov; typename itk::ImageIOBase::Pointer imageIO = itk::ImageIOFactory::CreateImageIO(moving_image_filename, itk::ImageIOFactory::ReadMode); imageIO->SetFileName(moving_image_filename); imageIO->ReadImageInformation(); // std::cout << " Dimension " << imageIO->GetNumberOfDimensions() << " Components " <GetNumberOfComponents() << std::endl; unsigned int veclength=imageIO->GetNumberOfComponents(); std::cout <<" read veclength as:: " << veclength << std::endl; ReadImage(img_mov,moving_image_filename); typename ImageType::Pointer img_ref; // = ImageType::New(); typename ImageFileReaderType::Pointer reader_img_ref = ImageFileReaderType::New(); if (misc_opt.reference_image_filename){ reader_img_ref->SetFileName(misc_opt.reference_image_filename); reader_img_ref->Update(); img_ref = reader_img_ref->GetOutput(); } typename VectorImageType::Pointer img_output = VectorImageType::New(); img_output->SetNumberOfComponentsPerPixel(veclength); img_output->SetLargestPossibleRegion( img_ref->GetLargestPossibleRegion() ); img_output->SetBufferedRegion( img_ref->GetLargestPossibleRegion() ); img_output->SetLargestPossibleRegion( img_ref->GetLargestPossibleRegion() ); img_output->Allocate(); img_output->SetSpacing(img_ref->GetSpacing()); img_output->SetOrigin(img_ref->GetOrigin()); img_output->SetDirection(img_ref->GetDirection()); typename ImageType::IndexType index; index.Fill(0); typename VectorImageType::PixelType vec=img_mov->GetPixel(index); vec.Fill(0); img_output->FillBuffer( vec ); for (unsigned int tensdim=0; tensdim < veclength; tensdim++) { typedef itk::VectorIndexSelectionCastImageFilter IndexSelectCasterType; typename IndexSelectCasterType::Pointer fieldCaster = IndexSelectCasterType::New(); fieldCaster->SetInput( img_mov ); fieldCaster->SetIndex( tensdim ); fieldCaster->Update(); typename ImageType::Pointer tenscomponent=fieldCaster->GetOutput(); tenscomponent->SetSpacing(img_mov->GetSpacing()); tenscomponent->SetOrigin(img_mov->GetOrigin()); tenscomponent->SetDirection(img_mov->GetDirection()); typename WarperType::Pointer warper = WarperType::New(); warper->SetInput(tenscomponent); // PixelType nullPix; // nullPix.Fill(0); warper->SetEdgePaddingValue(0); if (misc_opt.use_NN_interpolator){ typedef typename itk::NearestNeighborInterpolateImageFunction NNInterpolateType; typename NNInterpolateType::Pointer interpolator_NN = NNInterpolateType::New(); std::cout << "Haha" << std::endl; warper->SetInterpolator(interpolator_NN); } typedef itk::TransformFileReader TranReaderType; typedef itk::VectorImageFileReader FieldReaderType; unsigned int transcount=0; const int kOptQueueSize = opt_queue.size(); for(int i=0; iSetFileName(opt.filename); tran_reader->Update(); typename AffineTransformType::Pointer aff = dynamic_cast< AffineTransformType* > ((tran_reader->GetTransformList())->front().GetPointer()); if (opt.do_affine_inv) { typename AffineTransformType::Pointer aff_inv = AffineTransformType::New(); aff->GetInverse(aff_inv); aff = aff_inv; } // std::cout <<" aff " << transcount << std::endl; warper->PushBackAffineTransform(aff); if (transcount==0){ warper->SetOutputSize(img_mov->GetLargestPossibleRegion().GetSize()); warper->SetOutputSpacing(img_mov->GetSpacing()); warper->SetOutputOrigin(img_mov->GetOrigin()); warper->SetOutputDirection(img_mov->GetDirection()); } transcount++; break; } case IDENTITY_TRANSFORM:{ typename AffineTransformType::Pointer aff; GetIdentityTransform(aff); // std::cout << " aff id" << transcount << std::endl; warper->PushBackAffineTransform(aff); transcount++; break; } case IMAGE_AFFINE_HEADER:{ typename AffineTransformType::Pointer aff = AffineTransformType::New(); typename ImageType::Pointer img_affine = ImageType::New(); typename ImageFileReaderType::Pointer reader_image_affine = ImageFileReaderType::New(); reader_image_affine->SetFileName(opt.filename); reader_image_affine->Update(); img_affine = reader_image_affine->GetOutput(); GetAffineTransformFromImage(img_affine, aff); if (opt.do_affine_inv) { typename AffineTransformType::Pointer aff_inv = AffineTransformType::New(); aff->GetInverse(aff_inv); aff = aff_inv; } // std::cout <<" aff from image header " << transcount << std::endl; warper->PushBackAffineTransform(aff); // if (transcount==0){ // warper->SetOutputSize(img_mov->GetLargestPossibleRegion().GetSize()); // warper->SetOutputSpacing(img_mov->GetSpacing()); // warper->SetOutputOrigin(img_mov->GetOrigin()); // warper->SetOutputDirection(img_mov->GetDirection()); // } transcount++; break; } case DEFORMATION_FILE:{ typename FieldReaderType::Pointer field_reader = FieldReaderType::New(); field_reader->SetFileName( opt.filename ); field_reader->Update(); typename DeformationFieldType::Pointer field = field_reader->GetOutput(); warper->PushBackDeformationFieldTransform(field); warper->SetOutputSize(field->GetLargestPossibleRegion().GetSize()); warper->SetOutputOrigin(field->GetOrigin()); warper->SetOutputSpacing(field->GetSpacing()); warper->SetOutputDirection(field->GetDirection()); transcount++; break; } default: std::cout << "Unknown file type!" << std::endl; } } // warper->PrintTransformList(); if (img_ref.IsNotNull()){ warper->SetOutputSize(img_ref->GetLargestPossibleRegion().GetSize()); warper->SetOutputSpacing(img_ref->GetSpacing()); warper->SetOutputOrigin(img_ref->GetOrigin()); warper->SetOutputDirection(img_ref->GetDirection()); } else { if (misc_opt.use_TightestBoundingBox == true){ // compute the desired spacking after inputting all the transform files using the typename ImageType::SizeType largest_size; typename ImageType::PointType origin_warped; GetLaregstSizeAfterWarp(warper, img_mov, largest_size, origin_warped); warper->SetOutputSize(largest_size); warper->SetOutputSpacing(img_mov->GetSpacing()); warper->SetOutputOrigin(origin_warped); typename ImageType::DirectionType d; d.SetIdentity(); warper->SetOutputDirection(d); } } warper->DetermineFirstDeformNoInterp(); warper->Update(); typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter2( img_output, img_output->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { typename VectorImageType::PixelType tens=vfIter2.Get(); tens[tensdim]=warper->GetOutput()->GetPixel(vfIter2.GetIndex()); vfIter2.Set(tens); } } WriteImage(img_output, output_image_filename); } int main(int argc, char **argv){ if (argc<=3){ std::cout << argv[0] << " ImageDimension moving_image output_image -R fixed_image MyWarp.nii.gz MyAffine.txt --use-NN (Nearest Neighbor Interpolator option) " << std::endl; std::cout << argv[0] << " ImageDimension fixed_image output_image -R moving_image -i MyAffine.txt MyInverseWarp.nii.gz --use-NN (Nearest Neighbor Interpolator option ) " << std::endl; std::cout << " you can also string together series of mappings --- e.g.: MyAffine.txt MySecondAffine.txt MyWarp.nii.gz MySecondWarp.nii.gz -i MyInverseAffine.txt --- this can be an arbitrarily long series. " << std::endl; std::cout << " For this program, your moving_image will be either a 3-D image with vector voxels or a 4D image with scalar voxels. " << std::endl; std::cout << " You must define whether the image is 3/4D via the first parameter to the program " << std::endl; std::cout << " Output will be of the same type as input, but will be resampled to the domain size defined by the -R image ... " << std::endl; std::cout <<" See WarpImageMultiTransform for more detailed usage " << std::endl; exit(0); } TRAN_OPT_QUEUE opt_queue; char *moving_image_filename = NULL; char *output_image_filename = NULL; MISC_OPT misc_opt; bool is_parsing_ok = false; int kImageDim = atoi(argv[1]); is_parsing_ok = ParseInput(argc-2, argv+2, moving_image_filename, output_image_filename, opt_queue, misc_opt); if (is_parsing_ok){ std::cout << "moving_image_filename: " << moving_image_filename << std::endl; std::cout << "output_image_filename: " << output_image_filename << std::endl; std::cout << "reference_image_filename: "; if (misc_opt.reference_image_filename) std::cout << misc_opt.reference_image_filename << std::endl; else std::cout << "NULL" << std::endl; DisplayOptQueue(opt_queue); switch (kImageDim){ case 2:{ WarpImageMultiTransform<2>(moving_image_filename, output_image_filename, opt_queue, misc_opt); break; } case 3:{ WarpImageMultiTransform<3>(moving_image_filename, output_image_filename, opt_queue, misc_opt); break; } case 4:{ WarpImageMultiTransformFourD<4>(moving_image_filename, output_image_filename, opt_queue, misc_opt); break; } } } else{ std::cout << "Input error!" << std::endl; } exit(0); } ants-1.9.2+svn680.dfsg/Examples/WarpVTKPolyDataMultiTransform.cxx000066400000000000000000000525231147325206600246340ustar00rootroot00000000000000#include #include #include #include "itkImageFileReader.h" #include "itkVector.h" #include "itkVectorImageFileReader.h" #include "itkVectorImageFileWriter.h" #include "itkImageFileWriter.h" #include "itkMatrixOffsetTransformBase.h" #include "itkTransformFactory.h" #include "vtkPolyDataReader.h" //#include "itkWarpImageMultiTransformFilter.h" #include "itkDeformationFieldFromMultiTransformFilter.h" #include "itkTransformFileReader.h" #include "itkTransformFileWriter.h" #include "itkLabeledPointSetFileReader.h" #include "itkLabeledPointSetFileWriter.h" #include "itkMesh.h" #include #include #include #include #include #include #include typedef enum { INVALID_FILE = 1, AFFINE_FILE, DEFORMATION_FILE } TRAN_FILE_TYPE; typedef struct { char * filename; TRAN_FILE_TYPE file_type; bool do_affine_inv; } TRAN_OPT; typedef std::vector TRAN_OPT_QUEUE; template typename TImage::PointType TransformPoint(TDeformationField* field, typename TImage::PointType point ) { enum { ImageDimension = TImage::ImageDimension }; typename TImage::PointType newpoint; newpoint.Fill(0); for (unsigned int row=0; rowGetDirection()[row][col]; return newpoint; } vnl_matrix_fixed ConstructNiftiSform( vnl_matrix m_dir, vnl_vector v_origin, vnl_vector v_spacing) { // Set the NIFTI/RAS transform vnl_matrix m_ras_matrix; vnl_diag_matrix m_scale, m_lps_to_ras; vnl_vector v_ras_offset; // Compute the matrix m_scale.set(v_spacing); m_lps_to_ras.set(vnl_vector(3, 1.0)); m_lps_to_ras[0] = -1; m_lps_to_ras[1] = -1; m_ras_matrix = m_lps_to_ras * m_dir * m_scale; // Compute the vector v_ras_offset = m_lps_to_ras * v_origin; // Create the larger matrix vnl_vector vcol(4, 1.0); vcol.update(v_ras_offset); vnl_matrix_fixed m_sform; m_sform.set_identity(); m_sform.update(m_ras_matrix); m_sform.set_column(3, vcol); return m_sform; } vnl_matrix_fixed ConstructVTKtoNiftiTransform( vnl_matrix m_dir, vnl_vector v_origin, vnl_vector v_spacing) { vnl_matrix_fixed vox2nii = ConstructNiftiSform(m_dir, v_origin, v_spacing); vnl_matrix_fixed vtk2vox; vtk2vox.set_identity(); for(size_t i = 0; i < 3; i++) { vtk2vox(i,i) = 1.0 / v_spacing[i]; vtk2vox(i,3) = - v_origin[i] / v_spacing[i]; } return vox2nii * vtk2vox; } TRAN_FILE_TYPE CheckFileType(char *str) { std::string filename = str; std::string::size_type pos = filename.rfind("."); std::string filepre = std::string(filename, 0, pos); if (pos != std::string::npos) { std::string extension = std::string(filename, pos, filename.length() - 1); if (extension == std::string(".gz")) { pos = filepre.rfind("."); extension = std::string(filepre, pos, filepre.length() - 1); } if (extension == ".txt") return AFFINE_FILE; else return DEFORMATION_FILE; } else { return INVALID_FILE; } return AFFINE_FILE; } bool ParseInput(int argc, char **argv, char *&input_vtk_filename, char *&output_vtk_filename, char *&reference_image_filename, TRAN_OPT_QUEUE &opt_queue) { opt_queue.clear(); opt_queue.reserve(argc - 2); input_vtk_filename = argv[0]; output_vtk_filename = argv[1]; reference_image_filename = NULL; int ind = 2; while (ind < argc) { if (strcmp(argv[ind], "-R") == 0) { ind++; if (ind >= argc) return false; reference_image_filename = argv[ind]; } else if (strcmp(argv[ind], "-i") == 0) { ind++; if (ind >= argc) return false; TRAN_OPT opt; opt.filename = argv[ind]; if (CheckFileType(opt.filename) != AFFINE_FILE) { std::cout << "file: " << opt.filename << " is not an affine .txt file. Invalid to use '-i' " << std::endl; return false; } opt.file_type = AFFINE_FILE; opt.do_affine_inv = true; opt_queue.push_back(opt); } else { TRAN_OPT opt; opt.filename = argv[ind]; opt.file_type = CheckFileType(opt.filename); opt.do_affine_inv = false; opt_queue.push_back(opt); } ind++; } // if (reference_image_filename == NULL) { // std::cout << "the reference image file (-R) must be given!!!" // << std::endl; // return false; // } return true; } void DisplayOptQueue(const TRAN_OPT_QUEUE &opt_queue) { const int kQueueSize = opt_queue.size(); for (int i = 0; i < kQueueSize; i++) { std::cout << "[" << i << "/" << kQueueSize << "]: "; switch (opt_queue[i].file_type) { case AFFINE_FILE: std::cout << "AFFINE"; if (opt_queue[i].do_affine_inv) std::cout << "-INV"; break; case DEFORMATION_FILE: std::cout << "FIELD"; break; default: std::cout << "Invalid Format!!!"; break; } std::cout << ": " << opt_queue[i].filename << std::endl; } } template void WarpLabeledPointSetFileMultiTransform(char *input_vtk_filename, char *output_vtk_filename, char *reference_image_filename, TRAN_OPT_QUEUE &opt_queue) { typedef itk::Image ImageType; typedef itk::Vector VectorType; typedef itk::Image DeformationFieldType; typedef itk::MatrixOffsetTransformBase AffineTransformType; // typedef itk::WarpImageMultiTransformFilter WarperType; typedef itk::DeformationFieldFromMultiTransformFilter WarperType; typedef itk::LinearInterpolateImageFunction FuncType; itk::TransformFactory::RegisterTransform(); typedef itk::ImageFileReader ImageFileReaderType; typename ImageFileReaderType::Pointer reader_img = ImageFileReaderType::New(); typename ImageType::Pointer img_ref = ImageType::New(); typename ImageFileReaderType::Pointer reader_img_ref = ImageFileReaderType::New(); if (reference_image_filename) { reader_img_ref->SetFileName(reference_image_filename); reader_img_ref->Update(); img_ref = reader_img_ref->GetOutput(); } else { std::cout << "the reference image file (-R) must be given!!!" << std::endl; return; } typename WarperType::Pointer warper = WarperType::New(); // warper->SetInput(img_mov); // warper->SetEdgePaddingValue( 0); VectorType pad; pad.Fill(0); // warper->SetEdgePaddingValue(pad); typedef itk::TransformFileReader TranReaderType; typedef itk::VectorImageFileReader FieldReaderType; const int kOptQueueSize = opt_queue.size(); for (int i = 0; i < kOptQueueSize; i++) { const TRAN_OPT &opt = opt_queue[i]; switch (opt_queue[i].file_type) { case AFFINE_FILE: { typename TranReaderType::Pointer tran_reader = TranReaderType::New(); tran_reader->SetFileName(opt.filename); tran_reader->Update(); typename AffineTransformType::Pointer aff = dynamic_cast ((tran_reader->GetTransformList())->front().GetPointer()); if (opt_queue[i].do_affine_inv) { aff->GetInverse(aff); } // std::cout << aff << std::endl; warper->PushBackAffineTransform(aff); break; } case DEFORMATION_FILE: { typename FieldReaderType::Pointer field_reader = FieldReaderType::New(); field_reader->SetFileName(opt.filename); field_reader->Update(); typename DeformationFieldType::Pointer field = field_reader->GetOutput(); // std::cout << field << std::endl; warper->PushBackDeformationFieldTransform(field); break; } default: std::cout << "Unknown file type!" << std::endl; } } warper->SetOutputSize(img_ref->GetLargestPossibleRegion().GetSize()); warper->SetOutputSpacing(img_ref->GetSpacing()); warper->SetOutputOrigin(img_ref->GetOrigin()); warper->SetOutputDirection(img_ref->GetDirection()); std::cout << "output size: " << warper->GetOutputSize() << std::endl; std::cout << "output spacing: " << warper->GetOutputSpacing() << std::endl; // warper->PrintTransformList(); warper->DetermineFirstDeformNoInterp(); warper->Update(); typename DeformationFieldType::Pointer field_output = DeformationFieldType::New(); field_output = warper->GetOutput(); /** * Code to read the mesh */ typename ImageType::PointType point; typename ImageType::PointType warpedPoint; typedef itk::Mesh MeshType; // typedef itk::LabeledPointSetFileReader VTKReaderType; vtkPolyDataReader *vtkreader = vtkPolyDataReader::New(); vtkreader->SetFileName( input_vtk_filename ); vtkreader->Update(); vtkPolyData *mesh = vtkreader->GetOutput(); /* typename VTKReaderType::Pointer vtkreader = VTKReaderType::New(); vtkreader->SetFileName( input_vtk_filename ); vtkreader->Update(); typename MeshType::PointsContainerIterator It = vtkreader->GetOutput()->GetPoints()->Begin(); while( It != vtkreader->GetOutput()->GetPoints()->End() ) { point.CastFrom( It.Value() ); */ vnl_matrix_fixed ijk2ras, vtk2ras, lps2ras; vtk2ras = ConstructVTKtoNiftiTransform( field_output->GetDirection().GetVnlMatrix(), field_output->GetOrigin().GetVnlVector(), field_output->GetSpacing().GetVnlVector()); ijk2ras.set_identity(); vtk2ras.set_identity(); lps2ras.set_identity(); lps2ras(0,0) = -1; lps2ras(1,1) = -1; // Set up the transforms ijk2ras = ConstructNiftiSform( field_output->GetDirection().GetVnlMatrix(), field_output->GetOrigin().GetVnlVector(), field_output->GetSpacing().GetVnlVector()); vtk2ras = ConstructVTKtoNiftiTransform( field_output->GetDirection().GetVnlMatrix(), field_output->GetOrigin().GetVnlVector(), field_output->GetSpacing().GetVnlVector()); vnl_matrix_fixed ras2ijk = vnl_inverse(ijk2ras); vnl_matrix_fixed ras2vtk = vnl_inverse(vtk2ras); vnl_matrix_fixed ras2lps = vnl_inverse(lps2ras); // Update the coordinates for(int k = 0; k < mesh->GetNumberOfPoints(); k++) { // Get the point (in whatever format that it's stored) vnl_vector_fixed x_mesh, x_ras, x_ijk, v_warp, v_ras; vnl_vector_fixed y_ras, y_mesh; x_mesh[0] = mesh->GetPoint(k)[0]; x_mesh[1] = mesh->GetPoint(k)[1]; x_mesh[2] = mesh->GetPoint(k)[2]; x_mesh[3] = 1.0; // Map the point into RAS coordinates // if(parm.mesh_coord == RAS) x_ras = x_mesh; // else if(parm.mesh_coord == LPS) x_ras = lps2ras * x_mesh; // else if(parm.mesh_coord == IJKOS) x_ras = vtk2ras * x_mesh; // else x_ras = ijk2ras * x_mesh; // Map the point to IJK coordinates (continuous index) x_ijk = ras2ijk * x_ras; typename FuncType::ContinuousIndexType ind; ind[0]=x_ijk[0]; ind[1]=x_ijk[1]; ind[2]=x_ijk[2]; field_output->TransformContinuousIndexToPhysicalPoint(ind, point); // std::cout << " point " << point << std::endl; // std::cout << " point-t " << point << std::endl; bool isInside = warper->MultiTransformSinglePoint( point, warpedPoint ); // if ( isInside ) std::cout << " point-w " << warpedPoint << std::endl; if( isInside ) { typename MeshType::PointType newPoint; newPoint.CastFrom( warpedPoint ); // vtkreader->GetOutput()->SetPoint( It.Index(), newPoint ); if ( ImageDimension == 3 ) { mesh->GetPoints()->SetPoint(k, warpedPoint[0], warpedPoint[1], warpedPoint[2]); } } // ++It; } std::string fn=std::string(output_vtk_filename); if(fn.rfind(".vtk") == fn.length() - 4) { vtkPolyDataWriter *writer = vtkPolyDataWriter::New(); writer->SetFileName(output_vtk_filename); writer->SetInput(mesh); writer->Update(); } /* typedef itk::LabeledPointSetFileWriter VTKWriterType; typename VTKWriterType::Pointer vtkwriter = VTKWriterType::New(); vtkwriter->SetFileName( output_vtk_filename ); vtkwriter->SetInput( vtkreader->GetOutput() ); vtkwriter->SetMultiComponentScalars( vtkreader->GetMultiComponentScalars() ); vtkwriter->SetLines( vtkreader->GetLines() ); vtkwriter->Update(); */ // std::string filePrefix = output_vtk_filename; // std::string::size_type pos = filePrefix.rfind("."); // std::string extension = std::string(filePrefix, pos, filePrefix.length() // - 1); // filePrefix = std::string(filePrefix, 0, pos); // // std::cout << "output extension is: " << extension << std::endl; // // if (extension != std::string(".mha")) { // typedef itk::VectorImageFileWriter // WriterType; // typename WriterType::Pointer writer = WriterType::New(); // writer->SetFileName(output_vtk_filename); // writer->SetUseAvantsNamingConvention(true); // writer->SetInput(field_output); // writer->Update(); // } else { // typedef itk::ImageFileWriter WriterType; // typename WriterType::Pointer writer = WriterType::New(); // writer->SetFileName(output_vtk_filename); // writer->SetInput(field_output); // writer->Update(); // } } template void ComposeMultiAffine(char *input_affine_txt, char *output_affine_txt, char *reference_affine_txt, TRAN_OPT_QUEUE &opt_queue) { typedef itk::Image ImageType; typedef itk::Vector VectorType; typedef itk::Image DeformationFieldType; typedef itk::MatrixOffsetTransformBase AffineTransformType; typedef itk::WarpImageMultiTransformFilter WarperType; // typedef itk::DeformationFieldFromMultiTransformFilter WarperType; itk::TransformFactory::RegisterTransform(); // typedef itk::ImageFileReader ImageFileReaderType; // typename ImageFileReaderType::Pointer reader_img = ImageFileReaderType::New(); // typename ImageType::Pointer img_ref = ImageType::New(); // typename ImageFileReaderType::Pointer reader_img_ref = ImageFileReaderType::New(); typename WarperType::Pointer warper = WarperType::New(); // warper->SetInput(img_mov); // warper->SetEdgePaddingValue( 0); VectorType pad; pad.Fill(0); // warper->SetEdgePaddingValue(pad); typedef itk::TransformFileReader TranReaderType; typedef itk::VectorImageFileReader FieldReaderType; int cnt_affine = 0; const int kOptQueueSize = opt_queue.size(); for (int i = 0; i < kOptQueueSize; i++) { const TRAN_OPT &opt = opt_queue[i]; switch (opt_queue[i].file_type) { case AFFINE_FILE: { typename TranReaderType::Pointer tran_reader = TranReaderType::New(); tran_reader->SetFileName(opt.filename); tran_reader->Update(); typename AffineTransformType::Pointer aff = dynamic_cast ( (tran_reader->GetTransformList())->front().GetPointer()); if (opt_queue[i].do_affine_inv) { aff->GetInverse(aff); } // std::cout << aff << std::endl; warper->PushBackAffineTransform(aff); cnt_affine++; break; } case DEFORMATION_FILE: { std::cout << "Compose affine only files: ignore " << opt.filename << std::endl; break; } default: std::cout << "Unknown file type!" << std::endl; } } typedef typename AffineTransformType::CenterType PointType; PointType aff_center; typename AffineTransformType::Pointer aff_ref_tmp; if (reference_affine_txt) { typename TranReaderType::Pointer tran_reader = TranReaderType::New(); tran_reader->SetFileName(reference_affine_txt); tran_reader->Update(); aff_ref_tmp = dynamic_cast ((tran_reader->GetTransformList())->front().GetPointer()); } else { if (cnt_affine > 0) { std::cout << "the reference affine file for center is selected as the first affine!" << std::endl; aff_ref_tmp = ((warper->GetTransformList()).begin())->second.aex.aff; } else { std::cout << "No affine input is given. nothing to do ......" << std::endl; return; } } aff_center = aff_ref_tmp->GetCenter(); std::cout << "new center is : " << aff_center << std::endl; // warper->PrintTransformList(); // typename AffineTransformType::Pointer aff_output = warper->ComposeAffineOnlySequence(aff_center); typename AffineTransformType::Pointer aff_output = AffineTransformType::New(); warper->ComposeAffineOnlySequence(aff_center, aff_output); typedef itk::TransformFileWriter TranWriterType; typename TranWriterType::Pointer tran_writer = TranWriterType::New(); tran_writer->SetFileName(output_affine_txt); tran_writer->SetInput(aff_output); tran_writer->Update(); std::cout << "wrote file to : " << output_affine_txt << std::endl; } int main(int argc, char **argv) { if ( argc <= 4 ) { std::cout << "WarpLabeledPointSetFileMultiTransform ImageDimension inputVTKFile " << "outputVTKFile [-R reference_image] " << "{[deformation_field | [-i] affine_transform_txt ]}" << std::endl; exit(0); } TRAN_OPT_QUEUE opt_queue; // char *moving_image_filename = NULL; char *input_vtk_filename = NULL; char *output_vtk_filename = NULL; char *reference_image_filename = NULL; bool is_parsing_ok = false; int kImageDim = atoi(argv[1]); is_parsing_ok = ParseInput(argc - 2, argv + 2, input_vtk_filename, output_vtk_filename, reference_image_filename, opt_queue); if (is_parsing_ok) { switch (CheckFileType(output_vtk_filename)) { case DEFORMATION_FILE: { if (reference_image_filename == NULL) { std::cout << "the reference image file (-R) must be given!!!" << std::endl; return false; } std::cout << "output_vtk_filename: " << output_vtk_filename << std::endl; std::cout << "reference_image_filename: "; if (reference_image_filename) std::cout << reference_image_filename << std::endl; else std::cout << "NULL" << std::endl; DisplayOptQueue(opt_queue); switch (kImageDim) { case 2: { WarpLabeledPointSetFileMultiTransform<2> (input_vtk_filename, output_vtk_filename, reference_image_filename, opt_queue); break; } case 3: { WarpLabeledPointSetFileMultiTransform<3> (input_vtk_filename, output_vtk_filename, reference_image_filename, opt_queue); break; } } break; } case AFFINE_FILE: { std::cout << "output_affine_txt: " << output_vtk_filename << std::endl; std::cout << "reference_affine_txt: "; if (reference_image_filename) std::cout << reference_image_filename << std::endl; else std::cout << "NULL" << std::endl; DisplayOptQueue(opt_queue); switch (kImageDim) { case 2: { ComposeMultiAffine<2>(input_vtk_filename, output_vtk_filename, reference_image_filename, opt_queue); break; } case 3: { ComposeMultiAffine<3> (input_vtk_filename, output_vtk_filename, reference_image_filename, opt_queue); break; } } break; } default: std::cout << "Unknow output file format: " << output_vtk_filename << std::endl; break; } } else { std::cout << "Input error!" << std::endl; } exit(0); } ants-1.9.2+svn680.dfsg/Examples/itkCommandLineParserTest.cxx000066400000000000000000000042661147325206600237050ustar00rootroot00000000000000 #include "itkPICSLAdvancedNormalizationToolKit.h" #include "itkCommandLineParser.h" int main( int argc, char *argv[] ) { typedef itk::PICSLAdvancedNormalizationToolKit<3> RegistrationType; RegistrationType::Pointer registration = RegistrationType::New(); registration->ParseCommandLine( argc, argv ); typedef itk::CommandLineParser ParserType; ParserType::Pointer parser = ParserType::New(); typedef ParserType::OptionType OptionType; OptionType::Pointer fileOption = OptionType::New(); fileOption->SetShortName( 'f' ); fileOption->SetLongName( "file" ); fileOption->SetDescription( "The fixed image file used in the image registration algorithm." ); parser->AddOption( fileOption ); typedef ParserType::OptionType OptionType; OptionType::Pointer metricOption = OptionType::New(); metricOption->SetShortName( 'm' ); metricOption->SetLongName( "metric" ); metricOption->SetDescription( "The metric used by the image registration algorithm." ); parser->AddOption( metricOption ); typedef ParserType::OptionType OptionType; OptionType::Pointer verbosityOption = OptionType::New(); verbosityOption->SetLongName( "verbosity" ); verbosityOption->SetDescription( "Mundi vult decipi. " ); parser->AddOption( verbosityOption ); typedef ParserType::OptionType OptionType; OptionType::Pointer dirOption = OptionType::New(); dirOption->SetLongName( "directionality" ); // dirOption->SetShortName( 'd' ); dirOption->SetDescription( "Mundi vult decipi. " ); parser->AddOption( dirOption ); parser->Parse( argc, argv ); if ( !parser->GetOption( "directionality" ) ) { std::cout << "N ULL" << std::endl; } else { for ( unsigned int j = 0; j < 3; j++ ) { std::cout << parser->ConvertVector( parser->GetOption( "directionality" )->GetValue( 0 ) )[j] << " x "; } } parser->PrintMenu( std::cout, 7 ); // std::cout << std::endl << std::endl << "--------------------------------------------" // << std::endl << std::endl; // parser->Print( std::cout << 7 ); return 0; }; ants-1.9.2+svn680.dfsg/ImageRegistration/000077500000000000000000000000001147325206600200755ustar00rootroot00000000000000ants-1.9.2+svn680.dfsg/ImageRegistration/ANTS_affine_registration.h000066400000000000000000001567561147325206600251410ustar00rootroot00000000000000#ifndef ANTS_AFFINE_REGISTRATION_H_ #define ANTS_AFFINE_REGISTRATION_H_ #include #include #include #include "itkImage.h" #include "itkPoint.h" #include "itkCastImageFilter.h" #include "itkImageMaskSpatialObject.h" #include "itkCenteredEuler3DTransform.h" #include "itkRegularStepGradientDescentOptimizer.h" #include "itkGradientDescentOptimizer.h" #include "itkCenteredTransformInitializer.h" #include "itkMattesMutualInformationImageToImageMetric.h" #include "itkMultiResolutionImageRegistrationMethod.h" #include "itkLinearInterpolateImageFunction.h" #include "itkANTSAffine3DTransform.h" #include "itkCenteredRigid2DTransform.h" #include "itkANTSCenteredAffine2DTransform.h" #include "itkTransformFactory.h" #include "itkTransformFileReader.h" #include "itkTransformFileWriter.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkResampleImageFilter.h" #include "itkLinearInterpolateImageFunction.h" #include "itkWarpImageFilter.h" #include "itkWarpImageWAffineFilter.h" template void read_transform_file(StringType filename, CastTransformPointerType &transform); template void write_transform_file(TransformPointerType &transform, StringType str); template void compute_single_affine_transform_3d(ImagePointerType I_fixed, ImagePointerType I_moving, MaskImagePointerType mask_fixed, TransformPointerType &transform, TransformPointerType &transform_initial); template void compute_single_affine_transform_2d(ImagePointerType I_fixed, ImagePointerType I_moving, MaskImagePointerType mask_fixed, TransformPointerType &transform, TransformPointerType &transform_initial); template void compute_single_affine_transform(ImagePointerType fixedImage, ImagePointerType movingImage, MaskImagePointerType maskImage, TransformPointerType &transform, TransformPointerType &transform_initial); //void compute_single_affine_transform_2d(itk::Image::Pointer I_fixed, // itk::Image::Pointer I_moving, // itk::Image::Pointer mask_fixed, // itk::CenteredAffine2DTransform::Pointer &transform); template void create_deformation_field_byref(const DeformationFieldPointerType &ref, DeformationFieldPointerType &field); // this is obsolet, use itkWarpImageWAffineFilter template void compose_affine_with_field(const TransformPointerType &aff, const DeformationFieldPointerType &field, DeformationFieldPointerType &field_output); template void warp_image_field(const ImagePointerType &img_input, const DeformationFieldPointerType &field, ImagePointerType &img_output); template void warp_image_field_waffine(const ImagePointerType &img_input, const TransformPointerType &aff, const DeformationFieldPointerType &field, ImagePointerType &img_output); template void affine_image(const ImageTypePointer &input_image, const TransformTypePointer &transform, const RefImageTypePointer &ref_image, ImageTypePointer &img_aff ); template void write_transform_file(TransformPointerType &transform, StringType filename){ itk::TransformFileWriter::Pointer transform_writer; transform_writer = itk::TransformFileWriter::New(); transform_writer->SetFileName(filename); transform_writer->SetInput(transform); try{ transform_writer->Update(); } catch( itk::ExceptionObject &err){ std::cout << "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!" << std::endl <<"Exception in writing tranform file: " << std::endl << filename << std::endl; return; } return; } template void read_transform_file(StringType filename, CastTransformPointerType &transform){ typedef typename CastTransformPointerType::ObjectType CastTransformType; // const unsigned int InputSpaceDimension = CastTransformType::InputSpaceDimension; // const unsigned int OutputSpaceDimension = CastTransformType::OutputSpaceDimension; itk::TransformFactory::RegisterTransform(); itk::TransformFactory >::RegisterTransform(); typedef typename itk::TransformFileReader TranReaderType; TranReaderType::Pointer tran_reader = TranReaderType::New(); tran_reader->SetFileName(filename); try{ tran_reader->Update(); } catch( itk::ExceptionObject &err) { std::cerr << err << std::endl; std::cerr << "Exception caught in reading tran para file: " << filename << std::endl; return; } transform = dynamic_cast< CastTransformType* >((tran_reader->GetTransformList())->front().GetPointer()); return; } template void convert_affine_para_to_deformation_field(TransformPointerType &transform, DeformationFieldType &def){ return; } template class SEARCH_POINT_TYPE { public: ParaType para0; // seed parameter ParaType para1; // local optimal parameter itk::Point center; // transformation center double rval; // registered value unsigned int index; // the index in the list, //for sorting int number_of_iteration; // the number of iteration used for gradient descent }; template void generate_search_seed_3d(SEARCH_LIST &search_list, ParaType &ret); template void generate_search_seed_2d(SEARCH_LIST &search_list, ParaType &ret); template void get_best_search_point(SEARCH_LIST &search_list, SEARCH_POINT &spt); template void add_search_point(SEARCH_LIST &search_list, SEARCH_POINT spt); // use moment of the image initializer to get cx and cy only template bool register_image_cxyz(ImagePointerType fixed_image, ImagePointerType moving_image, ParaType ¶1, double &rval); // use an optional mask for the fixed image template bool register_image_affine3d_mres_mask(ImagePointerType fixed_image, ImagePointerType moving_image, ImageMaskSpatialObjectPointerType mask_fixed_object, ParaType para0, itk::Point center, int number_of_iteration, int MI_bins, int MI_samples, ParaType ¶1, double &rval); template bool register_image_affine2d_mres_mask(ImagePointerType fixed_image, ImagePointerType moving_image, ImageMaskSpatialObjectPointerType mask_fixed_object, ParaType para0, itk::Point center, int number_of_iteration, int MI_bins, int MI_samples, ParaType ¶1, double &rval); template double get_cost_value_mmi(ImagePointerType fixedImage, ImagePointerType movingImage, ParaType para, PointType center, TransformTypePointer null_transform); template bool compare_search_point(const SEARCH_POINT& lhs, const SEARCH_POINT& rhs){ return lhs.rval < rhs.rval; } template void compute_single_affine_transform_3d(ImagePointerType I_fixed, ImagePointerType I_moving, MaskImagePointerType mask_fixed, TransformPointerType &transform, TransformPointerType &transform_initial){ typedef typename ImagePointerType::ObjectType ImageType; const int ImageDimension = ImageType::ImageDimension; typedef typename ImageType::IOPixelType PixelType; typedef typename MaskImagePointerType::ObjectType MaskImageType; typedef typename MaskImageType::IOPixelType MaskPixelType; typedef typename TransformPointerType::ObjectType TransformType; typedef typename TransformType::ParametersType ParaType; // option parameters int number_of_seeds = 0; int number_of_iteration = 10000; int MI_bins = 32; int MI_samples = 6000; unsigned int time_seed = (unsigned int) time(NULL) ; srand(time_seed); //TODO: need to fix here bool b_use_mask = 0; //(mask_fixed == NULL); std::cout<<"number_of_seeds: " << number_of_seeds << std::endl; std::cout<<"rand_time_seed: " << time_seed << std::endl; std::cout<<"number_of_iteration: " << number_of_iteration << std::endl; std::cout<<"MI_bins: " << MI_bins << std::endl; std::cout<<"MI_samples: " << MI_samples < SEARCH_POINT; typedef std::vector SEARCH_LIST; SEARCH_LIST search_list; // typedef itk::ImageMaskSpatialObject ImageMaskSpatialObject; typedef itk::ImageMaskSpatialObject ImageMaskSpatialObject; typename ImageMaskSpatialObject::Pointer mask_fixed_object = 0; if (b_use_mask){ typedef itk::Image CharMaskImageType; typedef itk::CastImageFilter CastFilterType; typename CastFilterType::Pointer cast_filter = CastFilterType::New(); cast_filter->SetInput(mask_fixed); cast_filter->Update(); typename CharMaskImageType::Pointer mask_fixed_char = cast_filter->GetOutput(); mask_fixed_object = ImageMaskSpatialObject::New(); mask_fixed_object->SetImage(mask_fixed_char); } typename ImageType::PointType origin; origin.Fill(0); I_moving->SetOrigin(origin); I_fixed->SetOrigin(origin); typename TransformType::Pointer trans = TransformType::New(); ParaType para0(trans->GetNumberOfParameters()), para1(trans->GetNumberOfParameters()); double rval; SEARCH_POINT spt; typedef itk::CenteredEuler3DTransform TransformType_Euler3D; ParaType para_cxy(TransformType_Euler3D::New()->GetNumberOfParameters()); // translated from pre registration // find initial center bool is_ok = false; itk::Point center; if (transform_initial.IsNull()){ is_ok = register_image_cxyz(I_fixed, I_moving, para_cxy, rval); if (!is_ok){ std::cout << "!!!!!!!!!!!!!!!!!!!!!!!!!!" << std::endl <<"initial affine registeration falied" << std::endl; exit(-1); } center[0] = para_cxy[3]; center[1] = para_cxy[4]; center[2] = para_cxy[5]; } else { center[0] = transform_initial->GetCenter()[0]; center[1] = transform_initial->GetCenter()[1]; center[2] = transform_initial->GetCenter()[2]; } std::cout << std::endl; for (int n = 0; (number_of_seeds > 0) ? (n < number_of_seeds) : (n <= number_of_seeds) ; n++){ if (n==0){ if (transform_initial.IsNull()) { para0[0] = 0.0; para0[1] = 0.0; para0[2] = 0.0; para0[3] = 1.0; para0[4] = 1.0; para0[5] = 1.0; para0[6] = 1.0; para0[7] = 0.0; para0[8] = 0.0; para0[9] = 0.0; para0[10] = 0.0; para0[11] = 0.0; para0[12] = 0.0; } else { // use input as intial for(unsigned int i=0; iGetParameters().Size(); i++){ para0[i] = transform_initial->GetParameters()[i]; } } } else{ generate_search_seed_3d(search_list, para0); } // main registration using affine transform is_ok = register_image_affine3d_mres_mask(I_fixed, I_moving, mask_fixed_object, para0, center, number_of_iteration, MI_bins, MI_samples, para1, rval); if (!is_ok){ std::cout << "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!" << std::endl << "affine registration failed!" << std::endl << "use the initial parameters" << std::endl; // return -1; } spt.para0 = para0; spt.para1 = para1; spt.center = center; spt.rval = rval; spt.index= n; spt.number_of_iteration = number_of_iteration; std::cout << "para0: " << para0 << std::endl; std::cout << "para1: " << para1 << std::endl; std::cout << "center: " << center << std::endl; std::cout << "rval: " << rval << std::endl; std::cout << "add the search result to the list ... seed [" << n << "]" << std::endl << std::endl; add_search_point(search_list, spt); } get_best_search_point(search_list, spt); std::cout<< std::endl << "History: " << std::endl; for(int ii = 0; ii < search_list.size(); ii++){ std::cout << "[" << ii << "]: " << search_list[ii].rval << std::endl; std::cout << "center: " << search_list[ii].center << std::endl; std::cout << "para0: " << search_list[ii].para0 << std::endl; std::cout << "para1: " << search_list[ii].para1 << std::endl; } typename TransformType::Pointer transform_final = TransformType::New(); transform_final->SetParameters(spt.para1); transform_final->SetCenter(center); std::cout << "final transform parameters = " << transform_final->GetParameters() << std::endl; transform = transform_final; } double myrand(double l, double u) { // std::cout<<"in myrand" << std::endl; double r = l + (u-l)*rand()/(RAND_MAX+1.0); return r; // std::cout<<"out myrand" << std::endl; } template double dist2_search_point(ParaType para0, ParaType para1) { // use theta / scale / skew to compare two nodes double d2 = 0; // double scale[4] = {1.0, 1.0, 1.0, 1.0}; for (int ii = 0; ii<12; ii++){ double a = para0[ii]-para1[ii]; d2 += a * a; // * scale[ii]; } return d2; } template void generate_search_seed_3d(SEARCH_LIST &search_list, ParaType ¶){ bool b_found = 0; double s1, s2, s3, k1, k2, k3 = 0; double a=0; // rotation alpha double u=0, v=0, w=0; // rotation axis // ParaType para(13); const double scale_upper = 1.5; const double scale_lower = 1 / 1.5; const double skew_lower = -1.0; const double skew_upper = 1.0; const double dist2_thres = 0.3; const double mypi = 3.1415926536; const double rot_angle_upper = mypi * (0.5); const double rot_angle_lower = mypi * (-0.5); unsigned int iteration = 0; unsigned int maxiteration = 50; while(b_found==0 && iteration < maxiteration ){ iteration++; s1 = myrand(scale_lower, scale_upper); s2 = myrand(scale_lower, scale_upper); s3 = myrand(scale_lower, scale_upper); k1 = myrand(skew_lower, skew_upper); k2 = myrand(skew_lower, skew_upper); k3 = myrand(skew_lower, skew_upper); a = myrand(rot_angle_lower, rot_angle_upper); u = myrand(-1, 1); v = myrand(-1, 1); w = myrand(-1, 1); double n1 = 1.0/sqrt(u*u + v*v + w*w); double sin_half_a = sin(a*0.5); para.Fill(0.0); para[0] = sin_half_a * u * n1; para[1] = sin_half_a * v * n1; para[2] = sin_half_a * w * n1; para[3] = cos(a*0.5); para[4] = s1; para[5] = s2; para[6] = s3; para[7] = k1; para[8] = k2; para[9] = k3; // std::cout << "test rand: " << para << " iteration " << iteration << std::endl; // search nearby search points bool bfar = 1; for (int i=0; (bfar) & (i dist2_thres) & (d1 > dist2_thres); } b_found = bfar; // std::cout << "b_found = " << b_found << " bfar = " << bfar << std::endl; } // ret = para; } template void generate_search_seed_2d(SEARCH_LIST &search_list, ParaType ¶){ bool b_found = 0; double r1, s1, s2, k = 0; const double pi = 3.1415927; const double theta_upper = pi / 4 ; const double theta_lower = -pi / 4; const double scale_upper = 1.5; const double scale_lower = 1 / 1.5; const double skew_lower = -1.0; const double skew_upper = 1.0; const double dist2_thres = 0.1; unsigned int iteration = 0; unsigned int maxiteration = 50; while(b_found==0 && iteration < maxiteration ){ // for(;~b_found; ){ // std::cout << "b_found = " << b_found << std::endl; iteration++; r1 = myrand(theta_lower, theta_upper); s1 = myrand(scale_lower, scale_upper); s2 = myrand(scale_lower, scale_upper); k = myrand(skew_lower, skew_upper); para.Fill(0.0); para[0] = r1; para[1] = s1; para[2] = s2; para[3] = k; // // std::cout << "test rand: " << para << " iteration " << iteration << std::endl; // search nearby search points bool bfar = 1; for (int i=0; (bfar) & (i dist2_thres) & (d1 > dist2_thres); } b_found = bfar; // std::cout << "b_found = " << b_found << " bfar = " << bfar << std::endl; } } template void get_best_search_point(SEARCH_LIST &search_list, SEARCH_POINT &spt){ std::sort(search_list.begin(), search_list.end(), compare_search_point); spt = search_list[0]; } template void add_search_point(SEARCH_LIST &search_list, SEARCH_POINT spt){ search_list.push_back(spt); } template double get_cost_value_mmi(ImagePointerType fixedImage, ImagePointerType movingImage, ParaType para, PointType center, TransformTypePointer null_transform){ typedef typename ImagePointerType::ObjectType ImageType; typedef typename TransformTypePointer::ObjectType TransformType; typename TransformType::Pointer transform = TransformType::New(); transform->SetCenter(center); // transform->SetParameters(para); typedef typename itk::MattesMutualInformationImageToImageMetric mattesMutualInfoMetricType; typename mattesMutualInfoMetricType::Pointer mattesMutualInfo=mattesMutualInfoMetricType::New(); typedef typename itk::LinearInterpolateImageFunction InterpolatorType; typename InterpolatorType::Pointer interpolator=InterpolatorType::New(); interpolator->SetInputImage(movingImage); mattesMutualInfo->SetFixedImage(fixedImage); mattesMutualInfo->SetMovingImage(movingImage); mattesMutualInfo->SetFixedImageRegion(fixedImage->GetBufferedRegion()); // mattesMutualInfo->SetMovingImage(outputImage); mattesMutualInfo->SetTransform(transform); mattesMutualInfo->SetInterpolator(interpolator); mattesMutualInfo->SetNumberOfHistogramBins( 32 ); mattesMutualInfo->SetNumberOfSpatialSamples( 5000 ); mattesMutualInfo->SetTransformParameters(para); mattesMutualInfo->Initialize(); double rval = 0; try{ rval = mattesMutualInfo->GetValue(para); } catch (itk::ExceptionObject &err){ std::cout << "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!" << std::endl << "Exception caught in computing mattesMutualInfo after registration" << std::endl << "Maybe: Too many samples map outside moving image buffer" << std::endl << "Set the cost value = 0 (max for MutualInfo) " << std::endl; rval = 0; } // test the cost before registration transform->SetIdentity(); transform->SetCenter(center); ParaType para0 = transform->GetParameters(); mattesMutualInfo->SetTransformParameters(para0); mattesMutualInfo->Initialize(); double rval0 = 0; try{ rval0 = mattesMutualInfo->GetValue(para0); } catch(itk::ExceptionObject &err) { std::cout << "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!" << std::endl << "Exception caught in computing mattesMutualInfo before registration" << std::endl << "Maybe: Too many samples map outside moving image buffer" << std::endl << "Set the cost value = 0 (max for MutualInfo) " << std::endl; rval0 = 0; } std::cout << "in cost: before register: cost = " << rval0 << std::endl; std::cout << "in cost: after register: cost = " << rval << std::endl; return rval; } template bool register_image_cxy(ImagePointerType fixed_image, ImagePointerType moving_image, ParaType ¶1, double &rval){ // for 3d image use CenteredRigid2DTransform typedef itk::RegularStepGradientDescentOptimizer OptimizerType; typedef itk::CenteredRigid2DTransform TransformType_Rigid2D; typedef typename ImagePointerType::ObjectType ImageType; typename TransformType_Rigid2D::Pointer transform = TransformType_Rigid2D::New(); typedef itk::CenteredTransformInitializer< TransformType_Rigid2D, ImageType, ImageType > TransformInitializerType; typename TransformInitializerType::Pointer initializer = TransformInitializerType::New(); try { transform->SetIdentity(); initializer->SetTransform( transform ); initializer->SetFixedImage( fixed_image ); initializer->SetMovingImage( moving_image ); initializer->MomentsOn(); initializer->InitializeTransform(); transform->SetAngle( 0.0 ); } catch (itk::ExceptionObject &err) { std::cout << "!!!!!!!!!!!!!!!!!!!!!!!!1" << std::endl << "Exception in InitializeTransform" << std::endl; return false; } para1 = transform->GetParameters(); std::cout << "finish initialize cx/cy/cz ..." << std::endl; std::cout << "cx/cy parameters (Euler3D): " << para1 << std::endl; return true; } template bool register_image_cxyz(ImagePointerType fixed_image, ImagePointerType moving_image, ParaType ¶1, double &rval){ // for 3d image use CenteredRigid2DTransform typedef itk::RegularStepGradientDescentOptimizer OptimizerType; typedef itk::CenteredEuler3DTransform TransformType_Euler3D; typedef typename ImagePointerType::ObjectType ImageType; TransformType_Euler3D::Pointer transform = TransformType_Euler3D::New(); typedef itk::CenteredTransformInitializer< TransformType_Euler3D, ImageType, ImageType > TransformInitializerType; typename TransformInitializerType::Pointer initializer = TransformInitializerType::New(); try { transform->SetIdentity(); initializer->SetTransform( transform ); initializer->SetFixedImage( fixed_image ); initializer->SetMovingImage( moving_image ); initializer->MomentsOn(); initializer->InitializeTransform(); } catch (itk::ExceptionObject &err) { std::cout << "!!!!!!!!!!!!!!!!!!!!!!!!1" << std::endl << "Exception in InitializeTransform" << std::endl; return false; } para1 = transform->GetParameters(); std::cout << "finish initialize cx/cy/cz ..." << std::endl; std::cout << "cx/cy parameters (Euler3D): " << para1 << std::endl; return true; } template bool register_image_affine3d_mres_mask(ImagePointerType fixed_image, ImagePointerType moving_image, ImageMaskSpatialObjectPointerType mask_fixed_object, ParaType para0, itk::Point center, int number_of_iteration, int MI_bins, int MI_samples, ParaType ¶1, double &rval){ typedef typename ImagePointerType::ObjectType ImageType; typedef itk::RegularStepGradientDescentOptimizer OptimizerType; typedef itk::MattesMutualInformationImageToImageMetric< ImageType, ImageType> MetricType; typedef typename ImagePointerType::ObjectType ImageType; typename MetricType::Pointer metric = MetricType::New(); // for mattesMutualInfo metric->SetNumberOfHistogramBins( MI_bins ); /** 32 BA */ metric->SetNumberOfSpatialSamples( MI_samples );/** 6000 BA */ if (mask_fixed_object){ metric->SetFixedImageMask(mask_fixed_object); std::cout << mask_fixed_object << std::endl; std::cout << mask_fixed_object->GetImage() << std::endl; } // typedef TransformType_Rigid2D TransformType_Pre; typedef itk::LinearInterpolateImageFunction InterpolatorType; typedef itk::MultiResolutionImageRegistrationMethod RegistrationType; typedef itk::MultiResolutionPyramidImageFilter fPyramidType; typename fPyramidType::Pointer fixedImagePyramid = fPyramidType::New(); typename fPyramidType::Pointer movingImagePyramid = fPyramidType::New(); typename ImageType::PointType origin; origin.Fill(0); moving_image->SetOrigin(origin); fixed_image->SetOrigin(origin); typename OptimizerType::Pointer optimizer = OptimizerType::New(); typename InterpolatorType::Pointer interpolator = InterpolatorType::New(); typename RegistrationType::Pointer registration = RegistrationType::New(); typedef OptimizerType::ScalesType OptimizerScalesType; /*******************************************/ /* translate to para0 here */ std::cout << "pre registration para :" << para0 << std::endl; typedef itk::ANTSAffine3DTransform TransformType_ANTSAffine3D; typename TransformType_ANTSAffine3D::Pointer transform_a = TransformType_ANTSAffine3D::New(); transform_a->SetCenter(center); std::cout<<"initial center: " << transform_a->GetCenter() << std::endl; // typedef OptimizerType::ScalesType OptimizerScalesType; OptimizerScalesType optimizerScales_a( transform_a->GetNumberOfParameters() ); const double translationScale = 1.0/1.e4; /** BA */// /1.e6; // 1.e6; //1.e6; //1.0 / 1000.0; optimizerScales_a[0] = 1.0; // quaternion optimizerScales_a[1] = 1.0; // quaternion optimizerScales_a[2] = 1.0; // quaternion optimizerScales_a[3] = 1.0; // quaternion optimizerScales_a[4] = 1.0; // s1 optimizerScales_a[5] = 1.0; // s2 optimizerScales_a[6] = 1.0; // s3 optimizerScales_a[7] = 1.0; // k1 optimizerScales_a[8] = 1.0; // k2 optimizerScales_a[9] = 1.0; // k3 optimizerScales_a[10] = translationScale; optimizerScales_a[11] = translationScale; optimizerScales_a[12] = translationScale; optimizer->SetScales( optimizerScales_a ); optimizer->SetMaximumStepLength(0.1); optimizer->SetMinimumStepLength( 1.e-5 ); // optimizer->SetNumberOfIterations( 1000 ); // optimizer->SetNumberOfIterations( 500 ); optimizer->SetNumberOfIterations(number_of_iteration); // (500); /** BA */ optimizer->MinimizeOn(); // Create the Command observer and register it with the optimizer. // // CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New(); // optimizer->AddObserver( itk::IterationEvent(), observer ); registration = RegistrationType::New(); registration->SetNumberOfLevels(3); registration->SetFixedImagePyramid( fixedImagePyramid ); registration->SetMovingImagePyramid( movingImagePyramid ); registration->SetMetric( metric ); registration->SetOptimizer( optimizer ); registration->SetInterpolator( interpolator ); registration->SetFixedImage( fixed_image ); registration->SetMovingImage( moving_image ); registration->SetFixedImageRegion( fixed_image->GetLargestPossibleRegion() ); registration->SetTransform( transform_a ); // reset the parameter // registration->SetInitialTransformParameters(para_pre ); registration->SetInitialTransformParameters(para0 ); std::cout << "reset initial transform parameters" << std::endl; std::cout << "para_pre: " << para0 << std::endl; // std::cout << "transform_a: " << transform_a << std::endl; rval = get_cost_value_mmi(fixed_image, moving_image, para0, center, transform_a); std::cout << "init measure value: rval = " << rval << std::endl; // rval = optimizer->GetValue(para0); // rval = metric->GetValue(para0); bool bsuc = 1; try { registration->StartRegistration(); } catch( itk::ExceptionObject & err ) { std::cerr << "ExceptionObject caught !" << std::endl; std::cerr << err << std::endl; bsuc = 0; // exit(-1); } if (bsuc){ // OptimizerType::ParametersType // finalParameters = registration->GetLastTransformParameters(); para1 = registration->GetLastTransformParameters(); // para1 = finalParameters; // rval = optimizer->GetValue(para1); // rval = metric->GetValue(para1); // rval = registration->GetMetric()->GetValue(para1); rval = get_cost_value_mmi(fixed_image, moving_image, para1, center, transform_a); // double rval2 = optimizer->GetValue(); // std::cout << "measure value: rval2 = " << rval2 << std::endl; } else{ // register failed para1 = para0; // registration->GetLastTransformParameters(); rval = get_cost_value_mmi(fixed_image, moving_image, para1, center, transform_a); } std::cout << "final affine3d registration para :" << para1 << std::endl; std::cout << "use iteration: " << optimizer->GetNumberOfIterations() << std::endl; std::cout << "measure value: rval = " << rval << std::endl; std::cout << "finish register..." << std::endl; return bsuc; } template bool register_image_affine2d_mres_mask(ImagePointerType fixed_image, ImagePointerType moving_image, ImageMaskSpatialObjectPointerType mask_fixed_object, ParaType para0, itk::Point center, int number_of_iteration, int MI_bins, int MI_samples, ParaType ¶1, double &rval){ typedef typename ImagePointerType::ObjectType ImageType; typedef itk::RegularStepGradientDescentOptimizer OptimizerType; typedef itk::MattesMutualInformationImageToImageMetric< ImageType, ImageType> MetricType; typedef typename ImagePointerType::ObjectType ImageType; typename MetricType::Pointer metric = MetricType::New(); // for mattesMutualInfo metric->SetNumberOfHistogramBins( MI_bins ); /** 32 BA */ metric->SetNumberOfSpatialSamples( MI_samples );/** 6000 BA */ if (mask_fixed_object){ metric->SetFixedImageMask(mask_fixed_object); std::cout << mask_fixed_object << std::endl; std::cout << mask_fixed_object->GetImage() << std::endl; } // typedef TransformType_Rigid2D TransformType_Pre; typedef itk::LinearInterpolateImageFunction InterpolatorType; typedef itk::MultiResolutionImageRegistrationMethod RegistrationType; typedef itk::MultiResolutionPyramidImageFilter fPyramidType; typename fPyramidType::Pointer fixedImagePyramid = fPyramidType::New(); typename fPyramidType::Pointer movingImagePyramid = fPyramidType::New(); typename ImageType::PointType origin; origin.Fill(0); moving_image->SetOrigin(origin); fixed_image->SetOrigin(origin); typename OptimizerType::Pointer optimizer = OptimizerType::New(); typename InterpolatorType::Pointer interpolator = InterpolatorType::New(); typename RegistrationType::Pointer registration = RegistrationType::New(); typedef OptimizerType::ScalesType OptimizerScalesType; /*******************************************/ /* translate to para0 here */ std::cout << "pre registration para :" << para0 << std::endl; // typedef itk::CenteredAffine2DTransform TransformType; // typedef itk::CenteredAffine2DTransform TransformType_GSAffine2D; // typename TransformType_GSAffine2D::Pointer transform_a = TransformType_GSAffine2D::New(); typedef itk::ANTSCenteredAffine2DTransform TransformType_ANTSAffine2D; typename TransformType_ANTSAffine2D::Pointer transform_a = TransformType_ANTSAffine2D::New(); // transform_a->SetCenter(center); // std::cout<<"initial center: " << transform_a->GetCenter() << std::endl; OptimizerScalesType optimizerScales_a( transform_a->GetNumberOfParameters() ); const double translationScale = 1.0 / 1000.0; optimizerScales_a[0] = 1.0; optimizerScales_a[1] = 1.0; optimizerScales_a[2] = 1.0; optimizerScales_a[3] = 1.0; optimizerScales_a[4] = translationScale; optimizerScales_a[5] = translationScale; optimizerScales_a[6] = translationScale; optimizerScales_a[7] = translationScale; optimizer->SetScales( optimizerScales_a ); optimizer->SetMaximumStepLength( 0.1 ); optimizer->SetMinimumStepLength( 0.01 ); optimizer->SetNumberOfIterations( number_of_iteration ); optimizer->MinimizeOn(); // Create the Command observer and register it with the optimizer. // // CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New(); // optimizer->AddObserver( itk::IterationEvent(), observer ); registration = RegistrationType::New(); registration->SetNumberOfLevels(3); registration->SetFixedImagePyramid( fixedImagePyramid ); registration->SetMovingImagePyramid( movingImagePyramid ); registration->SetMetric( metric ); registration->SetOptimizer( optimizer ); registration->SetInterpolator( interpolator ); registration->SetFixedImage( fixed_image ); registration->SetMovingImage( moving_image ); registration->SetFixedImageRegion( fixed_image->GetLargestPossibleRegion() ); registration->SetTransform( transform_a ); // reset the parameter // registration->SetInitialTransformParameters(para_pre ); registration->SetInitialTransformParameters(para0 ); std::cout << "reset initial transform parameters" << std::endl; std::cout << "para_pre: " << para0 << std::endl; // std::cout << "transform_a: " << transform_a << std::endl; rval = get_cost_value_mmi(fixed_image, moving_image, para0, center, transform_a); std::cout << "init measure value: rval = " << rval << std::endl; // rval = optimizer->GetValue(para0); // rval = metric->GetValue(para0); bool bsuc = 1; try { registration->StartRegistration(); } catch( itk::ExceptionObject & err ) { std::cerr << "ExceptionObject caught !" << std::endl; std::cerr << err << std::endl; bsuc = 0; // exit(-1); } if (bsuc){ // OptimizerType::ParametersType // finalParameters = registration->GetLastTransformParameters(); para1 = registration->GetLastTransformParameters(); // para1 = finalParameters; // rval = optimizer->GetValue(para1); // rval = metric->GetValue(para1); // rval = registration->GetMetric()->GetValue(para1); rval = get_cost_value_mmi(fixed_image, moving_image, para1, center, transform_a); // double rval2 = optimizer->GetValue(); // std::cout << "measure value: rval2 = " << rval2 << std::endl; } else{ // register failed para1 = para0; // registration->GetLastTransformParameters(); rval = get_cost_value_mmi(fixed_image, moving_image, para1, center, transform_a); } std::cout << "final affine2d registration para :" << para1 << std::endl; std::cout << "use iteration: " << optimizer->GetNumberOfIterations() << std::endl; std::cout << "measure value: rval = " << rval << std::endl; std::cout << "finish register..." << std::endl; return bsuc; } // template // void compute_single_affine_transform_2d(ImagePointerType I_fixed, ImagePointerType I_moving, MaskImagePointerType mask_fixed, TransformPointerType &transform){ template void compute_single_affine_transform_2d(ImagePointerType I_fixed, ImagePointerType I_moving, MaskImagePointerType mask_fixed, TransformPointerType &transform, TransformPointerType &transform_initial){ typedef typename ImagePointerType::ObjectType ImageType; const int ImageDimension = ImageType::ImageDimension; typedef typename ImageType::IOPixelType PixelType; typedef typename MaskImagePointerType::ObjectType MaskImageType; typedef typename MaskImageType::IOPixelType MaskPixelType; typedef typename TransformPointerType::ObjectType TransformType; typedef typename TransformType::ParametersType ParaType; // option parameters int number_of_seeds = 0; int number_of_iteration = 500; int MI_bins = 32; int MI_samples = 6000; unsigned int time_seed = (unsigned int) time(NULL) ; srand(time_seed); //TODO: need to fix here bool b_use_mask = 0; //(mask_fixed == NULL); std::cout<<"number_of_seeds: " << number_of_seeds << std::endl; std::cout<<"rand_time_seed: " << time_seed << std::endl; std::cout<<"number_of_iteration: " << number_of_iteration << std::endl; std::cout<<"MI_bins: " << MI_bins << std::endl; std::cout<<"MI_samples: " << MI_samples < SEARCH_POINT; typedef std::vector SEARCH_LIST; SEARCH_LIST search_list; // typedef itk::ImageMaskSpatialObject ImageMaskSpatialObject; typedef typename itk::ImageMaskSpatialObject ImageMaskSpatialObject; typename ImageMaskSpatialObject::Pointer mask_fixed_object = 0; if (b_use_mask){ typedef typename itk::Image CharMaskImageType; typedef typename itk::CastImageFilter CastFilterType; typename CastFilterType::Pointer cast_filter = CastFilterType::New(); cast_filter->SetInput(mask_fixed); cast_filter->Update(); typename CharMaskImageType::Pointer mask_fixed_char = cast_filter->GetOutput(); mask_fixed_object = ImageMaskSpatialObject::New(); mask_fixed_object->SetImage(mask_fixed_char); } typename ImageType::PointType origin; origin.Fill(0); I_moving->SetOrigin(origin); I_fixed->SetOrigin(origin); typename TransformType::Pointer trans = TransformType::New(); ParaType para0(trans->GetNumberOfParameters()), para1(trans->GetNumberOfParameters()); double rval; SEARCH_POINT spt; typedef typename itk::CenteredRigid2DTransform TransformType_Rigid2D; ParaType para_cxy(TransformType_Rigid2D::New()->GetNumberOfParameters()); // translated from pre registration // find initial center bool is_ok = false; itk::Point center; if (transform_initial.IsNull()){ // if (1) { is_ok = register_image_cxy(I_fixed, I_moving, para_cxy, rval); if (!is_ok){ std::cout << "!!!!!!!!!!!!!!!!!!!!!!!!!!" << std::endl <<"initial affine registeration falied" << std::endl; exit(-1); } center[0] = para_cxy[1]; center[1] = para_cxy[2]; std::cout << "is_ok=" << is_ok << "para_cxy:" << para_cxy << std::endl; } else { center[0] = transform_initial->GetCenter()[0]; center[1] = transform_initial->GetCenter()[1]; std::cout << "input transform: " << transform_initial << std::endl; } std::cout << "initial center: (" << center[0] << "," << center[1] << ")" << std::endl; for (int n = 0; (number_of_seeds > 0) ? (n < number_of_seeds) : (n <= number_of_seeds) ; n++){ if (n==0){ // if (1) { if (transform_initial.IsNull()){ para0[0] = 0; // para1[0]; // theta para0[1] = 1.0; //s1 para0[2] = 1.0; //s2 para0[3] = 0.0; //k para0[4] = center[0]; // para1[1]; //c1 para0[5] = center[1]; // para1[2]; //c2 para0[6] = para_cxy[3]; // 0;//para1[3]; //t1 para0[7] = para_cxy[4]; //0; //para1[4]; //t2 std::cout << "ABC: " << std::endl; } else { for(unsigned int i=0; iGetParameters().Size(); i++){ para0[i] = transform_initial->GetParameters()[i]; } std::cout << "DEF: " << std::endl; } } else{ generate_search_seed_2d(search_list, para0); } // main registration using affine transform is_ok = register_image_affine2d_mres_mask(I_fixed, I_moving, mask_fixed_object, para0, center, number_of_iteration, MI_bins, MI_samples, para1, rval); if (!is_ok){ std::cout << "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!" << std::endl << "affine registration failed!" << std::endl << "use the initial parameters" << std::endl; // return -1; } spt.para0 = para0; spt.para1 = para1; spt.center = center; spt.rval = rval; spt.index= n; spt.number_of_iteration = number_of_iteration; std::cout << "para0: " << para0 << std::endl; std::cout << "para1: " << para1 << std::endl; std::cout << "center: " << center << std::endl; std::cout << "rval: " << rval << std::endl; std::cout << "add the search result to the list ... seed [" << n << "]" << std::endl << std::endl; add_search_point(search_list, spt); } get_best_search_point(search_list, spt); std::cout<< std::endl << "History: " << std::endl; for(int ii = 0; ii < search_list.size(); ii++){ std::cout << "[" << ii << "]: " << search_list[ii].rval << std::endl; std::cout << "center: " << search_list[ii].center << std::endl; std::cout << "para0: " << search_list[ii].para0 << std::endl; std::cout << "para1: " << search_list[ii].para1 << std::endl; } typename TransformType::Pointer transform_final = TransformType::New(); transform_final->SetParameters(spt.para1); transform_final->SetCenter(center); std::cout << "final transform parameters = " << transform_final->GetParameters() << std::endl; transform = transform_final; } template void compute_single_affine_transform(ImagePointerType fixedImage, ImagePointerType movingImage, MaskImagePointerType maskImage, TransformPointerType &transform, TransformPointerType &transform_initial){ typedef typename ImagePointerType::ObjectType ImageType; const int ImageDimension = ImageType::ImageDimension; typedef typename ImageType::IOPixelType PixelType; typedef typename MaskImagePointerType::ObjectType MaskImageType; typedef typename MaskImageType::IOPixelType MaskPixelType; typedef typename TransformPointerType::ObjectType TransformType; std::cout << "transform_initial: IsNotNull():" << transform_initial.IsNotNull() << std::endl; if (ImageDimension==2) { typedef itk::ANTSCenteredAffine2DTransform RunningAffineTransformType; RunningAffineTransformType::Pointer transform_running = RunningAffineTransformType::New(); RunningAffineTransformType::Pointer transform_running_initial; // = RunningAffineTransformType::New(); std::cout << "1: transform_running_initial: IsNotNull():" << transform_running_initial.IsNotNull() << std::endl; if (transform_initial.IsNotNull()) { transform_running_initial->SetCenter(*(reinterpret_cast (const_cast (&(transform_initial->GetCenter()))))); transform_running_initial->SetMatrix(*(reinterpret_cast (const_cast (&(transform_initial->GetMatrix()))))); transform_running_initial->SetTranslation(*(reinterpret_cast (const_cast (&(transform_initial->GetTranslation()))))); } // Use type casting typedef typename itk::Image::Pointer R_ImagePointerType; R_ImagePointerType & R_fixedImage = reinterpret_cast (fixedImage); R_ImagePointerType & R_movingImage = reinterpret_cast (movingImage); R_ImagePointerType & R_maskImage = reinterpret_cast (maskImage); std::cout << "2: transform_running_initial: IsNotNull():" << transform_running_initial.IsNotNull() << std::endl; compute_single_affine_transform_2d(R_fixedImage, R_movingImage, R_maskImage, transform_running, transform_running_initial); //TODO: transform->SetCenter(*(reinterpret_cast (const_cast (&(transform_running->GetCenter()))))); transform->SetTranslation(*(reinterpret_cast (const_cast (&(transform_running->GetTranslation()))))); transform->SetMatrix(*(reinterpret_cast (const_cast (&(transform_running->GetMatrix()))))); // transform->SetFixedParameters(transform_running->GetFixedParameters()); // transform->SetParameters(transform_running->GetParameters()); } else if (ImageDimension==3){ typedef itk::ANTSAffine3DTransform RunningAffineTransformType; RunningAffineTransformType::Pointer transform_running = RunningAffineTransformType::New(); RunningAffineTransformType::Pointer transform_running_initial = RunningAffineTransformType::New();; // compute_single_affine_transform_3d(fixedImage, movingImage, maskImage, transform_running); if (transform_initial.IsNotNull()) { transform_running_initial->SetCenter(*(reinterpret_cast (const_cast (&(transform_initial->GetCenter()))))); transform_running_initial->SetMatrix(*(reinterpret_cast (const_cast (&(transform_initial->GetMatrix()))))); transform_running_initial->SetTranslation(*(reinterpret_cast (const_cast (&(transform_initial->GetTranslation()))))); } // Use type casting typedef typename itk::Image::Pointer R_ImagePointerType; R_ImagePointerType & R_fixedImage = reinterpret_cast (fixedImage); R_ImagePointerType & R_movingImage = reinterpret_cast (movingImage); R_ImagePointerType & R_maskImage = reinterpret_cast (maskImage); compute_single_affine_transform_3d(R_fixedImage, R_movingImage, R_maskImage, transform_running, transform_running_initial); //TODO: transform->SetCenter(*(reinterpret_cast (const_cast (&(transform_running->GetCenter()))))); transform->SetTranslation(*(reinterpret_cast (const_cast (&(transform_running->GetTranslation()))))); transform->SetMatrix(*(reinterpret_cast (const_cast (&(transform_running->GetMatrix()))))); // transform->SetFixedParameters(transform_running->GetFixedParameters()); // transform->SetParameters(transform_running->GetParameters()); } else { std::cout << "Unsupported, not 2D/ 3D" << std::endl; return; } } template void create_deformation_field_byref(const DeformationFieldPointerType &ref, DeformationFieldPointerType &field){ typedef typename DeformationFieldPointerType::ObjectType DeformationFieldType; // field = DeformationFieldType::New(); typename DeformationFieldType::RegionType region; region.SetSize(ref->GetLargestPossibleRegion().GetSize() ); region.SetIndex(ref->GetLargestPossibleRegion().GetIndex() ); field->SetRegions( region ); field->SetSpacing(ref->GetSpacing()); field->SetOrigin(ref->GetOrigin() ); field->Allocate(); } // compose affine transform (in a matrix format A: (Ax+b)) with a deformation field F: // the new field is: F_new (x) = F ( A (x) ) // output should be allocated outside template void compose_affine_with_field(const TransformPointerType &aff, const DeformationFieldPointerType &field, DeformationFieldPointerType &field_output){ typedef typename DeformationFieldPointerType::ObjectType DeformationFieldType; typedef itk::ImageRegionIteratorWithIndex FieldIterator; typedef typename DeformationFieldType::IndexType IndexType; typedef typename DeformationFieldType::PointType PointType; typedef typename DeformationFieldType::PixelType VectorType; const unsigned int ImageDimension = DeformationFieldType::ImageDimension; // PointType zeroorigin; // zeroorigin.Fill(0); // field->SetOrigin(zeroorigin); // field_output->SetOrigin(zeroorigin); PointType pointIn1; PointType pointIn2; PointType pointIn3; // iterate through field_output finding the points that it maps to via field. // then take the difference from the original point and put it in the output field. // std::cout << " begin iteration " << std::endl; FieldIterator iter_field(field, field->GetLargestPossibleRegion()); // std::cout << field_output->GetLargestPossibleRegion() << std::endl; int cnt = 0; for(iter_field.GoToBegin(); !iter_field.IsAtEnd(); ++iter_field) { IndexType index = iter_field.GetIndex(); field_output->TransformIndexToPhysicalPoint( index, pointIn1 ); VectorType disp=iter_field.Get(); for (int jj=0; jjTransformPoint(pointIn2); VectorType out; for (int jj=0; jjSetPixel(iter_field.GetIndex(), out); } // std::cout << " end iteration " << std::endl; } // this is obsolet, use itkWarpImageWAffineFilter template void warp_image_field(const ImagePointerType &img_input, const DeformationFieldPointerType &field, ImagePointerType &img_output){ typedef typename ImagePointerType::ObjectType ImageType; typedef typename DeformationFieldPointerType::ObjectType DeformationFieldType; typedef typename itk::WarpImageFilter WarperType; typename WarperType::Pointer warper = WarperType::New(); warper->SetInput(img_input); warper->SetDeformationField(field); warper->SetEdgePaddingValue( 0); warper->SetOutputSpacing(field->GetSpacing() ); warper->SetOutputOrigin( field->GetOrigin() ); warper->Update(); img_output=warper->GetOutput(); } template void affine_image(const ImageTypePointer &input_image, const TransformPointerType &transform, const RefImageTypePointer &ref_image, ImageTypePointer &img_aff ){ typedef typename ImageTypePointer::ObjectType ImageType; typedef typename TransformPointerType::ObjectType TransformType; // apply the transform typedef itk::ResampleImageFilter< ImageType, ImageType> ResampleFilterType; typename ResampleFilterType::Pointer resampler = ResampleFilterType::New(); typedef itk::LinearInterpolateImageFunction< ImageType, double> InterpolatorType; typename InterpolatorType::Pointer interpolator = InterpolatorType::New(); resampler->SetInterpolator( interpolator ); resampler->SetInput( input_image ); resampler->SetSize( ref_image->GetLargestPossibleRegion().GetSize() ); resampler->SetOutputOrigin( ref_image->GetOrigin() ); resampler->SetOutputSpacing( ref_image->GetSpacing() ); resampler->SetDefaultPixelValue( 0 ); resampler->SetTransform( transform ); resampler->Update(); img_aff = resampler->GetOutput(); } template void warp_image_field_waffine(const ImagePointerType &img_input, const TransformPointerType &aff, const DeformationFieldPointerType &field, ImagePointerType &img_output){ // TODO: add a new WarpImageFilter to support affine as an input // temporary solution: typedef typename DeformationFieldPointerType::ObjectType DeformationFieldType; typename DeformationFieldType::Pointer field_comp = DeformationFieldType::New(); // create_deformation_field_byref(field, field_comp); // compose_affine_with_field(aff, field, field_comp); // warp_image_field(img_input, field_comp, img_output); typedef typename TransformPointerType::ObjectType TransformType; typedef typename ImagePointerType::ObjectType ImageType; typedef itk::WarpImageWAffineFilter WarperType; typename WarperType::Pointer warper = WarperType::New(); warper->SetInput(img_input); warper->SetDeformationField(field); warper->SetAffineTransform(aff); warper->SetEdgePaddingValue( 0); warper->SetOutputSpacing(field->GetSpacing() ); warper->SetOutputOrigin( field->GetOrigin() ); warper->SetOutputSize(field->GetLargestPossibleRegion().GetSize()); warper->Update(); img_output=warper->GetOutput(); return; } //TODO: use my own code to implement all the optimization codes #endif /*ANTS_AFFINE_REGISTRATION_H_*/ ants-1.9.2+svn680.dfsg/ImageRegistration/ANTS_affine_registration2.h000066400000000000000000001562111147325206600252050ustar00rootroot00000000000000#ifndef ANTS_AFFINE_REGISTRATION2_H_ #define ANTS_AFFINE_REGISTRATION2_H_ #include #include #include #include "itkImage.h" #include "itkPoint.h" #include "itkCastImageFilter.h" #include "itkImageMaskSpatialObject.h" #include "itkCenteredEuler3DTransform.h" #include "itkRegularStepGradientDescentOptimizer.h" #include "itkGradientDescentOptimizer.h" #include "itkCenteredTransformInitializer.h" #include "itkMattesMutualInformationImageToImageMetric.h" #include "itkCorrelationCoefficientHistogramImageToImageMetric.h" #include "itkMultiResolutionImageRegistrationMethod.h" #include "itkLinearInterpolateImageFunction.h" #include "itkANTSAffine3DTransform.h" #include "itkCenteredRigid2DTransform.h" #include "itkANTSCenteredAffine2DTransform.h" #include "itkTransformFactory.h" #include "itkTransformFileReader.h" #include "itkTransformFileWriter.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkResampleImageFilter.h" #include "itkLinearInterpolateImageFunction.h" #include "itkWarpImageFilter.h" #include "itkWarpImageWAffineFilter.h" #include "itkImageMomentsCalculator.h" #include #include "ReadWriteImage.h" #include "itkMeanSquaresImageToImageMetric.h" #include "itkGradientDifferenceImageToImageMetric.h" #include "itkNormalizedCorrelationImageToImageMetric.h" typedef enum{AffineWithMutualInformation=1, AffineWithMeanSquareDifference , AffineWithHistogramCorrelation, AffineWithNormalizedCorrelation , AffineWithGradientDifference } AffineMetricType; template class OptAffine{ public: typedef TAffineTransformPointer AffineTransformPointerType; typedef TMaskImagePointer MaskImagePointerType; typedef typename AffineTransformPointerType::ObjectType AffineTransformType; OptAffine(){ MI_bins = 32; MI_samples = 6000; number_of_seeds = 0; time_seed = (unsigned int) time(NULL) ; number_of_levels = 3; number_of_iteration_list.resize(number_of_levels); for(int i = 0; i number_of_iteration_list; std::vector gradient_scales; AffineMetricType metric_type; bool is_rigid; double maximum_step_length; double relaxation_factor; double minimum_step_length; double translation_scales; bool use_rotation_header; bool ignore_void_orgin; }; template std::ostream& operator<< (std::ostream& os, const OptAffine& p) { typedef OptAffine OptAffineType; os << "OptAffine: "; os << "metric_type="; switch(p.metric_type){ case AffineWithMutualInformation: os << "AffineWithMutualInformation" << std::endl; break; case AffineWithMeanSquareDifference: os << "AffineWithMeanSquareDifference" << std::endl; break; case AffineWithHistogramCorrelation: os << "AffineWithHistogramCorrelation" << std::endl; break; case AffineWithNormalizedCorrelation: os << "AffineWithNormalizedCorrelation" << std::endl; break; case AffineWithGradientDifference: os << "AffineWithGradientDifference" << std::endl; break; } os << "MI_bins="<< p.MI_bins << " " << "MI_samples=" << p.MI_samples << std::endl; os << "number_of_seeds="< 0) os << p.number_of_iteration_list[p.number_of_iteration_list.size()-1]; os << "]" << std::endl; os << "graident_scales="<<"["; for(unsigned int i=0; i 0) os << p.gradient_scales[p.gradient_scales.size()-1]; os << "]" << std::endl; os << "is_rigid = " << p.is_rigid << std::endl; os << "mask null: " << p.mask_fixed.IsNull() << std::endl; os << "maximum_step_length="< void ReadAffineTransformFile(StringType filename, CastTransformPointerType &transform){ typedef typename CastTransformPointerType::ObjectType CastTransformType; // const unsigned int InputSpaceDimension = CastTransformType::InputSpaceDimension; // const unsigned int OutputSpaceDimension = CastTransformType::OutputSpaceDimension; itk::TransformFactory::RegisterTransform(); itk::TransformFactory >::RegisterTransform(); typedef typename itk::TransformFileReader TranReaderType; TranReaderType::Pointer tran_reader = TranReaderType::New(); tran_reader->SetFileName(filename); try{ tran_reader->Update(); } catch( itk::ExceptionObject &err) { std::cerr << err << std::endl; std::cerr << "Exception caught in reading tran para file: " << filename << std::endl; return; } transform = dynamic_cast< CastTransformType* >((tran_reader->GetTransformList())->front().GetPointer()); return; } template void InitializeAffineOptmizationParameters(OptAffine &opt, double translationScale){ const int kImageDim = OptAffine::MaskImagePointerType::ObjectType::ImageDimension; switch(kImageDim){ case 2:{ // const double translationScale = 1.0 / 1000.0; opt.gradient_scales[0] = 1.0; opt.gradient_scales[1] = 1.0; opt.gradient_scales[2] = 1.0; opt.gradient_scales[3] = 1.0; opt.gradient_scales[4] = translationScale; opt.gradient_scales[5] = translationScale; opt.gradient_scales[6] = translationScale; opt.gradient_scales[7] = translationScale; } break; case 3:{ // const double translationScale = 1.0/1.e4; opt.gradient_scales[0] = 1.0; // quaternion opt.gradient_scales[1] = 1.0; // quaternion opt.gradient_scales[2] = 1.0; // quaternion opt.gradient_scales[3] = 1.0; // quaternion opt.gradient_scales[4] = 1.0; // s1 opt.gradient_scales[5] = 1.0; // s2 opt.gradient_scales[6] = 1.0; // s3 opt.gradient_scales[7] = 1.0; // k1 opt.gradient_scales[8] = 1.0; // k2 opt.gradient_scales[9] = 1.0; // k3 opt.gradient_scales[10] = translationScale; opt.gradient_scales[11] = translationScale; opt.gradient_scales[12] = translationScale; } break; } std::cout << opt; } template class RunningAffineCache{ public: typedef TImagePyramid ImagePyramidType; typedef typename ImagePyramidType::value_type ImagePointerType; typedef typename ImagePointerType::ObjectType ImageType; typedef TMetricPointerType MetricPointerType; typedef typename MetricPointerType::ObjectType MetricType; typedef TInterpolatorPointerType InterpolatorPointerType; typedef typename InterpolatorPointerType::ObjectType InterpolatorType; typedef TMaskObjectPointerType MaskObjectPointerType; RunningAffineCache(){ }; ~RunningAffineCache(){ }; TMaskObjectPointerType mask_fixed_object; TImagePyramid fixed_image_pyramid; TImagePyramid moving_image_pyramid; TMetricPointerType metric; TMetricPointerType invmetric; TInterpolatorPointerType interpolator; }; template void ComputeSingleAffineTransform(ImagePointerType fixedImage, ImagePointerType movingImage, OptAffineType &opt, TransformPointerType &transform); template void ComputeSingleAffineTransform2D3D(ImagePointerType I_fixed, ImagePointerType I_moving, OptAffineType &opt, TransformPointerType &transform); //////////////////////////////////////////////////////////////////////// template inline void PreConversionInAffine(ImagePointerType &fixedImage, RunningImagePointerType& R_fixedImage, ImagePointerType &movingImage, RunningImagePointerType& R_movingImage, OptAffineType &opt, RunningOptAffineType &R_opt){ typedef typename OptAffineType::AffineTransformPointerType::ObjectType AffineTransformType; typedef typename RunningOptAffineType::AffineTransformPointerType::ObjectType RunningAffineTransformType; if (opt.use_rotation_header){ std::cout << "===================>initialize from rotation header ... " << std::endl; // use the rotation header to initialize the affine: inv(Tm) * Tf typename AffineTransformType::Pointer aff_Im = AffineTransformType::New(); GetAffineTransformFromImage(movingImage, aff_Im); typename AffineTransformType::Pointer aff_If = AffineTransformType::New(); GetAffineTransformFromImage(fixedImage, aff_If); typename AffineTransformType::Pointer aff_combined = AffineTransformType::New(); aff_combined->SetFixedParameters(aff_If->GetFixedParameters()); aff_combined->SetParameters(aff_If->GetParameters()); typename AffineTransformType::Pointer aff_Im_inv = AffineTransformType::New(); aff_Im->GetInverse(aff_Im_inv); aff_combined->Compose(aff_Im_inv, 0); opt.transform_initial = aff_combined; // std::cout << "aff_If: " << aff_If << std::endl; // std::cout << "aff_Im: " << aff_Im << std::endl; // std::cout << "aff_combined: " << aff_combined << std::endl; } if (!opt.use_rotation_header && opt.ignore_void_orgin){ std::cout << "===================> ignore void origins which are too far away to be possible alignments: use 0 instead." << std::endl; typename AffineTransformType::Pointer aff_Im = AffineTransformType::New(); GetAffineTransformFromImage(movingImage, aff_Im); typename AffineTransformType::Pointer aff_If = AffineTransformType::New(); GetAffineTransformFromImage(fixedImage, aff_If); bool b_far_origin_without_rotation=false; // bool b_far_origin_without_rotation = HaveFarOriginWithoutRotation(aff_If, aff_Im); if (b_far_origin_without_rotation){ typename AffineTransformType::Pointer aff_combined = AffineTransformType::New(); aff_combined->SetFixedParameters(aff_If->GetFixedParameters()); aff_combined->SetParameters(aff_If->GetParameters()); typename AffineTransformType::Pointer aff_Im_inv = AffineTransformType::New(); aff_Im->GetInverse(aff_Im_inv); aff_combined->Compose(aff_Im_inv, 0); opt.transform_initial = aff_combined; } } if (opt.transform_initial.IsNotNull()) { R_opt.transform_initial = RunningAffineTransformType::New(); R_opt.transform_initial->SetCenter(*(reinterpret_cast (const_cast (&(opt.transform_initial->GetCenter()))))); R_opt.transform_initial->SetMatrix(*(reinterpret_cast (const_cast (&(opt.transform_initial->GetMatrix()))))); R_opt.transform_initial->SetTranslation(*(reinterpret_cast (const_cast (&(opt.transform_initial->GetTranslation()))))); } // std::cout << "R_opt.transform_initial" << R_opt.transform_initial << std::endl; if (opt.mask_fixed.IsNotNull()){ R_opt.mask_fixed = RunningOptAffineType::MaskImagePointerType::ObjectType::New(); R_opt.mask_fixed = reinterpret_cast (opt.mask_fixed); // have to set " -fno-strict-aliasing " in gcc to remove the following compilation warning: // warning: dereferencing type-punned pointer will break strict-aliasing rules } R_fixedImage = reinterpret_cast (fixedImage); R_movingImage = reinterpret_cast (movingImage); R_opt.MI_bins = opt.MI_bins; R_opt.MI_samples = opt.MI_samples; R_opt.number_of_seeds = opt.number_of_seeds; R_opt.time_seed = opt.time_seed; R_opt.number_of_levels = opt.number_of_levels; R_opt.number_of_iteration_list = opt.number_of_iteration_list; // R_opt.gradient_scales = opt.gradient_scales; // does not need, will assign value later. R_opt.metric_type = opt.metric_type; R_opt.is_rigid = opt.is_rigid; R_opt.maximum_step_length = opt.maximum_step_length ; R_opt.relaxation_factor = opt.relaxation_factor ; R_opt.minimum_step_length = opt.minimum_step_length ; R_opt.translation_scales = opt.translation_scales ; R_opt.use_rotation_header = opt.use_rotation_header; R_opt.ignore_void_orgin = opt.ignore_void_orgin; } //////////////////////////////////////////////////////////////////////// template inline void PostConversionInAffine(RunningAffineTransformPointerType& transform_running, AffineTransformPointerType &transform){ typedef typename RunningAffineTransformPointerType::ObjectType RunningAffineTransformType; typedef typename AffineTransformPointerType::ObjectType AffineTransformType; transform->SetCenter(*(reinterpret_cast (const_cast (&(transform_running->GetCenter()))))); transform->SetTranslation(*(reinterpret_cast (const_cast (&(transform_running->GetTranslation()))))); transform->SetMatrix(*(reinterpret_cast (const_cast (&(transform_running->GetMatrix()))))); // std::cout << "transform_running" << transform_running << std::endl; // std::cout << "transform" << transform << std::endl; } /////////////////////////////////////////////////////////////////////////// // the initial transform maybe any derivative class type from MatrixOffsetTransformBase, // it will be automatically converted to the my 2D/3D affine type template void ComputeSingleAffineTransform(ImagePointerType fixedImage, ImagePointerType movingImage, OptAffineType &opt, TransformPointerType &transform){ typedef typename ImagePointerType::ObjectType ImageType; const int ImageDimension = ImageType::ImageDimension; typedef typename ImageType::IOPixelType PixelType; std::cout << "transform_initial: IsNotNull():" << opt.transform_initial.IsNotNull() << std::endl; if (ImageDimension==2) { typedef itk::ANTSCenteredAffine2DTransform::Pointer RunningAffineTransformPointerType; const unsigned int RunningImageDimension = 2; typedef typename itk::Image::Pointer RunningImagePointerType; typedef OptAffine RunningOptAffineType; RunningImagePointerType R_fixedImage, R_movingImage; RunningOptAffineType R_opt; RunningAffineTransformPointerType transform_running; PreConversionInAffine(fixedImage, R_fixedImage, movingImage, R_movingImage, opt, R_opt); ComputeSingleAffineTransform2D3D(R_fixedImage, R_movingImage, R_opt, transform_running); PostConversionInAffine(transform_running, transform); } else if (ImageDimension==3){ typedef itk::ANTSAffine3DTransform::Pointer RunningAffineTransformPointerType; const unsigned int RunningImageDimension = 3; typedef typename itk::Image::Pointer RunningImagePointerType; typedef OptAffine RunningOptAffineType; RunningImagePointerType R_fixedImage, R_movingImage; RunningOptAffineType R_opt; RunningAffineTransformPointerType transform_running; PreConversionInAffine(fixedImage, R_fixedImage, movingImage, R_movingImage, opt, R_opt); ComputeSingleAffineTransform2D3D(R_fixedImage, R_movingImage, R_opt, transform_running); PostConversionInAffine(transform_running, transform); } else { std::cout << "Unsupported, not 2D/ 3D" << std::endl; return; } } /////////////////////////////////////////////////////////////////////////////// template void InitialzeImageMask(MaskImagePointerType &mask_fixed, ImageMaskSpatialObjectPointerType &mask_fixed_object){ if (mask_fixed.IsNull()) return; const unsigned int ImageDimension = MaskImagePointerType::ObjectType::ImageDimension; typedef typename MaskImagePointerType::ObjectType MaskImageType; typedef typename ImageMaskSpatialObjectPointerType::ObjectType ImageMaskSpatialObjectType; typedef itk::Image CharMaskImageType; typedef itk::CastImageFilter CastFilterType; typename CastFilterType::Pointer cast_filter = CastFilterType::New(); cast_filter->SetInput(mask_fixed); cast_filter->Update(); typename CharMaskImageType::Pointer mask_fixed_char = cast_filter->GetOutput(); mask_fixed_object = ImageMaskSpatialObjectType::New(); mask_fixed_object->SetImage(mask_fixed_char); } /////////////////////////////////////////////////////////////////////////////// /////////////////////////////////////////////////////////////////////////////// template void ComputeInitialPosition(ImagePointerType &I_fixed, ImagePointerType &I_moving, PointType ¢er, VectorType &translation_vec){ typedef typename ImagePointerType::ObjectType ImageType; typedef typename itk::ImageMomentsCalculator< ImageType > ImageCalculatorType; const unsigned int ImageDimension = ImageType::ImageDimension; typename ImageCalculatorType::Pointer calculator = ImageCalculatorType::New(); calculator->SetImage( I_fixed ); calculator->Compute(); typename ImageCalculatorType::VectorType fixed_center = calculator->GetCenterOfGravity(); calculator->SetImage( I_moving ); calculator->Compute(); typename ImageCalculatorType::VectorType moving_center = calculator->GetCenterOfGravity(); for( unsigned int i=0; i void InjectInitialPara(PointType ¢er, VectorType &translation_vec, TransformPointerType &transform){ typedef typename TransformPointerType::ObjectType::ParametersType ParaType; ParaType para0(TransformPointerType::ObjectType::ParametersDimension); switch((unsigned int) PointType::PointDimension){ case 2: para0[0] = 0; // para1[0]; // theta para0[1] = 1.0; //s1 para0[2] = 1.0; //s2 para0[3] = 0.0; //k para0[4] = center[0]; // para1[1]; //c1 para0[5] = center[1]; // para1[2]; //c2 para0[6] = translation_vec[0]; // 0;//para1[3]; //t1 para0[7] = translation_vec[1]; //0; //para1[4]; //t2 transform->SetParameters(para0); transform->SetCenter(center); break; case 3: para0[0] = 0.0; para0[1] = 0.0; para0[2] = 0.0; para0[3] = 1.0; para0[4] = 1.0; para0[5] = 1.0; para0[6] = 1.0; para0[7] = 0.0; para0[8] = 0.0; para0[9] = 0.0; para0[10] = translation_vec[0]; para0[11] = translation_vec[1]; para0[12] = translation_vec[2]; //para0[10] = 0.0; para0[11] = 0.0; para0[12] = 0.0; transform->SetParameters(para0); transform->SetCenter(center); break; } } ////////////////////////////////////////////////////////////////////////////////////////// template double TestCostValueMMI(ImagePointerType fixedImage, ImagePointerType movingImage, ParaType para, PointType center, TransformTypePointer null_transform){ typedef typename ImagePointerType::ObjectType ImageType; typedef typename TransformTypePointer::ObjectType TransformType; typename TransformType::Pointer transform = TransformType::New(); transform->SetCenter(center); // transform->SetParameters(para); typedef typename itk::MattesMutualInformationImageToImageMetric mattesMutualInfoMetricType; typename mattesMutualInfoMetricType::Pointer mattesMutualInfo=mattesMutualInfoMetricType::New(); typedef typename itk::LinearInterpolateImageFunction InterpolatorType; typename InterpolatorType::Pointer interpolator=InterpolatorType::New(); interpolator->SetInputImage(movingImage); mattesMutualInfo->SetFixedImage(fixedImage); mattesMutualInfo->SetMovingImage(movingImage); mattesMutualInfo->SetFixedImageRegion(fixedImage->GetBufferedRegion()); mattesMutualInfo->SetTransform(transform); mattesMutualInfo->SetInterpolator(interpolator); mattesMutualInfo->SetNumberOfHistogramBins( 32 ); mattesMutualInfo->SetNumberOfSpatialSamples( 5000 ); mattesMutualInfo->SetTransformParameters(para); mattesMutualInfo->Initialize(); double rval = 0; try{ rval = mattesMutualInfo->GetValue(para); } catch (itk::ExceptionObject &err){ std::cout << "!!!!!!!!!!!!!!!!!!!!!!!!!!!!!" << std::endl << "Exception caught in computing mattesMutualInfo after registration" << std::endl << "Maybe: Too many samples map outside moving image buffer" << std::endl << "Set the cost value = 0 (max for MutualInfo) " << std::endl; rval = 0; } return rval; } template void InitializeRunningAffineCache(ImagePointerType &fixed_image, ImagePointerType &moving_image, OptAffineType &opt, RunningAffineCacheType &running_cache){ typedef typename ImagePointerType::ObjectType ImageType; // typedef itk::LinearInterpolateImageFunction InterpolatorType; typedef typename RunningAffineCacheType::InterpolatorType InterpolatorType; typedef typename RunningAffineCacheType::MetricType MetricType; // typedef itk::MattesMutualInformationImageToImageMetric MetricType; BuildImagePyramid(fixed_image, opt.number_of_levels, running_cache.fixed_image_pyramid); BuildImagePyramid(moving_image, opt.number_of_levels, running_cache.moving_image_pyramid); InitialzeImageMask(opt.mask_fixed, running_cache.mask_fixed_object); running_cache.interpolator = InterpolatorType::New(); running_cache.metric = MetricType::New(); running_cache.invmetric = MetricType::New(); } template void GetAffineTransformFromImage(const ImageTypePointer& img, AffineTransformPointer &aff){ typedef typename ImageTypePointer::ObjectType ImageType; typedef typename ImageType::DirectionType DirectionType; typedef typename ImageType::PointType PointType; typedef typename ImageType::SpacingType SpacingType; typedef typename AffineTransformPointer::ObjectType::TranslationType VectorType; DirectionType direction = img->GetDirection(); PointType pt = img->GetOrigin(); SpacingType spacing = img->GetSpacing(); VectorType translation; translation.Fill(0); aff->SetMatrix(direction); aff->SetCenter(pt); aff->SetTranslation(translation); } template void InitializeAffineTransform(ImagePointerType &fixed_image, ImagePointerType &moving_image, OptAffineType& opt){ typedef typename OptAffineType::AffineTransformType TransformType; typedef typename TransformType::ParametersType ParaType; typedef typename TransformType::InputPointType PointType; typedef typename TransformType::OutputVectorType VectorType; std::cout << "opt.transform_initial.IsNull(): " << opt.transform_initial.IsNull() << std::endl; std::cout << " opt.use_rotation_header: " << opt.use_rotation_header << std::endl; std::cout << " opt.ignore_void_orgin: " << opt.ignore_void_orgin << std::endl; if (opt.transform_initial.IsNull()){ PointType center; VectorType translation_vec; ComputeInitialPosition(fixed_image, moving_image, center, translation_vec); opt.transform_initial = TransformType::New(); InjectInitialPara(center, translation_vec, opt.transform_initial); } } template ImagePointer ShrinkImageToScale(ImagePointer image , float scalingFactor ) { typedef float RealType; typedef typename ImagePointer::ObjectType ImageType; typename ImageType::SpacingType inputSpacing = image->GetSpacing(); typename ImageType::RegionType::SizeType inputSize = image->GetRequestedRegion().GetSize(); typename ImageType::SpacingType outputSpacing; typename ImageType::RegionType::SizeType outputSize; typedef itk::ResampleImageFilter ResampleFilterType; typename ResampleFilterType::Pointer resampler = ResampleFilterType::New(); RealType minimumSpacing = inputSpacing.GetVnlVector().min_value(); // RealType maximumSpacing = inputSpacing.GetVnlVector().max_value(); ImagePointer current_image = image; for ( unsigned int d = 0; d < ImageType::ImageDimension; d++ ) { RealType scaling = vnl_math_min( scalingFactor * minimumSpacing / inputSpacing[d], static_cast( inputSize[d] ) / 32.0 ); outputSpacing[d] = inputSpacing[d] * scaling; outputSize[d] = static_cast( inputSpacing[d] * static_cast( inputSize[d] ) / outputSpacing[d] + 0.5 ); typedef itk::RecursiveGaussianImageFilter GaussianFilterType; typename GaussianFilterType::Pointer smoother = GaussianFilterType::New(); smoother->SetInputImage( current_image ); smoother->SetDirection( d ); smoother->SetNormalizeAcrossScale( false ); smoother->SetSigma( 0.25 * ( outputSpacing[d] / inputSpacing[d] ) ); if ( smoother->GetSigma() > 0.0 ) { smoother->Update(); current_image = smoother->GetOutput(); } } resampler->SetInput(current_image ); resampler->SetSize(outputSize); resampler->SetOutputSpacing(outputSpacing); resampler->SetOutputOrigin(image->GetOrigin()); resampler->SetOutputDirection(image->GetDirection()); resampler->Update(); image = resampler->GetOutput(); // std::cout << "DEBUG: " << outputSize << std::endl; return image; } template void BuildImagePyramid(const ImagePointerType &image, int number_of_levels, ImagePyramidType &image_pyramid){ image_pyramid.resize(number_of_levels); image_pyramid[number_of_levels-1] = image; double scale_factor = 2; for(int i = 0; i < number_of_levels-1; i++){ image_pyramid[number_of_levels-2-i] = ShrinkImageToScale(image, scale_factor); scale_factor *= 2; } // for(int i=0; i < number_of_levels; i++) // std::cout << "level " << i << ": size: " << image_pyramid[i]->GetLargestPossibleRegion().GetSize() << std::endl; } template ParaType NormalizeGradientForRigidTransform(ParaType &original_gradient, int kImageDim){ ParaType new_gradient(original_gradient.Size()); new_gradient = original_gradient; switch(kImageDim){ case 2: //theta, s1, s2, k for(int j=1; j<=3; j++) new_gradient[j] = 0.; break; case 3: // q1,q2,q3,q4,s1,s2,s3,k1,k2,k3 for(int j=4; j<=9; j++) new_gradient[j] = 0.; break; } return new_gradient; } /////////////////////////////////////////////////////////////////////////////// //template template bool SymmRegisterImageAffineMutualInformationMultiResolution(RunningAffineCacheType &running_cache, OptAffine &opt, ParaType ¶_final){ typedef typename RunningAffineCacheType::ImagePyramidType ImagePyramidType; typedef typename RunningAffineCacheType::ImagePointerType ImagePointerType; typedef typename ImagePointerType::ObjectType ImageType; typedef typename RunningAffineCacheType::MetricType MetricType; typedef typename RunningAffineCacheType::InterpolatorType InterpolatorType; typedef typename OptAffine::AffineTransformType TransformType; typedef typename RunningAffineCacheType::MaskObjectPointerType MaskObjectPointerType; const unsigned int kImageDim = ImageType::ImageDimension; ImagePyramidType& fixed_image_pyramid = running_cache.fixed_image_pyramid; ImagePyramidType& moving_image_pyramid = running_cache.moving_image_pyramid; MaskObjectPointerType& mask_fixed_object = running_cache.mask_fixed_object; int number_of_levels = opt.number_of_levels; std::vector& number_of_iteration_list = opt.number_of_iteration_list; std::vector& gradient_scales = opt.gradient_scales; bool is_rigid = opt.is_rigid; // use my own's registration routine of image pyramid and gradient descent , only use ITK's implementation of mutual information // try to use my own transform class together with image pyramid when transform are required (not much for MI though) typename TransformType::Pointer transform = TransformType::New(); typename TransformType::Pointer invtransform = TransformType::New(); typename InterpolatorType::Pointer& interpolator = running_cache.interpolator; typename InterpolatorType::Pointer invinterpolator = InterpolatorType::New(); typename MetricType::Pointer& metric = running_cache.metric; typename MetricType::Pointer& invmetric = running_cache.invmetric; const int kParaDim = TransformType::ParametersDimension; ParaType current_para(kParaDim); current_para = opt.transform_initial->GetParameters(); double maximum_step_length = opt.maximum_step_length; double relaxation_factor = opt.relaxation_factor; double minimum_step_length = opt.minimum_step_length; double current_step_length; for(int i = 0; i < number_of_levels; i++){ transform->SetParameters(current_para); transform->SetCenter(opt.transform_initial->GetCenter()); ImagePointerType fixed_image = fixed_image_pyramid[i]; ImagePointerType moving_image = moving_image_pyramid[i]; int number_of_iteration_current_level = number_of_iteration_list[i]; interpolator->SetInputImage( moving_image ); metric->SetMovingImage( moving_image ); metric->SetFixedImage( fixed_image ); metric->SetTransform( transform ); metric->SetInterpolator( interpolator ); metric->SetFixedImageRegion(fixed_image->GetLargestPossibleRegion()); if (mask_fixed_object.IsNotNull()) metric->SetFixedImageMask(mask_fixed_object); metric->Initialize(); ParaType last_gradient(kParaDim); ParaType invlast_gradient(kParaDim); for(int j=0; j < kParaDim; j++) { last_gradient[j] = 0; invlast_gradient[j] = 0; } current_step_length = maximum_step_length; bool is_converged = false; int used_iterations = 0; for(used_iterations=0; used_iterations < number_of_iteration_current_level; used_iterations++){ transform->GetInverse(invtransform); invinterpolator->SetInputImage( fixed_image ); invmetric->SetMovingImage( fixed_image ); invmetric->SetFixedImage( moving_image ); invmetric->SetTransform( invtransform ); invmetric->SetInterpolator( invinterpolator ); invmetric->SetFixedImageRegion(moving_image->GetLargestPossibleRegion()); invmetric->Initialize(); ParaType original_gradient(kParaDim); ParaType current_gradient(kParaDim); ParaType invoriginal_gradient(kParaDim); ParaType invcurrent_gradient(kParaDim); double value,invvalue; try{ metric->GetValueAndDerivative(current_para, value, original_gradient); invmetric->GetValueAndDerivative( invtransform->GetParameters() , invvalue, invoriginal_gradient); } catch(itk::ExceptionObject & err){ std::cerr << "ExceptionObject caught !" << std::endl; std::cerr << err << std::endl; return false; break; } // use the similar routine as RegularStepGradientDescentBaseOptimizer::AdvanceOneStep // to use oscillation as the minimization convergence // notice this is always a minimization procedure if (is_rigid) { original_gradient = NormalizeGradientForRigidTransform(original_gradient, kImageDim); invoriginal_gradient = NormalizeGradientForRigidTransform(invoriginal_gradient, kImageDim); } for(int j=0; jGetRequestedRegion().GetSize() << "-mov" << moving_image->GetRequestedRegion().GetSize(); std::cout << ", affine para: " << current_para << std::endl; if (is_converged) std::cout << " reach oscillation, current step: " << current_step_length << "<" << minimum_step_length << std::endl; else std::cout << " does not reach oscillation, current step: " << current_step_length << ">" << minimum_step_length << std::endl; } para_final = current_para; return true; } /////////////////////////////////////////////////////////////////////////////// //template template bool RegisterImageAffineMutualInformationMultiResolution(RunningAffineCacheType &running_cache, OptAffine &opt, ParaType ¶_final){ typedef typename RunningAffineCacheType::ImagePyramidType ImagePyramidType; typedef typename RunningAffineCacheType::ImagePointerType ImagePointerType; typedef typename ImagePointerType::ObjectType ImageType; typedef typename RunningAffineCacheType::MetricType MetricType; typedef typename RunningAffineCacheType::InterpolatorType InterpolatorType; typedef typename OptAffine::AffineTransformType TransformType; typedef typename RunningAffineCacheType::MaskObjectPointerType MaskObjectPointerType; const unsigned int kImageDim = ImageType::ImageDimension; ImagePyramidType& fixed_image_pyramid = running_cache.fixed_image_pyramid; ImagePyramidType& moving_image_pyramid = running_cache.moving_image_pyramid; MaskObjectPointerType& mask_fixed_object = running_cache.mask_fixed_object; int number_of_levels = opt.number_of_levels; std::vector& number_of_iteration_list = opt.number_of_iteration_list; std::vector& gradient_scales = opt.gradient_scales; bool is_rigid = opt.is_rigid; // use my own's registration routine of image pyramid and gradient descent , only use ITK's implementation of mutual information // try to use my own transform class together with image pyramid when transform are required (not much for MI though) typename TransformType::Pointer transform = TransformType::New(); typename InterpolatorType::Pointer& interpolator = running_cache.interpolator; typename MetricType::Pointer& metric = running_cache.metric; const int kParaDim = TransformType::ParametersDimension; ParaType current_para(kParaDim); current_para = opt.transform_initial->GetParameters(); double maximum_step_length = opt.maximum_step_length; double relaxation_factor = opt.relaxation_factor; double minimum_step_length = opt.minimum_step_length; double current_step_length; double value=0; for(int i = 0; i < number_of_levels; i++){ transform->SetParameters(current_para); transform->SetCenter(opt.transform_initial->GetCenter()); ImagePointerType fixed_image = fixed_image_pyramid[i]; ImagePointerType moving_image = moving_image_pyramid[i]; int number_of_iteration_current_level = number_of_iteration_list[i]; interpolator->SetInputImage( moving_image ); metric->SetMovingImage( moving_image ); metric->SetFixedImage( fixed_image ); metric->SetTransform( transform ); metric->SetInterpolator( interpolator ); metric->SetFixedImageRegion(fixed_image->GetLargestPossibleRegion()); if (mask_fixed_object.IsNotNull()) metric->SetFixedImageMask(mask_fixed_object); metric->Initialize(); ParaType last_gradient(kParaDim); ParaType invlast_gradient(kParaDim); for(int j=0; j < kParaDim; j++) { last_gradient[j] = 0; invlast_gradient[j] = 0; } current_step_length = maximum_step_length; bool is_converged = false; int used_iterations = 0; for(used_iterations=0; used_iterations < number_of_iteration_current_level; used_iterations++){ ParaType original_gradient(kParaDim); ParaType current_gradient(kParaDim); value=0; try{ metric->GetValueAndDerivative(current_para, value, original_gradient); } catch(itk::ExceptionObject & err){ std::cerr << "ExceptionObject caught !" << std::endl; std::cerr << err << std::endl; return false; break; } // use the similar routine as RegularStepGradientDescentBaseOptimizer::AdvanceOneStep // to use oscillation as the minimization convergence // notice this is always a minimization procedure if (is_rigid) { original_gradient = NormalizeGradientForRigidTransform(original_gradient, kImageDim); } for(int j=0; jGetRequestedRegion().GetSize() << "-mov" << moving_image->GetRequestedRegion().GetSize(); std::cout << ", affine para: " << current_para << std::endl; if (is_converged) std::cout << " reach oscillation, current step: " << current_step_length << "<" << minimum_step_length << std::endl; else std::cout << " does not reach oscillation, current step: " << current_step_length << ">" << minimum_step_length << std::endl; } double value1=value; if ( ! mask_fixed_object.IsNotNull()) { typename TransformType::Pointer transform2 = TransformType::New(); ParaType current_para2(kParaDim); current_para2 = opt.transform_initial->GetParameters(); maximum_step_length = opt.maximum_step_length; relaxation_factor = opt.relaxation_factor; minimum_step_length = opt.minimum_step_length; value=0; for(int i = 0; i < number_of_levels; i++){ transform2->SetParameters(current_para2); transform2->SetCenter(opt.transform_initial->GetCenter()); /** see below -- we switch fixed and moving!! */ ImagePointerType fixed_image = moving_image_pyramid[i]; ImagePointerType moving_image = fixed_image_pyramid[i]; int number_of_iteration_current_level = number_of_iteration_list[i]; interpolator->SetInputImage( moving_image ); metric->SetMovingImage( moving_image ); metric->SetFixedImage( fixed_image ); metric->SetTransform( transform2 ); metric->SetInterpolator( interpolator ); metric->SetFixedImageRegion(fixed_image->GetLargestPossibleRegion()); /** FIXME --- need a moving mask ... */ // if (mask_fixed_object.IsNotNull()) metric->SetFixedImageMask(mask_fixed_object); metric->Initialize(); ParaType last_gradient(kParaDim); for(int j=0; j < kParaDim; j++) { last_gradient[j] = 0; } current_step_length = maximum_step_length; bool is_converged = false; int used_iterations = 0; for(used_iterations=0; used_iterations < number_of_iteration_current_level; used_iterations++){ ParaType original_gradient(kParaDim); ParaType current_gradient(kParaDim); value=0; try{ metric->GetValueAndDerivative(current_para2, value, original_gradient); } catch(itk::ExceptionObject & err){ std::cerr << "ExceptionObject caught !" << std::endl; std::cerr << err << std::endl; return false; break; } // use the similar routine as RegularStepGradientDescentBaseOptimizer::AdvanceOneStep // to use oscillation as the minimization convergence // notice this is always a minimization procedure if (is_rigid) { original_gradient = NormalizeGradientForRigidTransform(original_gradient, kImageDim); } for(int j=0; jGetRequestedRegion().GetSize() << "-mov" << moving_image->GetRequestedRegion().GetSize(); std::cout << ", affine para: " << current_para2 << std::endl; if (is_converged) std::cout << " reach oscillation, current step: " << current_step_length << "<" << minimum_step_length << std::endl; else std::cout << " does not reach oscillation, current step: " << current_step_length << ">" << minimum_step_length << std::endl; } std::cout << " v1 " << value1 << " v2 " << value << std::endl; if ( value < value1 ) { std::cout << " last params " << transform->GetParameters() << std::endl; std::cout << " my params " << transform2->GetParameters() << std::endl; transform2->GetInverse(transform); std::cout << " new params " << transform->GetParameters() << std::endl; para_final = transform->GetParameters(); return true; } } para_final = current_para; std::cout << "final " << para_final << std::endl; return true; } /////////////////////////////////////////////////////////////////////////////// template void ComputeSingleAffineTransform2D3D(ImagePointerType fixed_image, ImagePointerType moving_image, OptAffineType &opt, TransformPointerType &transform){ typedef typename ImagePointerType::ObjectType ImageType; const int ImageDimension = ImageType::ImageDimension; typedef std::vector ImagePyramidType; typedef itk::ImageMaskSpatialObject ImageMaskSpatialObjectType; typedef typename ImageMaskSpatialObjectType::Pointer MaskObjectPointerType; typedef itk::LinearInterpolateImageFunction InterpolatorType; typedef typename InterpolatorType::Pointer InterpolatorPointerType; typedef typename TransformPointerType::ObjectType TransformType; typedef typename TransformType::ParametersType ParaType; InitializeAffineOptmizationParameters(opt, opt.translation_scales); // std::cout << "DEBUG: opt.gradient_scales.size() = " << opt.gradient_scales.size() << std::endl; InitializeAffineTransform(fixed_image, moving_image, opt); std::cout << "input affine center: " << opt.transform_initial->GetCenter() << std::endl; std::cout << "input affine para: " << opt.transform_initial->GetParameters() << std::endl; transform = TransformType::New(); ParaType para_final(TransformType::ParametersDimension); switch (opt.metric_type){ case AffineWithMeanSquareDifference:{ typedef itk::MeanSquaresImageToImageMetric MetricType; typedef typename MetricType::Pointer MetricPointerType; typedef RunningAffineCache RunningAffineCacheType; RunningAffineCacheType running_cache; InitializeRunningAffineCache(fixed_image, moving_image, opt, running_cache); RegisterImageAffineMutualInformationMultiResolution(running_cache, opt, para_final); } break; case AffineWithHistogramCorrelation:{ typedef itk::CorrelationCoefficientHistogramImageToImageMetric MetricType; typedef typename MetricType::Pointer MetricPointerType; typedef RunningAffineCache RunningAffineCacheType; RunningAffineCacheType running_cache; InitializeRunningAffineCache(fixed_image, moving_image, opt, running_cache); unsigned int nBins = 32; typename MetricType::HistogramType::SizeType histSize; histSize[0] = nBins; histSize[1] = nBins; running_cache.metric->SetHistogramSize(histSize); RegisterImageAffineMutualInformationMultiResolution(running_cache, opt, para_final); } break; case AffineWithNormalizedCorrelation:{ typedef itk::NormalizedCorrelationImageToImageMetric MetricType; typedef typename MetricType::Pointer MetricPointerType; typedef RunningAffineCache RunningAffineCacheType; RunningAffineCacheType running_cache; InitializeRunningAffineCache(fixed_image, moving_image, opt, running_cache); RegisterImageAffineMutualInformationMultiResolution(running_cache, opt, para_final); } break; case AffineWithGradientDifference:{ typedef itk::GradientDifferenceImageToImageMetric MetricType; typedef typename MetricType::Pointer MetricPointerType; typedef RunningAffineCache RunningAffineCacheType; RunningAffineCacheType running_cache; InitializeRunningAffineCache(fixed_image, moving_image, opt, running_cache); RegisterImageAffineMutualInformationMultiResolution(running_cache, opt, para_final); } break; case AffineWithMutualInformation: { typedef itk::MattesMutualInformationImageToImageMetric MetricType; typedef typename MetricType::Pointer MetricPointerType; typedef RunningAffineCache RunningAffineCacheType; RunningAffineCacheType running_cache; InitializeRunningAffineCache(fixed_image, moving_image, opt, running_cache); running_cache.metric->SetNumberOfHistogramBins( opt.MI_bins ); running_cache.metric->SetNumberOfSpatialSamples( opt.MI_samples ); RegisterImageAffineMutualInformationMultiResolution(running_cache, opt, para_final); } break; default: break; } bool noaffine=true; for(int i = 0; i 0) noaffine=false; if (noaffine) for (int i=TransformType::ParametersDimension-ImageDimension; iSetParameters(para_final); transform->SetCenter(opt.transform_initial->GetCenter()); double rval_init = TestCostValueMMI(fixed_image, moving_image, opt.transform_initial->GetParameters(), opt.transform_initial->GetCenter(), transform); // std::cerr << "ABCDABCD: " << transform << std::endl; double rval_final = TestCostValueMMI(fixed_image, moving_image, para_final, opt.transform_initial->GetCenter(), transform); std::cout << "outputput affine center: " << transform->GetCenter() << std::endl; std::cout << "output affine para: " << transform->GetParameters() << std::endl; std::cout << "initial measure value (MMI): rval = " << rval_init << std::endl; std::cout << "final measure value (MMI): rval = " << rval_final << std::endl; std::cout << "finish affine registeration..." << std::endl; }; #endif /*ANTS_AFFINE_REGISTRATION2_H_*/ ants-1.9.2+svn680.dfsg/ImageRegistration/itkANTSAffine3DTransform.h000066400000000000000000000133521147325206600247230ustar00rootroot00000000000000#ifndef __itkANTSAffine3DTransform_h #define __itkANTSAffine3DTransform_h #include #include "itkRigid3DTransform.h" #include "vnl/vnl_quaternion.h" namespace itk { /** \brief ANTSAffine3DTransform of a vector space (e.g. space coordinates). * * This transform applies a rotation and translation to the space * * \ingroup Transforms */ template < class TScalarType=double > // Data type for scalars (float or double) class ITK_EXPORT ANTSAffine3DTransform : public MatrixOffsetTransformBase< TScalarType, 3, 3> // public Rigid3DTransform< TScalarType > { public: /** Standard class typedefs. */ typedef ANTSAffine3DTransform Self; // typedef Rigid3DTransform< TScalarType > Superclass; typedef MatrixOffsetTransformBase< TScalarType, 3, 3 > Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** New macro for creation of through a Smart Pointer */ itkNewMacro( Self ); /** Run-time type information (and related methods). */ // itkTypeMacro( ANTSAffine3DTransform, Rigid3DTransform ); itkTypeMacro( ANTSAffine3DTransform, MatrixOffsetTransformBase ); /** Dimension of parameters */ itkStaticConstMacro(InputSpaceDimension, unsigned int, 3); itkStaticConstMacro(OutputSpaceDimension, unsigned int, 3); itkStaticConstMacro(SpaceDimension, unsigned int, 3); itkStaticConstMacro(ParametersDimension, unsigned int, 13); /** Parameters Type */ typedef typename Superclass::ParametersType ParametersType; typedef typename Superclass::JacobianType JacobianType; typedef typename Superclass::ScalarType ScalarType; typedef typename Superclass::InputPointType InputPointType; typedef typename Superclass::OutputPointType OutputPointType; typedef typename Superclass::InputVectorType InputVectorType; typedef typename Superclass::OutputVectorType OutputVectorType; typedef typename Superclass::InputVnlVectorType InputVnlVectorType; typedef typename Superclass::OutputVnlVectorType OutputVnlVectorType; typedef typename Superclass::InputCovariantVectorType InputCovariantVectorType; typedef typename Superclass::OutputCovariantVectorType OutputCovariantVectorType; typedef typename Superclass::MatrixType MatrixType; typedef typename Superclass::InverseMatrixType InverseMatrixType; typedef typename Superclass::CenterType CenterType; typedef typename Superclass::OffsetType OffsetType; typedef typename Superclass::TranslationType TranslationType; /** VnlQuaternion type. */ typedef vnl_quaternion VnlQuaternionType; /** Compute the Jacobian Matrix of the transformation at one point */ /** Set the rotation of the rigid transform. * This method sets the rotation of a ANTSAffine3DTransform to a * value specified by the user. */ void SetRotation(const VnlQuaternionType &rotation); void SetS1(const TScalarType S1); void SetS2(const TScalarType S2); void SetS3(const TScalarType S3); void SetK1(const TScalarType K1); void SetK2(const TScalarType K2); void SetK3(const TScalarType K3); /** Get the rotation from an ANTSAffine3DTransform. * This method returns the value of the rotation of the * ANTSAffine3DTransform. **/ const VnlQuaternionType & GetRotation(void) const { return m_Rotation; } MatrixType ComputeMyRotationMatrix(); itkGetConstReferenceMacro( S1, TScalarType ); itkGetConstReferenceMacro( S2, TScalarType ); itkGetConstReferenceMacro( S3, TScalarType ); itkGetConstReferenceMacro( K1, TScalarType ); itkGetConstReferenceMacro( K2, TScalarType ); itkGetConstReferenceMacro( K3, TScalarType ); /** Set the parameters to the IdentityTransform */ virtual void SetIdentity(void); /** Set the transformation from a container of parameters. * This is typically used by optimizers. * There are 7 parameters. The first four represents the * quaternion and the last three represents the * offset. */ void SetParameters( const ParametersType & parameters ); const ParametersType & GetParameters() const; /** Compute the Jacobian of the transformation. * This method computes the Jacobian matrix of the transformation. * given point or vector, returning the transformed point or * vector. The rank of the Jacobian will also indicate if the transform * is invertible at this point. */ const JacobianType & GetJacobian(const InputPointType &point ) const; protected: /* ANTSAffine3DTransform(const MatrixType &matrix, */ /* const OutputVectorType &offset); */ ANTSAffine3DTransform(unsigned int outputDims, unsigned int paramDims); ANTSAffine3DTransform(); ~ANTSAffine3DTransform(){}; virtual void ComputeMatrix(); virtual void ComputeMatrixParameters(); void SetVarRotation(const VnlQuaternionType & rotation) { m_Rotation = rotation; }; // const InverseMatrixType & GetInverseMatrix( void ) const; void PrintSelf(std::ostream &os, Indent indent) const; private: ANTSAffine3DTransform(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented /** Rotation of the transformation. */ VnlQuaternionType m_Rotation; /** added affine parameters **/ TScalarType m_S1; TScalarType m_S2; TScalarType m_S3; TScalarType m_K1; TScalarType m_K2; TScalarType m_K3; }; //class ANTSAffine3DTransform } // namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkANTSAffine3DTransform.txx" #endif #endif /* __itkANTSAffine3DTransform_h */ ants-1.9.2+svn680.dfsg/ImageRegistration/itkANTSAffine3DTransform.txx000066400000000000000000000370571147325206600253270ustar00rootroot00000000000000#ifndef _itkANTSAffine3DTransform_txx #define _itkANTSAffine3DTransform_txx #include "itkANTSAffine3DTransform.h" #include "vnl/algo/vnl_qr.h" namespace itk { // Constructor with default arguments template ANTSAffine3DTransform ::ANTSAffine3DTransform() : Superclass(SpaceDimension, ParametersDimension) { m_Rotation = VnlQuaternionType(0,0,0,1); // axis * vcl_sin(t/2), vcl_cos(t/2) m_S1 = NumericTraits< TScalarType >::One; m_S2 = NumericTraits< TScalarType >::One; m_S3 = NumericTraits< TScalarType >::One; m_K1 = NumericTraits< TScalarType >::Zero; m_K2 = NumericTraits< TScalarType >::Zero; m_K3 = NumericTraits< TScalarType >::Zero; } // Constructor with default arguments template ANTSAffine3DTransform:: ANTSAffine3DTransform( unsigned int outputSpaceDimension, unsigned int parametersDimension ) : Superclass(outputSpaceDimension, parametersDimension) { m_Rotation = VnlQuaternionType(0,0,0,1); // axis * vcl_sin(t/2), vcl_cos(t/2) m_S1 = NumericTraits< TScalarType >::One; m_S2 = NumericTraits< TScalarType >::One; m_S3 = NumericTraits< TScalarType >::One; m_K1 = NumericTraits< TScalarType >::Zero; m_K2 = NumericTraits< TScalarType >::Zero; m_K3 = NumericTraits< TScalarType >::Zero; } // // Constructor with explicit arguments // template // ANTSAffine3DTransform:: // ANTSAffine3DTransform( const MatrixType & matrix, // const OutputVectorType & offset ) : // Superclass(matrix, offset) // { // this->ComputeMatrixParameters(); // } // Print self template void ANTSAffine3DTransform:: PrintSelf(std::ostream &os, Indent indent ) const { Superclass::PrintSelf(os,indent); os << indent << "Rotation: " << m_Rotation << std::endl; os << indent << "S1, S2, S3: " << m_S1 <<", "< void ANTSAffine3DTransform:: SetRotation(const VnlQuaternionType &rotation ) { m_Rotation = rotation; this->ComputeMatrix(); this->ComputeOffset(); this->Modified(); return; } template void ANTSAffine3DTransform:: SetS1(const TScalarType S1) { m_S1 = S1; this->ComputeMatrix(); this->ComputeOffset(); this->Modified(); return; } template void ANTSAffine3DTransform:: SetS2(const TScalarType S2) { m_S2 = S2; this->ComputeMatrix(); this->ComputeOffset(); this->Modified(); return; } template void ANTSAffine3DTransform:: SetS3(const TScalarType S3) { m_S3 = S3; this->ComputeMatrix(); this->ComputeOffset(); this->Modified(); return; } template void ANTSAffine3DTransform:: SetK1(const TScalarType K1) { m_K1 = K1; this->ComputeMatrix(); this->ComputeOffset(); this->Modified(); return; } template void ANTSAffine3DTransform:: SetK2(const TScalarType K2) { m_K2 = K2; this->ComputeMatrix(); this->ComputeOffset(); this->Modified(); return; } template void ANTSAffine3DTransform:: SetK3(const TScalarType K3) { m_K3 = K3; this->ComputeMatrix(); this->ComputeOffset(); this->Modified(); return; } // Set the parameters in order to fit an Identity transform template void ANTSAffine3DTransform:: SetIdentity( void ) { m_Rotation = VnlQuaternionType(0,0,0,1); m_S1 = NumericTraits< TScalarType >::One; m_S2 = NumericTraits< TScalarType >::One; m_S3 = NumericTraits< TScalarType >::One; m_K1 = NumericTraits< TScalarType >::Zero; m_K2 = NumericTraits< TScalarType >::Zero; m_K3 = NumericTraits< TScalarType >::Zero; this->Superclass::SetIdentity(); } // Set Parameters template void ANTSAffine3DTransform ::SetParameters( const ParametersType & parameters ) { OutputVectorType translation; // Transfer the quaternion part unsigned int par = 0; for(unsigned int j=0; j < 4; j++) { m_Rotation[j] = parameters[par]; ++par; } m_S1 = parameters[par++]; m_S2 = parameters[par++]; m_S3 = parameters[par++]; m_K1 = parameters[par++]; m_K2 = parameters[par++]; m_K3 = parameters[par++]; this->ComputeMatrix(); // Transfer the constant part for(unsigned int i=0; i < SpaceDimension; i++) { translation[i] = parameters[par]; ++par; } this->SetVarTranslation( translation ); this->ComputeOffset(); // Modified is always called since we just have a pointer to the // parameters and cannot know if the parameters have changed. this->Modified(); } // Set Parameters template const typename ANTSAffine3DTransform::ParametersType & ANTSAffine3DTransform ::GetParameters() const { VnlQuaternionType quaternion = this->GetRotation(); OutputVectorType translation = this->GetTranslation(); // Transfer the quaternion part unsigned int par = 0; for(unsigned int j=0; j < 4; j++) { this->m_Parameters[par] = quaternion[j]; ++par; } this->m_Parameters[par++] = m_S1; this->m_Parameters[par++] = m_S2; this->m_Parameters[par++] = m_S3; this->m_Parameters[par++] = m_K1; this->m_Parameters[par++] = m_K2; this->m_Parameters[par++] = m_K3; // Transfer the constant part for(unsigned int i=0; i < SpaceDimension; i++) { this->m_Parameters[par] = translation[i]; ++par; } return this->m_Parameters; } // Get parameters template const typename ANTSAffine3DTransform::JacobianType & ANTSAffine3DTransform:: GetJacobian( const InputPointType & p ) const { this->m_Jacobian.Fill(0.0); TScalarType c1 = this->GetCenter()[0]; TScalarType c2 = this->GetCenter()[1]; TScalarType c3 = this->GetCenter()[2]; TScalarType s1 = this->m_S1; TScalarType s2 = this->m_S2; TScalarType s3 = this->m_S3; TScalarType k1 = this->m_K1; TScalarType k2 = this->m_K2; TScalarType k3 = this->m_K3; TScalarType x1 = p[0]; TScalarType x2 = p[1]; TScalarType x3 = p[2]; // z1,z2,z3 is the S*K*point p TScalarType w1 = (x1-c1)+k1*(x2-c2)+k2*(x3-c3); TScalarType w2 = (x2-c2)+k3*(x3-c2); TScalarType w3 = (x3-c3); TScalarType z1 = s1*w1; TScalarType z2 = s2*w2; TScalarType z3 = s3*w3; // compute Jacobian with respect to quaternion parameters this->m_Jacobian[0][0] = 2.0 * ( m_Rotation.x() * z1 + m_Rotation.y() * z2 + m_Rotation.z() * z3 ); this->m_Jacobian[0][1] = 2.0 * (- m_Rotation.y() * z1 + m_Rotation.x() * z2 + m_Rotation.r() * z3 ); this->m_Jacobian[0][2] = 2.0 * (- m_Rotation.z() * z1 - m_Rotation.r() * z2 + m_Rotation.x() * z3 ); this->m_Jacobian[0][3] = - 2.0 * (- m_Rotation.r() * z1 + m_Rotation.z() * z2 - m_Rotation.y() * z3 ); this->m_Jacobian[1][0] = - this->m_Jacobian[0][1]; this->m_Jacobian[1][1] = this->m_Jacobian[0][0]; this->m_Jacobian[1][2] = this->m_Jacobian[0][3]; this->m_Jacobian[1][3] = - this->m_Jacobian[0][2]; this->m_Jacobian[2][0] = - this->m_Jacobian[0][2]; this->m_Jacobian[2][1] = - this->m_Jacobian[0][3]; this->m_Jacobian[2][2] = this->m_Jacobian[0][0]; this->m_Jacobian[2][3] = this->m_Jacobian[0][1]; // get rotation matrix first // this is done to compensate for the transposed representation // between VNL and ITK VnlQuaternionType conjugateRotation = m_Rotation.conjugate(); MatrixType newMatrix; newMatrix = conjugateRotation.rotation_matrix_transpose(); TScalarType r11 = newMatrix[0][0]; TScalarType r12 = newMatrix[0][1]; TScalarType r13 = newMatrix[0][2]; TScalarType r21 = newMatrix[1][0]; TScalarType r22 = newMatrix[1][1]; TScalarType r23 = newMatrix[1][2]; TScalarType r31 = newMatrix[2][0]; TScalarType r32 = newMatrix[2][1]; TScalarType r33 = newMatrix[2][2]; // compute Jacobian wrt S1/S2/S3 this->m_Jacobian[0][4] = r11 * w1; this->m_Jacobian[0][5] = r12 * w2; this->m_Jacobian[0][6] = r13 * w3; this->m_Jacobian[1][4] = r21 * w1; this->m_Jacobian[1][5] = r22 * w2; this->m_Jacobian[1][6] = r23 * w3; this->m_Jacobian[2][4] = r31 * w1; this->m_Jacobian[2][5] = r32 * w2; this->m_Jacobian[2][6] = r33 * w3; // compute Jacobian wrt K1/K2/K3 this->m_Jacobian[0][7] = r11 * s1 * (x2-c2); this->m_Jacobian[0][8] = r11 * s1 * (x3-c3); this->m_Jacobian[0][9] = r12 * s2 * (x3-c3); this->m_Jacobian[1][7] = r21 * s1 * (x2-c2); this->m_Jacobian[1][8] = r21 * s1 * (x3-c3); this->m_Jacobian[1][9] = r22 * s2 * (x3-c3); this->m_Jacobian[2][7] = r31 * s1 * (x2-c2); this->m_Jacobian[2][8] = r31 * s1 * (x3-c3); this->m_Jacobian[2][9] = r32 * s2 * (x3-c3); // for test purpose // FORCE ONLY DO DERIVATIVE ON S // for(int ii=0; ii <3; ii++){ // for(int jj=0; jj<=9;jj++){ // this->m_Jacobian[ii][jj] = 0.0; // } // } // this->m_Jacobian[0][4] = r11 * w1; // this->m_Jacobian[1][4] = r21 * w1; // this->m_Jacobian[2][4] = r31 * w1; // compute derivatives for the translation part unsigned int blockOffset = 10; for(unsigned int dim=0; dim < SpaceDimension; dim++ ) { this->m_Jacobian[ dim ][ blockOffset + dim ] = 1.0; } return this->m_Jacobian; // // compute derivatives with respect to rotation // this->m_Jacobian.Fill(0.0); // const TScalarType x = p[0] - this->GetCenter()[0]; // const TScalarType y = p[1] - this->GetCenter()[1]; // const TScalarType z = p[2] - this->GetCenter()[2]; // // compute Jacobian with respect to quaternion parameters // this->m_Jacobian[0][0] = 2.0 * ( m_Rotation.x() * x + m_Rotation.y() * y // + m_Rotation.z() * z ); // this->m_Jacobian[0][1] = 2.0 * (- m_Rotation.y() * x + m_Rotation.x() * y // + m_Rotation.r() * z ); // this->m_Jacobian[0][2] = 2.0 * (- m_Rotation.z() * x - m_Rotation.r() * y // + m_Rotation.x() * z ); // this->m_Jacobian[0][3] = - 2.0 * (- m_Rotation.r() * x + m_Rotation.z() * y // - m_Rotation.y() * z ); // this->m_Jacobian[1][0] = - this->m_Jacobian[0][1]; // this->m_Jacobian[1][1] = this->m_Jacobian[0][0]; // this->m_Jacobian[1][2] = this->m_Jacobian[0][3]; // this->m_Jacobian[1][3] = - this->m_Jacobian[0][2]; // this->m_Jacobian[2][0] = - this->m_Jacobian[0][2]; // this->m_Jacobian[2][1] = - this->m_Jacobian[0][3]; // this->m_Jacobian[2][2] = this->m_Jacobian[0][0]; // this->m_Jacobian[2][3] = this->m_Jacobian[0][1]; // // compute derivatives for the translation part // unsigned int blockOffset = 4; // for(unsigned int dim=0; dim < SpaceDimension; dim++ ) // { // this->m_Jacobian[ dim ][ blockOffset + dim ] = 1.0; // } // return this->m_Jacobian; } // template // const typename ANTSAffine3DTransform< TScalarType >::InverseMatrixType & // ANTSAffine3DTransform:: // GetInverseMatrix() const // { // // If the transform has been modified we recompute the inverse // if(this->InverseMatrixIsOld()) // { // InverseMatrixType newMatrix; // VnlQuaternionType conjugateRotation = m_Rotation.conjugate(); // VnlQuaternionType inverseRotation = conjugateRotation.inverse(); // newMatrix = inverseRotation.rotation_matrix_transpose(); // this->SetVarInverseMatrix(newMatrix); // } // return this->GetVarInverseMatrix(); // } template typename ANTSAffine3DTransform::MatrixType ANTSAffine3DTransform:: ComputeMyRotationMatrix() { VnlQuaternionType conjugateRotation = m_Rotation.conjugate(); // this is done to compensate for the transposed representation // between VNL and ITK MatrixType R; R = conjugateRotation.rotation_matrix_transpose(); return R; } template void ANTSAffine3DTransform:: ComputeMatrix() { VnlQuaternionType conjugateRotation = m_Rotation.conjugate(); // this is done to compensate for the transposed representation // between VNL and ITK MatrixType R; R = conjugateRotation.rotation_matrix_transpose(); MatrixType S; S.Fill(NumericTraits< TScalarType >::Zero); S[0][0] = this->m_S1; S[1][1] = this->m_S2; S[2][2] = this->m_S3; MatrixType K; K.Fill(NumericTraits< TScalarType >::Zero); K[0][0] = NumericTraits< TScalarType >::One; K[0][1] = this->m_K1; K[0][2] = this->m_K2; K[1][1] = NumericTraits< TScalarType >::One; K[1][2] = this->m_K3; K[2][2] = NumericTraits< TScalarType >::One; MatrixType newMatrix; newMatrix = R * S * K; this->SetVarMatrix(newMatrix); } template void ANTSAffine3DTransform:: ComputeMatrixParameters() { // VnlQuaternionType quat(this->GetMatrix().GetVnlMatrix()); // m_Rotation = quat; // std::cout << "compute para: to be done!" << std::endl; // InternalMatrixType A, Q, R; typedef vnl_matrix TMatrix; TMatrix A,Q,R; A = this->GetMatrix().GetVnlMatrix(); vnl_qr myqr(A); R = myqr.Q(); // Q() is the rotation Q = myqr.R(); // R() is the upper triangluar // this is not necessary, for the mirror case // the scale factor could not negative // normalize Q // TMatrix dq(3,3,0); // for(unsigned i=0;i<3;i++){ // dq(i,i) = (Q(i,i)>=0)? 1 : -1; // } // R = R * dq; // Q = dq * Q; double tr = 1 + R(0,0)+R(1,1)+R(2,2); double s,r,u,v,w; if (tr > 0){ s = 0.5 / sqrt(tr); r = 0.25 / s; u = (R(2,1)-R(1,2))*s; v = (R(0,2)-R(2,0))*s; w = (R(1,0)-R(0,1))*s; } else if (R(0,0)>R(1,1) && R(0,0)>R(2,2)) { s = 2 * sqrt(1 + R(0,0) - R(1,1) - R(2,2)); u = 0.25 * s; v = (R(0,1)+R(1,0)) / s; w = (R(0,2)+R(2,0)) / s; r = (R(1,2)-R(2,1)) / s; } else if (R(0,0)>R(1,1)) { s = 2 * sqrt(1 + R(1,1) - R(0,0) - R(2,2)); u = (R(0,1)+R(1,0)) / s; v = 0.25 * s; w = (R(1,2)+R(2,1)) / s; r = (R(0,2)-R(2,0)) / s; } else { s = 2 * sqrt(1 + R(2,2) - R(0,0) - R(1,1)); u = (R(0,2)+R(2,0)) / s; v = (R(1,2)+R(2,1)) / s; w = 0.25 * s; r = (R(0,1)-R(1,0)) / s; } m_Rotation = VnlQuaternionType(u,v,w,r); m_S1 = Q(0,0); m_S2 = Q(1,1); m_S3 = Q(2,2); m_K1 = Q(0,1) / Q(0,0); m_K2 = Q(0,2) / Q(0,0); m_K3 = Q(1,2) / Q(1,1); // std::cout << "before: this->GetMatrix(): " << this->GetMatrix(); this->ComputeMatrix(); // std::cout << "after: this->GetMatrix(): " << this->GetMatrix(); // std::cout << "A=" << A << std::endl; // std::cout << "Q=" << Q << std::endl; // std::cout << "R=" << R << std::endl; // std::cout << "dq=" << dq << std::endl; } } // namespace #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkANTSCenteredAffine2DTransform.h000066400000000000000000000202261147325206600263720ustar00rootroot00000000000000#ifndef __itkANTSCenteredAffine2DTransform_h #define __itkANTSCenteredAffine2DTransform_h #include #include "itkMatrixOffsetTransformBase.h" #include "itkExceptionObject.h" namespace itk { template < class TScalarType=double > // Data type for scalars (float or double) //class ITK_EXPORT Rigid2DTransform : class ITK_EXPORT ANTSCenteredAffine2DTransform: public MatrixOffsetTransformBase< TScalarType, 2, 2> // Dimensions of input and output spaces { public: /** Standard class typedefs. */ // typedef Rigid2DTransform Self; typedef ANTSCenteredAffine2DTransform Self; typedef MatrixOffsetTransformBase< TScalarType, 2, 2 > Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Run-time type information (and related methods). */ // itkTypeMacro( Rigid2DTransform, MatrixOffsetTransformBase ); itkTypeMacro( ANTSCenteredAffine2DTransform, MatrixOffsetTransformBase ); /** New macro for creation of through a Smart Pointer */ itkNewMacro( Self ); /** Dimension of the space. */ itkStaticConstMacro(InputSpaceDimension, unsigned int, 2); itkStaticConstMacro(OutputSpaceDimension, unsigned int, 2); // itkStaticConstMacro(ParametersDimension, unsigned int, 3); itkStaticConstMacro(ParametersDimension, unsigned int, 8); /** Scalar type. */ typedef typename Superclass::ScalarType ScalarType; /** Parameters type. */ typedef typename Superclass::ParametersType ParametersType; /** Jacobian type. */ typedef typename Superclass::JacobianType JacobianType; /// Standard matrix type for this class typedef typename Superclass::MatrixType MatrixType; /// Standard vector type for this class typedef typename Superclass::OffsetType OffsetType; /// Standard vector type for this class typedef typename Superclass::InputVectorType InputVectorType; typedef typename Superclass::OutputVectorType OutputVectorType; /// Standard covariant vector type for this class typedef typename Superclass::InputCovariantVectorType InputCovariantVectorType; typedef typename Superclass::OutputCovariantVectorType OutputCovariantVectorType; /// Standard vnl_vector type for this class typedef typename Superclass::InputVnlVectorType InputVnlVectorType; typedef typename Superclass::OutputVnlVectorType OutputVnlVectorType; /// Standard coordinate point type for this class typedef typename Superclass::InputPointType InputPointType; typedef typename Superclass::OutputPointType OutputPointType; /** * Set the rotation Matrix of a Rigid2D Transform * * This method sets the 2x2 matrix representing the rotation * in the transform. The Matrix is expected to be orthogonal * with a certain tolerance. * * \warning This method will throw an exception is the matrix * provided as argument is not orthogonal. * * \sa MatrixOffsetTransformBase::SetMatrix() **/ // virtual void SetMatrix( const MatrixType & matrix ); /** * Set/Get the rotation matrix. These methods are old and are * retained for backward compatibility. Instead, use SetMatrix() * GetMatrix(). **/ // virtual void SetRotationMatrix(const MatrixType &matrix) // { this->SetMatrix( matrix ); } // const MatrixType & GetRotationMatrix() const // { return this->GetMatrix(); } /** * Compose the transformation with a translation * * This method modifies self to include a translation of the * origin. The translation is precomposed with self if pre is * true, and postcomposed otherwise. **/ // void Translate(const OffsetType &offset, bool pre=false); /** * Back transform by an rigid transformation. * * The BackTransform() methods are slated to be removed from ITK. * Instead, please use GetInverse() or CloneInverseTo() to generate * an inverse transform and then perform the transform using that * inverted transform. **/ inline InputPointType BackTransform(const OutputPointType &point ) const; inline InputVectorType BackTransform(const OutputVectorType &vector) const; inline InputVnlVectorType BackTransform(const OutputVnlVectorType &vector) const; inline InputCovariantVectorType BackTransform( const OutputCovariantVectorType &vector) const; /** Set/Get the angle of rotation in radians */ void SetAngle(TScalarType angle); void SetS1(TScalarType S1); void SetS2(TScalarType S2); void SetK(TScalarType K); itkGetConstReferenceMacro( Angle, TScalarType ); itkGetConstReferenceMacro( S1, TScalarType ); itkGetConstReferenceMacro( S2, TScalarType ); itkGetConstReferenceMacro( K, TScalarType ); /** Set the angle of rotation in degrees. */ void SetAngleInDegrees(TScalarType angle); /** Set/Get the angle of rotation in radians. These methods * are old and are retained for backward compatibility. * Instead, use SetAngle() and GetAngle(). */ // void SetRotation(TScalarType angle) // { this->SetAngle(angle); } // virtual const TScalarType & GetRotation() const // { return m_Angle; } /** Set the transformation from a container of parameters * This is typically used by optimizers. * There are 3 parameters. The first one represents the * angle of rotation in radians and the last two represents the translation. * The center of rotation is fixed. * * \sa Transform::SetParameters() * \sa Transform::SetFixedParameters() */ void SetParameters( const ParametersType & parameters ); /** Get the parameters that uniquely define the transform * This is typically used by optimizers. * There are 3 parameters. The first one represents the * angle or rotation in radians and the last two represents the translation. * The center of rotation is fixed. * * \sa Transform::GetParameters() * \sa Transform::GetFixedParameters() */ const ParametersType & GetParameters( void ) const; /** This method computes the Jacobian matrix of the transformation * at a given input point. * * \sa Transform::GetJacobian() */ const JacobianType & GetJacobian(const InputPointType &point ) const; /** * This method creates and returns a new ANTSCenteredAffine2DTransform object * which is the inverse of self. **/ // void CloneInverseTo( Pointer & newinverse ) const; /** * This method creates and returns a new ANTSCenteredAffine2DTransform object * which has the same parameters. **/ void CloneTo( Pointer & clone ) const; /** Reset the parameters to create and identity transform. */ virtual void SetIdentity(void); protected: // Rigid2DTransform(); ANTSCenteredAffine2DTransform(); // Rigid2DTransform( unsigned int outputSpaceDimension, // unsigned int parametersDimension); ANTSCenteredAffine2DTransform( unsigned int outputSpaceDimension, unsigned int parametersDimension); // ~Rigid2DTransform(); ~ANTSCenteredAffine2DTransform(); /** * Print contents of an ANTSCenteredAffine2DTransform **/ void PrintSelf(std::ostream &os, Indent indent) const; /** Compute the matrix from angle. This is used in Set methods * to update the underlying matrix whenever a transform parameter * is changed. */ virtual void ComputeMatrix(void); /** Compute the angle from the matrix. This is used to compute * transform parameters from a given matrix. This is used in * MatrixOffsetTransformBase::Compose() and * MatrixOffsetTransformBase::GetInverse(). */ virtual void ComputeMatrixParameters(void); /** Update angle without recomputation of other internal variables. */ void SetVarAngle( TScalarType angle ) { m_Angle = angle; } void SetVarS1( TScalarType S1) { m_S1 = S1; } void SetVarS2( TScalarType S2) { m_S2 = S2; } void SetVarK( TScalarType K) { m_K = K; } private: ANTSCenteredAffine2DTransform(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented TScalarType m_Angle; TScalarType m_S1; TScalarType m_S2; TScalarType m_K; }; //class ANTSCenteredAffine2DTransform } // namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkANTSCenteredAffine2DTransform.txx" #endif #endif /* __itkANTSCenteredAffine2DTransform_h */ ants-1.9.2+svn680.dfsg/ImageRegistration/itkANTSCenteredAffine2DTransform.txx000066400000000000000000000327251147325206600267750ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkANTSCenteredAffine2DTransform.txx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkANTSCenteredAffine2DTransform_txx #define _itkANTSCenteredAffine2DTransform_txx // #include "itkRigid2DTransform.h" #include "itkANTSCenteredAffine2DTransform.h" #include "vnl/algo/vnl_qr.h" namespace itk { // Constructor with default arguments // template // ANTSCenteredAffine2DTransform:: // ANTSCenteredAffine2DTransform(): // Superclass(OutputSpaceDimension, ParametersDimension) // { // m_Angle = NumericTraits< TScalarType >::Zero; // } template ANTSCenteredAffine2DTransform:: ANTSCenteredAffine2DTransform(): Superclass(OutputSpaceDimension, ParametersDimension) { m_Angle = NumericTraits< TScalarType >::Zero; m_S1 = NumericTraits< TScalarType >::One; m_S2 = NumericTraits< TScalarType >::One; m_K = NumericTraits< TScalarType >::Zero; } // Constructor with arguments template ANTSCenteredAffine2DTransform:: ANTSCenteredAffine2DTransform( unsigned int spaceDimension, unsigned int parametersDimension): Superclass(spaceDimension,parametersDimension) { m_Angle = NumericTraits< TScalarType >::Zero; m_S1 = NumericTraits< TScalarType >::One; m_S2 = NumericTraits< TScalarType >::One; m_K = NumericTraits< TScalarType >::Zero; } // Destructor template ANTSCenteredAffine2DTransform:: ~ANTSCenteredAffine2DTransform() { } // Print self template void ANTSCenteredAffine2DTransform:: PrintSelf(std::ostream &os, Indent indent) const { Superclass::PrintSelf(os,indent); os << indent << "Angle = " << m_Angle << std::endl; os << indent << "S1 = " << m_S1 << std::endl; os << indent << "S2 = " << m_S2 << std::endl; os << indent << "K = " << m_K << std::endl; // os << indent << "S2 = " << m_ << std::endl; } // Set the rotation matrix // template // void // ANTSCenteredAffine2DTransform:: // SetMatrix(const MatrixType & matrix ) // { // itkDebugMacro("setting m_Matrix to " << matrix ); // // The matrix must be orthogonal otherwise it is not // // representing a valid rotaion in 2D space // typename MatrixType::InternalMatrixType test = // matrix.GetVnlMatrix() * matrix.GetTranspose(); // const double tolerance = 1e-10; // if( !test.is_identity( tolerance ) ) // { // itk::ExceptionObject ex(__FILE__,__LINE__,"Attempt to set a Non-Orthogonal matrix",ITK_LOCATION); // throw ex; // } // this->SetVarMatrix( matrix ); // this->ComputeOffset(); // this->ComputeMatrixParameters(); // this->Modified(); // } /** Compute the Angle from the Rotation Matrix */ template void ANTSCenteredAffine2DTransform ::ComputeMatrixParameters( void ) { typedef vnl_matrix TMatrix; TMatrix A,Q,R; A = this->GetMatrix().GetVnlMatrix(); vnl_qr myqr(A); R = myqr.Q(); // Q() is the rotation Q = myqr.R(); // R() is the upper triangluar TMatrix dq(2,2,0); for(unsigned i=0;i<2;i++){ dq(i,i) = (Q(i,i)>=0)? 1 : -1; } R = R * dq; Q = dq * Q; // std::cout << "A=" << A << std::endl; // std::cout << "Q=" << Q << std::endl; // std::cout << "R=" << R << std::endl; // std::cout << "dq=" << dq << std::endl; m_Angle = vcl_acos(R[0][0]); if(this->GetMatrix()[1][0]<0.0) { m_Angle = -m_Angle; } m_S1 = Q[0][0]; m_S2 = Q[1][1]; m_K = Q[0][1] / Q[0][0]; this->ComputeMatrix(); if(this->GetMatrix()[1][0]-sin(m_Angle) > 0.000001) { itkWarningMacro("Bad Rotation Matrix " << this->GetMatrix() ); } } // // Compose with a translation // template // void // ANTSCenteredAffine2DTransform:: // Translate(const OffsetType &offset, bool) // { // OutputVectorType newOffset = this->GetOffset(); // newOffset += offset; // this->SetOffset(newOffset); // } // Create and return an inverse transformation // template // void // ANTSCenteredAffine2DTransform:: // CloneInverseTo( Pointer & result ) const // { // result = New(); // result->SetCenter( this->GetCenter() ); // inverse have the same center // result->SetAngle( -this->GetAngle() ); // result->SetTranslation( -( this->GetInverseMatrix() * this->GetTranslation() ) ); // } // Create and return a clone of the transformation template void ANTSCenteredAffine2DTransform:: CloneTo( Pointer & result ) const { result = New(); result->SetCenter( this->GetCenter() ); result->SetAngle( this->GetAngle() ); result->SetS1( this->GetS1() ); result->SetS2( this->GetS2() ); result->SetK( this->GetK() ); result->SetTranslation( this->GetTranslation() ); } // Reset the transform to an identity transform template void ANTSCenteredAffine2DTransform< TScalarType >:: SetIdentity( void ) { this->Superclass::SetIdentity(); m_Angle = NumericTraits< TScalarType >::Zero; m_S1 = NumericTraits< TScalarType >::One; m_S2 = NumericTraits< TScalarType >::One; m_K = NumericTraits< TScalarType >::Zero; } // Set the angle of rotation template void ANTSCenteredAffine2DTransform ::SetAngle(TScalarType angle) { m_Angle = angle; this->ComputeMatrix(); this->ComputeOffset(); this->Modified(); } template void ANTSCenteredAffine2DTransform ::SetS1(TScalarType S1) { m_S1 = S1; this->ComputeMatrix(); this->ComputeOffset(); this->Modified(); } template void ANTSCenteredAffine2DTransform ::SetS2(TScalarType S2) { m_S2 = S2; this->ComputeMatrix(); this->ComputeOffset(); this->Modified(); } template void ANTSCenteredAffine2DTransform ::SetK(TScalarType K) { m_K = K; this->ComputeMatrix(); this->ComputeOffset(); this->Modified(); } // Set the angle of rotation template void ANTSCenteredAffine2DTransform ::SetAngleInDegrees(TScalarType angle) { const TScalarType angleInRadians = angle * vcl_atan(1.0) / 45.0; this->SetAngle( angleInRadians ); } // Compute the matrix from the angle template void ANTSCenteredAffine2DTransform ::ComputeMatrix( void ) { const double ca = vcl_cos(m_Angle ); const double sa = vcl_sin(m_Angle ); const double s1 = m_S1; const double s2 = m_S2; const double k = m_K; MatrixType rotationMatrix; rotationMatrix[0][0]= ca; rotationMatrix[0][1]=-sa; rotationMatrix[1][0]= sa; rotationMatrix[1][1]= ca; MatrixType scaleMatrix; scaleMatrix[0][0]= s1; scaleMatrix[0][1]=0; scaleMatrix[1][0]= 0; scaleMatrix[1][1]= s2; MatrixType shearMatrix; shearMatrix[0][0]= 1; shearMatrix[0][1]=k; shearMatrix[1][0]= 0; shearMatrix[1][1]= 1; MatrixType varMatrix; varMatrix = rotationMatrix * scaleMatrix * shearMatrix; this->SetVarMatrix( varMatrix ); } // Set Parameters template void ANTSCenteredAffine2DTransform:: SetParameters( const ParametersType & parameters ) { itkDebugMacro( << "Setting parameters " << parameters ); // Set angle/s1/s2/k this->SetVarAngle( parameters[0] ); this->SetVarS1( parameters[1] ); this->SetVarS2( parameters[2] ); this->SetVarK( parameters[3] ); // Set the center InputPointType center; for(unsigned int i=0; i < OutputSpaceDimension; i++) { center[i] = parameters[i+4]; } this->SetVarCenter( center ); // Set translation OutputVectorType translation; for(unsigned int i=0; i < OutputSpaceDimension; i++) { translation[i] = parameters[i+6]; } this->SetVarTranslation( translation ); // Update matrix and offset this->ComputeMatrix(); this->ComputeOffset(); // Modified is always called since we just have a pointer to the // parameters and cannot know if the parameters have changed. this->Modified(); itkDebugMacro(<<"After setting parameters "); } // Get Parameters template const typename ANTSCenteredAffine2DTransform::ParametersType & ANTSCenteredAffine2DTransform:: GetParameters( void ) const { itkDebugMacro( << "Getting parameters "); // Get the angle/s1/s2/k this->m_Parameters[0] = this->GetAngle(); this->m_Parameters[1] = this->GetS1(); this->m_Parameters[2] = this->GetS2(); this->m_Parameters[3] = this->GetK(); // Get the center for(unsigned int i=0; i < OutputSpaceDimension; i++) { this->m_Parameters[i+4] = this->GetCenter()[i]; } // Get the translation for(unsigned int i=0; i < OutputSpaceDimension; i++) { this->m_Parameters[i+6] = this->GetTranslation()[i]; } itkDebugMacro(<<"After getting parameters " << this->m_Parameters ); return this->m_Parameters; } // Compute transformation Jacobian template const typename ANTSCenteredAffine2DTransform::JacobianType & ANTSCenteredAffine2DTransform:: GetJacobian( const InputPointType & p ) const { const double ca = vcl_cos(this->GetAngle() ); const double sa = vcl_sin(this->GetAngle() ); const double s1 = m_S1; const double s2 = m_S2; const double k = m_K; this->m_Jacobian.Fill(0.0); const double cx = this->GetCenter()[0]; const double cy = this->GetCenter()[1]; // derivatives with respect to the angle // this->m_Jacobian[0][0] = -sa * ( p[0] - cx ) - ca * ( p[1] - cy ); // this->m_Jacobian[1][0] = ca * ( p[0] - cx ) - sa * ( p[1] - cy ); double pxoff = (p[0]-cx)+k*(p[1]-cy); double pyoff = p[1]-cy; // wrt. theta this->m_Jacobian[0][0] = s1*( pxoff )*(-sa) + s2*( pyoff )*(-ca); this->m_Jacobian[1][0] = s1*( pxoff )*(ca) + s2*( pyoff )*(-sa); // wrt. s1/s2 this->m_Jacobian[0][1] = ca * pxoff; this->m_Jacobian[0][2] = -sa * pyoff; this->m_Jacobian[1][1] = sa * pxoff; this->m_Jacobian[1][2] = ca * pyoff; // wrt. k this->m_Jacobian[0][3] = ca * s1 * pyoff; this->m_Jacobian[1][3] = sa * s1 * pyoff; // wrt. cx/cy this->m_Jacobian[0][4] = - s1*ca + 1.0; this->m_Jacobian[0][5] = -(k*s1*ca - s2*sa); this->m_Jacobian[1][4] = - s1*sa; this->m_Jacobian[1][5] = -(k*s1*sa + s2*ca) + 1.0; // wrt. t1/t2 this->m_Jacobian[0][6] = 1.0; this->m_Jacobian[1][7] = 1.0; // compute derivatives for the translation part // unsigned int blockOffset = 1; // for(unsigned int dim=0; dim < OutputSpaceDimension; dim++ ) // { // this->m_Jacobian[ dim ][ blockOffset + dim ] = 1.0; // } return this->m_Jacobian; } // Back transform a point template typename ANTSCenteredAffine2DTransform::InputPointType ANTSCenteredAffine2DTransform:: BackTransform(const OutputPointType &point) const { itkWarningMacro(<<"BackTransform(): This method is slated to be removed from ITK. Instead, please use GetInverse() to generate an inverse transform and then perform the transform using that inverted transform."); return this->GetInverseMatrix() * (point - this->GetOffset()); } // Back transform a vector template typename ANTSCenteredAffine2DTransform::InputVectorType ANTSCenteredAffine2DTransform:: BackTransform(const OutputVectorType &vect ) const { itkWarningMacro(<<"BackTransform(): This method is slated to be removed from ITK. Instead, please use GetInverse() to generate an inverse transform and then perform the transform using that inverted transform."); return this->GetInverseMatrix() * vect; } // Back transform a vnl_vector template typename ANTSCenteredAffine2DTransform::InputVnlVectorType ANTSCenteredAffine2DTransform:: BackTransform(const OutputVnlVectorType &vect ) const { itkWarningMacro(<<"BackTransform(): This method is slated to be removed from ITK. Instead, please use GetInverse() to generate an inverse transform and then perform the transform using that inverted transform."); return this->GetInverseMatrix() * vect; } // Back Transform a CovariantVector template typename ANTSCenteredAffine2DTransform::InputCovariantVectorType ANTSCenteredAffine2DTransform:: BackTransform(const OutputCovariantVectorType &vect) const { itkWarningMacro(<<"BackTransform(): This method is slated to be removed from ITK. Instead, please use GetInverse() to generate an inverse transform and then perform the transform using that inverted transform."); return this->GetMatrix() * vect; } } // namespace #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkANTSImageRegistrationOptimizer.cxx000066400000000000000000003272211147325206600273460ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkANTSImageRegistrationOptimizer.cxx,v $ Language: C++ Date: $Date: 2009/04/22 01:00:16 $ Version: $Revision: 1.47 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkANTSImageRegistrationOptimizer_txx_ #define _itkANTSImageRegistrationOptimizer_txx_ // disable debug warnings in MS compiler #ifdef _MSC_VER #pragma warning(disable: 4786) #endif #include "itkVectorParameterizedNeighborhoodOperatorImageFilter.h" #include "itkANTSImageRegistrationOptimizer.h" #include "itkIdentityTransform.h" #include "itkLinearInterpolateImageFunction.h" #include "itkRecursiveGaussianImageFilter.h" #include "itkVectorGaussianInterpolateImageFunction.h" #include "itkResampleImageFilter.h" #include "itkVectorNeighborhoodOperatorImageFilter.h" #include "vnl/vnl_math.h" #include "ANTS_affine_registration2.h" #include "itkWarpImageMultiTransformFilter.h" #include "itkVectorImageFileWriter.h" namespace itk { template ANTSImageRegistrationOptimizer ::ANTSImageRegistrationOptimizer() { this->m_DeformationField=NULL; this->m_InverseDeformationField=NULL; this->m_AffineTransform=NULL; itk::TransformFactory::RegisterTransform(); itk::TransformFactory >::RegisterTransform(); itk::TransformFactory >::RegisterTransform(); this->m_FixedPointSet=NULL; this->m_MovingPointSet=NULL; this->m_UseMulti=true; this->m_UseROI=false; this->m_MaskImage=NULL; this->m_ScaleFactor=1.0; this->m_Debug=false; this->m_SyNF=NULL; this->m_SyNFInv=NULL; this->m_SyNM=NULL; this->m_SyNMInv=NULL; this->m_Parser=NULL; this->m_GaussianTruncation=256; this->m_TimeVaryingVelocity=NULL; this->m_LastTimeVaryingVelocity=NULL; this->m_LastTimeVaryingUpdate=NULL; this->m_DeltaTime=0.1; this->m_SyNType=0; this->m_UseNN=false; this->m_UseBSplineInterpolation=false; this->m_VelocityFieldInterpolator=VelocityFieldInterpolatorType::New(); this->m_HitImage=NULL; this->m_ThickImage=NULL; this->m_SyNFullTime=0; } template typename ANTSImageRegistrationOptimizer::ImagePointer ANTSImageRegistrationOptimizer ::SubsampleImage( ImagePointer image, RealType scalingFactor , typename ImageType::PointType outputOrigin, typename ImageType::DirectionType outputDirection , AffineTransformPointer aff ) { typename ImageType::SpacingType inputSpacing = image->GetSpacing(); typename ImageType::RegionType::SizeType inputSize = image->GetRequestedRegion().GetSize(); typename ImageType::SpacingType outputSpacing=this->m_CurrentDomainSpacing; typename ImageType::RegionType::SizeType outputSize=this->m_CurrentDomainSize; // RealType minimumSpacing = inputSpacing.GetVnlVector().min_value(); // RealType maximumSpacing = inputSpacing.GetVnlVector().max_value(); typedef ResampleImageFilter ResamplerType; typename ResamplerType::Pointer resampler = ResamplerType::New(); typedef LinearInterpolateImageFunction InterpolatorType; typename InterpolatorType::Pointer interpolator = InterpolatorType::New(); interpolator->SetInputImage( image ); resampler->SetInterpolator( interpolator ); typedef itk::IdentityTransform< double, TDimension > TransformType; typename TransformType::Pointer transform = TransformType::New(); transform->SetIdentity(); resampler->SetTransform( transform ); if ( aff ) { // std::cout << " Setting Aff to " << this->m_AffineTransform << std::endl; resampler->SetTransform( aff ); } resampler->SetInput( image ); resampler->SetOutputSpacing( outputSpacing ); resampler->SetOutputOrigin( outputOrigin ); resampler->SetOutputDirection( outputDirection ); resampler->SetSize( outputSize ); resampler->Update(); ImagePointer outimage = resampler->GetOutput(); if (this->m_UseROI ) { outimage=this->MakeSubImage(outimage); // WriteImage(outimage,"temps.hdr"); // warp with affine & deformable } return outimage; } template typename ANTSImageRegistrationOptimizer::DeformationFieldPointer ANTSImageRegistrationOptimizer ::CopyDeformationField( DeformationFieldPointer input ) { DeformationFieldPointer output=DeformationFieldType::New(); output->SetSpacing( input->GetSpacing() ); output->SetOrigin( input->GetOrigin() ); output->SetDirection( input->GetDirection() ); output->SetLargestPossibleRegion(input->GetLargestPossibleRegion() ); output->SetRequestedRegion(input->GetLargestPossibleRegion() ); output->SetBufferedRegion( input->GetLargestPossibleRegion() ); output->Allocate(); typedef ImageRegionIterator Iterator; Iterator inIter( input, input->GetBufferedRegion() ); Iterator outIter( output, output->GetBufferedRegion() ); inIter.GoToBegin(); outIter.GoToBegin(); for( ; !inIter.IsAtEnd(); ++inIter, ++outIter ) { outIter.Set( inIter.Get() ); } output->SetSpacing( input->GetSpacing()); output->SetOrigin(input->GetOrigin()); return output; } template void ANTSImageRegistrationOptimizer ::SmoothDeformationFieldGauss(DeformationFieldPointer field, float sig, bool useparamimage, unsigned int lodim) { if (this->m_Debug ) std::cout << " enter gauss smooth " << sig << std::endl; if (sig <= 0) return; if (!field) { std::cout << " No Field in gauss Smoother " << std::endl; return; } DeformationFieldPointer tempField=DeformationFieldType::New(); tempField->SetSpacing( field->GetSpacing() ); tempField->SetOrigin( field->GetOrigin() ); tempField->SetDirection( field->GetDirection() ); tempField->SetLargestPossibleRegion( field->GetLargestPossibleRegion() ); tempField->SetRequestedRegion( field->GetRequestedRegion() ); tempField->SetBufferedRegion( field->GetBufferedRegion() ); tempField->Allocate(); typedef typename DeformationFieldType::PixelType VectorType; typedef typename VectorType::ValueType ScalarType; typedef GaussianOperator OperatorType; // typedef VectorNeighborhoodOperatorImageFilter< DeformationFieldType, DeformationFieldType> SmootherType; typedef VectorParameterizedNeighborhoodOperatorImageFilter< DeformationFieldType, DeformationFieldType, ImageType> SmootherType; OperatorType * oper = new OperatorType; typename SmootherType::Pointer smoother = SmootherType::New(); typedef typename DeformationFieldType::PixelContainerPointer PixelContainerPointer; PixelContainerPointer swapPtr; // graft the output field onto the mini-pipeline smoother->GraftOutput( tempField ); typename ImageType::SpacingType spacing=field->GetSpacing(); for( unsigned int j = 0; j < lodim; j++ ) { // smooth along this dimension oper->SetDirection( j ); float sigt=sig; oper->SetVariance( sigt ); oper->SetMaximumError(0.001 ); oper->SetMaximumKernelWidth( (unsigned int) this->m_GaussianTruncation ); oper->CreateDirectional(); // todo: make sure we only smooth within the buffered region smoother->SetOperator( *oper ); smoother->SetInput( field ); smoother->Update(); if ( j < lodim - 1 ) { // swap the containers swapPtr = smoother->GetOutput()->GetPixelContainer(); smoother->GraftOutput( field ); field->SetPixelContainer( swapPtr ); smoother->Modified(); } } // graft the output back to this filter tempField->SetPixelContainer( field->GetPixelContainer() ); //make sure boundary does not move float weight=1.0; if (sig < 0.5) weight=1.0-1.0*(sig/0.5); float weight2=1.0-weight; typedef itk::ImageRegionIteratorWithIndex Iterator; typename DeformationFieldType::SizeType size = field->GetLargestPossibleRegion().GetSize(); Iterator outIter( field, field->GetLargestPossibleRegion() ); for( outIter.GoToBegin(); !outIter.IsAtEnd(); ++outIter ) { bool onboundary=false; typename DeformationFieldType::IndexType index= outIter.GetIndex(); for (int i=0;i= static_cast( size[i] )-1 ) onboundary=true; } if (onboundary) { VectorType vec; vec.Fill(0.0); outIter.Set(vec); } else { //field=this->CopyDeformationField( VectorType svec=smoother->GetOutput()->GetPixel(index); outIter.Set( svec*weight+outIter.Get()*weight2); } } if (this->m_Debug ) std::cout << " done gauss smooth " << std::endl; delete oper; } template void ANTSImageRegistrationOptimizer ::SmoothVelocityGauss(TimeVaryingVelocityFieldPointer field, float sig , unsigned int lodim) { if (sig <= 0) return; if (!field) { std::cout << " No Field in gauss Smoother " << std::endl; return; } TimeVaryingVelocityFieldPointer tempField=TimeVaryingVelocityFieldType::New(); tempField->SetSpacing( field->GetSpacing() ); tempField->SetOrigin( field->GetOrigin() ); tempField->SetDirection( field->GetDirection() ); tempField->SetLargestPossibleRegion( field->GetLargestPossibleRegion() ); tempField->SetRequestedRegion( field->GetRequestedRegion() ); tempField->SetBufferedRegion( field->GetBufferedRegion() ); tempField->Allocate(); typedef typename TimeVaryingVelocityFieldType::PixelType VectorType; typedef typename VectorType::ValueType ScalarType; typedef GaussianOperator OperatorType; typedef VectorNeighborhoodOperatorImageFilter SmootherType; OperatorType * oper = new OperatorType; typename SmootherType::Pointer smoother = SmootherType::New(); typedef typename TimeVaryingVelocityFieldType::PixelContainerPointer PixelContainerPointer; PixelContainerPointer swapPtr; // graft the output field onto the mini-pipeline smoother->GraftOutput( tempField ); for( unsigned int j = 0; j < lodim; j++ ) { // smooth along this dimension oper->SetDirection( j ); oper->SetVariance( sig ); oper->SetMaximumError(0.001 ); oper->SetMaximumKernelWidth( (unsigned int) this->m_GaussianTruncation ); oper->CreateDirectional(); // todo: make sure we only smooth within the buffered region smoother->SetOperator( *oper ); smoother->SetInput( field ); smoother->Update(); if ( j < lodim - 1 ) { // swap the containers swapPtr = smoother->GetOutput()->GetPixelContainer(); smoother->GraftOutput( field ); field->SetPixelContainer( swapPtr ); smoother->Modified(); } } // graft the output back to this filter tempField->SetPixelContainer( field->GetPixelContainer() ); //make sure boundary does not move float weight=1.0; if (sig < 0.5) weight=1.0-1.0*(sig/0.5); float weight2=1.0-weight; typedef itk::ImageRegionIteratorWithIndex Iterator; typename TimeVaryingVelocityFieldType::SizeType size = field->GetLargestPossibleRegion().GetSize(); Iterator outIter( field, field->GetLargestPossibleRegion() ); for( outIter.GoToBegin(); !outIter.IsAtEnd(); ++outIter ) { bool onboundary=false; typename TimeVaryingVelocityFieldType::IndexType index= outIter.GetIndex(); for (int i=0;i= static_cast( size[i] )-1 ) onboundary=true; } if (onboundary) { VectorType vec; vec.Fill(0.0); outIter.Set(vec); } else { //field=this->CopyDeformationField( VectorType svec=smoother->GetOutput()->GetPixel(index); outIter.Set( svec*weight+outIter.Get()*weight2); } } if (this->m_Debug ) std::cout << " done gauss smooth " << std::endl; delete oper; } template void ANTSImageRegistrationOptimizer ::SmoothDeformationFieldBSpline( DeformationFieldPointer field, ArrayType meshsize, unsigned int splineorder, unsigned int numberoflevels ) { if (this->m_Debug ) std::cout << " enter bspline smooth " << std::endl; if (!field) { std::cout << " No Field in bspline Smoother " << std::endl; return; } if ( splineorder <= 0 ) { return; } typename BSplineFilterType::ArrayType numberofcontrolpoints; for ( unsigned int d = 0; d < ImageDimension; d++ ) { if ( meshsize[d] <= 0 ) { return; } numberofcontrolpoints[d] = static_cast( meshsize[d] ) + splineorder; } VectorType zeroVector; zeroVector.Fill( 0.0 ); // typedef VectorImageFileWriter // DeformationFieldWriterType; // typename DeformationFieldWriterType::Pointer writer = DeformationFieldWriterType::New(); // writer->SetInput( field ); // writer->SetFileName( "field.nii.gz" ); // writer->Update(); // exit( 0 ); typename ImageType::DirectionType originalDirection = field->GetDirection(); typename ImageType::DirectionType identityDirection; identityDirection.SetIdentity(); field->SetDirection( identityDirection ); typename BSplineFilterType::Pointer bspliner = BSplineFilterType::New(); bspliner->SetInput( field ); bspliner->SetNumberOfLevels( numberoflevels ); bspliner->SetSplineOrder( splineorder ); bspliner->SetNumberOfControlPoints( numberofcontrolpoints ); bspliner->SetIgnorePixelValue( zeroVector ); bspliner->Update(); field->SetDirection( originalDirection ); //make sure boundary does not move typedef itk::ImageRegionIteratorWithIndex Iterator; typename DeformationFieldType::SizeType size = field->GetLargestPossibleRegion().GetSize(); Iterator bIter( bspliner->GetOutput(), bspliner->GetOutput()->GetLargestPossibleRegion() ); Iterator outIter( field, field->GetLargestPossibleRegion() ); for( outIter.GoToBegin(), bIter.GoToBegin(); !outIter.IsAtEnd(); ++outIter, ++bIter ) { // bool onboundary=false; // typename DeformationFieldType::IndexType index = outIter.GetIndex(); // for( int i = 0; i < ImageDimension; i++ ) // { // if ( index[i] < 1 || index[i] >= static_cast( size[i] )-1 ) // onboundary = true; // } // if (onboundary) // { // VectorType vec; // vec.Fill(0.0); // outIter.Set(vec); // } // else // { outIter.Set( bIter.Get() ); // } } if (this->m_Debug ) std::cout << " done bspline smooth " << std::endl; } template void ANTSImageRegistrationOptimizer ::ComposeDiffs(DeformationFieldPointer fieldtowarpby, DeformationFieldPointer field, DeformationFieldPointer fieldout, float timesign) { typedef Point VPointType; // field->SetSpacing( fieldtowarpby->GetSpacing() ); // field->SetOrigin( fieldtowarpby->GetOrigin() ); // field->SetDirection( fieldtowarpby->GetDirection() ); if (!fieldout) { fieldout=DeformationFieldType::New(); fieldout->SetSpacing( fieldtowarpby->GetSpacing() ); fieldout->SetOrigin( fieldtowarpby->GetOrigin() ); fieldout->SetDirection( fieldtowarpby->GetDirection() ); fieldout->SetLargestPossibleRegion(fieldtowarpby->GetLargestPossibleRegion() ); fieldout->SetRequestedRegion( fieldtowarpby->GetLargestPossibleRegion() ); fieldout->SetBufferedRegion( fieldtowarpby->GetLargestPossibleRegion() ); fieldout->Allocate(); VectorType zero; zero.Fill(0); fieldout->FillBuffer(zero); } typedef typename DeformationFieldType::PixelType VectorType; typedef itk::WarpImageFilter WarperType; typedef DeformationFieldType FieldType; enum { ImageDimension = FieldType::ImageDimension }; typedef itk::ImageRegionIteratorWithIndex FieldIterator; typedef ImageType FloatImageType; typedef itk::ImageFileWriter writertype; typename ImageType::SpacingType oldspace = field->GetSpacing(); typename ImageType::SpacingType newspace = fieldtowarpby->GetSpacing(); typedef typename DeformationFieldType::IndexType IndexType; typedef typename DeformationFieldType::PointType PointType; typedef itk::VectorLinearInterpolateImageFunction DefaultInterpolatorType; typedef itk::VectorGaussianInterpolateImageFunction DefaultInterpolatorType2; typename DefaultInterpolatorType::Pointer vinterp = DefaultInterpolatorType::New(); vinterp->SetInputImage(field); // vinterp->SetParameters(NULL,1); VPointType pointIn1; VPointType pointIn2; typename DefaultInterpolatorType::ContinuousIndexType contind; // married to pointIn2 VPointType pointIn3; unsigned int ct=0; // iterate through fieldtowarpby finding the points that it maps to via field. // then take the difference from the original point and put it in the output field. // std::cout << " begin iteration " << std::endl; FieldIterator m_FieldIter( fieldtowarpby, fieldtowarpby->GetLargestPossibleRegion()); for( m_FieldIter.GoToBegin(); !m_FieldIter.IsAtEnd(); ++m_FieldIter ) { IndexType index = m_FieldIter.GetIndex(); bool dosample = true; // if (sub && m_FloatImage->GetPixel(index) < 0.5) dosample=false; if (dosample) { fieldtowarpby->TransformIndexToPhysicalPoint( index, pointIn1 ); VectorType disp=m_FieldIter.Get(); for (int jj=0; jjIsInsideBuffer(pointIn2)) disp2 = vinterp->Evaluate( pointIn2 ); else disp2.Fill(0); for (int jj=0; jjSetPixel(m_FieldIter.GetIndex(),out); ct++; }//endif }//end iteration } template typename ANTSImageRegistrationOptimizer::DeformationFieldPointer ANTSImageRegistrationOptimizer ::IntegrateConstantVelocity(DeformationFieldPointer totalField, unsigned int ntimesteps, float timestep) { VectorType zero; zero.Fill(0); DeformationFieldPointer diffmap=DeformationFieldType::New(); diffmap->SetSpacing( totalField->GetSpacing() ); diffmap->SetOrigin( totalField->GetOrigin() ); diffmap->SetDirection( totalField->GetDirection() ); diffmap->SetLargestPossibleRegion(totalField->GetLargestPossibleRegion() ); diffmap->SetRequestedRegion( totalField->GetLargestPossibleRegion() ); diffmap->SetBufferedRegion( totalField->GetLargestPossibleRegion() ); diffmap->Allocate(); diffmap->FillBuffer(zero); for (unsigned int nts=0; ntsComposeDiffs(diffmap,totalField,diffmap, timestep); } return diffmap; } template typename ANTSImageRegistrationOptimizer::DeformationFieldPointer ANTSImageRegistrationOptimizer ::ComputeUpdateField(DeformationFieldPointer fixedwarp, DeformationFieldPointer movingwarp , PointSetPointer fpoints, PointSetPointer wpoints, DeformationFieldPointer totalUpdateInvField, bool updateenergy) { ImagePointer mask=NULL; if ( movingwarp && this->m_MaskImage && !this->m_ComputeThickness ) mask= this->WarpMultiTransform( this->m_MaskImage, this->m_MaskImage, NULL, movingwarp, false , this->m_FixedImageAffineTransform ); else if (this->m_MaskImage && !this->m_ComputeThickness ) mask=this->SubsampleImage( this->m_MaskImage, this->m_ScaleFactor , this->m_MaskImage->GetOrigin() , this->m_MaskImage->GetDirection() , NULL); if ( !fixedwarp) {std::cout<< " NO F WARP " << std::endl; fixedwarp=this->m_DeformationField; } //if ( !movingwarp) std::cout<< " NO M WARP " << std::endl; /// std::cout << " get upd field " << std::endl; typename ImageType::SpacingType spacing=fixedwarp->GetSpacing(); VectorType zero; zero.Fill(0); DeformationFieldPointer updateField=NULL,totalUpdateField=NULL,updateFieldInv=NULL; totalUpdateField=DeformationFieldType::New(); totalUpdateField->SetSpacing( fixedwarp->GetSpacing() ); totalUpdateField->SetOrigin( fixedwarp->GetOrigin() ); totalUpdateField->SetDirection( fixedwarp->GetDirection() ); totalUpdateField->SetLargestPossibleRegion(fixedwarp->GetLargestPossibleRegion() ); totalUpdateField->SetRequestedRegion( fixedwarp->GetLargestPossibleRegion() ); totalUpdateField->SetBufferedRegion( fixedwarp->GetLargestPossibleRegion() ); totalUpdateField->Allocate(); totalUpdateField->FillBuffer(zero); // bool hadpointsetmetric=false; RealType sumWeights = 0.0; for( unsigned int n = 0; n < this->m_SimilarityMetrics.size(); n++ ) { sumWeights += this->m_SimilarityMetrics[n]->GetWeightScalar(); } sumWeights=1; for ( unsigned int metricCount = 0; metricCount < this->m_SimilarityMetrics.size(); metricCount++ ) { bool ispointsetmetric=false; /** build an update field */ if ( this->m_SimilarityMetrics.size() == 1 ) { updateField=totalUpdateField; if (totalUpdateInvField) updateFieldInv=totalUpdateInvField; } else { updateField=DeformationFieldType::New(); updateField->SetSpacing( fixedwarp->GetSpacing() ); updateField->SetOrigin( fixedwarp->GetOrigin() ); updateField->SetDirection( fixedwarp->GetDirection() ); updateField->SetLargestPossibleRegion(fixedwarp->GetLargestPossibleRegion() ); updateField->SetRequestedRegion( fixedwarp->GetLargestPossibleRegion() ); updateField->SetBufferedRegion( fixedwarp->GetLargestPossibleRegion() ); updateField->Allocate(); updateField->FillBuffer(zero); if (totalUpdateInvField){ updateFieldInv=DeformationFieldType::New(); updateFieldInv->SetSpacing( fixedwarp->GetSpacing() ); updateFieldInv->SetOrigin( fixedwarp->GetOrigin() ); updateFieldInv->SetDirection( fixedwarp->GetDirection() ); updateFieldInv->SetLargestPossibleRegion(fixedwarp->GetLargestPossibleRegion() ); updateFieldInv->SetRequestedRegion( fixedwarp->GetLargestPossibleRegion() ); updateFieldInv->SetBufferedRegion( fixedwarp->GetLargestPossibleRegion() ); updateFieldInv->Allocate(); updateFieldInv->FillBuffer(zero); } } /** get the update */ typedef DeformationFieldType DeformationFieldType; typedef typename FiniteDifferenceFunctionType::NeighborhoodType NeighborhoodIteratorType; typedef ImageRegionIterator UpdateIteratorType; // TimeStepType timeStep; void *globalData; // std::cout << " B " << std::endl; AffineTransformPointer faffinverse=NULL; if (this->m_FixedImageAffineTransform ){ faffinverse=AffineTransformType::New(); this->m_FixedImageAffineTransform->GetInverse(faffinverse); } AffineTransformPointer affinverse=NULL; if (this->m_AffineTransform ){ affinverse=AffineTransformType::New(); this->m_AffineTransform->GetInverse(affinverse); } // for each metric, warp the assoc. Images /** We loop Over This To Do MultiVariate */ /** FIXME really should pass an image list and then warp each one in turn then expand the update field to fit size of total deformation */ ImagePointer wmimage=NULL; if ( fixedwarp) wmimage= this->WarpMultiTransform( this->m_SmoothFixedImages[metricCount],this->m_SmoothMovingImages[metricCount], this->m_AffineTransform, fixedwarp, false , NULL ); else wmimage=this->SubsampleImage( this->m_SmoothMovingImages[metricCount] , this->m_ScaleFactor , this->m_SmoothMovingImages[metricCount]->GetOrigin() , this->m_SmoothMovingImages[metricCount]->GetDirection() , NULL); // std::cout << " C " << std::endl; ImagePointer wfimage=NULL; if ( movingwarp) wfimage= this->WarpMultiTransform( this->m_SmoothFixedImages[metricCount], this->m_SmoothFixedImages[metricCount], NULL, movingwarp, false , this->m_FixedImageAffineTransform ); else wfimage=this->SubsampleImage( this->m_SmoothFixedImages[metricCount] , this->m_ScaleFactor , this->m_SmoothFixedImages[metricCount]->GetOrigin() , this->m_SmoothFixedImages[metricCount]->GetDirection() , NULL); /* if (this->m_TimeVaryingVelocity && ! this->m_MaskImage ) { std::string outname=this->localANTSGetFilePrefix(this->m_OutputNamingConvention.c_str())+std::string("thick.nii.gz"); /// WriteImage(wmimage,outname.c_str()); outname=this->localANTSGetFilePrefix(this->m_OutputNamingConvention.c_str())+std::string("thick2.nii.gz"); WriteImage(wfimage,outname.c_str()); } */ // std::string outname=this->localANTSGetFilePrefix(this->m_OutputNamingConvention.c_str())+std::string("temp.nii.gz"); // WriteImage(wmimage,outname.c_str()); // std::string outname2=this->localANTSGetFilePrefix(this->m_OutputNamingConvention.c_str())+std::string("temp2.nii.gz"); // WriteImage(wfimage,outname2.c_str()); /** MV Loop END -- Would have to collect update fields then add them * together somehow -- Would also have to eliminate the similarity * metric loop within ComputeUpdateField */ // Get the FiniteDifferenceFunction to use in calculations. MetricBaseTypePointer df = this->m_SimilarityMetrics[metricCount]->GetMetric(); df->SetFixedImage(wfimage); df->SetMovingImage(wmimage); if (df->ThisIsAPointSetMetric()) ispointsetmetric=true; if (fpoints && ispointsetmetric ) df->SetFixedPointSet(fpoints); else if (ispointsetmetric ) std::cout << "NO POINTS!! " << std::endl; if (wpoints && ispointsetmetric ) df->SetMovingPointSet(wpoints); else if (ispointsetmetric ) std::cout << "NO POINTS!! " << std::endl; typename ImageType::SizeType radius = df->GetRadius(); df->InitializeIteration(); typename DeformationFieldType::Pointer output = updateField; typedef NeighborhoodAlgorithm::ImageBoundaryFacesCalculator FaceCalculatorType; typedef typename FaceCalculatorType::FaceListType FaceListType; FaceCalculatorType faceCalculator; FaceListType faceList = faceCalculator(updateField, updateField->GetLargestPossibleRegion(), radius); typename FaceListType::iterator fIt = faceList.begin(); globalData = df->GetGlobalDataPointer(); // Process the non-boundary region. NeighborhoodIteratorType nD(radius, updateField, *fIt); UpdateIteratorType nU(updateField, *fIt); nD.GoToBegin(); nU.GoToBegin(); while( !nD.IsAtEnd() ) { bool oktosample=true; float maskprob=1.0; if (mask) { maskprob=mask->GetPixel( nD.GetIndex() ); if (maskprob > 1.0) maskprob=1.0; if ( maskprob < 0.1) oktosample=false; } if ( oktosample ) { VectorType temp=df->ComputeUpdate(nD, globalData)*maskprob; nU.Value() += temp; if (totalUpdateInvField) { typename ImageType::IndexType index=nD.GetIndex(); temp = df->ComputeUpdateInv(nD, globalData)*maskprob+updateFieldInv->GetPixel(index); updateFieldInv->SetPixel(index,temp); }// else nU.Value() -= df->ComputeUpdateInv(nD, globalData)*maskprob; ++nD; ++nU; } else { ++nD; ++nU; } } // begin restriction of deformation field bool restrict=false; for (unsigned int jj=0; jjm_RestrictDeformation.size(); jj++ ) { float temp=this->m_RestrictDeformation[jj]; if ( fabs( temp - 1 ) > 1.e-5 ) restrict=true; } if (restrict && this->m_RestrictDeformation.size() == ImageDimension ) { nU.GoToBegin(); while( !nU.IsAtEnd() ) { for (unsigned int jj=0; jjm_RestrictDeformation.size(); jj++ ) { typename ImageType::IndexType index=nU.GetIndex(); VectorType temp = updateField->GetPixel(index); temp[jj]*=this->m_RestrictDeformation[jj]; updateField->SetPixel(index,temp); if (updateFieldInv ) { temp = updateFieldInv->GetPixel(index); temp[jj]*=this->m_RestrictDeformation[jj]; updateFieldInv->SetPixel(index,temp); } } ++nU; } } // end restrict deformation field if (updateenergy){ this->m_LastEnergy[metricCount]=this->m_Energy[metricCount]; this->m_Energy[metricCount]=df->GetEnergy();// *this->m_SimilarityMetrics[metricCount]->GetWeightScalar()/sumWeights; } // smooth the fields //if (!ispointsetmetric || ImageDimension == 2 ){ this->SmoothDeformationField(updateField,true); if (updateFieldInv) this->SmoothDeformationField(updateFieldInv,true); ///} /* else // use another strategy -- exact lm? / something like Laplacian { float tmag=0; for (unsigned int ff=0; ff<5; ff++) { tmag=0; this->SmoothDeformationField(updateField,true); if (updateFieldInv) this->SmoothDeformationField(updateFieldInv,true); nD.GoToBegin(); nU.GoToBegin(); while( !nD.IsAtEnd() ) { typename ImageType::IndexType index=nD.GetIndex(); bool oktosample=true; float maskprob=1.0; if (mask) { maskprob=mask->GetPixel( nD.GetIndex() ); if (maskprob > 1.0) maskprob=1.0; if ( maskprob < 0.1) oktosample=false; } VectorType F1; F1.Fill(0); VectorType F2; F2.Fill(0); if ( oktosample ) { F1 = df->ComputeUpdate(nD, globalData)*maskprob; if (totalUpdateInvField) { F2 = df->ComputeUpdateInv(nD, globalData)*maskprob; } ++nD; ++nU; } else { ++nD; ++nU; } // compute mags of F1 and F2 -- if large enough, reset them float f1mag=0,f2mag=0,umag=0; for (unsigned int dim=0; dimGetPixel(index)[dim]/spacing[dim]*updateField->GetPixel(index)[dim]/spacing[dim]; } f1mag=sqrt(f1mag); f2mag=sqrt(f2mag); umag=sqrt(umag); if ( f1mag > 0.05 ) updateField->SetPixel(index,F1); if ( f2mag > 0.05 ) updateFieldInv->SetPixel(index,F2); tmag+=umag; } // std::cout << " total mag " << tmag << std::endl; } //smooth the total field this->SmoothDeformationField(updateField,true); if (updateFieldInv) this->SmoothDeformationField(updateFieldInv,true); } */ //normalize update field then add to total field typedef ImageRegionIteratorWithIndex Iterator; Iterator dIter(totalUpdateField,totalUpdateField->GetLargestPossibleRegion() ); float mag=0.0; float max=0.0; unsigned long ct=0; float total=0; for( dIter.GoToBegin(); !dIter.IsAtEnd(); ++dIter ) { typename ImageType::IndexType index=dIter.GetIndex(); VectorType vec=updateField->GetPixel(index); mag=0; for (unsigned int jj=0; jj 0. ) std::cout << " mag " << mag << " max " << max << " vec " << vec << std::endl; if (mag > max) max=mag; ct++; total+=mag; // std::cout << " mag " << mag << std::endl; } if (this->m_Debug) std::cout << "PRE MAX " << max << std::endl; float max2=0; if (max <= 0) max=1; for( dIter.GoToBegin(); !dIter.IsAtEnd(); ++dIter ) { typename ImageType::IndexType index=dIter.GetIndex(); VectorType vec=updateField->GetPixel(index); vec=vec/max; mag=0; for (unsigned int jj=0; jj max2 ) max2=mag; // if (mag > 0.95) std::cout << " mag " << mag << " max " << max << " vec " << vec << " ind " << index << std::endl; /** FIXME need weights between metrics */ RealType normalizedWeight = this->m_SimilarityMetrics[metricCount]->GetWeightScalar() / sumWeights; // RealType weight = this->m_SimilarityMetrics[metricCount]->GetWeightImage()->GetPixel( diter.GetIndex() ); if (ispointsetmetric ) { VectorType intensityupdate=dIter.Get(); VectorType lmupdate=vec; float lmag=0; for (unsigned int li=0; li 1) modi=0; else modi=1.0-lmag; float iwt=1*modi; float lmwt=normalizedWeight; VectorType totalv=intensityupdate*iwt+lmupdate*lmwt; dIter.Set(totalv); } else dIter.Set(dIter.Get()+vec*normalizedWeight); } if (totalUpdateInvField){ Iterator dIter(totalUpdateInvField,totalUpdateInvField->GetLargestPossibleRegion() ); float mag=0.0; float max=0.0; unsigned long ct=0; float total=0; for( dIter.GoToBegin(); !dIter.IsAtEnd(); ++dIter ) { typename ImageType::IndexType index=dIter.GetIndex(); VectorType vec=updateFieldInv->GetPixel(index); mag=0; for (unsigned int jj=0; jj 0. ) std::cout << " mag " << mag << " max " << max << " vec " << vec << std::endl; if (mag > max) max=mag; ct++; total+=mag; // std::cout << " mag " << mag << std::endl; } if (this->m_Debug) std::cout << "PRE MAX " << max << std::endl; float max2=0; if (max <= 0) max=1; for( dIter.GoToBegin(); !dIter.IsAtEnd(); ++dIter ) { typename ImageType::IndexType index=dIter.GetIndex(); VectorType vec=updateFieldInv->GetPixel(index); vec=vec/max; mag=0; for (unsigned int jj=0; jj max2 ) max2=mag; // if (mag > 0.95) std::cout << " mag " << mag << " max " << max << " vec " << vec << " ind " << index << std::endl; /** FIXME need weights between metrics */ RealType normalizedWeight = this->m_SimilarityMetrics[metricCount]->GetWeightScalar() / sumWeights; // RealType weight = this->m_SimilarityMetrics[metricCount]->GetWeightImage()->GetPixel( diter.GetIndex() ); if (ispointsetmetric ) { VectorType intensityupdate=dIter.Get(); VectorType lmupdate=vec; float lmag=0; for (unsigned int li=0; li 1) modi=0; else modi=1.0-lmag; float iwt=1*modi; float lmwt=normalizedWeight; VectorType totalv=intensityupdate*iwt+lmupdate*lmwt; dIter.Set(totalv); } else dIter.Set(dIter.Get()+vec*normalizedWeight); } } if (this->m_Debug) std::cout << "PO MAX " << max2 << " sz" << totalUpdateField->GetLargestPossibleRegion().GetSize() << std::endl; } // this->SmoothDeformationField( totalUpdateField,true); // if (totalUpdateInvField) this->SmoothDeformationField( totalUpdateInvField,true); return totalUpdateField; } template typename ANTSImageRegistrationOptimizer::DeformationFieldPointer ANTSImageRegistrationOptimizer ::ComputeUpdateFieldAlternatingMin(DeformationFieldPointer fixedwarp, DeformationFieldPointer movingwarp , PointSetPointer fpoints, PointSetPointer wpoints, DeformationFieldPointer totalUpdateInvField, bool updateenergy) { ImagePointer mask=NULL; if ( movingwarp && this->m_MaskImage) mask= this->WarpMultiTransform( this->m_MaskImage, this->m_MaskImage, NULL, movingwarp, false , this->m_FixedImageAffineTransform ); else if (this->m_MaskImage) mask=this->SubsampleImage( this->m_MaskImage, this->m_ScaleFactor , this->m_MaskImage->GetOrigin() , this->m_MaskImage->GetDirection() , NULL); if ( !fixedwarp) {std::cout<< " NO F WARP " << std::endl; fixedwarp=this->m_DeformationField; } //if ( !movingwarp) std::cout<< " NO M WARP " << std::endl; /// std::cout << " get upd field " << std::endl; typename ImageType::SpacingType spacing=fixedwarp->GetSpacing(); VectorType zero; zero.Fill(0); DeformationFieldPointer updateField=NULL,totalUpdateField=NULL,updateFieldInv=NULL; totalUpdateField=DeformationFieldType::New(); totalUpdateField->SetSpacing( fixedwarp->GetSpacing() ); totalUpdateField->SetOrigin( fixedwarp->GetOrigin() ); totalUpdateField->SetDirection( fixedwarp->GetDirection() ); totalUpdateField->SetLargestPossibleRegion(fixedwarp->GetLargestPossibleRegion() ); totalUpdateField->SetRequestedRegion( fixedwarp->GetLargestPossibleRegion() ); totalUpdateField->SetBufferedRegion( fixedwarp->GetLargestPossibleRegion() ); totalUpdateField->Allocate(); totalUpdateField->FillBuffer(zero); //bool hadpointsetmetric=false; RealType sumWeights = 0.0; for( unsigned int n = 0; n < this->m_SimilarityMetrics.size(); n++ ) { sumWeights += this->m_SimilarityMetrics[n]->GetWeightScalar(); } sumWeights=1; // for ( unsigned int metricCount = 0; metricCount < this->m_SimilarityMetrics.size(); metricCount++ ) //{ //MetricBaseTypePointer df = this->m_SimilarityMetrics[metricCount]->GetMetric(); // if (df->ThisIsAPointSetMetric()) hadpointsetmetric=true; //} //for ( unsigned int metricCount = 0; metricCount < this->m_SimilarityMetrics.size(); metricCount++ ) unsigned int metricCount= this->m_CurrentIteration % this->m_SimilarityMetrics.size(); { bool ispointsetmetric=false; /** build an update field */ if ( true )// this->m_SimilarityMetrics.size() == 1 ) { updateField=totalUpdateField; if (totalUpdateInvField) updateFieldInv=totalUpdateInvField; } else { updateField=DeformationFieldType::New(); updateField->SetSpacing( fixedwarp->GetSpacing() ); updateField->SetOrigin( fixedwarp->GetOrigin() ); updateField->SetDirection( fixedwarp->GetDirection() ); updateField->SetLargestPossibleRegion(fixedwarp->GetLargestPossibleRegion() ); updateField->SetRequestedRegion( fixedwarp->GetLargestPossibleRegion() ); updateField->SetBufferedRegion( fixedwarp->GetLargestPossibleRegion() ); updateField->Allocate(); updateField->FillBuffer(zero); if (totalUpdateInvField){ updateFieldInv=DeformationFieldType::New(); updateFieldInv->SetSpacing( fixedwarp->GetSpacing() ); updateFieldInv->SetOrigin( fixedwarp->GetOrigin() ); updateFieldInv->SetDirection( fixedwarp->GetDirection() ); updateFieldInv->SetLargestPossibleRegion(fixedwarp->GetLargestPossibleRegion() ); updateFieldInv->SetRequestedRegion( fixedwarp->GetLargestPossibleRegion() ); updateFieldInv->SetBufferedRegion( fixedwarp->GetLargestPossibleRegion() ); updateFieldInv->Allocate(); updateFieldInv->FillBuffer(zero); } } /** get the update */ typedef DeformationFieldType DeformationFieldType; typedef typename FiniteDifferenceFunctionType::NeighborhoodType NeighborhoodIteratorType; typedef ImageRegionIterator UpdateIteratorType; // TimeStepType timeStep; void *globalData; // std::cout << " B " << std::endl; // for each metric, warp the assoc. Images /** We loop Over This To Do MultiVariate */ /** FIXME really should pass an image list and then warp each one in turn then expand the update field to fit size of total deformation */ ImagePointer wmimage=NULL; if ( fixedwarp) wmimage= this->WarpMultiTransform( this->m_SmoothFixedImages[metricCount],this->m_SmoothMovingImages[metricCount], this->m_AffineTransform, fixedwarp, false , this->m_FixedImageAffineTransform ); else wmimage=this->SubsampleImage( this->m_SmoothMovingImages[metricCount] , this->m_ScaleFactor , this->m_SmoothMovingImages[metricCount]->GetOrigin() , this->m_SmoothMovingImages[metricCount]->GetDirection() , NULL); // std::cout << " C " << std::endl; ImagePointer wfimage=NULL; if ( movingwarp) wfimage= this->WarpMultiTransform( this->m_SmoothFixedImages[metricCount], this->m_SmoothFixedImages[metricCount], NULL, movingwarp, false , this->m_FixedImageAffineTransform ); else wfimage=this->SubsampleImage( this->m_SmoothFixedImages[metricCount] , this->m_ScaleFactor , this->m_SmoothFixedImages[metricCount]->GetOrigin() , this->m_SmoothFixedImages[metricCount]->GetDirection() , NULL); // std::cout << " D " << std::endl; /** MV Loop END -- Would have to collect update fields then add them * together somehow -- Would also have to eliminate the similarity * metric loop within ComputeUpdateField */ // Get the FiniteDifferenceFunction to use in calculations. MetricBaseTypePointer df = this->m_SimilarityMetrics[metricCount]->GetMetric(); df->SetFixedImage(wfimage); df->SetMovingImage(wmimage); if (df->ThisIsAPointSetMetric()) ispointsetmetric=true; if (fpoints && ispointsetmetric ) df->SetFixedPointSet(fpoints); else if (ispointsetmetric ) std::cout << "NO POINTS!! " << std::endl; if (wpoints && ispointsetmetric ) df->SetMovingPointSet(wpoints); else if (ispointsetmetric ) std::cout << "NO POINTS!! " << std::endl; typename ImageType::SizeType radius = df->GetRadius(); df->InitializeIteration(); typename DeformationFieldType::Pointer output = updateField; typedef NeighborhoodAlgorithm::ImageBoundaryFacesCalculator FaceCalculatorType; typedef typename FaceCalculatorType::FaceListType FaceListType; FaceCalculatorType faceCalculator; FaceListType faceList = faceCalculator(updateField, updateField->GetLargestPossibleRegion(), radius); typename FaceListType::iterator fIt = faceList.begin(); globalData = df->GetGlobalDataPointer(); // Process the non-boundary region. NeighborhoodIteratorType nD(radius, updateField, *fIt); UpdateIteratorType nU(updateField, *fIt); nD.GoToBegin(); nU.GoToBegin(); while( !nD.IsAtEnd() ) { bool oktosample=true; float maskprob=1.0; if (mask) { maskprob=mask->GetPixel( nD.GetIndex() ); if (maskprob > 1.0) maskprob=1.0; if ( maskprob < 0.1) oktosample=false; } if ( oktosample ) { nU.Value() += df->ComputeUpdate(nD, globalData)*maskprob; if (totalUpdateInvField) { typename ImageType::IndexType index=nD.GetIndex(); VectorType temp = df->ComputeUpdateInv(nD, globalData)*maskprob; updateFieldInv->SetPixel(index,temp); } //else nU.Value() -= df->ComputeUpdateInv(nD, globalData)*maskprob; ++nD; ++nU; } else { ++nD; ++nU; } } if (updateenergy){ this->m_LastEnergy[metricCount]=this->m_Energy[metricCount]; this->m_Energy[metricCount]=df->GetEnergy();//*this->m_SimilarityMetrics[metricCount]->GetWeightScalar()/sumWeights; } // smooth the fields // if (!ispointsetmetric || ImageDimension == 2 ){ this->SmoothDeformationField(updateField,true); if (updateFieldInv) this->SmoothDeformationField(updateFieldInv,true); // } /* else // use another strategy -- exact lm? / something like Laplacian { float tmag=0; for (unsigned int ff=0; ff<5; ff++) { tmag=0; this->SmoothDeformationField(updateField,true); if (updateFieldInv) this->SmoothDeformationField(updateFieldInv,true); nD.GoToBegin(); nU.GoToBegin(); while( !nD.IsAtEnd() ) { typename ImageType::IndexType index=nD.GetIndex(); bool oktosample=true; float maskprob=1.0; if (mask) { maskprob=mask->GetPixel( nD.GetIndex() ); if (maskprob > 1.0) maskprob=1.0; if ( maskprob < 0.1) oktosample=false; } VectorType F1; F1.Fill(0); VectorType F2; F2.Fill(0); if ( oktosample ) { F1 = df->ComputeUpdate(nD, globalData)*maskprob; if (totalUpdateInvField) { F2 = df->ComputeUpdateInv(nD, globalData)*maskprob; } ++nD; ++nU; } else { ++nD; ++nU; } // compute mags of F1 and F2 -- if large enough, reset them float f1mag=0,f2mag=0,umag=0; for (unsigned int dim=0; dimGetPixel(index)[dim]/spacing[dim]*updateField->GetPixel(index)[dim]/spacing[dim]; } f1mag=sqrt(f1mag); f2mag=sqrt(f2mag); umag=sqrt(umag); if ( f1mag > 0.05 ) updateField->SetPixel(index,F1); if ( f2mag > 0.05 ) updateFieldInv->SetPixel(index,F2); tmag+=umag; } // std::cout << " total mag " << tmag << std::endl; } //smooth the total field this->SmoothDeformationField(updateField,true); if (updateFieldInv) this->SmoothDeformationField(updateFieldInv,true); } */ //normalize update field then add to total field typedef ImageRegionIteratorWithIndex Iterator; Iterator dIter(totalUpdateField,totalUpdateField->GetLargestPossibleRegion() ); float mag=0.0; float max=0.0; unsigned long ct=0; float total=0; for( dIter.GoToBegin(); !dIter.IsAtEnd(); ++dIter ) { typename ImageType::IndexType index=dIter.GetIndex(); VectorType vec=updateField->GetPixel(index); mag=0; for (unsigned int jj=0; jj 0. ) std::cout << " mag " << mag << " max " << max << " vec " << vec << std::endl; if (mag > max) max=mag; ct++; total+=mag; // std::cout << " mag " << mag << std::endl; } if (this->m_Debug) std::cout << "PRE MAX " << max << std::endl; float max2=0; if (max <= 0) max=1; for( dIter.GoToBegin(); !dIter.IsAtEnd(); ++dIter ) { typename ImageType::IndexType index=dIter.GetIndex(); VectorType vec=updateField->GetPixel(index); vec=vec/max; mag=0; for (unsigned int jj=0; jj max2 ) max2=mag; // if (mag > 0.95) std::cout << " mag " << mag << " max " << max << " vec " << vec << " ind " << index << std::endl; /** FIXME need weights between metrics */ RealType normalizedWeight = this->m_SimilarityMetrics[metricCount]->GetWeightScalar() / sumWeights; /* RealType weight = this->m_SimilarityMetrics[metricCount]->GetWeightImage()->GetPixel( diter.GetIndex() ); if (ispointsetmetric ) { VectorType intensityupdate=dIter.Get(); VectorType lmupdate=vec; float lmag=0; for (unsigned int li=0; li 1) modi=0; else modi=1.0-lmag; float iwt=1*modi; float lmwt=normalizedWeight; VectorType totalv=intensityupdate*iwt+lmupdate*lmwt; dIter.Set(totalv); } else */ dIter.Set(dIter.Get()+vec*normalizedWeight); } if (totalUpdateInvField){ Iterator dIter(totalUpdateInvField,totalUpdateInvField->GetLargestPossibleRegion() ); float mag=0.0; float max=0.0; unsigned long ct=0; float total=0; for( dIter.GoToBegin(); !dIter.IsAtEnd(); ++dIter ) { typename ImageType::IndexType index=dIter.GetIndex(); VectorType vec=updateFieldInv->GetPixel(index); mag=0; for (unsigned int jj=0; jj 0. ) std::cout << " mag " << mag << " max " << max << " vec " << vec << std::endl; if (mag > max) max=mag; ct++; total+=mag; // std::cout << " mag " << mag << std::endl; } if (this->m_Debug) std::cout << "PRE MAX " << max << std::endl; float max2=0; if (max <= 0) max=1; for( dIter.GoToBegin(); !dIter.IsAtEnd(); ++dIter ) { typename ImageType::IndexType index=dIter.GetIndex(); VectorType vec=updateFieldInv->GetPixel(index); vec=vec/max; mag=0; for (unsigned int jj=0; jj max2 ) max2=mag; // if (mag > 0.95) std::cout << " mag " << mag << " max " << max << " vec " << vec << " ind " << index << std::endl; /** FIXME need weights between metrics */ RealType normalizedWeight = this->m_SimilarityMetrics[metricCount]->GetWeightScalar() / sumWeights; // RealType weight = this->m_SimilarityMetrics[metricCount]->GetWeightImage()->GetPixel( diter.GetIndex() ); /* if (ispointsetmetric ) { VectorType intensityupdate=dIter.Get(); VectorType lmupdate=vec; float lmag=0; for (unsigned int li=0; li 1) modi=0; else modi=1.0-lmag; float iwt=1*modi; float lmwt=normalizedWeight; VectorType totalv=intensityupdate*iwt+lmupdate*lmwt; dIter.Set(totalv); } else */ dIter.Set(dIter.Get()+vec*normalizedWeight); } } if (this->m_Debug) std::cout << "PO MAX " << max2 << " sz" << totalUpdateField->GetLargestPossibleRegion().GetSize() << std::endl; } // this->SmoothDeformationField( totalUpdateField,true); // if (totalUpdateInvField) this->SmoothDeformationField( totalUpdateInvField,true); return totalUpdateField; } template void ANTSImageRegistrationOptimizer ::DiffeomorphicExpRegistrationUpdate(ImagePointer fixedImage, ImagePointer movingImage, PointSetPointer fpoints, PointSetPointer mpoints) { // this function computes a velocity field that --- when composed with itself --- optimizes the registration solution. // it's very different than the Exp approach used by DiffeomorphicDemons which just composes small deformation over time. // DiffDem cannot reconstruct the path between images without recomputing the registration. // DiffDem also cannot create an inverse mapping. /** FIXME really should pass an image list and then warp each one in turn then expand the update field to fit size of total deformation */ typename ImageType::SpacingType spacing=fixedImage->GetSpacing(); VectorType zero; zero.Fill(0); DeformationFieldPointer totalUpdateField=NULL; DeformationFieldPointer totalField=this->m_DeformationField; /** generate phi and phi gradient */ DeformationFieldPointer diffmap=DeformationFieldType::New(); diffmap->SetSpacing( totalField->GetSpacing() ); diffmap->SetOrigin( totalField->GetOrigin() ); diffmap->SetDirection( totalField->GetDirection() ); diffmap->SetLargestPossibleRegion(totalField->GetLargestPossibleRegion() ); diffmap->SetRequestedRegion( totalField->GetLargestPossibleRegion() ); diffmap->SetBufferedRegion( totalField->GetLargestPossibleRegion() ); diffmap->Allocate(); DeformationFieldPointer invdiffmap=DeformationFieldType::New(); invdiffmap->SetSpacing( totalField->GetSpacing() ); invdiffmap->SetOrigin( totalField->GetOrigin() ); invdiffmap->SetDirection( totalField->GetDirection() ); invdiffmap->SetLargestPossibleRegion(totalField->GetLargestPossibleRegion() ); invdiffmap->SetRequestedRegion( totalField->GetLargestPossibleRegion() ); invdiffmap->SetBufferedRegion( totalField->GetLargestPossibleRegion() ); invdiffmap->Allocate(); // float timestep=1.0/(float)this->m_NTimeSteps; // for (unsigned int nts=0; nts<=this->m_NTimeSteps; nts+=this->m_NTimeSteps) unsigned int nts=(unsigned int)this->m_NTimeSteps; { diffmap->FillBuffer(zero); invdiffmap->FillBuffer(zero); DeformationFieldPointer diffmap = this->IntegrateConstantVelocity(totalField, nts, 1); //DeformationFieldPointer invdiffmap = this->IntegrateConstantVelocity(totalField,(unsigned int)( this->m_NTimeSteps)-nts, (-1.)); ImagePointer wfimage,wmimage; PointSetPointer wfpoints=NULL,wmpoints=NULL; AffineTransformPointer aff =this->m_AffineTransform; if ( mpoints ) {// need full inverse map DeformationFieldPointer tinvdiffmap = this->IntegrateConstantVelocity(totalField, nts, (-1.)); wmpoints = this->WarpMultiTransform(fixedImage,movingImage, mpoints , aff , tinvdiffmap , true , this->m_FixedImageAffineTransform ); } DeformationFieldPointer updateField=this->ComputeUpdateField( diffmap, NULL, fpoints, wmpoints); // updateField = this->IntegrateConstantVelocity( updateField, nts, timestep); float maxl= this->MeasureDeformation(updateField); if (maxl <= 0) maxl=1; typedef ImageRegionIteratorWithIndex Iterator; Iterator dIter(updateField,updateField->GetLargestPossibleRegion() ); for( dIter.GoToBegin(); !dIter.IsAtEnd(); ++dIter ) dIter.Set( dIter.Get()*this->m_GradstepAltered/maxl); this->ComposeDiffs(updateField,totalField,totalField,1); /* float maxl= this->MeasureDeformation(updateField); if (maxl <= 0) maxl=1; typedef ImageRegionIteratorWithIndex Iterator; Iterator dIter(updateField,updateField->GetLargestPossibleRegion() ); for( dIter.GoToBegin(); !dIter.IsAtEnd(); ++dIter ) { dIter.Set( dIter.Get()*this->m_GradstepAltered/this->m_NTimeSteps ); totalField->SetPixel(dIter.GetIndex(), dIter.Get() + totalField->GetPixel(dIter.GetIndex()) ); } */ } this->SmoothDeformationField(totalField,false); return; } template void ANTSImageRegistrationOptimizer ::GreedyExpRegistrationUpdate(ImagePointer fixedImage, ImagePointer movingImage, PointSetPointer fpoints, PointSetPointer mpoints) { // similar approach to christensen 96 and diffeomorphic demons typename ImageType::SpacingType spacing=fixedImage->GetSpacing(); VectorType zero; zero.Fill(0); DeformationFieldPointer totalUpdateField=NULL; // we compose the update with this field. DeformationFieldPointer totalField=this->m_DeformationField; float timestep=1.0/(float)this->m_NTimeSteps; unsigned int nts=(unsigned int)this->m_NTimeSteps; ImagePointer wfimage,wmimage; PointSetPointer wfpoints=NULL,wmpoints=NULL; AffineTransformPointer aff =this->m_AffineTransform; DeformationFieldPointer updateField=this->ComputeUpdateField( totalField, NULL, fpoints, wmpoints); updateField = this->IntegrateConstantVelocity( updateField, nts, timestep); float maxl= this->MeasureDeformation(updateField); if (maxl <= 0) maxl=1; typedef ImageRegionIteratorWithIndex Iterator; Iterator dIter(updateField,updateField->GetLargestPossibleRegion() ); for( dIter.GoToBegin(); !dIter.IsAtEnd(); ++dIter ) dIter.Set( dIter.Get()*this->m_GradstepAltered/maxl ); this->ComposeDiffs(updateField,totalField,totalField,1); // maxl= this->MeasureDeformation(totalField); // std::cout << " maxl " << maxl << " gsa " << this->m_GradstepAltered << std::endl; // totalField=this->CopyDeformationField(totalUpdateField); this->SmoothDeformationField(totalField,false); return; } //added by songgang template typename ANTSImageRegistrationOptimizer::AffineTransformPointer ANTSImageRegistrationOptimizer::AffineOptimization(OptAffineType &affine_opt) { // typedef itk::Image TempImageType; // typename TempImageType::Pointer fixedImage = TempImageType::New(); // typename TempImageType::Pointer movingImage = TempImageType::New(); ImagePointer fixedImage; ImagePointer movingImage; /** FIXME -- here we assume the metrics all have the same image */ fixedImage = this->m_SimilarityMetrics[0]->GetFixedImage(); movingImage = this->m_SimilarityMetrics[0]->GetMovingImage(); //TODO: get mask image pointer / type for mask image if (this->m_MaskImage) affine_opt.mask_fixed = this->m_MaskImage; // AffineTransformPointer &transform_init = affine_opt.transform_initial; // ImagePointer &maskImage = affine_opt.mask_fixed; AffineTransformPointer transform = AffineTransformType::New(); // std::cout << "In AffineOptimization: transform_init.IsNotNull()=" << transform_init.IsNotNull() << std::endl; // compute_single_affine_transform(fixedImage, movingImage, maskImage, transform, transform_init); // OptAffine opt; ComputeSingleAffineTransform(fixedImage, movingImage, affine_opt, transform); return transform; } template void ANTSImageRegistrationOptimizer ::SyNRegistrationUpdate(ImagePointer fixedImage, ImagePointer movingImage, PointSetPointer fpoints, PointSetPointer mpoints) { typename ImageType::SpacingType spacing=fixedImage->GetSpacing(); VectorType zero; zero.Fill(0); DeformationFieldPointer totalUpdateField,totalUpdateInvField=DeformationFieldType::New(); totalUpdateInvField->SetSpacing( this->m_DeformationField->GetSpacing() ); totalUpdateInvField->SetOrigin( this->m_DeformationField->GetOrigin() ); totalUpdateInvField->SetDirection( this->m_DeformationField->GetDirection() ); totalUpdateInvField->SetLargestPossibleRegion(this->m_DeformationField->GetLargestPossibleRegion() ); totalUpdateInvField->SetRequestedRegion( this->m_DeformationField->GetLargestPossibleRegion() ); totalUpdateInvField->SetBufferedRegion( this->m_DeformationField->GetLargestPossibleRegion() ); totalUpdateInvField->Allocate(); totalUpdateInvField->FillBuffer(zero); if (!this->m_SyNF) { std::cout <<" Allocating " << std::endl; this->m_SyNF=this->CopyDeformationField(totalUpdateInvField); this->m_SyNFInv=this->CopyDeformationField(this->m_SyNF); this->m_SyNM=this->CopyDeformationField(totalUpdateInvField); this->m_SyNMInv=this->CopyDeformationField(this->m_SyNF); //this->m_Debug=true; if (this->m_Debug) std::cout << " SyNFInv" << this->m_SyNFInv->GetLargestPossibleRegion().GetSize() << std::endl; if (this->m_Debug) std::cout << " t updIf " << totalUpdateInvField->GetLargestPossibleRegion().GetSize() << std::endl; if (this->m_Debug) std::cout << " synf " << this->m_SyNF->GetLargestPossibleRegion().GetSize() << std::endl; //this->m_Debug=false; std::cout <<" Allocating Done " << std::endl; } if (!this->m_SyNF) { std::cout<<" F'D UP " << std::endl;} PointSetPointer wfpoints=NULL,wmpoints=NULL; AffineTransformPointer aff =this->m_AffineTransform; AffineTransformPointer affinverse=NULL; if (aff){ affinverse=AffineTransformType::New(); aff->GetInverse(affinverse); } if ( mpoints ) { wmpoints = this->WarpMultiTransform(fixedImage,movingImage, mpoints , aff , this->m_SyNM , true , this->m_FixedImageAffineTransform ); } if ( fpoints ) {// need full inverse map wfpoints = this->WarpMultiTransform(fixedImage,fixedImage, fpoints , NULL , this->m_SyNF , false , this->m_FixedImageAffineTransform ); } //syncom totalUpdateField=this->ComputeUpdateField(this->m_SyNMInv, this->m_SyNFInv, wfpoints, wmpoints,totalUpdateInvField); this->ComposeDiffs(this->m_SyNF,totalUpdateField,this->m_SyNF,this->m_GradstepAltered); this->ComposeDiffs(this->m_SyNM,totalUpdateInvField,this->m_SyNM,this->m_GradstepAltered); if ( this->m_TotalSmoothingparam > 0 || this->m_TotalSmoothingMeshSize[0] > 0 ) { this->SmoothDeformationField( this->m_SyNF,false); this->SmoothDeformationField( this->m_SyNM,false); } this->InvertField(this->m_SyNF,this->m_SyNFInv); this->InvertField(this->m_SyNM,this->m_SyNMInv); this->InvertField(this->m_SyNFInv,this->m_SyNF); this->InvertField(this->m_SyNMInv,this->m_SyNM); // std::cout << " F " << this->MeasureDeformation(this->m_SyNF) << " F1 " << this->MeasureDeformation(this->m_SyNFInv) << std::endl; // std::cout << " M " << this->MeasureDeformation(this->m_SyNM) << " M1 " << this->MeasureDeformation(this->m_SyNMInv) << std::endl; return; } template void ANTSImageRegistrationOptimizer ::SyNExpRegistrationUpdate(ImagePointer fixedImage, ImagePointer movingImage, PointSetPointer fpoints, PointSetPointer mpoints) { std::cout << " SyNEX" ; typename ImageType::SpacingType spacing=fixedImage->GetSpacing(); VectorType zero; zero.Fill(0); DeformationFieldPointer totalUpdateField,totalUpdateInvField=DeformationFieldType::New(); totalUpdateInvField->SetSpacing( this->m_DeformationField->GetSpacing() ); totalUpdateInvField->SetOrigin( this->m_DeformationField->GetOrigin() ); totalUpdateInvField->SetDirection( this->m_DeformationField->GetDirection() ); totalUpdateInvField->SetLargestPossibleRegion(this->m_DeformationField->GetLargestPossibleRegion() ); totalUpdateInvField->SetRequestedRegion( this->m_DeformationField->GetLargestPossibleRegion() ); totalUpdateInvField->SetBufferedRegion( this->m_DeformationField->GetLargestPossibleRegion() ); totalUpdateInvField->Allocate(); totalUpdateInvField->FillBuffer(zero); if (!this->m_SyNF) { std::cout <<" Allocating " << std::endl; this->m_SyNF=this->CopyDeformationField(totalUpdateInvField); this->m_SyNFInv=this->CopyDeformationField(this->m_SyNF); this->m_SyNM=this->CopyDeformationField(totalUpdateInvField); this->m_SyNMInv=this->CopyDeformationField(this->m_SyNF); std::cout <<" Allocating Done " << std::endl; } if (!this->m_SyNF) { std::cout<<" F'D UP " << std::endl;} ImagePointer wfimage,wmimage; PointSetPointer wfpoints=NULL,wmpoints=NULL; AffineTransformPointer aff =this->m_AffineTransform; AffineTransformPointer affinverse=NULL; //here, SyNF holds the moving velocity field, SyNM holds the fixed //velocity field and we integrate both to generate the inv/fwd fields float timestep=1.0/(float)this->m_NTimeSteps; unsigned int nts=this->m_NTimeSteps; DeformationFieldPointer fdiffmap = this->IntegrateConstantVelocity(this->m_SyNF, nts, 1); this->m_SyNFInv = this->IntegrateConstantVelocity(this->m_SyNF, nts, (-1.)); DeformationFieldPointer mdiffmap = this->IntegrateConstantVelocity(this->m_SyNM, nts, 1); this->m_SyNMInv = this->IntegrateConstantVelocity(this->m_SyNM, nts, (-1.)); if (aff){ affinverse=AffineTransformType::New(); aff->GetInverse(affinverse); } if ( mpoints ) { wmpoints = this->WarpMultiTransform(fixedImage,movingImage, mpoints , aff , this->m_SyNM , true , this->m_FixedImageAffineTransform ); } if ( fpoints ) {// need full inverse map wfpoints = this->WarpMultiTransform(fixedImage,fixedImage, fpoints , NULL , this->m_SyNF , false , this->m_FixedImageAffineTransform ); } totalUpdateField=this->ComputeUpdateField( this->m_SyNMInv , this->m_SyNFInv , wfpoints, wmpoints,totalUpdateInvField); //then addd typedef ImageRegionIteratorWithIndex Iterator; Iterator dIter(this->m_SyNF,this->m_SyNF->GetLargestPossibleRegion() ); float max=0,max2=0; for( dIter.GoToBegin(); !dIter.IsAtEnd(); ++dIter ) { typename ImageType::IndexType index=dIter.GetIndex(); VectorType vecf=totalUpdateField->GetPixel(index); VectorType vecm=totalUpdateInvField->GetPixel(index); dIter.Set(dIter.Get()+vecf*this->m_GradstepAltered); this->m_SyNM->SetPixel( index, this->m_SyNM->GetPixel( index )+vecm*this->m_GradstepAltered); // min field difference => geodesic => DV/dt=0 float geowt1=0.99; float geowt2=1.0-geowt1; VectorType synmv=this->m_SyNM->GetPixel( index ); VectorType synfv =this->m_SyNF->GetPixel( index ); this->m_SyNM->SetPixel( index, synmv*geowt1-synfv*geowt2); this->m_SyNF->SetPixel( index, synfv*geowt1-synmv*geowt2); } if (this->m_TotalSmoothingparam > 0 || this->m_TotalSmoothingMeshSize[0] > 0 ) { this->SmoothDeformationField( this->m_SyNF,false); this->SmoothDeformationField( this->m_SyNM,false); } // std::cout << " TUF " << this->MeasureDeformation(this->m_SyNF) << " TUM " << this->MeasureDeformation(this->m_SyNM) << std::endl; return; } template void ANTSImageRegistrationOptimizer ::UpdateTimeVaryingVelocityFieldWithSyNFandSyNM( ) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image DeformationFieldType; typedef itk::Image ImageType; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef TimeVaryingVelocityFieldType tvt; bool generatetvfield=false; if (!this->m_TimeVaryingVelocity) generatetvfield=true; else { for (int jj=0; jjm_CurrentDomainSize[jj] != this->m_TimeVaryingVelocity->GetLargestPossibleRegion().GetSize()[jj]) generatetvfield=true; } VectorType zero; zero.Fill(0); if (generatetvfield) { typename tvt::RegionType gregion; typename tvt::SizeType gsize; typename tvt::SpacingType gspace; typename tvt::PointType gorigin; gorigin.Fill(0); for (unsigned int dim=0; dimm_CurrentDomainSize[dim]; gspace[dim]=this->m_CurrentDomainSpacing[dim]; gorigin[dim]=this->m_CurrentDomainOrigin[dim]; } gsize[TDimension]=2;//this->m_NTimeSteps; gspace[TDimension]=1; gregion.SetSize(gsize); /** The TV Field has the direction of the sub-image -- the time domain has identity transform */ typename tvt::DirectionType iddir; iddir.Fill(0); iddir[ImageDimension][ImageDimension]=1; for (unsigned int i=0; iGetDeformationField()->GetDirection()[i][j]; this->m_TimeVaryingVelocity=tvt::New(); this->m_TimeVaryingVelocity->SetSpacing( gspace ); this->m_TimeVaryingVelocity->SetOrigin( gorigin ); this->m_TimeVaryingVelocity->SetDirection( iddir ); this->m_TimeVaryingVelocity->SetLargestPossibleRegion(gregion); this->m_TimeVaryingVelocity->SetRequestedRegion( gregion); this->m_TimeVaryingVelocity->SetBufferedRegion( gregion ); this->m_TimeVaryingVelocity->Allocate(); this->m_TimeVaryingVelocity->FillBuffer(zero); } typedef tvt TimeVaryingVelocityFieldType; typedef itk::ImageRegionIteratorWithIndex FieldIterator; typedef itk::ImageRegionIteratorWithIndex TVFieldIterator; typedef typename DeformationFieldType::IndexType DIndexType; typedef typename DeformationFieldType::PointType DPointType; typedef typename TimeVaryingVelocityFieldType::IndexType VIndexType; typedef typename TimeVaryingVelocityFieldType::PointType VPointType; TVFieldIterator m_FieldIter( this->m_TimeVaryingVelocity,this->m_TimeVaryingVelocity->GetLargestPossibleRegion()); for( m_FieldIter.GoToBegin(); !m_FieldIter.IsAtEnd(); ++m_FieldIter ) { typename tvt::IndexType velind=m_FieldIter.GetIndex(); IndexType ind; for (unsigned int j=0; jm_SyNF->GetPixel(ind); m_FieldIter.Set(vel); } else if (velind[ImageDimension]==1) { VectorType vel=this->m_SyNM->GetPixel(ind)*(-1.0); m_FieldIter.Set(vel); } } // std::cout <<" ALlocated TV F "<< std::endl; } template void ANTSImageRegistrationOptimizer ::CopyOrAddToVelocityField( TimeVaryingVelocityFieldPointer velocity, DeformationFieldPointer update1, DeformationFieldPointer update2 , float timept) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image DeformationFieldType; typedef itk::Image ImageType; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef TimeVaryingVelocityFieldType tvt; VectorType zero; typedef tvt TimeVaryingVelocityFieldType; typedef itk::ImageRegionIteratorWithIndex FieldIterator; typedef itk::ImageRegionIteratorWithIndex TVFieldIterator; typedef typename DeformationFieldType::IndexType DIndexType; typedef typename DeformationFieldType::PointType DPointType; typedef typename TimeVaryingVelocityFieldType::IndexType VIndexType; typedef typename TimeVaryingVelocityFieldType::PointType VPointType; int tpupdate=(unsigned int) (((float)this->m_NTimeSteps-1.0)*timept+0.5); //std::cout <<" add to " << tpupdate << std::endl; float tmag=0; TVFieldIterator m_FieldIter(velocity, velocity->GetLargestPossibleRegion()); for( m_FieldIter.GoToBegin(); !m_FieldIter.IsAtEnd(); ++m_FieldIter ) { typename tvt::IndexType velind=m_FieldIter.GetIndex(); IndexType ind; for (unsigned int j=0; jGetPixel(ind); float mag=0; for (unsigned int jj=0; jjGetPixel(ind)*(-1); m_FieldIter.Set(vel+m_FieldIter.Get() ); } } // std::cout << " tmag " << tmag << std::endl; } template void ANTSImageRegistrationOptimizer ::SyNTVRegistrationUpdate(ImagePointer fixedImage, ImagePointer movingImage, PointSetPointer fpoints, PointSetPointer mpoints) { typename ImageType::SpacingType spacing=fixedImage->GetSpacing(); VectorType zero; zero.Fill(0); DeformationFieldPointer totalUpdateField,totalUpdateInvField=DeformationFieldType::New(); totalUpdateInvField->SetSpacing( this->m_DeformationField->GetSpacing() ); totalUpdateInvField->SetOrigin( this->m_DeformationField->GetOrigin() ); totalUpdateInvField->SetDirection( this->m_DeformationField->GetDirection() ); totalUpdateInvField->SetLargestPossibleRegion(this->m_DeformationField->GetLargestPossibleRegion() ); totalUpdateInvField->SetRequestedRegion( this->m_DeformationField->GetLargestPossibleRegion() ); totalUpdateInvField->SetBufferedRegion( this->m_DeformationField->GetLargestPossibleRegion() ); totalUpdateInvField->Allocate(); totalUpdateInvField->FillBuffer(zero); if (!this->m_SyNF) { std::cout <<" Allocating " << std::endl; this->m_SyNF=this->CopyDeformationField(totalUpdateInvField); this->m_SyNFInv=this->CopyDeformationField(this->m_SyNF); this->m_SyNM=this->CopyDeformationField(totalUpdateInvField); this->m_SyNMInv=this->CopyDeformationField(this->m_SyNF); std::cout <<" Allocating Done " << std::endl; } if (!this->m_SyNF) { std::cout<<" F'D UP " << std::endl;} ImagePointer wfimage,wmimage; PointSetPointer wfpoints=NULL,wmpoints=NULL; AffineTransformPointer aff =this->m_AffineTransform; AffineTransformPointer affinverse=NULL; typedef ImageRegionIteratorWithIndex Iterator; Iterator dIter(this->m_SyNF,this->m_SyNF->GetLargestPossibleRegion() ); //here, SyNF holds the moving velocity field, SyNM holds the fixed //velocity field and we integrate both to generate the inv/fwd fields typename JacobianFunctionType::Pointer jfunction = JacobianFunctionType::New(); float lot=0,hit=0.5; float lot2=1.0; this->UpdateTimeVaryingVelocityFieldWithSyNFandSyNM( );// sets tvt to SyNF and SyNM -- works only for 2 time points! this->m_SyNFInv = this->IntegrateVelocity(hit,lot); this->m_SyNMInv = this->IntegrateVelocity(hit,lot2); if (aff){ affinverse=AffineTransformType::New(); aff->GetInverse(affinverse); } if ( mpoints ) { /**FIXME -- NEED INTEGRATION FOR POINTS ONLY -- warp landmarks for * tv-field */ // std::cout <<" aff " << std::endl; /** NOte, totalUpdateInvField is filled with zeroes! -- we only want affine mapping */ wmpoints = this->WarpMultiTransform(fixedImage,movingImage, mpoints , aff , totalUpdateInvField , true, NULL ); DeformationFieldPointer mdiffmap = this->IntegrateLandmarkSetVelocity(lot2,hit,wmpoints,movingImage); wmpoints = this->WarpMultiTransform(fixedImage,movingImage, wmpoints , NULL , mdiffmap , true , NULL ); } if ( fpoints ) {// need full inverse map wfpoints = this->WarpMultiTransform(fixedImage,movingImage, fpoints , NULL , totalUpdateInvField , true, this->m_FixedImageAffineTransform ); DeformationFieldPointer fdiffmap = this->IntegrateLandmarkSetVelocity(lot,hit,wfpoints,fixedImage); wfpoints = this->WarpMultiTransform(fixedImage,fixedImage, wfpoints , NULL , fdiffmap , false , NULL ); } totalUpdateField=this->ComputeUpdateField( this->m_SyNMInv, this->m_SyNFInv , wfpoints, wmpoints,totalUpdateInvField,true); for( dIter.GoToBegin(); !dIter.IsAtEnd(); ++dIter ) { typename ImageType::IndexType index=dIter.GetIndex(); VectorType vecf=totalUpdateField->GetPixel(index)*1; VectorType vecm=totalUpdateInvField->GetPixel(index); // update time components 1 & 2 this->m_SyNF->SetPixel( index, this->m_SyNF->GetPixel( index )+vecf*this->m_GradstepAltered); this->m_SyNM->SetPixel( index, this->m_SyNM->GetPixel( index )+vecm*this->m_GradstepAltered); // min field difference => geodesic => DV/dt=0 float geowt1=0.95; float geowt2=1.0-geowt1; VectorType synmv=this->m_SyNM->GetPixel( index ); VectorType synfv =this->m_SyNF->GetPixel( index ); this->m_SyNM->SetPixel( index, synmv*geowt1-synfv*geowt2); this->m_SyNF->SetPixel( index, synfv*geowt1-synmv*geowt2); } if ( this->m_TotalSmoothingparam > 0 || this->m_TotalSmoothingMeshSize[0] > 0 ) { //smooth time components separately this->SmoothDeformationField( this->m_SyNF,false); this->SmoothDeformationField( this->m_SyNM,false); } return; } template void ANTSImageRegistrationOptimizer ::DiReCTUpdate(ImagePointer fixedImage, ImagePointer movingImage, PointSetPointer fpoints, PointSetPointer mpoints) { typedef TimeVaryingVelocityFieldType tvt; TimeVaryingVelocityFieldPointer velocityUpdate=NULL; typename ImageType::SpacingType spacing=fixedImage->GetSpacing(); VectorType zero; zero.Fill(0); DeformationFieldPointer totalUpdateField,totalUpdateInvField=DeformationFieldType::New(); totalUpdateInvField->SetSpacing( this->m_DeformationField->GetSpacing() ); totalUpdateInvField->SetOrigin( this->m_DeformationField->GetOrigin() ); totalUpdateInvField->SetDirection( this->m_DeformationField->GetDirection() ); totalUpdateInvField->SetLargestPossibleRegion(this->m_DeformationField->GetLargestPossibleRegion() ); totalUpdateInvField->SetRequestedRegion( this->m_DeformationField->GetLargestPossibleRegion() ); totalUpdateInvField->SetBufferedRegion( this->m_DeformationField->GetLargestPossibleRegion() ); totalUpdateInvField->Allocate(); totalUpdateInvField->FillBuffer(zero); unsigned long numpx=this->m_DeformationField->GetBufferedRegion().GetNumberOfPixels(); bool generatetvfield=false; bool enlargefield=false; if (!this->m_TimeVaryingVelocity) generatetvfield=true; else { for (int jj=0; jjm_CurrentDomainSize[jj] != this->m_TimeVaryingVelocity->GetLargestPossibleRegion().GetSize()[jj]) enlargefield=true; } velocityUpdate=tvt::New(); typename tvt::RegionType gregion; typename tvt::SizeType gsize; typename tvt::SpacingType gspace; typename tvt::PointType gorigin; gorigin.Fill(0); for (unsigned int dim=0; dimm_CurrentDomainSize[dim]; gspace[dim]=this->m_CurrentDomainSpacing[dim]; gorigin[dim]=this->m_CurrentDomainOrigin[dim]; } if ( this->m_NTimeSteps < 2 ) this->m_NTimeSteps=2; gsize[TDimension]=(unsigned long) this->m_NTimeSteps; float hitstep=1.0/((float)this->m_NTimeSteps-1); gspace[TDimension]=1; gregion.SetSize(gsize); velocityUpdate->SetSpacing( gspace ); velocityUpdate->SetOrigin( gorigin ); /** The TV Field has the direction of the sub-image -- the time domain has identity transform */ typename tvt::DirectionType iddir; iddir.Fill(0); iddir[ImageDimension][ImageDimension]=1; for (unsigned int i=0; iGetDeformationField()->GetDirection()[i][j]; velocityUpdate->SetDirection( iddir ); velocityUpdate->SetLargestPossibleRegion(gregion); velocityUpdate->SetRequestedRegion( gregion); velocityUpdate->SetBufferedRegion( gregion ); velocityUpdate->Allocate(); velocityUpdate->FillBuffer(zero); if (generatetvfield) { this->m_TimeVaryingVelocity=tvt::New(); this->m_TimeVaryingVelocity->SetSpacing( gspace ); this->m_TimeVaryingVelocity->SetOrigin( gorigin ); this->m_TimeVaryingVelocity->SetDirection( iddir ); this->m_TimeVaryingVelocity->SetLargestPossibleRegion(gregion); this->m_TimeVaryingVelocity->SetRequestedRegion( gregion); this->m_TimeVaryingVelocity->SetBufferedRegion( gregion ); this->m_TimeVaryingVelocity->Allocate(); this->m_TimeVaryingVelocity->FillBuffer(zero); /* this->m_LastTimeVaryingVelocity=tvt::New(); this->m_LastTimeVaryingVelocity->SetSpacing( gspace ); this->m_LastTimeVaryingVelocity->SetOrigin( gorigin ); this->m_LastTimeVaryingVelocity->SetDirection( iddir ); this->m_LastTimeVaryingVelocity->SetLargestPossibleRegion(gregion); this->m_LastTimeVaryingVelocity->SetRequestedRegion( gregion); this->m_LastTimeVaryingVelocity->SetBufferedRegion( gregion ); this->m_LastTimeVaryingVelocity->Allocate(); this->m_LastTimeVaryingVelocity->FillBuffer(zero); */ this->m_LastTimeVaryingUpdate=tvt::New(); this->m_LastTimeVaryingUpdate->SetSpacing( gspace ); this->m_LastTimeVaryingUpdate->SetOrigin( gorigin ); this->m_LastTimeVaryingUpdate->SetDirection( iddir ); this->m_LastTimeVaryingUpdate->SetLargestPossibleRegion(gregion); this->m_LastTimeVaryingUpdate->SetRequestedRegion( gregion); this->m_LastTimeVaryingUpdate->SetBufferedRegion( gregion ); this->m_LastTimeVaryingUpdate->Allocate(); this->m_LastTimeVaryingUpdate->FillBuffer(zero); } else if ( enlargefield ){ this->m_TimeVaryingVelocity=this->ExpandVelocity(); this->m_TimeVaryingVelocity->SetSpacing(gspace); this->m_TimeVaryingVelocity->SetOrigin(gorigin); /* this->m_LastTimeVaryingVelocity=tvt::New(); this->m_LastTimeVaryingVelocity->SetSpacing( gspace ); this->m_LastTimeVaryingVelocity->SetOrigin( gorigin ); this->m_LastTimeVaryingVelocity->SetDirection( iddir ); this->m_LastTimeVaryingVelocity->SetLargestPossibleRegion(gregion); this->m_LastTimeVaryingVelocity->SetRequestedRegion( gregion); this->m_LastTimeVaryingVelocity->SetBufferedRegion( gregion ); this->m_LastTimeVaryingVelocity->Allocate(); this->m_LastTimeVaryingVelocity->FillBuffer(zero);*/ this->m_LastTimeVaryingUpdate=tvt::New(); this->m_LastTimeVaryingUpdate->SetSpacing( gspace ); this->m_LastTimeVaryingUpdate->SetOrigin( gorigin ); this->m_LastTimeVaryingUpdate->SetDirection( iddir ); this->m_LastTimeVaryingUpdate->SetLargestPossibleRegion(gregion); this->m_LastTimeVaryingUpdate->SetRequestedRegion( gregion); this->m_LastTimeVaryingUpdate->SetBufferedRegion( gregion ); this->m_LastTimeVaryingUpdate->Allocate(); this->m_LastTimeVaryingUpdate->FillBuffer(zero); } if (!this->m_SyNF) { std::cout <<" Allocating " << std::endl; this->m_SyNF=this->CopyDeformationField(totalUpdateInvField); this->m_SyNFInv=this->CopyDeformationField(this->m_SyNF); this->m_SyNM=this->CopyDeformationField(totalUpdateInvField); this->m_SyNMInv=this->CopyDeformationField(this->m_SyNF); std::cout <<" Allocating Done " << std::endl; } if (!this->m_SyNF) { std::cout<<" F'D UP " << std::endl;} ImagePointer wfimage,wmimage; PointSetPointer wfpoints=NULL,wmpoints=NULL; AffineTransformPointer aff =this->m_AffineTransform; AffineTransformPointer affinverse=NULL; typedef ImageRegionIteratorWithIndex Iterator; Iterator dIter(this->m_SyNF,this->m_SyNF->GetLargestPossibleRegion() ); //here, SyNF holds the moving velocity field, SyNM holds the fixed //velocity field and we integrate both to generate the inv/fwd fields typename JacobianFunctionType::Pointer jfunction = JacobianFunctionType::New(); float lot=0, lot2=1.0; unsigned int fct=100; for (float hit=0; hit<=1; hit=hit+hitstep) { this->m_SyNFInv = this->IntegrateVelocity(hit,lot); this->m_SyNMInv = this->IntegrateVelocity(hit,lot2); if ( false && this->m_CurrentIteration == 1 && this->m_SyNFInv ) { typedef itk::VectorImageFileWriter DeformationFieldWriterType; typename DeformationFieldWriterType::Pointer writer = DeformationFieldWriterType::New(); std::ostringstream osstream; osstream << fct; fct++; std::string fnm = std::string("field1")+osstream.str()+std::string("warp.nii.gz"); std::string fnm2 = std::string("field2")+osstream.str()+std::string("warp.nii.gz"); writer->SetUseAvantsNamingConvention( true ); writer->SetInput( this->m_SyNFInv ); writer->SetFileName( fnm.c_str() ); std::cout << " write " << fnm << std::endl; writer->Update(); // writer->SetInput( this->m_SyNMInv ); // writer->SetFileName( fnm2.c_str() ); // writer->Update(); } if (aff){ affinverse=AffineTransformType::New(); aff->GetInverse(affinverse); } if ( mpoints ) { /**FIXME -- NEED INTEGRATION FOR POINTS ONLY -- warp landmarks for * tv-field */ // std::cout <<" aff " << std::endl; /** NOte, totalUpdateInvField is filled with zeroes! -- we only want affine mapping */ wmpoints = this->WarpMultiTransform(fixedImage,movingImage, mpoints , aff , totalUpdateInvField , true, NULL ); DeformationFieldPointer mdiffmap = this->IntegrateLandmarkSetVelocity(lot2,hit,wmpoints,movingImage); wmpoints = this->WarpMultiTransform(fixedImage,movingImage, wmpoints , NULL , mdiffmap , true , NULL ); } if ( fpoints ) {// need full inverse map wfpoints = this->WarpMultiTransform(fixedImage,movingImage, fpoints , NULL , totalUpdateInvField , true, this->m_FixedImageAffineTransform ); DeformationFieldPointer fdiffmap = this->IntegrateLandmarkSetVelocity(lot,hit,wfpoints,fixedImage); wfpoints = this->WarpMultiTransform(fixedImage,fixedImage, wfpoints , NULL , fdiffmap , false , NULL ); } totalUpdateField=this->ComputeUpdateField( this->m_SyNMInv, this->m_SyNFInv , wfpoints, wmpoints,totalUpdateInvField,true); if ( this->m_SyNFullTime == 2 ) totalUpdateInvField=NULL; this->CopyOrAddToVelocityField( velocityUpdate, totalUpdateField , totalUpdateInvField, hit ); } // http://en.wikipedia.org/wiki/Conjugate_gradient_method // below is r_k+1 this->SmoothVelocityGauss( velocityUpdate , this->m_GradSmoothingparam , ImageDimension ); // update total velocity with v-update float tmag=0; typedef itk::ImageRegionIteratorWithIndex TVFieldIterator; TVFieldIterator m_FieldIter( this->m_TimeVaryingVelocity,this->m_TimeVaryingVelocity->GetLargestPossibleRegion()); for( m_FieldIter.GoToBegin(); !m_FieldIter.IsAtEnd(); ++m_FieldIter ) { float A=1; float alpha=0,alpha1=0,alpha2=0,beta=0,beta1=0,beta2=0; VectorType vec1=velocityUpdate->GetPixel(m_FieldIter.GetIndex()); // r_k+1 VectorType vec2=vec1;//this->m_LastTimeVaryingVelocity->GetPixel(m_FieldIter.GetIndex()); // r_k VectorType upd=this->m_LastTimeVaryingUpdate->GetPixel(m_FieldIter.GetIndex()); // p_k for (unsigned int ii=0; ii 0 ) alpha=alpha1/(A*alpha2+0.001); if (beta2 > 0 ) beta=beta1/(beta2+0.001); if (beta > 1) beta=1; if (alpha > 1) alpha=1; // std::cout <<" beta " << beta << " alpha " << alpha << " it " << this->m_CurrentIteration << std::endl; VectorType newupd=(vec1); if ( this->m_CurrentIteration > 2) { newupd=(vec1+upd)*0.5; } VectorType newsoln=m_FieldIter.Get()+ this->m_GradstepAltered*newupd; m_FieldIter.Set( newsoln ); // VectorType vec2u=vec2 - alpha*A*upd; // this->m_LastTimeVaryingVelocity->SetPixel(m_FieldIter.GetIndex(), vec1 ); this->m_LastTimeVaryingUpdate->SetPixel(m_FieldIter.GetIndex(), newupd); float mag=0; VectorType vv=m_FieldIter.Get(); for (unsigned int jj=0; jjm_NTimeSteps*(float)numpx); std::cout << " DiffLength " << tmag << std::endl; if ( this->m_TotalSmoothingparam > 0 || this->m_TotalSmoothingMeshSize[0] > 0 ) { this->SmoothVelocityGauss( this->m_TimeVaryingVelocity , this->m_TotalSmoothingparam , ImageDimension ); // this->SmoothDeformationField( this->m_SyNF,false); // this->SmoothDeformationField( this->m_SyNM,false); } return; } template typename ANTSImageRegistrationOptimizer::DeformationFieldPointer ANTSImageRegistrationOptimizer ::IntegrateVelocity(float starttimein, float finishtimein ) { ImagePointer mask=NULL; if ( this->m_SyNMInv && this->m_MaskImage) mask= this->WarpMultiTransform( this->m_MaskImage, this->m_MaskImage, NULL, this->m_SyNMInv, false , this->m_FixedImageAffineTransform ); else if (this->m_MaskImage) mask=this->SubsampleImage( this->m_MaskImage, this->m_ScaleFactor , this->m_MaskImage->GetOrigin() , this->m_MaskImage->GetDirection() , NULL); // std::cout << " st " << starttimein << " ft " << finishtimein << std::endl; typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image DeformationFieldType; typedef itk::Image ImageType; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef TimeVaryingVelocityFieldType tvt; bool dothick=false; if ( finishtimein > starttimein && this->m_ComputeThickness ) dothick=true; if ( dothick && this->m_CurrentIteration > 2 ) { this->m_ThickImage=ImageType::New(); this->m_ThickImage->SetSpacing( this->m_SyNF->GetSpacing() ); this->m_ThickImage->SetOrigin( this->m_SyNF->GetOrigin() ); this->m_ThickImage->SetDirection( this->m_SyNF->GetDirection() ); this->m_ThickImage->SetLargestPossibleRegion(this->m_SyNF->GetLargestPossibleRegion() ); this->m_ThickImage->SetRequestedRegion( this->m_SyNF->GetLargestPossibleRegion() ); this->m_ThickImage->SetBufferedRegion( this->m_SyNF->GetLargestPossibleRegion() ); this->m_ThickImage->Allocate(); this->m_ThickImage->FillBuffer(0); this->m_HitImage=ImageType::New(); this->m_HitImage->SetSpacing( this->m_SyNF->GetSpacing() ); this->m_HitImage->SetOrigin( this->m_SyNF->GetOrigin() ); this->m_HitImage->SetDirection( this->m_SyNF->GetDirection() ); this->m_HitImage->SetLargestPossibleRegion(this->m_SyNF->GetLargestPossibleRegion() ); this->m_HitImage->SetRequestedRegion( this->m_SyNF->GetLargestPossibleRegion() ); this->m_HitImage->SetBufferedRegion( this->m_SyNF->GetLargestPossibleRegion() ); this->m_HitImage->Allocate(); this->m_HitImage->FillBuffer(0); } else { this->m_HitImage=NULL; this->m_ThickImage=NULL; } DeformationFieldPointer intfield=DeformationFieldType::New(); intfield->SetSpacing( this->m_CurrentDomainSpacing ); intfield->SetOrigin( this->m_DeformationField->GetOrigin() ); intfield->SetDirection( this->m_DeformationField->GetDirection() ); intfield->SetLargestPossibleRegion( this->m_DeformationField->GetLargestPossibleRegion()); intfield->SetRequestedRegion( this->m_DeformationField->GetLargestPossibleRegion()); intfield->SetBufferedRegion( this->m_DeformationField->GetLargestPossibleRegion() ); intfield->Allocate(); VectorType zero; zero.Fill(0); intfield->FillBuffer(zero); if (starttimein == finishtimein) return intfield; if (!this->m_TimeVaryingVelocity) { std::cout << " No TV Field " << std::endl; return intfield; } this->m_VelocityFieldInterpolator->SetInputImage(this->m_TimeVaryingVelocity); typedef tvt TimeVaryingVelocityFieldType; typedef itk::ImageRegionIteratorWithIndex FieldIterator; typedef itk::ImageRegionIteratorWithIndex TVFieldIterator; typedef typename DeformationFieldType::IndexType DIndexType; typedef typename DeformationFieldType::PointType DPointType; typedef typename TimeVaryingVelocityFieldType::IndexType VIndexType; typedef typename TimeVaryingVelocityFieldType::PointType VPointType; if (starttimein < 0) starttimein=0; if (starttimein > 1) starttimein=1; if (finishtimein < 0) finishtimein=0; if (finishtimein > 1) finishtimein=1; float timesign=1.0; if (starttimein > finishtimein ) timesign= -1.0; FieldIterator m_FieldIter(this->GetDeformationField(), this->GetDeformationField()->GetLargestPossibleRegion()); // std::cout << " Start Int " << starttimein << std::endl; if ( mask && !this->m_ComputeThickness ) { for( m_FieldIter.GoToBegin(); !m_FieldIter.IsAtEnd(); ++m_FieldIter ) { IndexType velind=m_FieldIter.GetIndex(); VectorType disp; if (mask->GetPixel(velind) > 0.05 ) disp=this->IntegratePointVelocity(starttimein, finishtimein , velind)*mask->GetPixel(velind); else disp.Fill(0); intfield->SetPixel(velind,disp); } } else { for( m_FieldIter.GoToBegin(); !m_FieldIter.IsAtEnd(); ++m_FieldIter ) { IndexType velind=m_FieldIter.GetIndex(); VectorType disp=this->IntegratePointVelocity(starttimein, finishtimein , velind); intfield->SetPixel(velind,disp); } } if (this->m_ThickImage && this->m_MaskImage ){ std::string outname=this->localANTSGetFilePrefix(this->m_OutputNamingConvention.c_str())+std::string("thick.nii.gz"); std::cout << " write " << outname << std::endl; WriteImage(this->m_ThickImage,outname.c_str()); } return intfield; } template typename ANTSImageRegistrationOptimizer::DeformationFieldPointer ANTSImageRegistrationOptimizer ::IntegrateLandmarkSetVelocity(float starttimein, float finishtimein, typename ANTSImageRegistrationOptimizer::PointSetPointer mypoints , typename ANTSImageRegistrationOptimizer::ImagePointer refimage ) { typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image DeformationFieldType; typedef itk::Image ImageType; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef TimeVaryingVelocityFieldType tvt; DeformationFieldPointer intfield=DeformationFieldType::New(); intfield->SetSpacing( this->m_CurrentDomainSpacing ); intfield->SetOrigin( this->m_DeformationField->GetOrigin() ); intfield->SetDirection( this->m_DeformationField->GetDirection() ); intfield->SetLargestPossibleRegion( this->m_DeformationField->GetLargestPossibleRegion()); intfield->SetRequestedRegion( this->m_DeformationField->GetLargestPossibleRegion()); intfield->SetBufferedRegion( this->m_DeformationField->GetLargestPossibleRegion() ); intfield->Allocate(); VectorType zero; zero.Fill(0); intfield->FillBuffer(zero); if (starttimein == finishtimein) return intfield; if (!this->m_TimeVaryingVelocity) { std::cout << " No TV Field " << std::endl; return intfield; } this->m_VelocityFieldInterpolator->SetInputImage(this->m_TimeVaryingVelocity); typedef tvt TimeVaryingVelocityFieldType; typedef itk::ImageRegionIteratorWithIndex FieldIterator; typedef itk::ImageRegionIteratorWithIndex TVFieldIterator; typedef typename DeformationFieldType::IndexType DIndexType; typedef typename DeformationFieldType::PointType DPointType; typedef typename TimeVaryingVelocityFieldType::IndexType VIndexType; typedef typename TimeVaryingVelocityFieldType::PointType VPointType; if (starttimein < 0) starttimein=0; if (starttimein > 1) starttimein=1; if (finishtimein < 0) finishtimein=0; if (finishtimein > 1) finishtimein=1; float timesign=1.0; if (starttimein > finishtimein ) timesign= -1.0; unsigned long sz1 = mypoints->GetNumberOfPoints(); for (unsigned long ii=0; iiGetPoint(ii,&point); //std::cout <<" get point index " << point << std::endl; ImagePointType pt,wpt; for (unsigned int jj=0; jjTransformPhysicalPointToIndex( pt, velind ); //std::cout <<" inside? " << bisinside << std::endl; if (bisinside) { // std::cout << "integrate " << std::endl; VectorType disp=this->IntegratePointVelocity(starttimein, finishtimein , velind); // std::cout << "put inside " << std::endl; intfield->SetPixel(velind,disp); } } return intfield; } template typename ANTSImageRegistrationOptimizer::VectorType ANTSImageRegistrationOptimizer ::IntegratePointVelocity(float starttimein, float finishtimein , IndexType velind) { typedef Point xPointType; this->m_Debug=false; // std::cout <<"Enter IP "<< std::endl; typedef typename VelocityFieldInterpolatorType::OutputType InterpPointType; typedef float PixelType; typedef itk::Vector VectorType; typedef itk::Image DeformationFieldType; typedef itk::Image ImageType; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::SpacingType SpacingType; typedef TimeVaryingVelocityFieldType tvt; VectorType zero; zero.Fill(0); if (starttimein == finishtimein) return zero; typedef tvt TimeVaryingVelocityFieldType; typedef itk::ImageRegionIteratorWithIndex FieldIterator; typedef itk::ImageRegionIteratorWithIndex TVFieldIterator; typedef typename DeformationFieldType::IndexType DIndexType; typedef typename DeformationFieldType::PointType DPointType; typedef typename TimeVaryingVelocityFieldType::IndexType VIndexType; typedef typename TimeVaryingVelocityFieldType::PointType VPointType; this->m_VelocityFieldInterpolator->SetInputImage(this->m_TimeVaryingVelocity); double dT=this->m_DeltaTime; unsigned int m_NumberOfTimePoints = this->m_TimeVaryingVelocity->GetLargestPossibleRegion().GetSize()[TDimension]; if (starttimein < 0) starttimein=0; if (starttimein > 1) starttimein=1; if (finishtimein < 0) finishtimein=0; if (finishtimein > 1) finishtimein=1; float timesign=1.0; if (starttimein > finishtimein ) timesign= -1.0; VectorType velo; velo.Fill(0); xPointType pointIn1; xPointType pointIn2; xPointType pointIn3; typename VelocityFieldInterpolatorType::ContinuousIndexType vcontind; float itime = starttimein; unsigned long ct = 0; float inverr=0; float thislength=0,euclideandist=0; bool timedone = false; inverr=1110; VectorType disp; double deltaTime=dT,vecsign=1.0; SpacingType spacing= this->m_DeformationField->GetSpacing(); if (starttimein > finishtimein ) vecsign=-1.0; VIndexType vind; vind.Fill(0); for (unsigned int jj=0; jjm_TimeVaryingVelocity->TransformIndexToPhysicalPoint( vind, pointIn1); // time is in [0,1] pointIn1[TDimension]= starttimein*(m_NumberOfTimePoints-1); bool isinside=true; xPointType Y1x; xPointType Y2x; xPointType Y3x; xPointType Y4x; // set up parameters for start of integration disp.Fill(0.0); timedone=false; itime = starttimein; ct = 0; while ( !timedone ) { double itimetn1 = itime - timesign*deltaTime; double itimetn1h = itime - timesign*deltaTime*0.5; if (itimetn1h < 0 ) itimetn1h=0; if (itimetn1h > 1 ) itimetn1h=1; if (itimetn1 < 0 ) itimetn1=0; if (itimetn1 > 1 ) itimetn1=1; float totalmag=0; // first get current position of particle typename VelocityFieldInterpolatorType::OutputType f1; f1.Fill(0); typename VelocityFieldInterpolatorType::OutputType f2; f2.Fill(0); typename VelocityFieldInterpolatorType::OutputType f3; f3.Fill(0); typename VelocityFieldInterpolatorType::OutputType f4; f4.Fill(0); for (unsigned int jj=0; jjm_Debug) std::cout << " p2 " << pointIn2<< std::endl; Y1x[TDimension]=itimetn1*(float)(m_NumberOfTimePoints-1); Y2x[TDimension]=itimetn1h*(float)(m_NumberOfTimePoints-1); Y3x[TDimension]=itimetn1h*(float)(m_NumberOfTimePoints-1); Y4x[TDimension]=itime*(float)(m_NumberOfTimePoints-1); if (this->m_Debug) std::cout << " p2 " << pointIn2<< " y1 " << Y1x[TDimension] << " y4 " << Y4x[TDimension] << std::endl; if ( this->m_VelocityFieldInterpolator->IsInsideBuffer(Y1x) ) { f1 = this->m_VelocityFieldInterpolator->Evaluate( Y1x ); for (unsigned int jj=0; jjm_VelocityFieldInterpolator->IsInsideBuffer(Y2x) ) { f2 = this->m_VelocityFieldInterpolator->Evaluate( Y2x ); for (unsigned int jj=0; jjm_VelocityFieldInterpolator->IsInsideBuffer(Y3x) ) { f3 = this->m_VelocityFieldInterpolator->Evaluate( Y3x ); for (unsigned int jj=0; jjm_VelocityFieldInterpolator->IsInsideBuffer(Y4x) ) { f4 = this->m_VelocityFieldInterpolator->Evaluate( Y4x ); } for (unsigned int jj=0; jj finishtimein) { if (itime <= finishtimein ) timedone=true; } else if (thislength == 0) timedone=true; else { if (itime >= finishtimein ) timedone=true; } } // now we have the thickness value stored in thislength if ( this->m_ThickImage && this->m_HitImage){ // set up parameters for start of integration velo.Fill(0); itime = starttimein; timedone = false; vind.Fill(0); for (unsigned int jj=0; jjm_TimeVaryingVelocity->TransformIndexToPhysicalPoint( vind, pointIn1); // time is in [0,1] pointIn1[TDimension]= starttimein*(m_NumberOfTimePoints-1); // set up parameters for start of integration disp.Fill(0.0); timedone=false; itime = starttimein; ct = 0; while ( !timedone ) { double itimetn1 = itime - timesign*deltaTime; double itimetn1h = itime - timesign*deltaTime*0.5; if (itimetn1h < 0 ) itimetn1h=0; if (itimetn1h > 1 ) itimetn1h=1; if (itimetn1 < 0 ) itimetn1=0; if (itimetn1 > 1 ) itimetn1=1; // float totalmag=0; // first get current position of particle typename VelocityFieldInterpolatorType::OutputType f1; f1.Fill(0); typename VelocityFieldInterpolatorType::OutputType f2; f2.Fill(0); typename VelocityFieldInterpolatorType::OutputType f3; f3.Fill(0); typename VelocityFieldInterpolatorType::OutputType f4; f4.Fill(0); for (unsigned int jj=0; jjm_Debug) std::cout << " p2 " << pointIn2<< std::endl; Y1x[TDimension]=itimetn1*(float)(m_NumberOfTimePoints-1); Y2x[TDimension]=itimetn1h*(float)(m_NumberOfTimePoints-1); Y3x[TDimension]=itimetn1h*(float)(m_NumberOfTimePoints-1); Y4x[TDimension]=itime*(float)(m_NumberOfTimePoints-1); if (this->m_Debug) std::cout << " p2 " << pointIn2<< " y1 " << Y1x[TDimension] << " y4 " << Y4x[TDimension] << std::endl; if ( this->m_VelocityFieldInterpolator->IsInsideBuffer(Y1x) ) { f1 = this->m_VelocityFieldInterpolator->Evaluate( Y1x ); for (unsigned int jj=0; jjm_VelocityFieldInterpolator->IsInsideBuffer(Y2x) ) { f2 = this->m_VelocityFieldInterpolator->Evaluate( Y2x ); for (unsigned int jj=0; jjm_VelocityFieldInterpolator->IsInsideBuffer(Y3x) ) { f3 = this->m_VelocityFieldInterpolator->Evaluate( Y3x ); for (unsigned int jj=0; jjm_VelocityFieldInterpolator->IsInsideBuffer(Y4x) ) { f4 = this->m_VelocityFieldInterpolator->Evaluate( Y4x ); } for (unsigned int jj=0; jj finishtimein) { if (itime <= finishtimein ) timedone=true; } else { if (itime >= finishtimein ) timedone=true; } // bool isingray=true; if ( this->m_MaskImage ) { if ( this->m_MaskImage->GetPixel(velind) ) { VIndexType thind2; IndexType thind; bool isin=this->m_TimeVaryingVelocity->TransformPhysicalPointToIndex( pointIn3, thind2 ); for (unsigned int ij=0; ijm_HitImage->GetPixel(thind); unsigned long newct=lastct+1; float oldthick=this->m_ThickImage->GetPixel(thind); float newthick=(float)lastct/(float)newct*oldthick+1.0/(float)newct*euclideandist; this->m_HitImage->SetPixel( thind, newct ); this->m_ThickImage->SetPixel(thind, newthick ); } else std::cout << " thind " << thind << " edist " << euclideandist << " p3 " << pointIn3 << " p1 " << pointIn1 << std::endl; // this->m_ThickImage->SetPixel(thind, thislength ); } } } } // if (!isinside) { std::cout << " velind " << velind << " not inside " << Y1 << std::endl; } if (this->m_Debug) std::cout << " Length " << thislength << std::endl; this->m_Debug=false; return disp; } /** * Standard "PrintSelf" method */ template void ANTSImageRegistrationOptimizer ::PrintSelf( std::ostream& os, Indent indent) const { Superclass::PrintSelf( os, indent ); } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkANTSImageRegistrationOptimizer.h000066400000000000000000002204411147325206600267670ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkANTSImageRegistrationOptimizer.h,v $ Language: C++ Date: $Date: 2009/04/22 01:00:17 $ Version: $Revision: 1.44 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkANTSImageRegistrationOptimizer_h #define __itkANTSImageRegistrationOptimizer_h #include "itkObject.h" #include "itkObjectFactory.h" #include "itkVectorGaussianInterpolateImageFunction.h" #include "antsCommandLineParser.h" #include "itkShiftScaleImageFilter.h" #include "itkMinimumMaximumImageFilter.h" #include "itkImage.h" #include "itkMacro.h" #include "ReadWriteImage.h" #include "itkCenteredEuler3DTransform.h" #include "itkQuaternionRigidTransform.h" #include "itkANTSAffine3DTransform.h" #include "itkANTSCenteredAffine2DTransform.h" #include "itkCenteredTransformInitializer.h" #include "itkTransformFileReader.h" #include "itkTransformFileWriter.h" #include "itkFiniteDifferenceFunction.h" #include "itkFixedArray.h" #include "itkANTSSimilarityMetric.h" #include "itkVectorExpandImageFilter.h" //#include "itkNeighbohoodAlgorithm.h" #include "itkPDEDeformableRegistrationFilter.h" #include "itkWarpImageFilter.h" #include "itkWarpImageMultiTransformFilter.h" #include "itkDeformationFieldFromMultiTransformFilter.h" #include "itkWarpImageWAffineFilter.h" #include "itkPointSet.h" #include "itkVector.h" #include "itkBSplineScatteredDataPointSetToImageFilter.h" #include "itkGeneralToBSplineDeformationFieldFilter.h" #include "ANTS_affine_registration2.h" #include "itkVectorFieldGradientImageFunction.h" #include "itkBSplineInterpolateImageFunction.h" namespace itk { template class ITK_EXPORT ANTSImageRegistrationOptimizer : public Object { public: /** Standard class typedefs. */ typedef ANTSImageRegistrationOptimizer Self; typedef Object Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); /** Run-time type information (and related methods). */ itkTypeMacro( ANTSImageRegistrationOptimizer, Object ); itkStaticConstMacro( Dimension, unsigned int, TDimension ); itkStaticConstMacro( ImageDimension, unsigned int, TDimension ); typedef TReal RealType; typedef Image ImageType; typedef typename ImageType::Pointer ImagePointer; typedef itk::MatrixOffsetTransformBase< double, ImageDimension, ImageDimension > TransformType; /** Point Types for landmarks and labeled point-sets */ typedef itk::ANTSLabeledPointSet LabeledPointSetType; typedef typename LabeledPointSetType::Pointer LabeledPointSetPointer; typedef typename LabeledPointSetType::PointSetType PointSetType; typedef typename PointSetType::Pointer PointSetPointer; typedef typename PointSetType::PointType PointType; typedef typename PointSetType::PixelType PointDataType; typedef typename ImageType::PointType ImagePointType; typedef itk::MatrixOffsetTransformBase AffineTransformType; typedef typename AffineTransformType::Pointer AffineTransformPointer; typedef OptAffine OptAffineType; typedef itk::Vector VectorType; typedef itk::Image DeformationFieldType; typedef typename DeformationFieldType::Pointer DeformationFieldPointer; typedef itk::Image TimeVaryingVelocityFieldType; typedef typename TimeVaryingVelocityFieldType::Pointer TimeVaryingVelocityFieldPointer; typedef itk::VectorLinearInterpolateImageFunction VelocityFieldInterpolatorType; typedef itk::VectorGaussianInterpolateImageFunction VelocityFieldInterpolatorType2; typedef typename DeformationFieldType::IndexType IndexType; typedef ants::CommandLineParser ParserType; typedef typename ParserType::OptionType OptionType; typedef GeneralToBSplineDeformationFieldFilter BSplineFilterType; typedef FixedArray ArrayType; /** Typedefs for similarity metrics */ typedef ANTSSimilarityMetric SimilarityMetricType; typedef typename SimilarityMetricType::Pointer SimilarityMetricPointer; typedef std::vector SimilarityMetricListType; /** FiniteDifferenceFunction type. */ typedef FiniteDifferenceFunction FiniteDifferenceFunctionType; typedef typename FiniteDifferenceFunctionType::TimeStepType TimeStepType; typedef typename FiniteDifferenceFunctionType::Pointer FiniteDifferenceFunctionPointer; typedef AvantsPDEDeformableRegistrationFunction MetricBaseType; typedef typename MetricBaseType::Pointer MetricBaseTypePointer; /* Jacobian and other calculations */ typedef itk::VectorFieldGradientImageFunction JacobianFunctionType; /** Set functions */ void SetAffineTransform(AffineTransformPointer A) {this->m_AffineTransform=A;} void SetDeformationField(DeformationFieldPointer A) {this->m_DeformationField=A;} void SetInverseDeformationField(DeformationFieldPointer A) {this->m_InverseDeformationField=A;} void SetMaskImage( ImagePointer m) { this->m_MaskImage=m; } void SetFixedImageAffineTransform(AffineTransformPointer A) {this->m_FixedImageAffineTransform=A;} AffineTransformPointer GetFixedImageAffineTransform() {return this->m_FixedImageAffineTransform;} /** Get functions */ AffineTransformPointer GetAffineTransform() {return this->m_AffineTransform;} DeformationFieldPointer GetDeformationField( ) {return this->m_DeformationField;} DeformationFieldPointer GetInverseDeformationField() {return this->m_InverseDeformationField;} /** Initialize all parameters */ void SetNumberOfLevels(unsigned int i) {this->m_NumberOfLevels=i;} void SetParser( typename ParserType::Pointer P ) {this->m_Parser=P;} /** Basic operations */ DeformationFieldPointer CopyDeformationField( DeformationFieldPointer input ); std::string localANTSGetFilePrefix(const char *str){ std::string filename = str; std::string::size_type pos = filename.rfind( "." ); std::string filepre = std::string( filename, 0, pos ); if ( pos != std::string::npos ){ std::string extension = std::string( filename, pos, filename.length()-1); if (extension==std::string(".gz")){ pos = filepre.rfind( "." ); extension = std::string( filepre, pos, filepre.length()-1 ); } // if (extension==".txt") return AFFINE_FILE; // else return DEFORMATION_FILE; } // else{ // return INVALID_FILE; //} return filepre; } void SmoothDeformationField(DeformationFieldPointer field, bool TrueEqualsGradElseTotal ) { typename ParserType::OptionType::Pointer regularizationOption = this->m_Parser->GetOption( "regularization" ); if ( ( regularizationOption->GetValue() ).find( "DMFFD" ) != std::string::npos ) { if( ( !TrueEqualsGradElseTotal && this->m_TotalSmoothingparam == 0.0 ) || ( TrueEqualsGradElseTotal && this->m_GradSmoothingparam == 0.0 ) ) { return; } ArrayType meshSize; unsigned int splineOrder = this->m_BSplineFieldOrder; float bsplineKernelVariance = static_cast( splineOrder + 1 ) / 12.0; unsigned int numberOfLevels = 1; if( TrueEqualsGradElseTotal ) { if( this->m_GradSmoothingparam < 0.0 ) { meshSize = this->m_GradSmoothingMeshSize; for( unsigned int d = 0; d < ImageDimension; d++ ) { meshSize[d] *= static_cast( vcl_pow( 2.0, static_cast( this->m_CurrentLevel ) ) ); } } else { float spanLength = vcl_sqrt( this->m_GradSmoothingparam / bsplineKernelVariance ); for( unsigned int d = 0; d < ImageDimension; d++ ) { meshSize[d] = static_cast( field->GetLargestPossibleRegion().GetSize()[d] / spanLength + 0.5 ); } } this->SmoothDeformationFieldBSpline( field, meshSize, splineOrder, numberOfLevels ); } else { if( this->m_TotalSmoothingparam < 0.0 ) { meshSize = this->m_TotalSmoothingMeshSize; for( unsigned int d = 0; d < ImageDimension; d++ ) { meshSize[d] *= static_cast( vcl_pow( 2.0, static_cast( this->m_CurrentLevel ) ) ); } } else { float spanLength = vcl_sqrt( this->m_TotalSmoothingparam / bsplineKernelVariance ); for( unsigned int d = 0; d < ImageDimension; d++ ) { meshSize[d] = static_cast( field->GetLargestPossibleRegion().GetSize()[d] / spanLength + 0.5 ); } } RealType maxMagnitude = 0.0; ImageRegionIterator It( field, field->GetLargestPossibleRegion() ); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { RealType magnitude = ( It.Get() ).GetNorm(); if( magnitude > maxMagnitude ) { maxMagnitude = magnitude; } } this->SmoothDeformationFieldBSpline( field, meshSize, splineOrder, numberOfLevels ); if( maxMagnitude > 0.0 ) { for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { It.Set( It.Get() / maxMagnitude ); } } } } else // Gaussian { float sig=0; if (TrueEqualsGradElseTotal) sig=this->m_GradSmoothingparam; else sig=this->m_TotalSmoothingparam; this->SmoothDeformationFieldGauss(field,sig); } } void SmoothDeformationFieldGauss(DeformationFieldPointer field = NULL, float sig=0.0, bool useparamimage=false, unsigned int lodim=ImageDimension); // float = smoothingparam, int = maxdim to smooth void SmoothVelocityGauss(TimeVaryingVelocityFieldPointer field,float,unsigned int); void SmoothDeformationFieldBSpline(DeformationFieldPointer field, ArrayType meshSize, unsigned int splineorder, unsigned int numberoflevels ); DeformationFieldPointer ComputeUpdateFieldAlternatingMin(DeformationFieldPointer fixedwarp, DeformationFieldPointer movingwarp, PointSetPointer fpoints=NULL, PointSetPointer wpoints=NULL,DeformationFieldPointer updateFieldInv=NULL, bool updateenergy=true); DeformationFieldPointer ComputeUpdateField(DeformationFieldPointer fixedwarp, DeformationFieldPointer movingwarp, PointSetPointer fpoints=NULL, PointSetPointer wpoints=NULL,DeformationFieldPointer updateFieldInv=NULL, bool updateenergy=true); TimeVaryingVelocityFieldPointer ExpandVelocity( ) { float expandFactors[ImageDimension+1]; expandFactors[ImageDimension]=1; m_Debug=false; for( int idim = 0; idim < ImageDimension; idim++ ) { expandFactors[idim] = (float)this->m_CurrentDomainSize[idim]/(float) this->m_TimeVaryingVelocity->GetLargestPossibleRegion().GetSize()[idim]; if( expandFactors[idim] < 1 ) expandFactors[idim] = 1; if (this->m_Debug) std::cout << " ExpFac " << expandFactors[idim] << " curdsz " << this->m_CurrentDomainSize[idim] << std::endl; } VectorType pad; pad.Fill(0); typedef VectorExpandImageFilter ExpanderType; typename ExpanderType::Pointer m_FieldExpander = ExpanderType::New(); m_FieldExpander->SetInput(this->m_TimeVaryingVelocity); m_FieldExpander->SetExpandFactors( expandFactors ); // m_FieldExpander->SetEdgePaddingValue( pad ); m_FieldExpander->UpdateLargestPossibleRegion(); return m_FieldExpander->GetOutput(); } DeformationFieldPointer ExpandField(DeformationFieldPointer field, typename ImageType::SpacingType targetSpacing) { // this->m_Debug=true; float expandFactors[ImageDimension]; for( int idim = 0; idim < ImageDimension; idim++ ) { expandFactors[idim] = (float)this->m_CurrentDomainSize[idim]/(float)field->GetLargestPossibleRegion().GetSize()[idim]; if( expandFactors[idim] < 1 ) expandFactors[idim] = 1; // if (this->m_Debug) std::cout << " ExpFac " << expandFactors[idim] << " curdsz " << this->m_CurrentDomainSize[idim] << std::endl; } VectorType pad; pad.Fill(0); typedef VectorExpandImageFilter ExpanderType; typename ExpanderType::Pointer m_FieldExpander = ExpanderType::New(); m_FieldExpander->SetInput(field); m_FieldExpander->SetExpandFactors( expandFactors ); // use default // m_FieldExpander->SetEdgePaddingValue( pad ); m_FieldExpander->UpdateLargestPossibleRegion(); typename DeformationFieldType::Pointer fieldout=m_FieldExpander->GetOutput(); fieldout->SetSpacing(targetSpacing); fieldout->SetOrigin(field->GetOrigin()); if (this->m_Debug) std::cout << " Field size " << fieldout->GetLargestPossibleRegion().GetSize() << std::endl; //this->m_Debug=false; return fieldout; } ImagePointer GetVectorComponent(DeformationFieldPointer field, unsigned int index) { // Initialize the Moving to the displacement field typedef DeformationFieldType FieldType; typename ImageType::Pointer sfield=ImageType::New(); sfield->SetSpacing( field->GetSpacing() ); sfield->SetOrigin( field->GetOrigin() ); sfield->SetDirection( field->GetDirection() ); sfield->SetLargestPossibleRegion(field->GetLargestPossibleRegion() ); sfield->SetRequestedRegion(field->GetRequestedRegion() ); sfield->SetBufferedRegion( field->GetBufferedRegion() ); sfield->Allocate(); typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator vfIter( field, field->GetLargestPossibleRegion() ); for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter) { VectorType v1=vfIter.Get(); sfield->SetPixel(vfIter.GetIndex(),v1[index]); } return sfield; } ImagePointer SubsampleImage( ImagePointer, RealType , typename ImageType::PointType outputOrigin, typename ImageType::DirectionType outputDirection, AffineTransformPointer aff = NULL); DeformationFieldPointer SubsampleField( DeformationFieldPointer field, typename ImageType::SizeType targetSize, typename ImageType::SpacingType targetSpacing ) { std::cout << "FIXME -- NOT DONE CORRECTLY " << std::endl; std::cout << "FIXME -- NOT DONE CORRECTLY " << std::endl; std::cout << "FIXME -- NOT DONE CORRECTLY " << std::endl; std::cout << "FIXME -- NOT DONE CORRECTLY " << std::endl; std::cout << " SUBSAM FIELD SUBSAM FIELD SUBSAM FIELD " << std::endl; typename DeformationFieldType::Pointer sfield=DeformationFieldType::New(); for (unsigned int i=0; i < ImageDimension; i++) { typename ImageType::Pointer precomp=this->GetVectorComponent(field,i); typename ImageType::Pointer comp=this->SubsampleImage(precomp,targetSize,targetSpacing); if ( i==0 ) { sfield->SetSpacing( comp->GetSpacing() ); sfield->SetOrigin( comp->GetOrigin() ); sfield->SetDirection( comp->GetDirection() ); sfield->SetLargestPossibleRegion(comp->GetLargestPossibleRegion() ); sfield->SetRequestedRegion(comp->GetRequestedRegion() ); sfield->SetBufferedRegion( comp->GetBufferedRegion() ); sfield->Allocate(); } typedef itk::ImageRegionIteratorWithIndex Iterator; typedef typename DeformationFieldType::PixelType VectorType; VectorType v1; VectorType zero; zero.Fill(0.0); Iterator vfIter( sfield, sfield->GetLargestPossibleRegion() ); for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter) { v1=vfIter.Get(); v1[i]=comp->GetPixel(vfIter.GetIndex()); vfIter.Set(v1); } } return sfield; } PointSetPointer WarpMultiTransform(ImagePointer referenceimage, ImagePointer movingImage, PointSetPointer movingpoints, AffineTransformPointer aff , DeformationFieldPointer totalField, bool doinverse , AffineTransformPointer fixedaff ) { if (!movingpoints) { std::cout << " NULL POINTS " << std::endl; return NULL; } AffineTransformPointer affinverse=NULL; if (aff) { affinverse=AffineTransformType::New(); aff->GetInverse(affinverse); } AffineTransformPointer fixedaffinverse=NULL; if (fixedaff) { fixedaffinverse=AffineTransformType::New(); fixedaff->GetInverse(fixedaffinverse); } typedef itk::WarpImageMultiTransformFilter WarperType; typename WarperType::Pointer warper = WarperType::New(); warper->SetInput(movingImage); warper->SetEdgePaddingValue( 0); warper->SetSmoothScale(1); if (!doinverse) { if (totalField) warper->PushBackDeformationFieldTransform(totalField); if (fixedaff) warper->PushBackAffineTransform(fixedaff); else if (aff) warper->PushBackAffineTransform(aff); } else { if (aff) warper->PushBackAffineTransform( affinverse ); else if (fixedaff) warper->PushBackAffineTransform(fixedaffinverse); if (totalField) warper->PushBackDeformationFieldTransform(totalField); } warper->SetOutputOrigin(referenceimage->GetOrigin()); typename ImageType::SizeType size=referenceimage->GetLargestPossibleRegion().GetSize(); if (totalField) size=totalField->GetLargestPossibleRegion().GetSize(); warper->SetOutputSize(size); typename ImageType::SpacingType spacing=referenceimage->GetSpacing(); if (totalField) spacing=totalField->GetSpacing(); warper->SetOutputSpacing(spacing); warper->SetOutputDirection(referenceimage->GetDirection()); totalField->SetOrigin(referenceimage->GetOrigin() ); totalField->SetDirection(referenceimage->GetDirection() ); // warper->Update(); // std::cout << " updated in point warp " << std::endl; PointSetPointer outputMesh = PointSetType::New(); unsigned long count = 0; unsigned long sz1 = movingpoints->GetNumberOfPoints(); if (this->m_Debug) std::cout << " BEFORE # points " << sz1 << std::endl; for (unsigned long ii=0; iiGetPoint(ii,&point); movingpoints->GetPointData(ii,&label); // convert pointtype to imagepointtype ImagePointType pt,wpt; for (unsigned int jj=0; jjMultiTransformSinglePoint(pt,wpt); if (bisinside) { for (unsigned int jj=0; jjSetPointData( count, label ); outputMesh->SetPoint( count, wpoint ); // if (ii % 100 == 0) std::cout << " pt " << pt << " wpt " << wpt << std::endl; count++; } } if (this->m_Debug) std::cout << " AFTER # points " << count << std::endl; // if (count != sz1 ) std::cout << " WARNING: POINTS ARE MAPPING OUT OF IMAGE DOMAIN " << 1.0 - (float) count/(float)(sz1+1) << std::endl; return outputMesh; } ImagePointer WarpMultiTransform( ImagePointer referenceimage, ImagePointer movingImage, AffineTransformPointer aff , DeformationFieldPointer totalField, bool doinverse , AffineTransformPointer fixedaff ) { typedef typename ImageType::DirectionType DirectionType; DirectionType rdirection=referenceimage->GetDirection(); DirectionType mdirection=movingImage->GetDirection(); AffineTransformPointer affinverse=NULL; if (aff) { affinverse=AffineTransformType::New(); aff->GetInverse(affinverse); } AffineTransformPointer fixedaffinverse=NULL; if (fixedaff) { fixedaffinverse=AffineTransformType::New(); fixedaff->GetInverse(fixedaffinverse); } DirectionType iddir; iddir.Fill(0); for (unsigned int i=0;i InterpolatorType1; typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; typedef itk::BSplineInterpolateImageFunction InterpolatorType3; typename InterpolatorType1::Pointer interp1 = InterpolatorType1::New(); typename InterpolatorType2::Pointer interpnn = InterpolatorType2::New(); typename InterpolatorType3::Pointer interpcu = InterpolatorType3::New(); this->m_UseMulti=true; if (!this->m_UseMulti){ ImagePointer wmimage = this->SubsampleImage(movingImage , this->m_ScaleFactor , movingImage->GetOrigin() , movingImage->GetDirection() , aff ); typedef itk::WarpImageFilter WarperType; typename WarperType::Pointer warper; warper = WarperType::New(); warper->SetInput( wmimage); warper->SetDeformationField(totalField); warper->SetOutputSpacing(totalField->GetSpacing()); warper->SetOutputOrigin(totalField->GetOrigin()); warper->SetInterpolator(interp1); if (this->m_UseNN) warper->SetInterpolator(interpnn); if (this->m_UseBSplineInterpolation) warper->SetInterpolator(interpcu); // warper->SetOutputSize(this->m_CurrentDomainSize); // warper->SetEdgePaddingValue( 0 ); warper->Update(); return warper->GetOutput(); } typedef itk::WarpImageMultiTransformFilter WarperType; typename WarperType::Pointer warper = WarperType::New(); warper->SetInput(movingImage); warper->SetEdgePaddingValue( 0); warper->SetSmoothScale(1); warper->SetInterpolator(interp1); if (this->m_UseNN) warper->SetInterpolator(interpnn); if (!doinverse) { if (totalField) warper->PushBackDeformationFieldTransform(totalField); if (fixedaff) warper->PushBackAffineTransform(fixedaff); else if (aff) warper->PushBackAffineTransform(aff); } else { if (aff) warper->PushBackAffineTransform( affinverse ); else if (fixedaff) warper->PushBackAffineTransform(fixedaffinverse); if (totalField) warper->PushBackDeformationFieldTransform(totalField); } warper->SetOutputOrigin(referenceimage->GetOrigin()); typename ImageType::SizeType size=referenceimage->GetLargestPossibleRegion().GetSize(); if (totalField) size=totalField->GetLargestPossibleRegion().GetSize(); warper->SetOutputSize(size); typename ImageType::SpacingType spacing=referenceimage->GetSpacing(); if (totalField) spacing=totalField->GetSpacing(); warper->SetOutputSpacing(spacing); warper->SetOutputDirection(referenceimage->GetDirection()); totalField->SetOrigin(referenceimage->GetOrigin() ); totalField->SetDirection(referenceimage->GetDirection() ); warper->Update(); if (this->m_Debug){ std::cout << " updated ok -- warped image output size " << warper->GetOutput()->GetLargestPossibleRegion().GetSize() << " requested size " << totalField->GetLargestPossibleRegion().GetSize() << std::endl; } typename ImageType::Pointer outimg=warper->GetOutput(); return outimg; } ImagePointer SmoothImageToScale(ImagePointer image , float scalingFactor ) { typename ImageType::SpacingType inputSpacing = image->GetSpacing(); typename ImageType::RegionType::SizeType inputSize = image->GetRequestedRegion().GetSize(); typename ImageType::SpacingType outputSpacing; typename ImageType::RegionType::SizeType outputSize; RealType minimumSpacing = inputSpacing.GetVnlVector().min_value(); // RealType maximumSpacing = inputSpacing.GetVnlVector().max_value(); for ( unsigned int d = 0; d < Dimension; d++ ) { RealType scaling = vnl_math_min( scalingFactor * minimumSpacing / inputSpacing[d], static_cast( inputSize[d] ) / 32.0 ); outputSpacing[d] = inputSpacing[d] * scaling; outputSize[d] = static_cast( inputSpacing[d] * static_cast( inputSize[d] ) / outputSpacing[d] + 0.5 ); typedef RecursiveGaussianImageFilter GaussianFilterType; typename GaussianFilterType::Pointer smoother = GaussianFilterType::New(); smoother->SetInputImage( image ); smoother->SetDirection( d ); smoother->SetNormalizeAcrossScale( false ); float sig = (outputSpacing[d]/inputSpacing[d]-1.0)*0.2;///(float)ImageDimension; smoother->SetSigma(sig ); if ( smoother->GetSigma() > 0.0 ) { smoother->Update(); image = smoother->GetOutput(); } } image=this->NormalizeImage(image); return image; } typename ANTSImageRegistrationOptimizer::DeformationFieldPointer IntegrateConstantVelocity(DeformationFieldPointer totalField, unsigned int ntimesteps, float timeweight); /** Base optimization functions */ // AffineTransformPointer AffineOptimization(AffineTransformPointer &aff_init, OptAffine &affine_opt); // {return NULL;} AffineTransformPointer AffineOptimization(OptAffineType &affine_opt); // {return NULL;} std::string GetTransformationModel( ) { return this->m_TransformationModel; } void SetTransformationModel( std::string s) { this->m_TransformationModel=s; std::cout << " Requested Transformation Model: " << this->m_TransformationModel << " : Using " << std::endl; if ( this->m_TransformationModel == std::string("Elast") ) { std::cout << "Elastic model for transformation. " << std::endl; } else if ( this->m_TransformationModel == std::string("SyN") ) { std::cout << "SyN diffeomorphic model for transformation. " << std::endl; } else if ( this->m_TransformationModel == std::string("GreedyExp") ) { std::cout << "Greedy Exp Diff model for transformation. Similar to Diffeomorphic Demons. Params same as Exp model. " << std::endl; this->m_TransformationModel=std::string("GreedyExp"); } else { std::cout << "Exp Diff model for transformation. " << std::endl; this->m_TransformationModel=std::string("Exp"); } } void SetUpParameters() { /** Univariate Deformable Mapping */ // set up parameters for deformation restriction std::string temp=this->m_Parser->GetOption( "Restrict-Deformation" )->GetValue(); this->m_RestrictDeformation = this->m_Parser->template ConvertVector(temp); if ( this->m_RestrictDeformation.size() != ImageDimension ) { std::cout <<" You input a vector of size : " << this->m_RestrictDeformation.size() << " for --Restrict-Deformation. The vector length does not match the image dimension. Ignoring. " << std::endl; for (unsigned int jj=0; jjm_RestrictDeformation.size(); jj++ ) this->m_RestrictDeformation[jj]=0; } // set up max iterations per level temp=this->m_Parser->GetOption( "number-of-iterations" )->GetValue(); this->m_Iterations = this->m_Parser->template ConvertVector(temp); this->SetNumberOfLevels(this->m_Iterations.size()); this->m_UseROI=false; if ( typename OptionType::Pointer option = this->m_Parser->GetOption( "roi" ) ) { temp=this->m_Parser->GetOption( "roi" )->GetValue(); this->m_RoiNumbers = this->m_Parser->template ConvertVector(temp); if ( temp.length() > 3 ) this->m_UseROI=true; } typename ParserType::OptionType::Pointer oOption = this->m_Parser->GetOption( "output-naming" ); this->m_OutputNamingConvention=oOption->GetValue(); typename ParserType::OptionType::Pointer thicknessOption = this->m_Parser->GetOption( "geodesic" ); if( thicknessOption->GetValue() == "true" || thicknessOption->GetValue() == "1" ) { this->m_ComputeThickness=1; this->m_SyNFullTime=2; }// asymm forces else if( thicknessOption->GetValue() == "2" ) { this->m_ComputeThickness=1; this->m_SyNFullTime=1; } // symmetric forces else this->m_ComputeThickness=0; // not full time varying stuff /** * Get transformation model and associated parameters */ typename ParserType::OptionType::Pointer transformOption = this->m_Parser->GetOption( "transformation-model" ); this->SetTransformationModel( transformOption->GetValue() ); if ( transformOption->GetNumberOfParameters() >= 1 ) { std::string parameter = transformOption->GetParameter( 0, 0 ); float temp=this->m_Parser->template Convert( parameter ); this->m_Gradstep = temp; this->m_GradstepAltered = temp; } else { this->m_Gradstep=0.5; this->m_GradstepAltered=0.5; } if ( transformOption->GetNumberOfParameters() >= 2 ) { std::string parameter = transformOption->GetParameter( 0, 1 ); this->m_NTimeSteps = this->m_Parser->template Convert( parameter ); } else this->m_NTimeSteps=1; if ( transformOption->GetNumberOfParameters() >= 3 ) { std::string parameter = transformOption->GetParameter( 0, 2 ); this->m_DeltaTime = this->m_Parser->template Convert( parameter ); if (this->m_DeltaTime > 1) this->m_DeltaTime=1; if (this->m_DeltaTime <= 0) this->m_DeltaTime=0.001; std::cout <<" set DT " << this->m_DeltaTime << std::endl; this->m_SyNType=1; } else this->m_DeltaTime=0.1; // if ( transformOption->GetNumberOfParameters() >= 3 ) // { // std::string parameter = transformOption->GetParameter( 0, 2 ); // this->m_SymmetryType // = this->m_Parser->template Convert( parameter ); // } /** * Get regularization and associated parameters */ this->m_GradSmoothingparam = -1; this->m_TotalSmoothingparam = -1; this->m_GradSmoothingMeshSize.Fill( 0 ); this->m_TotalSmoothingMeshSize.Fill( 0 ); typename ParserType::OptionType::Pointer regularizationOption = this->m_Parser->GetOption( "regularization" ); if( regularizationOption->GetValue() == "Gauss" ) { if ( regularizationOption->GetNumberOfParameters() >= 1 ) { std::string parameter = regularizationOption->GetParameter( 0, 0 ); this->m_GradSmoothingparam = this->m_Parser->template Convert( parameter ); } else this->m_GradSmoothingparam=3; if ( regularizationOption->GetNumberOfParameters() >= 2 ) { std::string parameter = regularizationOption->GetParameter( 0, 1 ); this->m_TotalSmoothingparam = this->m_Parser->template Convert( parameter ); } else this->m_TotalSmoothingparam=0.5; if ( regularizationOption->GetNumberOfParameters() >= 3 ) { std::string parameter = regularizationOption->GetParameter( 0, 2 ); this->m_GaussianTruncation = this->m_Parser->template Convert( parameter ); } else this->m_GaussianTruncation = 256; std::cout <<" Grad Step " << this->m_Gradstep << " total-smoothing " << this->m_TotalSmoothingparam << " gradient-smoothing " << this->m_GradSmoothingparam << std::endl; } else if( ( regularizationOption->GetValue() ).find( "DMFFD" ) != std::string::npos ) { if ( regularizationOption->GetNumberOfParameters() >= 1 ) { std::string parameter = regularizationOption->GetParameter( 0, 0 ); if( parameter.find( "x" ) != std::string::npos ) { std::vector gradMeshSize = this->m_Parser->template ConvertVector( parameter ); for( unsigned int d = 0; d < ImageDimension; d++ ) { this->m_GradSmoothingMeshSize[d] = gradMeshSize[d]; } } else { this->m_GradSmoothingparam = this->m_Parser->template Convert( parameter ); } } else { this->m_GradSmoothingparam = 3.0; } if ( regularizationOption->GetNumberOfParameters() >= 2 ) { std::string parameter = regularizationOption->GetParameter( 0, 1 ); if( parameter.find( "x" ) != std::string::npos ) { std::vector totalMeshSize = this->m_Parser->template ConvertVector( parameter ); for( unsigned int d = 0; d < ImageDimension; d++ ) { this->m_TotalSmoothingMeshSize[d] = totalMeshSize[d]; } } else { this->m_TotalSmoothingparam = this->m_Parser->template Convert( parameter ); } } else { this->m_TotalSmoothingparam=0.5; } if ( regularizationOption->GetNumberOfParameters() >= 3 ) { std::string parameter = regularizationOption->GetParameter( 0, 2 ); this->m_BSplineFieldOrder = this->m_Parser->template Convert( parameter ); } else this->m_BSplineFieldOrder = 3; std::cout <<" Grad Step " << this->m_Gradstep << " total-smoothing " << this->m_TotalSmoothingparam << " gradient-smoothing " << this->m_GradSmoothingparam << " bspline-field-order " << this->m_BSplineFieldOrder << std::endl; } else { this->m_GradSmoothingparam=3; this->m_TotalSmoothingparam=0.5; std::cout <<" Default Regularization is Gaussian smoothing with : " << this->m_GradSmoothingparam << " & " << this->m_TotalSmoothingparam << std::endl; // itkExceptionMacro( "Invalid regularization: " << regularizationOption->GetValue() ); } } void ComputeMultiResolutionParameters(ImagePointer fixedImage ) { VectorType zero; zero.Fill(0); /** Compute scale factors */ this->m_FullDomainSpacing = fixedImage->GetSpacing(); this->m_FullDomainSize = fixedImage->GetRequestedRegion().GetSize(); this->m_CurrentDomainSpacing = fixedImage->GetSpacing(); this->m_CurrentDomainSize = fixedImage->GetRequestedRegion().GetSize(); this->m_CurrentDomainDirection=fixedImage->GetDirection(); this->m_FullDomainOrigin.Fill(0); this->m_CurrentDomainOrigin.Fill(0); /** alter the input size based on information gained from the ROI information - if available */ if (this->m_UseROI) { for (unsigned int ii=0; iim_FullDomainSize[ii]= (typename ImageType::SizeType::SizeValueType) this->m_RoiNumbers[ii+ImageDimension]; this->m_FullDomainOrigin[ii]=this->m_RoiNumbers[ii]; } std::cout << " ROI #s : size " << this->m_FullDomainSize << " orig " << this->m_FullDomainOrigin << std::endl; } RealType minimumSpacing = this->m_FullDomainSpacing.GetVnlVector().min_value(); // RealType maximumSpacing = this->m_FullDomainSpacing.GetVnlVector().max_value(); for ( unsigned int d = 0; d < Dimension; d++ ) { RealType scaling = vnl_math_min( this->m_ScaleFactor * minimumSpacing / this->m_FullDomainSpacing[d], static_cast( this->m_FullDomainSize[d] ) / 32.0 ); if (scaling < 1) scaling=1; this->m_CurrentDomainSpacing[d] = this->m_FullDomainSpacing[d] * scaling; this->m_CurrentDomainSize[d] = static_cast( this->m_FullDomainSpacing[d] *static_cast( this->m_FullDomainSize[d] ) / this->m_CurrentDomainSpacing[d] + 0.5 ); this->m_CurrentDomainOrigin[d] = static_cast( this->m_FullDomainSpacing[d] *static_cast( this->m_FullDomainOrigin[d] ) / this->m_CurrentDomainSpacing[d] + 0.5 ); } // this->m_Debug=true; if (this->m_Debug) std::cout << " outsize " << this->m_CurrentDomainSize << " curspc " << this->m_CurrentDomainSpacing << " fullspc " << this->m_FullDomainSpacing << " fullsz " << this->m_FullDomainSize << std::endl; // this->m_Debug=false; if (!this->m_DeformationField) {/*FIXME -- need initial deformation strategy */ this->m_DeformationField=DeformationFieldType::New(); this->m_DeformationField->SetSpacing( this->m_CurrentDomainSpacing); this->m_DeformationField->SetOrigin( fixedImage->GetOrigin() ); this->m_DeformationField->SetDirection( fixedImage->GetDirection() ); typename ImageType::RegionType region; region.SetSize( this->m_CurrentDomainSize); this->m_DeformationField->SetLargestPossibleRegion(region); this->m_DeformationField->SetRequestedRegion(region); this->m_DeformationField->SetBufferedRegion(region); this->m_DeformationField->Allocate(); this->m_DeformationField->FillBuffer(zero); std::cout << " allocated def field " << this->m_DeformationField->GetDirection() << std::endl; //exit(0); } else { this->m_DeformationField=this->ExpandField(this->m_DeformationField,this->m_CurrentDomainSpacing); if ( this->m_TimeVaryingVelocity ) this->ExpandVelocity(); } } ImagePointer NormalizeImage( ImagePointer image) { typedef itk::MinimumMaximumImageFilter MinMaxFilterType; typename MinMaxFilterType::Pointer minMaxFilter = MinMaxFilterType::New(); minMaxFilter->SetInput( image ); minMaxFilter->Update(); double min = minMaxFilter->GetMinimum(); double shift = -1.0 * static_cast( min ); double scale = static_cast( minMaxFilter->GetMaximum() ); scale += shift; scale = 1.0 / scale; typedef itk::ShiftScaleImageFilter FilterType; typename FilterType::Pointer filter = FilterType::New(); filter->SetInput( image ); filter->SetShift( shift ); filter->SetScale( scale ); filter->Update(); return filter->GetOutput(); } void DeformableOptimization() { DeformationFieldPointer updateField = NULL; this->SetUpParameters(); typename ImageType::SpacingType spacing; VectorType zero; zero.Fill(0); std::cout << " setting N-TimeSteps = " << this->m_NTimeSteps << " trunc " << this->m_GaussianTruncation << std::endl; unsigned int maxits=0; for ( unsigned int currentLevel = 0; currentLevel < this->m_NumberOfLevels; currentLevel++ ) if ( this->m_Iterations[currentLevel] > maxits) maxits=this->m_Iterations[currentLevel]; if (maxits == 0) { this->m_DeformationField=NULL; this->m_InverseDeformationField=NULL; // this->ComputeMultiResolutionParameters(this->m_SimilarityMetrics[0]->GetFixedImage()); return; } /* this is a hack to force univariate mappings in the future, we will re-cast this framework s.t. multivariate images can be used */ unsigned int numberOfMetrics=this->m_SimilarityMetrics.size(); for ( unsigned int metricCount = 1; metricCount < numberOfMetrics; metricCount++) { this->m_SimilarityMetrics[metricCount]->GetFixedImage( )->SetOrigin( this->m_SimilarityMetrics[0]->GetFixedImage()->GetOrigin()); this->m_SimilarityMetrics[metricCount]->GetFixedImage( )->SetDirection( this->m_SimilarityMetrics[0]->GetFixedImage()->GetDirection()); this->m_SimilarityMetrics[metricCount]->GetMovingImage( )->SetOrigin( this->m_SimilarityMetrics[0]->GetMovingImage()->GetOrigin()); this->m_SimilarityMetrics[metricCount]->GetMovingImage( )->SetDirection( this->m_SimilarityMetrics[0]->GetMovingImage()->GetDirection()); } /* here, we assign all point set pointers to any single non-null point-set pointer */ for (unsigned int metricCount=0; metricCount < numberOfMetrics; metricCount++) { for (unsigned int metricCount2=0; metricCount2 < numberOfMetrics; metricCount2++) { if (this->m_SimilarityMetrics[metricCount]->GetFixedPointSet()) this->m_SimilarityMetrics[metricCount2]->SetFixedPointSet(this->m_SimilarityMetrics[metricCount]->GetFixedPointSet()); if (this->m_SimilarityMetrics[metricCount]->GetMovingPointSet()) this->m_SimilarityMetrics[metricCount2]->SetMovingPointSet(this->m_SimilarityMetrics[metricCount]->GetMovingPointSet()); } } this->m_SmoothFixedImages.resize(numberOfMetrics,NULL); this->m_SmoothMovingImages.resize(numberOfMetrics,NULL); for ( unsigned int currentLevel = 0; currentLevel < this->m_NumberOfLevels; currentLevel++ ) { this->m_CurrentLevel = currentLevel; typedef Vector ProfilePointDataType; typedef Image CurveType; typedef PointSet EnergyProfileType; typedef typename EnergyProfileType::PointType ProfilePointType; std::vector energyProfiles; energyProfiles.resize( numberOfMetrics ); for( unsigned int qq = 0; qq < numberOfMetrics; qq++ ) { energyProfiles[qq] = EnergyProfileType::New(); energyProfiles[qq]->Initialize(); } ImagePointer fixedImage; ImagePointer movingImage; this->m_GradstepAltered=this->m_Gradstep; this->m_ScaleFactor = pow( 2.0, (int)static_cast( this->m_NumberOfLevels-currentLevel-1 ) ); std::cout << " this->m_ScaleFactor " << this->m_ScaleFactor << " nlev " << this->m_NumberOfLevels << " curl " << currentLevel << std::endl; /** FIXME -- here we assume the metrics all have the same image */ fixedImage = this->m_SimilarityMetrics[0]->GetFixedImage(); movingImage = this->m_SimilarityMetrics[0]->GetMovingImage(); spacing=fixedImage->GetSpacing(); this->ComputeMultiResolutionParameters(fixedImage); std::cout << " Its at this level " << this->m_Iterations[currentLevel] << std::endl; /* generate smoothed images for all metrics */ for ( unsigned int metricCount=0; metricCount < numberOfMetrics; metricCount++) { this->m_SmoothFixedImages[metricCount] = this->SmoothImageToScale(this->m_SimilarityMetrics[metricCount]->GetFixedImage(), this->m_ScaleFactor); this->m_SmoothMovingImages[metricCount] = this->SmoothImageToScale(this->m_SimilarityMetrics[metricCount]->GetMovingImage(), this->m_ScaleFactor); } fixedImage=this->m_SmoothFixedImages[0]; movingImage=this->m_SmoothMovingImages[0]; unsigned int nmet=this->m_SimilarityMetrics.size(); this->m_LastEnergy.resize(nmet,1.e12); this->m_Energy.resize(nmet,1.e9); this->m_EnergyBad.resize(nmet,0); bool converged=false; this->m_CurrentIteration=0; if (this->GetTransformationModel() != std::string("SyN")) this->m_FixedImageAffineTransform=NULL; while (!converged) { for (unsigned int metricCount=0; metricCount < numberOfMetrics; metricCount++) this->m_SimilarityMetrics[metricCount]->GetMetric()->SetIterations(this->m_CurrentIteration); if ( this->GetTransformationModel() == std::string("Elast")) { if (this->m_Iterations[currentLevel] > 0) this->ElasticRegistrationUpdate(fixedImage, movingImage); } else if (this->GetTransformationModel() == std::string("SyN")) { if ( currentLevel > 0 ) { this->m_SyNF=this->ExpandField(this->m_SyNF,this->m_CurrentDomainSpacing); this->m_SyNFInv=this->ExpandField(this->m_SyNFInv,this->m_CurrentDomainSpacing); this->m_SyNM=this->ExpandField(this->m_SyNM,this->m_CurrentDomainSpacing); this->m_SyNMInv=this->ExpandField(this->m_SyNMInv,this->m_CurrentDomainSpacing); } if(this->m_Iterations[currentLevel] > 0) { if (this->m_SyNType && this->m_ComputeThickness ) this->DiReCTUpdate(fixedImage, movingImage, this->m_SimilarityMetrics[0]->GetFixedPointSet(), this->m_SimilarityMetrics[0]->GetMovingPointSet() ); else if (this->m_SyNType) this->SyNTVRegistrationUpdate(fixedImage, movingImage, this->m_SimilarityMetrics[0]->GetFixedPointSet(), this->m_SimilarityMetrics[0]->GetMovingPointSet() ); else this->SyNRegistrationUpdate(fixedImage, movingImage, this->m_SimilarityMetrics[0]->GetFixedPointSet(), this->m_SimilarityMetrics[0]->GetMovingPointSet() ); } else if (this->m_SyNType) this->UpdateTimeVaryingVelocityFieldWithSyNFandSyNM( ); // this->CopyOrAddToVelocityField( this->m_SyNF, 0 , false); } else if (this->GetTransformationModel() == std::string("Exp")) { if(this->m_Iterations[currentLevel] > 0) { this->DiffeomorphicExpRegistrationUpdate(fixedImage, movingImage,this->m_SimilarityMetrics[0]->GetFixedPointSet(), this->m_SimilarityMetrics[0]->GetMovingPointSet() ); } } else if (this->GetTransformationModel() == std::string("GreedyExp")) { if(this->m_Iterations[currentLevel] > 0) { this->GreedyExpRegistrationUpdate(fixedImage, movingImage,this->m_SimilarityMetrics[0]->GetFixedPointSet(), this->m_SimilarityMetrics[0]->GetMovingPointSet() ); } } this->m_CurrentIteration++; /** * This is where we track the energy profile to check for convergence. */ for( unsigned int qq = 0; qq < numberOfMetrics; qq++ ) { ProfilePointType point; point[0] = this->m_CurrentIteration-1; ProfilePointDataType energy; energy[0] = this->m_Energy[qq]; energyProfiles[qq]->SetPoint( this->m_CurrentIteration-1, point ); energyProfiles[qq]->SetPointData( this->m_CurrentIteration-1, energy ); } /** * If there are a sufficent number of iterations, fit a quadratic * single B-spline span to the number of energy profile points * in the first metric. To test convergence, evaluate the derivative * at the end of the profile to determine if >= 0. To change to a * window of the energy profile, simply change the origin (assuming that * the desired window will start at the user-specified origin and * end at the current iteration). */ unsigned int domtar=12; if( this->m_CurrentIteration > domtar ) { typedef BSplineScatteredDataPointSetToImageFilter BSplinerType; typename BSplinerType::Pointer bspliner = BSplinerType::New(); typename CurveType::PointType origin; unsigned int domainorigin=0; unsigned int domainsize=this->m_CurrentIteration - domainorigin; if ( this->m_CurrentIteration > domtar ) { domainsize=domtar; domainorigin=this->m_CurrentIteration-domainsize; } origin.Fill( domainorigin ); typename CurveType::SizeType size; size.Fill( domainsize ); typename CurveType::SpacingType spacing; spacing.Fill( 1 ); typename EnergyProfileType::Pointer energyProfileWindow = EnergyProfileType::New(); energyProfileWindow->Initialize(); unsigned int windowBegin = static_cast( origin[0] ); float totale=0; for( unsigned int qq = windowBegin; qq < this->m_CurrentIteration; qq++ ) { ProfilePointType point; point[0] = qq; ProfilePointDataType energy; energy.Fill( 0 ); energyProfiles[0]->GetPointData( qq, &energy ); totale+=energy[0]; energyProfileWindow->SetPoint( qq-windowBegin, point ); energyProfileWindow->SetPointData( qq-windowBegin, energy ); } // std::cout <<" totale " << totale << std::endl; if (totale > 0) totale*=(-1.0); for( unsigned int qq = windowBegin; qq < this->m_CurrentIteration; qq++ ) { ProfilePointDataType energy; energy.Fill(0); energyProfiles[0]->GetPointData( qq, &energy ); energyProfileWindow->SetPointData( qq-windowBegin, energy/totale); } bspliner->SetInput( energyProfileWindow ); bspliner->SetOrigin( origin ); bspliner->SetSpacing( spacing ); bspliner->SetSize( size ); bspliner->SetNumberOfLevels( 1 ); unsigned int order=1; bspliner->SetSplineOrder( order ); typename BSplinerType::ArrayType ncps; ncps.Fill( order+1); // single span, order = 2 bspliner->SetNumberOfControlPoints( ncps ); bspliner->Update(); ProfilePointType endPoint; endPoint[0] = static_cast( this->m_CurrentIteration-domainsize*0.5 ); typename BSplinerType::GradientType gradient; gradient.Fill(0); bspliner->EvaluateGradientAtPoint( endPoint, gradient ); this->m_ESlope=gradient[0][0] ; if ( this->m_ESlope < 0.0001 && this->m_CurrentIteration > domtar) converged=true; std::cout << " E-Slope " << this->m_ESlope;//<< std::endl; } for ( unsigned int qq=0; qq < this->m_Energy.size(); qq++ ) { if ( qq==0 ) std::cout << " iteration " << this->m_CurrentIteration; std::cout << " energy " << qq << " : " << this->m_Energy[qq];// << " Last " << this->m_LastEnergy[qq]; if (this->m_LastEnergy[qq] < this->m_Energy[qq]) { this->m_EnergyBad[qq]++; } } unsigned int numbade=0; for (unsigned int qq=0; qqm_Energy.size(); qq++) if (this->m_CurrentIteration <= 1) this->m_EnergyBad[qq] = 0; else if ( this->m_EnergyBad[qq] > 1 ) numbade += this->m_EnergyBad[qq]; //if ( this->m_EnergyBad[0] > 2) // { // this->m_GradstepAltered*=0.8; // std::cout <<" reducing gradstep " << this->m_GradstepAltered; // this->m_EnergyBad[this->m_Energy.size()-1]=0; // } std::cout << std::endl; if (this->m_CurrentIteration >= this->m_Iterations[currentLevel] )converged = true; // || this->m_EnergyBad[0] >= 6 ) // if ( converged && this->m_CurrentIteration >= this->m_Iterations[currentLevel] ) std::cout <<" tired convergence: reached max iterations " << std::endl; else if (converged) { std::cout << " Converged due to oscillation in optimization "; for (unsigned int qq=0; qqm_Energy.size(); qq++) std::cout<< " metric " << qq << " bad " << this->m_EnergyBad[qq] << " " ; std::cout <GetTransformationModel() == std::string("SyN")) { // float timestep=1.0/(float)this->m_NTimeSteps; // unsigned int nts=this->m_NTimeSteps; if (this->m_SyNType) { // this->m_SyNFInv = this->IntegrateConstantVelocity(this->m_SyNF, nts, timestep*(-1.)); // this->m_SyNMInv = this->IntegrateConstantVelocity(this->m_SyNM, nts, timestep*(-1.)); // this->m_SyNF= this->IntegrateConstantVelocity(this->m_SyNF, nts, timestep); // this->m_SyNM= this->IntegrateConstantVelocity(this->m_SyNM, // nts, timestep); // DeformationFieldPointer fdiffmap = this->IntegrateVelocity(0,0.5); // this->m_SyNFInv = this->IntegrateVelocity(0.5,0); // DeformationFieldPointer mdiffmap = this->IntegrateVelocity(0.5,1); // this->m_SyNMInv = this->IntegrateVelocity(1,0.5); // this->m_SyNM=this->CopyDeformationField(mdiffmap); // this->m_SyNF=this->CopyDeformationField(fdiffmap); this->m_DeformationField = this->IntegrateVelocity(0,1); // ImagePointer wmimage= this->WarpMultiTransform( this->m_SmoothFixedImages[0],this->m_SmoothMovingImages[0], this->m_AffineTransform, this->m_DeformationField, false , this->m_ScaleFactor ); this->m_InverseDeformationField=this->IntegrateVelocity(1,0); } else { this->m_InverseDeformationField=this->CopyDeformationField( this->m_SyNM); this->ComposeDiffs(this->m_SyNF,this->m_SyNMInv,this->m_DeformationField,1); this->ComposeDiffs(this->m_SyNM,this->m_SyNFInv,this->m_InverseDeformationField,1); } } else if (this->GetTransformationModel() == std::string("Exp")) { DeformationFieldPointer diffmap = this->IntegrateConstantVelocity( this->m_DeformationField, (unsigned int)this->m_NTimeSteps , 1 ); // 1.0/ (float)this->m_NTimeSteps); DeformationFieldPointer invdiffmap = this->IntegrateConstantVelocity(this->m_DeformationField,(unsigned int) this->m_NTimeSteps, -1 ); // -1.0/(float)this->m_NTimeSteps); this->m_InverseDeformationField=invdiffmap; this->m_DeformationField=diffmap; AffineTransformPointer invaff =NULL; if (this->m_AffineTransform) { invaff=AffineTransformType::New(); this->m_AffineTransform->GetInverse(invaff); if (this->m_Debug) std::cout << " ??????invaff " << this->m_AffineTransform << std::endl << std::endl; if (this->m_Debug) std::cout << " invaff?????? " << invaff << std::endl << std::endl; } } else if (this->GetTransformationModel() == std::string("GreedyExp")) { DeformationFieldPointer diffmap = this->m_DeformationField; this->m_InverseDeformationField=NULL; this->m_DeformationField=diffmap; AffineTransformPointer invaff =NULL; if (this->m_AffineTransform) { invaff=AffineTransformType::New(); this->m_AffineTransform->GetInverse(invaff); if (this->m_Debug) std::cout << " ??????invaff " << this->m_AffineTransform << std::endl << std::endl; if (this->m_Debug) std::cout << " invaff?????? " << invaff << std::endl << std::endl; } } this->m_DeformationField->SetOrigin( this->m_SimilarityMetrics[0]->GetFixedImage()->GetOrigin() ); this->m_DeformationField->SetDirection( this->m_SimilarityMetrics[0]->GetFixedImage()->GetDirection() ); if (this->m_InverseDeformationField) { this->m_InverseDeformationField->SetOrigin( this->m_SimilarityMetrics[0]->GetFixedImage()->GetOrigin() ); this->m_InverseDeformationField->SetDirection( this->m_SimilarityMetrics[0]->GetFixedImage()->GetDirection() ); } if ( this->m_TimeVaryingVelocity ) { std::string outname=localANTSGetFilePrefix(this->m_OutputNamingConvention.c_str())+std::string("velocity.mhd"); typename itk::ImageFileWriter::Pointer writer = itk::ImageFileWriter::New(); writer->SetFileName(outname.c_str()); writer->SetInput( this->m_TimeVaryingVelocity); writer->UpdateLargestPossibleRegion(); // writer->Write(); std::cout << " write tv field " << outname << std::endl; // WriteImage( this->m_TimeVaryingVelocity , outname.c_str()); } } void DiffeomorphicExpRegistrationUpdate(ImagePointer fixedImage, ImagePointer movingImage,PointSetPointer fpoints=NULL, PointSetPointer mpoints=NULL); void GreedyExpRegistrationUpdate(ImagePointer fixedImage, ImagePointer movingImage,PointSetPointer fpoints=NULL, PointSetPointer mpoints=NULL); void SyNRegistrationUpdate(ImagePointer fixedImage, ImagePointer movingImage, PointSetPointer fpoints=NULL, PointSetPointer mpoints=NULL); void SyNExpRegistrationUpdate(ImagePointer fixedImage, ImagePointer movingImage, PointSetPointer fpoints=NULL, PointSetPointer mpoints=NULL); void SyNTVRegistrationUpdate(ImagePointer fixedImage, ImagePointer movingImage, PointSetPointer fpoints=NULL, PointSetPointer mpoints=NULL); void DiReCTUpdate(ImagePointer fixedImage, ImagePointer movingImage, PointSetPointer fpoints=NULL, PointSetPointer mpoints=NULL); /** allows one to copy or add a field to a time index within the velocity * field */ void UpdateTimeVaryingVelocityFieldWithSyNFandSyNM( ); void CopyOrAddToVelocityField( TimeVaryingVelocityFieldPointer velocity, DeformationFieldPointer update1, DeformationFieldPointer update2 , float timept); //void CopyOrAddToVelocityField( DeformationFieldPointer update, unsigned int timeindex, bool CopyIsTrueOtherwiseAdd); void ElasticRegistrationUpdate(ImagePointer fixedImage, ImagePointer movingImage) { typename ImageType::SpacingType spacing; VectorType zero; zero.Fill(0); DeformationFieldPointer updateField; updateField=this->ComputeUpdateField(this->m_DeformationField,NULL,NULL,NULL,NULL); typedef ImageRegionIteratorWithIndex Iterator; Iterator dIter(this->m_DeformationField,this->m_DeformationField->GetLargestPossibleRegion() ); for( dIter.GoToBegin(); !dIter.IsAtEnd(); ++dIter ) { typename ImageType::IndexType index=dIter.GetIndex(); VectorType vec=updateField->GetPixel(index); dIter.Set(dIter.Get()+vec*this->m_Gradstep); } if (this->m_Debug) { std::cout << " updated elast " << " up-sz " << updateField->GetLargestPossibleRegion() << std::endl; std::cout << " t-sz " << this->m_DeformationField->GetLargestPossibleRegion() << std::endl; } this->SmoothDeformationField(this->m_DeformationField, false); return; } ImagePointer WarpImageBackward( ImagePointer image, DeformationFieldPointer field ) { typedef WarpImageFilter WarperType; typename WarperType::Pointer warper = WarperType::New(); typedef NearestNeighborInterpolateImageFunction InterpolatorType; warper->SetInput(image); warper->SetDeformationField( field ); warper->SetEdgePaddingValue( 0); warper->SetOutputSpacing(field->GetSpacing() ); warper->SetOutputOrigin( field->GetOrigin() ); warper->Update(); return warper->GetOutput(); } void ComposeDiffs(DeformationFieldPointer fieldtowarpby, DeformationFieldPointer field, DeformationFieldPointer fieldout, float sign); void SetSimilarityMetrics( SimilarityMetricListType S ) {this->m_SimilarityMetrics=S;} void SetFixedPointSet( PointSetPointer p ) { this->m_FixedPointSet=p; } void SetMovingPointSet( PointSetPointer p ) { this->m_MovingPointSet=p; } void SetDeltaTime( float t) {this->m_DeltaTime=t; } float InvertField(DeformationFieldPointer field, DeformationFieldPointer inverseField, float weight=1.0, float toler=0.1, int maxiter=20, bool print = false) { float mytoler=toler; unsigned int mymaxiter=maxiter; typename ParserType::OptionType::Pointer thicknessOption = this->m_Parser->GetOption( "go-faster" ); if( thicknessOption->GetValue() == "true" || thicknessOption->GetValue() == "1" ) { mytoler=0.5; maxiter=12; } VectorType zero; zero.Fill(0); // if (this->GetElapsedIterations() < 2 ) maxiter=10; ImagePointer floatImage = ImageType::New(); floatImage->SetLargestPossibleRegion( field->GetLargestPossibleRegion() ); floatImage->SetBufferedRegion( field->GetLargestPossibleRegion().GetSize() ); floatImage->SetSpacing(field->GetSpacing()); floatImage->SetOrigin(field->GetOrigin()); floatImage->SetDirection(field->GetDirection()); floatImage->Allocate(); typedef typename DeformationFieldType::PixelType VectorType; typedef typename DeformationFieldType::IndexType IndexType; typedef typename VectorType::ValueType ScalarType; typedef ImageRegionIteratorWithIndex Iterator; DeformationFieldPointer lagrangianInitCond=DeformationFieldType::New(); lagrangianInitCond->SetSpacing( field->GetSpacing() ); lagrangianInitCond->SetOrigin( field->GetOrigin() ); lagrangianInitCond->SetDirection( field->GetDirection() ); lagrangianInitCond->SetLargestPossibleRegion( field->GetLargestPossibleRegion() ); lagrangianInitCond->SetRequestedRegion(field->GetRequestedRegion() ); lagrangianInitCond->SetBufferedRegion( field->GetLargestPossibleRegion() ); lagrangianInitCond->Allocate(); DeformationFieldPointer eulerianInitCond=DeformationFieldType::New(); eulerianInitCond->SetSpacing( field->GetSpacing() ); eulerianInitCond->SetOrigin( field->GetOrigin() ); eulerianInitCond->SetDirection( field->GetDirection() ); eulerianInitCond->SetLargestPossibleRegion( field->GetLargestPossibleRegion() ); eulerianInitCond->SetRequestedRegion(field->GetRequestedRegion() ); eulerianInitCond->SetBufferedRegion( field->GetLargestPossibleRegion() ); eulerianInitCond->Allocate(); typedef typename DeformationFieldType::SizeType SizeType; SizeType size=field->GetLargestPossibleRegion().GetSize(); typename ImageType::SpacingType spacing = field->GetSpacing(); float subpix=0.0; unsigned long npix=1; for (int j=0; jGetLargestPossibleRegion().GetSize()[j]; } subpix=pow((float)ImageDimension,(float)ImageDimension)*0.5; float max=0; Iterator iter( field, field->GetLargestPossibleRegion() ); for( iter.GoToBegin(); !iter.IsAtEnd(); ++iter ) { IndexType index=iter.GetIndex(); VectorType vec1=iter.Get(); VectorType newvec=vec1*weight; lagrangianInitCond->SetPixel(index,newvec); float mag=0; for (unsigned int jj=0; jj max) max=mag; } eulerianInitCond->FillBuffer(zero); float scale=(1.)/max; if (scale > 1.) scale=1.0; // float initscale=scale; Iterator vfIter( inverseField, inverseField->GetLargestPossibleRegion() ); // int num=10; // for (int its=0; its subpix && meandif > subpix*0.1 && badct < 2 )//&& ct < 20 && denergy > 0) // float length=0.0; float stepl=2.; float lastdifmag=0; float epsilon = (float)size[0]/256; if (epsilon > 1) epsilon = 1; while ( difmag > mytoler && ct < mymaxiter && meandif > 0.001) { denergy=laste-difmag;//meandif; denergy2=laste-meandif; laste=difmag;//meandif; meandif=0.0; //this field says what position the eulerian field should contain in the E domain this->ComposeDiffs(inverseField,lagrangianInitCond, eulerianInitCond, 1); difmag=0.0; for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) { IndexType index=vfIter.GetIndex(); VectorType update=eulerianInitCond->GetPixel(index); float mag=0; for (int j=0; j difmag) {difmag=mag; } // if (mag < 1.e-2) update.Fill(0); eulerianInitCond->SetPixel(index,update); floatImage->SetPixel(index,mag); } meandif/=(float)npix; if (ct == 0) epsilon = 0.75; else epsilon=0.5; stepl=difmag*epsilon; for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) { float val = floatImage->GetPixel(vfIter.GetIndex()); VectorType update=eulerianInitCond->GetPixel(vfIter.GetIndex()); if (val > stepl) update = update * (stepl/val); VectorType upd=vfIter.Get()+update * (epsilon); vfIter.Set(upd); } ct++; lastdifmag=difmag; } // std::cout <<" difmag " << difmag << ": its " << ct << std::endl; return difmag; } void SetUseNearestNeighborInterpolation( bool useNN) { this->m_UseNN=useNN; } void SetUseBSplineInterpolation( bool useNN) { this->m_UseBSplineInterpolation=useNN; } protected: DeformationFieldPointer IntegrateVelocity(float,float); DeformationFieldPointer IntegrateLandmarkSetVelocity(float,float, PointSetPointer movingpoints, ImagePointer referenceimage ); VectorType IntegratePointVelocity(float starttimein, float finishtimein , IndexType startPoint); ImagePointer MakeSubImage( ImagePointer bigimage) { typedef itk::ImageRegionIteratorWithIndex Iterator; ImagePointer varimage=ImageType::New(); typename ImageType::RegionType region; typename ImageType::SizeType size=bigimage->GetLargestPossibleRegion().GetSize(); region.SetSize( this->m_CurrentDomainSize); typename ImageType::IndexType index; index.Fill(0); region.SetIndex(index); varimage->SetRegions( region ); varimage->SetSpacing(this->m_CurrentDomainSpacing); varimage->SetOrigin(bigimage->GetOrigin()); varimage->SetDirection(bigimage->GetDirection()); varimage->Allocate(); varimage->FillBuffer(0); typename ImageType::IndexType cornerind; cornerind.Fill(0); for (unsigned int ii=0; iim_CurrentDomainOrigin[ii]-(float)this->m_CurrentDomainSize[ii]/2; if (diff < 0) diff=0; cornerind[ii]=(unsigned long) diff; } // std::cout << " corner index " << cornerind << std::endl; Iterator vfIter2( bigimage, bigimage->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { typename ImageType::IndexType origindex=vfIter2.GetIndex(); typename ImageType::IndexType index=vfIter2.GetIndex(); bool oktosample=true; for (unsigned int ii=0; iim_CurrentDomainOrigin[ii]); // float diff = // index[ii]=origindex[ii]-cornerind[ii]; if ( fabs(centerbasedcoord) > (this->m_CurrentDomainSize[ii]/2-1)) oktosample=false; } if (oktosample) { // std::cout << " index " << index << " origindex " << origindex << " ok? " << oktosample << std::endl; varimage->SetPixel(index,bigimage->GetPixel(origindex)); } } //std::cout << " sizes " << varimage->GetLargestPossibleRegion().GetSize() << " bigimage " << bigimage->GetLargestPossibleRegion().GetSize() << std::endl; return varimage; } float MeasureDeformation(DeformationFieldPointer field, int option=0) { typedef typename DeformationFieldType::PixelType VectorType; typedef typename DeformationFieldType::IndexType IndexType; typedef typename DeformationFieldType::SizeType SizeType; typedef typename VectorType::ValueType ScalarType; typedef ImageRegionIteratorWithIndex Iterator; // all we have to do here is add the local field to the global field. Iterator vfIter( field, field->GetLargestPossibleRegion() ); SizeType size=field->GetLargestPossibleRegion().GetSize(); unsigned long ct=1; double totalmag=0; float maxstep=0; // this->m_EuclideanNorm=0; typename ImageType::SpacingType myspacing = field->GetSpacing(); for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter ) { IndexType index=vfIter.GetIndex(); IndexType rindex=vfIter.GetIndex(); IndexType lindex=vfIter.GetIndex(); VectorType update=vfIter.Get(); float mag=0.0; float stepl=0.0; for (int i=0;i2) lindex[i]=lindex[i]-1; VectorType rupdate=field->GetPixel(rindex); VectorType lupdate=field->GetPixel(lindex); VectorType dif=rupdate-lupdate; for (int tt=0; tt maxstep) maxstep=stepl; ct++; totalmag+=mag; // this->m_EuclideanNorm+=stepl; } //this->m_EuclideanNorm/=ct; //this->m_ElasticPathLength = totalmag/ct; //this->m_LinftyNorm = maxstep; // std::cout << " Elast path length " << this->m_ElasticPathLength << " L inf norm " << this->m_LinftyNorm << std::endl; //if (this->m_ElasticPathLength >= this->m_ArcLengthGoal) // if (maxstep >= this->m_ArcLengthGoal) { // this->StopRegistration(); // scale the field to the right length // float scale=this->m_ArcLengthGoal/this->m_ElasticPathLength; // for( vfIter.GoToBegin(); !vfIter.IsAtEnd(); ++vfIter )vfIter.Set(vfIter.Get()*scale); } //if (this->m_LinftyNorm <= 0) this->m_LinftyNorm=1; //if (this->m_ElasticPathLength <= 0) this->m_ElasticPathLength=0; //if (this->m_EuclideanNorm <= 0) this->m_EuclideanNorm=0; //if (option==0) return this->m_ElasticPathLength; //else if (option==2) return this->m_EuclideanNorm; // else return maxstep; } ANTSImageRegistrationOptimizer(); virtual ~ANTSImageRegistrationOptimizer() {} void PrintSelf( std::ostream& os, Indent indent ) const; private: ANTSImageRegistrationOptimizer( const Self& ); //purposely not implemented void operator=( const Self& ); //purposely not implemented typename VelocityFieldInterpolatorType::Pointer m_VelocityFieldInterpolator; typename ImageType::SizeType m_CurrentDomainSize; typename ImageType::PointType m_CurrentDomainOrigin; typename ImageType::SpacingType m_CurrentDomainSpacing; typename ImageType::DirectionType m_CurrentDomainDirection; typename ImageType::SizeType m_FullDomainSize; typename ImageType::PointType m_FullDomainOrigin; typename ImageType::SpacingType m_FullDomainSpacing; AffineTransformPointer m_AffineTransform; AffineTransformPointer m_FixedImageAffineTransform; DeformationFieldPointer m_DeformationField; DeformationFieldPointer m_InverseDeformationField; std::vector m_GradientDescentParameters; std::vector m_MetricScalarWeights; std::vector m_SmoothFixedImages; std::vector m_SmoothMovingImages; bool m_Debug; unsigned int m_NumberOfLevels; typename ParserType::Pointer m_Parser; SimilarityMetricListType m_SimilarityMetrics; ImagePointer m_MaskImage; float m_ScaleFactor; bool m_UseMulti; bool m_UseROI; bool m_UseNN; bool m_UseBSplineInterpolation; unsigned int m_CurrentIteration; unsigned int m_CurrentLevel; std::string m_TransformationModel; std::string m_OutputNamingConvention; PointSetPointer m_FixedPointSet; PointSetPointer m_MovingPointSet; std::vector m_Iterations; std::vector m_RestrictDeformation; std::vector m_RoiNumbers; float m_GradSmoothingparam; float m_TotalSmoothingparam; float m_Gradstep; float m_GradstepAltered; float m_NTimeSteps; float m_GaussianTruncation; float m_DeltaTime; float m_ESlope; /** energy stuff */ std::vector m_Energy; std::vector m_LastEnergy; std::vector m_EnergyBad; /** for SyN only */ DeformationFieldPointer m_SyNF; DeformationFieldPointer m_SyNFInv; DeformationFieldPointer m_SyNM; DeformationFieldPointer m_SyNMInv; TimeVaryingVelocityFieldPointer m_TimeVaryingVelocity; TimeVaryingVelocityFieldPointer m_LastTimeVaryingVelocity; TimeVaryingVelocityFieldPointer m_LastTimeVaryingUpdate; unsigned int m_SyNType; /** for BSpline stuff */ unsigned int m_BSplineFieldOrder; ArrayType m_GradSmoothingMeshSize; ArrayType m_TotalSmoothingMeshSize; /** For thickness calculation */ ImagePointer m_HitImage; ImagePointer m_ThickImage; unsigned int m_ComputeThickness; unsigned int m_SyNFullTime; }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkANTSImageRegistrationOptimizer.cxx" #endif #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkANTSImageTransformation.cxx000066400000000000000000000125641147325206600260000ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkANTSImageTransformation.cxx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkANTSImageTransformation_txx_ #define _itkANTSImageTransformation_txx_ // disable debug warnings in MS compiler #ifdef _MSC_VER #pragma warning(disable: 4786) #endif #include "ANTS_affine_registration2.h" #include "itkANTSImageTransformation.h" #include "itkIdentityTransform.h" #include "itkImageFileReader.h" #include "itkImageFileWriter.h" #include "itkLinearInterpolateImageFunction.h" #include "itkRecursiveGaussianImageFilter.h" #include "itkResampleImageFilter.h" #include "itkVectorImageFileWriter.h" #include "vnl/vnl_math.h" namespace itk { template ANTSImageTransformation ::ANTSImageTransformation() { this->m_DeformationField=NULL; this->m_InverseDeformationField=NULL; this->m_AffineTransform=NULL; this->m_WriteComponentImages = false; m_DeformationRegionOfInterestSize.Fill(0); m_DeformationRegionSpacing.Fill(1); m_DeformationRegionOfInterestCenter.Fill(0); } template void ANTSImageTransformation ::Compose() { } template void ANTSImageTransformation ::Write() { std::cout <<" begin writing " << m_NamingConvention << std::endl; std::string filePrefix = this->m_NamingConvention; std::string::size_type pos = filePrefix.rfind( "." ); std::string extension; std::string gzExtension( "" ); if ( pos != std::string::npos && std::string( filePrefix, pos, pos+2 ) != std::string( "./" ) ) { filePrefix = std::string( filePrefix, 0, pos ); extension = std::string( this->m_NamingConvention, pos, this->m_NamingConvention.length()-1 ); if ( extension == std::string( ".gz" ) ) { gzExtension = std::string( ".gz" ); pos = filePrefix.rfind( "." ); extension = std::string( filePrefix, pos, filePrefix.length()-1 ); filePrefix = std::string( filePrefix, 0, pos ); } } else { extension = std::string( ".nii.gz" ); } //Added by songgang if (this->m_AffineTransform) { std::cout << " writing " << filePrefix << " affine " << std::endl; std::string filename = filePrefix + std::string( "Affine.txt" ); WriteAffineTransformFile(this->m_AffineTransform, filename); } if ( this->m_DeformationField ) { std::cout <<" writing " << filePrefix << " def " << std::endl; if ( extension != std::string( ".mha" ) ) { std::string filename = filePrefix + std::string( "Warp" ) + extension + gzExtension; typedef VectorImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); std::cout << "filename " << filename << std::endl; writer->SetFileName( filename ); writer->SetUseAvantsNamingConvention( true ); writer->SetInput( this->m_DeformationField ); writer->Update(); } else { std::string filename = filePrefix + std::string( "Warp.mha" ); typedef ImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetFileName( filename ); writer->SetInput( this->m_DeformationField ); writer->Update(); } } if ( this->m_InverseDeformationField ) { if ( extension != std::string( ".mha" ) ) { std::string filename = filePrefix + std::string( "InverseWarp" ) + extension + gzExtension; typedef VectorImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetFileName( filename ); writer->SetUseAvantsNamingConvention( true ); writer->SetInput( this->m_InverseDeformationField ); writer->Update(); } else { std::string filename = filePrefix + std::string( "InverseWarp.mha" ); typedef ImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetFileName( filename ); writer->SetInput( this->m_InverseDeformationField ); writer->Update(); } } } /** * Standard "PrintSelf" method */ template void ANTSImageTransformation ::PrintSelf( std::ostream& os, Indent indent) const { Superclass::PrintSelf( os, indent ); } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkANTSImageTransformation.h000066400000000000000000000146441147325206600254260ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkANTSImageTransformation.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkANTSImageTransformation_h #define __itkANTSImageTransformation_h #include "itkObject.h" #include "itkObjectFactory.h" #include "antsCommandLineParser.h" #include "itkImage.h" #include "itkMacro.h" #include "itkCenteredEuler3DTransform.h" #include "itkQuaternionRigidTransform.h" #include "itkANTSCenteredAffine2DTransform.h" #include "itkANTSAffine3DTransform.h" // #include "itkCenteredRigid2DTransform.h" #include "itkCenteredTransformInitializer.h" #include "itkTransformFileReader.h" #include "itkTransformFileWriter.h" namespace itk { template class ITK_EXPORT ANTSImageTransformation : public Object { public: /** Standard class typedefs. */ typedef ANTSImageTransformation Self; typedef Object Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); /** Run-time type information (and related methods). */ itkTypeMacro( ANTSImageTransformation, Object ); itkStaticConstMacro( Dimension, unsigned int, TDimension ); itkStaticConstMacro( ImageDimension, unsigned int, TDimension ); typedef TReal RealType; typedef Image ImageType; /** declare transformation types */ typedef itk::MatrixOffsetTransformBase AffineTransformType; typedef typename AffineTransformType::Pointer AffineTransformPointer; typedef itk::Vector VectorType; typedef itk::Image DeformationFieldType; typedef typename DeformationFieldType::Pointer DeformationFieldPointer; typedef typename DeformationFieldType::RegionType DeformationRegionOfInterestType; typedef typename DeformationFieldType::SizeType DeformationRegionOfInterestSizeType; typedef typename DeformationFieldType::PointType DeformationRegionOfInterestCenterType; typedef typename ants::CommandLineParser ParserType; typedef typename ParserType::OptionType OptionType; /** Set functions */ void SetDeformationRegionOfInterest( DeformationRegionOfInterestCenterType DRC, DeformationRegionOfInterestSizeType DRS , typename DeformationFieldType::SpacingType DRSp) { m_DeformationRegionOfInterestCenter=DRC; m_DeformationRegionOfInterestSize=DRS; m_DeformationRegionSpacing=DRSp; } void SetAffineTransform(AffineTransformPointer A) {this->m_AffineTransform=A;} void SetDeformationField(DeformationFieldPointer A) {this->m_DeformationField=A;} void SetInverseDeformationField(DeformationFieldPointer A) {this->m_InverseDeformationField=A;} void SetNamingConvention(std::string name) { this->m_NamingConvention=name; } /** Get functions */ AffineTransformPointer GetAffineTransform() { return this->m_AffineTransform; } DeformationFieldPointer GetDeformationField( ) { return this->m_DeformationField; } DeformationFieldPointer GetInverseDeformationField() { return this->m_InverseDeformationField; } void SetFixedImageAffineTransform(AffineTransformPointer A) {this->m_FixedImageAffineTransform=A;} AffineTransformPointer GetFixedImageAffineTransform() {return this->m_FixedImageAffineTransform;} /** Initialize the mapping */ void InitializeTransform() { if (!this->m_AffineTransform) { this->m_AffineTransform=AffineTransformType::New(); this->m_AffineTransform->SetIdentity(); } // deformation fields too if (!this->m_DeformationField) { VectorType zero; zero.Fill(0); this->m_DeformationField=DeformationFieldType::New(); /* m_DeformationRegionOfInterest.SetSize( m_DeformationRegionOfInterestSize ); this->m_DeformationField->SetSpacing( m_DeformationRegionSpacing ); this->m_DeformationField->SetOrigin( m_DeformationRegionOfInterestCenter ); this->m_DeformationField->SetLargestPossibleRegion( m_DeformationRegionOfInterest ); this->m_DeformationField->SetRequestedRegion( m_DeformationRegionOfInterest ); this->m_DeformationField->SetBufferedRegion( m_DeformationRegionOfInterest ); this->m_DeformationField->Allocate(); this->m_DeformationField->FillBuffer(zero); */ } } /** Write the transformations out */ void Write(); /** Concatenate all transformations */ void Compose(); /** Concatenate all transformations in inverse direction */ void ComposeInverse(); itkSetMacro( WriteComponentImages, bool ); itkGetMacro( WriteComponentImages, bool ); itkBooleanMacro( WriteComponentImages ); protected: ANTSImageTransformation(); virtual ~ANTSImageTransformation() {} void PrintSelf( std::ostream& os, Indent indent ) const; private: ANTSImageTransformation( const Self& ); //purposely not implemented void operator=( const Self& ); //purposely not implemented AffineTransformPointer m_AffineTransform; AffineTransformPointer m_FixedImageAffineTransform; DeformationFieldPointer m_DeformationField; DeformationRegionOfInterestType m_DeformationRegionOfInterest; DeformationRegionOfInterestCenterType m_DeformationRegionOfInterestCenter; DeformationRegionOfInterestSizeType m_DeformationRegionOfInterestSize; typename DeformationFieldType::SpacingType m_DeformationRegionSpacing; DeformationFieldPointer m_InverseDeformationField; std::string m_NamingConvention; bool m_WriteComponentImages; }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkANTSImageTransformation.cxx" #endif #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkANTSLabeledPointSet.h000066400000000000000000000064231147325206600244670ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkANTSLabeledPointSet.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkANTSLabeledPointSet_h #define __itkANTSLabeledPointSet_h #include "itkObject.h" #include "itkObjectFactory.h" #include "itkMacro.h" #include "itkVector.h" #include "itkPointSet.h" namespace itk { template class ITK_EXPORT ANTSLabeledPointSet : public Object { public: /** Standard class typedefs. */ typedef ANTSLabeledPointSet Self; typedef Object Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); /** Run-time type information (and related methods). */ itkTypeMacro( ANTSLabeledPointSet, Object ); itkStaticConstMacro( Dimension, unsigned int, TDimension ); typedef float RealType; typedef Image ImageType; typedef typename ImageType::Pointer ImagePointer; typedef Vector VectorType; typedef Image DeformationFieldType; /** Point Types for landmarks and labeled point-sets */ typedef long PointDataVectorType; typedef itk::PointSet PointSetType; typedef typename PointSetType::Pointer PointSetPointer; typedef typename PointSetType::PointType PointType; typedef typename PointSetType::PixelType PointDataType; typedef typename ImageType::PointType ImagePointType; itkSetMacro( PointSet, PointSetPointer ); itkGetConstMacro( PointSet, PointSetPointer ); PointType GetPoint( unsigned long ii) { PointType point; this->m_PointSet->GetPoint(ii,&point); return point; } PointDataType GetPointData( unsigned long ii) { PointDataType data; this->m_PointSet->GetPointData(ii,&data); return data; } void SetPoint( unsigned long ii , PointType point ) { this->m_PointSet->SetPoint(ii,point); } void SetPointData( unsigned long ii , PointDataType label ) { this->m_PointSet->SetPointData(ii,label); } void SetPointAndData( unsigned long ii , PointType point , PointDataType label ) { this->m_PointSet->SetPoint(ii,point); this->m_PointSet->SetPointData(ii,label); } private: PointSetPointer m_PointSet; }; } // end namespace itk #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkANTSSimilarityMetric.h000066400000000000000000000105321147325206600247370ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkANTSSimilarityMetric.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkANTSSimilarityMetric_h #define __itkANTSSimilarityMetric_h #include "itkObject.h" #include "itkObjectFactory.h" #include "itkAvantsPDEDeformableRegistrationFunction.h" #include "itkMacro.h" #include "itkVector.h" #include "itkANTSLabeledPointSet.h" namespace itk { template class ITK_EXPORT ANTSSimilarityMetric : public Object { public: /** Standard class typedefs. */ typedef ANTSSimilarityMetric Self; typedef Object Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); /** Run-time type information (and related methods). */ itkTypeMacro( ANTSSimilarityMetric, Object ); itkStaticConstMacro( Dimension, unsigned int, TDimension ); typedef TReal RealType; typedef Image ImageType; typedef typename ImageType::Pointer ImagePointer; typedef Vector VectorType; typedef Image DeformationFieldType; /** Point Types for landmarks and labeled point-sets */ typedef itk::ANTSLabeledPointSet LabeledPointSetType; typedef typename LabeledPointSetType::Pointer LabeledPointSetPointer; typedef typename LabeledPointSetType::PointSetType PointSetType; typedef typename PointSetType::Pointer PointSetPointer; typedef typename PointSetType::PointType PointType; typedef typename PointSetType::PixelType PointDataType; typedef typename ImageType::PointType ImagePointType; /** Typedefs for similarity metrics */ typedef AvantsPDEDeformableRegistrationFunction MetricType; typedef typename MetricType::Pointer MetricPointer; typedef typename MetricType::RadiusType RadiusType; itkSetMacro( FixedImage, ImagePointer ); itkGetConstMacro( FixedImage, ImagePointer ); itkSetMacro( MovingImage, ImagePointer ); itkGetConstMacro( MovingImage, ImagePointer ); itkSetMacro( FixedPointSet, PointSetPointer ); itkGetConstMacro( FixedPointSet, PointSetPointer ); itkSetMacro( MovingPointSet, PointSetPointer ); itkGetConstMacro( MovingPointSet, PointSetPointer ); itkSetMacro( WeightImage, ImagePointer ); itkGetConstMacro( WeightImage, ImagePointer ); itkSetClampMacro( WeightScalar, RealType, 0.0, NumericTraits::max() ); itkGetConstMacro( WeightScalar, RealType ); itkSetObjectMacro( Metric, MetricType ); itkGetObjectMacro( Metric, MetricType ); itkSetMacro( MaximizeMetric, bool ); itkGetConstMacro( MaximizeMetric, bool ); itkBooleanMacro( MaximizeMetric ); private: MetricPointer m_Metric; bool m_MaximizeMetric; ImagePointer m_FixedImage; ImagePointer m_MovingImage; PointSetPointer m_FixedPointSet; PointSetPointer m_MovingPointSet; ImagePointer m_WeightImage; RealType m_WeightScalar; }; } // end namespace itk #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkAvantsMutualInformationRegistrationFunction.cxx000066400000000000000000000523041147325206600322700ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkAvantsMutualInformationRegistrationFunction.cxx,v $ Language: C++ Date: $Date: 2009/01/08 15:14:48 $ Version: $Revision: 1.21 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkAvantsMutualInformationRegistrationFunction_txx #define _itkAvantsMutualInformationRegistrationFunction_txx #include "itkAvantsMutualInformationRegistrationFunction.h" #include "itkBSplineInterpolateImageFunction.h" #include "itkCovariantVector.h" #include "itkImageRandomConstIteratorWithIndex.h" #include "itkImageRegionConstIterator.h" #include "itkImageRegionIterator.h" #include "itkImageIterator.h" #include "vnl/vnl_math.h" #include "itkDiscreteGaussianImageFilter.h" #include "itkBSplineDeformableTransform.h" #include "itkImageRegionConstIteratorWithIndex.h" namespace itk { /** * Constructor */ template < class TFixedImage, class TMovingImage , class TDeformationField > AvantsMutualInformationRegistrationFunction ::AvantsMutualInformationRegistrationFunction() { this-> Superclass::m_NormalizeGradient=true; this->m_NumberOfSpatialSamples = 5000; this->m_NumberOfHistogramBins = 50; // this->SetComputeGradient(false); // don't use the default gradient for no // Initialize PDFs to NULL m_JointPDF = NULL; m_OpticalFlow=false; typename TransformType::Pointer transformer = TransformType::New(); this->SetTransform(transformer); typename BSplineInterpolatorType::Pointer interpolator = BSplineInterpolatorType::New(); this->SetInterpolator (interpolator); m_FixedImageMask=NULL; m_MovingImageMask=NULL; // Initialize memory m_MovingImageNormalizedMin = 0.0; m_FixedImageNormalizedMin = 0.0; m_MovingImageTrueMin = 0.0; m_MovingImageTrueMax = 0.0; m_FixedImageBinSize = 0.0; m_MovingImageBinSize = 0.0; m_CubicBSplineDerivativeKernel = NULL; m_BSplineInterpolator = NULL; m_DerivativeCalculator = NULL; m_NumParametersPerDim = 0; m_NumBSplineWeights = 0; m_BSplineTransform = NULL; m_NumberOfParameters = ImageDimension; m_FixedImageGradientCalculator = GradientCalculatorType::New(); m_MovingImageGradientCalculator = GradientCalculatorType::New(); this->m_Padding=2; typename DefaultInterpolatorType::Pointer interp = DefaultInterpolatorType::New(); typename DefaultInterpolatorType::Pointer interp2 = DefaultInterpolatorType::New(); m_MovingImageInterpolator = static_cast( interp.GetPointer() ); m_FixedImageInterpolator = static_cast( interp2.GetPointer() ); m_Interpolator = static_cast( interp.GetPointer() ); this->m_RobustnessParameter=-1.e19; } /** * Print out internal information about this class */ template < class TFixedImage, class TMovingImage , class TDeformationField> void AvantsMutualInformationRegistrationFunction ::PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf(os, indent); os << indent << "NumberOfSpatialSamples: "; os << m_NumberOfSpatialSamples << std::endl; os << indent << "NumberOfHistogramBins: "; os << m_NumberOfHistogramBins << std::endl; // Debugging information os << indent << "NumberOfParameters: "; os << m_NumberOfParameters << std::endl; os << indent << "FixedImageNormalizedMin: "; os << m_FixedImageNormalizedMin << std::endl; os << indent << "MovingImageNormalizedMin: "; os << m_MovingImageNormalizedMin << std::endl; os << indent << "MovingImageTrueMin: "; os << m_MovingImageTrueMin << std::endl; os << indent << "MovingImageTrueMax: "; os << m_MovingImageTrueMax << std::endl; os << indent << "FixedImageBinSize: "; os << m_FixedImageBinSize << std::endl; os << indent << "MovingImageBinSize: "; os << m_MovingImageBinSize << std::endl; os << indent << "InterpolatorIsBSpline: "; os << m_InterpolatorIsBSpline << std::endl; os << indent << "TransformIsBSpline: "; os << m_TransformIsBSpline << std::endl; } /** * Initialize */ template void AvantsMutualInformationRegistrationFunction ::InitializeIteration() { m_CubicBSplineKernel = CubicBSplineFunctionType::New(); m_CubicBSplineDerivativeKernel = CubicBSplineDerivativeFunctionType::New(); this->m_Energy=0; pdfinterpolator = pdfintType::New(); dpdfinterpolator = dpdfintType::New(); pdfinterpolator2 = pdfintType2::New(); pdfinterpolator3 = pdfintType2::New(); m_DerivativeCalculator = DerivativeFunctionType::New(); // this->ComputeMetricImage(); // bool makenewimage=false; typedef ImageRegionIteratorWithIndex ittype; TFixedImage* img =const_cast(this->m_FixedImage.GetPointer()); typename TFixedImage::SizeType imagesize=img->GetLargestPossibleRegion().GetSize(); /* if (!this->m_MetricImage )makenewimage=true; else if (imagesize[0] != this->m_MetricImage->GetLargestPossibleRegion().GetSize()[0]) makenewimage = true; else this->m_MetricImage->FillBuffer(0); if (makenewimage) { this->m_MetricImage = TFixedImage::New(); this->m_MetricImage->SetLargestPossibleRegion(img->GetLargestPossibleRegion() ); this->m_MetricImage->SetBufferedRegion(img->GetLargestPossibleRegion()); this->m_MetricImage->SetSpacing(img->GetSpacing()); this->m_MetricImage->SetOrigin(img->GetOrigin()); this->m_MetricImage->Allocate(); ittype it(this->m_MetricImage,this->m_MetricImage->GetLargestPossibleRegion().GetSize()); for( it.GoToBegin(); !it.IsAtEnd(); ++it ) it.Set(0); } */ m_FixedImageGradientCalculator->SetInputImage( this->m_FixedImage ); m_MovingImageGradientCalculator->SetInputImage( this->m_MovingImage ); m_FixedImageInterpolator->SetInputImage( this->m_FixedImage ); m_Interpolator->SetInputImage( this->m_MovingImage ); m_FixedImageSpacing = this->m_FixedImage->GetSpacing(); m_FixedImageOrigin = this->m_FixedImage->GetOrigin(); m_Normalizer = 0.0; m_NumberOfSpatialSamples = 1; for( unsigned int k = 0; k < ImageDimension; k++ ) { m_Normalizer += m_FixedImageSpacing[k] * m_FixedImageSpacing[k]; m_NumberOfSpatialSamples *= this->m_FixedImage->GetLargestPossibleRegion().GetSize()[k]; } m_Normalizer /= static_cast( ImageDimension ); /** * Compute binsize for the histograms. * * The binsize for the image intensities needs to be adjusted so that * we can avoid dealing with boundary conditions using the cubic * spline as the Parzen window. We do this by increasing the size * of the bins so that the joint histogram becomes "padded" at the * borders. Because we are changing the binsize, * we also need to shift the minimum by the padded amount in order to * avoid minimum values filling in our padded region. * * Note that there can still be non-zero bin values in the padded region, * it's just that these bins will never be a central bin for the Parzen * window. * double fixedImageMax = 1.0; double fixedImageMin = 0.0; double movingImageMax = 1.0; double movingImageMin = 0.0; m_MovingImageTrueMin = movingImageMin; m_MovingImageTrueMax = movingImageMax; */ double movingImageMin = NumericTraits::max(); double movingImageMax = NumericTraits::NonpositiveMin(); double fixedImageMin = NumericTraits::max(); double fixedImageMax = NumericTraits::NonpositiveMin(); typedef ImageRegionConstIterator MovingIteratorType; MovingIteratorType movingImageIterator( this->m_MovingImage, this->m_MovingImage->GetBufferedRegion() ); for ( movingImageIterator.GoToBegin(); !movingImageIterator.IsAtEnd(); ++movingImageIterator) { bool takesample = true; if (this->m_FixedImageMask) if (this->m_FixedImageMask->GetPixel( movingImageIterator.GetIndex() ) < 1.e-6 ) takesample=false; if (takesample) { double sample = static_cast( movingImageIterator.Get() ); double fsample = static_cast( this->m_FixedImage->GetPixel( movingImageIterator.GetIndex() )); if ( sample < movingImageMin ) { movingImageMin = sample; } if ( sample > movingImageMax ) { movingImageMax = sample; } if ( fsample < fixedImageMin ) { fixedImageMin = fsample; } if ( fsample > fixedImageMax ) { fixedImageMax = fsample; } } } this->m_MovingImageTrueMax=movingImageMax; this->m_FixedImageTrueMax=fixedImageMax; this->m_MovingImageTrueMin=movingImageMin; this->m_FixedImageTrueMin=fixedImageMin; fixedImageMax=1.*( fixedImageMax - fixedImageMin )+fixedImageMin; movingImageMax=1.*( movingImageMax - movingImageMin )+movingImageMin; m_FixedImageBinSize = ( fixedImageMax - fixedImageMin ) / static_cast( m_NumberOfHistogramBins - 2 * this->m_Padding ); m_FixedImageNormalizedMin = fixedImageMin / m_FixedImageBinSize - static_cast( this->m_Padding ); m_MovingImageBinSize = ( movingImageMax - movingImageMin ) / static_cast( m_NumberOfHistogramBins - 2 * this->m_Padding ); m_MovingImageNormalizedMin = movingImageMin / m_MovingImageBinSize - static_cast( this->m_Padding ); m_FixedImageMarginalPDF = MarginalPDFType::New(); m_MovingImageMarginalPDF = MarginalPDFType::New(); typename MarginalPDFType::RegionType mPDFRegion; typename MarginalPDFType::SizeType mPDFSize; typename MarginalPDFType::IndexType mPDFIndex; typename MarginalPDFType::SpacingType mPDFspacing; mPDFspacing.Fill(1); mPDFIndex.Fill( 0 ); mPDFSize.Fill( m_NumberOfHistogramBins ); mPDFRegion.SetIndex( mPDFIndex ); mPDFRegion.SetSize( mPDFSize ); m_FixedImageMarginalPDF->SetRegions( mPDFRegion ); m_FixedImageMarginalPDF->Allocate(); m_FixedImageMarginalPDF->SetSpacing(mPDFspacing); m_MovingImageMarginalPDF->SetRegions( mPDFRegion ); m_MovingImageMarginalPDF->Allocate(); m_MovingImageMarginalPDF->SetSpacing(mPDFspacing); /** * Allocate memory for the joint PDF and joint PDF derivatives. * The joint PDF and joint PDF derivatives are store as itk::Image. */ m_JointPDF = JointPDFType::New(); // Instantiate a region, index, size JointPDFRegionType jointPDFRegion; JointPDFIndexType jointPDFIndex; JointPDFSizeType jointPDFSize; // For the joint PDF define a region starting from {0,0} // with size {m_NumberOfHistogramBins, m_NumberOfHistogramBins}. // The dimension represents fixed image parzen window index // and moving image parzen window index, respectively. jointPDFIndex.Fill( 0 ); jointPDFSize.Fill( m_NumberOfHistogramBins ); jointPDFRegion.SetIndex( jointPDFIndex ); jointPDFRegion.SetSize( jointPDFSize ); // Set the regions and allocate m_JointPDF->SetRegions( jointPDFRegion ); m_JointPDF->Allocate(); m_JointHist = JointPDFType::New(); m_JointHist->SetRegions(jointPDFRegion ); m_JointHist->Allocate(); // For the derivatives of the joint PDF define a region starting from {0,0,0} // with size {m_NumberOfParameters,m_NumberOfHistogramBins, // m_NumberOfHistogramBins}. The dimension represents transform parameters, // fixed image parzen window index and moving image parzen window index, // respectively. typename JointPDFType::SpacingType jspacing; jspacing.Fill(1); this->m_JointHist->SetSpacing(jspacing); this->m_JointPDF->SetSpacing(jspacing); m_NormalizeMetric=1.0; for (int i=0; im_FixedImage->GetLargestPossibleRegion().GetSize()[i]; this->GetProbabilities(); this->ComputeMutualInformation(); pdfinterpolator->SetInputImage(m_JointPDF); pdfinterpolator2->SetInputImage(m_FixedImageMarginalPDF); pdfinterpolator3->SetInputImage(m_MovingImageMarginalPDF); pdfinterpolator->SetSplineOrder(3); pdfinterpolator2->SetSplineOrder(3); pdfinterpolator3->SetSplineOrder(3); } /** * Get the both Value and Derivative Measure */ template < class TFixedImage, class TMovingImage , class TDeformationField> void AvantsMutualInformationRegistrationFunction ::GetProbabilities() { typedef ImageRegionConstIteratorWithIndex RandomIterator; RandomIterator randIter( this->m_FixedImage, this->m_FixedImage->GetLargestPossibleRegion() ); for ( unsigned int j = 0; j < m_NumberOfHistogramBins; j++ ) { MarginalPDFIndexType mind; mind[0]=j; m_FixedImageMarginalPDF->SetPixel(mind,0); m_MovingImageMarginalPDF->SetPixel(mind,0); } // Reset the joint pdfs to zero m_JointPDF->FillBuffer( 0.0 ); m_JointHist->FillBuffer( 0.0 ); unsigned long nSamples=0; RandomIterator iter( this->m_FixedImage, this->m_FixedImage->GetLargestPossibleRegion() ); for( iter.GoToBegin(); !iter.IsAtEnd(); ++iter ) { bool takesample=true; if (this->m_FixedImageMask) if (this->m_FixedImageMask->GetPixel( iter.GetIndex() ) < 1.e-6 ) takesample=false; if (takesample) { // Get sampled index FixedImageIndexType index = iter.GetIndex(); typename FixedImageType::SizeType imagesize=this->m_FixedImage->GetLargestPossibleRegion().GetSize(); /* bool inimage=true; for (unsigned int dd=0; dd= static_cast(imagesize[dd]-1) ) inimage=false; } */ double movingImageValue = this->GetMovingParzenTerm( this->m_MovingImage->GetPixel( index ) ); double fixedImageValue = this->GetFixedParzenTerm( this->m_FixedImage->GetPixel( index ) ); unsigned int movingImageParzenWindowIndex=this->FitIndexInBins( movingImageValue ); unsigned int fixedImageParzenWindowIndex=this->FitIndexInBins( fixedImageValue ); // std::cout << " fiv " << fixedImageValue << " fip " << fixedImageParzenWindowIndex << " miv " << movingImageValue << " mip " << movingImageParzenWindowIndex << std::endl; JointPDFValueType *pdfPtr = m_JointPDF->GetBufferPointer() + ( fixedImageParzenWindowIndex* m_NumberOfHistogramBins ); int pdfMovingIndex = static_cast( movingImageParzenWindowIndex ); pdfPtr += pdfMovingIndex; *(pdfPtr) += static_cast( 1 ); ++nSamples; } } /** * Normalize the PDFs, compute moving image marginal PDF * */ typedef ImageRegionIterator JointPDFIteratorType; JointPDFIteratorType jointPDFIterator ( m_JointPDF, m_JointPDF->GetBufferedRegion() ); // Compute joint PDF normalization factor (to ensure joint PDF sum adds to 1.0) double jointPDFSum = 0.0; float max = 0; float max2 = 0; float max3 = 0; float max4 = 0; jointPDFIterator.GoToBegin(); while( !jointPDFIterator.IsAtEnd() ) { float temp = jointPDFIterator.Get(); m_JointHist->SetPixel(jointPDFIterator.GetIndex(),temp); if (temp > max) max = temp; if (temp > max2 && temp < max) max2=temp; if (temp > max3 && temp < max2) max3=temp; if (temp > max4 && temp < max3) max4=temp; ++jointPDFIterator; } jointPDFIterator.GoToBegin(); while( !jointPDFIterator.IsAtEnd() ) { float temp = jointPDFIterator.Get(); jointPDFIterator.Set(temp); jointPDFSum += temp; ++jointPDFIterator; } //std::cout << " Joint PDF Summation? " << jointPDFSum << std::endl; // of derivatives if ( jointPDFSum == 0.0 ) { itkExceptionMacro( "Joint PDF summed to zero" ); } // Normalize the PDF bins jointPDFIterator.GoToEnd(); while( !jointPDFIterator.IsAtBegin() ) { --jointPDFIterator; jointPDFIterator.Value() /= static_cast( jointPDFSum ); } bool smoothjh=true; if (smoothjh) { typedef DiscreteGaussianImageFilter dgtype; typename dgtype::Pointer dg=dgtype::New(); dg->SetInput(this->m_JointPDF); dg->SetVariance(1.); dg->SetUseImageSpacingOff(); dg->SetMaximumError(.01f); dg->Update(); this->m_JointPDF=dg->GetOutput(); } // Compute moving image marginal PDF by summing over fixed image bins. typedef ImageLinearIteratorWithIndex JointPDFLinearIterator; JointPDFLinearIterator linearIter( m_JointPDF, m_JointPDF->GetBufferedRegion() ); linearIter.SetDirection( 0 ); linearIter.GoToBegin(); unsigned int fixedIndex = 0; while( !linearIter.IsAtEnd() ) { double sum = 0.0; while( !linearIter.IsAtEndOfLine() ) { sum += linearIter.Get(); ++linearIter; } MarginalPDFIndexType mind; mind[0]=fixedIndex; m_FixedImageMarginalPDF->SetPixel(mind,static_cast(sum)); linearIter.NextLine(); ++fixedIndex; } linearIter.SetDirection( 1 ); linearIter.GoToBegin(); unsigned int movingIndex = 0; while( !linearIter.IsAtEnd() ) { double sum = 0.0; while( !linearIter.IsAtEndOfLine() ) { sum += linearIter.Get(); ++linearIter; } MarginalPDFIndexType mind; mind[0]=movingIndex; m_MovingImageMarginalPDF->SetPixel(mind,static_cast(sum)); linearIter.NextLine(); ++movingIndex; } } /** * Get the both Value and Derivative Measure */ template < class TFixedImage, class TMovingImage , class TDeformationField> double AvantsMutualInformationRegistrationFunction ::GetValueAndDerivative(IndexType oindex, MeasureType& valuei, DerivativeType& derivative1,DerivativeType& derivative2) { double value=0; DerivativeType zero(ImageDimension); zero.Fill(0); double movingImageValue = this->GetMovingParzenTerm( this->m_MovingImage->GetPixel( oindex ) ); double fixedImageValue = this->GetFixedParzenTerm( this->m_FixedImage->GetPixel( oindex ) ); double movingImageParzenWindowContIndex=this->FitContIndexInBins( movingImageValue ); double fixedImageParzenWindowContIndex=this->FitContIndexInBins( fixedImageValue ); double dJPDF=0,dFmPDF=0,jointPDFValue=0,fixedImagePDFValue=0; { typename pdfintType::ContinuousIndexType pdfind; pdfind[1]= fixedImageParzenWindowContIndex; pdfind[0]= movingImageParzenWindowContIndex; jointPDFValue=pdfinterpolator->EvaluateAtContinuousIndex(pdfind); dJPDF = (1.0)*(pdfinterpolator->EvaluateDerivativeAtContinuousIndex( pdfind ))[1]; } { typename pdfintType2::ContinuousIndexType mind; mind[0]=fixedImageParzenWindowContIndex; fixedImagePDFValue = pdfinterpolator2->EvaluateAtContinuousIndex(mind); dFmPDF = (pdfinterpolator2->EvaluateDerivativeAtContinuousIndex(mind))[0]; } double term1=0,term2=0,eps=1.e-12; if( jointPDFValue > eps && (fixedImagePDFValue) > 0) { term1 = dJPDF/jointPDFValue; term2 = dFmPDF/fixedImagePDFValue; value = (term1*(-1.0)+term2); } // end if-block to check non-zero bin contribution else value = 0; return value; } template < class TFixedImage, class TMovingImage , class TDeformationField> double AvantsMutualInformationRegistrationFunction ::GetValueAndDerivativeInv(IndexType oindex, MeasureType& valuei, DerivativeType& derivative1,DerivativeType& derivative2) { double value=0; DerivativeType zero(ImageDimension); zero.Fill(0); double movingImageValue = this->GetMovingParzenTerm( this->m_MovingImage->GetPixel( oindex ) ); double fixedImageValue = this->GetFixedParzenTerm( this->m_FixedImage->GetPixel( oindex ) ); double movingImageParzenWindowContIndex=this->FitContIndexInBins( movingImageValue ); double fixedImageParzenWindowContIndex=this->FitContIndexInBins( fixedImageValue ); double dJPDF=0,dMmPDF=0,jointPDFValue=0,movingImagePDFValue=0; { typename pdfintType::ContinuousIndexType pdfind; pdfind[1]= fixedImageParzenWindowContIndex; pdfind[0]= movingImageParzenWindowContIndex; jointPDFValue=pdfinterpolator->EvaluateAtContinuousIndex(pdfind); dJPDF = (1.0)*(pdfinterpolator->EvaluateDerivativeAtContinuousIndex( pdfind ))[0]; } { typename pdfintType2::ContinuousIndexType mind; mind[0]=movingImageParzenWindowContIndex; movingImagePDFValue = pdfinterpolator3->EvaluateAtContinuousIndex(mind); dMmPDF = (pdfinterpolator3->EvaluateDerivativeAtContinuousIndex(mind))[0]; } double term1=0,term2=0,eps=1.e-12; if( jointPDFValue > eps && (movingImagePDFValue) > 0 ) { term1 = dJPDF/jointPDFValue; term2 = dMmPDF/movingImagePDFValue; value = (term1*(-1.0)+term2); } // end if-block to check non-zero bin contribution else value = 0; // double pmr=jointPDFValue; // double pmt=jointPDFValue; // double Fx=log( ( pmr / pr ) / ( pmt / pt ) ); return value; } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkAvantsMutualInformationRegistrationFunction.h000066400000000000000000000645001147325206600317160ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkAvantsMutualInformationRegistrationFunction.h,v $ Language: C++ Date: $Date: 2009/01/08 15:14:48 $ Version: $Revision: 1.20 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkAvantsMutualInformationRegistrationFunction_h #define __itkAvantsMutualInformationRegistrationFunction_h #include "itkImageFileWriter.h" #include "itkImageToImageMetric.h" #include "itkAvantsPDEDeformableRegistrationFunction.h" #include "itkCovariantVector.h" #include "itkPoint.h" #include "itkIndex.h" #include "itkBSplineKernelFunction.h" #include "itkBSplineDerivativeKernelFunction.h" #include "itkCentralDifferenceImageFunction.h" #include "itkBSplineInterpolateImageFunction.h" #include "itkLinearInterpolateImageFunction.h" #include "itkBSplineDeformableTransform.h" #include "itkTranslationTransform.h" #include "itkArray2D.h" #include "itkImageBase.h" #include "itkTransform.h" #include "itkInterpolateImageFunction.h" #include "itkSingleValuedCostFunction.h" #include "itkExceptionObject.h" #include "itkGradientRecursiveGaussianImageFilter.h" #include "itkSpatialObject.h" #include "itkConstNeighborhoodIterator.h" namespace itk { /** \class AvantsMutualInformationRegistrationFunction * \brief Computes the mutual information between two images to be * registered using the method of Avants et al. * * AvantsMutualInformationRegistrationFunction computes the mutual * information between a fixed and moving image to be registered. * * This class is templated over the FixedImage type and the MovingImage * type. * * The fixed and moving images are set via methods SetFixedImage() and * SetMovingImage(). This metric makes use of user specified Transform and * Interpolator. The Transform is used to map points from the fixed image to * the moving image domain. The Interpolator is used to evaluate the image * intensity at user specified geometric points in the moving image. * The Transform and Interpolator are set via methods SetTransform() and * SetInterpolator(). * * If a BSplineInterpolationFunction is used, this class obtain * image derivatives from the BSpline interpolator. Otherwise, * image derivatives are computed using central differencing. * * \warning This metric assumes that the moving image has already been * connected to the interpolator outside of this class. * * The method GetValue() computes of the mutual information * while method GetValueAndDerivative() computes * both the mutual information and its derivatives with respect to the * transform parameters. * * The calculations are based on the method of Avants et al [1,2] * where the probability density distribution are estimated using * Parzen histograms. Since the fixed image PDF does not contribute * to the derivatives, it does not need to be smooth. Hence, * a zero order (box car) BSpline kernel is used * for the fixed image intensity PDF. On the other hand, to ensure * smoothness a third order BSpline kernel is used for the * moving image intensity PDF. * * On Initialize(), the FixedImage is uniformly sampled within * the FixedImageRegion. The number of samples used can be set * via SetNumberOfSpatialSamples(). Typically, the number of * spatial samples used should increase with the image size. * * During each call of GetValue(), GetDerivatives(), * GetValueAndDerivatives(), marginal and joint intensity PDF's * values are estimated at discrete position or bins. * The number of bins used can be set via SetNumberOfHistogramBins(). * To handle data with arbitray magnitude and dynamic range, * the image intensity is scale such that any contribution to the * histogram will fall into a valid bin. * * One the PDF's have been contructed, the mutual information * is obtained by doubling summing over the discrete PDF values. * * * Notes: * 1. This class returns the negative mutual information value. * 2. This class in not thread safe due the private data structures * used to the store the sampled points and the marginal and joint pdfs. * * References: * [1] "Nonrigid multimodality image registration" * D. Avants, D. R. Haynor, H. Vesselle, T. Lewellen and W. Eubank * Medical Imaging 2001: Image Processing, 2001, pp. 1609-1620. * [2] "PET-CT Image Registration in the Chest Using Free-form Deformations" * D. Avants, D. R. Haynor, H. Vesselle, T. Lewellen and W. Eubank * IEEE Transactions in Medical Imaging. Vol.22, No.1, January 2003. pp.120-128. * [3] "Optimization of Mutual Information for MultiResolution Image * Registration" * P. Thevenaz and M. Unser * IEEE Transactions in Image Processing, 9(12) December 2000. * * \ingroup RegistrationMetrics * \ingroup ThreadUnSafe */ template class ITK_EXPORT AvantsMutualInformationRegistrationFunction : public AvantsPDEDeformableRegistrationFunction< TFixedImage, TMovingImage , TDeformationField> { public: /** Standard class typedefs. */ typedef AvantsMutualInformationRegistrationFunction Self; typedef AvantsPDEDeformableRegistrationFunction< TFixedImage, TMovingImage, TDeformationField > Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Run-time type information (and related methods). */ itkTypeMacro( AvantsMutualInformationRegistrationFunction, AvantsPDEDeformableRegistrationFunction ); /** MovingImage image type. */ typedef typename Superclass::MovingImageType MovingImageType; typedef typename Superclass::MovingImagePointer MovingImagePointer; /** FixedImage image type. */ typedef typename Superclass::FixedImageType FixedImageType; typedef typename Superclass::FixedImagePointer FixedImagePointer; typedef typename FixedImageType::IndexType IndexType; typedef typename FixedImageType::SizeType SizeType; typedef typename FixedImageType::SpacingType SpacingType; /** Deformation field type. */ typedef typename Superclass::VectorType VectorType; typedef typename Superclass::DeformationFieldType DeformationFieldType; typedef typename Superclass::DeformationFieldTypePointer DeformationFieldTypePointer; /** Inherit some enums from the superclass. */ itkStaticConstMacro(ImageDimension, unsigned int,Superclass::ImageDimension); /** Inherit some enums from the superclass. */ typedef typename Superclass::PixelType PixelType; typedef typename Superclass::RadiusType RadiusType; typedef typename Superclass::NeighborhoodType NeighborhoodType; typedef typename Superclass::FloatOffsetType FloatOffsetType; typedef typename Superclass::TimeStepType TimeStepType; /** Interpolator type. */ typedef double CoordRepType; typedef // // LinearInterpolateImageFunction BSplineInterpolateImageFunction InterpolatorType; typedef typename InterpolatorType::Pointer InterpolatorPointer; typedef typename InterpolatorType::PointType PointType; typedef InterpolatorType DefaultInterpolatorType; // typedef LinearInterpolateImageFunction //DefaultInterpolatorType; /** Covariant vector type. */ typedef CovariantVector CovariantVectorType; /** Gradient calculator type. */ typedef CentralDifferenceImageFunction GradientCalculatorType; typedef typename GradientCalculatorType::Pointer GradientCalculatorPointer; /** Set the moving image interpolator. */ void SetMovingImageInterpolator( InterpolatorType * ptr ) { m_MovingImageInterpolator = ptr; } /** Get the moving image interpolator. */ InterpolatorType * GetMovingImageInterpolator(void) { return m_MovingImageInterpolator; } /** This class uses a constant timestep of 1. */ virtual TimeStepType ComputeGlobalTimeStep(void *itkNotUsed(GlobalData)) const { return 1; } /** Return a pointer to a global data structure that is passed to * this object from the solver at each calculation. */ virtual void *GetGlobalDataPointer() const { GlobalDataStruct *global = new GlobalDataStruct(); // global->m_SumOfSquaredDifference = 0.0; /// global->m_NumberOfPixelsProcessed = 0L; // global->m_SumOfSquaredChange = 0; return global; } /** Release memory for global data structure. */ virtual void ReleaseGlobalDataPointer( void *GlobalData ) const { delete (GlobalDataStruct *) GlobalData; } /** Set the object's state before each iteration. */ virtual void InitializeIteration(); typedef double CoordinateRepresentationType; /** Types inherited from Superclass. */ typedef TranslationTransform TransformType; typedef ImageToImageMetric< TFixedImage, TMovingImage > Metricclass; typedef typename TransformType::Pointer TransformPointer; typedef typename Metricclass::TransformJacobianType TransformJacobianType; // typedef typename Metricclass::InterpolatorType InterpolatorType; typedef typename Metricclass::MeasureType MeasureType; typedef typename Metricclass::DerivativeType DerivativeType; typedef typename TransformType::ParametersType ParametersType; typedef typename Metricclass::FixedImageConstPointer FixedImageConstPointer; typedef typename Metricclass::MovingImageConstPointer MovingImageCosntPointer; // typedef typename TransformType::CoordinateRepresentationType CoordinateRepresentationType; /** Index and Point typedef support. */ typedef typename FixedImageType::IndexType FixedImageIndexType; typedef typename FixedImageIndexType::IndexValueType FixedImageIndexValueType; typedef typename MovingImageType::IndexType MovingImageIndexType; typedef typename TransformType::InputPointType FixedImagePointType; typedef typename TransformType::OutputPointType MovingImagePointType; /** The marginal PDFs are stored as std::vector. */ typedef float PDFValueType; // typedef std::vector MarginalPDFType; typedef Image MarginalPDFType; typedef typename MarginalPDFType::IndexType MarginalPDFIndexType; /** Typedef for the joint PDF and PDF derivatives are stored as ITK Images. */ typedef Image JointPDFType; typedef Image JointPDFDerivativesType; typedef JointPDFType::IndexType JointPDFIndexType; typedef JointPDFType::PixelType JointPDFValueType; typedef JointPDFType::RegionType JointPDFRegionType; typedef JointPDFType::SizeType JointPDFSizeType; typedef JointPDFDerivativesType::IndexType JointPDFDerivativesIndexType; typedef JointPDFDerivativesType::PixelType JointPDFDerivativesValueType; typedef JointPDFDerivativesType::RegionType JointPDFDerivativesRegionType; typedef JointPDFDerivativesType::SizeType JointPDFDerivativesSizeType; /** Get the value and derivatives for single valued optimizers. */ double GetValueAndDerivative( IndexType index, MeasureType& Value, DerivativeType& Derivative1 , DerivativeType& Derivative2 ) ; /** Get the value and derivatives for single valued optimizers. */ double GetValueAndDerivativeInv( IndexType index, MeasureType& Value, DerivativeType& Derivative1 , DerivativeType& Derivative2 ) ; /** Number of spatial samples to used to compute metric */ // itkSetClampMacro( NumberOfSpatialSamples, unsigned long, // 1, NumericTraits::max() ); //itkGetConstReferenceMacro( NumberOfSpatialSamples, unsigned long); /** Number of bins to used in the histogram. Typical value is 50. */ // itkSetClampMacro( NumberOfHistogramBins, unsigned long, // 1, NumericTraits::max() ); // itkGetConstReferenceMacro( NumberOfHistogramBins, unsigned long); void SetNumberOfHistogramBins(unsigned long nhb) { m_NumberOfHistogramBins=nhb+2*this->m_Padding;} unsigned long GetNumberOfHistogramBins() {return m_NumberOfHistogramBins;} void SetTransform(TransformPointer t){m_Transform=t;} TransformPointer GetTransform(){return m_Transform;} void SetInterpolator(InterpolatorPointer t){m_Interpolator=t;} InterpolatorPointer GetInterpolator(){ return m_Interpolator; } void GetProbabilities(); double ComputeMutualInformation() { /** Ray Razlighi changes : May 3, 2010: improves convergence. 1- The padding is lowered to 2. 2- The MI energy is changed to actual MI, please note the value of this MI is in the range of [0 min(H(x),H(y))]. in case you need it to be normalized. 3- The Natural logarithm is changed to log2. 4- In the ComputeMutualInformation() the iterator range has been changed to cover the entire PDF. 5- In the FitIndexInBins() the truncation by floor has been modified to round. 6- The normalization is done based on NomberOfHistogramBins-1 instead of NomberOfHistogramBins. */ float px,py,pxy; double mival = 0; double mi; unsigned long ct = 0; typename JointPDFType::IndexType index; for (unsigned int ii=0; iiGetPixel(mind); for (unsigned int jj=0; jjGetPixel(mind); float denom = px*py; index[0]=ii; index[1]=jj; //pxy=m_JointPDF->GetPixel(index); JointPDFValueType *pdfPtr = m_JointPDF->GetBufferPointer() + ( ii* m_NumberOfHistogramBins ); // Move the pointer to the first affected bin int pdfMovingIndex = static_cast( jj ); pdfPtr += pdfMovingIndex; pxy=*(pdfPtr); mi=0; if (fabs(denom) > 0 ) { if (pxy/denom > 0) { //true mi mi = pxy*log(pxy/denom); //test mi //mi = 1.0 + log(pxy/denom); ct++; } } mival += mi; } // std::cout << " II " << ii << " JJ " << ii << " pxy " << pxy << " px " << px << std::endl; } this->m_Energy = -1.0*mival/log(2); return this->m_Energy; } virtual VectorType ComputeUpdateInv(const NeighborhoodType &neighborhood, void *globalData, const FloatOffsetType &offset = FloatOffsetType(0.0)) { VectorType update; update.Fill(0.0); IndexType oindex = neighborhood.GetIndex(); FixedImageType* img =const_cast(this->Superclass::m_FixedImage.GetPointer()); if (!img) return update; typename FixedImageType::SpacingType spacing=img->GetSpacing(); typename FixedImageType::SizeType imagesize=img->GetLargestPossibleRegion().GetSize(); for (unsigned int dd=0; dd= static_cast(imagesize[dd]-1) ) return update; } CovariantVectorType fixedGradient; double loce=0.0; ParametersType fdvec1(ImageDimension); ParametersType fdvec2(ImageDimension); fdvec1.Fill(0); fdvec2.Fill(0); fixedGradient = m_FixedImageGradientCalculator->EvaluateAtIndex( oindex ); double nccm1=0; loce=this->GetValueAndDerivativeInv(oindex,nccm1,fdvec1,fdvec2); float eps=10; if ( loce > eps ) loce=eps; if ( loce < eps*(-1.0) ) loce=eps*(-1.0); for (int imd=0; imdm_MetricImage) this->m_MetricImage->SetPixel(oindex,loce); return update; } /* Normalizing the image to the range of [0 1] */ double GetMovingParzenTerm( double intensity ) { double windowTerm = static_cast( intensity ) - this->m_MovingImageTrueMin; windowTerm = windowTerm / ( this->m_MovingImageTrueMax - this->m_MovingImageTrueMin ) ; return windowTerm ; } double GetFixedParzenTerm( double intensity ) { double windowTerm = static_cast( intensity ) - this->m_FixedImageTrueMin; windowTerm = windowTerm / ( this->m_FixedImageTrueMax - this->m_FixedImageTrueMin ) ; return windowTerm ; } /* find the image index in the number of histogram bins */ unsigned int FitIndexInBins( double windowTerm ) { unsigned int movingImageParzenWindowIndex = static_cast( this->m_Padding + round( windowTerm * (float)(this->m_NumberOfHistogramBins - 1 - this->m_Padding))) ; // Make sure the extreme values are in valid bins if ( movingImageParzenWindowIndex < this->m_Padding ) { movingImageParzenWindowIndex = this->m_Padding -1 ; } else if ( movingImageParzenWindowIndex > (m_NumberOfHistogramBins - this->m_Padding ) ) { movingImageParzenWindowIndex = m_NumberOfHistogramBins - this->m_Padding - 1; } return movingImageParzenWindowIndex; } double FitContIndexInBins( double windowTerm ) { return ( (double) this->m_Padding + windowTerm*(float)(this->m_NumberOfHistogramBins-this->m_Padding)); } virtual VectorType ComputeUpdate(const NeighborhoodType &neighborhood, void *globalData, const FloatOffsetType &offset = FloatOffsetType(0.0)) { VectorType update; update.Fill(0.0); IndexType oindex = neighborhood.GetIndex(); FixedImageType* img =const_cast(this->Superclass::m_MovingImage.GetPointer()); if (!img) return update; typename FixedImageType::SpacingType spacing=img->GetSpacing(); typename FixedImageType::SizeType imagesize=img->GetLargestPossibleRegion().GetSize(); for (unsigned int dd=0; dd= static_cast(imagesize[dd]-1) ) return update; } CovariantVectorType movingGradient; double loce=0.0; ParametersType fdvec1(ImageDimension); ParametersType fdvec2(ImageDimension); fdvec1.Fill(0); fdvec2.Fill(0); movingGradient = m_MovingImageGradientCalculator->EvaluateAtIndex( oindex ); double nccm1=0; loce=this->GetValueAndDerivative(oindex,nccm1,fdvec1,fdvec2); float eps=10; if ( loce > eps ) loce=eps; if ( loce < eps*(-1.0) ) loce=eps*(-1.0); for (int imd=0; imdm_MetricImage) { typedef ImageFileWriter writertype; typename writertype::Pointer w= writertype::New(); w->SetInput( this->m_MetricImage); w->SetFileName("ZZmetric.nii.gz"); w->Write(); } } void SetOpticalFlow(bool b){ m_OpticalFlow = b; } typename JointPDFType::Pointer GetJointPDF() { return m_JointPDF; } typename JointPDFType::Pointer GetJointHist() { return m_JointHist; } void SetFixedImageMask( FixedImageType* img) {m_FixedImageMask=img; } /** FixedImage image neighborhood iterator type. */ typedef ConstNeighborhoodIterator FixedImageNeighborhoodIteratorType; /** A global data type for this class of equation. Used to store * iterators for the fixed image. */ struct GlobalDataStruct { FixedImageNeighborhoodIteratorType m_FixedImageIterator; }; /** The fixed image marginal PDF. */ mutable MarginalPDFType::Pointer m_FixedImageMarginalPDF; /** The moving image marginal PDF. */ mutable MarginalPDFType::Pointer m_MovingImageMarginalPDF; /** The joint PDF and PDF derivatives. */ typename JointPDFType::Pointer m_JointPDF; unsigned long m_NumberOfSpatialSamples; unsigned long m_NumberOfParameters; /** Variables to define the marginal and joint histograms. */ unsigned long m_NumberOfHistogramBins; double m_MovingImageNormalizedMin; double m_FixedImageNormalizedMin; double m_MovingImageTrueMin; double m_MovingImageTrueMax; double m_FixedImageTrueMin; double m_FixedImageTrueMax; double m_FixedImageBinSize; double m_MovingImageBinSize; protected: AvantsMutualInformationRegistrationFunction(); virtual ~AvantsMutualInformationRegistrationFunction() {}; void PrintSelf(std::ostream& os, Indent indent) const; private: AvantsMutualInformationRegistrationFunction(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented typename JointPDFType::Pointer m_JointHist; typename JointPDFDerivativesType::Pointer m_JointPDFDerivatives; /** Typedefs for BSpline kernel and derivative functions. */ typedef BSplineKernelFunction<3> CubicBSplineFunctionType; typedef BSplineDerivativeKernelFunction<3> CubicBSplineDerivativeFunctionType; /** Cubic BSpline kernel for computing Parzen histograms. */ typename CubicBSplineFunctionType::Pointer m_CubicBSplineKernel; typename CubicBSplineDerivativeFunctionType::Pointer m_CubicBSplineDerivativeKernel; /** * Types and variables related to image derivative calculations. * If a BSplineInterpolationFunction is used, this class obtain * image derivatives from the BSpline interpolator. Otherwise, * image derivatives are computed using central differencing. */ typedef CovariantVector< double, itkGetStaticConstMacro(ImageDimension) > ImageDerivativesType; /** Boolean to indicate if the interpolator BSpline. */ bool m_InterpolatorIsBSpline; // boolean to determine if we use mono-modality assumption bool m_OpticalFlow; /** Typedefs for using BSpline interpolator. */ typedef BSplineInterpolateImageFunction BSplineInterpolatorType; /** Pointer to BSplineInterpolator. */ typename BSplineInterpolatorType::Pointer m_BSplineInterpolator; /** Typedefs for using central difference calculator. */ typedef CentralDifferenceImageFunction DerivativeFunctionType; /** Pointer to central difference calculator. */ typename DerivativeFunctionType::Pointer m_DerivativeCalculator; /** * Types and variables related to BSpline deformable transforms. * If the transform is of type third order BSplineDeformableTransform, * then we can speed up the metric derivative calculation by * only inspecting the parameters within the support region * of a mapped point. */ /** Boolean to indicate if the transform is BSpline deformable. */ bool m_TransformIsBSpline; /** The number of BSpline parameters per image dimension. */ long m_NumParametersPerDim; /** * The number of BSpline transform weights is the number of * of parameter in the support region (per dimension ). */ unsigned long m_NumBSplineWeights; /** * Enum of the deformabtion field spline order. */ enum { DeformationSplineOrder = 3 }; /** * Typedefs for the BSplineDeformableTransform. */ typedef BSplineDeformableTransform< CoordinateRepresentationType, ::itk::GetImageDimension::ImageDimension, DeformationSplineOrder> BSplineTransformType; typedef typename BSplineTransformType::WeightsType BSplineTransformWeightsType; typedef typename BSplineTransformType::ParameterIndexArrayType BSplineTransformIndexArrayType; /** * Variables used when transform is of type BSpline deformable. */ typename BSplineTransformType::Pointer m_BSplineTransform; /** * Cache pre-transformed points, weights, indices and * within support region flag. */ typedef typename BSplineTransformWeightsType::ValueType WeightsValueType; typedef Array2D BSplineTransformWeightsArrayType; typedef typename BSplineTransformIndexArrayType::ValueType IndexValueType; typedef Array2D BSplineTransformIndicesArrayType; typedef std::vector MovingImagePointArrayType; typedef std::vector BooleanArrayType; BSplineTransformWeightsArrayType m_BSplineTransformWeightsArray; BSplineTransformIndicesArrayType m_BSplineTransformIndicesArray; MovingImagePointArrayType m_PreTransformPointsArray; BooleanArrayType m_WithinSupportRegionArray; typename TFixedImage::SpacingType m_FixedImageSpacing; typename TFixedImage::PointType m_FixedImageOrigin; typedef FixedArray::ImageDimension> ParametersOffsetType; ParametersOffsetType m_ParametersOffset; mutable TransformPointer m_Transform; InterpolatorPointer m_Interpolator; InterpolatorPointer m_FixedImageInterpolator; InterpolatorPointer m_MovingImageInterpolator; GradientCalculatorPointer m_FixedImageGradientCalculator; GradientCalculatorPointer m_MovingImageGradientCalculator; FixedImagePointer m_FixedImageMask; MovingImagePointer m_MovingImageMask; double m_NormalizeMetric; float m_Normalizer; typedef BSplineInterpolateImageFunction pdfintType; typename pdfintType::Pointer pdfinterpolator; typedef BSplineInterpolateImageFunction dpdfintType; typename dpdfintType::Pointer dpdfinterpolator; typedef BSplineInterpolateImageFunction pdfintType2; typename pdfintType2::Pointer pdfinterpolator2; typename pdfintType2::Pointer pdfinterpolator3; unsigned int m_Padding; }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkAvantsMutualInformationRegistrationFunction.cxx" #endif #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkAvantsPDEDeformableRegistrationFunction.h000066400000000000000000000250271147325206600306330ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkAvantsPDEDeformableRegistrationFunction.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkAvantsPDEDeformableRegistrationFunction_h_ #define _itkAvantsPDEDeformableRegistrationFunction_h_ #include "itkPDEDeformableRegistrationFunction.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkPointSet.h" namespace itk { /** \class AvantsPDEDeformableRegistrationFunction * * This is an abstract base class for all PDE functions which drives a * deformable registration algorithm. It is used by * PDEDeformationRegistrationFilter subclasses to compute the * output deformation field which will map a moving image onto * a fixed image. * * This class is templated over the fixed image type, moving image type * and the deformation field type. * * \sa AvantsPDEDeformableRegistrationFilter * \ingroup PDEDeformableRegistrationFunctions */ template class ITK_EXPORT AvantsPDEDeformableRegistrationFunction : public PDEDeformableRegistrationFunction { public: /** Standard class typedefs. */ typedef AvantsPDEDeformableRegistrationFunction Self; typedef PDEDeformableRegistrationFunction Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Run-time type information (and related methods) */ itkTypeMacro( AvantsPDEDeformableRegistrationFunction, PDEDeformableRegistrationFunction ); /** MovingImage image type. */ typedef TMovingImage MovingImageType; typedef typename MovingImageType::ConstPointer MovingImagePointer; typedef typename TMovingImage::IndexType IndexType; enum { ImageDimension = MovingImageType::ImageDimension }; /** FixedImage image type. */ typedef TFixedImage FixedImageType; typedef typename FixedImageType::ConstPointer FixedImagePointer; typedef typename Superclass::NeighborhoodType NeighborhoodType; typedef typename Superclass::FloatOffsetType FloatOffsetType; /** Deformation field type. */ typedef TDeformationField DeformationFieldType; typedef typename DeformationFieldType::Pointer DeformationFieldTypePointer; typedef typename TDeformationField::PixelType VectorType; /** PointSet Types */ typedef itk::PointSet PointSetType; typedef typename PointSetType::Pointer PointSetPointer; typedef typename PointSetType::PointType PointType; typedef typename PointSetType::PixelType PointDataType; typedef Image MetricImageType; typedef typename Image::Pointer MetricImagePointer; /** Set the moving image. */ void SetMovingImage( const MovingImageType * ptr ) { Superclass::m_MovingImage = ptr; } /** Get the moving image. */ MovingImageType * GetMovingImage(void) { return const_cast(Superclass::m_MovingImage.GetPointer()); } /** Set the fixed image. */ void SetFixedImage( const FixedImageType * ptr ) { Superclass::m_FixedImage = ptr; } /** Get the fixed image. */ FixedImageType * GetFixedImage(void) { return const_cast(Superclass::m_FixedImage.GetPointer()); } /** Set the fixed image. */ void SetDeformationField( DeformationFieldTypePointer ptr ) { Superclass::m_DeformationField = ptr; } /** Get the fixed image. */ DeformationFieldTypePointer GetDeformationField(void) { return Superclass::m_DeformationField; } void SetEnergy( double e) { this->m_Energy=0.0;} double GetEnergy( ) { return this->m_Energy;} void SetGradientStep( double e) { this->m_GradientStep = e;} void SetMaxAllowedStep( double e) { this->m_MaxAllowedStep = e;} double GetGradientStep( ) { return this->m_GradientStep ;} void SetNormalizeGradient( bool e) { this->m_NormalizeGradient=e;} bool GetNormalizeGradient( ) { return this->m_NormalizeGradient;} /* * allows one to compute the metric everywhere */ void ComputeMetricImage() { bool makenewimage=false; typedef ImageRegionIteratorWithIndex ittype; FixedImageType* img =const_cast(this->m_FixedImage.GetPointer()); typename FixedImageType::SizeType imagesize=img->GetLargestPossibleRegion().GetSize(); if (!m_MetricImage )makenewimage=true; else if (imagesize[0] != m_MetricImage->GetLargestPossibleRegion().GetSize()[0]) makenewimage = true; if (makenewimage) { m_MetricImage = MetricImageType::New(); m_MetricImage->SetLargestPossibleRegion(img->GetLargestPossibleRegion() ); m_MetricImage->SetBufferedRegion(img->GetLargestPossibleRegion()); m_MetricImage->SetSpacing(img->GetSpacing()); m_MetricImage->SetOrigin(img->GetOrigin()); m_MetricImage->Allocate(); ittype it(m_MetricImage,m_MetricImage->GetLargestPossibleRegion().GetSize()); for( it.GoToBegin(); !it.IsAtEnd(); ++it ) it.Set(0); } return; } virtual double ComputeMetricAtPair(IndexType fixedindex, typename TDeformationField::PixelType vec) { return 0.0; } virtual VectorType ComputeUpdateInv(const NeighborhoodType &neighborhood, void *globalData, const FloatOffsetType &offset = FloatOffsetType(0.0)) { bool m_Use1SidedDiff=false; VectorType update; update.Fill(0.0); typename FixedImageType::SpacingType spacing=this->GetFixedImage()->GetSpacing(); IndexType oindex = neighborhood.GetIndex(); typename TDeformationField::PixelType vec = this->m_DeformationField->GetPixel(oindex); float loce=0.0; double nccp1,nccm1; if(m_Use1SidedDiff) nccp1=this->ComputeMetricAtPair(oindex,vec); for (int imd=0; imdm_DeformationField->GetPixel(oindex); typename TDeformationField::PixelType fdvec2 = this->m_DeformationField->GetPixel(oindex); float step=0.1*spacing[imd]; fdvec1[imd]=vec[imd]+step; if (!m_Use1SidedDiff) nccp1=this->ComputeMetricAtPair(oindex,fdvec1); fdvec2[imd]=vec[imd]-step; nccm1=this->ComputeMetricAtPair(oindex,fdvec2); update[imd]=nccp1-nccm1; loce+=(nccp1+nccm1); } loce/=(2.0*(float)ImageDimension);//this->ComputeMetricAtPair(oindex,vec); this->m_Energy+=loce; if (m_MetricImage) m_MetricImage->SetPixel(oindex,loce); return update*this->m_GradientStep; } virtual VectorType ComputeUpdate(const NeighborhoodType &neighborhood, void *globalData, const FloatOffsetType &offset = FloatOffsetType(0.0)) { bool m_Use1SidedDiff=false; VectorType update; update.Fill(0.0); typename FixedImageType::SpacingType spacing=this->GetFixedImage()->GetSpacing(); IndexType oindex = neighborhood.GetIndex(); typename TDeformationField::PixelType vec = this->m_DeformationField->GetPixel(oindex); float loce=0.0; double nccp1,nccm1; if(m_Use1SidedDiff) nccp1=this->ComputeMetricAtPair(oindex,vec); for (int imd=0; imdm_DeformationField->GetPixel(oindex); typename TDeformationField::PixelType fdvec2 = this->m_DeformationField->GetPixel(oindex); float step=0.1*spacing[imd]; fdvec1[imd]=vec[imd]+step; if (!m_Use1SidedDiff) nccp1=this->ComputeMetricAtPair(oindex,fdvec1); fdvec2[imd]=vec[imd]-step; nccm1=this->ComputeMetricAtPair(oindex,fdvec2); update[imd]=nccp1-nccm1; loce+=(nccp1+nccm1); } loce/=(2.0*(float)ImageDimension);//this->ComputeMetricAtPair(oindex,vec); this->m_Energy+=loce; if (m_MetricImage) m_MetricImage->SetPixel(oindex,loce); // float mag=0; // for (int imd=0; imd 1) update.Fill(0.0); // //std::cout << " update " << update << " ind " << oindex << std::endl; return update*this->m_GradientStep; } void SetIterations( unsigned int i ) { this->m_Iterations=i; } /** this parameter is used to set a minimum value for the metric -- a minimum value that is treated as " close enough" -- */ void SetRobustnessParameter( float i ) { this->m_RobustnessParameter=i; } void SetFixedPointSet( PointSetPointer p ) { this->m_FixedPointSet=p; } void SetMovingPointSet( PointSetPointer p ) { this->m_MovingPointSet=p; } bool ThisIsAPointSetMetric() { return this->m_IsPointSetMetric; } protected: AvantsPDEDeformableRegistrationFunction() { this->m_MovingImage = NULL; m_MetricImage = NULL; this->m_FixedImage = NULL; this->m_DeformationField = NULL; this->m_Energy = 0.0; m_BestEnergy = 0.0; this->m_NormalizeGradient = true; this->m_GradientStep = 1.0; m_AverageStepMag=0; m_MaxStepMag=0; m_AvgCt=0; m_Iterations=0; this->m_FixedPointSet=NULL; this->m_MovingPointSet=NULL; this->m_IsPointSetMetric=false; this->m_RobustnessParameter=-1.e12; } ~AvantsPDEDeformableRegistrationFunction() {} void PrintSelf(std::ostream& os, Indent indent) const { this->PrintSelf(os, indent); }; mutable double m_BestEnergy; mutable double m_LastLastEnergy; mutable double m_MaxAllowedStep; mutable double m_AverageStepMag; mutable unsigned long m_AvgCt; mutable unsigned long m_Iterations; mutable double m_MaxStepMag; PointSetPointer m_FixedPointSet; PointSetPointer m_MovingPointSet; bool m_IsPointSetMetric; MetricImagePointer m_MetricImage; float m_RobustnessParameter; private: AvantsPDEDeformableRegistrationFunction(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace itk #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkCrossCorrelationRegistrationFunction.cxx000066400000000000000000000365041147325206600307350ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkCrossCorrelationRegistrationFunction.cxx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkCrossCorrelationRegistrationFunction_txx_ #define _itkCrossCorrelationRegistrationFunction_txx_ #include "itkCrossCorrelationRegistrationFunction.h" #include "itkExceptionObject.h" #include "vnl/vnl_math.h" #include "itkImageFileWriter.h" #include "itkImageLinearConstIteratorWithIndex.h" #include "itkDiscreteGaussianImageFilter.h" #include "itkMeanImageFilter.h" #include "itkMedianImageFilter.h" #include "itkImageFileWriter.h" #include namespace itk { /* * Default constructor */ template CrossCorrelationRegistrationFunction ::CrossCorrelationRegistrationFunction() { m_AvgMag=0; m_Iteration=0; RadiusType r; unsigned int j; for( j = 0; j < ImageDimension; j++ ) { r[j] = 2; } this->SetRadius(r); this->m_Energy = 0.0; m_TimeStep = 1.0; m_DenominatorThreshold = 1e-9; m_IntensityDifferenceThreshold = 0.001; Superclass::m_MovingImage = NULL; m_MetricGradientImage = NULL; Superclass::m_FixedImage = NULL; m_FixedImageSpacing.Fill( 1.0 ); m_FixedImageOrigin.Fill( 0.0 ); m_FixedImageGradientCalculator = GradientCalculatorType::New(); binaryimage=NULL; m_FullyRobust=false; m_MovingImageGradientCalculator = GradientCalculatorType::New(); typename DefaultInterpolatorType::Pointer interp = DefaultInterpolatorType::New(); m_MovingImageInterpolator = static_cast( interp.GetPointer() ); for (int i=0; i<5; i++) finitediffimages[i]=NULL; m_NumberOfHistogramBins=32; m_FixedImageMask=NULL; m_MovingImageMask=NULL; } /* * Standard "PrintSelf" method. */ template void CrossCorrelationRegistrationFunction ::PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf(os, indent); /* os << indent << "MovingImageIterpolator: "; os << m_MovingImageInterpolator.GetPointer() << std::endl; os << indent << "FixedImageGradientCalculator: "; os << m_FixedImageGradientCalculator.GetPointer() << std::endl; os << indent << "DenominatorThreshold: "; os << m_DenominatorThreshold << std::endl; os << indent << "IntensityDifferenceThreshold: "; os << m_IntensityDifferenceThreshold << std::endl; */ } /* * Set the function state values before each iteration */ template void CrossCorrelationRegistrationFunction ::InitializeIteration() { typedef ImageRegionIteratorWithIndex ittype; if( !Superclass::m_MovingImage || !Superclass::m_FixedImage || !m_MovingImageInterpolator ) { itkExceptionMacro( << "MovingImage, FixedImage and/or Interpolator not set" ); throw ExceptionObject(__FILE__,__LINE__); } // cache fixed image information m_FixedImageSpacing = Superclass::m_FixedImage->GetSpacing(); m_FixedImageOrigin = Superclass::m_FixedImage->GetOrigin(); // setup gradient calculator m_FixedImageGradientCalculator->SetInputImage( Superclass::m_FixedImage ); m_MovingImageGradientCalculator->SetInputImage( Superclass::m_MovingImage ); // setup moving image interpolator m_MovingImageInterpolator->SetInputImage( Superclass::m_MovingImage ); unsigned long numpix=1; for (int i=0; iGetLargestPossibleRegion().GetSize()[i]; m_MetricTotal=0.0; this->m_Energy=0.0; typedef itk::DiscreteGaussianImageFilter dgf1; // typedef itk::DiscreteGaussianImageFilter dgf; typedef itk::MeanImageFilter dgf; typedef itk::MedianImageFilter dgf2; // compute the normalizer m_Normalizer = 0.0; for( unsigned int k = 0; k < ImageDimension; k++ ) { m_Normalizer += m_FixedImageSpacing[k] * m_FixedImageSpacing[k]; } m_Normalizer /= static_cast( ImageDimension ); typename FixedImageType::SpacingType spacing=this->GetFixedImage()->GetSpacing(); bool makeimg=false; if ( m_Iteration==0 ) makeimg=true; else if (!finitediffimages[0] ) makeimg=true; else { for (unsigned int dd=0; ddGetLargestPossibleRegion().GetSize()[dd] != this->GetFixedImage()->GetLargestPossibleRegion().GetSize()[dd] ) makeimg=true; } } if (makeimg) { finitediffimages[0]=this->MakeImage(); finitediffimages[1]=this->MakeImage(); finitediffimages[2]=this->MakeImage(); finitediffimages[3]=this->MakeImage(); finitediffimages[4]=this->MakeImage(); } //float sig=15.; RadiusType r; for( int j = 0; j < ImageDimension; j++ ) r[j] = this->GetRadius()[j]; typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator tIter(this->GetFixedImage(),this->GetFixedImage()->GetLargestPossibleRegion() ); typename FixedImageType::SizeType imagesize=this->GetFixedImage()->GetLargestPossibleRegion().GetSize(); // compute local means // typedef itk::ImageRegionIteratorWithIndex Iterator; // // The following change was made to speed up the correlation calculation. // typedef std::deque SumQueueType; SumQueueType Qsuma2; SumQueueType Qsumb2; SumQueueType Qsuma; SumQueueType Qsumb; SumQueueType Qsumab; SumQueueType Qcount; ImageLinearConstIteratorWithIndex outIter( this->finitediffimages[0], this->finitediffimages[0]->GetLargestPossibleRegion() ); outIter.SetDirection( 0 ); outIter.GoToBegin(); while( !outIter.IsAtEnd() ) { // Push the zeros onto the stack that are outsized the image boundary at // the beginning of the line. Qsuma2 = SumQueueType( r[0], 0.0 ); Qsumb2 = SumQueueType( r[0], 0.0 ); Qsuma = SumQueueType( r[0], 0.0 ); Qsumb = SumQueueType( r[0], 0.0 ); Qsumab = SumQueueType( r[0], 0.0 ); Qcount = SumQueueType( r[0], 0.0 ); NeighborhoodIterator hoodIt( this->GetRadius(), this->finitediffimages[0], this->finitediffimages[0]->GetLargestPossibleRegion() ); IndexType oindex = outIter.GetIndex(); hoodIt.SetLocation( oindex ); unsigned int hoodlen = hoodIt.Size(); // Now add the rest of the values from each hyperplane for( unsigned int i = r[0]; i < ( 2*r[0] + 1 ); i++ ) { float suma2 = 0.0; float sumb2 = 0.0; float suma = 0.0; float sumb = 0.0; float sumab = 0.0; float count = 0.0; for( unsigned int indct = i; indct < hoodlen; indct += ( 2*r[0] + 1 ) ) { bool isInBounds = true; hoodIt.GetPixel( indct, isInBounds ); IndexType index = hoodIt.GetIndex( indct ); if ( !isInBounds || ( this->m_FixedImageMask && this->m_FixedImageMask->GetPixel( index ) < 0.25 ) ) { continue; } float a = this->GetFixedImage()->GetPixel( index ); float b = this->GetMovingImage()->GetPixel( index ); suma2 += a*a; sumb2 += b*b; suma += a; sumb += b; sumab += a*b; count += 1.0; } Qsuma2.push_back( suma2 ); Qsumb2.push_back( sumb2 ); Qsuma.push_back( suma ); Qsumb.push_back( sumb ); Qsumab.push_back( sumab ); Qcount.push_back( count ); } while( !outIter.IsAtEndOfLine() ) { // Test to see if there are any voxels we need to handle in the current // window. float suma2 = 0.0; float sumb2 = 0.0; float suma = 0.0; float sumb = 0.0; float sumab = 0.0; float count = 0.0; typename SumQueueType::iterator itcount = Qcount.begin(); while( itcount != Qcount.end() ) { count += *itcount; ++itcount; } // If there are values, we need to calculate the different quantities if( count > 0 ) { typename SumQueueType::iterator ita2 = Qsuma2.begin(); typename SumQueueType::iterator itb2 = Qsumb2.begin(); typename SumQueueType::iterator ita = Qsuma.begin(); typename SumQueueType::iterator itb = Qsumb.begin(); typename SumQueueType::iterator itab = Qsumab.begin(); while( ita2 != Qsuma2.end() ) { suma2 += *ita2; sumb2 += *itb2; suma += *ita; sumb += *itb; sumab += *itab; ++ita2; ++itb2; ++ita; ++itb; ++itab; } float fixedMean = suma / count; float movingMean = sumb / count; float sff = suma2 - fixedMean*suma - fixedMean*suma + count*fixedMean*fixedMean; float smm = sumb2 - movingMean*sumb - movingMean*sumb + count*movingMean*movingMean; float sfm = sumab - movingMean*suma - fixedMean*sumb + count*movingMean*fixedMean; IndexType oindex = outIter.GetIndex(); float val = this->GetFixedImage()->GetPixel( oindex ) - fixedMean; this->finitediffimages[0]->SetPixel( oindex, val ); val = this->GetMovingImage()->GetPixel( oindex ) - movingMean; this->finitediffimages[1]->SetPixel( oindex, val ); this->finitediffimages[2]->SetPixel( oindex, sfm );//A this->finitediffimages[3]->SetPixel( oindex, sff );//B this->finitediffimages[4]->SetPixel( oindex, smm );//C } // Increment the iterator and check to see if we're at the end of the // line. If so, go to the next line. Otherwise, add the // the values for the next hyperplane. ++outIter; if( !outIter.IsAtEndOfLine() ) { hoodIt.SetLocation( outIter.GetIndex() ); suma2 = 0.0; sumb2 = 0.0; suma = 0.0; sumb = 0.0; sumab = 0.0; count = 0.0; for( unsigned int indct = 2*r[0]; indct < hoodlen; indct += ( 2*r[0] + 1 ) ) { bool isInBounds = true; hoodIt.GetPixel( indct, isInBounds ); IndexType index = hoodIt.GetIndex( indct ); if ( !isInBounds || ( this->m_FixedImageMask && this->m_FixedImageMask->GetPixel( index ) < 0.25 ) ) { continue; } float a = this->GetFixedImage()->GetPixel( index ); float b = this->GetMovingImage()->GetPixel( index ); suma2 += a*a; sumb2 += b*b; suma += a; sumb += b; sumab += a*b; count += 1.0; } Qsuma2.push_back( suma2 ); Qsumb2.push_back( sumb2 ); Qsuma.push_back( suma ); Qsumb.push_back( sumb ); Qsumab.push_back( sumab ); Qcount.push_back( count ); } Qsuma2.pop_front(); Qsumb2.pop_front(); Qsuma.pop_front(); Qsumb.pop_front(); Qsumab.pop_front(); Qcount.pop_front(); } outIter.NextLine(); } //m_FixedImageGradientCalculator->SetInputImage(finitediffimages[0]); m_MaxMag=0.0; m_MinMag=9.e9; m_AvgMag=0.0; m_Iteration++; } /* * Compute the ncc metric everywhere */ template typename TDeformationField::PixelType CrossCorrelationRegistrationFunction ::ComputeMetricAtPairB(IndexType oindex, typename TDeformationField::PixelType vec) { typename TDeformationField::PixelType deriv; deriv.Fill(0.0); double sff=0.0; double smm=0.0; double sfm=0.0; // double fixedValue; // double movingValue; sff=0.0; smm=0.0; sfm=0.0; PointType mappedPoint; CovariantVectorType gradI,gradJ; if (this->m_FixedImageMask) if (this->m_FixedImageMask->GetPixel( oindex ) < 0.25 ) return deriv; sfm=finitediffimages[2]->GetPixel(oindex); sff=finitediffimages[3]->GetPixel(oindex); smm=finitediffimages[4]->GetPixel(oindex); if ( sff == 0.0 || smm == 0.0) return deriv; this->localCrossCorrelation=0; if (sff*smm > 1.e-5) this->localCrossCorrelation = sfm*sfm / ( sff * smm ); IndexType index=oindex;//hoodIt.GetIndex(indct); gradI = m_FixedImageGradientCalculator->EvaluateAtIndex( index ); // gradJ = m_MovingImageGradientCalculator->EvaluateAtIndex( index ); float Ji=finitediffimages[1]->GetPixel(index); float Ii=finitediffimages[0]->GetPixel(index); m_TEMP=2.0*sfm/(sff*smm)*( Ji - sfm/sff*Ii ); for (int qq=0; qqlocalCrossCorrelation*(-1.0) < this->m_RobustnessParameter) deriv.Fill(0); // if ( this->localCrossCorrelation*(-1.0) < this->m_RobustnessParameter) { // std::cout << " localC " << this->localCrossCorrelation << std::endl; } if ( this->localCrossCorrelation < 1 ) this->m_Energy-=this->localCrossCorrelation; return deriv;//localCrossCorrelation; } /* * Compute the ncc metric everywhere */ template typename TDeformationField::PixelType CrossCorrelationRegistrationFunction ::ComputeMetricAtPairC(IndexType oindex, typename TDeformationField::PixelType vec) { typename TDeformationField::PixelType deriv; deriv.Fill(0.0); double sff=0.0; double smm=0.0; double sfm=0.0; // double fixedValue; // double movingValue; sff=0.0; smm=0.0; sfm=0.0; PointType mappedPoint; CovariantVectorType gradI,gradJ; if (this->m_FixedImageMask) if (this->m_FixedImageMask->GetPixel( oindex ) < 0.25 ) return deriv; sfm=finitediffimages[2]->GetPixel(oindex); sff=finitediffimages[3]->GetPixel(oindex); smm=finitediffimages[4]->GetPixel(oindex); if ( sff == 0.0 || smm == 0.0) return deriv; IndexType index=oindex;//hoodIt.GetIndex(indct); if (sff == 0.0) sff=1.0; if (smm == 0.0) smm=1.0; ///gradI = m_FixedImageGradientCalculator->EvaluateAtIndex( index ); gradJ = m_MovingImageGradientCalculator->EvaluateAtIndex( index ); float Ji=finitediffimages[1]->GetPixel(index); float Ii=finitediffimages[0]->GetPixel(index); for (int qq=0; qqlocalCrossCorrelation = sfm*sfm / ( sff * smm ); else if (sff == 0.0 && smm == 0) this->localCrossCorrelation = 1.0; else this->localCrossCorrelation = 1.0; // if ( this->localCrossCorrelation*(-1.0) < this->m_RobustnessParameter) deriv.Fill(0); return deriv;//localCrossCorrelation; } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkCrossCorrelationRegistrationFunction.h000066400000000000000000000347511147325206600303640ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkCrossCorrelationRegistrationFunction.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkCrossCorrelationRegistrationFunction_h_ #define _itkCrossCorrelationRegistrationFunction_h_ #include "itkAvantsPDEDeformableRegistrationFunction.h" #include "itkPoint.h" #include "itkCovariantVector.h" #include "itkInterpolateImageFunction.h" #include "itkLinearInterpolateImageFunction.h" #include "itkCentralDifferenceImageFunction.h" #include "itkGradientRecursiveGaussianImageFilter.h" #include "itkAvantsMutualInformationRegistrationFunction.h" namespace itk { /** * \class CrossCorrelationRegistrationFunction * * This class encapsulate the PDE which drives the demons registration * algorithm. It is used by CrossCorrelationRegistrationFilter to compute the * output deformation field which will map a moving image onto a * a fixed image. * * Non-integer moving image values are obtained by using * interpolation. The default interpolator is of type * LinearInterpolateImageFunction. The user may set other * interpolators via method SetMovingImageInterpolator. Note that the input * interpolator must derive from baseclass InterpolateImageFunction. * * This class is templated over the fixed image type, moving image type, * and the deformation field type. * * \warning This filter assumes that the fixed image type, moving image type * and deformation field type all have the same number of dimensions. * * \sa CrossCorrelationRegistrationFilter * \ingroup FiniteDifferenceFunctions */ template class ITK_EXPORT CrossCorrelationRegistrationFunction : public AvantsPDEDeformableRegistrationFunction< TFixedImage, TMovingImage, TDeformationField> { public: /** Standard class typedefs. */ typedef CrossCorrelationRegistrationFunction Self; typedef AvantsPDEDeformableRegistrationFunction< TFixedImage, TMovingImage, TDeformationField > Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Run-time type information (and related methods). */ itkTypeMacro( CrossCorrelationRegistrationFunction, AvantsPDEDeformableRegistrationFunction ); /** MovingImage image type. */ typedef typename Superclass::MovingImageType MovingImageType; typedef typename Superclass::MovingImagePointer MovingImagePointer; /** FixedImage image type. */ typedef typename Superclass::MetricImageType MetricImageType; typedef typename Superclass::MetricImageType::Pointer MetricImagePointer; typedef typename Superclass::FixedImageType FixedImageType; typedef typename Superclass::FixedImagePointer FixedImagePointer; typedef typename FixedImageType::IndexType IndexType; typedef typename FixedImageType::SizeType SizeType; /** Deformation field type. */ typedef typename Superclass::DeformationFieldType DeformationFieldType; typedef typename Superclass::DeformationFieldTypePointer DeformationFieldTypePointer; typedef typename TDeformationField::PixelType VectorType; typedef CovariantVector GradientPixelType; typedef Image GradientImageType; typedef SmartPointer GradientImagePointer; typedef GradientRecursiveGaussianImageFilter< MetricImageType,GradientImageType > GradientImageFilterType; typedef typename GradientImageFilterType::Pointer GradientImageFilterPointer; typedef Image BinaryImageType; typedef typename BinaryImageType::Pointer BinaryImagePointer; /** Inherit some enums from the superclass. */ itkStaticConstMacro(ImageDimension, unsigned int,Superclass::ImageDimension); /** Inherit some enums from the superclass. */ typedef typename Superclass::PixelType PixelType; typedef typename Superclass::RadiusType RadiusType; typedef typename Superclass::NeighborhoodType NeighborhoodType; // typedef typename Superclass::NeighborhoodType BoundaryNeighborhoodType; typedef typename Superclass::FloatOffsetType FloatOffsetType; typedef typename Superclass::TimeStepType TimeStepType; /** Interpolator type. */ typedef double CoordRepType; typedef InterpolateImageFunction InterpolatorType; typedef typename InterpolatorType::Pointer InterpolatorPointer; typedef typename InterpolatorType::PointType PointType; typedef LinearInterpolateImageFunction DefaultInterpolatorType; /** Covariant vector type. */ typedef CovariantVector CovariantVectorType; /** Gradient calculator type. */ typedef CentralDifferenceImageFunction GradientCalculatorType; typedef typename GradientCalculatorType::Pointer GradientCalculatorPointer; /** Set the moving image interpolator. */ void SetMovingImageInterpolator( InterpolatorType * ptr ) { m_MovingImageInterpolator = ptr; } /** Get the moving image interpolator. */ InterpolatorType * GetMovingImageInterpolator(void) { return m_MovingImageInterpolator; } typename TDeformationField::PixelType ComputeMetricAtPairB(IndexType fixedindex, typename TDeformationField::PixelType vec ); typename TDeformationField::PixelType ComputeMetricAtPairC(IndexType fixedindex, typename TDeformationField::PixelType vec ); /** This class uses a constant timestep of 1. */ virtual TimeStepType ComputeGlobalTimeStep(void *GlobalData) const { return m_TimeStep; } /** Return a pointer to a global data structure that is passed to * this object from the solver at each calculation. */ virtual void *GetGlobalDataPointer() const { GlobalDataStruct *global = new GlobalDataStruct(); return global; } /** Release memory for global data structure. */ virtual void ReleaseGlobalDataPointer( void *GlobalData ) const { delete (GlobalDataStruct *) GlobalData; } /** Set the object's state before each iteration. */ virtual void InitializeIteration(); double ComputeCrossCorrelation() { if (finitediffimages[0]) { double totalcc=0; unsigned long ct=0; typedef ImageRegionIteratorWithIndex ittype; ittype it(this->finitediffimages[0],this->finitediffimages[0]->GetLargestPossibleRegion().GetSize()); for( it.GoToBegin(); !it.IsAtEnd(); ++it ) { IndexType oindex=it.GetIndex(); double sfm=finitediffimages[2]->GetPixel(oindex); double sff=finitediffimages[3]->GetPixel(oindex); double smm=finitediffimages[4]->GetPixel(oindex); double cc=0; if ( fabs(sff*smm) > 0) { cc+=sfm*sfm/(sff*smm); ct++; } if ( this->m_MetricImage) { this->m_MetricImage->SetPixel(oindex,cc); //if ( fabs(cc) > 0) // std::cout << " set cc " << cc << std::endl; } totalcc+=cc; } this->m_Energy=totalcc/(float)ct*(-1.0); return this->m_Energy; } else return 0; } virtual VectorType OpticalFlowUpdate(const NeighborhoodType &neighborhood) { // Get fixed image related information IndexType index=neighborhood.GetIndex(); typename TDeformationField::PixelType vec = Superclass::m_DeformationField->GetPixel(index); VectorType update; update.Fill(0.0); double fixedValue; CovariantVectorType fixedGradient; double fixedGradientSquaredMagnitude = 0; fixedValue = (double) Superclass::Superclass::m_FixedImage->GetPixel( index ); fixedGradient = m_FixedImageGradientCalculator->EvaluateAtIndex( index ); for( unsigned int j = 0; j < ImageDimension; j++ ) { fixedGradientSquaredMagnitude += vnl_math_sqr( fixedGradient[j] ); } double movingValue; int j; PointType mappedPoint; for( j = 0; j < ImageDimension; j++ ) { mappedPoint[j] = double( index[j] ) * m_FixedImageSpacing[j] + m_FixedImageOrigin[j]; mappedPoint[j] += vec[j]; } if( m_MovingImageInterpolator->IsInsideBuffer( mappedPoint ) ) { movingValue = m_MovingImageInterpolator->Evaluate( mappedPoint ); } else { for( j = 0; j < ImageDimension; j++ ) { update[j] = 0.0; } return update; } double speedValue = fixedValue - movingValue; if (fabs(speedValue) < this->m_RobustnessParameter) speedValue=0; double denominator = vnl_math_sqr( speedValue ) / m_Normalizer + fixedGradientSquaredMagnitude; double m_DenominatorThreshold = 1e-9; double m_IntensityDifferenceThreshold = 0.001; if ( vnl_math_abs(speedValue) < m_IntensityDifferenceThreshold || denominator < m_DenominatorThreshold ) { for( j = 0; j < ImageDimension; j++ ) { update[j] = 0.0; } return update; } for( j = 0; j < ImageDimension; j++ ) { update[j] = speedValue * fixedGradient[j] / denominator; } return update; } void SetFixedImageMask( MetricImageType* img) {m_FixedImageMask=img; } virtual VectorType ComputeUpdate(const NeighborhoodType &neighborhood, void *globalData, const FloatOffsetType &offset = FloatOffsetType(0.0)) { VectorType update; update.Fill(0.0); IndexType oindex = neighborhood.GetIndex(); FixedImageType* img =const_cast(Superclass::m_FixedImage.GetPointer()); if (!img) return update; update=this->ComputeMetricAtPairB(oindex,update); return update; } virtual VectorType ComputeUpdateInv(const NeighborhoodType &neighborhood, void *globalData, const FloatOffsetType &offset = FloatOffsetType(0.0)) { VectorType update; update.Fill(0.0); IndexType oindex = neighborhood.GetIndex(); FixedImageType* img =const_cast(Superclass::m_FixedImage.GetPointer()); if (!img) return update; update=this->ComputeMetricAtPairC(oindex,update); return update; } void SetFullyRobust(bool b){m_FullyRobust=b;} void GetProbabilities(); double localCrossCorrelation; float m_TEMP; MetricImagePointer MakeImage() { typedef ImageRegionIteratorWithIndex ittype; typedef ImageRegionIteratorWithIndex ittype2; FixedImageType* img =const_cast(Superclass::m_FixedImage.GetPointer()); typename FixedImageType::SizeType imagesize=img->GetLargestPossibleRegion().GetSize(); this->m_MetricImage = MetricImageType::New(); this->m_MetricImage->SetLargestPossibleRegion(img->GetLargestPossibleRegion() ); this->m_MetricImage->SetBufferedRegion(img->GetLargestPossibleRegion()); this->m_MetricImage->SetSpacing(img->GetSpacing()); this->m_MetricImage->SetOrigin(img->GetOrigin()); this->m_MetricImage->SetDirection(img->GetDirection()); this->m_MetricImage->Allocate(); this->m_MetricImage->FillBuffer(0); bool makebinimg=false; if (makebinimg) { m_Iteration=0; binaryimage = BinaryImageType::New(); binaryimage->SetLargestPossibleRegion(img->GetLargestPossibleRegion() ); binaryimage->SetBufferedRegion(img->GetLargestPossibleRegion()); binaryimage->SetSpacing(img->GetSpacing()); binaryimage->SetOrigin(img->GetOrigin()); binaryimage->Allocate(); ittype2 it(binaryimage,binaryimage->GetLargestPossibleRegion().GetSize()); for( it.GoToBegin(); !it.IsAtEnd(); ++it ) it.Set(1); } return this->m_MetricImage; } protected: CrossCorrelationRegistrationFunction(); ~CrossCorrelationRegistrationFunction() {} void PrintSelf(std::ostream& os, Indent indent) const; /** FixedImage image neighborhood iterator type. */ typedef ConstNeighborhoodIterator FixedImageNeighborhoodIteratorType; /** A global data type for this class of equation. Used to store * iterators for the fixed image. */ struct GlobalDataStruct { FixedImageNeighborhoodIteratorType m_FixedImageIterator; }; private: CrossCorrelationRegistrationFunction(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented /** Cache fixed image information. */ typename TFixedImage::SpacingType m_FixedImageSpacing; typename TFixedImage::PointType m_FixedImageOrigin; /** Function to compute derivatives of the fixed image. */ GradientCalculatorPointer m_FixedImageGradientCalculator; GradientCalculatorPointer m_MovingImageGradientCalculator; /** Function to interpolate the moving image. */ InterpolatorPointer m_MovingImageInterpolator; /** The global timestep. */ TimeStepType m_TimeStep; /** Threshold below which the denominator term is considered zero. */ double m_DenominatorThreshold; /** Threshold below which two intensity value are assumed to match. */ double m_IntensityDifferenceThreshold; mutable double m_MetricTotal; mutable float m_MinMag; mutable float m_MaxMag; mutable float m_AvgMag; mutable float m_Thresh; GradientImagePointer m_MetricGradientImage; MetricImagePointer finitediffimages[5]; BinaryImagePointer binaryimage; MetricImagePointer m_FixedImageMask; MetricImagePointer m_MovingImageMask; typedef itk::AvantsMutualInformationRegistrationFunction MetricType2; typename MetricType2::Pointer m_MIFunct; unsigned int m_NumberOfHistogramBins; bool m_FullyRobust; unsigned int m_Iteration; float m_Normalizer; }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkCrossCorrelationRegistrationFunction.cxx" #endif #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkExpectationBasedPointSetRegistrationFunction.h000066400000000000000000000320401147325206600317660ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkExpectationBasedPointSetRegistrationFunction.h,v $ Language: C++ Date: $Date: 2009/02/11 02:37:46 $ Version: $Revision: 1.21 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkExpectationBasedPointSetRegistrationFunction_h_ #define _itkExpectationBasedPointSetRegistrationFunction_h_ #include "itkPointSetFunction.h" #include "itkGaussianProbabilityDensityFunction.h" #include "itkKdTreeGenerator.h" #include "itkListSample.h" #include "itkMatrix.h" #include "itkMersenneTwisterRandomVariateGenerator.h" #include "itkMeshSource.h" #include "itkPointSet.h" #include "itkVector.h" #include "itkWeightedCentroidKdTreeGenerator.h" #include "itkPDEDeformableRegistrationFunction.h" #include "itkPoint.h" #include "itkCovariantVector.h" #include "itkInterpolateImageFunction.h" #include "itkLinearInterpolateImageFunction.h" #include "itkCentralDifferenceImageFunction.h" namespace itk { /** * \class ExpectationBasedPointSetRegistrationFunction * * This class encapsulate the PDE which drives the demons registration * algorithm. It is used by ExpectationBasedPointSetRegistrationFilter to compute the * output deformation field which will map a moving image onto a * a fixed image. * * Non-integer moving image values are obtained by using * interpolation. The default interpolator is of type * LinearInterpolateImageFunction. The user may set other * interpolators via method SetMovingImageInterpolator. Note that the input * interpolator must derive from baseclass InterpolateImageFunction. * * This class is templated over the fixed image type, moving image type, * and the deformation field type. * * \warning This filter assumes that the fixed image type, moving image type * and deformation field type all have the same number of dimensions. * * \sa ExpectationBasedPointSetRegistrationFilter * \ingroup FiniteDifferenceFunctions */ template class ITK_EXPORT ExpectationBasedPointSetRegistrationFunction : public AvantsPDEDeformableRegistrationFunction< TFixedImage, TMovingImage, TDeformationField> { public: /** Standard class typedefs. */ typedef ExpectationBasedPointSetRegistrationFunction Self; typedef PDEDeformableRegistrationFunction< TFixedImage, TMovingImage, TDeformationField > Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Run-time type information (and related methods). */ itkTypeMacro( ExpectationBasedPointSetRegistrationFunction, PDEDeformableRegistrationFunction ); /** MovingImage image type. */ typedef typename Superclass::MovingImageType MovingImageType; typedef typename Superclass::MovingImagePointer MovingImagePointer; /** FixedImage image type. */ typedef typename Superclass::FixedImageType FixedImageType; typedef typename Superclass::FixedImagePointer FixedImagePointer; typedef typename FixedImageType::PointType ImagePointType; typedef typename FixedImageType::IndexType IndexType; typedef typename FixedImageType::SizeType SizeType; typedef typename FixedImageType::SpacingType SpacingType; /** Deformation field type. */ typedef typename Superclass::DeformationFieldType DeformationFieldType; typedef typename Superclass::DeformationFieldTypePointer DeformationFieldTypePointer; typedef typename DeformationFieldType::PixelType VectorType; /** Inherit some enums from the superclass. */ itkStaticConstMacro(ImageDimension, unsigned int,Superclass::ImageDimension); itkStaticConstMacro(MeasurementDimension, unsigned int,Superclass::ImageDimension); /** Inherit some enums from the superclass. */ typedef typename Superclass::PixelType PixelType; typedef typename Superclass::RadiusType RadiusType; typedef typename Superclass::NeighborhoodType NeighborhoodType; typedef typename Superclass::FloatOffsetType FloatOffsetType; typedef typename Superclass::TimeStepType TimeStepType; /** Covariant vector type. */ typedef CovariantVector CovariantVectorType; /** PointSet Types */ typedef TPointSet PointSetType; typedef typename PointSetType::Pointer PointSetPointer; typedef typename PointSetType::PointType PointType; typedef typename PointSetType::PixelType PointDataType; typedef std::vector LabelSetType; // typedef long PointDataType; typedef Vector MeasurementVectorType; typedef typename Statistics::ListSample SampleType; typedef typename Statistics::WeightedCentroidKdTreeGenerator TreeGeneratorType; typedef typename TreeGeneratorType::KdTreeType:: InstanceIdentifierVectorType NeighborhoodIdentifierType; /** Bspline stuff */ typedef PointSet BSplinePointSetType; typedef BSplineScatteredDataPointSetToImageFilter BSplineFilterType; typedef typename BSplineFilterType::WeightsContainerType BSplineWeightsType; typedef typename BSplineFilterType::PointDataImageType ControlPointLatticeType; typedef typename BSplineFilterType::ArrayType ArrayType; /** Other typedef */ typedef float RealType; typedef float OutputType; typedef typename Statistics ::MersenneTwisterRandomVariateGenerator RandomizerType; typedef typename Statistics ::GaussianProbabilityDensityFunction GaussianType; /** Fixed image gradient calculator type. */ typedef CentralDifferenceImageFunction GradientCalculatorType; typedef typename GradientCalculatorType::Pointer GradientCalculatorPointer; /** Moving image gradient calculator type. */ typedef CentralDifferenceImageFunction MovingImageGradientCalculatorType; typedef typename MovingImageGradientCalculatorType::Pointer MovingImageGradientCalculatorPointer; /** This class uses a constant timestep of 1. */ virtual TimeStepType ComputeGlobalTimeStep(void * itkNotUsed(GlobalData)) const { return m_TimeStep; } /** Return a pointer to a global data structure that is passed to * this object from the solver at each calculation. */ virtual void *GetGlobalDataPointer() const { GlobalDataStruct *global = new GlobalDataStruct(); global->m_SumOfSquaredDifference = 0.0; global->m_NumberOfPixelsProcessed = 0L; global->m_SumOfSquaredChange = 0; return global; } /** Release memory for global data structure. */ virtual void ReleaseGlobalDataPointer( void *GlobalData ) const; void ExpectationLandmarkField(float weight, bool whichdirection); void FastExpectationLandmarkField(float weight, bool whichdirection, long whichlabel, bool dobsp); /** Set the object's state before each iteration. */ virtual void InitializeIteration(); /** This method is called by a finite difference solver image filter at * each pixel that does not lie on a data set boundary */ virtual PixelType ComputeUpdate(const NeighborhoodType &neighborhood, void *globalData, const FloatOffsetType &offset = FloatOffsetType(0.0)); virtual PixelType ComputeUpdateInv(const NeighborhoodType &neighborhood, void *globalData, const FloatOffsetType &offset = FloatOffsetType(0.0)); /** Get the metric value. The metric value is the mean square difference * in intensity between the fixed image and transforming moving image * computed over the the overlapping region between the two images. */ virtual double GetMetric() const { return m_Metric; } /** Get the rms change in deformation field. */ virtual double GetRMSChange() const { return m_RMSChange; } /** Set/Get the threshold below which the absolute difference of * intensity yields a match. When the intensities match between a * moving and fixed image pixel, the update vector (for that * iteration) will be the zero vector. Default is 0.001. */ virtual void SetEuclideanDistanceThreshold(double); virtual double GetEuclideanDistanceThreshold() const; void SetFixedPointSetSigma ( float f ) { this->m_FixedPointSetSigma=f; } float GetFixedPointSetSigma ( ) { return this->m_FixedPointSetSigma; } void SetMovingPointSetSigma ( float f ) { this->m_MovingPointSetSigma=f; } float GetMovingPointSetSigma ( ) { return this->m_MovingPointSetSigma; } void SetKNeighborhood( unsigned int n ) { this->m_KNeighborhood = n; } unsigned int GetKNeighborhood() { return this->m_KNeighborhood; } void SetUseSymmetricMatching(unsigned int b) { this->m_UseSymmetricMatching=b; } protected: ExpectationBasedPointSetRegistrationFunction(); ~ExpectationBasedPointSetRegistrationFunction() {} void PrintSelf(std::ostream& os, Indent indent) const; /** FixedImage image neighborhood iterator type. */ typedef ConstNeighborhoodIterator FixedImageNeighborhoodIteratorType; /** A global data type for this class of equation. Used to store * information for computing the metric. */ struct GlobalDataStruct { double m_SumOfSquaredDifference; unsigned long m_NumberOfPixelsProcessed; double m_SumOfSquaredChange; }; void SetUpKDTrees(long whichlabel); private: ExpectationBasedPointSetRegistrationFunction(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented /** Cache fixed image information. */ SpacingType m_FixedImageSpacing; ImagePointType m_FixedImageOrigin; double m_Normalizer; /** Function to compute derivatives of the fixed image. */ GradientCalculatorPointer m_FixedImageGradientCalculator; /** Function to compute derivatives of the moving image. */ MovingImageGradientCalculatorPointer m_MovingImageGradientCalculator; bool m_UseMovingImageGradient; /** The global timestep. */ TimeStepType m_TimeStep; /** Threshold below which the denominator term is considered zero. */ double m_DenominatorThreshold; /** Threshold below which two intensity value are assumed to match. */ double m_EuclideanDistanceThreshold; /** The metric value is the mean square difference in intensity between * the fixed image and transforming moving image computed over the * the overlapping region between the two images. */ mutable double m_Metric; mutable double m_SumOfSquaredDifference; mutable unsigned long m_NumberOfPixelsProcessed; mutable double m_RMSChange; mutable double m_SumOfSquaredChange; /** Mutex lock to protect modification to metric. */ mutable SimpleFastMutexLock m_MetricCalculationLock; DeformationFieldTypePointer m_DerivativeFixedField; DeformationFieldTypePointer m_DerivativeMovingField; float m_FixedPointSetSigma; float m_MovingPointSetSigma; float m_LandmarkEnergy; unsigned int m_KNeighborhood; unsigned int m_BucketSize; RealType m_Sigma; typename TreeGeneratorType::Pointer m_FixedKdTreeGenerator; typename SampleType::Pointer m_FixedSamplePoints; typename TreeGeneratorType::Pointer m_MovingKdTreeGenerator; typename SampleType::Pointer m_MovingSamplePoints; typename RandomizerType::Pointer m_Randomizer; bool m_Normalize; LabelSetType m_LabelSet; unsigned int m_UseSymmetricMatching; typename BSplinePointSetType::Pointer m_bpoints; typename BSplineWeightsType::Pointer m_bweights; unsigned int m_bcount; }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkExpectationBasedPointSetRegistrationFunction.txx" #endif #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkExpectationBasedPointSetRegistrationFunction.txx000066400000000000000000000604211147325206600323660ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkExpectationBasedPointSetRegistrationFunction.txx,v $ Language: C++ Date: $Date: 2009/03/09 17:03:46 $ Version: $Revision: 1.25 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkExpectationBasedPointSetRegistrationFunction_txx_ #define _itkExpectationBasedPointSetRegistrationFunction_txx_ #include "itkExpectationBasedPointSetRegistrationFunction.h" #include "itkExceptionObject.h" #include "vnl/vnl_math.h" #include "itkBSplineScatteredDataPointSetToImageFilter.h" #include "itkPointSet.h" namespace itk { /* * Default constructor */ template ExpectationBasedPointSetRegistrationFunction ::ExpectationBasedPointSetRegistrationFunction() { RadiusType r; unsigned int j; for( j = 0; j < ImageDimension; j++ ) { r[j] = 0; } this->SetRadius(r); m_TimeStep = 1.0; m_DenominatorThreshold = 1e-9; m_EuclideanDistanceThreshold =0.01; this->SetMovingImage(NULL); this->SetFixedImage(NULL); m_FixedImageSpacing.Fill( 1.0 ); m_FixedImageOrigin.Fill( 0.0 ); m_Normalizer = 1.0; m_FixedImageGradientCalculator = GradientCalculatorType::New(); m_Metric = NumericTraits::max(); m_SumOfSquaredDifference = 0.0; m_NumberOfPixelsProcessed = 0L; m_RMSChange = NumericTraits::max(); m_SumOfSquaredChange = 0.0; this->m_KNeighborhood=100; m_MovingImageGradientCalculator = MovingImageGradientCalculatorType::New(); m_UseMovingImageGradient = false; this->m_FixedPointSet=NULL; this->m_MovingPointSet=NULL; this->m_DerivativeFixedField=NULL; this->m_DerivativeMovingField=NULL; this->m_IsPointSetMetric=true; this->m_UseSymmetricMatching=100000; this->m_Iterations=0; } /* * Standard "PrintSelf" method. */ template void ExpectationBasedPointSetRegistrationFunction ::PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf(os, indent); } /** * */ template void ExpectationBasedPointSetRegistrationFunction ::SetEuclideanDistanceThreshold(double threshold) { m_EuclideanDistanceThreshold = threshold; } /** * */ template double ExpectationBasedPointSetRegistrationFunction ::GetEuclideanDistanceThreshold() const { return m_EuclideanDistanceThreshold; } /* * Set the function state values before each iteration */ template void ExpectationBasedPointSetRegistrationFunction ::ExpectationLandmarkField(float weight, bool whichdirection) { typedef ImageRegionIteratorWithIndex Iterator; SpacingType spacing = this->GetFixedImage()->GetSpacing(); unsigned long sz1=this->m_FixedPointSet->GetNumberOfPoints(); unsigned long sz2=this->m_MovingPointSet->GetNumberOfPoints(); if (!whichdirection) { sz2=this->m_FixedPointSet->GetNumberOfPoints(); sz1=this->m_MovingPointSet->GetNumberOfPoints(); } typedef vnl_matrix MatrixType; MatrixType EucDist(sz1,sz2); EucDist.fill(0); MatrixType fixedlms(sz1,ImageDimension); MatrixType movinglms(sz2,ImageDimension); fixedlms.fill(0); movinglms.fill(0); if ( sz1 <= 0 || sz2 <= 0 ) { return; } DeformationFieldTypePointer lmField=this->m_DerivativeFixedField; if (!whichdirection) lmField=this->m_DerivativeMovingField; float inweight=weight; this->m_LandmarkEnergy=0.0; float max=0; unsigned long bestindex=0; std::cout << " sz1 " << sz1 << " sz2 "<m_FixedPointSet->GetPoint(ii,&fixedpoint); else this->m_MovingPointSet->GetPoint(ii,&fixedpoint); if (whichdirection) this->m_FixedPointSet->GetPointData(ii,&fixedlabel); else this->m_MovingPointSet->GetPointData(ii,&fixedlabel); float min = 1.e9; ImagePointType fpt; IndexType oindex; for (int j=0; jGetFixedImage()->TransformPhysicalPointToIndex(fpt,oindex); if (!convok) std::cout <<" fpt " << fpt << std::endl; // if whichdirection is true, then the fixed direction, else moving for (unsigned long jj=0; jjm_MovingPointSet->GetPoint(jj,&movingpoint); else this->m_FixedPointSet->GetPoint(jj,&movingpoint); ImagePointType mpt; for (int j=0; jGetMovingImage()->TransformPhysicalPointToIndex(mpt,movingindex); double prob=0; if (convok) { float mag=0.0; VectorType force; for (int j=0; jm_FixedPointSetSigma; if (!whichdirection) sigma=this->m_MovingPointSetSigma; prob=exp(-1.0*mag/sigma); PointDataType movinglabel=0; if (whichdirection) this->m_MovingPointSet->GetPointData(jj,&movinglabel); else this->m_FixedPointSet->GetPointData(jj,&movinglabel); // if (ii == 2 && jj==2) std::cout << "prob " << prob << " sigma " << sigma << " " << mag << " fl " << fixedlabel << " ml " << movinglabel << std::endl; if ( fixedlabel != movinglabel) prob=0; // || fixedlabel !=4) prob=0; mag=sqrt(mag); } EucDist(ii,jj)=prob; } if (min < 1.e5) this->m_LandmarkEnergy+=min; } MatrixType sinkhorn=EucDist; for (unsigned int iter=0; iter<1; iter++) { for (unsigned int jj=0; jjm_FixedPointSet->GetPoint(ii,&fixedpoint); else this->m_MovingPointSet->GetPoint(ii,&fixedpoint); if (whichdirection) this->m_FixedPointSet->GetPointData(ii,&fixedlabel); else this->m_MovingPointSet->GetPointData(ii,&fixedlabel); ImagePointType mpt; ImagePointType fpt; for (int j=0; jGetFixedImage()->TransformPhysicalPointToIndex(fpt,fixedindex); if (convok) { float mag=0.0; VectorType force; for (int j=0; j 50) { float tot=0; for (int k=0; k < sz2; k++) { tot+=sinkhorn(ii,k); } std::cout << "TOT " << tot << std::endl; force.Fill(0); mag=0; } */ if (mag > maxerr) maxerr=mag; energy+=mag; std::cout <<" ii " << ii << " force " << force << " mag " << mag << " mpt " << mpt << " fpt " << fixedpoint << " nrg " << energy /(float)ii << std::endl; lmField->SetPixel(fixedindex,force+lmField->GetPixel(fixedindex)); } // lmField->SetPixel(fixedindex,sforce); } // std::cout << " max " << maxerr << std::endl; this->m_LandmarkEnergy=energy/(float)sz1; this->m_Energy=this->m_LandmarkEnergy; } /* * Set the function state values before each iteration */ template void ExpectationBasedPointSetRegistrationFunction ::InitializeIteration() { // std::cout << " INIT ITER " << std::endl; if( !this->GetMovingImage() || !this->GetFixedImage() ) { itkExceptionMacro( << "MovingImage, FixedImage not set" ); } // cache fixed image information m_FixedImageSpacing = this->GetFixedImage()->GetSpacing(); m_FixedImageOrigin = this->GetFixedImage()->GetOrigin(); // compute the normalizer m_Normalizer = 0.0; for( unsigned int k = 0; k < ImageDimension; k++ ) { m_Normalizer += m_FixedImageSpacing[k] * m_FixedImageSpacing[k]; } m_Normalizer /= static_cast( ImageDimension ); // setup gradient calculator m_FixedImageGradientCalculator->SetInputImage( this->GetFixedImage() ); m_MovingImageGradientCalculator->SetInputImage( this->GetMovingImage() ); // initialize metric computation variables m_SumOfSquaredDifference = 0.0; m_NumberOfPixelsProcessed = 0L; m_SumOfSquaredChange = 0.0; typename DeformationFieldType::PixelType zero; zero.Fill(0); this->m_DerivativeFixedField=DeformationFieldType::New(); this->m_DerivativeFixedField->SetSpacing( this->GetFixedImage()->GetSpacing() ); this->m_DerivativeFixedField->SetOrigin( this->GetFixedImage()->GetOrigin() ); this->m_DerivativeFixedField->SetLargestPossibleRegion(this->GetFixedImage()->GetLargestPossibleRegion() ); this->m_DerivativeFixedField->SetRequestedRegion(this->GetFixedImage()->GetLargestPossibleRegion() ); this->m_DerivativeFixedField->SetBufferedRegion( this->GetFixedImage()->GetLargestPossibleRegion() ); this->m_DerivativeFixedField->Allocate(); this->m_DerivativeMovingField=DeformationFieldType::New(); this->m_DerivativeMovingField->SetSpacing( this->GetMovingImage()->GetSpacing() ); this->m_DerivativeMovingField->SetOrigin( this->GetMovingImage()->GetOrigin() ); this->m_DerivativeMovingField->SetLargestPossibleRegion(this->GetMovingImage()->GetLargestPossibleRegion() ); this->m_DerivativeMovingField->SetRequestedRegion(this->GetMovingImage()->GetLargestPossibleRegion() ); this->m_DerivativeMovingField->SetBufferedRegion( this->GetMovingImage()->GetLargestPossibleRegion() ); this->m_DerivativeMovingField->Allocate(); this->m_DerivativeMovingField->FillBuffer(zero); this->m_DerivativeFixedField->FillBuffer(zero); //acquire labels if (this->m_LabelSet.size() < 1) { this->m_LabelSet.clear(); unsigned long sz1=this->m_FixedPointSet->GetNumberOfPoints(); std::cout << " NPTS " << sz1 << std::endl; for (unsigned long ii=0; iim_FixedPointSet->GetPoint(ii,&fixedpoint); this->m_FixedPointSet->GetPointData(ii,&label); if ( label > 0 ) { if ( find( this->m_LabelSet.begin(), this->m_LabelSet.end(), label ) == this->m_LabelSet.end() ) { this->m_LabelSet.push_back( label ); } } } } else { std::cout << " #of Label Values to match " << this->m_LabelSet.size() << std::endl; } this->m_bpoints = BSplinePointSetType::New(); this->m_bpoints->Initialize(); this->m_bweights = BSplineWeightsType::New(); this->m_bweights->Initialize(); this->m_bcount=0; unsigned int lct=0; typename LabelSetType::const_iterator it; for ( it = this->m_LabelSet.begin(); it != this->m_LabelSet.end(); ++it ) { lct++; PointDataType label=(PointDataType)*it; // std::cout << " doing label " << label << std::endl; this->SetUpKDTrees(label); bool dobsp=false; // if (lct == this->m_LabelSet.size() ) dobsp=true; this->FastExpectationLandmarkField(1.0,true,label,dobsp); this->FastExpectationLandmarkField(1.0,false,label,dobsp); } // follow up with BSpline if dospb is true . } /* * Compute update at a specify neighbourhood */ template typename ExpectationBasedPointSetRegistrationFunction ::PixelType ExpectationBasedPointSetRegistrationFunction ::ComputeUpdate(const NeighborhoodType &it, void * gd, const FloatOffsetType& itkNotUsed(offset)) { IndexType index = it.GetIndex(); PixelType update = this->m_DerivativeFixedField->GetPixel(index); if (this->m_Iterations > this->m_UseSymmetricMatching) update.Fill(0); return update; } /* * Compute update at a specify neighbourhood */ template typename ExpectationBasedPointSetRegistrationFunction ::PixelType ExpectationBasedPointSetRegistrationFunction ::ComputeUpdateInv(const NeighborhoodType &it, void * gd, const FloatOffsetType& itkNotUsed(offset)) { IndexType index = it.GetIndex(); return this->m_DerivativeMovingField->GetPixel(index); } /* * Update the metric and release the per-thread-global data. */ template void ExpectationBasedPointSetRegistrationFunction ::ReleaseGlobalDataPointer( void *gd ) const { GlobalDataStruct * globalData = (GlobalDataStruct *) gd; m_MetricCalculationLock.Lock(); m_SumOfSquaredDifference += globalData->m_SumOfSquaredDifference; m_NumberOfPixelsProcessed += globalData->m_NumberOfPixelsProcessed; m_SumOfSquaredChange += globalData->m_SumOfSquaredChange; if ( m_NumberOfPixelsProcessed ) { m_Metric = m_SumOfSquaredDifference / static_cast( m_NumberOfPixelsProcessed ); m_RMSChange = vcl_sqrt( m_SumOfSquaredChange / static_cast( m_NumberOfPixelsProcessed ) ); } m_MetricCalculationLock.Unlock(); delete globalData; } /* * Set the function state values before each iteration */ template void ExpectationBasedPointSetRegistrationFunction::SetUpKDTrees(long whichlabel) { // convert this->m_FixedPointSet to a sample type this->m_FixedSamplePoints = SampleType::New(); this->m_FixedSamplePoints->SetMeasurementVectorSize( MeasurementDimension ); MeasurementVectorType mv; unsigned int bucketsize=4; unsigned int npts= this->m_FixedPointSet->GetNumberOfPoints(); // std::cout << " NP MOV " << npts << std::endl; for ( unsigned int i = 0; i < npts; i++ ) { PointType fixedpoint; this->m_FixedPointSet->GetPoint(i,&fixedpoint); PointDataType fixedlabel=0; this->m_FixedPointSet->GetPointData(i,&fixedlabel); for ( unsigned int d = 0; d < ImageDimension; d++ ) { mv[d] = fixedpoint[d]; } //mv[ImageDimension]=(float) fixedlabel*1.e6; if (fixedlabel == whichlabel) this->m_FixedSamplePoints->PushBack( mv ); } this->m_FixedKdTreeGenerator = TreeGeneratorType::New(); this->m_FixedKdTreeGenerator->SetSample( this->m_FixedSamplePoints ); this->m_FixedKdTreeGenerator->SetBucketSize( bucketsize ); this->m_FixedKdTreeGenerator->Update(); this->m_MovingSamplePoints = SampleType::New(); this->m_MovingSamplePoints->SetMeasurementVectorSize( ImageDimension ); npts= this->m_MovingPointSet->GetNumberOfPoints(); // std::cout << " NP MOV " << npts << std::endl; for ( unsigned int i = 0; i < npts; i++ ) { PointType movingpoint; this->m_MovingPointSet->GetPoint(i,&movingpoint); PointDataType movinglabel=0; this->m_MovingPointSet->GetPointData(i,&movinglabel); for ( unsigned int d = 0; d < ImageDimension; d++ ) { mv[d] = movingpoint[d]; } //mv[ImageDimension]=(float) movinglabel*1.e6; if (movinglabel == whichlabel) this->m_MovingSamplePoints->PushBack( mv ); } this->m_MovingKdTreeGenerator = TreeGeneratorType::New(); this->m_MovingKdTreeGenerator->SetSample( this->m_MovingSamplePoints ); this->m_MovingKdTreeGenerator->SetBucketSize( bucketsize ); this->m_MovingKdTreeGenerator->Update(); } /* * Set the function state values before each iteration */ template void ExpectationBasedPointSetRegistrationFunction ::FastExpectationLandmarkField(float weight, bool whichdirection, long whichlabel, bool dobspline) { /** * BSpline typedefs */ /** Typedefs for B-spline filter */ unsigned int m_SplineOrder=3; unsigned int m_NumberOfBLevels=5; ArrayType m_MeshResolution; m_MeshResolution.Fill(1); unsigned int PointDimension=ImageDimension; typedef ImageRegionIteratorWithIndex Iterator; SpacingType spacing = this->GetFixedImage()->GetSpacing(); typename TreeGeneratorType::Pointer fkdtree; typename TreeGeneratorType::Pointer mkdtree; if (whichdirection) { mkdtree=this->m_MovingKdTreeGenerator; fkdtree=this->m_FixedKdTreeGenerator; }else{ fkdtree=this->m_MovingKdTreeGenerator; mkdtree=this->m_FixedKdTreeGenerator; } unsigned long sz1=fkdtree->GetOutput()->Size(); unsigned long sz2=mkdtree->GetOutput()->Size(); // std::cout << " s1 " << sz1 << " s2 " << sz2 << std::endl; if ( sz1 <= 0 || sz2 <= 0 ) { return; } DeformationFieldTypePointer lmField=this->m_DerivativeFixedField; if (!whichdirection) lmField=this->m_DerivativeMovingField; unsigned int KNeighbors=this->m_KNeighborhood; if (KNeighbors > sz2) KNeighbors=sz2; float inweight=weight; this->m_LandmarkEnergy=0.0; // float max=0; vnl_vector probabilities(KNeighbors); probabilities.fill(0); float energy=0,maxerr=0; for (unsigned long ii=0; iiGetOutput()->GetMeasurementVector(ii); ImagePointType mpt; mpt.Fill(0); ImagePointType fpt; fpt.Fill(0); for (int j=0; jGetFixedImage()->TransformPhysicalPointToIndex(fpt,fixedindex); // std::cout << " Orig " << this->GetFixedImage()->GetOrigin() << " ind " << fixedindex << " pt " << fpt << std::endl; if (convok) { float mag=0.0; VectorType force; force.Fill(0); typename TreeGeneratorType::KdTreeType::InstanceIdentifierVectorType neighbors; mkdtree->GetOutput()->Search( fixedpoint, static_cast( KNeighbors ), neighbors ); double probtotal=0.0; for (unsigned int dd=0; ddGetOutput()->GetMeasurementVector(wpt); float mag=0; for (unsigned int qq=0; qqm_FixedPointSetSigma; if (!whichdirection) sigma=this->m_MovingPointSetSigma; double prob=exp(-1.0*mag/sigma); probtotal+=prob; probabilities(dd)=prob; } if (probtotal > 0) for (unsigned int dd=0; ddGetOutput()->GetMeasurementVector(wpt); double pp=probabilities(dd)/probtotal; if (pp > 0)for (int j=0; j 1.e-3) std::cout << " prob " << pp << " mpt " << mpt << " dd " << dd <<" wpt " << wpt << " movinpoint " << movingpoint << " ptot " << probtotal << std::endl; } typename BSplinePointSetType::PointType bpoint; for (int j=0; jm_bpoints->SetPoint( this->m_bcount, bpoint ); this->m_bpoints->SetPointData( this->m_bcount, distance ); float bwt=1; this->m_bweights->InsertElement( this->m_bcount, static_cast( bwt ) ); this->m_bcount++; mag=sqrt(mag); if (mag > maxerr) maxerr=mag; energy+=mag; lmField->SetPixel(fixedindex,force+lmField->GetPixel(fixedindex)); } } // std::cout << " max " << maxerr << std::endl; this->m_LandmarkEnergy=energy/(float)sz1; this->m_Energy=this->m_LandmarkEnergy; if (dobspline) { /** * Calculate derivative field with respect to the moving points */ { //std::cout << " start bsp " << std::endl; typename BSplineFilterType::ArrayType nlevels; typename BSplineFilterType::ArrayType ncps; nlevels.Fill( m_NumberOfBLevels ); for( unsigned int d = 0; d < PointDimension; d++ ) { ncps[d] = m_MeshResolution[d] +m_SplineOrder; } typename BSplineFilterType::Pointer bspliner = BSplineFilterType::New(); bspliner->SetInput( this->m_bpoints ); bspliner->SetOrigin( this->GetMovingImage()->GetOrigin() ); bspliner->SetSpacing( this->GetMovingImage()->GetSpacing() ); bspliner->SetSize( this->GetMovingImage()->GetLargestPossibleRegion().GetSize() ); bspliner->SetNumberOfLevels( nlevels ); bspliner->SetSplineOrder( m_SplineOrder ); bspliner->SetNumberOfControlPoints( ncps ); bspliner->SetGenerateOutputImage( true ); bspliner->SetPointWeights( this->m_bweights ); bspliner->Update(); lmField = bspliner->GetOutput(); /** * Now calculate the distances after matching. ItM = this->m_MovingPointSet->GetPoints()->Begin(); ItMD = this->m_MovingPointSet->GetPointData()->Begin(); ItF = this->m_FixedPointSet->GetPoints()->Begin(); ItFD = this->m_FixedPointSet->GetPointData()->Begin(); typename BSplineWeightsType::ConstIterator ItW = weights->Begin(); while( ItM != this->m_MovingPointSet->GetPoints()->End() && ItF != this->m_FixedPointSet->GetPoints()->End() ) { typename BSplinePointSetType::PixelType vector; bspliner->EvaluateAtPoint( ItM.Value(), vector ); RealType distance = 0.0; for( unsigned int d = 0; d < PointDimension; d++ ) { distance += vnl_math_sqr( ItM.Value()[d] + vector[d] - ItF.Value()[d] ); } this->m_Energy += ItW.Value() * vcl_sqrt( distance ); ++ItF; ++ItFD; ++ItM; ++ItMD; ++ItW; } */ } } } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkJensenHavrdaCharvatTsallisLabeledPointSetMetric.h000066400000000000000000000207001147325206600322750ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkJensenHavrdaCharvatTsallisLabeledPointSetMetric.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkJensenHavrdaCharvatTsallisLabeledPointSetMetric_h #define _itkJensenHavrdaCharvatTsallisLabeledPointSetMetric_h #include "itkPointSetToPointSetMetric.h" #include "itkIdentityTransform.h" namespace itk { /** \class JensenHavrdaCharvatTsallisLabeledPointSetMetric * * * This class is templated over the fixed point-set type, moving * point-set type. * */ template class ITK_EXPORT JensenHavrdaCharvatTsallisLabeledPointSetMetric : public PointSetToPointSetMetric { public: /** Standard class typedefs. */ typedef JensenHavrdaCharvatTsallisLabeledPointSetMetric Self; typedef PointSetToPointSetMetric Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); /** Run-time type information (and related methods) */ itkTypeMacro( JensenHavrdaCharvatTsallisLabeledPointSetMetric, PointSetToLabelPointSetMetric ); itkStaticConstMacro( PointDimension, unsigned int, TPointSet::PointDimension ); /** Types transferred from the base class */ typedef typename Superclass::TransformType TransformType; typedef typename Superclass::TransformPointer TransformPointer; typedef typename Superclass::TransformParametersType TransformParametersType; typedef typename Superclass::TransformJacobianType TransformJacobianType; typedef typename Superclass::MeasureType MeasureType; typedef typename Superclass::DerivativeType DerivativeType; typedef TPointSet PointSetType; typedef typename PointSetType::PointType PointType; typedef typename PointSetType::PixelType PixelType; /** * Other typedefs */ typedef double RealType; typedef IdentityTransform DefaultTransformType; typedef std::vector LabelSetType; /** * Public function definitions */ itkSetConstObjectMacro( FixedPointSet, PointSetType ); itkGetConstObjectMacro( FixedPointSet, PointSetType ); itkSetConstObjectMacro( MovingPointSet, PointSetType ); itkGetConstObjectMacro( MovingPointSet, PointSetType ); itkSetObjectMacro( Transform, TransformType ); itkGetObjectMacro( Transform, TransformType ); /** * The only transform type used is Identity */ unsigned int GetNumberOfParameters( void ) const { return m_Transform->GetNumberOfParameters(); } /** Initialize the Metric by making sure that all the components * are present and plugged together correctly */ virtual void Initialize( void ) throw ( ExceptionObject ); /** Get the number of values */ unsigned int GetNumberOfValues() const; /** Get the derivatives of the match measure. */ void GetDerivative( const TransformParametersType & parameters, DerivativeType & Derivative ) const; /** Get the value for single valued optimizers. */ MeasureType GetValue( const TransformParametersType & parameters ) const; /** Get value and derivatives for multiple valued optimizers. */ void GetValueAndDerivative( const TransformParametersType & parameters, MeasureType& Value, DerivativeType& Derivative ) const; itkSetClampMacro( Alpha, RealType, 1.0, 2.0 ); itkGetConstMacro( Alpha, RealType ); itkSetMacro( UseRegularizationTerm, bool ); itkGetConstMacro( UseRegularizationTerm, bool ); itkBooleanMacro( UseRegularizationTerm ); itkSetMacro( UseWithRespectToTheMovingPointSet, bool ); itkGetConstMacro( UseWithRespectToTheMovingPointSet, bool ); itkBooleanMacro( UseWithRespectToTheMovingPointSet ); itkSetMacro( MovingPointSetSigma, RealType ); itkGetConstMacro( MovingPointSetSigma, RealType ); itkSetMacro( MovingEvaluationKNeighborhood, unsigned int ); itkGetConstMacro( MovingEvaluationKNeighborhood, unsigned int ); itkSetMacro( FixedPointSetSigma, RealType ); itkGetConstMacro( FixedPointSetSigma, RealType ); itkSetMacro( FixedEvaluationKNeighborhood, unsigned int ); itkGetConstMacro( FixedEvaluationKNeighborhood, unsigned int ); itkSetMacro( UseInputAsSamples, bool ); itkGetConstMacro( UseInputAsSamples, bool ); itkBooleanMacro( UseInputAsSamples ); /** * If this->m_UseInputAsSamples = true, the following * two variables are not used. */ itkSetMacro( NumberOfMovingSamples, unsigned long ); itkGetConstMacro( NumberOfMovingSamples, unsigned long ); itkSetMacro( NumberOfFixedSamples, unsigned long ); itkGetConstMacro( NumberOfFixedSamples, unsigned long ); itkSetMacro( UseAnisotropicCovariances, bool ); itkGetConstMacro( UseAnisotropicCovariances, bool ); itkBooleanMacro( UseAnisotropicCovariances ); /** * If this->m_UseAnisotropicCovariances = false, the * following four variables are not used. */ itkSetMacro( FixedCovarianceKNeighborhood, unsigned int ); itkGetConstMacro( FixedCovarianceKNeighborhood, unsigned int ); itkSetMacro( FixedKernelSigma, RealType ); itkGetConstMacro( FixedKernelSigma, RealType ); itkSetMacro( MovingCovarianceKNeighborhood, unsigned int ); itkGetConstMacro( MovingCovarianceKNeighborhood, unsigned int ); itkSetMacro( MovingKernelSigma, RealType ); itkGetConstMacro( MovingKernelSigma, RealType ); void SetFixedLabelSet( LabelSetType labels ) { typename LabelSetType::const_iterator iter; for ( iter = labels.begin(); iter != labels.end(); ++iter ) { this->m_FixedLabelSet.push_back( *iter ); } this->Modified(); } LabelSetType* GetFixedLabelSet() { return &this->m_FixedLabelSet; } void SetMovingLabelSet( LabelSetType labels ) { typename LabelSetType::const_iterator iter; for ( iter = labels.begin(); iter != labels.end(); ++iter ) { this->m_MovingLabelSet.push_back( *iter ); } this->Modified(); } LabelSetType* GetMovingLabelSet() { return &this->m_MovingLabelSet; } protected: JensenHavrdaCharvatTsallisLabeledPointSetMetric(); ~JensenHavrdaCharvatTsallisLabeledPointSetMetric() {} void PrintSelf( std::ostream& os, Indent indent ) const; private: JensenHavrdaCharvatTsallisLabeledPointSetMetric(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented bool m_UseRegularizationTerm; bool m_UseInputAsSamples; bool m_UseAnisotropicCovariances; bool m_UseWithRespectToTheMovingPointSet; RealType m_MovingPointSetSigma; RealType m_MovingKernelSigma; unsigned int m_MovingCovarianceKNeighborhood; unsigned int m_MovingEvaluationKNeighborhood; unsigned long m_NumberOfMovingSamples; RealType m_FixedPointSetSigma; RealType m_FixedKernelSigma; unsigned int m_FixedCovarianceKNeighborhood; unsigned int m_FixedEvaluationKNeighborhood; unsigned long m_NumberOfFixedSamples; RealType m_Alpha; TransformPointer m_Transform; LabelSetType m_FixedLabelSet; LabelSetType m_MovingLabelSet; }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkJensenHavrdaCharvatTsallisLabeledPointSetMetric.txx" #endif #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkJensenHavrdaCharvatTsallisLabeledPointSetMetric.txx000066400000000000000000000451531147325206600327020ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkJensenHavrdaCharvatTsallisLabeledPointSetMetric.txx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkJensenHavrdaCharvatTsallisLabeledPointSetMetric_txx #define _itkJensenHavrdaCharvatTsallisLabeledPointSetMetric_txx #include "itkJensenHavrdaCharvatTsallisLabeledPointSetMetric.h" #include "itkJensenHavrdaCharvatTsallisPointSetMetric.h" namespace itk { template JensenHavrdaCharvatTsallisLabeledPointSetMetric ::JensenHavrdaCharvatTsallisLabeledPointSetMetric() { this->m_UseRegularizationTerm = false; this->m_UseInputAsSamples = true; this->m_UseAnisotropicCovariances = false; this->m_NumberOfFixedSamples = 100; this->m_FixedPointSetSigma = 1.0; this->m_FixedKernelSigma = 10.0; this->m_FixedCovarianceKNeighborhood = 5; this->m_FixedEvaluationKNeighborhood = 50; this->m_NumberOfMovingSamples = 100; this->m_MovingPointSetSigma = 1.0; this->m_MovingKernelSigma = 10.0; this->m_MovingCovarianceKNeighborhood = 5; this->m_MovingEvaluationKNeighborhood = 50; this->m_Alpha = 2.0; this->m_UseWithRespectToTheMovingPointSet = true; typename DefaultTransformType::Pointer transform = DefaultTransformType::New(); transform->SetIdentity(); Superclass::SetTransform( transform ); } /** Initialize the metric */ template void JensenHavrdaCharvatTsallisLabeledPointSetMetric ::Initialize( void ) throw ( ExceptionObject ) { Superclass::Initialize(); if ( this->m_FixedLabelSet.size() <= 0 && this->m_FixedPointSet->GetNumberOfPoints() > 0 ) { typename PointSetType::PointDataContainerIterator It = this->m_FixedPointSet->GetPointData()->Begin(); while ( It != this->m_FixedPointSet->GetPointData()->End() ) { if ( find( this->m_FixedLabelSet.begin(), this->m_FixedLabelSet.end(), It.Value() ) == this->m_FixedLabelSet.end() ) { this->m_FixedLabelSet.push_back( It.Value() ); } ++It; } } if ( this->m_MovingLabelSet.size() <= 0 && this->m_MovingPointSet->GetNumberOfPoints() > 0 ) { typename PointSetType::PointDataContainerIterator It = this->m_MovingPointSet->GetPointData()->Begin(); while ( It != this->m_MovingPointSet->GetPointData()->End() ) { if ( find( this->m_MovingLabelSet.begin(), this->m_MovingLabelSet.end(), It.Value() ) == this->m_MovingLabelSet.end() ) { this->m_MovingLabelSet.push_back( It.Value() ); } ++It; } } } /** Return the number of values, i.e the number of points in the moving set */ template unsigned int JensenHavrdaCharvatTsallisLabeledPointSetMetric ::GetNumberOfValues() const { if ( this->m_UseWithRespectToTheMovingPointSet ) { if ( this->m_MovingPointSet ) { return this->m_MovingPointSet->GetPoints()->Size(); } } else { if ( this->m_FixedPointSet ) { return this->m_FixedPointSet->GetPoints()->Size(); } } return 0; } /** Get the match Measure */ template typename JensenHavrdaCharvatTsallisLabeledPointSetMetric::MeasureType JensenHavrdaCharvatTsallisLabeledPointSetMetric ::GetValue( const TransformParametersType & parameters ) const { MeasureType measure; measure.SetSize( 1 ); measure.Fill( 0 ); typename LabelSetType::const_iterator iter; for ( iter = this->m_FixedLabelSet.begin(); iter != this->m_FixedLabelSet.end(); ++iter ) { PixelType currentLabel = *iter; /** * check to see if the moving label set contains the same label */ if ( find( this->m_MovingLabelSet.begin(), this->m_MovingLabelSet.end(), currentLabel ) == this->m_MovingLabelSet.end() ) { continue; } /** * Collect all the fixed and moving points with the currentLabel */ typename PointSetType::Pointer fixedLabelPoints = PointSetType::New(); fixedLabelPoints->Initialize(); unsigned long fixedCount = 0; typename PointSetType::PointsContainerConstIterator ItF = this->m_FixedPointSet->GetPoints()->Begin(); typename PointSetType::PointDataContainerIterator ItFD = this->m_FixedPointSet->GetPointData()->Begin(); while ( ItF != this->m_FixedPointSet->GetPoints()->End() ) { if ( ItFD.Value() == currentLabel ) { fixedLabelPoints->SetPoint( fixedCount++, ItF.Value() ); } ++ItF; ++ItFD; } typename PointSetType::Pointer movingLabelPoints = PointSetType::New(); movingLabelPoints->Initialize(); unsigned long movingCount = 0; typename PointSetType::PointsContainerConstIterator ItM = this->m_MovingPointSet->GetPoints()->Begin(); typename PointSetType::PointDataContainerIterator ItMD = this->m_MovingPointSet->GetPointData()->Begin(); while ( ItM != this->m_MovingPointSet->GetPoints()->End() ) { if ( ItMD.Value() == currentLabel ) { movingLabelPoints->SetPoint( movingCount++, ItM.Value() ); } ++ItM; ++ItMD; } /** * Invoke the single label JensenTsallis measure */ typedef JensenHavrdaCharvatTsallisPointSetMetric MetricType; typename MetricType::Pointer metric = MetricType::New(); metric->SetFixedPointSet( fixedLabelPoints ); metric->SetNumberOfFixedSamples( this->m_NumberOfFixedSamples ); metric->SetFixedPointSetSigma( this->m_FixedPointSetSigma ); metric->SetFixedKernelSigma( this->m_FixedKernelSigma ); metric->SetFixedCovarianceKNeighborhood( this->m_FixedCovarianceKNeighborhood ); metric->SetFixedEvaluationKNeighborhood( this->m_FixedEvaluationKNeighborhood ); metric->SetMovingPointSet( movingLabelPoints ); metric->SetNumberOfMovingSamples( this->m_NumberOfMovingSamples ); metric->SetMovingPointSetSigma( this->m_MovingPointSetSigma ); metric->SetMovingKernelSigma( this->m_MovingKernelSigma ); metric->SetMovingCovarianceKNeighborhood( this->m_MovingCovarianceKNeighborhood ); metric->SetMovingEvaluationKNeighborhood( this->m_MovingEvaluationKNeighborhood ); metric->SetUseRegularizationTerm( this->m_UseRegularizationTerm ); metric->SetUseInputAsSamples( this->m_UseInputAsSamples ); metric->SetUseAnisotropicCovariances( this->m_UseAnisotropicCovariances ); metric->SetUseWithRespectToTheMovingPointSet( this->m_UseWithRespectToTheMovingPointSet ); metric->SetAlpha( this->m_Alpha ); metric->Initialize(); MeasureType value = metric->GetValue( parameters ); measure[0] += value[0]; } return measure; } /** Get the Derivative Measure */ template void JensenHavrdaCharvatTsallisLabeledPointSetMetric ::GetDerivative( const TransformParametersType & parameters, DerivativeType & derivative ) const { unsigned long numberOfDerivatives = this->m_FixedPointSet->GetNumberOfPoints(); if ( this->m_UseWithRespectToTheMovingPointSet ) { numberOfDerivatives = this->m_MovingPointSet->GetNumberOfPoints(); } derivative.SetSize( numberOfDerivatives, PointDimension ); derivative.Fill( 0 ); typename LabelSetType::const_iterator iter; for ( iter = this->m_FixedLabelSet.begin(); iter != this->m_FixedLabelSet.end(); ++iter ) { PixelType currentLabel = *iter; /** * check to see if the moving label set contains the same label */ if ( find( this->m_MovingLabelSet.begin(), this->m_MovingLabelSet.end(), currentLabel ) == this->m_MovingLabelSet.end() ) { continue; } /** * Collect all the fixed and moving points with the currentLabel */ typename PointSetType::Pointer fixedLabelPoints = PointSetType::New(); fixedLabelPoints->Initialize(); unsigned long fixedCount = 0; std::vector fixedIndices; fixedIndices.clear(); typename PointSetType::PointsContainerConstIterator ItF = this->m_FixedPointSet->GetPoints()->Begin(); typename PointSetType::PointDataContainerIterator ItFD = this->m_FixedPointSet->GetPointData()->Begin(); while ( ItF != this->m_FixedPointSet->GetPoints()->End() ) { if ( ItFD.Value() == currentLabel ) { fixedLabelPoints->SetPoint( fixedCount++, ItF.Value() ); fixedIndices.push_back( ItF.Index() ); } ++ItF; ++ItFD; } typename PointSetType::Pointer movingLabelPoints = PointSetType::New(); movingLabelPoints->Initialize(); unsigned long movingCount = 0; std::vector movingIndices; movingIndices.clear(); typename PointSetType::PointsContainerConstIterator ItM = this->m_MovingPointSet->GetPoints()->Begin(); typename PointSetType::PointDataContainerIterator ItMD = this->m_MovingPointSet->GetPointData()->Begin(); while ( ItM != this->m_MovingPointSet->GetPoints()->End() ) { if ( ItMD.Value() == currentLabel ) { movingLabelPoints->SetPoint( movingCount++, ItM.Value() ); movingIndices.push_back( ItM.Index() ); } ++ItM; ++ItMD; } /** * Invoke the single label JensenTsallis measure */ typedef JensenHavrdaCharvatTsallisPointSetMetric MetricType; typename MetricType::Pointer metric = MetricType::New(); metric->SetFixedPointSet( fixedLabelPoints ); metric->SetNumberOfFixedSamples( this->m_NumberOfFixedSamples ); metric->SetFixedPointSetSigma( this->m_FixedPointSetSigma ); metric->SetFixedKernelSigma( this->m_FixedKernelSigma ); metric->SetFixedCovarianceKNeighborhood( this->m_FixedCovarianceKNeighborhood ); metric->SetFixedEvaluationKNeighborhood( this->m_FixedEvaluationKNeighborhood ); metric->SetMovingPointSet( movingLabelPoints ); metric->SetNumberOfMovingSamples( this->m_NumberOfMovingSamples ); metric->SetMovingPointSetSigma( this->m_MovingPointSetSigma ); metric->SetMovingKernelSigma( this->m_MovingKernelSigma ); metric->SetMovingCovarianceKNeighborhood( this->m_MovingCovarianceKNeighborhood ); metric->SetMovingEvaluationKNeighborhood( this->m_MovingEvaluationKNeighborhood ); metric->SetUseRegularizationTerm( this->m_UseRegularizationTerm ); metric->SetUseInputAsSamples( this->m_UseInputAsSamples ); metric->SetUseAnisotropicCovariances( this->m_UseAnisotropicCovariances ); metric->SetUseWithRespectToTheMovingPointSet( this->m_UseWithRespectToTheMovingPointSet ); metric->SetAlpha( this->m_Alpha ); metric->Initialize(); DerivativeType labelDerivative; metric->GetDerivative( parameters, labelDerivative ); RealType avgNorm = 0.0; for ( unsigned int i = 0; i < metric->GetNumberOfValues(); i++ ) { RealType norm = 0.0; for ( unsigned int j = 0; j < PointDimension; j++ ) { norm += ( labelDerivative(i, j) * labelDerivative(i, j) ); } avgNorm += vcl_sqrt( norm ); } avgNorm /= static_cast( metric->GetNumberOfValues() ); labelDerivative /= avgNorm; if ( this->m_UseWithRespectToTheMovingPointSet ) { std::vector::const_iterator it; unsigned long index = 0; for ( it = movingIndices.begin(); it != movingIndices.end(); ++it ) { for ( unsigned int d = 0; d < PointDimension; d++ ) { derivative( *it, d ) = labelDerivative( index, d ); } index++; } } else { std::vector::const_iterator it; unsigned long index = 0; for ( it = fixedIndices.begin(); it != fixedIndices.end(); ++it ) { for ( unsigned int d = 0; d < PointDimension; d++ ) { derivative( *it, d ) = labelDerivative( index, d ); } index++; } } } } /** Get both the match Measure and theDerivative Measure */ template void JensenHavrdaCharvatTsallisLabeledPointSetMetric ::GetValueAndDerivative( const TransformParametersType & parameters, MeasureType & value, DerivativeType & derivative ) const { unsigned long numberOfDerivatives = this->m_FixedPointSet->GetNumberOfPoints(); if ( this->m_UseWithRespectToTheMovingPointSet ) { numberOfDerivatives = this->m_MovingPointSet->GetNumberOfPoints(); } derivative.SetSize( numberOfDerivatives, PointDimension ); derivative.Fill( 0 ); value.SetSize( 1 ); value.Fill( 0 ); typename LabelSetType::const_iterator iter; for ( iter = this->m_FixedLabelSet.begin(); iter != this->m_FixedLabelSet.end(); ++iter ) { PixelType currentLabel = *iter; /** * check to see if the moving label set contains the same label */ if ( find( this->m_MovingLabelSet.begin(), this->m_MovingLabelSet.end(), currentLabel ) == this->m_MovingLabelSet.end() ) { continue; } /** * Collect all the fixed and moving points with the currentLabel */ typename PointSetType::Pointer fixedLabelPoints = PointSetType::New(); fixedLabelPoints->Initialize(); unsigned long fixedCount = 0; std::vector fixedIndices; fixedIndices.clear(); typename PointSetType::PointsContainerConstIterator ItF = this->m_FixedPointSet->GetPoints()->Begin(); typename PointSetType::PointDataContainerIterator ItFD = this->m_FixedPointSet->GetPointData()->Begin(); while ( ItF != this->m_FixedPointSet->GetPoints()->End() ) { if ( ItFD.Value() == currentLabel ) { fixedLabelPoints->SetPoint( fixedCount++, ItF.Value() ); fixedIndices.push_back( ItF.Index() ); } ++ItF; ++ItFD; } typename PointSetType::Pointer movingLabelPoints = PointSetType::New(); movingLabelPoints->Initialize(); unsigned long movingCount = 0; std::vector movingIndices; movingIndices.clear(); typename PointSetType::PointsContainerConstIterator ItM = this->m_MovingPointSet->GetPoints()->Begin(); typename PointSetType::PointDataContainerIterator ItMD = this->m_MovingPointSet->GetPointData()->Begin(); while ( ItM != this->m_MovingPointSet->GetPoints()->End() ) { if ( ItMD.Value() == currentLabel ) { movingLabelPoints->SetPoint( movingCount++, ItM.Value() ); movingIndices.push_back( ItM.Index() ); } ++ItM; ++ItMD; } /** * Invoke the single label JensenTsallis measure */ typedef JensenHavrdaCharvatTsallisPointSetMetric MetricType; typename MetricType::Pointer metric = MetricType::New(); metric->SetFixedPointSet( fixedLabelPoints ); metric->SetNumberOfFixedSamples( this->m_NumberOfFixedSamples ); metric->SetFixedPointSetSigma( this->m_FixedPointSetSigma ); metric->SetFixedKernelSigma( this->m_FixedKernelSigma ); metric->SetFixedCovarianceKNeighborhood( this->m_FixedCovarianceKNeighborhood ); metric->SetFixedEvaluationKNeighborhood( this->m_FixedEvaluationKNeighborhood ); metric->SetMovingPointSet( movingLabelPoints ); metric->SetNumberOfMovingSamples( this->m_NumberOfMovingSamples ); metric->SetMovingPointSetSigma( this->m_MovingPointSetSigma ); metric->SetMovingKernelSigma( this->m_MovingKernelSigma ); metric->SetMovingCovarianceKNeighborhood( this->m_MovingCovarianceKNeighborhood ); metric->SetMovingEvaluationKNeighborhood( this->m_MovingEvaluationKNeighborhood ); metric->SetUseRegularizationTerm( this->m_UseRegularizationTerm ); metric->SetUseInputAsSamples( this->m_UseInputAsSamples ); metric->SetUseAnisotropicCovariances( this->m_UseAnisotropicCovariances ); metric->SetUseWithRespectToTheMovingPointSet( this->m_UseWithRespectToTheMovingPointSet ); metric->SetAlpha( this->m_Alpha ); metric->Initialize(); DerivativeType labelDerivative; MeasureType labelValue; metric->GetValueAndDerivative( parameters, labelValue, labelDerivative ); value[0] += labelValue[0]; RealType avgNorm = 0.0; for ( unsigned int i = 0; i < metric->GetNumberOfValues(); i++ ) { RealType norm = 0.0; for ( unsigned int j = 0; j < PointDimension; j++ ) { norm += ( labelDerivative(i, j) * labelDerivative(i, j) ); } avgNorm += vcl_sqrt( norm ); } avgNorm /= static_cast( metric->GetNumberOfValues() ); labelDerivative /= avgNorm; if ( this->m_UseWithRespectToTheMovingPointSet ) { std::vector::const_iterator it; unsigned long index = 0; for ( it = movingIndices.begin(); it != movingIndices.end(); ++it ) { for ( unsigned int d = 0; d < PointDimension; d++ ) { derivative( *it, d ) = labelDerivative( index, d ); } index++; } } else { std::vector::const_iterator it; unsigned long index = 0; for ( it = fixedIndices.begin(); it != fixedIndices.end(); ++it ) { for ( unsigned int d = 0; d < PointDimension; d++ ) { derivative( *it, d ) = labelDerivative( index, d ); } index++; } } } } template void JensenHavrdaCharvatTsallisLabeledPointSetMetric ::PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf( os, indent ); os << indent << "Use with respect to the moving point set: " << this->m_UseWithRespectToTheMovingPointSet << std::endl; os << indent << "Use regularization term: " << this->m_UseRegularizationTerm << std::endl; if ( !this->m_UseInputAsSamples ) { os << indent << "Number of fixed samples: " << this->m_NumberOfFixedSamples << std::endl; os << indent << "Number of moving samples: " << this->m_NumberOfMovingSamples << std::endl; } os << indent << "Alpha: " << this->m_Alpha << std::endl; os << indent << "Fixed sigma: " << this->m_FixedPointSetSigma << std::endl; os << indent << "Moving sigma: " << this->m_MovingPointSetSigma << std::endl; } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkJensenHavrdaCharvatTsallisPointSetMetric.h000066400000000000000000000176601147325206600310370ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkJensenHavrdaCharvatTsallisPointSetMetric.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkJensenHavrdaCharvatTsallisPointSetMetric_h #define __itkJensenHavrdaCharvatTsallisPointSetMetric_h #include "itkPointSetToPointSetMetric.h" #include "itkIdentityTransform.h" #include "itkManifoldParzenWindowsPointSetFunction.h" namespace itk { /** \class JensenHavrdaCharvatTsallisPointSetMetric * * * * */ template class ITK_EXPORT JensenHavrdaCharvatTsallisPointSetMetric : public PointSetToPointSetMetric { public: /** Standard class typedefs. */ typedef JensenHavrdaCharvatTsallisPointSetMetric Self; typedef PointSetToPointSetMetric Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); /** Run-time type information (and related methods) */ itkTypeMacro( JensenHavrdaCharvatTsallisPointSetMetric, PointSetToPointSetMetric ); itkStaticConstMacro( PointDimension, unsigned int, TPointSet::PointDimension ); /** Types transferred from the base class */ typedef typename Superclass::TransformType TransformType; typedef typename Superclass::TransformPointer TransformPointer; typedef typename Superclass::TransformParametersType TransformParametersType; typedef typename Superclass::TransformJacobianType TransformJacobianType; typedef typename Superclass::MeasureType MeasureType; typedef typename Superclass::DerivativeType DerivativeType; typedef TPointSet PointSetType; typedef typename PointSetType::Pointer PointSetPointer; typedef typename PointSetType::PointType PointType; /** * Other typedefs */ typedef double RealType; typedef ManifoldParzenWindowsPointSetFunction DensityFunctionType; typedef typename DensityFunctionType::GaussianType GaussianType; typedef IdentityTransform DefaultTransformType; /** * Public function definitions */ itkSetConstObjectMacro( FixedPointSet, PointSetType ); itkGetConstObjectMacro( FixedPointSet, PointSetType ); itkSetConstObjectMacro( MovingPointSet, PointSetType ); itkGetConstObjectMacro( MovingPointSet, PointSetType ); itkSetObjectMacro( Transform, TransformType ); itkGetObjectMacro( Transform, TransformType ); /** * The only transform type used is Identity */ unsigned int GetNumberOfParameters( void ) const { return m_Transform->GetNumberOfParameters(); } /** Initialize the Metric by making sure that all the components * are present and plugged together correctly */ virtual void Initialize( void ) throw ( ExceptionObject ); /** Get the number of values */ unsigned int GetNumberOfValues() const; /** Get the derivatives of the match measure. */ void GetDerivative( const TransformParametersType & parameters, DerivativeType & Derivative ) const; /** Get the value for single valued optimizers. */ MeasureType GetValue( const TransformParametersType & parameters ) const; /** Get value and derivatives for multiple valued optimizers. */ void GetValueAndDerivative( const TransformParametersType & parameters, MeasureType& Value, DerivativeType& Derivative ) const; itkSetClampMacro( Alpha, RealType, 1.0, 2.0 ); itkGetConstMacro( Alpha, RealType ); itkSetMacro( UseRegularizationTerm, bool ); itkGetConstMacro( UseRegularizationTerm, bool ); itkBooleanMacro( UseRegularizationTerm ); itkSetMacro( UseWithRespectToTheMovingPointSet, bool ); itkGetConstMacro( UseWithRespectToTheMovingPointSet, bool ); itkBooleanMacro( UseWithRespectToTheMovingPointSet ); itkSetMacro( MovingPointSetSigma, RealType ); itkGetConstMacro( MovingPointSetSigma, RealType ); itkSetMacro( MovingEvaluationKNeighborhood, unsigned int ); itkGetConstMacro( MovingEvaluationKNeighborhood, unsigned int ); itkSetMacro( FixedPointSetSigma, RealType ); itkGetConstMacro( FixedPointSetSigma, RealType ); itkSetMacro( FixedEvaluationKNeighborhood, unsigned int ); itkGetConstMacro( FixedEvaluationKNeighborhood, unsigned int ); itkSetMacro( UseInputAsSamples, bool ); itkGetConstMacro( UseInputAsSamples, bool ); itkBooleanMacro( UseInputAsSamples ); /** * If this->m_UseInputAsSamples = true, the following * two variables are not used. */ itkSetMacro( NumberOfMovingSamples, unsigned long ); itkGetConstMacro( NumberOfMovingSamples, unsigned long ); itkSetMacro( NumberOfFixedSamples, unsigned long ); itkGetConstMacro( NumberOfFixedSamples, unsigned long ); itkSetMacro( UseAnisotropicCovariances, bool ); itkGetConstMacro( UseAnisotropicCovariances, bool ); itkBooleanMacro( UseAnisotropicCovariances ); /** * If this->m_UseAnisotropicCovariances = false, the * following four variables are not used. */ itkSetMacro( FixedCovarianceKNeighborhood, unsigned int ); itkGetConstMacro( FixedCovarianceKNeighborhood, unsigned int ); itkSetMacro( FixedKernelSigma, RealType ); itkGetConstMacro( FixedKernelSigma, RealType ); itkSetMacro( MovingCovarianceKNeighborhood, unsigned int ); itkGetConstMacro( MovingCovarianceKNeighborhood, unsigned int ); itkSetMacro( MovingKernelSigma, RealType ); itkGetConstMacro( MovingKernelSigma, RealType ); protected: JensenHavrdaCharvatTsallisPointSetMetric(); ~JensenHavrdaCharvatTsallisPointSetMetric() {} void PrintSelf( std::ostream& os, Indent indent ) const; private: //purposely not implemented JensenHavrdaCharvatTsallisPointSetMetric(const Self&); void operator=(const Self&); bool m_UseRegularizationTerm; bool m_UseInputAsSamples; bool m_UseAnisotropicCovariances; bool m_UseWithRespectToTheMovingPointSet; typename DensityFunctionType::Pointer m_MovingDensityFunction; PointSetPointer m_MovingSamplePoints; RealType m_MovingPointSetSigma; RealType m_MovingKernelSigma; unsigned int m_MovingCovarianceKNeighborhood; unsigned int m_MovingEvaluationKNeighborhood; unsigned long m_NumberOfMovingSamples; typename DensityFunctionType::Pointer m_FixedDensityFunction; PointSetPointer m_FixedSamplePoints; RealType m_FixedPointSetSigma; RealType m_FixedKernelSigma; unsigned int m_FixedCovarianceKNeighborhood; unsigned int m_FixedEvaluationKNeighborhood; unsigned long m_NumberOfFixedSamples; RealType m_Alpha; TransformPointer m_Transform; }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkJensenHavrdaCharvatTsallisPointSetMetric.txx" #endif #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkJensenHavrdaCharvatTsallisPointSetMetric.txx000066400000000000000000000616121147325206600314270ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkJensenHavrdaCharvatTsallisPointSetMetric.txx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkJensenHavrdaCharvatTsallisPointSetMetric_txx #define __itkJensenHavrdaCharvatTsallisPointSetMetric_txx #include "itkJensenHavrdaCharvatTsallisPointSetMetric.h" namespace itk { template JensenHavrdaCharvatTsallisPointSetMetric ::JensenHavrdaCharvatTsallisPointSetMetric() { this->m_UseRegularizationTerm = false; this->m_UseInputAsSamples = true; this->m_UseAnisotropicCovariances = false; this->m_NumberOfFixedSamples = 100; this->m_FixedPointSetSigma = 1.0; this->m_FixedKernelSigma = 10.0; this->m_FixedCovarianceKNeighborhood = 5; this->m_FixedEvaluationKNeighborhood = 50; this->m_NumberOfMovingSamples = 100; this->m_MovingPointSetSigma = 1.0; this->m_MovingKernelSigma = 10.0; this->m_MovingCovarianceKNeighborhood = 5; this->m_MovingEvaluationKNeighborhood = 50; this->m_Alpha = 2.0; this->m_UseWithRespectToTheMovingPointSet = true; typename DefaultTransformType::Pointer transform = DefaultTransformType::New(); transform->SetIdentity(); Superclass::SetTransform( transform ); } /** Initialize the metric */ template void JensenHavrdaCharvatTsallisPointSetMetric ::Initialize( void ) throw ( ExceptionObject ) { Superclass::Initialize(); /** * Initialize the fixed points */ this->m_FixedDensityFunction = DensityFunctionType::New(); this->m_FixedDensityFunction->SetBucketSize( 4 ); this->m_FixedDensityFunction->SetKernelSigma( this->m_FixedKernelSigma ); this->m_FixedDensityFunction->SetRegularizationSigma( this->m_FixedPointSetSigma ); this->m_FixedDensityFunction->SetNormalize( true ); this->m_FixedDensityFunction->SetUseAnisotropicCovariances( this->m_UseAnisotropicCovariances ); this->m_FixedDensityFunction->SetCovarianceKNeighborhood( this->m_FixedCovarianceKNeighborhood ); this->m_FixedDensityFunction->SetEvaluationKNeighborhood( this->m_FixedEvaluationKNeighborhood ); this->m_FixedDensityFunction->SetInputPointSet( this->m_FixedPointSet ); if( !this->m_UseInputAsSamples ) { this->m_FixedSamplePoints = PointSetType::New(); this->m_FixedSamplePoints->Initialize(); for( unsigned long i = 0; i < this->m_NumberOfFixedSamples; i++ ) { this->m_FixedSamplePoints->SetPoint( i, this->m_FixedDensityFunction->GenerateRandomSample() ); } } /** * Initialize the moving points */ this->m_MovingDensityFunction = DensityFunctionType::New(); this->m_MovingDensityFunction->SetBucketSize( 4 ); this->m_MovingDensityFunction->SetKernelSigma( this->m_MovingKernelSigma ); this->m_MovingDensityFunction->SetRegularizationSigma( this->m_MovingPointSetSigma ); this->m_MovingDensityFunction->SetNormalize( true ); this->m_MovingDensityFunction->SetUseAnisotropicCovariances( this->m_UseAnisotropicCovariances ); this->m_MovingDensityFunction->SetCovarianceKNeighborhood( this->m_MovingCovarianceKNeighborhood ); this->m_MovingDensityFunction->SetEvaluationKNeighborhood( this->m_MovingEvaluationKNeighborhood ); this->m_MovingDensityFunction->SetInputPointSet( this->m_MovingPointSet ); if( !this->m_UseInputAsSamples ) { this->m_MovingSamplePoints = PointSetType::New(); this->m_MovingSamplePoints->Initialize(); for( unsigned long i = 0; i < this->m_NumberOfMovingSamples; i++ ) { this->m_MovingSamplePoints->SetPoint( i, this->m_MovingDensityFunction->GenerateRandomSample() ); } } } /** Return the number of values, i.e the number of points in the moving set */ template unsigned int JensenHavrdaCharvatTsallisPointSetMetric ::GetNumberOfValues() const { if( this->m_UseWithRespectToTheMovingPointSet ) { if( this->m_MovingPointSet ) { return this->m_MovingPointSet->GetPoints()->Size(); } } else { if( this->m_FixedPointSet ) { return this->m_FixedPointSet->GetPoints()->Size(); } } return 0; } /** Get the match Measure */ template typename JensenHavrdaCharvatTsallisPointSetMetric ::MeasureType JensenHavrdaCharvatTsallisPointSetMetric ::GetValue( const TransformParametersType & parameters ) const { /** * Only identity transform is valid */ // this->SetTransformParameters( parameters ); PointSetPointer points[2]; PointSetPointer samples[2]; typename DensityFunctionType::Pointer densityFunctions[2]; if( this->m_UseWithRespectToTheMovingPointSet ) { points[0] = const_cast( static_cast( this->m_FixedPointSet ) ); points[1] = const_cast( static_cast( this->m_MovingPointSet ) ); if( this->m_UseInputAsSamples ) { samples[0] = points[0]; samples[1] = points[1]; } else { samples[0] = this->m_FixedSamplePoints; samples[1] = this->m_MovingSamplePoints; } densityFunctions[0] = this->m_FixedDensityFunction; densityFunctions[1] = this->m_MovingDensityFunction; } else { points[1] = const_cast( static_cast( this->m_FixedPointSet ) ); points[0] = const_cast( static_cast( this->m_MovingPointSet ) ); if( this->m_UseInputAsSamples ) { samples[1] = points[1]; samples[0] = points[0]; } else { samples[1] = this->m_FixedSamplePoints; samples[0] = this->m_MovingSamplePoints; } densityFunctions[1] = this->m_FixedDensityFunction; densityFunctions[0] = this->m_MovingDensityFunction; } RealType totalNumberOfPoints = static_cast( points[0]->GetNumberOfPoints() ) + static_cast( points[1]->GetNumberOfPoints() ); RealType totalNumberOfSamples = static_cast( samples[0]->GetNumberOfPoints() ) + static_cast( samples[1]->GetNumberOfPoints() ); MeasureType measure; measure.SetSize( 1 ); measure.Fill( 0 ); RealType energyTerm1 = 0.0; RealType energyTerm2 = 0.0; /** * first term */ RealType prefactor = -1.0 / totalNumberOfSamples; if( this->m_Alpha != 1.0 ) { prefactor /= ( this->m_Alpha - 1.0 ); } typename PointSetType::PointsContainerConstIterator It = samples[0]->GetPoints()->Begin(); while( It != samples[0]->GetPoints()->End() ) { PointType samplePoint = It.Value(); RealType probabilityStar = densityFunctions[0]->Evaluate( samplePoint ) * static_cast( points[0]->GetNumberOfPoints() ) + densityFunctions[1]->Evaluate( samplePoint ) * static_cast( points[1]->GetNumberOfPoints() ); probabilityStar /= totalNumberOfPoints; if( probabilityStar == 0 ) { ++It; continue; } if( this->m_Alpha == 1.0 ) { energyTerm1 += ( prefactor * vcl_log( probabilityStar ) / vnl_math::ln2 ); } else { energyTerm1 += ( prefactor * vcl_pow( probabilityStar, static_cast( this->m_Alpha - 1.0 ) ) ); } ++It; } /** * second term, i.e. regularization term */ if( this->m_UseRegularizationTerm ) { RealType prefactor2 = -static_cast( points[1]->GetNumberOfPoints() ) / ( totalNumberOfPoints * static_cast( samples[1]->GetNumberOfPoints() ) ); if( this->m_Alpha != 1.0 ) { prefactor2 /= ( this->m_Alpha - 1.0 ); } typename PointSetType::PointsContainerConstIterator It = samples[1]->GetPoints()->Begin(); while( It != samples[1]->GetPoints()->End() ) { PointType samplePoint = It.Value(); RealType probability = densityFunctions[1]->Evaluate( samplePoint ); if( probability == 0 ) { ++It; continue; } if( this->m_Alpha == 1.0 ) { energyTerm2 += ( prefactor2 * vcl_log( probability ) / vnl_math::ln2 ); } else { energyTerm2 += ( prefactor2 * vcl_pow( probability, static_cast( this->m_Alpha - 1.0 ) ) ); } ++It; } } measure[0] = energyTerm1 - energyTerm2; return measure; } /** Get the Derivative Measure */ template void JensenHavrdaCharvatTsallisPointSetMetric ::GetDerivative( const TransformParametersType & parameters, DerivativeType & derivative ) const { /** * Only identity transform is valid */ // this->SetTransformParameters( parameters ); PointSetPointer points[2]; PointSetPointer samples[2]; typename DensityFunctionType::Pointer densityFunctions[2]; unsigned int kNeighborhood; if( this->m_UseWithRespectToTheMovingPointSet ) { points[0] = const_cast( static_cast( this->m_FixedPointSet ) ); points[1] = const_cast( static_cast( this->m_MovingPointSet ) ); if( this->m_UseInputAsSamples ) { samples[0] = points[0]; samples[1] = points[1]; } else { samples[0] = this->m_FixedSamplePoints; samples[1] = this->m_MovingSamplePoints; } densityFunctions[0] = this->m_FixedDensityFunction; densityFunctions[1] = this->m_MovingDensityFunction; kNeighborhood = this->m_MovingEvaluationKNeighborhood; } else { points[1] = const_cast( static_cast( this->m_FixedPointSet ) ); points[0] = const_cast( static_cast( this->m_MovingPointSet ) ); if( this->m_UseInputAsSamples ) { samples[1] = points[1]; samples[0] = points[0]; } else { samples[1] = this->m_FixedSamplePoints; samples[0] = this->m_MovingSamplePoints; } densityFunctions[1] = this->m_FixedDensityFunction; densityFunctions[0] = this->m_MovingDensityFunction; kNeighborhood = this->m_FixedEvaluationKNeighborhood; } RealType totalNumberOfPoints = static_cast( points[0]->GetNumberOfPoints() ) + static_cast( points[1]->GetNumberOfPoints() ); RealType totalNumberOfSamples = static_cast( samples[0]->GetNumberOfPoints() ) + static_cast( samples[1]->GetNumberOfPoints() ); derivative.SetSize( points[1]->GetPoints()->Size(), PointDimension ); derivative.Fill( 0 ); /** * first term */ RealType prefactor = 1.0 / ( totalNumberOfSamples * totalNumberOfPoints ); typename PointSetType::PointsContainerConstIterator It = samples[0]->GetPoints()->Begin(); while( It != samples[0]->GetPoints()->End() ) { PointType fixedSamplePoint = It.Value(); RealType probabilityStar = densityFunctions[0]->Evaluate( fixedSamplePoint ) * static_cast( points[0]->GetNumberOfPoints() ) + densityFunctions[1]->Evaluate( fixedSamplePoint ) * static_cast( points[1]->GetNumberOfPoints() ); probabilityStar /= totalNumberOfPoints; if( probabilityStar == 0 ) { ++It; continue; } RealType probabilityStarFactor = vcl_pow( probabilityStar, static_cast( 2.0 - this->m_Alpha ) ); typename GaussianType::MeasurementVectorType sampleMeasurement; for( unsigned int d = 0; d < PointDimension; d++ ) { sampleMeasurement[d] = fixedSamplePoint[d]; } typename DensityFunctionType::NeighborhoodIdentifierType neighbors = densityFunctions[1]->GetNeighborhoodIdentifiers( sampleMeasurement, kNeighborhood ); for( unsigned int n = 0; n < neighbors.size(); n++ ) { RealType gaussian = densityFunctions[1]-> GetGaussian( neighbors[n] )->Evaluate( sampleMeasurement ); if( gaussian == 0 ) { continue; } typename GaussianType::MeanType mean = densityFunctions[1]->GetGaussian( neighbors[n] )->GetMean(); for( unsigned int d = 0; d < PointDimension; d++ ) { mean[d] -= fixedSamplePoint[d]; } if( this->m_UseAnisotropicCovariances ) { typename GaussianType::MatrixType Ci = densityFunctions[1]->GetGaussian( neighbors[n] ) ->GetInverseCovariance(); mean = Ci * mean; } else { mean /= vnl_math_sqr( densityFunctions[1]->GetGaussian( neighbors[n] )->GetSigma() ); } mean *= ( prefactor * gaussian / probabilityStarFactor ); for( unsigned int d = 0; d < PointDimension; d++ ) { derivative(neighbors[n], d) += mean[d]; } } ++It; } /** * second term, i.e. regularization term */ if( this->m_UseRegularizationTerm ) { RealType prefactor2 = -1.0 / ( static_cast( samples[1]->GetNumberOfPoints() ) * totalNumberOfPoints ); typename PointSetType::PointsContainerConstIterator It = samples[1]->GetPoints()->Begin(); while( It != samples[1]->GetPoints()->End() ) { PointType movingSamplePoint = It.Value(); RealType probability = densityFunctions[1]->Evaluate( movingSamplePoint ); if( probability == 0 ) { ++It; continue; } RealType probabilityFactor = vcl_pow( probability, static_cast( 2.0 - this->m_Alpha ) ); probabilityFactor *= ( samples[1]->GetNumberOfPoints() / totalNumberOfSamples ); typename GaussianType::MeasurementVectorType sampleMeasurement; for( unsigned int d = 0; d < PointDimension; d++ ) { sampleMeasurement[d] = movingSamplePoint[d]; } typename DensityFunctionType::NeighborhoodIdentifierType neighbors = densityFunctions[1]->GetNeighborhoodIdentifiers( sampleMeasurement, kNeighborhood ); for( unsigned int i = 0; i < neighbors.size(); i++ ) { RealType gaussian = densityFunctions[1]-> GetGaussian( neighbors[i] )->Evaluate( sampleMeasurement ); if( gaussian == 0 ) { continue; } typename GaussianType::MeanType mean = densityFunctions[1]->GetGaussian( neighbors[i] )->GetMean(); for( unsigned int d = 0; d < PointDimension; d++ ) { mean[d] -= movingSamplePoint[d]; } if( this->m_UseAnisotropicCovariances ) { typename GaussianType::MatrixType Ci = densityFunctions[1]->GetGaussian( neighbors[i] ) ->GetInverseCovariance(); mean = Ci * mean; } else { mean /= vnl_math_sqr( densityFunctions[1]->GetGaussian( neighbors[i] )->GetSigma() ); } mean *= ( prefactor2 * gaussian / probabilityFactor ); for( unsigned int d = 0; d < PointDimension; d++ ) { derivative(neighbors[i], d) += mean[d]; } } ++It; } } } /** Get both the match Measure and theDerivative Measure */ template void JensenHavrdaCharvatTsallisPointSetMetric ::GetValueAndDerivative( const TransformParametersType & parameters, MeasureType & value, DerivativeType & derivative ) const { /** * Only identity transform is valid */ // this->SetTransformParameters( parameters ); PointSetPointer points[2]; PointSetPointer samples[2]; typename DensityFunctionType::Pointer densityFunctions[2]; unsigned int kNeighborhood; if( this->m_UseWithRespectToTheMovingPointSet ) { points[0] = const_cast( static_cast( this->m_FixedPointSet ) ); points[1] = const_cast( static_cast( this->m_MovingPointSet ) ); if( this->m_UseInputAsSamples ) { samples[0] = points[0]; samples[1] = points[1]; } else { samples[0] = this->m_FixedSamplePoints; samples[1] = this->m_MovingSamplePoints; } densityFunctions[0] = this->m_FixedDensityFunction; densityFunctions[1] = this->m_MovingDensityFunction; kNeighborhood = this->m_MovingEvaluationKNeighborhood; } else { points[1] = const_cast( static_cast( this->m_FixedPointSet ) ); points[0] = const_cast( static_cast( this->m_MovingPointSet ) ); if( this->m_UseInputAsSamples ) { samples[1] = points[1]; samples[0] = points[0]; } else { samples[1] = this->m_FixedSamplePoints; samples[0] = this->m_MovingSamplePoints; } densityFunctions[1] = this->m_FixedDensityFunction; densityFunctions[0] = this->m_MovingDensityFunction; kNeighborhood = this->m_FixedEvaluationKNeighborhood; } RealType totalNumberOfPoints = static_cast( points[0]->GetNumberOfPoints() ) + static_cast( points[1]->GetNumberOfPoints() ); RealType totalNumberOfSamples = static_cast( samples[0]->GetNumberOfPoints() ) + static_cast( samples[1]->GetNumberOfPoints() ); derivative.SetSize( points[1]->GetPoints()->Size(), PointDimension ); derivative.Fill( 0 ); value.SetSize( 1 ); value.Fill( 0 ); /** * first term */ RealType energyTerm1 = 0.0; RealType energyTerm2 = 0.0; RealType prefactor[2]; prefactor[0] = -1.0 / totalNumberOfSamples; if( this->m_Alpha != 1.0 ) { prefactor[0] /= ( this->m_Alpha - 1.0 ); } prefactor[1] = 1.0 / ( totalNumberOfSamples * totalNumberOfPoints ); typename PointSetType::PointsContainerConstIterator It = samples[0]->GetPoints()->Begin(); while( It != samples[0]->GetPoints()->End() ) { PointType fixedSamplePoint = It.Value(); RealType probabilityStar = densityFunctions[0]->Evaluate( fixedSamplePoint ) * static_cast( points[0]->GetNumberOfPoints() ) + densityFunctions[1]->Evaluate( fixedSamplePoint ) * static_cast( points[1]->GetNumberOfPoints() ); probabilityStar /= totalNumberOfPoints; if( probabilityStar == 0 ) { ++It; continue; } if( this->m_Alpha == 1.0 ) { energyTerm1 += ( prefactor[0] * vcl_log( probabilityStar ) / vnl_math::ln2 ); } else { energyTerm1 += ( prefactor[0] * vcl_pow( probabilityStar, static_cast( this->m_Alpha - 1.0 ) ) ); } RealType probabilityStarFactor = vcl_pow( probabilityStar, static_cast( 2.0 - this->m_Alpha ) ); typename GaussianType::MeasurementVectorType sampleMeasurement; for( unsigned int d = 0; d < PointDimension; d++ ) { sampleMeasurement[d] = fixedSamplePoint[d]; } typename DensityFunctionType::NeighborhoodIdentifierType neighbors = densityFunctions[1]->GetNeighborhoodIdentifiers( sampleMeasurement, kNeighborhood ); for( unsigned int n = 0; n < neighbors.size(); n++ ) { RealType gaussian = densityFunctions[1]-> GetGaussian( neighbors[n] )->Evaluate( sampleMeasurement ); if( gaussian == 0 ) { continue; } typename GaussianType::MeanType mean = densityFunctions[1]->GetGaussian( neighbors[n] )->GetMean(); for( unsigned int d = 0; d < PointDimension; d++ ) { mean[d] -= fixedSamplePoint[d]; } if( this->m_UseAnisotropicCovariances ) { typename GaussianType::MatrixType Ci = densityFunctions[1]->GetGaussian( neighbors[n] ) ->GetInverseCovariance(); mean = Ci * mean; } else { mean /= vnl_math_sqr( densityFunctions[1]->GetGaussian( neighbors[n] )->GetSigma() ); } mean *= ( prefactor[1] * gaussian / probabilityStarFactor ); for( unsigned int d = 0; d < PointDimension; d++ ) { derivative(neighbors[n], d) += mean[d]; } } ++It; } /** * second term, i.e. regularization term */ if( this->m_UseRegularizationTerm ) { RealType prefactor2[2]; prefactor2[0] = -static_cast( points[1]->GetNumberOfPoints() ) / ( totalNumberOfPoints * static_cast( samples[1]->GetNumberOfPoints() ) ); prefactor2[1] = -1.0 / ( static_cast( samples[1]->GetNumberOfPoints() ) * totalNumberOfPoints ); if( this->m_Alpha != 1.0 ) { prefactor2[0] /= ( this->m_Alpha - 1.0 ); } typename PointSetType::PointsContainerConstIterator It = samples[1]->GetPoints()->Begin(); while( It != samples[1]->GetPoints()->End() ) { PointType movingSamplePoint = It.Value(); RealType probability = densityFunctions[1]->Evaluate( movingSamplePoint ); if( probability == 0 ) { ++It; continue; } if( this->m_Alpha == 1.0 ) { energyTerm2 += ( prefactor2[0] * vcl_log( probability ) / vnl_math::ln2 ); } else { energyTerm2 += ( prefactor2[0] * vcl_pow( probability, static_cast( this->m_Alpha - 1.0 ) ) ); } RealType probabilityFactor = vcl_pow( probability, static_cast( 2.0 - this->m_Alpha ) ); probabilityFactor *= ( samples[1]->GetNumberOfPoints() / totalNumberOfSamples ); typename GaussianType::MeasurementVectorType sampleMeasurement; for( unsigned int d = 0; d < PointDimension; d++ ) { sampleMeasurement[d] = movingSamplePoint[d]; } typename DensityFunctionType::NeighborhoodIdentifierType neighbors = densityFunctions[1]->GetNeighborhoodIdentifiers( sampleMeasurement, kNeighborhood ); for( unsigned int i = 0; i < neighbors.size(); i++ ) { RealType gaussian = densityFunctions[1]-> GetGaussian( neighbors[i] )->Evaluate( sampleMeasurement ); if( gaussian == 0 ) { continue; } typename GaussianType::MeanType mean = densityFunctions[1]->GetGaussian( neighbors[i] )->GetMean(); for( unsigned int d = 0; d < PointDimension; d++ ) { mean[d] -= movingSamplePoint[d]; } if( this->m_UseAnisotropicCovariances ) { typename GaussianType::MatrixType Ci = densityFunctions[1]->GetGaussian( neighbors[i] ) ->GetInverseCovariance(); mean = Ci * mean; } else { mean /= vnl_math_sqr( densityFunctions[1]->GetGaussian( neighbors[i] )->GetSigma() ); } mean *= ( prefactor2[1] * gaussian / probabilityFactor ); for( unsigned int d = 0; d < PointDimension; d++ ) { derivative(neighbors[i], d) += mean[d]; } } ++It; } } value[0] = energyTerm1 - energyTerm2; } template void JensenHavrdaCharvatTsallisPointSetMetric ::PrintSelf( std::ostream& os, Indent indent ) const { Superclass::PrintSelf( os, indent ); os << indent << "Use with respect to the moving point set: " << this->m_UseWithRespectToTheMovingPointSet << std::endl; os << indent << "Use regularization term: " << this->m_UseRegularizationTerm << std::endl; os << indent << "Alpha: " << this->m_Alpha << std::endl; os << indent << "Fixed sigma: " << this->m_FixedPointSetSigma << std::endl; os << indent << "Moving sigma: " << this->m_MovingPointSetSigma << std::endl; if( !this->m_UseInputAsSamples ) { os << indent << "Number of fixed samples: " << this->m_NumberOfFixedSamples << std::endl; os << indent << "Number of moving samples: " << this->m_NumberOfMovingSamples << std::endl; } else { os << indent << "Use input points as samples." << std::endl; } if( this->m_UseAnisotropicCovariances ) { os << indent << "Fixed kernel sigma: " << this->m_FixedKernelSigma << std::endl; os << indent << "Moving kernel sigma: " << this->m_MovingKernelSigma << std::endl; os << indent << "Fixed covariance k-neighborhood: " << this->m_FixedCovarianceKNeighborhood << std::endl; os << indent << "Moving covariance k-neighborhood: " << this->m_MovingCovarianceKNeighborhood << std::endl; } else { os << indent << "Isotropic covariances are used." << std::endl; } } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkJensenTsallisBSplineRegistrationFunction.h000066400000000000000000000247761147325206600311320ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkJensenTsallisBSplineRegistrationFunction.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkJensenTsallisBSplineRegistrationFunction_h_ #define _itkJensenTsallisBSplineRegistrationFunction_h_ #include "itkAvantsPDEDeformableRegistrationFunction.h" #include "itkJensenHavrdaCharvatTsallisLabeledPointSetMetric.h" #include "itkBSplineScatteredDataPointSetToImageFilter.h" #include "itkPointSet.h" namespace itk { /** * \class JensenTsallisBSplineRegistrationFunction * \ingroup FiniteDifferenceFunctions */ template class ITK_EXPORT JensenTsallisBSplineRegistrationFunction : public AvantsPDEDeformableRegistrationFunction { public: /** Standard class typedefs. */ typedef JensenTsallisBSplineRegistrationFunction Self; typedef AvantsPDEDeformableRegistrationFunction Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); /** Run-time type information (and related methods) */ itkTypeMacro( JensenTsallisBSplineRegistrationFunction, AvantsPDEDeformableRegistrationFunction ); /** * Inherit some enums from the superclass. */ itkStaticConstMacro( ImageDimension, unsigned int, Superclass::ImageDimension); itkStaticConstMacro( PointDimension, unsigned int, TFixedPointSet::PointDimension ); typedef typename Superclass::NeighborhoodType NeighborhoodType; typedef typename Superclass::FloatOffsetType FloatOffsetType; typedef typename Superclass::TimeStepType TimeStepType; typedef JensenHavrdaCharvatTsallisLabeledPointSetMetric PointSetMetricType; typedef typename PointSetMetricType::RealType RealType; /** * Moving typedefs */ typedef TMovingImage MovingImageType; typedef TMovingPointSet MovingPointSetType; typedef typename MovingImageType::Pointer MovingImagePointer; typedef typename MovingPointSetType::Pointer MovingPointSetPointer; /** * Fixed typedefs */ typedef TFixedImage FixedImageType; typedef TFixedPointSet FixedPointSetType; typedef typename FixedImageType::Pointer FixedImagePointer; typedef typename FixedPointSetType::Pointer FixedPointSetPointer; /** * Deformation field typedefs */ typedef TDeformationField DeformationFieldType; typedef typename DeformationFieldType::Pointer DeformationFieldPointer; typedef typename DeformationFieldType::PixelType VectorType; /** * BSpline typedefs */ /** Typedefs for B-spline filter */ typedef PointSet BSplinePointSetType; typedef BSplineScatteredDataPointSetToImageFilter BSplineFilterType; typedef typename BSplineFilterType::WeightsContainerType BSplineWeightsType; typedef typename BSplineFilterType::PointDataImageType ControlPointLatticeType; typedef typename BSplineFilterType::ArrayType ArrayType; void SetAlpha( RealType r ) { this->m_Alpha = r; } RealType GetAlpha() { return this->m_Alpha; } void SetUseRegularizationTerm( bool b ) { this->m_UseRegularizationTerm = b; } RealType GetUseRegularizationTerm() { return this->m_UseRegularizationTerm; } void SetUseInputAsSamples( bool b ) { this->m_UseInputAsSamples = b; } RealType GetUseInputAsSamples() { return this->m_UseInputAsSamples; } void SetUseAnisotropicCovariances( bool b ) { this->m_UseAnisotropicCovariances = b; } RealType GetUseAnisotropicCovariances() { return this->m_UseAnisotropicCovariances; } void SetFixedPointSetSigma( RealType r ) { this->m_FixedPointSetSigma = r; } RealType GetFixedPointSetSigma() { return this->m_FixedPointSetSigma; } void SetFixedKernelSigma( RealType r ) { this->m_FixedKernelSigma = r; } RealType GetFixedKernelSigma() { return this->m_FixedKernelSigma; } void SetFixedEvaluationKNeighborhood( unsigned int r ) { this->m_FixedEvaluationKNeighborhood = r; } unsigned int GetFixedEvaluationKNeighborhood() { return this->m_FixedEvaluationKNeighborhood; } void SetFixedCovarianceKNeighborhood( unsigned int r ) { this->m_FixedCovarianceKNeighborhood = r; } unsigned int GetFixedCovarianceKNeighborhood() { return this->m_FixedCovarianceKNeighborhood; } void SetNumberOfFixedSamples( unsigned long r ) { this->m_NumberOfFixedSamples = r; } unsigned long GetNumberOfFixedSamples() { return this->m_NumberOfFixedSamples; } void SetMovingPointSetSigma( RealType r ) { this->m_MovingPointSetSigma = r; } RealType GetMovingPointSetSigma() { return this->m_MovingPointSetSigma; } void SetMovingKernelSigma( RealType r ) { this->m_MovingKernelSigma = r; } RealType GetMovingKernelSigma() { return this->m_MovingKernelSigma; } void SetMovingEvaluationKNeighborhood( unsigned int r ) { this->m_MovingEvaluationKNeighborhood = r; } unsigned int GetMovingEvaluationKNeighborhood() { return this->m_MovingEvaluationKNeighborhood; } void SetMovingCovarianceKNeighborhood( unsigned int r ) { this->m_MovingCovarianceKNeighborhood = r; } unsigned int GetMovingCovarianceKNeighborhood() { return this->m_MovingCovarianceKNeighborhood; } void SetNumberOfMovingSamples( unsigned long r ) { this->m_NumberOfMovingSamples = r; } unsigned long GetNumberOfMovingSamples() { return this->m_NumberOfMovingSamples; } void SetMeshResolution( ArrayType a ) { this->m_MeshResolution = a; } ArrayType GetMeshResolution() { return this->m_MeshResolution; } void SetNumberOfLevels( unsigned int r ) { this->m_NumberOfLevels = r; } unsigned int GetNumberOfLevels() { return this->m_NumberOfLevels; } void SetSplineOrder( unsigned int r ) { this->m_SplineOrder = r; } unsigned int GetSplineOrder() { return this->m_SplineOrder; } /** This class uses a constant timestep of 1. */ virtual TimeStepType ComputeGlobalTimeStep(void *itkNotUsed(GlobalData)) const { return m_TimeStep; } /** Return a pointer to a global data structure that is passed to * this object from the solver at each calculation. */ virtual void *GetGlobalDataPointer() const { GlobalDataStruct *global = new GlobalDataStruct(); return global; } /** Release memory for global data structure. */ virtual void ReleaseGlobalDataPointer( void *GlobalData ) const { delete (GlobalDataStruct *) GlobalData; } /** Set the object's state before each iteration. */ virtual void InitializeIteration(); virtual VectorType ComputeUpdate( const NeighborhoodType &neighborhood, void *globalData, const FloatOffsetType &offset = FloatOffsetType( 0.0 ) ); virtual VectorType ComputeUpdateInv( const NeighborhoodType &neighborhood, void *globalData, const FloatOffsetType &offset = FloatOffsetType( 0.0 ) ); protected: JensenTsallisBSplineRegistrationFunction(); ~JensenTsallisBSplineRegistrationFunction() {} /** FixedImage image neighborhood iterator type. */ typedef ConstNeighborhoodIterator FixedImageNeighborhoodIteratorType; /** A global data type for this class of equation. Used to store * iterators for the fixed image. */ struct GlobalDataStruct { FixedImageNeighborhoodIteratorType m_FixedImageIterator; }; void PrintSelf(std::ostream& os, Indent indent) const; private: // typename ControlPointLatticeType::Pointer m_FixedControlPointLattice; // typename ControlPointLatticeType::Pointer m_MovingControlPointLattice; /** The global timestep. */ TimeStepType m_TimeStep; /** * point-set related variables */ bool m_UseRegularizationTerm; bool m_UseInputAsSamples; bool m_UseAnisotropicCovariances; typename DeformationFieldType::Pointer m_DerivativeFixedField; typename DeformationFieldType::Pointer m_DerivativeMovingField; RealType m_FixedPointSetSigma; RealType m_FixedKernelSigma; unsigned int m_FixedEvaluationKNeighborhood; unsigned int m_FixedCovarianceKNeighborhood; unsigned long m_NumberOfFixedSamples; RealType m_MovingPointSetSigma; RealType m_MovingKernelSigma; unsigned int m_MovingEvaluationKNeighborhood; unsigned int m_MovingCovarianceKNeighborhood; unsigned long m_NumberOfMovingSamples; RealType m_Alpha; /** * Bspline related variables */ unsigned int m_SplineOrder; unsigned int m_NumberOfLevels; ArrayType m_MeshResolution; }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkJensenTsallisBSplineRegistrationFunction.txx" #endif #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkJensenTsallisBSplineRegistrationFunction.txx000066400000000000000000000413401147325206600315100ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkJensenTsallisBSplineRegistrationFunction.txx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkJensenTsallisBSplineRegistrationFunction_txx_ #define _itkJensenTsallisBSplineRegistrationFunction_txx_ #include "itkJensenTsallisBSplineRegistrationFunction.h" #include "itkImageLinearConstIteratorWithIndex.h" //#include "itkBSplineControlPointImageFilter.h" namespace itk { template JensenTsallisBSplineRegistrationFunction ::JensenTsallisBSplineRegistrationFunction() { this->m_UseRegularizationTerm = false; this->m_UseInputAsSamples = true; this->m_UseAnisotropicCovariances = false; this->m_NumberOfFixedSamples = 100; this->m_FixedPointSetSigma = 1.0; this->m_FixedKernelSigma = 0.0; this->m_FixedEvaluationKNeighborhood = 50; this->m_NumberOfMovingSamples = 100; this->m_MovingPointSetSigma = 1.0; this->m_MovingKernelSigma = 0.0; this->m_MovingEvaluationKNeighborhood = 50; unsigned int covarianceKNeighborhood = static_cast( vcl_pow( 3.0, static_cast( ImageDimension ) ) ) - 1; this->m_FixedCovarianceKNeighborhood = covarianceKNeighborhood; this->m_MovingCovarianceKNeighborhood = covarianceKNeighborhood; this->m_Alpha = 2.0; // this->m_FixedControlPointLattice = NULL; // this->m_MovingControlPointLattice = NULL; this->m_DerivativeFixedField = NULL; this->m_DerivativeMovingField = NULL; this->m_IsPointSetMetric=true; this->m_SplineOrder = 3; this->m_NumberOfLevels = 1; this->m_MeshResolution.Fill( 1 ); } template void JensenTsallisBSplineRegistrationFunction ::InitializeIteration( void ) { if( this->m_FixedKernelSigma == 0 ) { double maxFixedSpacing = 0.0; for( unsigned int d = 0; d < ImageDimension; d++ ) { if( this->GetFixedImage()->GetSpacing()[d] ) { maxFixedSpacing = this->GetFixedImage()->GetSpacing()[d]; } } this->m_FixedKernelSigma = 2.0 * maxFixedSpacing; } if( this->m_MovingKernelSigma == 0 ) { double maxMovingSpacing = 0.0; for( unsigned int d = 0; d < ImageDimension; d++ ) { if( this->GetMovingImage()->GetSpacing()[d] ) { maxMovingSpacing = this->GetMovingImage()->GetSpacing()[d]; } } this->m_MovingKernelSigma = 2.0 * maxMovingSpacing; } typename PointSetMetricType::Pointer pointSetMetric = PointSetMetricType::New(); pointSetMetric->SetFixedPointSet( this->m_FixedPointSet ); pointSetMetric->SetMovingPointSet( this->m_MovingPointSet ); pointSetMetric->SetUseRegularizationTerm( this->m_UseRegularizationTerm ); pointSetMetric->SetUseInputAsSamples( this->m_UseInputAsSamples ); pointSetMetric->SetUseAnisotropicCovariances( this->m_UseAnisotropicCovariances ); pointSetMetric->SetNumberOfFixedSamples( this->m_NumberOfFixedSamples ); pointSetMetric->SetFixedPointSetSigma( this->m_FixedPointSetSigma ); pointSetMetric->SetFixedKernelSigma( this->m_FixedKernelSigma ); pointSetMetric->SetFixedCovarianceKNeighborhood( this->m_FixedCovarianceKNeighborhood ); pointSetMetric->SetFixedEvaluationKNeighborhood( this->m_FixedEvaluationKNeighborhood ); pointSetMetric->SetNumberOfMovingSamples( this->m_NumberOfMovingSamples ); pointSetMetric->SetMovingPointSetSigma( this->m_MovingPointSetSigma ); pointSetMetric->SetMovingKernelSigma( this->m_MovingKernelSigma ); pointSetMetric->SetMovingCovarianceKNeighborhood( this->m_MovingCovarianceKNeighborhood ); pointSetMetric->SetMovingEvaluationKNeighborhood( this->m_MovingEvaluationKNeighborhood ); pointSetMetric->SetAlpha( this->m_Alpha ); pointSetMetric->Initialize(); typename PointSetMetricType::DefaultTransformType::ParametersType parameters; parameters.Fill( 0.0 ); /** * Calculate with respect to the moving point set */ pointSetMetric->SetUseWithRespectToTheMovingPointSet( true ); typename PointSetMetricType::DerivativeType movingGradient; typename PointSetMetricType::MeasureType movingMeasure; pointSetMetric->GetValueAndDerivative( parameters, movingMeasure, movingGradient ); this->m_Energy += movingMeasure[0]; typename BSplinePointSetType::Pointer movingGradientPoints = BSplinePointSetType::New(); movingGradientPoints->Initialize(); typename BSplineWeightsType::Pointer movingWeights = BSplineWeightsType::New(); movingWeights->Initialize(); unsigned long count = 0; for( unsigned int n = 0; n < pointSetMetric->GetNumberOfValues(); n++ ) { typename MovingPointSetType::PointType point; this->m_MovingPointSet->GetPoint( n, &point ); typename BSplinePointSetType::PointType bsplinePoint; VectorType gradient; bsplinePoint.CastFrom( point ); bool isInside = true; for( unsigned int d = 0; d < PointDimension; d++ ) { if( bsplinePoint[d] <= this->GetMovingImage()->GetOrigin()[d] || bsplinePoint[d] >= this->GetMovingImage()->GetOrigin()[d] + ( this->GetMovingImage()->GetLargestPossibleRegion().GetSize()[d] - 1 ) * this->GetMovingImage()->GetSpacing()[d] ) { isInside = false; break; } } if( isInside ) { for( unsigned int d = 0; d < PointDimension; d++ ) { gradient[d] = movingGradient(n, d); } movingGradientPoints->SetPoint( count, bsplinePoint ); movingGradientPoints->SetPointData( count, gradient ); movingWeights->InsertElement( count, 1.0 ); count++; } } VectorType zeroVector; zeroVector.Fill( 0.0 ); ImageLinearConstIteratorWithIndex ItM( this->GetMovingImage(), this->GetMovingImage()->GetLargestPossibleRegion() ); for( unsigned int d = 0; d < PointDimension; d++ ) { ItM.SetDirection( d ); ItM.GoToBegin(); while( !ItM.IsAtEnd() ) { typename MovingImageType::PointType point; typename BSplinePointSetType::PointType bsplinePoint; ItM.GoToBeginOfLine(); this->GetMovingImage()->TransformIndexToPhysicalPoint( ItM.GetIndex(), point ); bsplinePoint.CastFrom( point ); movingGradientPoints->SetPoint( count, bsplinePoint ); movingGradientPoints->SetPointData( count, zeroVector ); count++; ItM.GoToEndOfLine(); --ItM; this->GetMovingImage()->TransformIndexToPhysicalPoint( ItM.GetIndex(), point ); bsplinePoint.CastFrom( point ); movingGradientPoints->SetPoint( count, bsplinePoint ); movingGradientPoints->SetPointData( count, zeroVector ); movingWeights->InsertElement( count, 1000.0 ); count++; ItM.NextLine(); } } ArrayType numberOfMovingControlPoints; for( unsigned int d = 0; d < ImageDimension; d++ ) { numberOfMovingControlPoints[d] = this->m_SplineOrder + static_cast( vcl_floor( 0.5 + static_cast( this->GetMovingImage()->GetLargestPossibleRegion().GetSize()[d] ) / static_cast( this->m_MeshResolution[d] ) ) ); } typename BSplineFilterType::Pointer movingBSpliner = BSplineFilterType::New(); movingBSpliner->SetInput( movingGradientPoints ); movingBSpliner->SetPointWeights( movingWeights.GetPointer() ); movingBSpliner->SetOrigin( this->GetMovingImage()->GetOrigin() ); movingBSpliner->SetSpacing( this->GetMovingImage()->GetSpacing() ); movingBSpliner->SetSize( this->GetMovingImage()->GetLargestPossibleRegion().GetSize() ); movingBSpliner->SetNumberOfLevels( this->m_NumberOfLevels ); movingBSpliner->SetSplineOrder( this->m_SplineOrder ); movingBSpliner->SetNumberOfControlPoints( numberOfMovingControlPoints ); movingBSpliner->SetGenerateOutputImage( true ); movingBSpliner->Update(); this->m_DerivativeMovingField = movingBSpliner->GetOutput(); // this->m_MovingControlPointLattice = movingBSpliner->GetPhiLattice(); /** * Calculate with respect to the fixed point set */ pointSetMetric->SetUseWithRespectToTheMovingPointSet( false ); typename PointSetMetricType::DerivativeType fixedGradient; typename PointSetMetricType::MeasureType fixedMeasure; pointSetMetric->GetValueAndDerivative( parameters, fixedMeasure, fixedGradient ); this->m_Energy += fixedMeasure[0]; typename BSplinePointSetType::Pointer fixedGradientPoints = BSplinePointSetType::New(); fixedGradientPoints->Initialize(); typename BSplineWeightsType::Pointer fixedWeights = BSplineWeightsType::New(); fixedWeights->Initialize(); count = 0; for( unsigned int n = 0; n < pointSetMetric->GetNumberOfValues(); n++ ) { typename FixedPointSetType::PointType point; this->m_FixedPointSet->GetPoint( n, &point ); typename BSplinePointSetType::PointType bsplinePoint; VectorType gradient; bsplinePoint.CastFrom( point ); bool isInside = true; for( unsigned int d = 0; d < PointDimension; d++ ) { if( bsplinePoint[d] <= this->GetFixedImage()->GetOrigin()[d] || bsplinePoint[d] >= this->GetFixedImage()->GetOrigin()[d] + ( this->GetFixedImage()->GetLargestPossibleRegion().GetSize()[d] - 1 ) * this->GetFixedImage()->GetSpacing()[d] ) { isInside = false; break; } } if( isInside ) { for( unsigned int d = 0; d < PointDimension; d++ ) { gradient[d] = fixedGradient(n, d); } fixedGradientPoints->SetPoint( count, bsplinePoint ); fixedGradientPoints->SetPointData( count, gradient ); fixedWeights->InsertElement( count, 1.0 ); count++; } } ImageLinearConstIteratorWithIndex ItF( this->GetFixedImage(), this->GetFixedImage()->GetLargestPossibleRegion() ); for( unsigned int d = 0; d < PointDimension; d++ ) { ItF.SetDirection( d ); ItF.GoToBegin(); while( !ItF.IsAtEnd() ) { typename FixedImageType::PointType point; typename BSplinePointSetType::PointType bsplinePoint; ItF.GoToBeginOfLine(); this->GetFixedImage()->TransformIndexToPhysicalPoint( ItF.GetIndex(), point ); bsplinePoint.CastFrom( point ); fixedGradientPoints->SetPoint( count, bsplinePoint ); fixedGradientPoints->SetPointData( count, zeroVector ); fixedWeights->InsertElement( count, 1.0 ); count++; ItF.GoToEndOfLine(); --ItF; this->GetFixedImage()->TransformIndexToPhysicalPoint( ItF.GetIndex(), point ); bsplinePoint.CastFrom( point ); fixedGradientPoints->SetPoint( count, bsplinePoint ); fixedGradientPoints->SetPointData( count, zeroVector ); fixedWeights->InsertElement( count, 1000.0 ); count++; ItF.NextLine(); } } ArrayType numberOfFixedControlPoints; for( unsigned int d = 0; d < ImageDimension; d++ ) { numberOfFixedControlPoints[d] = this->m_SplineOrder + static_cast( vcl_floor( 0.5 + static_cast( this->GetFixedImage()->GetLargestPossibleRegion().GetSize()[d] ) / static_cast( this->m_MeshResolution[d] ) ) ); } typename BSplineFilterType::Pointer fixedBSpliner = BSplineFilterType::New(); fixedBSpliner->SetInput( fixedGradientPoints ); fixedBSpliner->SetPointWeights( fixedWeights.GetPointer() ); fixedBSpliner->SetOrigin( this->GetFixedImage()->GetOrigin() ); fixedBSpliner->SetSpacing( this->GetFixedImage()->GetSpacing() ); fixedBSpliner->SetSize( this->GetFixedImage()->GetLargestPossibleRegion().GetSize() ); fixedBSpliner->SetNumberOfLevels( this->m_NumberOfLevels ); fixedBSpliner->SetSplineOrder( this->m_SplineOrder ); fixedBSpliner->SetNumberOfControlPoints( numberOfFixedControlPoints ); fixedBSpliner->SetGenerateOutputImage( true ); fixedBSpliner->Update(); this->m_DerivativeFixedField = fixedBSpliner->GetOutput(); // this->m_FixedControlPointLattice = fixedBSpliner->GetPhiLattice(); } template typename JensenTsallisBSplineRegistrationFunction::VectorType JensenTsallisBSplineRegistrationFunction ::ComputeUpdate( const NeighborhoodType &neighborhood, void *globalData, const FloatOffsetType &offset ) { if( this->m_DerivativeFixedField ) { return -this->m_DerivativeFixedField->GetPixel( neighborhood.GetIndex() ); } else { itkExceptionMacro( "Initialize() has not been called." ); } /* typedef BSplineControlPointImageFilter BSplineControlPointImageFilterType; typename BSplineControlPointImageFilterType::Pointer movingBSpliner = BSplineControlPointImageFilterType::New(); movingBSpliner->SetInput( this->m_MovingControlPointLattice ); movingBSpliner->SetOrigin( this->GetMovingImage()->GetOrigin() ); movingBSpliner->SetSpacing( this->GetMovingImage()->GetSpacing() ); movingBSpliner->SetSize( this->GetMovingImage()->GetLargestPossibleRegion().GetSize() ); movingBSpliner->SetSplineOrder( this->m_SplineOrder ); VectorType gradient; movingBSpliner->EvaluateAtIndex( neighborhood.GetIndex(), gradient ); return gradient; */ } template typename JensenTsallisBSplineRegistrationFunction::VectorType JensenTsallisBSplineRegistrationFunction ::ComputeUpdateInv( const NeighborhoodType &neighborhood, void *globalData, const FloatOffsetType &offset ) { if( this->m_DerivativeMovingField ) { return -this->m_DerivativeMovingField->GetPixel( neighborhood.GetIndex() ); } else { itkExceptionMacro( "Initialize() has not been called." ); } /* typedef BSplineControlPointImageFilter BSplineControlPointImageFilterType; typename BSplineControlPointImageFilterType::Pointer fixedBSpliner = BSplineControlPointImageFilterType::New(); fixedBSpliner->SetInput( this->m_FixedControlPointLattice ); fixedBSpliner->SetOrigin( this->GetFixedImage()->GetOrigin() ); fixedBSpliner->SetSpacing( this->GetFixedImage()->GetSpacing() ); fixedBSpliner->SetSize( this->GetFixedImage()->GetLargestPossibleRegion().GetSize() ); fixedBSpliner->SetSplineOrder( this->m_SplineOrder ); VectorType gradient; fixedBSpliner->EvaluateAtIndex( neighborhood.GetIndex(), gradient ); return gradient; */ } template void JensenTsallisBSplineRegistrationFunction ::PrintSelf( std::ostream& os, Indent indent ) const { Superclass::PrintSelf( os, indent ); os << indent << "Use regularization term: " << this->m_UseRegularizationTerm << std::endl; if( !this->m_UseInputAsSamples ) { os << indent << "Number of fixed samples: " << this->m_NumberOfFixedSamples << std::endl; os << indent << "Number of moving samples: " << this->m_NumberOfMovingSamples << std::endl; } os << indent << "Alpha: " << this->m_Alpha << std::endl; os << indent << "Fixed sigma: " << this->m_FixedPointSetSigma << std::endl; os << indent << "Moving sigma: " << this->m_MovingPointSetSigma << std::endl; os << indent << "Spline order: " << this->m_SplineOrder << std::endl; os << indent << "Number of levels: " << this->m_NumberOfLevels << std::endl; os << indent << "Number of control points: " << this->m_MeshResolution << std::endl; } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkPICSLAdvancedNormalizationToolKit.h000066400000000000000000000120121147325206600273270ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkPICSLAdvancedNormalizationToolKit.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkPICSLAdvancedNormalizationToolKit_h #define __itkPICSLAdvancedNormalizationToolKit_h #include "itkObject.h" #include "itkObjectFactory.h" #include "itkANTSImageTransformation.h" #include "itkANTSImageRegistrationOptimizer.h" #include "itkANTSSimilarityMetric.h" #include "antsCommandLineParser.h" #include "itkImage.h" #include "itkMacro.h" #include "itkANTSLabeledPointSet.h" namespace itk { template class ITK_EXPORT PICSLAdvancedNormalizationToolKit : public Object { public: /** Standard class typedefs. */ typedef PICSLAdvancedNormalizationToolKit Self; typedef Object Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); /** Run-time type information (and related methods). */ itkTypeMacro( PICSLAdvancedNormalizationToolKit, Object ); itkStaticConstMacro( Dimension, unsigned int, TDimension ); typedef TReal RealType; typedef Image ImageType; typedef typename ImageType::Pointer ImagePointer; typedef typename ImageType::PixelType PixelType; typedef itk::ANTSImageTransformation TransformationModelType; typedef typename TransformationModelType::Pointer TransformationModelPointer; typedef itk::ANTSImageRegistrationOptimizer RegistrationOptimizerType; typedef typename RegistrationOptimizerType::Pointer RegistrationOptimizerPointer; typedef typename TransformationModelType::DeformationFieldType DeformationFieldType; typedef typename TransformationModelType::AffineTransformType AffineTransformType; typedef typename RegistrationOptimizerType::OptAffineType OptAffineType; /** Point Set Type */ typedef itk::ANTSLabeledPointSet LabeledPointSetType; typedef typename LabeledPointSetType::Pointer LabeledPointSetPointer; typedef typename LabeledPointSetType::PointSetType PointSetType; /** Typedefs for similarity metrics */ typedef ANTSSimilarityMetric SimilarityMetricType; typedef typename SimilarityMetricType::Pointer SimilarityMetricPointer; typedef std::vector SimilarityMetricListType; typedef typename SimilarityMetricType::MetricType MetricBaseType; typedef ants::CommandLineParser ParserType; typedef typename ParserType::OptionType OptionType; void ParseCommandLine( int argc, char **argv ); TransformationModelPointer GetTransformationModel( ) { return this->m_TransformationModel; } RegistrationOptimizerPointer SetRegistrationOptimizer( ) { return this->m_RegistrationOptimizer; } void SetTransformationModel( TransformationModelPointer T ) {this->m_TransformationModel=T; } void SetRegistrationOptimizer( RegistrationOptimizerPointer T ) {this->m_RegistrationOptimizer=T; } void InitializeTransformAndOptimizer(); void RunRegistration(); protected: PICSLAdvancedNormalizationToolKit(); virtual ~PICSLAdvancedNormalizationToolKit() {} void PrintSelf( std::ostream& os, Indent indent ) const; private: PICSLAdvancedNormalizationToolKit( const Self& ); //purposely not implemented void operator=( const Self& ); //purposely not implemented ImagePointer SubsampleImage( ImagePointer, RealType ); ImagePointer PreprocessImage( ImagePointer ); ImagePointer ReplaceProblematicPixelValues( ImagePointer, PixelType ); void InitializeCommandLineOptions(); void ReadImagesAndMetrics(); typename ParserType::Pointer m_Parser; TransformationModelPointer m_TransformationModel; RegistrationOptimizerPointer m_RegistrationOptimizer; SimilarityMetricListType m_SimilarityMetrics; }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkPICSLAdvancedNormalizationToolKit.txx" #endif #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkPICSLAdvancedNormalizationToolKit.txx000066400000000000000000001536641147325206600277460ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkPICSLAdvancedNormalizationToolKit.txx,v $ Language: C++ Date: $Date: 2009/03/08 19:51:49 $ Version: $Revision: 1.22 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkPICSLAdvancedNormalizationToolKit_txx_ #define _itkPICSLAdvancedNormalizationToolKit_txx_ // disable debug warnings in MS compiler #ifdef _MSC_VER #pragma warning(disable: 4786) #endif #include "itkPICSLAdvancedNormalizationToolKit.h" #include "itkHistogramMatchingImageFilter.h" #include "itkSpatialMutualInformationRegistrationFunction.h" #include "itkIdentityTransform.h" #include "itkImageFileReader.h" #include "itkImageFileWriter.h" #include "itkLabeledPointSetFileReader.h" #include "itkLinearInterpolateImageFunction.h" #include "itkRecursiveGaussianImageFilter.h" #include "itkResampleImageFilter.h" // Image similarity metrics #include "itkAvantsMutualInformationRegistrationFunction.h" #include "itkSyNDemonsRegistrationFunction.h" #include "itkProbabilisticRegistrationFunction.h" #include "itkCrossCorrelationRegistrationFunction.h" #include "itkExpectationBasedPointSetRegistrationFunction.h" //#include "itkRobustDemonsRegistrationFunction.h" //#include "itkRobustOpticalFlow.h" //#include "itkSectionMutualInformationRegistrationFunction.h" #include "itkJensenTsallisBSplineRegistrationFunction.h" #include "vnl/vnl_math.h" #include "ANTS_affine_registration2.h" namespace itk { template PICSLAdvancedNormalizationToolKit ::PICSLAdvancedNormalizationToolKit() { this->InitializeCommandLineOptions(); } template void PICSLAdvancedNormalizationToolKit ::ParseCommandLine( int argc, char **argv ) { this->m_Parser->Parse( argc, argv ); typename ParserType::OptionListType unknownOptions = this->m_Parser->GetUnknownOptions(); if( unknownOptions.size() ) { std::cout << std::endl << "WARNING: Unknown options" << std::endl; typename ParserType::OptionListType::const_iterator its; for( its = unknownOptions.begin(); its != unknownOptions.end(); its++ ) { if( (*its)->GetShortName() != '\0' ) { std::cout << " " << '-' << (*its)->GetShortName() << std::endl; } else { std::cout << " " << "--" << (*its)->GetLongName() << std::endl; } } std::cout << std::endl; } std::string printhelp_long = this->m_Parser->GetOption( "help" )->GetValue(); unsigned int help_long = this->m_Parser->template Convert( printhelp_long ); if ( help_long ) { this->m_Parser->PrintMenu( std::cout, 7, false ); exit( 0 ); } std::string printhelp_short = this->m_Parser->GetOption( 'h' )->GetValue(); unsigned int help_short = this->m_Parser->template Convert( printhelp_short ); if ( help_short ) { this->m_Parser->PrintMenu( std::cout, 7, true ); exit( 0 ); } } template void PICSLAdvancedNormalizationToolKit ::InitializeTransformAndOptimizer() { if (!this->m_TransformationModel) { this->m_TransformationModel=TransformationModelType::New(); } if (!this->m_RegistrationOptimizer) { this->m_RegistrationOptimizer=RegistrationOptimizerType::New(); } std::string temp = this->m_Parser->GetOption( "output-naming" )->GetValue(); this->m_TransformationModel->SetNamingConvention( temp ); this->m_TransformationModel->InitializeTransform(); this->m_RegistrationOptimizer->SetParser( this->m_Parser ); this->m_RegistrationOptimizer->SetSimilarityMetrics( this->m_SimilarityMetrics ); } template void PICSLAdvancedNormalizationToolKit ::RunRegistration() { /** parse the command line and get input objects */ this->ReadImagesAndMetrics(); // exit(0); /** initializes the transformation model and the optimizer */ this->InitializeTransformAndOptimizer(); /** Get the mask if there is one */ if ( typename OptionType::Pointer option = this->m_Parser->GetOption( "mask-image" ) ) { typedef ImageFileReader ReaderType; std::string maskfn = this->m_Parser->GetOption( "mask-image" )->GetValue(); if (maskfn.length() > 4) { typename ReaderType::Pointer maskImageFileReader = ReaderType::New(); maskImageFileReader->SetFileName( maskfn.c_str() ); maskImageFileReader->Update(); ImagePointer maskImage = this->PreprocessImage( maskImageFileReader->GetOutput() ); this->m_RegistrationOptimizer->SetMaskImage(maskImage); } } typename TransformationModelType::AffineTransformPointer aff_init, aff, fixed_aff_init; //added by songgang // try initialize the affine transform typename OptionType::ValueType initial_affine_filename = this->m_Parser->GetOption( "initial-affine" )->GetValue(); if ( initial_affine_filename != "" ){ std::cout << "Loading affine registration from: " << initial_affine_filename << std::endl; aff_init = TransformationModelType::AffineTransformType::New(); ReadAffineTransformFile(initial_affine_filename, aff_init); } else{ std::cout << "Use identity affine transform as initial affine para." << std::endl; std::cout << "aff_init.IsNull()==" << aff_init.IsNull() << std::endl; } typename OptionType::ValueType fixed_initial_affine_filename = this->m_Parser->GetOption( "fixed-image-initial-affine" )->GetValue(); if ( fixed_initial_affine_filename != "" ){ std::cout << "Loading affine registration from: " << fixed_initial_affine_filename << std::endl; fixed_aff_init = TransformationModelType::AffineTransformType::New(); ReadAffineTransformFile(fixed_initial_affine_filename, fixed_aff_init); std::cout << " FIXME! currently, if one passes a fixed initial affine mapping, then NO affine mapping will be performed subsequently! " << std::endl; } else{ std::cout << "Use identity affine transform as initial fixed affine para." << std::endl; std::cout << "fixed_aff_init.IsNull()==" << fixed_aff_init.IsNull() << std::endl; } bool useNN = this->m_Parser->template Convert( this->m_Parser->GetOption( "use-NN" )->GetValue() ); if ( useNN) this->m_RegistrationOptimizer->SetUseNearestNeighborInterpolation(true); else this->m_RegistrationOptimizer->SetUseNearestNeighborInterpolation(false); typename OptionType::ValueType continue_affine = this->m_Parser->GetOption( "continue-affine" )->GetValue(); if ( fixed_initial_affine_filename != "" ) continue_affine=std::string("false"); if ( continue_affine == "true" ){ std::cout << "Continue affine registration from the input" << std::endl; //<< aff_init << std::endl; OptAffineType affine_opt; //InitializeAffineOption() { affine_opt.transform_initial = aff_init; std::string temp=this->m_Parser->GetOption( "number-of-affine-iterations" )->GetValue(); affine_opt.number_of_iteration_list = this->m_Parser->template ConvertVector(temp); affine_opt.number_of_levels = affine_opt.number_of_iteration_list.size(); temp=this->m_Parser->GetOption( "affine-metric-type" )->GetValue(); if (temp == "MI") affine_opt.metric_type = AffineWithMutualInformation; if (temp == "MSQ") affine_opt.metric_type = AffineWithMeanSquareDifference; if (temp == "CCH") affine_opt.metric_type = AffineWithHistogramCorrelation; if (temp == "CC") affine_opt.metric_type = AffineWithNormalizedCorrelation; if (temp == "GD") affine_opt.metric_type = AffineWithGradientDifference; temp=this->m_Parser->GetOption( "MI-option" )->GetValue(); std::vector mi_option = this->m_Parser->template ConvertVector(temp); affine_opt.MI_bins = mi_option[0]; affine_opt.MI_samples = mi_option[1]; temp=this->m_Parser->GetOption( "rigid-affine" )->GetValue(); std::string temp2=this->m_Parser->GetOption( "do-rigid" )->GetValue(); affine_opt.is_rigid = ( ( temp=="true" ) || ( temp2=="true" ) || ( temp == "1" ) || ( temp2 == "1" ) ); temp=this->m_Parser->GetOption( "affine-gradient-descent-option" )->GetValue(); std::vector gradient_option = this->m_Parser->template ConvertVector(temp); affine_opt.maximum_step_length = gradient_option[0]; affine_opt.relaxation_factor = gradient_option[1]; affine_opt.minimum_step_length = gradient_option[2]; affine_opt.translation_scales = gradient_option[3]; // std::cout << affine_opt; temp=this->m_Parser->GetOption( "use-rotation-header" )->GetValue(); affine_opt.use_rotation_header = (temp=="true"); std::cout << "affine_opt.use_rotation_header = " << affine_opt.use_rotation_header << std::endl; temp=this->m_Parser->GetOption( "ignore-void-origin")->GetValue(); affine_opt.ignore_void_orgin = (temp=="true"); std::cout << "affine_opt.ignore_void_orgin = " << affine_opt.ignore_void_orgin << std::endl; } aff = this->m_RegistrationOptimizer->AffineOptimization(affine_opt); } else{ std::cout << "Use fixed initial affine para." << std::endl; if (aff_init.IsNull()){ aff_init = TransformationModelType::AffineTransformType::New(); aff_init->SetIdentity(); } if (fixed_aff_init.IsNull()){ fixed_aff_init = TransformationModelType::AffineTransformType::New(); fixed_aff_init->SetIdentity(); } aff = aff_init; } // std::cout << aff << std::endl; this->m_TransformationModel->SetAffineTransform(aff); this->m_TransformationModel->SetFixedImageAffineTransform(fixed_aff_init); this->m_RegistrationOptimizer->SetAffineTransform(aff); this->m_RegistrationOptimizer->SetFixedImageAffineTransform( this->m_TransformationModel->GetFixedImageAffineTransform()); /** Second, optimize Diff */ this->m_RegistrationOptimizer->DeformableOptimization(); std::cout << " Registration Done " << std::endl; this->m_TransformationModel->SetDeformationField(this->m_RegistrationOptimizer->GetDeformationField()); this->m_TransformationModel->SetInverseDeformationField(this->m_RegistrationOptimizer->GetInverseDeformationField()); } template typename PICSLAdvancedNormalizationToolKit::ImagePointer PICSLAdvancedNormalizationToolKit ::PreprocessImage( ImagePointer image ) { image = this->ReplaceProblematicPixelValues( image, 0 ); return image; } template typename PICSLAdvancedNormalizationToolKit::ImagePointer PICSLAdvancedNormalizationToolKit ::ReplaceProblematicPixelValues( ImagePointer image, PixelType value = 0 ) { ImageRegionIterator It( image, image->GetRequestedRegion() ); for ( It.GoToBegin(); !It.IsAtEnd(); ++It ) { PixelType pixel = It.Get(); if ( vnl_math_isinf( pixel ) || vnl_math_isnan( pixel ) ) { It.Set( value ); } } typedef itk::MinimumMaximumImageFilter MinMaxFilterType; typename MinMaxFilterType::Pointer minMaxFilter = MinMaxFilterType::New(); minMaxFilter->SetInput( image ); minMaxFilter->Update(); double min = minMaxFilter->GetMinimum(); double shift = -1.0 * static_cast( min ); double scale = static_cast( minMaxFilter->GetMaximum() ); scale += shift; scale = 1.0 / scale; typedef itk::ShiftScaleImageFilter FilterType; typename FilterType::Pointer filter = FilterType::New(); filter->SetInput( image ); filter->SetShift( shift ); filter->SetScale( scale ); filter->Update(); return filter->GetOutput(); } /** * Standard "PrintSelf" method */ template void PICSLAdvancedNormalizationToolKit ::PrintSelf( std::ostream& os, Indent indent) const { Superclass::PrintSelf( os, indent ); } template void PICSLAdvancedNormalizationToolKit ::ReadImagesAndMetrics() { /** * Read in all images and preprocess them before * storing them in their corresponding image lists. */ this->m_SimilarityMetrics.clear(); typedef ImageFileReader ReaderType; bool useHistMatch = this->m_Parser->template Convert( this->m_Parser->GetOption( "use-Histogram-Matching" )->GetValue() ); /** * Read the metrics and image files */ if ( typename OptionType::Pointer option = this->m_Parser->GetOption( "image-metric" ) ) { std::cout <<" values " << option->GetNumberOfValues() << std::endl; for ( unsigned int i = 0; i < option->GetNumberOfValues(); i++ ) { SimilarityMetricPointer similarityMetric = SimilarityMetricType::New(); RealType similarityMetricScalarWeight = 1.0; similarityMetric->SetWeightScalar( similarityMetricScalarWeight ); unsigned int parameterCount = 0; typename ReaderType::Pointer fixedImageFileReader = ReaderType::New(); fixedImageFileReader->SetFileName( option->GetParameter( i, parameterCount ) ); fixedImageFileReader->Update(); ImagePointer fixedImage = this->PreprocessImage( fixedImageFileReader->GetOutput() ); similarityMetric->SetFixedImage( fixedImage ); parameterCount++; std::cout << " Fixed image file: " << fixedImageFileReader->GetFileName() << std::endl; typename ReaderType::Pointer movingImageFileReader = ReaderType::New(); movingImageFileReader->SetFileName( option->GetParameter( i, parameterCount ) ); movingImageFileReader->Update(); ImagePointer movingImage = this->PreprocessImage( movingImageFileReader->GetOutput() ); similarityMetric->SetMovingImage( movingImage ); typename SimilarityMetricType::RadiusType radius; radius.Fill( 0 ); parameterCount++; std::cout << " Moving image file: " << movingImageFileReader->GetFileName() << std::endl; /** * Check if similarity metric is image based or point-set based. * Image metrics: * > mean-squares/MeanSquares/MSQ * > mutual-information/MutualInformation/MI * > cross-correlation/CrossCorrelation/CC * > probabilistic/Probabilistic/PR * * Point-set metrics: * > point-set-expectation/PointSetExpectation/PSE * > jensen-tsallis-bspline/JensenTsallisBSpline/JTB * */ bool isMetricPointSetBased = false; typename OptionType::ValueType whichMetric = option->GetValue( i ); if ( whichMetric == "point-set-expectation" || whichMetric == "PointSetExpectation" || whichMetric == "PSE" || whichMetric == "jensen-tsallis-bspline" || whichMetric == "JensenTsallisBSpline" || whichMetric == "JTB" ) { isMetricPointSetBased = true; } if ( isMetricPointSetBased ) { std::cout << "Metric " << i << ": " << " Point-set " << whichMetric << " n-params " << option->GetNumberOfParameters( i ) << std::endl; /** * Read in the point-set metric parameters */ typedef LabeledPointSetFileReader PointSetReaderType; typename PointSetReaderType::Pointer fixedPointSetReader = PointSetReaderType::New(); fixedPointSetReader->SetFileName( option->GetParameter( i, parameterCount ) ); parameterCount++; typename PointSetReaderType::Pointer movingPointSetReader = PointSetReaderType::New(); movingPointSetReader->SetFileName( option->GetParameter( i, parameterCount ) ); parameterCount++; if ( option->GetNumberOfParameters( i ) > parameterCount ) { similarityMetricScalarWeight = this->m_Parser->template Convert( option->GetParameter( i, parameterCount ) ); parameterCount++; } similarityMetric->SetWeightScalar( similarityMetricScalarWeight ); std::cout << " similarity metric weight: " << similarityMetricScalarWeight << std::endl; float pointSetPercent=0.5; float pointSetSigma = 5.0; bool extractBoundaryPointsOnly = false; unsigned int kNeighborhood = 50; if ( option->GetNumberOfParameters( i ) > parameterCount ) { pointSetPercent = this->m_Parser->template Convert( option->GetParameter( i, parameterCount ) ); parameterCount++; } if ( option->GetNumberOfParameters( i ) > parameterCount ) { pointSetSigma = this->m_Parser->template Convert( option->GetParameter( i, parameterCount ) ); parameterCount++; } if ( option->GetNumberOfParameters( i ) > parameterCount ) { extractBoundaryPointsOnly = this->m_Parser->template Convert( option->GetParameter( i, parameterCount ) ); parameterCount++; } if ( option->GetNumberOfParameters( i ) > parameterCount ) { kNeighborhood = this->m_Parser->template Convert( option->GetParameter( i, parameterCount ) ); parameterCount++; } std::cout << " point-set sigma = " << pointSetSigma << std::endl; std::cout << " percentage of points = " << pointSetPercent << std::endl; std::cout << " k-neighborhood = " << kNeighborhood << std::endl; if( extractBoundaryPointsOnly ) { std::cout << " use only boundary points. " << pointSetPercent << std::endl; } fixedPointSetReader->SetRandomPercentage( pointSetPercent ); fixedPointSetReader->SetExtractBoundaryPoints( extractBoundaryPointsOnly ); fixedPointSetReader->Update(); std::cout << " Fixed point-set file: " << fixedPointSetReader->GetFileName() << std::endl; std::cout << " Number of fixed labels: " << fixedPointSetReader->GetLabelSet()->size() << std::endl; std::cout << " Distinct fixed labels: "; for ( unsigned int n = 0; n < fixedPointSetReader->GetLabelSet()->size(); n++ ) { std::cout << fixedPointSetReader->GetLabelSet()->operator[]( n ) << " "; } std::cout << std::endl; movingPointSetReader->SetRandomPercentage( pointSetPercent ); movingPointSetReader->SetExtractBoundaryPoints( extractBoundaryPointsOnly ); movingPointSetReader->Update(); movingPointSetReader->SetRandomPercentage( pointSetPercent ); movingPointSetReader->SetExtractBoundaryPoints( extractBoundaryPointsOnly ); movingPointSetReader->Update(); std::cout << " Moving point-set file: " << movingPointSetReader->GetFileName() << std::endl; std::cout << " Number of moving labels: " << movingPointSetReader->GetLabelSet()->size() << std::endl; std::cout << " Distinct moving labels: "; for ( unsigned int n = 0; n < movingPointSetReader->GetLabelSet()->size(); n++ ) { std::cout << movingPointSetReader->GetLabelSet()->operator[]( n ) << " "; } std::cout << std::endl; if ( whichMetric == "point-set-expectation" || whichMetric == "PointSetExpectation" || whichMetric == "PSE" ) { typedef itk::ExpectationBasedPointSetRegistrationFunction MetricType; typename MetricType::Pointer metric = MetricType::New(); metric->SetRadius( radius ); metric->SetFixedPointSet( fixedPointSetReader->GetOutput() ); metric->SetMovingPointSet( movingPointSetReader->GetOutput() ); metric->SetFixedPointSetSigma( pointSetSigma ); metric->SetMovingPointSetSigma( pointSetSigma ); metric->SetKNeighborhood( kNeighborhood ); if ( option->GetNumberOfParameters( i ) > parameterCount ) { unsigned int pm = this->m_Parser->template Convert( option->GetParameter( i, parameterCount ) ); metric->SetUseSymmetricMatching( pm ); std::cout << " Symmetric match iterations -- going Asymmeric for the rest " << pm << std::endl; parameterCount++; } similarityMetric->SetMetric( metric ); similarityMetric->SetMaximizeMetric( true ); similarityMetric->SetFixedPointSet( fixedPointSetReader->GetOutput() ); similarityMetric->SetMovingPointSet(movingPointSetReader->GetOutput() ); this->m_SimilarityMetrics.push_back( similarityMetric ); } else if ( whichMetric == "jensen-tsallis-bspline" || whichMetric == "JensenTsallisBSpline" || whichMetric == "JTB" ) { typedef itk::JensenTsallisBSplineRegistrationFunction MetricType; typename MetricType::Pointer metric = MetricType::New(); metric->SetRadius( radius ); metric->SetFixedPointSet( fixedPointSetReader->GetOutput() ); metric->SetMovingPointSet( movingPointSetReader->GetOutput() ); metric->SetFixedPointSetSigma( pointSetSigma ); metric->SetMovingPointSetSigma( pointSetSigma ); metric->SetFixedEvaluationKNeighborhood( kNeighborhood ); metric->SetMovingEvaluationKNeighborhood( kNeighborhood ); if ( option->GetNumberOfParameters( i ) > parameterCount ) { metric->SetAlpha( this->m_Parser->template Convert( option->GetParameter( i, parameterCount ) ) ); parameterCount++; } if ( option->GetNumberOfParameters( i ) > parameterCount ) { typename RegistrationOptimizerType::ArrayType meshResolution; std::vector resolution = this->m_Parser->template ConvertVector( option->GetParameter( i, parameterCount ) ); if ( resolution.size() != TDimension ) { itkExceptionMacro( "Mesh resolution does not match image dimension." ); } for ( unsigned int d = 0; d < TDimension; d++ ) { meshResolution[d] = resolution[d]; } metric->SetMeshResolution( meshResolution ); parameterCount++; } if ( option->GetNumberOfParameters( i ) > parameterCount ) { metric->SetSplineOrder( this->m_Parser->template Convert( option->GetParameter( i, parameterCount ) ) ); parameterCount++; } if ( option->GetNumberOfParameters( i ) > parameterCount ) { metric->SetNumberOfLevels( this->m_Parser->template Convert( option->GetParameter( i, parameterCount ) ) ); parameterCount++; } if ( option->GetNumberOfParameters( i ) > parameterCount ) { metric->SetUseAnisotropicCovariances( this->m_Parser->template Convert( option->GetParameter( i, parameterCount ) ) ); parameterCount++; } std::cout << " B-spline parameters " << std::endl; std::cout << " mesh resolution: " << metric->GetMeshResolution() << std::endl; std::cout << " spline order: " << metric->GetSplineOrder() << std::endl; std::cout << " number of levels: " << metric->GetNumberOfLevels() << std::endl; std::cout << " Alpha: " << metric->GetAlpha() << std::endl; if ( metric->GetUseAnisotropicCovariances() ) { std::cout << " using anisotropic covariances." << std::endl; } similarityMetric->SetMetric( metric ); similarityMetric->SetMaximizeMetric( true ); similarityMetric->SetFixedPointSet( fixedPointSetReader->GetOutput() ); similarityMetric->SetMovingPointSet( movingPointSetReader->GetOutput() ); this->m_SimilarityMetrics.push_back( similarityMetric ); } } else // similarity metric is image-based { std::cout << "Metric " << i << ": " << " Not a Point-set" << std::endl; std::cout << " Fixed image file: " << fixedImageFileReader->GetFileName() << std::endl; std::cout << " Moving image file: " << movingImageFileReader->GetFileName() << std::endl; similarityMetric->SetFixedPointSet( NULL); similarityMetric->SetMovingPointSet( NULL ); if ( option->GetNumberOfParameters( i ) > parameterCount ) { similarityMetricScalarWeight = this->m_Parser->template Convert( option->GetParameter( i, parameterCount ) ); parameterCount++; } similarityMetric->SetWeightScalar( similarityMetricScalarWeight ); std::cout << " similarity metric weight: " << similarityMetricScalarWeight << std::endl; typename SimilarityMetricType::RadiusType radius; radius.Fill( 0 ); if ( option->GetNumberOfParameters( i ) > parameterCount ) { std::vector rad = this->m_Parser->template ConvertVector( option->GetParameter( i, parameterCount ) ); if( rad.size() == 1 ) { radius.Fill( rad[0] ); } else if( rad.size() == TDimension ) { for( unsigned int n = 0; n < TDimension; n++ ) { radius[n] = rad[n]; } } else { std::cerr << "Badly formed radius specification" << std::endl; exit( 0 ); } parameterCount++; } std::cout << " Radius: " << radius << std::endl; float extraparam= -1.e12; if ( option->GetNumberOfParameters( i ) > parameterCount ) { extraparam=this->m_Parser->template Convert( option->GetParameter( i, parameterCount ) ); std::cout <<" Setting Extra Param to : " << extraparam << " often used as a robustness parameter for longitudinal studies " << std::endl; parameterCount++; } std::cout << " Radius: " << radius << std::endl; unsigned int numberOfHistogramBins = 64; if ( Dimension == 2 ) { numberOfHistogramBins = 32; } if ( whichMetric == "mean-squares" || whichMetric == "MeanSquares" || whichMetric == "MSQ" ) { typedef itk::HistogramMatchingImageFilter FilterType; typename FilterType::Pointer filter = FilterType::New(); filter->SetInput(movingImage ); filter->SetReferenceImage(fixedImage); filter->SetNumberOfHistogramLevels( 256 ); filter->SetNumberOfMatchPoints( 12 ); filter->ThresholdAtMeanIntensityOn(); // filter->ThresholdAtMeanIntensityOff(); if (useHistMatch){ filter->Update(); std::cout << " use Histogram Matching " << std::endl; movingImage=filter->GetOutput(); movingImage = this->PreprocessImage(movingImage); similarityMetric->SetMovingImage( movingImage );} typedef SyNDemonsRegistrationFunction MetricType; typename MetricType::Pointer metric = MetricType::New(); if ( radius[0] > 0) metric->SetUseMovingImageGradient( true ); metric->SetIntensityDifferenceThreshold( extraparam ); metric->SetRobustnessParameter( extraparam ); // metric->SetRobust( true ); // metric->SetSymmetric( false ); /// metric->SetNormalizeGradient( false ); metric->SetRadius( radius ); similarityMetric->SetMetric( metric ); similarityMetric->SetMaximizeMetric( true ); this->m_SimilarityMetrics.push_back( similarityMetric ); } else if ( whichMetric == "mutual-information" || whichMetric == "MutualInformation" || whichMetric == "MI" ) { typedef itk::AvantsMutualInformationRegistrationFunction MetricType; typename MetricType::Pointer metric = MetricType::New(); metric->SetNumberOfHistogramBins( numberOfHistogramBins ); metric->SetNormalizeGradient( false ); metric->SetRobustnessParameter( extraparam ); unsigned int histbins=radius[0]; if (histbins < 8) histbins=8; metric->SetNumberOfHistogramBins(histbins); radius.Fill(0); metric->SetRadius( radius ); similarityMetric->SetMetric( metric ); similarityMetric->SetMaximizeMetric( true ); this->m_SimilarityMetrics.push_back( similarityMetric ); } else if ( whichMetric == "spatial-mutual-information" || whichMetric == "SpatialMutualInformation" || whichMetric == "SMI" ) { typedef itk::SpatialMutualInformationRegistrationFunction MetricType; typename MetricType::Pointer metric = MetricType::New(); metric->SetNumberOfHistogramBins( numberOfHistogramBins ); metric->SetNormalizeGradient( false ); metric->SetRobustnessParameter( extraparam ); unsigned int histbins=radius[0]; if (histbins < 8) histbins=8; metric->SetNumberOfHistogramBins(histbins); radius.Fill(0); metric->SetRadius( radius ); similarityMetric->SetMetric( metric ); similarityMetric->SetMaximizeMetric( true ); this->m_SimilarityMetrics.push_back( similarityMetric ); } else if ( whichMetric == "cross-correlation" || whichMetric == "CrossCorrelation" || whichMetric == "CC" ) { typedef itk::HistogramMatchingImageFilter FilterType; typename FilterType::Pointer filter = FilterType::New(); filter->SetInput(movingImage ); filter->SetReferenceImage(fixedImage); filter->SetNumberOfHistogramLevels( 256 ); filter->SetNumberOfMatchPoints( 12 ); filter->ThresholdAtMeanIntensityOn(); // filter->ThresholdAtMeanIntensityOff(); if (useHistMatch){ filter->Update(); std::cout << " use Histogram Matching " << std::endl; movingImage=filter->GetOutput(); movingImage = this->PreprocessImage(movingImage); similarityMetric->SetMovingImage( movingImage );} typedef itk::CrossCorrelationRegistrationFunction MetricType; typename MetricType::Pointer metric = MetricType::New(); metric->SetNormalizeGradient( false ); metric->SetRadius( radius ); metric->SetRobustnessParameter( extraparam ); similarityMetric->SetMetric( metric ); similarityMetric->SetMaximizeMetric( true ); this->m_SimilarityMetrics.push_back( similarityMetric ); } else if ( whichMetric == "probabilistic" || whichMetric == "Probabilistic" || whichMetric == "PR" ) { typedef itk::HistogramMatchingImageFilter FilterType; typename FilterType::Pointer filter = FilterType::New(); filter->SetInput(movingImage ); filter->SetReferenceImage(fixedImage); filter->SetNumberOfHistogramLevels( 256 ); filter->SetNumberOfMatchPoints( 12 ); filter->ThresholdAtMeanIntensityOn(); // filter->ThresholdAtMeanIntensityOff(); if (useHistMatch){ filter->Update(); std::cout << " use Histogram Matching " << std::endl; movingImage=filter->GetOutput(); movingImage = this->PreprocessImage(movingImage); similarityMetric->SetMovingImage( movingImage );} typedef itk::ProbabilisticRegistrationFunction MetricType; typename MetricType::Pointer metric = MetricType::New(); metric->SetNormalizeGradient( false ); metric->SetRadius( radius ); metric->SetRobustnessParameter( extraparam ); similarityMetric->SetMetric( metric ); similarityMetric->SetMaximizeMetric( true ); this->m_SimilarityMetrics.push_back( similarityMetric ); } else { itkWarningMacro( "Could not decipher image metric choice: " << whichMetric ); } } } } else { itkExceptionMacro( "No image metric specified on the command line." ); } } template void PICSLAdvancedNormalizationToolKit ::InitializeCommandLineOptions() { this->m_Parser = ParserType::New(); // this->m_Parser->SetCommandDescription( " PICSL Advanced Image Normalization Toolkit" ); if (false) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "weight-image" ); option->SetShortName( 'w' ); option->SetDescription( "blah-blah" ); this->m_Parser->AddOption( option ); } if (true) { std::string description = std::string( "this mask -- defined in the 'fixed' image space defines " ) + std::string( "the region of interest over which the registration is " ) + std::string( "computed ==> above 0.1 means inside mask ==> continuous " ) + std::string( "values in range [0.1,1.0] effect optimization like a " ) + std::string( "probability. ==> values > 1 are treated as = 1.0 " ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "mask-image" ); option->SetShortName( 'x' ); // option->SetDescription( "this mask -- defined in the 'fixed' image space defines the region of interest over which the registration is computed ==> above 0.1 means inside mask \n\t\t==> continuous values in range [0.1 , 1.0] effect optimization like a probability. \n\t\t==> values > 1 are treated as = 1.0 " ); option->SetDescription( description ); option->SetUsageOption( 0, "maskFileName" ); this->m_Parser->AddOption( option ); } if (false) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "mask-threshold" ); option->SetShortName( 'M' ); option->SetDescription( "blah-blah" ); this->m_Parser->AddOption( option ); } if (true) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "image-metric" ); option->SetShortName( 'm' ); std::string newLineTabs( "\n\t\t" ); std::string intensityBasedDescription( "Intensity-Based Metrics: " ); std::string intensityBasedOptions( "[fixedImage,movingImage,weight,radius/OrForMI-#histogramBins]" ); std::string ccDescription( "CC/cross-correlation/CrossCorrelation" ); std::string miDescription( "MI/mutual-information/MutualInformation" ); std::string smiDescription( "SMI/spatial-mutual-information/SpatialMutualInformation" ); std::string prDescription( "PR/probabilistic/Probabilistic" ); std::string msqDescription( "MSQ/mean-squares/MeanSquares -- radius > 0 uses moving image gradient in metric deriv." ); intensityBasedDescription += ( newLineTabs + ccDescription + intensityBasedOptions + newLineTabs + miDescription + intensityBasedOptions + newLineTabs + smiDescription + intensityBasedOptions + newLineTabs + prDescription + intensityBasedOptions + newLineTabs + msqDescription + intensityBasedOptions ); std::string pointBasedDescription( "\n\t Point-Set-Based Metrics: " ); std::string pointBasedOptions = std::string( "[fixedImage,movingImage,fixedPoints,movingPoints" ) + std::string( ",weight,pointSetPercentage,pointSetSigma,boundaryPointsOnly" ) + std::string( ",kNeighborhood" ); std::string pseDescription( "PSE/point-set-expectation/PointSetExpectation" ); std::string pseOptions(", PartialMatchingIterations=100000] \n the partial matching option assumes the complete labeling is in the first set of label parameters ... more iterations leads to more symmetry in the matching - 0 iterations means full asymmetry " ); std::string jtbDescription( "JTB/jensen-tsallis-bspline/JensenTsallisBSpline" ); std::string jtbOptions = std::string( ",alpha,meshResolution,splineOrder,numberOfLevels" ) + std::string( ",useAnisotropicCovariances]" ); pointBasedDescription += ( newLineTabs + pseDescription + pointBasedOptions + pseOptions + newLineTabs + jtbDescription + pointBasedOptions + jtbOptions ); std::string description = intensityBasedDescription + pointBasedDescription; option->SetDescription( description ); this->m_Parser->AddOption( option ); } if (true) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "output-naming" ); option->SetShortName( 'o' ); option->SetDescription( "The name for the output - a prefix or a name+type : e.g. -o OUT or -o OUT.nii or -o OUT.mha " ); this->m_Parser->AddOption( option ); } if (true) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "roi" ); option->SetLongName( "R" ); option->SetDescription( "TODO/FIXME: the --R sets an ROI option -- it passes a vector of parameters that sets the center and bounding box \n of the region of interest for a sub-field registration. e.g. in 3D the option setting \n -r 10x12x15x50x50x25 \n sets up a bounding box of size 50,50,25 with origin at 10,12,15 in voxel (should this be physical?) coordinates. " ); std::string roidefault=std::string("0"); /** set up a default parameter */ option->AddValue(roidefault); this->m_Parser->AddOption( option ); } if (false) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "number-of-levels" ); option->SetShortName( 'n' ); option->SetDescription( "number of levels in multi-resolution optimization -- an integer :: 3 is a common choice " ); std::string nlevdefault=std::string("3"); /** set up a default parameter */ option->AddValue(nlevdefault); this->m_Parser->AddOption( option ); } if (true) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "number-of-iterations" ); option->SetShortName( 'i' ); option->SetDescription( "number of iterations per level -- a 'vector' e.g. : 100x100x20 " ); std::string nitdefault=std::string("10x10x5"); /** set up a default parameter */ option->AddValue(nitdefault); this->m_Parser->AddOption( option ); } if (true) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "Restrict-Deformation" ); option->SetDescription( "restrict the gradient that drives the deformation by scalar factors along specified dimensions -- a float 'vector' of length ImageDimension to multiply against the similarity metric's gradient values --- e.g. in 3D : 0.1x1x0 --- will set the z gradient to zero and scale the x gradient by 0.1 and y by 1 (no change). Thus, you get a 2.5-Dimensional registration as there is still 3D continuity in the mapping. " ); std::string nitdefault; if (TDimension == 2) nitdefault=std::string("1x1"); if (TDimension == 3) nitdefault=std::string("1x1x1"); /** set up a default parameter */ option->AddValue(nitdefault); this->m_Parser->AddOption( option ); } if (false) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "number-to-interpolate" ); option->SetShortName( 'b' ); option->SetDescription( "blah-blah" ); this->m_Parser->AddOption( option ); } if (false) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "use-converge-criteria" ); option->SetShortName( 'a' ); option->SetDescription( "blah-blah" ); this->m_Parser->AddOption( option ); } if (false) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "thickness-limit" ); option->SetShortName( 'T' ); option->SetDescription( "blah-blah" ); this->m_Parser->AddOption( option ); } if (false) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "prior-image-list" ); option->SetShortName( 'P' ); option->SetDescription( "blah-blah" ); this->m_Parser->AddOption( option ); } if (false) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "use-laplacian" ); option->SetShortName( 'i' ); option->SetDescription( "blah-blah" ); this->m_Parser->AddOption( option ); } if (false) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "use-histogram-matching" ); option->SetShortName( 'e' ); option->SetDescription( "blah-blah" ); this->m_Parser->AddOption( option ); } if (true) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "verbose" ); option->SetShortName( 'v' ); option->SetDescription( " verbose output " ); this->m_Parser->AddOption( option ); } if( true ) { std::string description = std::string( "Print the help menu (short version)." ); OptionType::Pointer option = OptionType::New(); option->SetShortName( 'h' ); option->SetDescription( description ); std::string zero = std::string( "0" ); option->AddValue( zero ); this->m_Parser->AddOption( option ); } if( true ) { std::string description = std::string( "Print the help menu." ); OptionType::Pointer option = OptionType::New(); option->SetLongName( "help" ); option->SetDescription( description ); std::string zero = std::string( "0" ); option->AddValue( zero ); this->m_Parser->AddOption( option ); } if (true) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "transformation-model" ); option->SetShortName( 't' ); option->SetDescription( "TRANSFORMATION[gradient-step-length,number-of-time-steps,DeltaTime,symmetry-type].\n\t Choose one of the following TRANSFORMATIONS:\n\t\tDiff = diffeomorphic\n\t\tElast = Elastic\n\t\tExp = exponential diff\n\t\t Greedy Exp = greedy exponential diff, like diffeomorphic demons. same parameters. \n\t\tSyN -- symmetric normalization \n \n DeltaTime is the integration time-discretization step - sub-voxel - n-time steps currently fixed at 2 " ); std::string nitdefault=std::string("SyN[0.5]"); /** set up a default parameter */ option->AddValue(nitdefault); this->m_Parser->AddOption( option ); } // if (true) // { // OptionType::Pointer option = OptionType::New(); // option->SetLongName( "def-field-sigma" ); // option->SetShortName( 's' ); // option->SetDescription( "smoothing of deformation field " ); // // this->m_Parser->AddOption( option ); // } // // if (true) // { // OptionType::Pointer option = OptionType::New(); // option->SetLongName( "gradient-field-sigma" ); // option->SetShortName( 'g' ); // option->SetDescription( "this smooths the gradient update field" ); // option->AddValue("0.0"); // this->m_Parser->AddOption( option ); // } // // if (true) // { // OptionType::Pointer option = OptionType::New(); // option->SetLongName( "gradient-step-length" ); // option->SetShortName( 'l' ); // option->SetDescription( "gradient descent parameter - a float :: e.g. 1.0 " ); // option->AddValue("1.0"); // this->m_Parser->AddOption( option ); // } if (true) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "regularization" ); option->SetShortName( 'r' ); option->SetDescription( "REGULARIZATION[gradient-field-sigma,def-field-sigma,truncation].\n\t Choose one of the following REGULARIZATIONS:\n\t\tGauss = gaussian\n\t\tDMFFD = directly manipulated free form deformation" ); std::string nitdefault=std::string("Gauss[3,0.5]"); /** set up a default parameter */ option->AddValue(nitdefault); this->m_Parser->AddOption( option ); } //added by songgang if (true) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "initial-affine" ); option->SetShortName( 'a' ); option->SetDescription( "use the input file as the initial affine parameter" ); this->m_Parser->AddOption( option ); } if (true) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "fixed-image-initial-affine" ); option->SetShortName( 'F' ); option->SetDescription( "use the input file as the initial affine parameter for the fixed image " ); this->m_Parser->AddOption( option ); } if (true) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "geodesic" ); option->SetShortName( 'T' ); option->SetDescription( " = 0 / 1 / 2, 0 = not time-dependent, 1 = asymmetric , 2 = symmetric "); // compute the Euclidean length of the diffeomorphism and write to an image -- OutputNamethick.nii.gz -- This is a Beta version of thickness computation -- Not Full-Blown DiReCT , Das, 2009, Neuroimage --- syn with time is the only model that can be used with this option " ); this->m_Parser->AddOption( option ); } if (true) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "go-faster" ); option->SetShortName( 'G' ); option->SetDescription( " true / false -- if true, SyN is faster but loses some accuracy wrt inverse-identity constraint, see Avants MIA 2008."); std::string nitdefault=std::string("false"); option->AddValue(nitdefault); this->m_Parser->AddOption( option ); } //added by songgang if (true){ OptionType::Pointer option = OptionType::New(); option->SetLongName( "continue-affine"); option->SetDescription( "true (default) | false, do (not) perform affine given the initial affine parameters"); std::string nitdefault=std::string("true"); option->AddValue(nitdefault); this->m_Parser->AddOption( option ); } //added by songgang if (true) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "number-of-affine-iterations" ); option->SetDescription( "number of iterations per level -- a 'vector' e.g. : 100x100x20 " ); std::string nitdefault=std::string("10000x10000x10000"); option->AddValue(nitdefault); this->m_Parser->AddOption( option ); } if (true) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "use-NN" ); option->SetDescription( "use nearest neighbor interpolation " ); std::string nitdefault=std::string("0"); option->AddValue(nitdefault); this->m_Parser->AddOption( option ); } if (true) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "use-Histogram-Matching" ); option->SetDescription( "use histogram matching of moving to fixed image " ); std::string nitdefault=std::string("0"); option->AddValue(nitdefault); this->m_Parser->AddOption( option ); } if (true) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "affine-metric-type" ); option->SetDescription( "MI: mutual information (default), MSQ: mean square error, CC: Normalized correlation, CCH: Histogram-based correlation coefficient (not recommended), GD: gradient difference (not recommended) " ); std::string nitdefault=std::string("MI"); option->AddValue(nitdefault); this->m_Parser->AddOption( option ); } if (true) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "MI-option" ); option->SetDescription( "option of mutual information: MI_bins x MI_samples (default: 32x32000)" ); switch(TDimension){ case 2: option->AddValue(std::string("32x5000")); break; case 3: option->AddValue(std::string("32x32000")); break; } //std::string nitdefault=std::string("32x8000"); // option->AddValue(nitdefault); this->m_Parser->AddOption( option ); } if (true) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "rigid-affine" ); option->SetDescription( "use rigid transformation : true / false(default)" ); std::string nitdefault=std::string("false"); option->AddValue(nitdefault); this->m_Parser->AddOption( option ); } if (true) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "do-rigid" ); option->SetDescription( "use rigid transformation : true / false(default)" ); std::string nitdefault=std::string("false"); option->AddValue(nitdefault); this->m_Parser->AddOption( option ); } if (true) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "affine-gradient-descent-option" ); option->SetDescription( "option of gradient descent in affine transformation: maximum_step_length x relaxation_factor x minimum_step_length x translation_scales "); std::string nitdefault=std::string("0.1x0.5x1.e-4x1.e-4"); option->AddValue(nitdefault); this->m_Parser->AddOption( option ); } if (true) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "use-rotation-header" ); option->SetDescription( "use rotation matrix in image headers: true (default) / false" ); std::string nitdefault=std::string("false"); option->AddValue(nitdefault); this->m_Parser->AddOption( option ); } if (true) { OptionType::Pointer option = OptionType::New(); option->SetLongName( "ignore-void-origin" ); option->SetDescription( "ignore the apparently unmatched origins (when use-rotation-header is false and the rotation matrix is identity: true (default) / false" ); std::string nitdefault=std::string("false"); option->AddValue(nitdefault); this->m_Parser->AddOption( option ); } } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkProbabilisticRegistrationFunction.cxx000066400000000000000000000670621147325206600302330ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkProbabilisticRegistrationFunction.cxx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkProbabilisticRegistrationFunction_txx_ #define _itkProbabilisticRegistrationFunction_txx_ #include "itkProbabilisticRegistrationFunction.h" #include "itkExceptionObject.h" #include "vnl/vnl_math.h" #include "itkImageFileWriter.h" #include "itkDiscreteGaussianImageFilter.h" #include "itkMeanImageFilter.h" #include "itkMedianImageFilter.h" #include "itkImageFileWriter.h" #include namespace itk { /* * Default constructor */ template ProbabilisticRegistrationFunction ::ProbabilisticRegistrationFunction() { m_AvgMag=0; m_Iteration=0; RadiusType r; unsigned int j; for( j = 0; j < ImageDimension; j++ ) { r[j] = 2; } this->SetRadius(r); this->m_Energy = 0.0; m_TimeStep = 1.0; m_DenominatorThreshold = 1e-9; m_IntensityDifferenceThreshold = 0.001; Superclass::m_MovingImage = NULL; m_MetricGradientImage = NULL; Superclass::m_FixedImage = NULL; m_FixedImageSpacing.Fill( 1.0 ); m_FixedImageOrigin.Fill( 0.0 ); m_FixedImageGradientCalculator = GradientCalculatorType::New(); binaryimage=NULL; m_FullyRobust=false; m_MovingImageGradientCalculator = GradientCalculatorType::New(); typename DefaultInterpolatorType::Pointer interp = DefaultInterpolatorType::New(); m_MovingImageInterpolator = static_cast( interp.GetPointer() ); for (int i=0; i<5; i++) finitediffimages[i]=NULL; m_NumberOfHistogramBins=32; m_FixedImageMask=NULL; m_MovingImageMask=NULL; } /* * Standard "PrintSelf" method. */ template void ProbabilisticRegistrationFunction ::PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf(os, indent); /* os << indent << "MovingImageIterpolator: "; os << m_MovingImageInterpolator.GetPointer() << std::endl; os << indent << "FixedImageGradientCalculator: "; os << m_FixedImageGradientCalculator.GetPointer() << std::endl; os << indent << "DenominatorThreshold: "; os << m_DenominatorThreshold << std::endl; os << indent << "IntensityDifferenceThreshold: "; os << m_IntensityDifferenceThreshold << std::endl; */ } /* * Set the function state values before each iteration */ template void ProbabilisticRegistrationFunction ::InitializeIteration() { typedef ImageRegionIteratorWithIndex ittype; if( !Superclass::m_MovingImage || !Superclass::m_FixedImage || !m_MovingImageInterpolator ) { itkExceptionMacro( << "MovingImage, FixedImage and/or Interpolator not set" ); throw ExceptionObject(__FILE__,__LINE__); } // cache fixed image information m_FixedImageSpacing = Superclass::m_FixedImage->GetSpacing(); m_FixedImageOrigin = Superclass::m_FixedImage->GetOrigin(); // setup gradient calculator m_FixedImageGradientCalculator->SetInputImage( Superclass::m_FixedImage ); m_MovingImageGradientCalculator->SetInputImage( Superclass::m_MovingImage ); // setup moving image interpolator m_MovingImageInterpolator->SetInputImage( Superclass::m_MovingImage ); unsigned long numpix=1; for (int i=0; iGetLargestPossibleRegion().GetSize()[i]; m_MetricTotal=0.0; this->m_Energy=0.0; typedef itk::DiscreteGaussianImageFilter dgf1; // typedef itk::DiscreteGaussianImageFilter dgf; typedef itk::MeanImageFilter dgf; typedef itk::MedianImageFilter dgf2; // compute the normalizer m_Normalizer = 0.0; for( unsigned int k = 0; k < ImageDimension; k++ ) { m_Normalizer += m_FixedImageSpacing[k] * m_FixedImageSpacing[k]; } m_Normalizer /= static_cast( ImageDimension ); typename FixedImageType::SpacingType spacing=this->GetFixedImage()->GetSpacing(); bool makeimg=false; if ( m_Iteration==0 ) makeimg=true; else if (!finitediffimages[0] ) makeimg=true; else { for (unsigned int dd=0; ddGetLargestPossibleRegion().GetSize()[dd] != this->GetFixedImage()->GetLargestPossibleRegion().GetSize()[dd] ) makeimg=true; } } if (makeimg) { finitediffimages[0]=this->MakeImage(); finitediffimages[1]=this->MakeImage(); finitediffimages[2]=this->MakeImage(); finitediffimages[3]=this->MakeImage(); finitediffimages[4]=this->MakeImage(); } //float sig=15.; RadiusType r; for( int j = 0; j < ImageDimension; j++ ) r[j] = this->GetRadius()[j]; typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator tIter(this->GetFixedImage(),this->GetFixedImage()->GetLargestPossibleRegion() ); typename FixedImageType::SizeType imagesize=this->GetFixedImage()->GetLargestPossibleRegion().GetSize(); // compute local means // typedef itk::ImageRegionIteratorWithIndex Iterator; // // The following change was made to speed up the correlation calculation. // // first round { typedef std::deque SumQueueType; SumQueueType Qsuma2; SumQueueType Qsumb2; SumQueueType Qsuma; SumQueueType Qsumb; SumQueueType Qsumab; SumQueueType Qcount; ImageLinearConstIteratorWithIndex outIter( this->finitediffimages[0], this->finitediffimages[0]->GetLargestPossibleRegion() ); outIter.SetDirection( 0 ); outIter.GoToBegin(); while( !outIter.IsAtEnd() ) { // Push the zeros onto the stack that are outsized the image boundary at // the beginning of the line. Qsuma2 = SumQueueType( r[0], 0.0 ); Qsumb2 = SumQueueType( r[0], 0.0 ); Qsuma = SumQueueType( r[0], 0.0 ); Qsumb = SumQueueType( r[0], 0.0 ); Qsumab = SumQueueType( r[0], 0.0 ); Qcount = SumQueueType( r[0], 0.0 ); NeighborhoodIterator hoodIt( this->GetRadius(), this->finitediffimages[0], this->finitediffimages[0]->GetLargestPossibleRegion() ); IndexType oindex = outIter.GetIndex(); hoodIt.SetLocation( oindex ); unsigned int hoodlen = hoodIt.Size(); // Now add the rest of the values from each hyperplane for( unsigned int i = r[0]; i < ( 2*r[0] + 1 ); i++ ) { float suma2 = 0.0; float sumb2 = 0.0; float suma = 0.0; float sumb = 0.0; float sumab = 0.0; float count = 0.0; for( unsigned int indct = i; indct < hoodlen; indct += ( 2*r[0] + 1 ) ) { bool isInBounds = true; hoodIt.GetPixel( indct, isInBounds ); IndexType index = hoodIt.GetIndex( indct ); if ( !isInBounds || ( this->m_FixedImageMask && this->m_FixedImageMask->GetPixel( index ) < 0.25 ) ) { continue; } float a = this->GetFixedImage()->GetPixel( index ); float b = this->GetMovingImage()->GetPixel( index ); suma2 += a*a; sumb2 += b*b; suma += a; sumb += b; sumab += a*b; count += 1.0; } Qsuma2.push_back( suma2 ); Qsumb2.push_back( sumb2 ); Qsuma.push_back( suma ); Qsumb.push_back( sumb ); Qsumab.push_back( sumab ); Qcount.push_back( count ); } while( !outIter.IsAtEndOfLine() ) { // Test to see if there are any voxels we need to handle in the current // window. float suma2 = 0.0; float sumb2 = 0.0; float suma = 0.0; float sumb = 0.0; float sumab = 0.0; float count = 0.0; typename SumQueueType::iterator itcount = Qcount.begin(); while( itcount != Qcount.end() ) { count += *itcount; ++itcount; } // If there are values, we need to calculate the different quantities if( count > 0 ) { typename SumQueueType::iterator ita2 = Qsuma2.begin(); typename SumQueueType::iterator itb2 = Qsumb2.begin(); typename SumQueueType::iterator ita = Qsuma.begin(); typename SumQueueType::iterator itb = Qsumb.begin(); typename SumQueueType::iterator itab = Qsumab.begin(); while( ita2 != Qsuma2.end() ) { suma2 += *ita2; sumb2 += *itb2; suma += *ita; sumb += *itb; sumab += *itab; ++ita2; ++itb2; ++ita; ++itb; ++itab; } float fixedMean = suma / count; float movingMean = sumb / count; float sff = suma2 - fixedMean*suma - fixedMean*suma + count*fixedMean*fixedMean; float smm = sumb2 - movingMean*sumb - movingMean*sumb + count*movingMean*movingMean; float sfm = sumab - movingMean*suma - fixedMean*sumb + count*movingMean*fixedMean; IndexType oindex = outIter.GetIndex(); float val = this->GetFixedImage()->GetPixel( oindex ) - fixedMean; this->finitediffimages[0]->SetPixel( oindex, val ); val = this->GetMovingImage()->GetPixel( oindex ) - movingMean; this->finitediffimages[1]->SetPixel( oindex, val ); this->finitediffimages[2]->SetPixel( oindex, sfm );//A this->finitediffimages[3]->SetPixel( oindex, sff );//B this->finitediffimages[4]->SetPixel( oindex, smm );//C } // Increment the iterator and check to see if we're at the end of the // line. If so, go to the next line. Otherwise, add the // the values for the next hyperplane. ++outIter; if( !outIter.IsAtEndOfLine() ) { hoodIt.SetLocation( outIter.GetIndex() ); suma2 = 0.0; sumb2 = 0.0; suma = 0.0; sumb = 0.0; sumab = 0.0; count = 0.0; for( unsigned int indct = 2*r[0]; indct < hoodlen; indct += ( 2*r[0] + 1 ) ) { bool isInBounds = true; hoodIt.GetPixel( indct, isInBounds ); IndexType index = hoodIt.GetIndex( indct ); if ( !isInBounds || ( this->m_FixedImageMask && this->m_FixedImageMask->GetPixel( index ) < 0.25 ) ) { continue; } float a = this->GetFixedImage()->GetPixel( index ); float b = this->GetMovingImage()->GetPixel( index ); suma2 += a*a; sumb2 += b*b; suma += a; sumb += b; sumab += a*b; count += 1.0; } Qsuma2.push_back( suma2 ); Qsumb2.push_back( sumb2 ); Qsuma.push_back( suma ); Qsumb.push_back( sumb ); Qsumab.push_back( sumab ); Qcount.push_back( count ); } Qsuma2.pop_front(); Qsumb2.pop_front(); Qsuma.pop_front(); Qsumb.pop_front(); Qsumab.pop_front(); Qcount.pop_front(); } outIter.NextLine(); } } // second round { typedef std::deque SumQueueType; SumQueueType Qsuma2; SumQueueType Qsumb2; SumQueueType Qsuma; SumQueueType Qsumb; SumQueueType Qsumab; SumQueueType Qcount; ImageLinearConstIteratorWithIndex outIter( this->finitediffimages[0], this->finitediffimages[0]->GetLargestPossibleRegion() ); outIter.SetDirection( 0 ); outIter.GoToBegin(); while( !outIter.IsAtEnd() ) { // Push the zeros onto the stack that are outsized the image boundary at // the beginning of the line. Qsuma2 = SumQueueType( r[0], 0.0 ); Qsumb2 = SumQueueType( r[0], 0.0 ); Qsuma = SumQueueType( r[0], 0.0 ); Qsumb = SumQueueType( r[0], 0.0 ); Qsumab = SumQueueType( r[0], 0.0 ); Qcount = SumQueueType( r[0], 0.0 ); NeighborhoodIterator hoodIt( this->GetRadius(), this->finitediffimages[0], this->finitediffimages[0]->GetLargestPossibleRegion() ); IndexType oindex = outIter.GetIndex(); hoodIt.SetLocation( oindex ); unsigned int hoodlen = hoodIt.Size(); // Now add the rest of the values from each hyperplane for( unsigned int i = r[0]; i < ( 2*r[0] + 1 ); i++ ) { float suma2 = 0.0; float sumb2 = 0.0; float suma = 0.0; float sumb = 0.0; float sumab = 0.0; float count = 0.0; for( unsigned int indct = i; indct < hoodlen; indct += ( 2*r[0] + 1 ) ) { bool isInBounds = true; hoodIt.GetPixel( indct, isInBounds ); IndexType index = hoodIt.GetIndex( indct ); if ( !isInBounds || ( this->m_FixedImageMask && this->m_FixedImageMask->GetPixel( index ) < 0.25 ) ) { continue; } float a = this->finitediffimages[0]->GetPixel( index ); float b = this->finitediffimages[1]->GetPixel( index ); suma2 += a*a; sumb2 += b*b; suma += a; sumb += b; sumab += a*b; count += 1.0; } Qsuma2.push_back( suma2 ); Qsumb2.push_back( sumb2 ); Qsuma.push_back( suma ); Qsumb.push_back( sumb ); Qsumab.push_back( sumab ); Qcount.push_back( count ); } while( !outIter.IsAtEndOfLine() ) { // Test to see if there are any voxels we need to handle in the current // window. float suma2 = 0.0; float sumb2 = 0.0; float suma = 0.0; float sumb = 0.0; float sumab = 0.0; float count = 0.0; typename SumQueueType::iterator itcount = Qcount.begin(); while( itcount != Qcount.end() ) { count += *itcount; ++itcount; } // If there are values, we need to calculate the different quantities if( count > 0 ) { typename SumQueueType::iterator ita2 = Qsuma2.begin(); typename SumQueueType::iterator itb2 = Qsumb2.begin(); typename SumQueueType::iterator ita = Qsuma.begin(); typename SumQueueType::iterator itb = Qsumb.begin(); typename SumQueueType::iterator itab = Qsumab.begin(); while( ita2 != Qsuma2.end() ) { suma2 += *ita2; sumb2 += *itb2; suma += *ita; sumb += *itb; sumab += *itab; ++ita2; ++itb2; ++ita; ++itb; ++itab; } float fixedMean = suma / count; float movingMean = sumb / count; float sff = suma2 - fixedMean*suma - fixedMean*suma + count*fixedMean*fixedMean; float smm = sumb2 - movingMean*sumb - movingMean*sumb + count*movingMean*movingMean; float sfm = sumab - movingMean*suma - fixedMean*sumb + count*movingMean*fixedMean; IndexType oindex = outIter.GetIndex(); // float val = this->GetFixedImage()->GetPixel( oindex ) - fixedMean; // this->finitediffimages[0]->SetPixel( oindex, val ); // val = this->GetMovingImage()->GetPixel( oindex ) - movingMean; // this->finitediffimages[1]->SetPixel( oindex, val ); this->finitediffimages[2]->SetPixel( oindex, sfm );//A this->finitediffimages[3]->SetPixel( oindex, sff );//B this->finitediffimages[4]->SetPixel( oindex, smm );//C } // Increment the iterator and check to see if we're at the end of the // line. If so, go to the next line. Otherwise, add the // the values for the next hyperplane. ++outIter; if( !outIter.IsAtEndOfLine() ) { hoodIt.SetLocation( outIter.GetIndex() ); suma2 = 0.0; sumb2 = 0.0; suma = 0.0; sumb = 0.0; sumab = 0.0; count = 0.0; for( unsigned int indct = 2*r[0]; indct < hoodlen; indct += ( 2*r[0] + 1 ) ) { bool isInBounds = true; hoodIt.GetPixel( indct, isInBounds ); IndexType index = hoodIt.GetIndex( indct ); if ( !isInBounds || ( this->m_FixedImageMask && this->m_FixedImageMask->GetPixel( index ) < 0.25 ) ) { continue; } float a = this->finitediffimages[0]->GetPixel( index ); float b = this->finitediffimages[1]->GetPixel( index ); suma2 += a*a; sumb2 += b*b; suma += a; sumb += b; sumab += a*b; count += 1.0; } Qsuma2.push_back( suma2 ); Qsumb2.push_back( sumb2 ); Qsuma.push_back( suma ); Qsumb.push_back( sumb ); Qsumab.push_back( sumab ); Qcount.push_back( count ); } Qsuma2.pop_front(); Qsumb2.pop_front(); Qsuma.pop_front(); Qsumb.pop_front(); Qsumab.pop_front(); Qcount.pop_front(); } outIter.NextLine(); } } m_MaxMag=0.0; m_MinMag=9.e9; m_AvgMag=0.0; m_Iteration++; } /* * Set the function state values before each iteration */ template void ProbabilisticRegistrationFunction ::InitializeIterationOld() { typedef ImageRegionIteratorWithIndex ittype; if( !Superclass::m_MovingImage || !Superclass::m_FixedImage || !m_MovingImageInterpolator ) { itkExceptionMacro( << "MovingImage, FixedImage and/or Interpolator not set" ); throw ExceptionObject(__FILE__,__LINE__); } // cache fixed image information m_FixedImageSpacing = Superclass::m_FixedImage->GetSpacing(); m_FixedImageOrigin = Superclass::m_FixedImage->GetOrigin(); // setup gradient calculator m_FixedImageGradientCalculator->SetInputImage( Superclass::m_FixedImage ); m_MovingImageGradientCalculator->SetInputImage( Superclass::m_MovingImage ); // setup moving image interpolator m_MovingImageInterpolator->SetInputImage( Superclass::m_MovingImage ); unsigned long numpix=1; for (int i=0; iGetLargestPossibleRegion().GetSize()[i]; m_MetricTotal=0.0; this->m_Energy=0.0; typedef itk::DiscreteGaussianImageFilter dgf1; // typedef itk::DiscreteGaussianImageFilter dgf; typedef itk::MeanImageFilter dgf; typedef itk::MedianImageFilter dgf2; // compute the normalizer m_Normalizer = 0.0; for( unsigned int k = 0; k < ImageDimension; k++ ) { m_Normalizer += m_FixedImageSpacing[k] * m_FixedImageSpacing[k]; } m_Normalizer /= static_cast( ImageDimension ); typename FixedImageType::SpacingType spacing=this->GetFixedImage()->GetSpacing(); bool makeimg=false; if ( m_Iteration==0 ) makeimg=true; else if (!finitediffimages[0] ) makeimg=true; else { for (unsigned int dd=0; ddGetLargestPossibleRegion().GetSize()[dd] != this->GetFixedImage()->GetLargestPossibleRegion().GetSize()[dd] ) makeimg=true; } } if (makeimg) { finitediffimages[0]=this->MakeImage(); finitediffimages[1]=this->MakeImage(); finitediffimages[2]=this->MakeImage(); finitediffimages[3]=this->MakeImage(); finitediffimages[4]=this->MakeImage(); } //float sig=15.; RadiusType r; for( int j = 0; j < ImageDimension; j++ ) r[j] = this->GetRadius()[j]; typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator tIter(this->GetFixedImage(),this->GetFixedImage()->GetLargestPossibleRegion() ); typename FixedImageType::SizeType imagesize=this->GetFixedImage()->GetLargestPossibleRegion().GetSize(); // compute local means // typedef itk::ImageRegionIteratorWithIndex Iterator; Iterator outIter(this->finitediffimages[0],this->finitediffimages[0]->GetLargestPossibleRegion() ); for( outIter.GoToBegin(); !outIter.IsAtEnd(); ++outIter ) { bool takesample = true; if (this->m_FixedImageMask) if (this->m_FixedImageMask->GetPixel( outIter.GetIndex() ) < 0.25 ) takesample=false; if (takesample) { NeighborhoodIterator hoodIt( this->GetRadius() ,this->finitediffimages[0] , this->finitediffimages[0]->GetLargestPossibleRegion()); IndexType oindex = outIter.GetIndex(); hoodIt.SetLocation(oindex); double fixedMean=0; double movingMean=0; PointType mappedPoint; unsigned int indct; unsigned int hoodlen=hoodIt.Size(); // unsigned int inct=0; double sumj=0,sumi=0; unsigned int cter=0; for(indct=0; indct static_cast(imagesize[dd]-1) ) inimage=false; } if (inimage && this->m_FixedImageMask) if (this->m_FixedImageMask->GetPixel( index ) < 0.25 ) inimage=false; if (inimage) { sumj+=this->GetMovingImage()->GetPixel(index); sumi+=this->GetFixedImage()->GetPixel(index); cter++; } } if (cter > 0) movingMean=sumj/(float)cter; if (cter > 0) fixedMean=sumi/(float)cter; float val = this->GetFixedImage()->GetPixel(oindex) - fixedMean; this->finitediffimages[0]->SetPixel( oindex, val ); val = this->GetMovingImage()->GetPixel(oindex) - movingMean; this->finitediffimages[1]->SetPixel( oindex, val ); } } for( outIter.GoToBegin(); !outIter.IsAtEnd(); ++outIter ) { IndexType oindex = outIter.GetIndex(); bool takesample = true; if (this->m_FixedImageMask) if (this->m_FixedImageMask->GetPixel( oindex ) < 0.25 ) takesample=false; if (takesample) { NeighborhoodIterator hoodIt( this->GetRadius() ,this->finitediffimages[0] , this->finitediffimages[0]->GetLargestPossibleRegion()); hoodIt.SetLocation(oindex); double sff=0.0; double smm=0.0; double sfm=0.0; PointType mappedPoint; unsigned int indct; unsigned int hoodlen=hoodIt.Size(); // unsigned int inct=0; for(indct=0; indct static_cast(imagesize[dd]-1) ) inimage=false; } if (inimage && this->m_FixedImageMask) if (this->m_FixedImageMask->GetPixel( index ) < 0.25 ) inimage=false; if (inimage) { double fixedValue =(double)this->finitediffimages[0]->GetPixel( index ); double movingValue=(double)this->finitediffimages[1]->GetPixel( index ); // double ofixedValue =(double)this->GetFixedImage()->GetPixel( index ); // double omovingValue=(double)this->GetMovingImage()->GetPixel( index ); sff+=fixedValue*fixedValue; smm+=movingValue*movingValue; sfm+=fixedValue*movingValue; } } this->finitediffimages[2]->SetPixel( oindex, sfm );//A this->finitediffimages[3]->SetPixel( oindex, sff );//B this->finitediffimages[4]->SetPixel( oindex, smm );//C // this->finitediffimages[5]->SetPixel( oindex , sumi);//B*C // this->finitediffimages[6]->SetPixel( oindex , sumj);//B*C } } //m_FixedImageGradientCalculator->SetInputImage(finitediffimages[0]); m_MaxMag=0.0; m_MinMag=9.e9; m_AvgMag=0.0; m_Iteration++; } /* * Compute the ncc metric everywhere */ template typename TDeformationField::PixelType ProbabilisticRegistrationFunction ::ComputeMetricAtPairB(IndexType oindex, typename TDeformationField::PixelType vec) { typename TDeformationField::PixelType deriv; deriv.Fill(0.0); double sff=0.0; double smm=0.0; double sfm=0.0; // double fixedValue; // double movingValue; sff=0.0; smm=0.0; sfm=0.0; PointType mappedPoint; CovariantVectorType gradI,gradJ; if (this->m_FixedImageMask) if (this->m_FixedImageMask->GetPixel( oindex ) < 0.25 ) return deriv; sfm=finitediffimages[2]->GetPixel(oindex); sff=finitediffimages[3]->GetPixel(oindex); smm=finitediffimages[4]->GetPixel(oindex); if ( sff == 0.0 || smm == 0.0) return deriv; IndexType index=oindex;//hoodIt.GetIndex(indct); // bool inimage=true; if (sff == 0.0) sff=1.0; if (smm == 0.0) smm=1.0; gradI = m_FixedImageGradientCalculator->EvaluateAtIndex( index ); // gradJ = m_MovingImageGradientCalculator->EvaluateAtIndex( index ); float Ji=finitediffimages[1]->GetPixel(index); float Ii=finitediffimages[0]->GetPixel(index); m_TEMP=2.0*sfm/(sff*smm)*( Ji - sfm/sff*Ii ); for (int qq=0; qqm_RobustnessParameter) deriv.Fill(0); // if ( localProbabilistic*(-1.0) < this->m_RobustnessParameter) { // std::cout << " localC " << localProbabilistic << std::endl; } this->m_Energy-=localProbabilistic; return deriv;//localProbabilistic; } /* * Compute the ncc metric everywhere */ template typename TDeformationField::PixelType ProbabilisticRegistrationFunction ::ComputeMetricAtPairC(IndexType oindex, typename TDeformationField::PixelType vec) { typename TDeformationField::PixelType deriv; deriv.Fill(0.0); double sff=0.0; double smm=0.0; double sfm=0.0; // double fixedValue; // double movingValue; sff=0.0; smm=0.0; sfm=0.0; PointType mappedPoint; CovariantVectorType gradI,gradJ; if (this->m_FixedImageMask) if (this->m_FixedImageMask->GetPixel( oindex ) < 0.25 ) return deriv; sfm=finitediffimages[2]->GetPixel(oindex); sff=finitediffimages[3]->GetPixel(oindex); smm=finitediffimages[4]->GetPixel(oindex); if ( sff == 0.0 || smm == 0.0) return deriv; IndexType index=oindex;//hoodIt.GetIndex(indct); if (sff == 0.0) sff=1.0; if (smm == 0.0) smm=1.0; ///gradI = m_FixedImageGradientCalculator->EvaluateAtIndex( index ); gradJ = m_MovingImageGradientCalculator->EvaluateAtIndex( index ); float Ji=finitediffimages[1]->GetPixel(index); float Ii=finitediffimages[0]->GetPixel(index); for (int qq=0; qqm_RobustnessParameter) deriv.Fill(0); return deriv;//localProbabilistic; } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkProbabilisticRegistrationFunction.h000066400000000000000000000344641147325206600276600ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkProbabilisticRegistrationFunction.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkProbabilisticRegistrationFunction_h_ #define _itkProbabilisticRegistrationFunction_h_ #include "itkAvantsPDEDeformableRegistrationFunction.h" #include "itkPoint.h" #include "itkCovariantVector.h" #include "itkInterpolateImageFunction.h" #include "itkLinearInterpolateImageFunction.h" #include "itkCentralDifferenceImageFunction.h" #include "itkGradientRecursiveGaussianImageFilter.h" #include "itkAvantsMutualInformationRegistrationFunction.h" namespace itk { /** * \class ProbabilisticRegistrationFunction * * This class encapsulate the PDE which drives the demons registration * algorithm. It is used by ProbabilisticRegistrationFilter to compute the * output deformation field which will map a moving image onto a * a fixed image. * * Non-integer moving image values are obtained by using * interpolation. The default interpolator is of type * LinearInterpolateImageFunction. The user may set other * interpolators via method SetMovingImageInterpolator. Note that the input * interpolator must derive from baseclass InterpolateImageFunction. * * This class is templated over the fixed image type, moving image type, * and the deformation field type. * * \warning This filter assumes that the fixed image type, moving image type * and deformation field type all have the same number of dimensions. * * \sa ProbabilisticRegistrationFilter * \ingroup FiniteDifferenceFunctions */ template class ITK_EXPORT ProbabilisticRegistrationFunction : public AvantsPDEDeformableRegistrationFunction< TFixedImage, TMovingImage, TDeformationField> { public: /** Standard class typedefs. */ typedef ProbabilisticRegistrationFunction Self; typedef AvantsPDEDeformableRegistrationFunction< TFixedImage, TMovingImage, TDeformationField > Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Run-time type information (and related methods). */ itkTypeMacro( ProbabilisticRegistrationFunction, AvantsPDEDeformableRegistrationFunction ); /** MovingImage image type. */ typedef typename Superclass::MovingImageType MovingImageType; typedef typename Superclass::MovingImagePointer MovingImagePointer; /** FixedImage image type. */ typedef typename Superclass::MetricImageType MetricImageType; typedef typename Superclass::MetricImageType::Pointer MetricImagePointer; typedef typename Superclass::FixedImageType FixedImageType; typedef typename Superclass::FixedImagePointer FixedImagePointer; typedef typename FixedImageType::IndexType IndexType; typedef typename FixedImageType::SizeType SizeType; /** Deformation field type. */ typedef typename Superclass::DeformationFieldType DeformationFieldType; typedef typename Superclass::DeformationFieldTypePointer DeformationFieldTypePointer; typedef typename TDeformationField::PixelType VectorType; typedef CovariantVector GradientPixelType; typedef Image GradientImageType; typedef SmartPointer GradientImagePointer; typedef GradientRecursiveGaussianImageFilter< MetricImageType,GradientImageType > GradientImageFilterType; typedef typename GradientImageFilterType::Pointer GradientImageFilterPointer; typedef Image BinaryImageType; typedef typename BinaryImageType::Pointer BinaryImagePointer; /** Inherit some enums from the superclass. */ itkStaticConstMacro(ImageDimension, unsigned int,Superclass::ImageDimension); /** Inherit some enums from the superclass. */ typedef typename Superclass::PixelType PixelType; typedef typename Superclass::RadiusType RadiusType; typedef typename Superclass::NeighborhoodType NeighborhoodType; // typedef typename Superclass::NeighborhoodType BoundaryNeighborhoodType; typedef typename Superclass::FloatOffsetType FloatOffsetType; typedef typename Superclass::TimeStepType TimeStepType; /** Interpolator type. */ typedef double CoordRepType; typedef InterpolateImageFunction InterpolatorType; typedef typename InterpolatorType::Pointer InterpolatorPointer; typedef typename InterpolatorType::PointType PointType; typedef LinearInterpolateImageFunction DefaultInterpolatorType; /** Covariant vector type. */ typedef CovariantVector CovariantVectorType; /** Gradient calculator type. */ typedef CentralDifferenceImageFunction GradientCalculatorType; typedef typename GradientCalculatorType::Pointer GradientCalculatorPointer; /** Set the moving image interpolator. */ void SetMovingImageInterpolator( InterpolatorType * ptr ) { m_MovingImageInterpolator = ptr; } /** Get the moving image interpolator. */ InterpolatorType * GetMovingImageInterpolator(void) { return m_MovingImageInterpolator; } typename TDeformationField::PixelType ComputeMetricAtPairB(IndexType fixedindex, typename TDeformationField::PixelType vec ); typename TDeformationField::PixelType ComputeMetricAtPairC(IndexType fixedindex, typename TDeformationField::PixelType vec ); /** This class uses a constant timestep of 1. */ virtual TimeStepType ComputeGlobalTimeStep(void *GlobalData) const { return m_TimeStep; } /** Return a pointer to a global data structure that is passed to * this object from the solver at each calculation. */ virtual void *GetGlobalDataPointer() const { GlobalDataStruct *global = new GlobalDataStruct(); return global; } /** Release memory for global data structure. */ virtual void ReleaseGlobalDataPointer( void *GlobalData ) const { delete (GlobalDataStruct *) GlobalData; } /** Set the object's state before each iteration. */ virtual void InitializeIteration(); /** Set the object's state before each iteration. */ void InitializeIterationOld(); double ComputeCrossCorrelation() { if (finitediffimages[0]) { double totalcc=0; unsigned long ct=0; typedef ImageRegionIteratorWithIndex ittype; ittype it(this->finitediffimages[0],this->finitediffimages[0]->GetLargestPossibleRegion().GetSize()); for( it.GoToBegin(); !it.IsAtEnd(); ++it ) { IndexType oindex=it.GetIndex(); double sfm=finitediffimages[2]->GetPixel(oindex); double sff=finitediffimages[3]->GetPixel(oindex); double smm=finitediffimages[4]->GetPixel(oindex); double cc=0; if ( fabs(sff*smm) > 0) { cc+=sfm*sfm/(sff*smm); ct++; } totalcc+=cc; } this->m_Energy=totalcc/(float)ct*(-1.0); return this->m_Energy; } else return 0; } virtual VectorType OpticalFlowUpdate(const NeighborhoodType &neighborhood) { // Get fixed image related information IndexType index=neighborhood.GetIndex(); typename TDeformationField::PixelType vec = Superclass::m_DeformationField->GetPixel(index); VectorType update; update.Fill(0.0); double fixedValue; CovariantVectorType fixedGradient; double fixedGradientSquaredMagnitude = 0; fixedValue = (double) Superclass::Superclass::m_FixedImage->GetPixel( index ); fixedGradient = m_FixedImageGradientCalculator->EvaluateAtIndex( index ); for( unsigned int j = 0; j < ImageDimension; j++ ) { fixedGradientSquaredMagnitude += vnl_math_sqr( fixedGradient[j] ); } double movingValue; int j; PointType mappedPoint; for( j = 0; j < ImageDimension; j++ ) { mappedPoint[j] = double( index[j] ) * m_FixedImageSpacing[j] + m_FixedImageOrigin[j]; mappedPoint[j] += vec[j]; } if( m_MovingImageInterpolator->IsInsideBuffer( mappedPoint ) ) { movingValue = m_MovingImageInterpolator->Evaluate( mappedPoint ); } else { for( j = 0; j < ImageDimension; j++ ) { update[j] = 0.0; } return update; } double speedValue = fixedValue - movingValue; if (fabs(speedValue) < this->m_RobustnessParameter) speedValue=0; double denominator = vnl_math_sqr( speedValue ) / m_Normalizer + fixedGradientSquaredMagnitude; double m_DenominatorThreshold = 1e-9; double m_IntensityDifferenceThreshold = 0.001; if ( vnl_math_abs(speedValue) < m_IntensityDifferenceThreshold || denominator < m_DenominatorThreshold ) { for( j = 0; j < ImageDimension; j++ ) { update[j] = 0.0; } return update; } for( j = 0; j < ImageDimension; j++ ) { update[j] = speedValue * fixedGradient[j] / denominator; } return update; } void SetFixedImageMask( MetricImageType* img) {m_FixedImageMask=img; } virtual VectorType ComputeUpdate(const NeighborhoodType &neighborhood, void *globalData, const FloatOffsetType &offset = FloatOffsetType(0.0)) { VectorType update; update.Fill(0.0); IndexType oindex = neighborhood.GetIndex(); FixedImageType* img =const_cast(Superclass::m_FixedImage.GetPointer()); if (!img) return update; update=this->ComputeMetricAtPairB(oindex,update); return update; } virtual VectorType ComputeUpdateInv(const NeighborhoodType &neighborhood, void *globalData, const FloatOffsetType &offset = FloatOffsetType(0.0)) { VectorType update; update.Fill(0.0); IndexType oindex = neighborhood.GetIndex(); FixedImageType* img =const_cast(Superclass::m_FixedImage.GetPointer()); if (!img) return update; update=this->ComputeMetricAtPairC(oindex,update); return update; } void SetFullyRobust(bool b){m_FullyRobust=b;} void GetProbabilities(); double localProbabilistic; float m_TEMP; protected: ProbabilisticRegistrationFunction(); ~ProbabilisticRegistrationFunction() {} void PrintSelf(std::ostream& os, Indent indent) const; /** FixedImage image neighborhood iterator type. */ typedef ConstNeighborhoodIterator FixedImageNeighborhoodIteratorType; /** A global data type for this class of equation. Used to store * iterators for the fixed image. */ struct GlobalDataStruct { FixedImageNeighborhoodIteratorType m_FixedImageIterator; }; MetricImagePointer MakeImage() { typedef ImageRegionIteratorWithIndex ittype; typedef ImageRegionIteratorWithIndex ittype2; FixedImageType* img =const_cast(Superclass::m_FixedImage.GetPointer()); typename FixedImageType::SizeType imagesize=img->GetLargestPossibleRegion().GetSize(); this->m_MetricImage = MetricImageType::New(); this->m_MetricImage->SetLargestPossibleRegion(img->GetLargestPossibleRegion() ); this->m_MetricImage->SetBufferedRegion(img->GetLargestPossibleRegion()); this->m_MetricImage->SetSpacing(img->GetSpacing()); this->m_MetricImage->SetOrigin(img->GetOrigin()); this->m_MetricImage->Allocate(); this->m_MetricImage->FillBuffer(0); bool makebinimg=false; if (makebinimg) { m_Iteration=0; binaryimage = BinaryImageType::New(); binaryimage->SetLargestPossibleRegion(img->GetLargestPossibleRegion() ); binaryimage->SetBufferedRegion(img->GetLargestPossibleRegion()); binaryimage->SetSpacing(img->GetSpacing()); binaryimage->SetOrigin(img->GetOrigin()); binaryimage->Allocate(); ittype2 it(binaryimage,binaryimage->GetLargestPossibleRegion().GetSize()); for( it.GoToBegin(); !it.IsAtEnd(); ++it ) it.Set(1); } return this->m_MetricImage; } private: ProbabilisticRegistrationFunction(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented /** Cache fixed image information. */ typename TFixedImage::SpacingType m_FixedImageSpacing; typename TFixedImage::PointType m_FixedImageOrigin; /** Function to compute derivatives of the fixed image. */ GradientCalculatorPointer m_FixedImageGradientCalculator; GradientCalculatorPointer m_MovingImageGradientCalculator; /** Function to interpolate the moving image. */ InterpolatorPointer m_MovingImageInterpolator; /** The global timestep. */ TimeStepType m_TimeStep; /** Threshold below which the denominator term is considered zero. */ double m_DenominatorThreshold; /** Threshold below which two intensity value are assumed to match. */ double m_IntensityDifferenceThreshold; mutable double m_MetricTotal; mutable float m_MinMag; mutable float m_MaxMag; mutable float m_AvgMag; mutable float m_Thresh; GradientImagePointer m_MetricGradientImage; MetricImagePointer finitediffimages[5]; BinaryImagePointer binaryimage; MetricImagePointer m_FixedImageMask; MetricImagePointer m_MovingImageMask; typedef itk::AvantsMutualInformationRegistrationFunction MetricType2; typename MetricType2::Pointer m_MIFunct; unsigned int m_NumberOfHistogramBins; bool m_FullyRobust; unsigned int m_Iteration; float m_Normalizer; }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkProbabilisticRegistrationFunction.cxx" #endif #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkSpatialMutualInformationRegistrationFunction.cxx000077500000000000000000001037451147325206600324420ustar00rootroot00000000000000/*========================================================================= Program: Insight Segmentation & Registration Toolkit Module: $RCSfile: itkSpatialMutualInformationRegistrationFunction.cxx,v $ Language: C++ Date: $Date: 2009/01/08 15:14:48 $ Version: $Revision: 1.21 $ Copyright (c) Insight Software Consortium. All rights reserved. See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkSpatialMutualInformationRegistrationFunction_txx #define _itkSpatialMutualInformationRegistrationFunction_txx #include "itkSpatialMutualInformationRegistrationFunction.h" #include "itkBSplineInterpolateImageFunction.h" #include "itkCovariantVector.h" #include "itkImageRandomConstIteratorWithIndex.h" #include "itkImageRegionConstIterator.h" #include "itkImageRegionIterator.h" #include "itkImageIterator.h" #include "vnl/vnl_math.h" #include "itkDiscreteGaussianImageFilter.h" #include "itkBSplineDeformableTransform.h" #include "itkImageRegionConstIteratorWithIndex.h" namespace itk { /** * Constructor */ template < class TFixedImage, class TMovingImage , class TDeformationField > SpatialMutualInformationRegistrationFunction ::SpatialMutualInformationRegistrationFunction() { this-> Superclass::m_NormalizeGradient=true; this->m_NumberOfSpatialSamples = 5000; this->m_NumberOfHistogramBins = 50; // this->SetComputeGradient(false); // don't use the default gradient for now this->m_InterpolatorIsBSpline = false; this->m_TransformIsBSpline = false; // Initialize PDFs to NULL m_JointPDF = NULL; m_OpticalFlow=false; typename TransformType::Pointer transformer = TransformType::New(); this->SetTransform(transformer); typename BSplineInterpolatorType::Pointer interpolator = BSplineInterpolatorType::New(); this->SetInterpolator (interpolator); m_FixedImageMask=NULL; m_MovingImageMask=NULL; // Initialize memory m_MovingImageNormalizedMin = 0.0; m_FixedImageNormalizedMin = 0.0; m_MovingImageTrueMin = 0.0; m_MovingImageTrueMax = 0.0; m_FixedImageBinSize = 0.0; m_MovingImageBinSize = 0.0; m_BSplineInterpolator = NULL; m_NumParametersPerDim = 0; m_NumBSplineWeights = 0; m_BSplineTransform = NULL; m_NumberOfParameters = ImageDimension; m_FixedImageGradientCalculator = GradientCalculatorType::New(); m_MovingImageGradientCalculator = GradientCalculatorType::New(); this->m_Padding=0; typename DefaultInterpolatorType::Pointer interp = DefaultInterpolatorType::New(); typename DefaultInterpolatorType::Pointer interp2 = DefaultInterpolatorType::New(); m_MovingImageInterpolator = static_cast( interp.GetPointer() ); m_FixedImageInterpolator = static_cast( interp2.GetPointer() ); m_Interpolator = static_cast( interp.GetPointer() ); this->m_RobustnessParameter=-1.e19; } /** * Print out internal information about this class */ template < class TFixedImage, class TMovingImage , class TDeformationField> void SpatialMutualInformationRegistrationFunction ::PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf(os, indent); os << indent << "NumberOfSpatialSamples: "; os << m_NumberOfSpatialSamples << std::endl; os << indent << "NumberOfHistogramBins: "; os << m_NumberOfHistogramBins << std::endl; // Debugging information os << indent << "NumberOfParameters: "; os << m_NumberOfParameters << std::endl; os << indent << "FixedImageNormalizedMin: "; os << m_FixedImageNormalizedMin << std::endl; os << indent << "MovingImageNormalizedMin: "; os << m_MovingImageNormalizedMin << std::endl; os << indent << "MovingImageTrueMin: "; os << m_MovingImageTrueMin << std::endl; os << indent << "MovingImageTrueMax: "; os << m_MovingImageTrueMax << std::endl; os << indent << "FixedImageBinSize: "; os << m_FixedImageBinSize << std::endl; os << indent << "MovingImageBinSize: "; os << m_MovingImageBinSize << std::endl; os << indent << "InterpolatorIsBSpline: "; os << m_InterpolatorIsBSpline << std::endl; os << indent << "TransformIsBSpline: "; os << m_TransformIsBSpline << std::endl; } /** * Initialize */ template void SpatialMutualInformationRegistrationFunction ::InitializeIteration() { m_CubicBSplineKernel = CubicBSplineFunctionType::New(); this->m_Energy=0; this->pdfinterpolator = pdfintType::New(); this->pdfinterpolatorXuY = pdfintType::New(); this->pdfinterpolatorXYu = pdfintType::New(); this->pdfinterpolatorXlY = pdfintType::New(); this->pdfinterpolatorXYl = pdfintType::New(); this->pdfinterpolatorXuYl = pdfintType::New(); this->pdfinterpolatorXlYu = pdfintType::New(); this->pdfinterpolatorXuYr = pdfintType::New(); this->pdfinterpolatorXrYu = pdfintType::New(); pdfinterpolator2 = pdfintType2::New(); pdfinterpolator3 = pdfintType2::New(); // this->ComputeMetricImage(); // std::cout << " A " << std::endl; // bool makenewimage=false; typedef ImageRegionIteratorWithIndex ittype; TFixedImage* img =const_cast(this->m_FixedImage.GetPointer()); typename TFixedImage::SizeType imagesize=img->GetLargestPossibleRegion().GetSize(); // std::cout << " B " << std::endl; /* if (!this->m_MetricImage )makenewimage=true; else if (imagesize[0] != this->m_MetricImage->GetLargestPossibleRegion().GetSize()[0]) makenewimage = true; else this->m_MetricImage->FillBuffer(0); if (makenewimage) { this->m_MetricImage = TFixedImage::New(); this->m_MetricImage->SetLargestPossibleRegion(img->GetLargestPossibleRegion() ); this->m_MetricImage->SetBufferedRegion(img->GetLargestPossibleRegion()); this->m_MetricImage->SetSpacing(img->GetSpacing()); this->m_MetricImage->SetOrigin(img->GetOrigin()); this->m_MetricImage->Allocate(); ittype it(this->m_MetricImage,this->m_MetricImage->GetLargestPossibleRegion().GetSize()); for( it.GoToBegin(); !it.IsAtEnd(); ++it ) it.Set(0); } */ m_FixedImageGradientCalculator->SetInputImage( this->m_FixedImage ); m_MovingImageGradientCalculator->SetInputImage( this->m_MovingImage ); m_FixedImageInterpolator->SetInputImage( this->m_FixedImage ); m_Interpolator->SetInputImage( this->m_MovingImage ); m_FixedImageSpacing = this->m_FixedImage->GetSpacing(); m_FixedImageOrigin = this->m_FixedImage->GetOrigin(); m_Normalizer = 0.0; m_NumberOfSpatialSamples = 1; for( unsigned int k = 0; k < ImageDimension; k++ ) { m_Normalizer += m_FixedImageSpacing[k] * m_FixedImageSpacing[k]; m_NumberOfSpatialSamples *= this->m_FixedImage->GetLargestPossibleRegion().GetSize()[k]; } m_Normalizer /= static_cast( ImageDimension ); /** * Compute binsize for the histograms. * * The binsize for the image intensities needs to be adjusted so that * we can avoid dealing with boundary conditions using the cubic * spline as the Parzen window. We do this by increasing the size * of the bins so that the joint histogram becomes "padded" at the * borders. Because we are changing the binsize, * we also need to shift the minimum by the padded amount in order to * avoid minimum values filling in our padded region. * * Note that there can still be non-zero bin values in the padded region, * it's just that these bins will never be a central bin for the Parzen * window. * double fixedImageMax = 1.0; double fixedImageMin = 0.0; double movingImageMax = 1.0; double movingImageMin = 0.0; m_MovingImageTrueMin = movingImageMin; m_MovingImageTrueMax = movingImageMax; */ double movingImageMin = NumericTraits::max(); double movingImageMax = NumericTraits::NonpositiveMin(); double fixedImageMin = NumericTraits::max(); double fixedImageMax = NumericTraits::NonpositiveMin(); typedef ImageRegionConstIterator MovingIteratorType; MovingIteratorType movingImageIterator( this->m_MovingImage, this->m_MovingImage->GetBufferedRegion() ); for ( movingImageIterator.GoToBegin(); !movingImageIterator.IsAtEnd(); ++movingImageIterator) { bool takesample = true; if (this->m_FixedImageMask) if (this->m_FixedImageMask->GetPixel( movingImageIterator.GetIndex() ) < 1.e-6 ) takesample=false; if (takesample) { double sample = static_cast( movingImageIterator.Get() ); double fsample = static_cast( this->m_FixedImage->GetPixel( movingImageIterator.GetIndex() )); if ( sample < movingImageMin ) { movingImageMin = sample; } if ( sample > movingImageMax ) { movingImageMax = sample; } if ( fsample < fixedImageMin ) { fixedImageMin = fsample; } if ( fsample > fixedImageMax ) { fixedImageMax = fsample; } } } this->m_MovingImageTrueMax=movingImageMax; this->m_FixedImageTrueMax=fixedImageMax; this->m_MovingImageTrueMin=movingImageMin; this->m_FixedImageTrueMin=fixedImageMin; fixedImageMax=1.*( fixedImageMax - fixedImageMin )+fixedImageMin; movingImageMax=1.*( movingImageMax - movingImageMin )+movingImageMin; m_FixedImageBinSize = ( fixedImageMax - fixedImageMin ) / static_cast( m_NumberOfHistogramBins - 2 * this->m_Padding ); m_FixedImageNormalizedMin = fixedImageMin / m_FixedImageBinSize - static_cast( this->m_Padding ); m_MovingImageBinSize = ( movingImageMax - movingImageMin ) / static_cast( m_NumberOfHistogramBins - 2 * this->m_Padding ); m_MovingImageNormalizedMin = movingImageMin / m_MovingImageBinSize - static_cast( this->m_Padding ); m_FixedImageMarginalPDF = MarginalPDFType::New(); m_MovingImageMarginalPDF = MarginalPDFType::New(); typename MarginalPDFType::RegionType mPDFRegion; typename MarginalPDFType::SizeType mPDFSize; typename MarginalPDFType::IndexType mPDFIndex; typename MarginalPDFType::SpacingType mPDFspacing; mPDFspacing.Fill(1); mPDFIndex.Fill( 0 ); mPDFSize.Fill( m_NumberOfHistogramBins ); mPDFRegion.SetIndex( mPDFIndex ); mPDFRegion.SetSize( mPDFSize ); m_FixedImageMarginalPDF->SetRegions( mPDFRegion ); m_FixedImageMarginalPDF->Allocate(); m_FixedImageMarginalPDF->SetSpacing(mPDFspacing); m_MovingImageMarginalPDF->SetRegions( mPDFRegion ); m_MovingImageMarginalPDF->Allocate(); m_MovingImageMarginalPDF->SetSpacing(mPDFspacing); // std::cout << " C " << std::endl; /** * Allocate memory for the joint PDF and joint PDF derivatives. * The joint PDF and joint PDF derivatives are store as itk::Image. */ this->m_JointPDF = JointPDFType::New(); this->m_JointPDFXuY = JointPDFType::New(); this->m_JointPDFXYu = JointPDFType::New(); this->m_JointPDFXlY = JointPDFType::New(); this->m_JointPDFXYl = JointPDFType::New(); this->m_JointPDFXuYl = JointPDFType::New(); this->m_JointPDFXlYu = JointPDFType::New(); this->m_JointPDFXrYu = JointPDFType::New(); this->m_JointPDFXuYr = JointPDFType::New(); // std::cout << " D " << std::endl; // Instantiate a region, index, size JointPDFRegionType jointPDFRegion; JointPDFIndexType jointPDFIndex; JointPDFSizeType jointPDFSize; typename JointPDFType::SpacingType jspacing; jspacing.Fill(1); // For the joint PDF define a region starting from {0,0} // with size {m_NumberOfHistogramBins, m_NumberOfHistogramBins}. // The dimension represents fixed image parzen window index // and moving image parzen window index, respectively. jointPDFIndex.Fill( 0 ); jointPDFSize.Fill( m_NumberOfHistogramBins ); jointPDFRegion.SetIndex( jointPDFIndex ); jointPDFRegion.SetSize( jointPDFSize ); m_JointHist = JointPDFType::New(); m_JointHist->SetRegions(jointPDFRegion ); m_JointHist->Allocate(); this->m_JointHist->SetSpacing(jspacing); // Set the regions and allocate this->m_JointPDF->SetRegions( jointPDFRegion ); this->m_JointPDF->Allocate(); this->m_JointPDF->SetSpacing(jspacing); this->m_JointPDFXYu->SetRegions( jointPDFRegion ); this->m_JointPDFXYu->Allocate(); this->m_JointPDFXYu->SetSpacing(jspacing); this->m_JointPDFXuY->SetRegions( jointPDFRegion ); this->m_JointPDFXuY->Allocate(); this->m_JointPDFXuY->SetSpacing(jspacing); this->m_JointPDFXlY->SetRegions( jointPDFRegion ); this->m_JointPDFXlY->Allocate(); this->m_JointPDFXlY->SetSpacing(jspacing); this->m_JointPDFXYl->SetRegions( jointPDFRegion ); this->m_JointPDFXYl->Allocate(); this->m_JointPDFXYl->SetSpacing(jspacing); // this->m_JointPDFXlYu->SetRegions( jointPDFRegion ); this->m_JointPDFXlYu->Allocate(); this->m_JointPDFXlYu->SetSpacing(jspacing); this->m_JointPDFXuYl->SetRegions( jointPDFRegion ); this->m_JointPDFXuYl->Allocate(); this->m_JointPDFXuYl->SetSpacing(jspacing); this->m_JointPDFXrYu->SetRegions( jointPDFRegion ); this->m_JointPDFXrYu->Allocate(); this->m_JointPDFXrYu->SetSpacing(jspacing); this->m_JointPDFXuYr->SetRegions( jointPDFRegion ); this->m_JointPDFXuYr->Allocate(); this->m_JointPDFXuYr->SetSpacing(jspacing); // std::cout << " E " << std::endl; m_NormalizeMetric=1.0; for (int i=0; im_FixedImage->GetLargestPossibleRegion().GetSize()[i]; // std::cout << " F " << std::endl; pdfinterpolator->SetInputImage(m_JointPDF); pdfinterpolator->SetSplineOrder(3); this->pdfinterpolatorXuY->SetInputImage(this->m_JointPDFXuY); this->pdfinterpolatorXYu->SetInputImage(this->m_JointPDFXYu); this->pdfinterpolatorXlY->SetInputImage(this->m_JointPDFXlY); this->pdfinterpolatorXYl->SetInputImage(this->m_JointPDFXYl); this->pdfinterpolatorXuYl->SetInputImage(this->m_JointPDFXuYl); this->pdfinterpolatorXlYu->SetInputImage(this->m_JointPDFXlYu); this->pdfinterpolatorXuYr->SetInputImage(this->m_JointPDFXuYr); this->pdfinterpolatorXrYu->SetInputImage(this->m_JointPDFXrYu); this->pdfinterpolatorXuY->SetSplineOrder(3); this->pdfinterpolatorXYu->SetSplineOrder(3); this->pdfinterpolatorXlY->SetSplineOrder(3); this->pdfinterpolatorXYl->SetSplineOrder(3); this->pdfinterpolatorXuYl->SetSplineOrder(3); this->pdfinterpolatorXlYu->SetSplineOrder(3); this->pdfinterpolatorXuYr->SetSplineOrder(3); this->pdfinterpolatorXrYu->SetSplineOrder(3); pdfinterpolator2->SetInputImage(m_FixedImageMarginalPDF); pdfinterpolator3->SetInputImage(m_MovingImageMarginalPDF); pdfinterpolator2->SetSplineOrder(3); pdfinterpolator3->SetSplineOrder(3); // std::cout << " Ga " << std::endl; this->GetProbabilities(); // std::cout << " G " << std::endl; this->ComputeSpatialMutualInformation(); // std::cout << " H " << std::endl; } /** * Get the both Value and Derivative Measure */ template < class TFixedImage, class TMovingImage , class TDeformationField> void SpatialMutualInformationRegistrationFunction ::GetProbabilities() { typedef ImageRegionConstIteratorWithIndex RandomIterator; RandomIterator randIter( this->m_FixedImage, this->m_FixedImage->GetLargestPossibleRegion() ); for ( unsigned int j = 0; j < m_NumberOfHistogramBins; j++ ) { MarginalPDFIndexType mind; mind[0]=j; m_FixedImageMarginalPDF->SetPixel(mind,0); m_MovingImageMarginalPDF->SetPixel(mind,0); } // Reset the joint pdfs to zero m_JointPDF->FillBuffer( 0.0 ); m_JointPDFXuY->FillBuffer( 0.0 ); m_JointPDFXYu->FillBuffer( 0.0 ); m_JointPDFXlY->FillBuffer( 0.0 ); m_JointPDFXYl->FillBuffer( 0.0 ); m_JointPDFXuYl->FillBuffer( 0.0 ); m_JointPDFXlYu->FillBuffer( 0.0 ); m_JointPDFXrYu->FillBuffer( 0.0 ); m_JointPDFXuYr->FillBuffer( 0.0 ); m_JointHist->FillBuffer( 0.0 ); unsigned long nSamples=0; RandomIterator iter( this->m_FixedImage, this->m_FixedImage->GetLargestPossibleRegion() ); for( iter.GoToBegin(); !iter.IsAtEnd(); ++iter ) { bool takesample=true; if (this->m_FixedImageMask) if (this->m_FixedImageMask->GetPixel( iter.GetIndex() ) < 1.e-6 ) takesample=false; if (takesample) { // Get sampled index FixedImageIndexType index = iter.GetIndex(); FixedImageIndexType IndexU = index; FixedImageIndexType IndexL = index; FixedImageIndexType IndexR = index; // check the neighboring voxel to be in the image typename FixedImageType::SizeType imagesize=this->m_FixedImage->GetLargestPossibleRegion().GetSize(); bool inimage=true; for (unsigned int dd=0; dd static_cast(imagesize[dd]-2) ) inimage=false; } // std::cout << " Image size? " << imagesize << std::endl; if (inimage) { IndexU[0] = index[0] - 1; IndexL[1] = index[1] - 1; IndexR[1] = index[1] + 1; double movingImageValue = this->GetMovingParzenTerm( this->m_MovingImage->GetPixel( index ) ); double fixedImageValue = this->GetFixedParzenTerm( this->m_FixedImage->GetPixel( index ) ); unsigned int movingImageParzenWindowIndex=this->FitIndexInBins( movingImageValue ); unsigned int fixedImageParzenWindowIndex=this->FitIndexInBins( fixedImageValue ); JointPDFValueType *pdfPtr = m_JointPDF->GetBufferPointer() + ( fixedImageParzenWindowIndex* m_NumberOfHistogramBins ); int pdfMovingIndex = static_cast( movingImageParzenWindowIndex ); pdfPtr += pdfMovingIndex; *(pdfPtr) += static_cast( 1 ); movingImageValue = this->GetMovingParzenTerm( this->m_MovingImage->GetPixel( index ) ); fixedImageValue = this->GetFixedParzenTerm( this->m_FixedImage->GetPixel( IndexU ) ); movingImageParzenWindowIndex=this->FitIndexInBins( movingImageValue ); fixedImageParzenWindowIndex=this->FitIndexInBins( fixedImageValue ); pdfPtr = m_JointPDFXuY->GetBufferPointer() + ( fixedImageParzenWindowIndex* m_NumberOfHistogramBins ); pdfMovingIndex = static_cast( movingImageParzenWindowIndex ); pdfPtr += pdfMovingIndex; *(pdfPtr) += static_cast( 1 ); movingImageValue = this->GetMovingParzenTerm( this->m_MovingImage->GetPixel( IndexU ) ); fixedImageValue = this->GetFixedParzenTerm( this->m_FixedImage->GetPixel( index ) ); movingImageParzenWindowIndex=this->FitIndexInBins( movingImageValue ); fixedImageParzenWindowIndex=this->FitIndexInBins( fixedImageValue ); pdfPtr = m_JointPDFXYu->GetBufferPointer() + ( fixedImageParzenWindowIndex* m_NumberOfHistogramBins ); pdfMovingIndex = static_cast( movingImageParzenWindowIndex ); pdfPtr += pdfMovingIndex; *(pdfPtr) += static_cast( 1 ); movingImageValue = this->GetMovingParzenTerm( this->m_MovingImage->GetPixel( index ) ); fixedImageValue = this->GetFixedParzenTerm( this->m_FixedImage->GetPixel( IndexL ) ); movingImageParzenWindowIndex=this->FitIndexInBins( movingImageValue ); fixedImageParzenWindowIndex=this->FitIndexInBins( fixedImageValue ); pdfPtr = m_JointPDFXlY->GetBufferPointer() + ( fixedImageParzenWindowIndex* m_NumberOfHistogramBins ); pdfMovingIndex = static_cast( movingImageParzenWindowIndex ); pdfPtr += pdfMovingIndex; *(pdfPtr) += static_cast( 1 ); movingImageValue = this->GetMovingParzenTerm( this->m_MovingImage->GetPixel( IndexL ) ); fixedImageValue = this->GetFixedParzenTerm( this->m_FixedImage->GetPixel( index ) ); movingImageParzenWindowIndex=this->FitIndexInBins( movingImageValue ); fixedImageParzenWindowIndex=this->FitIndexInBins( fixedImageValue ); pdfPtr = m_JointPDFXYl->GetBufferPointer() + ( fixedImageParzenWindowIndex* m_NumberOfHistogramBins ); pdfMovingIndex = static_cast( movingImageParzenWindowIndex ); pdfPtr += pdfMovingIndex; *(pdfPtr) += static_cast( 1 ); movingImageValue = this->GetMovingParzenTerm( this->m_MovingImage->GetPixel( IndexL ) ); fixedImageValue = this->GetFixedParzenTerm( this->m_FixedImage->GetPixel( IndexU ) ); movingImageParzenWindowIndex=this->FitIndexInBins( movingImageValue ); fixedImageParzenWindowIndex=this->FitIndexInBins( fixedImageValue ); pdfPtr = m_JointPDFXuYl->GetBufferPointer() + ( fixedImageParzenWindowIndex* m_NumberOfHistogramBins ); pdfMovingIndex = static_cast( movingImageParzenWindowIndex ); pdfPtr += pdfMovingIndex; *(pdfPtr) += static_cast( 1 ); movingImageValue = this->GetMovingParzenTerm( this->m_MovingImage->GetPixel( IndexU ) ); fixedImageValue = this->GetFixedParzenTerm( this->m_FixedImage->GetPixel( IndexL ) ); movingImageParzenWindowIndex=this->FitIndexInBins( movingImageValue ); fixedImageParzenWindowIndex=this->FitIndexInBins( fixedImageValue ); pdfPtr = m_JointPDFXlYu->GetBufferPointer() + ( fixedImageParzenWindowIndex* m_NumberOfHistogramBins ); pdfMovingIndex = static_cast( movingImageParzenWindowIndex ); pdfPtr += pdfMovingIndex; *(pdfPtr) += static_cast( 1 ); movingImageValue = this->GetMovingParzenTerm( this->m_MovingImage->GetPixel( IndexU ) ); fixedImageValue = this->GetFixedParzenTerm( this->m_FixedImage->GetPixel( IndexR ) ); movingImageParzenWindowIndex=this->FitIndexInBins( movingImageValue ); fixedImageParzenWindowIndex=this->FitIndexInBins( fixedImageValue ); pdfPtr = m_JointPDFXrYu->GetBufferPointer() + ( fixedImageParzenWindowIndex* m_NumberOfHistogramBins ); pdfMovingIndex = static_cast( movingImageParzenWindowIndex ); pdfPtr += pdfMovingIndex; *(pdfPtr) += static_cast( 1 ); movingImageValue = this->GetMovingParzenTerm( this->m_MovingImage->GetPixel( IndexR ) ); fixedImageValue = this->GetFixedParzenTerm( this->m_FixedImage->GetPixel( IndexU ) ); movingImageParzenWindowIndex=this->FitIndexInBins( movingImageValue ); fixedImageParzenWindowIndex=this->FitIndexInBins( fixedImageValue ); pdfPtr = m_JointPDFXuYr->GetBufferPointer() + ( fixedImageParzenWindowIndex* m_NumberOfHistogramBins ); pdfMovingIndex = static_cast( movingImageParzenWindowIndex ); pdfPtr += pdfMovingIndex; *(pdfPtr) += static_cast( 1 ); ++nSamples; } } } // std::cout << " Image Rotation Number " << nSamples << std::endl; /** * Normalize the PDFs, compute moving image marginal PDF * */ typedef ImageRegionIterator JointPDFIteratorType; JointPDFIteratorType jointPDFIterator ( m_JointPDF, m_JointPDF->GetBufferedRegion() ); JointPDFIteratorType jointPDFXuYIterator ( m_JointPDFXuY, m_JointPDFXuY->GetBufferedRegion() ); JointPDFIteratorType jointPDFXYuIterator ( m_JointPDFXYu, m_JointPDFXYu->GetBufferedRegion() ); JointPDFIteratorType jointPDFXlYIterator ( m_JointPDFXlY, m_JointPDFXlY->GetBufferedRegion() ); JointPDFIteratorType jointPDFXYlIterator ( m_JointPDFXYl, m_JointPDFXYl->GetBufferedRegion() ); JointPDFIteratorType jointPDFXuYlIterator ( m_JointPDFXuYl, m_JointPDFXuYl->GetBufferedRegion() ); JointPDFIteratorType jointPDFXlYuIterator ( m_JointPDFXlYu, m_JointPDFXlYu->GetBufferedRegion() ); JointPDFIteratorType jointPDFXrYuIterator ( m_JointPDFXrYu, m_JointPDFXrYu->GetBufferedRegion() ); JointPDFIteratorType jointPDFXuYrIterator ( m_JointPDFXuYr, m_JointPDFXuYr->GetBufferedRegion() ); // Compute joint PDF normalization factor (to ensure joint PDF sum adds to 1.0) double jointPDFSum = 0.0; jointPDFIterator.GoToBegin(); while( !jointPDFIterator.IsAtEnd() ) { float temp = jointPDFIterator.Get(); // jointPDFIterator.Set(temp); jointPDFSum += temp; ++jointPDFIterator; } // std::cout << " Joint PDF Summation? " << jointPDFSum << std::endl; // of derivatives if ( jointPDFSum == 0.0 ) { itkExceptionMacro( "Joint PDF summed to zero" ); } // Normalize the PDF bins jointPDFIterator.GoToEnd(); jointPDFXuYIterator.GoToEnd(); jointPDFXYuIterator.GoToEnd(); jointPDFXlYIterator.GoToEnd(); jointPDFXYlIterator.GoToEnd(); jointPDFXuYlIterator.GoToEnd(); jointPDFXlYuIterator.GoToEnd(); jointPDFXrYuIterator.GoToEnd(); jointPDFXuYrIterator.GoToEnd(); while( !jointPDFIterator.IsAtBegin() ) { --jointPDFIterator; --jointPDFXuYIterator; --jointPDFXYuIterator; --jointPDFXlYIterator; --jointPDFXYlIterator; --jointPDFXuYlIterator; --jointPDFXlYuIterator; --jointPDFXrYuIterator; --jointPDFXuYrIterator; jointPDFIterator.Value() /= static_cast( jointPDFSum ); jointPDFXuYIterator.Value() /= static_cast( jointPDFSum ); jointPDFXYuIterator.Value() /= static_cast( jointPDFSum ); jointPDFXlYIterator.Value() /= static_cast( jointPDFSum ); jointPDFXYlIterator.Value() /= static_cast( jointPDFSum ); jointPDFXuYlIterator.Value() /= static_cast( jointPDFSum ); jointPDFXlYuIterator.Value() /= static_cast( jointPDFSum ); jointPDFXrYuIterator.Value() /= static_cast( jointPDFSum ); jointPDFXuYrIterator.Value() /= static_cast( jointPDFSum ); } bool smoothjh=false; if (smoothjh) { typedef DiscreteGaussianImageFilter dgtype; typename dgtype::Pointer dg=dgtype::New(); dg->SetInput(this->m_JointPDF); dg->SetVariance(1.); dg->SetUseImageSpacingOff(); dg->SetMaximumError(.01f); dg->Update(); this->m_JointPDF=dg->GetOutput(); } // Compute moving image marginal PDF by summing over fixed image bins. typedef ImageLinearIteratorWithIndex JointPDFLinearIterator; JointPDFLinearIterator linearIter( m_JointPDF, m_JointPDF->GetBufferedRegion() ); linearIter.SetDirection( 0 ); linearIter.GoToBegin(); unsigned int fixedIndex = 0; while( !linearIter.IsAtEnd() ) { double sum = 0.0; while( !linearIter.IsAtEndOfLine() ) { sum += linearIter.Get(); ++linearIter; } MarginalPDFIndexType mind; mind[0]=fixedIndex; m_FixedImageMarginalPDF->SetPixel(mind,static_cast(sum)); linearIter.NextLine(); ++fixedIndex; } linearIter.SetDirection( 1 ); linearIter.GoToBegin(); unsigned int movingIndex = 0; while( !linearIter.IsAtEnd() ) { double sum = 0.0; while( !linearIter.IsAtEndOfLine() ) { sum += linearIter.Get(); ++linearIter; } MarginalPDFIndexType mind; mind[0]=movingIndex; m_MovingImageMarginalPDF->SetPixel(mind,static_cast(sum)); linearIter.NextLine(); ++movingIndex; } } /** * Get the both Value and Derivative Measure */ template < class TFixedImage, class TMovingImage , class TDeformationField> double SpatialMutualInformationRegistrationFunction ::GetValueAndDerivative(IndexType oindex, MeasureType& valuei, DerivativeType& derivative1,DerivativeType& derivative2) { double value=0; DerivativeType zero(ImageDimension); zero.Fill(0); double movingImageValue = this->GetMovingParzenTerm( this->m_MovingImage->GetPixel( oindex ) ); double fixedImageValue = this->GetFixedParzenTerm( this->m_FixedImage->GetPixel( oindex ) ); double movingImageParzenWindowContIndex=this->FitContIndexInBins( movingImageValue ); double fixedImageParzenWindowContIndex=this->FitContIndexInBins( fixedImageValue ); double dJPDF=0,jointPDFValue=0; double jointPDFValueXuY = 0, dJPDFXuY = 0, jointPDFValueXYu = 0, dJPDFXYu = 0, jointPDFValueXlY = 0, dJPDFXlY = 0, jointPDFValueXYl = 0; double dJPDFXYl = 0, jointPDFValueXuYl = 0, dJPDFXuYl = 0, jointPDFValueXlYu = 0, dJPDFXlYu = 0, jointPDFValueXuYr = 0, dJPDFXuYr = 0, jointPDFValueXrYu = 0, dJPDFXrYu = 0; { /** take derivative of joint pdf with respect to the b-spline */ typename pdfintType::ContinuousIndexType pdfind; pdfind[1]= fixedImageParzenWindowContIndex; pdfind[0]= movingImageParzenWindowContIndex; jointPDFValue = pdfinterpolator->EvaluateAtContinuousIndex(pdfind); dJPDF = (1.0)*(pdfinterpolator->EvaluateDerivativeAtContinuousIndex( pdfind ))[1]; jointPDFValueXuY = pdfinterpolatorXuY->EvaluateAtContinuousIndex(pdfind); dJPDFXuY = (1.0)*(pdfinterpolatorXuY->EvaluateDerivativeAtContinuousIndex( pdfind ))[1]; jointPDFValueXYu = pdfinterpolatorXYu->EvaluateAtContinuousIndex(pdfind); dJPDFXYu = (1.0)*(pdfinterpolatorXYu->EvaluateDerivativeAtContinuousIndex( pdfind ))[1]; jointPDFValueXlY = pdfinterpolatorXlY->EvaluateAtContinuousIndex(pdfind); dJPDFXlY = (1.0)*(pdfinterpolatorXlY->EvaluateDerivativeAtContinuousIndex( pdfind ))[1]; jointPDFValueXYl = pdfinterpolatorXYl->EvaluateAtContinuousIndex(pdfind); dJPDFXYl = (1.0)*(pdfinterpolatorXYl->EvaluateDerivativeAtContinuousIndex( pdfind ))[1]; jointPDFValueXuYl = pdfinterpolatorXuYl->EvaluateAtContinuousIndex(pdfind); dJPDFXuYl = (1.0)*(pdfinterpolatorXuYl->EvaluateDerivativeAtContinuousIndex( pdfind ))[1]; jointPDFValueXlYu = pdfinterpolatorXlYu->EvaluateAtContinuousIndex(pdfind); dJPDFXlYu = (1.0)*(pdfinterpolatorXlYu->EvaluateDerivativeAtContinuousIndex( pdfind ))[1]; jointPDFValueXuYr = pdfinterpolatorXuYr->EvaluateAtContinuousIndex(pdfind); dJPDFXuYr = (1.0)*(pdfinterpolatorXuYr->EvaluateDerivativeAtContinuousIndex( pdfind ))[1]; jointPDFValueXrYu = pdfinterpolatorXrYu->EvaluateAtContinuousIndex(pdfind); dJPDFXrYu = (1.0)*(pdfinterpolatorXrYu->EvaluateDerivativeAtContinuousIndex( pdfind ))[1]; } double eps=1.e-12; if( jointPDFValue > eps && jointPDFValueXuY > eps && jointPDFValueXYu > eps && jointPDFValueXlY > eps && jointPDFValueXYl > eps && jointPDFValueXuYl > eps && jointPDFValueXlYu > eps && jointPDFValueXrYu > eps && jointPDFValueXuYr > eps) { value = 4 * dJPDF / jointPDFValue + dJPDFXuYl / jointPDFValueXuYl + dJPDFXlYu / jointPDFValueXlYu + dJPDFXuYr / jointPDFValueXuYr + dJPDFXrYu / jointPDFValueXrYu - 2 * dJPDFXYu / jointPDFValueXYu - 2 * dJPDFXuY / jointPDFValueXuY - 2 * dJPDFXYl / jointPDFValueXYl - 2 * dJPDFXlY / jointPDFValueXlY; } // end if-block to check non-zero bin contribution else value = 0; return (value/4)*(-1); } template < class TFixedImage, class TMovingImage , class TDeformationField> double SpatialMutualInformationRegistrationFunction ::GetValueAndDerivativeInv(IndexType oindex, MeasureType& valuei, DerivativeType& derivative1,DerivativeType& derivative2) { double value=0; DerivativeType zero(ImageDimension); zero.Fill(0); double movingImageValue = this->GetMovingParzenTerm( this->m_MovingImage->GetPixel( oindex ) ); double fixedImageValue = this->GetFixedParzenTerm( this->m_FixedImage->GetPixel( oindex ) ); double movingImageParzenWindowContIndex=this->FitContIndexInBins( movingImageValue ); double fixedImageParzenWindowContIndex=this->FitContIndexInBins( fixedImageValue ); double dJPDF=0,jointPDFValue=0; double jointPDFValueXuY = 0, dJPDFXuY = 0, jointPDFValueXYu = 0, dJPDFXYu = 0, jointPDFValueXlY = 0, dJPDFXlY = 0, jointPDFValueXYl = 0; double dJPDFXYl = 0, jointPDFValueXuYl = 0, dJPDFXuYl = 0, jointPDFValueXlYu = 0, dJPDFXlYu = 0, jointPDFValueXuYr = 0, dJPDFXuYr = 0, jointPDFValueXrYu = 0, dJPDFXrYu = 0; { /** take derivative of joint pdf with respect to the b-spline */ typename pdfintType::ContinuousIndexType pdfind; pdfind[1]= fixedImageParzenWindowContIndex; pdfind[0]= movingImageParzenWindowContIndex; jointPDFValue = pdfinterpolator->EvaluateAtContinuousIndex(pdfind); dJPDF = (1.0)*(pdfinterpolator->EvaluateDerivativeAtContinuousIndex( pdfind ))[0]; jointPDFValueXuY = pdfinterpolatorXuY->EvaluateAtContinuousIndex(pdfind); dJPDFXuY = (1.0)*(pdfinterpolatorXuY->EvaluateDerivativeAtContinuousIndex( pdfind ))[0]; jointPDFValueXYu = pdfinterpolatorXYu->EvaluateAtContinuousIndex(pdfind); dJPDFXYu = (1.0)*(pdfinterpolatorXYu->EvaluateDerivativeAtContinuousIndex( pdfind ))[0]; jointPDFValueXlY = pdfinterpolatorXlY->EvaluateAtContinuousIndex(pdfind); dJPDFXlY = (1.0)*(pdfinterpolatorXlY->EvaluateDerivativeAtContinuousIndex( pdfind ))[0]; jointPDFValueXYl = pdfinterpolatorXYl->EvaluateAtContinuousIndex(pdfind); dJPDFXYl = (1.0)*(pdfinterpolatorXYl->EvaluateDerivativeAtContinuousIndex( pdfind ))[0]; jointPDFValueXuYl = pdfinterpolatorXuYl->EvaluateAtContinuousIndex(pdfind); dJPDFXuYl = (1.0)*(pdfinterpolatorXuYl->EvaluateDerivativeAtContinuousIndex( pdfind ))[0]; jointPDFValueXlYu = pdfinterpolatorXlYu->EvaluateAtContinuousIndex(pdfind); dJPDFXlYu = (1.0)*(pdfinterpolatorXlYu->EvaluateDerivativeAtContinuousIndex( pdfind ))[0]; jointPDFValueXuYr = pdfinterpolatorXuYr->EvaluateAtContinuousIndex(pdfind); dJPDFXuYr = (1.0)*(pdfinterpolatorXuYr->EvaluateDerivativeAtContinuousIndex( pdfind ))[0]; jointPDFValueXrYu = pdfinterpolatorXrYu->EvaluateAtContinuousIndex(pdfind); dJPDFXrYu = (1.0)*(pdfinterpolatorXrYu->EvaluateDerivativeAtContinuousIndex( pdfind ))[0]; } double eps=1.e-12; if( jointPDFValue > eps && jointPDFValueXuY > eps && jointPDFValueXYu > eps && jointPDFValueXlY > eps && jointPDFValueXYl > eps && jointPDFValueXuYl > eps && jointPDFValueXlYu > eps && jointPDFValueXrYu > eps && jointPDFValueXuYr > eps) { value = 4 * dJPDF / jointPDFValue + dJPDFXuYl / jointPDFValueXuYl + dJPDFXlYu / jointPDFValueXlYu + dJPDFXuYr / jointPDFValueXuYr + dJPDFXrYu / jointPDFValueXrYu - 2 * dJPDFXYu / jointPDFValueXYu - 2 * dJPDFXuY / jointPDFValueXuY - 2 * dJPDFXYl / jointPDFValueXYl - 2 * dJPDFXlY / jointPDFValueXlY; } // end if-block to check non-zero bin contribution else value = 0; // return 0; return (value/4)*(0); } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkSpatialMutualInformationRegistrationFunction.h000077500000000000000000000727721147325206600320740ustar00rootroot00000000000000/*========================================================================= Program: Insight Segmentation & Registration Toolkit Module: $RCSfile: itkSpatialMutualInformationRegistrationFunction.h,v $ Language: C++ Date: $Date: 2009/01/08 15:14:48 $ Version: $Revision: 1.20 $ Copyright (c) Insight Software Consortium. All rights reserved. See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkSpatialMutualInformationRegistrationFunction_h #define __itkSpatialMutualInformationRegistrationFunction_h #include "itkImageFileWriter.h" #include "itkImageToImageMetric.h" #include "itkAvantsPDEDeformableRegistrationFunction.h" #include "itkCovariantVector.h" #include "itkPoint.h" #include "itkIndex.h" #include "itkBSplineKernelFunction.h" #include "itkBSplineDerivativeKernelFunction.h" #include "itkCentralDifferenceImageFunction.h" #include "itkBSplineInterpolateImageFunction.h" #include "itkLinearInterpolateImageFunction.h" #include "itkBSplineDeformableTransform.h" #include "itkTranslationTransform.h" #include "itkArray2D.h" #include "itkImageBase.h" #include "itkTransform.h" #include "itkInterpolateImageFunction.h" #include "itkSingleValuedCostFunction.h" #include "itkExceptionObject.h" #include "itkGradientRecursiveGaussianImageFilter.h" #include "itkSpatialObject.h" #include "itkConstNeighborhoodIterator.h" namespace itk { /** \class SpatialMutualInformationRegistrationFunction * \brief Computes the mutual information between two images to be * registered using the method of Spatial et al. * * SpatialMutualInformationRegistrationFunction computes the mutual * information between a fixed and moving image to be registered. * * This class is templated over the FixedImage type and the MovingImage * type. * * The fixed and moving images are set via methods SetFixedImage() and * SetMovingImage(). This metric makes use of user specified Transform and * Interpolator. The Transform is used to map points from the fixed image to * the moving image domain. The Interpolator is used to evaluate the image * intensity at user specified geometric points in the moving image. * The Transform and Interpolator are set via methods SetTransform() and * SetInterpolator(). * * If a BSplineInterpolationFunction is used, this class obtain * image derivatives from the BSpline interpolator. Otherwise, * image derivatives are computed using central differencing. * * \warning This metric assumes that the moving image has already been * connected to the interpolator outside of this class. * * The method GetValue() computes of the mutual information * while method GetValueAndDerivative() computes * both the mutual information and its derivatives with respect to the * transform parameters. * * The calculations are based on the method of Spatial et al [1,2] * where the probability density distribution are estimated using * Parzen histograms. Since the fixed image PDF does not contribute * to the derivatives, it does not need to be smooth. Hence, * a zero order (box car) BSpline kernel is used * for the fixed image intensity PDF. On the other hand, to ensure * smoothness a third order BSpline kernel is used for the * moving image intensity PDF. * * On Initialize(), the FixedImage is uniformly sampled within * the FixedImageRegion. The number of samples used can be set * via SetNumberOfSpatialSamples(). Typically, the number of * spatial samples used should increase with the image size. * * During each call of GetValue(), GetDerivatives(), * GetValueAndDerivatives(), marginal and joint intensity PDF's * values are estimated at discrete position or bins. * The number of bins used can be set via SetNumberOfHistogramBins(). * To handle data with arbitray magnitude and dynamic range, * the image intensity is scale such that any contribution to the * histogram will fall into a valid bin. * * One the PDF's have been contructed, the mutual information * is obtained by doubling summing over the discrete PDF values. * * * Notes: * 1. This class returns the negative mutual information value. * 2. This class in not thread safe due the private data structures * used to the store the sampled points and the marginal and joint pdfs. * * References: * [1] "Nonrigid multimodality image registration" * D. Spatial, D. R. Haynor, H. Vesselle, T. Lewellen and W. Eubank * Medical Imaging 2001: Image Processing, 2001, pp. 1609-1620. * [2] "PET-CT Image Registration in the Chest Using Free-form Deformations" * D. Spatial, D. R. Haynor, H. Vesselle, T. Lewellen and W. Eubank * IEEE Transactions in Medical Imaging. Vol.22, No.1, January 2003. pp.120-128. * [3] "Optimization of Mutual Information for MultiResolution Image * Registration" * P. Thevenaz and M. Unser * IEEE Transactions in Image Processing, 9(12) December 2000. * * \ingroup RegistrationMetrics * \ingroup ThreadUnSafe */ template class ITK_EXPORT SpatialMutualInformationRegistrationFunction : public AvantsPDEDeformableRegistrationFunction< TFixedImage, TMovingImage , TDeformationField> { public: /** Standard class typedefs. */ typedef SpatialMutualInformationRegistrationFunction Self; typedef AvantsPDEDeformableRegistrationFunction< TFixedImage, TMovingImage, TDeformationField > Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Run-time type information (and related methods). */ itkTypeMacro( SpatialMutualInformationRegistrationFunction, AvantsPDEDeformableRegistrationFunction ); /** MovingImage image type. */ typedef typename Superclass::MovingImageType MovingImageType; typedef typename Superclass::MovingImagePointer MovingImagePointer; /** FixedImage image type. */ typedef typename Superclass::FixedImageType FixedImageType; typedef typename Superclass::FixedImagePointer FixedImagePointer; typedef typename FixedImageType::IndexType IndexType; typedef typename FixedImageType::SizeType SizeType; typedef typename FixedImageType::SpacingType SpacingType; /** Deformation field type. */ typedef typename Superclass::VectorType VectorType; typedef typename Superclass::DeformationFieldType DeformationFieldType; typedef typename Superclass::DeformationFieldTypePointer DeformationFieldTypePointer; /** Inherit some enums from the superclass. */ itkStaticConstMacro(ImageDimension, unsigned int,Superclass::ImageDimension); /** Inherit some enums from the superclass. */ typedef typename Superclass::PixelType PixelType; typedef typename Superclass::RadiusType RadiusType; typedef typename Superclass::NeighborhoodType NeighborhoodType; typedef typename Superclass::FloatOffsetType FloatOffsetType; typedef typename Superclass::TimeStepType TimeStepType; /** Interpolator type. */ typedef double CoordRepType; typedef // // LinearInterpolateImageFunction BSplineInterpolateImageFunction InterpolatorType; typedef typename InterpolatorType::Pointer InterpolatorPointer; typedef typename InterpolatorType::PointType PointType; typedef InterpolatorType DefaultInterpolatorType; // typedef LinearInterpolateImageFunction //DefaultInterpolatorType; /** Covariant vector type. */ typedef CovariantVector CovariantVectorType; /** Gradient calculator type. */ typedef CentralDifferenceImageFunction GradientCalculatorType; typedef typename GradientCalculatorType::Pointer GradientCalculatorPointer; /** Set the moving image interpolator. */ void SetMovingImageInterpolator( InterpolatorType * ptr ) { m_MovingImageInterpolator = ptr; } /** Get the moving image interpolator. */ InterpolatorType * GetMovingImageInterpolator(void) { return m_MovingImageInterpolator; } /** This class uses a constant timestep of 1. */ virtual TimeStepType ComputeGlobalTimeStep(void *itkNotUsed(GlobalData)) const { return 1; } /** Return a pointer to a global data structure that is passed to * this object from the solver at each calculation. */ virtual void *GetGlobalDataPointer() const { GlobalDataStruct *global = new GlobalDataStruct(); // global->m_SumOfSquaredDifference = 0.0; /// global->m_NumberOfPixelsProcessed = 0L; // global->m_SumOfSquaredChange = 0; return global; } /** Release memory for global data structure. */ virtual void ReleaseGlobalDataPointer( void *GlobalData ) const { delete (GlobalDataStruct *) GlobalData; } /** Set the object's state before each iteration. */ virtual void InitializeIteration(); typedef double CoordinateRepresentationType; /** Types inherited from Superclass. */ typedef TranslationTransform TransformType; typedef ImageToImageMetric< TFixedImage, TMovingImage > Metricclass; typedef typename TransformType::Pointer TransformPointer; typedef typename Metricclass::TransformJacobianType TransformJacobianType; // typedef typename Metricclass::InterpolatorType InterpolatorType; typedef typename Metricclass::MeasureType MeasureType; typedef typename Metricclass::DerivativeType DerivativeType; typedef typename TransformType::ParametersType ParametersType; typedef typename Metricclass::FixedImageConstPointer FixedImageConstPointer; typedef typename Metricclass::MovingImageConstPointer MovingImageCosntPointer; // typedef typename TransformType::CoordinateRepresentationType CoordinateRepresentationType; /** Index and Point typedef support. */ typedef typename FixedImageType::IndexType FixedImageIndexType; typedef typename FixedImageIndexType::IndexValueType FixedImageIndexValueType; typedef typename MovingImageType::IndexType MovingImageIndexType; typedef typename TransformType::InputPointType FixedImagePointType; typedef typename TransformType::OutputPointType MovingImagePointType; /** The marginal PDFs are stored as std::vector. */ typedef float PDFValueType; // typedef std::vector MarginalPDFType; typedef Image MarginalPDFType; typedef typename MarginalPDFType::IndexType MarginalPDFIndexType; /** Typedef for the joint PDF and PDF derivatives are stored as ITK Images. */ typedef Image JointPDFType; typedef Image JointPDFDerivativesType; typedef JointPDFType::IndexType JointPDFIndexType; typedef JointPDFType::PixelType JointPDFValueType; typedef JointPDFType::RegionType JointPDFRegionType; typedef JointPDFType::SizeType JointPDFSizeType; typedef JointPDFDerivativesType::IndexType JointPDFDerivativesIndexType; typedef JointPDFDerivativesType::PixelType JointPDFDerivativesValueType; typedef JointPDFDerivativesType::RegionType JointPDFDerivativesRegionType; typedef JointPDFDerivativesType::SizeType JointPDFDerivativesSizeType; /** Get the value and derivatives for single valued optimizers. */ double GetValueAndDerivative( IndexType index, MeasureType& Value, DerivativeType& Derivative1 , DerivativeType& Derivative2 ) ; /** Get the value and derivatives for single valued optimizers. */ double GetValueAndDerivativeInv( IndexType index, MeasureType& Value, DerivativeType& Derivative1 , DerivativeType& Derivative2 ) ; /** Number of spatial samples to used to compute metric */ // itkSetClampMacro( NumberOfSpatialSamples, unsigned long, // 1, NumericTraits::max() ); //itkGetConstReferenceMacro( NumberOfSpatialSamples, unsigned long); /** Number of bins to used in the histogram. Typical value is 50. */ // itkSetClampMacro( NumberOfHistogramBins, unsigned long, // 1, NumericTraits::max() ); // itkGetConstReferenceMacro( NumberOfHistogramBins, unsigned long); void SetNumberOfHistogramBins(unsigned long nhb) { m_NumberOfHistogramBins=nhb+2*this->m_Padding;} unsigned long GetNumberOfHistogramBins() {return m_NumberOfHistogramBins;} void SetTransform(TransformPointer t){m_Transform=t;} TransformPointer GetTransform(){return m_Transform;} void SetInterpolator(InterpolatorPointer t){m_Interpolator=t;} InterpolatorPointer GetInterpolator(){ return m_Interpolator; } void GetProbabilities(); double ComputeMutualInformation() { // typedef ImageRegionIterator JointPDFIteratorType; // JointPDFIteratorType jointPDFIterator ( m_JointPDF, m_JointPDF->GetBufferedRegion() ); float px,py,pxy; double mival = 0; double mi; unsigned long ct = 0; typename JointPDFType::IndexType index; // for (unsigned int ii=this->m_Padding+1; iim_Padding-2; ii++) for (unsigned int ii=0; iiGetPixel(mind); // for (unsigned int jj=this->m_Padding+1; jjm_Padding-2; jj++) for (unsigned int jj=0; jjGetPixel(mind); float denom = px*py; index[0]=ii; index[1]=jj; //pxy=m_JointPDF->GetPixel(index); JointPDFValueType *pdfPtr = m_JointPDF->GetBufferPointer() + ( ii* m_NumberOfHistogramBins ); // Move the pointer to the first affected bin int pdfMovingIndex = static_cast( jj ); pdfPtr += pdfMovingIndex; pxy=*(pdfPtr); mi=0; if (fabs(denom) > 0 ) { if (pxy/denom > 0) { //true mi mi = pxy*log(pxy/denom); //test mi //mi = 1.0 + log(pxy/denom); ct++; } } mival += mi; } // std::cout << " II " << ii << " JJ " << ii << " pxy " << pxy << " px " << px << std::endl; } this->m_Energy = (-1.0) * mival/log(2); return this->m_Energy; } double ComputeSpatialMutualInformation() { float pxy; double SMI = 0; double JointEntropy = 0, JointEntropyXuY = 0, JointEntropyXYu = 0, JointEntropyXlY = 0, JointEntropyXYl = 0; double JointEntropyXuYl = 0, JointEntropyXlYu = 0, JointEntropyXrYu = 0, JointEntropyXuYr = 0; for (unsigned int ii=0; ii( jj ); JointPDFValueType *pdfPtr = m_JointPDF->GetBufferPointer() + ( ii* m_NumberOfHistogramBins ) + pdfMovingIndex; pxy=*(pdfPtr); if (fabs(pxy) > 0 ) JointEntropy -= pxy * log(pxy); pdfPtr = m_JointPDFXuY->GetBufferPointer() + ( ii* m_NumberOfHistogramBins ) + pdfMovingIndex; pxy=*(pdfPtr); if (fabs(pxy) > 0 ) JointEntropyXuY -= pxy * log(pxy); pdfPtr = m_JointPDFXYu->GetBufferPointer() + ( ii* m_NumberOfHistogramBins ) + pdfMovingIndex; pxy=*(pdfPtr); if (fabs(pxy) > 0 ) JointEntropyXYu -= pxy * log(pxy); pdfPtr = m_JointPDFXlY->GetBufferPointer() + ( ii* m_NumberOfHistogramBins ) + pdfMovingIndex; pxy=*(pdfPtr); if (fabs(pxy) > 0 ) JointEntropyXlY -= pxy * log(pxy); pdfPtr = m_JointPDFXYl->GetBufferPointer() + ( ii* m_NumberOfHistogramBins ) + pdfMovingIndex; pxy=*(pdfPtr); if (fabs(pxy) > 0 ) JointEntropyXYl -= pxy * log(pxy); pdfPtr = m_JointPDFXuYl->GetBufferPointer() + ( ii* m_NumberOfHistogramBins ) + pdfMovingIndex; pxy=*(pdfPtr); if (fabs(pxy) > 0 ) JointEntropyXuYl -= pxy * log(pxy); pdfPtr = m_JointPDFXlYu->GetBufferPointer() + ( ii* m_NumberOfHistogramBins ) + pdfMovingIndex; pxy=*(pdfPtr); if (fabs(pxy) > 0 ) JointEntropyXlYu -= pxy * log(pxy); pdfPtr = m_JointPDFXrYu->GetBufferPointer() + ( ii* m_NumberOfHistogramBins ) + pdfMovingIndex; pxy=*(pdfPtr); if (fabs(pxy) > 0 ) JointEntropyXrYu -= pxy * log(pxy); pdfPtr = m_JointPDFXuYr->GetBufferPointer() + ( ii* m_NumberOfHistogramBins ) + pdfMovingIndex; pxy=*(pdfPtr); if (fabs(pxy) > 0 ) JointEntropyXuYr -= pxy * log(pxy); } } SMI = (0.5) * (JointEntropyXuY + JointEntropyXYu + JointEntropyXlY + JointEntropyXYl) - (0.25) * (4 * JointEntropy + JointEntropyXuYr + JointEntropyXrYu + JointEntropyXlYu + JointEntropyXuYl); // std::cout << " JE " << JointEntropy << " JEXuY " << JointEntropyXuY << " JEXYu " << JointEntropyXYu << " JEXuYr " << JointEntropyXuYr << std::endl; this->m_Energy = (-1.0) * fabs(SMI/log(2)); return this->m_Energy; } virtual VectorType ComputeUpdateInv(const NeighborhoodType &neighborhood, void *globalData, const FloatOffsetType &offset = FloatOffsetType(0.0)) { VectorType update; update.Fill(0.0); IndexType oindex = neighborhood.GetIndex(); FixedImageType* img =const_cast(this->Superclass::m_FixedImage.GetPointer()); if (!img) return update; typename FixedImageType::SpacingType spacing=img->GetSpacing(); typename FixedImageType::SizeType imagesize=img->GetLargestPossibleRegion().GetSize(); for (unsigned int dd=0; dd= static_cast(imagesize[dd]-1) ) return update; } CovariantVectorType fixedGradient; double loce=0.0; ParametersType fdvec1(ImageDimension); ParametersType fdvec2(ImageDimension); fdvec1.Fill(0); fdvec2.Fill(0); fixedGradient = m_FixedImageGradientCalculator->EvaluateAtIndex( oindex ); double nccm1=0; loce=this->GetValueAndDerivativeInv(oindex,nccm1,fdvec1,fdvec2); float eps=10; if ( loce > eps ) loce=eps; if ( loce < eps*(-1.0) ) loce=eps*(-1.0); for (int imd=0; imdm_MetricImage) this->m_MetricImage->SetPixel(oindex,loce); return update; } /* Normalizing the image to the range of [0 1] */ double GetMovingParzenTerm( double intensity ) { double windowTerm = static_cast( intensity ) - this->m_MovingImageTrueMin; windowTerm = windowTerm / ( this->m_MovingImageTrueMax - this->m_MovingImageTrueMin ) ; return windowTerm ; } double GetFixedParzenTerm( double intensity ) { double windowTerm = static_cast( intensity ) - this->m_FixedImageTrueMin; windowTerm = windowTerm / ( this->m_FixedImageTrueMax - this->m_FixedImageTrueMin ) ; return windowTerm ; } /* find the image index in the number of histogram bins */ unsigned int FitIndexInBins( double windowTerm ) { unsigned int movingImageParzenWindowIndex = static_cast( this->m_Padding + round( windowTerm * (float)(this->m_NumberOfHistogramBins - 1 - this->m_Padding))) ; // Make sure the extreme values are in valid bins if ( movingImageParzenWindowIndex < this->m_Padding ) { movingImageParzenWindowIndex = this->m_Padding -1 ; } else if ( movingImageParzenWindowIndex > (m_NumberOfHistogramBins - this->m_Padding ) ) { movingImageParzenWindowIndex = m_NumberOfHistogramBins - this->m_Padding - 1; } return movingImageParzenWindowIndex; } double FitContIndexInBins( double windowTerm ) { return ( (double) this->m_Padding + windowTerm*(float)(this->m_NumberOfHistogramBins-this->m_Padding)); } virtual VectorType ComputeUpdate(const NeighborhoodType &neighborhood, void *globalData, const FloatOffsetType &offset = FloatOffsetType(0.0)) { VectorType update; update.Fill(0.0); IndexType oindex = neighborhood.GetIndex(); FixedImageType* img =const_cast(this->Superclass::m_MovingImage.GetPointer()); if (!img) return update; typename FixedImageType::SpacingType spacing=img->GetSpacing(); typename FixedImageType::SizeType imagesize=img->GetLargestPossibleRegion().GetSize(); for (unsigned int dd=0; dd= static_cast(imagesize[dd]-1) ) return update; } CovariantVectorType movingGradient; double loce=0.0; ParametersType fdvec1(ImageDimension); ParametersType fdvec2(ImageDimension); fdvec1.Fill(0); fdvec2.Fill(0); movingGradient = m_MovingImageGradientCalculator->EvaluateAtIndex( oindex ); double nccm1=0; loce=this->GetValueAndDerivative(oindex,nccm1,fdvec1,fdvec2); float eps=10; if ( loce > eps ) loce=eps; if ( loce < eps*(-1.0) ) loce=eps*(-1.0); for (int imd=0; imdm_MetricImage) { typedef ImageFileWriter writertype; typename writertype::Pointer w= writertype::New(); w->SetInput( this->m_MetricImage); w->SetFileName("ZZmetric.nii.gz"); w->Write(); } } void SetOpticalFlow(bool b){ m_OpticalFlow = b; } typename JointPDFType::Pointer GetJointPDF() { return m_JointPDF; } typename JointPDFType::Pointer GetJointHist() { return m_JointHist; } void SetFixedImageMask( FixedImageType* img) {m_FixedImageMask=img; } /** FixedImage image neighborhood iterator type. */ typedef ConstNeighborhoodIterator FixedImageNeighborhoodIteratorType; /** A global data type for this class of equation. Used to store * iterators for the fixed image. */ struct GlobalDataStruct { FixedImageNeighborhoodIteratorType m_FixedImageIterator; }; /** The fixed image marginal PDF. */ mutable MarginalPDFType::Pointer m_FixedImageMarginalPDF; /** The moving image marginal PDF. */ mutable MarginalPDFType::Pointer m_MovingImageMarginalPDF; /** The joint PDF and PDF derivatives. */ typename JointPDFType::Pointer m_JointPDF; unsigned long m_NumberOfSpatialSamples; unsigned long m_NumberOfParameters; /** Variables to define the marginal and joint histograms. */ unsigned long m_NumberOfHistogramBins; double m_MovingImageNormalizedMin; double m_FixedImageNormalizedMin; double m_MovingImageTrueMin; double m_MovingImageTrueMax; double m_FixedImageTrueMin; double m_FixedImageTrueMax; double m_FixedImageBinSize; double m_MovingImageBinSize; protected: SpatialMutualInformationRegistrationFunction(); virtual ~SpatialMutualInformationRegistrationFunction() {}; void PrintSelf(std::ostream& os, Indent indent) const; private: SpatialMutualInformationRegistrationFunction(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented typename JointPDFType::Pointer m_JointHist; typename JointPDFDerivativesType::Pointer m_JointPDFDerivatives; typedef BSplineInterpolateImageFunction pdfintType; typename pdfintType::Pointer pdfinterpolator; // typename JointPDFType::Pointer m_JointEntropy; typename JointPDFType::Pointer m_JointPDFXuY; typename JointPDFType::Pointer m_JointPDFXYu; typename JointPDFType::Pointer m_JointPDFXlY; typename JointPDFType::Pointer m_JointPDFXYl; typename JointPDFType::Pointer m_JointPDFXuYl; typename JointPDFType::Pointer m_JointPDFXlYu; typename JointPDFType::Pointer m_JointPDFXrYu; typename JointPDFType::Pointer m_JointPDFXuYr; typename pdfintType::Pointer pdfinterpolatorXuY; typename pdfintType::Pointer pdfinterpolatorXYu; typename pdfintType::Pointer pdfinterpolatorXlY; typename pdfintType::Pointer pdfinterpolatorXYl; typename pdfintType::Pointer pdfinterpolatorXuYl; typename pdfintType::Pointer pdfinterpolatorXlYu; typename pdfintType::Pointer pdfinterpolatorXuYr; typename pdfintType::Pointer pdfinterpolatorXrYu; /** Typedefs for BSpline kernel and derivative functions. */ typedef BSplineKernelFunction<3> CubicBSplineFunctionType; typedef BSplineDerivativeKernelFunction<3> CubicBSplineDerivativeFunctionType; /** Cubic BSpline kernel for computing Parzen histograms. */ typename CubicBSplineFunctionType::Pointer m_CubicBSplineKernel; typename CubicBSplineDerivativeFunctionType::Pointer m_CubicBSplineDerivativeKernel; /** * Types and variables related to image derivative calculations. * If a BSplineInterpolationFunction is used, this class obtain * image derivatives from the BSpline interpolator. Otherwise, * image derivatives are computed using central differencing. */ typedef CovariantVector< double, itkGetStaticConstMacro(ImageDimension) > ImageDerivativesType; /** Boolean to indicate if the interpolator BSpline. */ bool m_InterpolatorIsBSpline; // boolean to determine if we use mono-modality assumption bool m_OpticalFlow; /** Typedefs for using BSpline interpolator. */ typedef BSplineInterpolateImageFunction BSplineInterpolatorType; /** Pointer to BSplineInterpolator. */ typename BSplineInterpolatorType::Pointer m_BSplineInterpolator; /** Typedefs for using central difference calculator. */ typedef CentralDifferenceImageFunction DerivativeFunctionType; /** Pointer to central difference calculator. */ typename DerivativeFunctionType::Pointer m_DerivativeCalculator; /** * Types and variables related to BSpline deformable transforms. * If the transform is of type third order BSplineDeformableTransform, * then we can speed up the metric derivative calculation by * only inspecting the parameters within the support region * of a mapped point. */ /** Boolean to indicate if the transform is BSpline deformable. */ bool m_TransformIsBSpline; /** The number of BSpline parameters per image dimension. */ long m_NumParametersPerDim; /** * The number of BSpline transform weights is the number of * of parameter in the support region (per dimension ). */ unsigned long m_NumBSplineWeights; /** * Enum of the deformabtion field spline order. */ enum { DeformationSplineOrder = 3 }; /** * Typedefs for the BSplineDeformableTransform. */ typedef BSplineDeformableTransform< CoordinateRepresentationType, ::itk::GetImageDimension::ImageDimension, DeformationSplineOrder> BSplineTransformType; typedef typename BSplineTransformType::WeightsType BSplineTransformWeightsType; typedef typename BSplineTransformType::ParameterIndexArrayType BSplineTransformIndexArrayType; /** * Variables used when transform is of type BSpline deformable. */ typename BSplineTransformType::Pointer m_BSplineTransform; /** * Cache pre-transformed points, weights, indices and * within support region flag. */ typedef typename BSplineTransformWeightsType::ValueType WeightsValueType; typedef Array2D BSplineTransformWeightsArrayType; typedef typename BSplineTransformIndexArrayType::ValueType IndexValueType; typedef Array2D BSplineTransformIndicesArrayType; typedef std::vector MovingImagePointArrayType; typedef std::vector BooleanArrayType; BSplineTransformWeightsArrayType m_BSplineTransformWeightsArray; BSplineTransformIndicesArrayType m_BSplineTransformIndicesArray; MovingImagePointArrayType m_PreTransformPointsArray; BooleanArrayType m_WithinSupportRegionArray; typename TFixedImage::SpacingType m_FixedImageSpacing; typename TFixedImage::PointType m_FixedImageOrigin; typedef FixedArray::ImageDimension> ParametersOffsetType; ParametersOffsetType m_ParametersOffset; mutable TransformPointer m_Transform; InterpolatorPointer m_Interpolator; InterpolatorPointer m_FixedImageInterpolator; InterpolatorPointer m_MovingImageInterpolator; GradientCalculatorPointer m_FixedImageGradientCalculator; GradientCalculatorPointer m_MovingImageGradientCalculator; FixedImagePointer m_FixedImageMask; MovingImagePointer m_MovingImageMask; double m_NormalizeMetric; float m_Normalizer; // typedef BSplineInterpolateImageFunction dpdfintType; // typename dpdfintType::Pointer dpdfinterpolator; typedef BSplineInterpolateImageFunction pdfintType2; typename pdfintType2::Pointer pdfinterpolator2; typename pdfintType2::Pointer pdfinterpolator3; unsigned int m_Padding; }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkSpatialMutualInformationRegistrationFunction.cxx" #endif #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkSyNDemonsRegistrationFunction.cxx000066400000000000000000000270111147325206600273120ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkSyNDemonsRegistrationFunction.cxx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkSyNDemonsRegistrationFunction_txx_ #define _itkSyNDemonsRegistrationFunction_txx_ #include "itkSyNDemonsRegistrationFunction.h" #include "itkExceptionObject.h" #include "vnl/vnl_math.h" namespace itk { /* * Default constructor */ template SyNDemonsRegistrationFunction ::SyNDemonsRegistrationFunction() { RadiusType r; unsigned int j; for( j = 0; j < ImageDimension; j++ ) { r[j] = 0; } this->SetRadius(r); m_TimeStep = 1.0; m_DenominatorThreshold = 1e-9; m_IntensityDifferenceThreshold =0.001; this->SetMovingImage(NULL); this->SetFixedImage(NULL); m_FixedImageSpacing.Fill( 1.0 ); m_FixedImageOrigin.Fill( 0.0 ); m_Normalizer = 1.0; m_FixedImageGradientCalculator = GradientCalculatorType::New(); typename DefaultInterpolatorType::Pointer interp = DefaultInterpolatorType::New(); m_MovingImageInterpolator = static_cast( interp.GetPointer() ); m_Metric = NumericTraits::max(); m_SumOfSquaredDifference = 0.0; m_NumberOfPixelsProcessed = 0L; m_RMSChange = NumericTraits::max(); m_SumOfSquaredChange = 0.0; m_MovingImageGradientCalculator = MovingImageGradientCalculatorType::New(); m_UseMovingImageGradient = false; } /* * Standard "PrintSelf" method. */ template void SyNDemonsRegistrationFunction ::PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf(os, indent); os << indent << "MovingImageIterpolator: "; os << m_MovingImageInterpolator.GetPointer() << std::endl; os << indent << "FixedImageGradientCalculator: "; os << m_FixedImageGradientCalculator.GetPointer() << std::endl; os << indent << "DenominatorThreshold: "; os << m_DenominatorThreshold << std::endl; os << indent << "IntensityDifferenceThreshold: "; os << m_IntensityDifferenceThreshold << std::endl; os << indent << "UseMovingImageGradient: "; os << m_UseMovingImageGradient << std::endl; os << indent << "Metric: "; os << m_Metric << std::endl; os << indent << "SumOfSquaredDifference: "; os << m_SumOfSquaredDifference << std::endl; os << indent << "NumberOfPixelsProcessed: "; os << m_NumberOfPixelsProcessed << std::endl; os << indent << "RMSChange: "; os << m_RMSChange << std::endl; os << indent << "SumOfSquaredChange: "; os << m_SumOfSquaredChange << std::endl; } /** * */ template void SyNDemonsRegistrationFunction ::SetIntensityDifferenceThreshold(double threshold) { m_IntensityDifferenceThreshold = threshold; } /** * */ template double SyNDemonsRegistrationFunction ::GetIntensityDifferenceThreshold() const { return m_IntensityDifferenceThreshold; } /* * Set the function state values before each iteration */ template void SyNDemonsRegistrationFunction ::InitializeIteration() { // std::cout << " INIT ITER " << std::endl; if( !this->GetMovingImage() || !this->GetFixedImage() || !m_MovingImageInterpolator ) { itkExceptionMacro( << "MovingImage, FixedImage and/or Interpolator not set" ); } // cache fixed image information m_FixedImageSpacing = this->GetFixedImage()->GetSpacing(); m_FixedImageOrigin = this->GetFixedImage()->GetOrigin(); // compute the normalizer m_Normalizer = 0.0; for( unsigned int k = 0; k < ImageDimension; k++ ) { m_Normalizer += m_FixedImageSpacing[k] * m_FixedImageSpacing[k]; } m_Normalizer /= static_cast( ImageDimension ); this->m_Energy=0; // setup gradient calculator m_FixedImageGradientCalculator->SetInputImage( this->GetFixedImage() ); m_MovingImageGradientCalculator->SetInputImage( this->GetMovingImage() ); // setup moving image interpolator m_MovingImageInterpolator->SetInputImage( this->GetMovingImage() ); // initialize metric computation variables m_SumOfSquaredDifference = 0.0; m_NumberOfPixelsProcessed = 0L; m_SumOfSquaredChange = 0.0; } /* * Compute update at a specify neighbourhood */ template typename SyNDemonsRegistrationFunction ::PixelType SyNDemonsRegistrationFunction ::ComputeUpdate(const NeighborhoodType &it, void * gd, const FloatOffsetType& itkNotUsed(offset)) { PixelType update; unsigned int j; IndexType index = it.GetIndex(); // Get fixed image related information double fixedValue; CovariantVectorType gradient; CovariantVectorType mgradient; double gradientSquaredMagnitude = 0; // Note: no need to check the index is within // fixed image buffer. This is done by the external filter. fixedValue = (double) this->GetFixedImage()->GetPixel( index ); double movingValue = (double)this->GetMovingImage()->GetPixel( index ); // if (fixedValue > 0)std::cout << " fxv " << fixedValue << " movingValue " << movingValue << std::endl; gradient = m_FixedImageGradientCalculator->EvaluateAtIndex( index ); mgradient = m_MovingImageGradientCalculator->EvaluateAtIndex( index ); for( j = 0; j < ImageDimension; j++ ) { if ( this->m_UseMovingImageGradient) gradient[j]=gradient[j]+mgradient[j]; gradientSquaredMagnitude += vnl_math_sqr( gradient[j] ); } /** * Compute Update. * In the original equation the denominator is defined as (g-f)^2 + grad_mag^2. * However there is a mismatch in units between the two terms. * The units for the second term is intensity^2/mm^2 while the * units for the first term is intensity^2. This mismatch is particularly * problematic when the fixed image does not have unit spacing. * In this implemenation, we normalize the first term by a factor K, * such that denominator = (g-f)^2/K + grad_mag^2 * where K = mean square spacing to compensate for the mismatch in units. */ double speedValue = fixedValue - movingValue; if ( fabs(speedValue) < this->m_RobustnessParameter) speedValue=0; // update the metric GlobalDataStruct *globalData = (GlobalDataStruct *)gd; if ( globalData ) { globalData->m_SumOfSquaredDifference += vnl_math_sqr( speedValue ); globalData->m_NumberOfPixelsProcessed += 1; } double denominator = vnl_math_sqr( speedValue ) / m_Normalizer + gradientSquaredMagnitude; this->m_Energy+=speedValue*speedValue; if ( vnl_math_abs(speedValue) < m_IntensityDifferenceThreshold || denominator < m_DenominatorThreshold ) { for( j = 0; j < ImageDimension; j++ ) { update[j] = 0.0; } return update; } for( j = 0; j < ImageDimension; j++ ) { update[j] = speedValue * gradient[j] / denominator; if ( globalData ) { globalData->m_SumOfSquaredChange += vnl_math_sqr( update[j] ); } } return update; } /* * Compute update at a specify neighbourhood */ template typename SyNDemonsRegistrationFunction ::PixelType SyNDemonsRegistrationFunction ::ComputeUpdateInv(const NeighborhoodType &it, void * gd, const FloatOffsetType& itkNotUsed(offset)) { PixelType update; unsigned int j; IndexType index = it.GetIndex(); // Get fixed image related information double fixedValue; CovariantVectorType gradient; double gradientSquaredMagnitude = 0; // Note: no need to check the index is within // fixed image buffer. This is done by the external filter. fixedValue = (double) this->GetFixedImage()->GetPixel( index ); double movingValue = (double)this->GetMovingImage()->GetPixel( index ); // if (fixedValue > 0)std::cout << " fxv " << fixedValue << " movingValue " << movingValue << std::endl; // gradient = m_FixedImageGradientCalculator->EvaluateAtIndex( index ); gradient = m_MovingImageGradientCalculator->EvaluateAtIndex( index ); for( j = 0; j < ImageDimension; j++ ) { gradientSquaredMagnitude += vnl_math_sqr( gradient[j] ); } /** * Compute Update. * In the original equation the denominator is defined as (g-f)^2 + grad_mag^2. * However there is a mismatch in units between the two terms. * The units for the second term is intensity^2/mm^2 while the * units for the first term is intensity^2. This mismatch is particularly * problematic when the fixed image does not have unit spacing. * In this implemenation, we normalize the first term by a factor K, * such that denominator = (g-f)^2/K + grad_mag^2 * where K = mean square spacing to compensate for the mismatch in units. */ double speedValue = movingValue - fixedValue; if ( fabs(speedValue) < this->m_RobustnessParameter) speedValue=0; // update the metric GlobalDataStruct *globalData = (GlobalDataStruct *)gd; if ( globalData ) { globalData->m_SumOfSquaredDifference += vnl_math_sqr( speedValue ); globalData->m_NumberOfPixelsProcessed += 1; } double denominator = vnl_math_sqr( speedValue ) / m_Normalizer + gradientSquaredMagnitude; if ( vnl_math_abs(speedValue) < m_IntensityDifferenceThreshold || denominator < m_DenominatorThreshold ) { for( j = 0; j < ImageDimension; j++ ) { update[j] = 0.0; } return update; } for( j = 0; j < ImageDimension; j++ ) { update[j] = speedValue * gradient[j] / denominator; if ( globalData ) { globalData->m_SumOfSquaredChange += vnl_math_sqr( update[j] ); } } return update; } /* * Update the metric and release the per-thread-global data. */ template void SyNDemonsRegistrationFunction ::ReleaseGlobalDataPointer( void *gd ) const { GlobalDataStruct * globalData = (GlobalDataStruct *) gd; m_MetricCalculationLock.Lock(); m_SumOfSquaredDifference += globalData->m_SumOfSquaredDifference; m_NumberOfPixelsProcessed += globalData->m_NumberOfPixelsProcessed; m_SumOfSquaredChange += globalData->m_SumOfSquaredChange; if ( m_NumberOfPixelsProcessed ) { m_Metric = m_SumOfSquaredDifference / static_cast( m_NumberOfPixelsProcessed ); m_RMSChange = vcl_sqrt( m_SumOfSquaredChange / static_cast( m_NumberOfPixelsProcessed ) ); } m_MetricCalculationLock.Unlock(); delete globalData; } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkSyNDemonsRegistrationFunction.h000066400000000000000000000237151147325206600267460ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkSyNDemonsRegistrationFunction.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkSyNDemonsRegistrationFunction_h_ #define _itkSyNDemonsRegistrationFunction_h_ #include "itkPDEDeformableRegistrationFunction.h" #include "itkPoint.h" #include "itkCovariantVector.h" #include "itkInterpolateImageFunction.h" #include "itkLinearInterpolateImageFunction.h" #include "itkCentralDifferenceImageFunction.h" namespace itk { /** * \class SyNDemonsRegistrationFunction * * This class encapsulate the PDE which drives the demons registration * algorithm. It is used by SyNDemonsRegistrationFilter to compute the * output deformation field which will map a moving image onto a * a fixed image. * * Non-integer moving image values are obtained by using * interpolation. The default interpolator is of type * LinearInterpolateImageFunction. The user may set other * interpolators via method SetMovingImageInterpolator. Note that the input * interpolator must derive from baseclass InterpolateImageFunction. * * This class is templated over the fixed image type, moving image type, * and the deformation field type. * * \warning This filter assumes that the fixed image type, moving image type * and deformation field type all have the same number of dimensions. * * \sa SyNDemonsRegistrationFilter * \ingroup FiniteDifferenceFunctions */ template class ITK_EXPORT SyNDemonsRegistrationFunction : public AvantsPDEDeformableRegistrationFunction< TFixedImage, TMovingImage, TDeformationField> { public: /** Standard class typedefs. */ typedef SyNDemonsRegistrationFunction Self; typedef PDEDeformableRegistrationFunction< TFixedImage, TMovingImage, TDeformationField > Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Run-time type information (and related methods). */ itkTypeMacro( SyNDemonsRegistrationFunction, PDEDeformableRegistrationFunction ); /** MovingImage image type. */ typedef typename Superclass::MovingImageType MovingImageType; typedef typename Superclass::MovingImagePointer MovingImagePointer; /** FixedImage image type. */ typedef typename Superclass::FixedImageType FixedImageType; typedef typename Superclass::FixedImagePointer FixedImagePointer; typedef typename FixedImageType::IndexType IndexType; typedef typename FixedImageType::SizeType SizeType; typedef typename FixedImageType::SpacingType SpacingType; /** Deformation field type. */ typedef typename Superclass::DeformationFieldType DeformationFieldType; typedef typename Superclass::DeformationFieldTypePointer DeformationFieldTypePointer; /** Inherit some enums from the superclass. */ itkStaticConstMacro(ImageDimension, unsigned int,Superclass::ImageDimension); /** Inherit some enums from the superclass. */ typedef typename Superclass::PixelType PixelType; typedef typename Superclass::RadiusType RadiusType; typedef typename Superclass::NeighborhoodType NeighborhoodType; typedef typename Superclass::FloatOffsetType FloatOffsetType; typedef typename Superclass::TimeStepType TimeStepType; /** Interpolator type. */ typedef double CoordRepType; typedef InterpolateImageFunction InterpolatorType; typedef typename InterpolatorType::Pointer InterpolatorPointer; typedef typename InterpolatorType::PointType PointType; typedef LinearInterpolateImageFunction DefaultInterpolatorType; /** Covariant vector type. */ typedef CovariantVector CovariantVectorType; /** Fixed image gradient calculator type. */ typedef CentralDifferenceImageFunction GradientCalculatorType; typedef typename GradientCalculatorType::Pointer GradientCalculatorPointer; /** Moving image gradient calculator type. */ typedef CentralDifferenceImageFunction MovingImageGradientCalculatorType; typedef typename MovingImageGradientCalculatorType::Pointer MovingImageGradientCalculatorPointer; /** Set the moving image interpolator. */ void SetMovingImageInterpolator( InterpolatorType * ptr ) { m_MovingImageInterpolator = ptr; } /** Get the moving image interpolator. */ InterpolatorType * GetMovingImageInterpolator(void) { return m_MovingImageInterpolator; } /** This class uses a constant timestep of 1. */ virtual TimeStepType ComputeGlobalTimeStep(void * itkNotUsed(GlobalData)) const { return m_TimeStep; } /** Return a pointer to a global data structure that is passed to * this object from the solver at each calculation. */ virtual void *GetGlobalDataPointer() const { GlobalDataStruct *global = new GlobalDataStruct(); global->m_SumOfSquaredDifference = 0.0; global->m_NumberOfPixelsProcessed = 0L; global->m_SumOfSquaredChange = 0; return global; } /** Release memory for global data structure. */ virtual void ReleaseGlobalDataPointer( void *GlobalData ) const; /** Set the object's state before each iteration. */ virtual void InitializeIteration(); /** This method is called by a finite difference solver image filter at * each pixel that does not lie on a data set boundary */ virtual PixelType ComputeUpdate(const NeighborhoodType &neighborhood, void *globalData, const FloatOffsetType &offset = FloatOffsetType(0.0)); virtual PixelType ComputeUpdateInv(const NeighborhoodType &neighborhood, void *globalData, const FloatOffsetType &offset = FloatOffsetType(0.0)); /** Get the metric value. The metric value is the mean square difference * in intensity between the fixed image and transforming moving image * computed over the the overlapping region between the two images. */ virtual double GetMetric() const { return m_Metric; } /** Get the rms change in deformation field. */ virtual double GetRMSChange() const { return m_RMSChange; } /** Select if the fixed image or moving image gradient is used for * the computating the demon forces. The fixed image gradient is used * by default. */ virtual void SetUseMovingImageGradient( bool flag ) { m_UseMovingImageGradient = flag; } virtual bool GetUseMovingImageGradient() const { return m_UseMovingImageGradient; } /** Set/Get the threshold below which the absolute difference of * intensity yields a match. When the intensities match between a * moving and fixed image pixel, the update vector (for that * iteration) will be the zero vector. Default is 0.001. */ virtual void SetIntensityDifferenceThreshold(double); virtual double GetIntensityDifferenceThreshold() const; protected: SyNDemonsRegistrationFunction(); ~SyNDemonsRegistrationFunction() {} void PrintSelf(std::ostream& os, Indent indent) const; /** FixedImage image neighborhood iterator type. */ typedef ConstNeighborhoodIterator FixedImageNeighborhoodIteratorType; /** A global data type for this class of equation. Used to store * information for computing the metric. */ struct GlobalDataStruct { double m_SumOfSquaredDifference; unsigned long m_NumberOfPixelsProcessed; double m_SumOfSquaredChange; }; private: SyNDemonsRegistrationFunction(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented /** Cache fixed image information. */ SpacingType m_FixedImageSpacing; PointType m_FixedImageOrigin; double m_Normalizer; /** Function to compute derivatives of the fixed image. */ GradientCalculatorPointer m_FixedImageGradientCalculator; /** Function to compute derivatives of the moving image. */ MovingImageGradientCalculatorPointer m_MovingImageGradientCalculator; bool m_UseMovingImageGradient; /** Function to interpolate the moving image. */ InterpolatorPointer m_MovingImageInterpolator; /** The global timestep. */ TimeStepType m_TimeStep; /** Threshold below which the denominator term is considered zero. */ double m_DenominatorThreshold; /** Threshold below which two intensity value are assumed to match. */ double m_IntensityDifferenceThreshold; /** The metric value is the mean square difference in intensity between * the fixed image and transforming moving image computed over the * the overlapping region between the two images. */ mutable double m_Metric; mutable double m_SumOfSquaredDifference; mutable unsigned long m_NumberOfPixelsProcessed; mutable double m_RMSChange; mutable double m_SumOfSquaredChange; /** Mutex lock to protect modification to metric. */ mutable SimpleFastMutexLock m_MetricCalculationLock; }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkSyNDemonsRegistrationFunction.cxx" #endif #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkVectorParameterizedNeighborhoodOperatorImageFilter.h000066400000000000000000000157241147325206600331230ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkVectorParameterizedNeighborhoodOperatorImageFilter.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkVectorParameterizedNeighborhoodOperatorImageFilter_h #define __itkVectorParameterizedNeighborhoodOperatorImageFilter_h #include "itkImageToImageFilter.h" #include "itkNeighborhoodOperator.h" #include "itkImage.h" #include "itkImageBoundaryCondition.h" #include "itkGaussianOperator.h" namespace itk { /** \class VectorParameterizedNeighborhoodOperatorImageFilter * \brief Applies a single scalar NeighborhoodOperator to an * itk::Vector image region. * * This filter calculates successive inner products between a single * NeighborhoodOperator and a NeighborhoodIterator, which is swept * across every pixel in an image region. For operators that are * symmetric across their axes, the result is a fast convolution * with the image region. Apply the mirror()'d operator for * non-symmetric NeighborhoodOperators. * * This filter assumes that the input and output images have * pixels which are itk::Vectors of the same vector dimension. * The input NeighbourhoodOperator must have a scalar type * that matches the ValueType of vector pixels. * * To apply a scalar NeighborhoodOperator to a scalar image * use NeighborhoodOperatorImageFilter instead. * * \ingroup ImageFilters * * \sa Image * \sa Neighborhood * \sa NeighborhoodOperator * \sa NeighborhoodIterator * \sa NeighborhoodOperatorImageFilter */ template class ITK_EXPORT VectorParameterizedNeighborhoodOperatorImageFilter : public ImageToImageFilter< TInputImage, TOutputImage > { public: /** Standard class typedefs. */ typedef VectorParameterizedNeighborhoodOperatorImageFilter Self; typedef ImageToImageFilter< TInputImage, TOutputImage > Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Run-time type information (and related methods) */ itkTypeMacro(VectorParameterizedNeighborhoodOperatorImageFilter, ImageToImageFilter); /** Extract some information from the image types. Dimensionality * of the two images is assumed to be the same. */ typedef typename TInputImage::Pointer InputImagePointer; typedef typename TOutputImage::Pointer OutputImagePointer; typedef typename TOutputImage::PixelType OutputPixelType; typedef typename TOutputImage::InternalPixelType OutputInternalPixelType; typedef typename TInputImage::PixelType InputPixelType; typedef typename TInputImage::InternalPixelType InputInternalPixelType; typedef typename OutputPixelType::ValueType ScalarValueType; typedef TParamImage ParameterImageType; typedef typename TParamImage::Pointer ParameterImagePointer; typedef typename TParamImage::PixelType ParameterImageTypePixelType; /** Determine image dimension. */ itkStaticConstMacro(ImageDimension, unsigned int, TOutputImage::ImageDimension ); /** Image typedef support */ typedef TInputImage InputImageType; typedef TOutputImage OutputImageType; /** Typedef for generic boundary condition pointer */ typedef ImageBoundaryCondition * ImageBoundaryConditionPointerType; /** Superclass typedefs. */ typedef typename Superclass::OutputImageRegionType OutputImageRegionType; typedef itk::GaussianOperator OperatorType; // Neighborhood /** Sets the operator that is used to filter the image. Note * that the operator is stored as an internal COPY (it * is not part of the pipeline). */ void SetOperator(OperatorType &p) { m_Operator = p; this->Modified(); } /** Allows a user to override the internal boundary condition. Care should be * be taken to ensure that the overriding boundary condition is a persistent * object during the time it is referenced. The overriding condition * can be of a different type than the default type as long as it is * a subclass of ImageBoundaryCondition. */ void OverrideBoundaryCondition(const ImageBoundaryConditionPointerType i) { m_BoundsCondition = i; } /** VectorParameterizedNeighborhoodOperatorImageFilter needs a larger input requested * region than the output requested region. As such, * VectorParameterizedNeighborhoodOperatorImageFilter needs to provide an implementation for * GenerateInputRequestedRegion() in order to inform the pipeline * execution model. * * \sa ProcessObject::GenerateInputRequestedRegion() */ virtual void GenerateInputRequestedRegion() throw (InvalidRequestedRegionError); void SetParameterImage( ParameterImagePointer I) { m_ParameterImage = I; } protected: VectorParameterizedNeighborhoodOperatorImageFilter() { m_ParameterImage = NULL; } virtual ~VectorParameterizedNeighborhoodOperatorImageFilter() {} /** VectorParameterizedNeighborhoodOperatorImageFilter can be implemented as a * multithreaded filter. Therefore, this implementation provides a * ThreadedGenerateData() routine which is called for each * processing thread. The output image data is allocated * automatically by the superclass prior to calling * ThreadedGenerateData(). ThreadedGenerateData can only write to * the portion of the output image specified by the parameter * "outputRegionForThread" * \sa ImageToImageFilter::ThreadedGenerateData(), * ImageToImageFilter::GenerateData() */ void ThreadedGenerateData(const OutputImageRegionType& outputRegionForThread, int threadId ); void PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf(os,indent); } private: VectorParameterizedNeighborhoodOperatorImageFilter(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented /** Pointer to the internal operator used to filter the image. */ OperatorType m_Operator; /** Pointer to a persistent boundary condition object used * for the image iterator. */ ImageBoundaryConditionPointerType m_BoundsCondition; ParameterImagePointer m_ParameterImage; }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkVectorParameterizedNeighborhoodOperatorImageFilter.txx" #endif #endif ants-1.9.2+svn680.dfsg/ImageRegistration/itkVectorParameterizedNeighborhoodOperatorImageFilter.txx000066400000000000000000000130301147325206600335030ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkVectorParameterizedNeighborhoodOperatorImageFilter.txx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkVectorParameterizedNeighborhoodOperatorImageFilter_txx #define _itkVectorParameterizedNeighborhoodOperatorImageFilter_txx #include "itkVectorParameterizedNeighborhoodOperatorImageFilter.h" #include "itkNeighborhoodAlgorithm.h" #include "itkVectorNeighborhoodInnerProduct.h" #include "itkImageRegionIterator.h" #include "itkConstNeighborhoodIterator.h" #include "itkProgressReporter.h" namespace itk { template void VectorParameterizedNeighborhoodOperatorImageFilter ::GenerateInputRequestedRegion() throw (InvalidRequestedRegionError) { // call the superclass' implementation of this method. this should // copy the output requested region to the input requested region Superclass::GenerateInputRequestedRegion(); // get pointers to the input and output InputImagePointer inputPtr = const_cast< InputImageType * >( this->GetInput() ); if ( !inputPtr ) { return; } // get a copy of the input requested region (should equal the output // requested region) typename TInputImage::RegionType inputRequestedRegion; inputRequestedRegion = inputPtr->GetRequestedRegion(); // pad the input requested region by the operator radius inputRequestedRegion.PadByRadius( m_Operator.GetRadius() ); // crop the input requested region at the input's largest possible region if ( inputRequestedRegion.Crop(inputPtr->GetLargestPossibleRegion()) ) { inputPtr->SetRequestedRegion( inputRequestedRegion ); return; } else { // Couldn't crop the region (requested region is outside the largest // possible region). Throw an exception. // store what we tried to request (prior to trying to crop) inputPtr->SetRequestedRegion( inputRequestedRegion ); // build an exception InvalidRequestedRegionError e(__FILE__, __LINE__); OStringStream msg; msg << static_cast(this->GetNameOfClass()) << "::GenerateInputRequestedRegion()"; e.SetLocation(msg.str().c_str()); e.SetDescription("Requested region is (at least partially) outside the largest possible region."); e.SetDataObject(inputPtr); throw e; } } template< class TInputImage, class TOutputImage, class TParamImage> void VectorParameterizedNeighborhoodOperatorImageFilter ::ThreadedGenerateData(const OutputImageRegionType& outputRegionForThread, int threadId) { typedef NeighborhoodAlgorithm::ImageBoundaryFacesCalculator BFC; typedef typename BFC::FaceListType FaceListType; VectorNeighborhoodInnerProduct smartInnerProduct; BFC faceCalculator; FaceListType faceList; // Allocate output OutputImageType *output = this->GetOutput(); const InputImageType *input = this->GetInput(); // Break the input into a series of regions. The first region is free // of boundary conditions, the rest with boundary conditions. Note, // we pass in the input image and the OUTPUT requested region. We are // only concerned with centering the neighborhood operator at the // pixels that correspond to output pixels. faceList = faceCalculator(input, outputRegionForThread, m_Operator.GetRadius()); typename FaceListType::iterator fit; // support progress methods/callbacks ProgressReporter progress(this, threadId, outputRegionForThread.GetNumberOfPixels()); ImageRegionIteratorWithIndex it; // Process non-boundary region and then each of the boundary faces. // These are N-d regions which border the edge of the buffer. ConstNeighborhoodIterator bit; for (fit=faceList.begin(); fit != faceList.end(); ++fit) { bit = ConstNeighborhoodIterator(m_Operator.GetRadius(), input, *fit); it = ImageRegionIteratorWithIndex(output, *fit); bit.GoToBegin(); while ( ! bit.IsAtEnd() ) { if (m_ParameterImage) { // float max=1.45; float param = m_ParameterImage->GetPixel(it.GetIndex()); // if (param > 0.5 || param < 2.0 ) param = 0.0; // if (param < 1./max && param > 0 ) param = 1./max; // if (param > max && param > 0 ) param = max; // if (param < 1.0 && param > 0) param=1.0/param; // std::cout << " param " << param ; if (param <= 0) it.Value() = input->GetPixel(it.GetIndex()); else { m_Operator.SetVariance( param ); m_Operator.CreateDirectional(); it.Value() = smartInnerProduct(bit, m_Operator); } } else it.Value() = smartInnerProduct(bit, m_Operator); ++bit; ++it; progress.CompletedPixel(); } } } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/ImageSegmentation/000077500000000000000000000000001147325206600200605ustar00rootroot00000000000000ants-1.9.2+svn680.dfsg/ImageSegmentation/antsAtroposSegmentationImageFilter.h000066400000000000000000000377411147325206600272510ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: antsAtroposSegmentationImageFilter.h,v $ Language: C++ Date: $Date: $ Version: $Revision: $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __antsAtroposSegmentationImageFilter_h #define __antsAtroposSegmentationImageFilter_h #include "itkImageToImageFilter.h" #include "antsListSampleFunction.h" #include "antsListSampleToListSampleFilter.h" #include "itkArray.h" #include "itkBSplineScatteredDataPointSetToImageFilter.h" #include "itkFixedArray.h" #include "itkListSample.h" #include "itkPointSet.h" #include "itkVector.h" #include #include #include #include namespace itk { namespace ants { /** \class AtroposSegmentationImageFilter * \brief Atropos: A Priori Classification with Registration Initialized * Template Assistance * * This filter provides an Expectation-Maximization framework for statistical * segmentation where the intensity profile of each class is modeled as a * mixture model and spatial smoothness is enforced by an MRF prior. * * Initial labeling can be performed by otsu thresholding, kmeans clustering, * a set of user-specified prior probability images, or a prior label image. * If specified, the latter two initialization options are also used as * priors in the MRF update step. * * The assumed labeling is such that classes are assigned consecutive * indices 1, 2, 3, etc. Label 0 is reserved for the background when a * mask is specified. * */ template::ImageDimension>, class TClassifiedImage = TMaskImage> class ITK_EXPORT AtroposSegmentationImageFilter : public ImageToImageFilter { public: /** Standard class typdedefs. */ typedef AtroposSegmentationImageFilter Self; typedef ImageToImageFilter Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); /** Run-time type information (and related methods). */ itkTypeMacro( AtroposSegmentationImageFilter, ImageToImageFilter ); /** Dimension of the images. */ itkStaticConstMacro( ImageDimension, unsigned int, TInputImage::ImageDimension ); itkStaticConstMacro( ClassifiedImageDimension, unsigned int, TClassifiedImage::ImageDimension ); itkStaticConstMacro( MaskImageDimension, unsigned int, TMaskImage::ImageDimension ); /** Typedef support of input types. */ typedef TInputImage ImageType; typedef typename ImageType::PixelType PixelType; typedef TMaskImage MaskImageType; typedef typename MaskImageType::PixelType MaskLabelType; typedef TClassifiedImage ClassifiedImageType; typedef typename ClassifiedImageType::PixelType LabelType; /** Some convenient typedefs. */ typedef float RealType; typedef Image RealImageType; typedef FixedArray ArrayType; typedef PointSet SparseImageType; /** Mixture model component typedefs */ typedef Array MeasurementVectorType; typedef typename itk::Statistics::ListSample SampleType; typedef SmartPointer SamplePointer; typedef ants::Statistics::ListSampleFunction LikelihoodFunctionType; typedef typename LikelihoodFunctionType::Pointer LikelihoodFunctionPointer; typedef typename LikelihoodFunctionType:: WeightArrayType WeightArrayType; /** Outlier handling typedefs */ typedef ants::Statistics:: ListSampleToListSampleFilter OutlierHandlingFilterType; /** B-spline fitting typedefs */ typedef Vector ScalarType; typedef Image ScalarImageType; typedef PointSet PointSetType; typedef BSplineScatteredDataPointSetToImageFilter BSplineFilterType; typedef typename BSplineFilterType::PointDataImageType ControlPointLatticeType; typedef std::vector ControlPointLatticeContainerType; /** Initialization typedefs */ enum InitializationStrategyType { Random, KMeans, Otsu, PriorProbabilityImages, PriorLabelImage }; typedef std::pair LabelParametersType; typedef std::map LabelParameterMapType; typedef Array ParametersType; enum PosteriorProbabilityFormulationType { Socrates, Plato, Aristotle /*, Zeno, Diogenes, Thales, Democritus*/ }; /** ivars Set/Get functionality */ itkSetClampMacro( NumberOfClasses, unsigned int, 2, NumericTraits::max() ); itkGetConstMacro( NumberOfClasses, unsigned int ); itkSetMacro( MaximumNumberOfIterations, unsigned int ); itkGetConstMacro( MaximumNumberOfIterations, unsigned int ); itkSetMacro( ConvergenceThreshold, RealType ); itkGetConstMacro( ConvergenceThreshold, RealType ); itkGetConstMacro( CurrentConvergenceMeasurement, RealType ); itkGetConstMacro( ElapsedIterations, unsigned int ); itkSetMacro( MRFSmoothingFactor, RealType ); itkGetConstMacro( MRFSmoothingFactor, RealType ); itkSetMacro( MRFRadius, ArrayType ); itkGetConstMacro( MRFRadius, ArrayType ); itkSetMacro( InitializationStrategy, InitializationStrategyType ); itkGetConstMacro( InitializationStrategy, InitializationStrategyType ); itkSetMacro( InitialKMeansParameters, ParametersType ); itkGetConstMacro( InitialKMeansParameters, ParametersType ); itkSetMacro( PosteriorProbabilityFormulation, PosteriorProbabilityFormulationType ); itkGetConstMacro( PosteriorProbabilityFormulation, PosteriorProbabilityFormulationType ); itkSetMacro( UseMixtureModelProportions, bool ); itkGetConstMacro( UseMixtureModelProportions, bool ); itkSetMacro( SplineOrder, unsigned int ); itkGetConstMacro( SplineOrder, unsigned int ); itkSetMacro( NumberOfLevels, ArrayType ); itkGetConstMacro( NumberOfLevels, ArrayType ); itkSetMacro( NumberOfControlPoints, ArrayType ); itkGetConstMacro( NumberOfControlPoints, ArrayType ); itkGetConstMacro( NumberOfIntensityImages, unsigned int ); itkSetMacro( MinimizeMemoryUsage, bool ); itkGetConstMacro( MinimizeMemoryUsage, bool ); itkBooleanMacro( MinimizeMemoryUsage ); void SetMaskImage( const MaskImageType * mask ); const MaskImageType * GetMaskImage() const; itkSetClampMacro( PriorProbabilityWeight, RealType, 0.0, 1.0 ); itkGetConstMacro( PriorProbabilityWeight, RealType ); itkSetClampMacro( ProbabilityThreshold, RealType, 0.0, 1.0 ); itkGetConstMacro( ProbabilityThreshold, RealType ); void SetAdaptiveSmoothingWeight( unsigned int idx, RealType weight ) { RealType clampedWeight = vnl_math_min( NumericTraits::One, vnl_math_max( NumericTraits::Zero, weight ) ); /** * Clamp values between 0 and 1. Also, index [0] corresponds to the * input image and [1]...[n], correspond to the auxiliary images. */ if( idx >= this->m_AdaptiveSmoothingWeights.size() ) { this->m_AdaptiveSmoothingWeights.resize( idx + 1 ); this->m_AdaptiveSmoothingWeights[idx] = clampedWeight; this->Modified(); } if( this->m_AdaptiveSmoothingWeights[idx] != weight ) { this->m_AdaptiveSmoothingWeights[idx] = clampedWeight; this->Modified(); } } RealType GetAdaptiveSmoothingWeight( unsigned int idx ) { /** * [0] corresponds to the input image and [1]...[n], correspond to * the auxiliary images. */ if( idx < this->m_AdaptiveSmoothingWeights.size() ) { return this->m_AdaptiveSmoothingWeights[idx]; } else { return 0; } } void SetPriorLabelParameterMap( LabelParameterMapType m ) { this->m_PriorLabelParameterMap = m; this->Modified(); } void GetPriorLabelParameterMap() { return this->m_PriorLabelParameterMap; } /** * Prior probability images (numbered between 1,...,numberOfClasses) */ void SetPriorProbabilityImage( unsigned int whichClass, RealImageType * prior ); typename RealImageType::Pointer GetPriorProbabilityImage( unsigned int whichClass ) const; void SetPriorLabelImage( const ClassifiedImageType * prior ); const ClassifiedImageType * GetPriorLabelImage() const; /** * Auxiliary images (numbered between 1,...,n) */ void SetIntensityImage( unsigned int which, const ImageType * image ); const ImageType * GetIntensityImage( unsigned int which ) const; /** * Euclidean distance uses Maurer to calculate the distance transform image. * Otherwise use the fast marching filter. The former option is faster but it * for non-Euclidean shapes (such as the cortex), it might be more accurate * to use the latter option. */ itkSetMacro( UseEuclideanDistanceForPriorLabels, bool ); itkGetConstMacro( UseEuclideanDistanceForPriorLabels, bool ); itkBooleanMacro( UseEuclideanDistanceForPriorLabels ); itkSetObjectMacro( OutlierHandlingFilter, OutlierHandlingFilterType ); itkGetObjectMacro( OutlierHandlingFilter, OutlierHandlingFilterType ); void SetLikelihoodFunction( unsigned int n, LikelihoodFunctionType *prob ) { if( n < this->m_MixtureModelComponents.size() && this->m_MixtureModelComponents[n] != prob ) { this->m_MixtureModelComponents[n] = prob; this->Modified(); } else if( n >= this->m_MixtureModelComponents.size() ) { this->m_MixtureModelComponents.resize( n + 1 ); this->m_MixtureModelComponents[n] = prob; this->Modified(); } } LikelihoodFunctionType * GetLikelihoodFunction( unsigned int n ) { if( n < this->m_MixtureModelComponents.size() ) { return this->m_MixtureModelComponents[n].GetPointer(); } else { return NULL; } } /** * Function to facilitate looking at the likelihood image. * Not used internally. */ typename RealImageType::Pointer GetLikelihoodImage( unsigned int ); RealType CalculateLocalPosteriorProbability( RealType, RealType, RealType, RealType, RealType ); typename RealImageType::Pointer GetPosteriorProbabilityImage( unsigned int ); typename RealImageType::Pointer GetSmoothIntensityImageFromPriorImage( unsigned int, unsigned int ); typename RealImageType::Pointer GetDistancePriorProbabilityImage( unsigned int ); typename SampleType::Pointer GetScalarSamples(); #ifdef ITK_USE_CONCEPT_CHECKING /** Begin concept checking */ itkConceptMacro( SameDimensionCheck1, ( Concept::SameDimension ) ); itkConceptMacro( SameDimensionCheck2, ( Concept::SameDimension ) ); /** End concept checking */ #endif protected: AtroposSegmentationImageFilter(); ~AtroposSegmentationImageFilter(); void PrintSelf( std::ostream& os, Indent indent ) const; void GenerateData(); private: AtroposSegmentationImageFilter(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented void GenerateInitialClassLabeling(); void GenerateInitialClassLabelingWithOtsuThresholding(); void GenerateInitialClassLabelingWithKMeansClustering(); void GenerateInitialClassLabelingWithPriorProbabilityImages(); RealType UpdateClassLabeling(); unsigned int m_NumberOfClasses; unsigned int m_NumberOfIntensityImages; unsigned int m_ElapsedIterations; unsigned int m_MaximumNumberOfIterations; RealType m_CurrentConvergenceMeasurement; RealType m_ConvergenceThreshold; MaskLabelType m_MaskLabel; std::vector m_MixtureModelComponents; Array m_MixtureModelProportions; InitializationStrategyType m_InitializationStrategy; ParametersType m_InitialKMeansParameters; PosteriorProbabilityFormulationType m_PosteriorProbabilityFormulation; bool m_UseMixtureModelProportions; typename OutlierHandlingFilterType::Pointer m_OutlierHandlingFilter; ArrayType m_MRFRadius; RealType m_MRFSmoothingFactor; std::vector m_AdaptiveSmoothingWeights; RealType m_PriorProbabilityWeight; LabelParameterMapType m_PriorLabelParameterMap; RealType m_ProbabilityThreshold; std::vector m_PriorProbabilityImages; std::vector m_PriorProbabilitySparseImages; unsigned int m_SplineOrder; ArrayType m_NumberOfLevels; ArrayType m_NumberOfControlPoints; std::vector m_ControlPointLattices; typename RealImageType::Pointer m_SumDistancePriorProbabilityImage; typename RealImageType::Pointer m_SumPosteriorProbabilityImage; bool m_MinimizeMemoryUsage; bool m_UseEuclideanDistanceForPriorLabels; std::vector m_DistancePriorProbabilityImages; std::vector m_PosteriorProbabilityImages; inline typename RealImageType::IndexType NumberToIndex( unsigned long number, const typename RealImageType::SizeType size ) const { typename RealImageType::IndexType k; k[0] = 1; for ( unsigned int i = 1; i < ImageDimension; i++ ) { k[i] = size[i-1]*k[i-1]; } typename RealImageType::IndexType index; for ( unsigned int i = 0; i < ImageDimension; i++ ) { index[ImageDimension-i-1] = static_cast( number/k[ImageDimension-i-1] ); number %= k[ImageDimension-i-1]; } return index; } inline unsigned long IndexToNumber( typename RealImageType::IndexType k, const typename RealImageType::SizeType size ) const { unsigned long number = k[0]; for ( unsigned int i = 1; i < ImageDimension; i++ ) { unsigned long s = 1; for( unsigned int j = 0; j < i; j++ ) { s *= size[j]; } number += s*k[i]; } return number; } }; } // namespace ants } // namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "antsAtroposSegmentationImageFilter.txx" #endif #endif ants-1.9.2+svn680.dfsg/ImageSegmentation/antsAtroposSegmentationImageFilter.txx000066400000000000000000003130471147325206600276410ustar00rootroot00000000000000/*========================================================================= Program: Insight Segmentation & Registration Toolkit Module: $RCSfile: antsAtroposSegmentationImageFilter.txx,v $ Language: C++ Date: $Date: $ Version: $Revision: $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __antsAtroposSegmentationImageFilter_txx #define __antsAtroposSegmentationImageFilter_txx #include "antsAtroposSegmentationImageFilter.h" #include "antsGaussianListSampleFunction.h" #include "itkAddImageFilter.h" #include "itkAddConstantToImageFilter.h" #include "itkBinaryContourImageFilter.h" #include "itkBinaryThresholdImageFilter.h" #include "itkBSplineControlPointImageFilter.h" #include "itkCastImageFilter.h" #include "itkConstNeighborhoodIterator.h" #include "itkDistanceToCentroidMembershipFunction.h" #include "itkFastMarchingImageFilter.h" #include "itkImageDuplicator.h" #include "itkImageRegionConstIteratorWithIndex.h" #include "itkImageRegionIterator.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkIterationReporter.h" #include "itkKdTreeBasedKmeansEstimator.h" #include "itkLabelStatisticsImageFilter.h" #include "itkMersenneTwisterRandomVariateGenerator.h" #include "itkMinimumDecisionRule2.h" #include "itkOtsuMultipleThresholdsCalculator.h" #include "itkSampleClassifierFilter.h" #include "itkSignedMaurerDistanceMapImageFilter.h" #include "itkTimeProbe.h" #include "itkVariableSizeMatrix.h" #include "itkVectorIndexSelectionCastImageFilter.h" #include "itkWeightedCentroidKdTreeGenerator.h" #include "vnl/vnl_vector.h" namespace itk { namespace ants { template AtroposSegmentationImageFilter ::AtroposSegmentationImageFilter() { this->ProcessObject::SetNumberOfRequiredInputs( 1 ); this->m_NumberOfIntensityImages = 1; this->m_NumberOfClasses = 3; this->m_MaximumNumberOfIterations = 5; this->m_ElapsedIterations = 0; this->m_ConvergenceThreshold = 0.001; this->m_MaskLabel = NumericTraits::One; this->m_InitializationStrategy = KMeans; this->m_InitialKMeansParameters.SetSize( 0 ); this->m_PosteriorProbabilityFormulation = Socrates; this->m_UseMixtureModelProportions = true; this->m_PriorProbabilityWeight = 0.0; this->m_AdaptiveSmoothingWeights.clear(); this->m_PriorLabelParameterMap.clear(); this->m_ProbabilityThreshold = 0.0; this->m_PriorProbabilityImages.clear(); this->m_PriorProbabilitySparseImages.clear(); this->m_MRFSmoothingFactor = 0.3; this->m_MRFRadius.Fill( 1 ); this->m_SplineOrder = 3; this->m_NumberOfLevels.Fill( 6 ); this->m_NumberOfControlPoints.Fill( this->m_SplineOrder + 1 ); this->m_MinimizeMemoryUsage = false; this->m_UseEuclideanDistanceForPriorLabels = false; this->m_PosteriorProbabilityImages.clear(); this->m_DistancePriorProbabilityImages.clear(); this->m_OutlierHandlingFilter = NULL; } template AtroposSegmentationImageFilter ::~AtroposSegmentationImageFilter() { } template void AtroposSegmentationImageFilter ::SetMaskImage( const MaskImageType * mask ) { this->SetNthInput( 1, const_cast( mask ) ); } template const typename AtroposSegmentationImageFilter ::MaskImageType * AtroposSegmentationImageFilter ::GetMaskImage() const { const MaskImageType * maskImage = dynamic_cast( this->ProcessObject::GetInput( 1 ) ); return maskImage; } template void AtroposSegmentationImageFilter ::SetPriorLabelImage( const ClassifiedImageType * prior ) { this->SetNthInput( 2, const_cast( prior ) ); } template const typename AtroposSegmentationImageFilter ::ClassifiedImageType * AtroposSegmentationImageFilter ::GetPriorLabelImage() const { const ClassifiedImageType * prior = dynamic_cast( this->ProcessObject::GetInput( 2 ) ); return prior; } template void AtroposSegmentationImageFilter ::SetPriorProbabilityImage( unsigned int whichClass, RealImageType * priorImage ) { if( whichClass < 1 || whichClass > this->m_NumberOfClasses ) { itkExceptionMacro( "The requested prior probability image = " << whichClass << " should be in the range [1, " << this->m_NumberOfClasses << "]" ); } if( this->m_MinimizeMemoryUsage ) { // To make matters simpler, we force the index to be zero // for each priorImage image. typename RealImageType::IndexType startIndex = priorImage->GetRequestedRegion().GetIndex(); typename SparseImageType::Pointer sparsePriorImage = SparseImageType::New(); sparsePriorImage->Initialize(); unsigned long count = 0; ImageRegionConstIteratorWithIndex It( priorImage, priorImage->GetRequestedRegion() ); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { if( It.Get() > this->m_ProbabilityThreshold ) { typename RealImageType::IndexType index = It.GetIndex(); for( unsigned int d = 0; d < ImageDimension; d++ ) { index[d] -= startIndex[d]; } unsigned long number = this->IndexToNumber( index, priorImage->GetRequestedRegion().GetSize() ); typename SparseImageType::PointType imageNumberIndex; imageNumberIndex[0] = number; sparsePriorImage->SetPoint( count, imageNumberIndex ); sparsePriorImage->SetPointData( count, It.Get() ); count++; } } if( this->m_PriorProbabilitySparseImages.size() < whichClass ) { this->m_PriorProbabilitySparseImages.resize( whichClass ); } this->m_PriorProbabilitySparseImages[whichClass-1] = sparsePriorImage; } else { if( this->m_PriorProbabilityImages.size() < whichClass ) { this->m_PriorProbabilityImages.resize( whichClass ); } this->m_PriorProbabilityImages[whichClass-1] = priorImage; } } template typename AtroposSegmentationImageFilter ::RealImageType::Pointer AtroposSegmentationImageFilter ::GetPriorProbabilityImage( unsigned int whichClass ) const { if( whichClass < 1 || whichClass > this->m_NumberOfClasses ) { itkExceptionMacro( "The requested prior probability image = " << whichClass << " should be in the range [1, " << this->m_NumberOfClasses << "]" ); } if( this->m_InitializationStrategy != PriorProbabilityImages ) { return NULL; } if( ( this->m_MinimizeMemoryUsage && this->m_PriorProbabilitySparseImages.size() != this->m_NumberOfClasses ) || ( !this->m_MinimizeMemoryUsage && this->m_PriorProbabilityImages.size() != this->m_NumberOfClasses ) ) { itkExceptionMacro( "The number of prior probability images does not " << "equal the number of classes." ); } if( this->m_MinimizeMemoryUsage ) { // To make matters simpler, we forced the prior probability images // to all have indices of zeros. typename RealImageType::RegionType region; region.SetSize( this->GetIntensityImage( 0 )->GetRequestedRegion().GetSize() ); typename RealImageType::RegionType::IndexType index; for( unsigned int d = 0; d < ImageDimension; d++ ) { index[d] = 0; } region.SetIndex( index ); typename RealImageType::Pointer priorImage = RealImageType::New(); priorImage->SetRegions( region ); priorImage->SetOrigin( this->GetIntensityImage( 0 )->GetOrigin() ); priorImage->SetDirection( this->GetIntensityImage( 0 )->GetDirection() ); priorImage->SetSpacing( this->GetIntensityImage( 0 )->GetSpacing() ); priorImage->Allocate(); priorImage->FillBuffer( 0 ); typename SparseImageType::Pointer sparsePriorImage = this->m_PriorProbabilitySparseImages[whichClass-1]; typename SparseImageType::PointsContainer::ConstIterator It = sparsePriorImage->GetPoints()->Begin(); typename SparseImageType::PointDataContainer::ConstIterator ItD = sparsePriorImage->GetPointData()->Begin(); while( It != sparsePriorImage->GetPoints()->End() ) { unsigned long number = static_cast( It.Value()[0] ); typename RealImageType::IndexType index = this->NumberToIndex( number, priorImage->GetRequestedRegion().GetSize() ); priorImage->SetPixel( index, ItD.Value() ); ++It; ++ItD; } return priorImage; } else { return this->m_PriorProbabilityImages[whichClass-1]; } } template void AtroposSegmentationImageFilter ::SetIntensityImage( unsigned int which, const ImageType * image ) { if( which == 0 ) { this->SetInput( image ); } if( which > this->m_NumberOfIntensityImages - 1 ) { this->m_NumberOfIntensityImages = which + 1; } this->SetNthInput( 2 + this->m_NumberOfClasses + which, const_cast( image ) ); } template const typename AtroposSegmentationImageFilter ::ImageType * AtroposSegmentationImageFilter ::GetIntensityImage( unsigned int which ) const { const ImageType *image; if( which == 0 ) { image = dynamic_cast( this->ProcessObject::GetInput( 0 ) ); } else if( which > 0 && which <= this->m_NumberOfIntensityImages ) { image = dynamic_cast( this->ProcessObject::GetInput( 2 + this->m_NumberOfClasses + which ) ); } else { itkExceptionMacro( "Image " << which << " is outside the range " << "[1+m_NumberOfClasses...1+m_NumberOfClasses+m_NumberOfIntensityImages]." ) } return image; } template void AtroposSegmentationImageFilter ::GenerateData() { /** * Assign Gaussian likelihood functions if mixture model components are absent */ typedef ants::Statistics::GaussianListSampleFunction LikelihoodType; for( unsigned int n = 0; n < this->m_NumberOfClasses; n++ ) { if( !this->GetLikelihoodFunction( n ) ) { typename LikelihoodType::Pointer gaussianLikelihood = LikelihoodType::New(); this->SetLikelihoodFunction( n, gaussianLikelihood ); } } /** * Initialize the class labeling and the likelihood parameters */ this->GenerateInitialClassLabeling(); /** * Iterate until convergence or iterative exhaustion. */ IterationReporter reporter( this, 0, 1 ); bool isConverged = false; RealType probabilityNew = 0.0; RealType probabilityOld = NumericTraits::NonpositiveMin(); unsigned int iteration = 0; while( !isConverged && iteration++ < this->m_MaximumNumberOfIterations ) { /** * Clear the current posterior probability images to force * recalculation of the posterior probability images. */ this->m_PosteriorProbabilityImages.clear(); TimeProbe timer; timer.Start(); probabilityNew = this->UpdateClassLabeling(); timer.Stop(); this->m_CurrentConvergenceMeasurement = probabilityNew - probabilityOld; if( this->m_CurrentConvergenceMeasurement < this->m_ConvergenceThreshold ) { isConverged = true; } probabilityOld = probabilityNew; itkDebugMacro( "Iteration: " << probabilityNew ); this->m_ElapsedIterations++; reporter.CompletedStep(); } } template void AtroposSegmentationImageFilter ::GenerateInitialClassLabeling() { this->AllocateOutputs(); this->GetOutput()->FillBuffer( NumericTraits::Zero ); switch( this->m_InitializationStrategy ) { case Random: { typedef itk::Statistics::MersenneTwisterRandomVariateGenerator GeneratorType; typename GeneratorType::Pointer generator = GeneratorType::New(); ImageRegionIterator It( this->GetOutput(), this->GetOutput()->GetRequestedRegion() ); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { LabelType label = generator->GetIntegerVariate( this->m_NumberOfClasses - 1 ) + 1; It.Set( label ); } break; } case KMeans: default: { this->GenerateInitialClassLabelingWithKMeansClustering(); break; } case Otsu: { this->GenerateInitialClassLabelingWithOtsuThresholding(); break; } case PriorProbabilityImages: { this->GenerateInitialClassLabelingWithPriorProbabilityImages(); break; } case PriorLabelImage: { typedef ImageDuplicator DuplicatorType; typename DuplicatorType::Pointer duplicator = DuplicatorType::New(); duplicator->SetInputImage( this->GetPriorLabelImage() ); duplicator->Update(); this->SetNthOutput( 0, duplicator->GetOutput() ); break; } } /** * Calculate the initial parameters of the mixture model from the * initial labeling, i.e. the proportion, mean, and covariance for each label. */ this->m_MixtureModelProportions.SetSize( this->m_NumberOfClasses ); unsigned int totalSampleSize = 0; std::vector samples; for( unsigned int n = 0; n < this->m_NumberOfClasses; n++ ) { typename SampleType::Pointer sample = SampleType::New(); sample->SetMeasurementVectorSize( this->m_NumberOfIntensityImages ); samples.push_back( sample ); } /** * Accumulate the sample array for all labels. Also accumulate the * prior probability weights, if applicable. */ ImageRegionIteratorWithIndex ItO( this->GetOutput(), this->GetOutput()->GetRequestedRegion() ); for( ItO.GoToBegin(); !ItO.IsAtEnd(); ++ItO ) { LabelType label = ItO.Get(); if( label == 0 ) { continue; } typename SampleType::MeasurementVectorType measurement; ::itk::Statistics::MeasurementVectorTraits::SetLength( measurement, this->m_NumberOfIntensityImages ); for( unsigned int i = 0; i < this->m_NumberOfIntensityImages; i++ ) { measurement[i] = this->GetIntensityImage( i )->GetPixel( ItO.GetIndex() ); } samples[label-1]->PushBack( measurement ); } /** * Create the weight array now that we know the sample sizes. */ Array count( this->m_NumberOfClasses ); count.Fill( 0 ); std::vector weights; for( unsigned int n = 0; n < this->m_NumberOfClasses; n++ ) { totalSampleSize += samples[n]->Size(); WeightArrayType weightArray( samples[n]->Size() ); weightArray.Fill( 1.0 ); weights.push_back( weightArray ); } if( this->m_InitializationStrategy == PriorProbabilityImages ) { for( ItO.GoToBegin(); !ItO.IsAtEnd(); ++ItO ) { LabelType label = ItO.Get(); if( label == 0 ) { continue; } typename RealImageType::Pointer priorProbabilityImage = this->GetPriorProbabilityImage( label ); weights[label-1].SetElement( count[label-1]++, priorProbabilityImage->GetPixel( ItO.GetIndex() ) ); } } for( unsigned int n = 0; n < this->m_NumberOfClasses; n++ ) { this->m_MixtureModelComponents[n]->SetWeights( &weights[n] ); this->m_MixtureModelComponents[n]->SetInputListSample( samples[n] ); if( this->m_UseMixtureModelProportions ) { this->m_MixtureModelProportions[n] = static_cast( samples[n]->Size() ) / static_cast( totalSampleSize ); } else { this->m_MixtureModelProportions[n] = 1.0 / static_cast( this->m_NumberOfClasses ); } this->m_MixtureModelComponents[n]->ClearInputListSample(); } for( unsigned int i = 0; i < this->m_NumberOfIntensityImages; i++ ) { ControlPointLatticeContainerType container; this->m_ControlPointLattices.push_back( container ); for( unsigned int n = 0; n < this->m_NumberOfClasses; n++ ) { this->m_ControlPointLattices[i].push_back( NULL ); } } } template void AtroposSegmentationImageFilter ::GenerateInitialClassLabelingWithPriorProbabilityImages() { // We first normalize prior probability images by keeping track of // 1. the sum of the prior probabilities at each pixel // 2. the max prior probability value // 3. which prior probability image corresponds to the max prior value typename RealImageType::Pointer sumPriorProbabilityImage = RealImageType::New(); sumPriorProbabilityImage->SetOrigin( this->GetInput()->GetOrigin() ); sumPriorProbabilityImage->SetSpacing( this->GetInput()->GetSpacing() ); sumPriorProbabilityImage->SetRegions( this->GetInput()->GetRequestedRegion() ); sumPriorProbabilityImage->SetDirection( this->GetInput()->GetDirection() ); sumPriorProbabilityImage->Allocate(); sumPriorProbabilityImage->FillBuffer( 0 ); typename RealImageType::Pointer maxPriorProbabilityImage = RealImageType::New(); maxPriorProbabilityImage->SetOrigin( this->GetInput()->GetOrigin() ); maxPriorProbabilityImage->SetSpacing( this->GetInput()->GetSpacing() ); maxPriorProbabilityImage->SetRegions( this->GetInput()->GetRequestedRegion() ); maxPriorProbabilityImage->SetDirection( this->GetInput()->GetDirection() ); maxPriorProbabilityImage->Allocate(); maxPriorProbabilityImage->FillBuffer( 0 ); for( unsigned int n = 0; n < this->m_NumberOfClasses; n++ ) { typename RealImageType::Pointer priorProbabilityImage = this->GetPriorProbabilityImage( n + 1 ); typedef AddImageFilter AdderType; typename AdderType::Pointer adder = AdderType::New(); adder->SetInput1( sumPriorProbabilityImage ); adder->SetInput2( priorProbabilityImage ); adder->Update(); sumPriorProbabilityImage = adder->GetOutput(); ImageRegionIteratorWithIndex ItP( priorProbabilityImage, priorProbabilityImage->GetRequestedRegion() ); ImageRegionIterator ItM( maxPriorProbabilityImage, maxPriorProbabilityImage->GetRequestedRegion() ); ImageRegionIterator ItO( this->GetOutput(), this->GetOutput()->GetRequestedRegion() ); ItP.GoToBegin(); ItM.GoToBegin(); ItO.GoToBegin(); while( !ItP.IsAtEnd() ) { if( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItP.GetIndex() ) == this->m_MaskLabel ) { if( ItP.Get() > ItM.Get() ) { ItM.Set( ItP.Get() ); ItO.Set( n + 1 ); } else if( ItP.Get() == ItM.Get() ) { if( n == 0 ) { ItO.Set( 1 ); } else // if maximum probabilities are the same, randomly select one { typedef itk::Statistics::MersenneTwisterRandomVariateGenerator GeneratorType; typename GeneratorType::Pointer generator = GeneratorType::New(); if( generator->GetIntegerVariate( 1 ) ) { ItO.Set( n + 1 ); } } } } ++ItP; ++ItM; ++ItO; } } // Now we can normalize each prior probability image by dividing by the sum for( unsigned int n = 0; n < this->m_NumberOfClasses; n++ ) { typename RealImageType::Pointer priorProbabilityImage = this->GetPriorProbabilityImage( n + 1 ); ImageRegionIteratorWithIndex ItP( priorProbabilityImage, priorProbabilityImage->GetRequestedRegion() ); ImageRegionIterator ItS( sumPriorProbabilityImage, sumPriorProbabilityImage->GetRequestedRegion() ); ImageRegionIterator ItM( maxPriorProbabilityImage, maxPriorProbabilityImage->GetRequestedRegion() ); ImageRegionIterator ItO( this->GetOutput(), this->GetOutput()->GetRequestedRegion() ); ItP.GoToBegin(); ItS.GoToBegin(); ItM.GoToBegin(); ItO.GoToBegin(); while( !ItP.IsAtEnd() ) { if( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItP.GetIndex() ) == this->m_MaskLabel ) { if( ItM.Get() <= this->m_ProbabilityThreshold || ItS.Get() == 0.0 ) { ItO.Set( NumericTraits::Zero ); ItP.Set( NumericTraits::Zero ); } else { ItP.Set( ItP.Get() / ItS.Get() ); } } ++ItP; ++ItS; ++ItM; ++ItO; } this->SetPriorProbabilityImage( n + 1, priorProbabilityImage ); } /** * Set the initial output to be the prior label image. This way we can * propogate the segmentation solution to regions of non-zero probability * but where the mask exists. */ typedef ImageDuplicator DuplicatorType; typename DuplicatorType::Pointer duplicator = DuplicatorType::New(); duplicator->SetInputImage( this->GetOutput() ); duplicator->Update(); this->SetPriorLabelImage( duplicator->GetOutput() ); } template void AtroposSegmentationImageFilter ::GenerateInitialClassLabelingWithOtsuThresholding() { RealType maxValue = NumericTraits::min(); RealType minValue = NumericTraits::max(); ImageRegionConstIteratorWithIndex ItI( this->GetInput(), this->GetInput()->GetRequestedRegion() ); for( ItI.GoToBegin(); !ItI.IsAtEnd(); ++ItI ) { if( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItI.GetIndex() ) == this->m_MaskLabel ) { if ( ItI.Get() < minValue ) { minValue = ItI.Get(); } else if ( ItI.Get() > maxValue ) { maxValue = ItI.Get(); } } } typedef LabelStatisticsImageFilter StatsType; typename StatsType::Pointer stats = StatsType::New(); stats->SetInput( this->GetInput() ); if( this->GetMaskImage() ) { stats->SetLabelInput( const_cast( this->GetMaskImage() ) ); } else { typename MaskImageType::Pointer maskImage = MaskImageType::New(); maskImage->SetOrigin( this->GetOutput()->GetOrigin() ); maskImage->SetSpacing( this->GetOutput()->GetSpacing() ); maskImage->SetRegions( this->GetOutput()->GetRequestedRegion() ); maskImage->SetDirection( this->GetOutput()->GetDirection() ); maskImage->Allocate(); maskImage->FillBuffer( this->m_MaskLabel ); stats->SetLabelInput( maskImage ); } stats->UseHistogramsOn(); stats->SetHistogramParameters( 200, minValue, maxValue ); stats->Update(); typedef OtsuMultipleThresholdsCalculator OtsuType; typename OtsuType::Pointer otsu = OtsuType::New(); otsu->SetInputHistogram( stats->GetHistogram( this->m_MaskLabel ) ); otsu->SetNumberOfThresholds( this->m_NumberOfClasses - 1 ); otsu->Update(); typename OtsuType::OutputType thresholds = otsu->GetOutput(); ImageRegionIterator ItO( this->GetOutput(), this->GetOutput()->GetRequestedRegion() ); for( ItI.GoToBegin(), ItO.GoToBegin(); !ItI.IsAtEnd(); ++ItI, ++ItO ) { LabelType label = NumericTraits::Zero; if( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItI.GetIndex() ) == this->m_MaskLabel ) { if( ItI.Get() < thresholds[0] ) { label = NumericTraits::One; } else { bool thresholdFound = false; for ( unsigned int i = 1; i < thresholds.size(); i++ ) { if( ItI.Get() >= thresholds[i-1] && ItI.Get() <= thresholds[i] ) { label = static_cast( i+1 ); thresholdFound = true; break; } } if( !thresholdFound ) { label = static_cast( thresholds.size() + 1 ); } } } ItO.Set( label ); } } template void AtroposSegmentationImageFilter ::GenerateInitialClassLabelingWithKMeansClustering() { /** * We first perform kmeans on the first image and use the results to * seed the second run of kmeans using all the images. */ typedef LabelStatisticsImageFilter StatsType; typename StatsType::Pointer stats = StatsType::New(); stats->SetInput( this->GetInput() ); if( this->GetMaskImage() ) { stats->SetLabelInput( const_cast( this->GetMaskImage() ) ); } else { typename MaskImageType::Pointer maskImage = MaskImageType::New(); maskImage->SetOrigin( this->GetOutput()->GetOrigin() ); maskImage->SetSpacing( this->GetOutput()->GetSpacing() ); maskImage->SetRegions( this->GetOutput()->GetRequestedRegion() ); maskImage->SetDirection( this->GetOutput()->GetDirection() ); maskImage->Allocate(); maskImage->FillBuffer( this->m_MaskLabel ); stats->SetLabelInput( maskImage ); } stats->UseHistogramsOff(); stats->Update(); RealType minValue = stats->GetMinimum( this->m_MaskLabel ); RealType maxValue = stats->GetMaximum( this->m_MaskLabel ); /** * The code below can be replaced by itkListSampleToImageFilter when we * migrate over to the Statistics classes current in the Review/ directory. */ typename SampleType::Pointer sample = SampleType::New(); sample->SetMeasurementVectorSize( 1 ); ImageRegionConstIteratorWithIndex ItI( this->GetInput(), this->GetInput()->GetRequestedRegion() ); for( ItI.GoToBegin(); !ItI.IsAtEnd(); ++ItI ) { if( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItI.GetIndex() ) == this->m_MaskLabel ) { typename SampleType::MeasurementVectorType measurement; measurement.SetSize( 1 ); measurement[0] = ItI.Get(); sample->PushBack( measurement ); } } typedef itk::Statistics::WeightedCentroidKdTreeGenerator TreeGeneratorType; typename TreeGeneratorType::Pointer treeGenerator = TreeGeneratorType::New(); treeGenerator->SetSample( sample ); treeGenerator->SetBucketSize( 16 ); treeGenerator->Update(); typedef typename TreeGeneratorType::KdTreeType TreeType; typedef itk::Statistics::KdTreeBasedKmeansEstimator EstimatorType; typename EstimatorType::Pointer estimator = EstimatorType::New(); estimator->SetKdTree( treeGenerator->GetOutput() ); estimator->SetMaximumIteration( 200 ); estimator->SetCentroidPositionChangesThreshold( 0.0 ); typedef typename EstimatorType::ParametersType ParametersType; ParametersType initialMeans( this->m_NumberOfClasses ); /** * If the initial KMeans parameters are not set, guess initial class means by * dividing the dynamic range of the first image into equal intervals. */ if( this->m_InitialKMeansParameters.Size() == this->m_NumberOfClasses ) { for( unsigned int n = 0; n < this->m_NumberOfClasses; n++ ) { initialMeans[n] = this->m_InitialKMeansParameters[n]; } } else { for( unsigned int n = 0; n < this->m_NumberOfClasses; n++ ) { initialMeans[n] = minValue + ( maxValue - minValue ) * ( static_cast( n ) + 0.5 ) / static_cast( this->m_NumberOfClasses ); } } estimator->SetParameters( initialMeans ); estimator->StartOptimization(); /** * Classify the samples */ typedef itk::Statistics::MinimumDecisionRule2 DecisionRuleType; typename DecisionRuleType::Pointer decisionRule = DecisionRuleType::New(); typedef itk::Statistics::SampleClassifierFilter ClassifierType; typename ClassifierType::Pointer classifier = ClassifierType::New(); classifier->SetDecisionRule( decisionRule ); classifier->SetInput( sample ); classifier->SetNumberOfClasses( this->m_NumberOfClasses ); typename ClassifierType::ClassLabelVectorObjectType::Pointer classLabels = ClassifierType::ClassLabelVectorObjectType::New(); classifier->SetClassLabels( classLabels ); typename ClassifierType::ClassLabelVectorType &classLabelVector = classLabels->Get(); /** * Order the cluster means so that the lowest mean of the input image * corresponds to label '1', the second lowest to label '2', etc. */ std::vector estimatorParameters; for( unsigned int n = 0; n < this->m_NumberOfClasses; n++ ) { estimatorParameters.push_back( estimator->GetParameters()[n] ); } std::sort( estimatorParameters.begin(), estimatorParameters.end() ); typedef itk::Statistics::DistanceToCentroidMembershipFunction MembershipFunctionType; typename ClassifierType::MembershipFunctionVectorObjectType::Pointer membershipFunctions = ClassifierType::MembershipFunctionVectorObjectType::New(); typename ClassifierType::MembershipFunctionVectorType &membershipFunctionsVector = membershipFunctions->Get(); classifier->SetMembershipFunctions( membershipFunctions ); for( unsigned int n = 0; n < this->m_NumberOfClasses; n++ ) { typename MembershipFunctionType::Pointer membershipFunction = MembershipFunctionType::New(); membershipFunction->SetMeasurementVectorSize( sample->GetMeasurementVectorSize() ); typename MembershipFunctionType::CentroidType centroid; ::itk::Statistics::MeasurementVectorTraits::SetLength( centroid, sample->GetMeasurementVectorSize() ); centroid[0] = estimatorParameters[n]; membershipFunction->SetCentroid( centroid ); membershipFunctionsVector.push_back( membershipFunction.GetPointer() ); classLabelVector.push_back( static_cast( n + 1 ) ); } classifier->Update(); /** * Classify the voxels */ typedef typename ClassifierType::MembershipSampleType ClassifierOutputType; typedef typename ClassifierOutputType::ConstIterator LabelIterator; ImageRegionIteratorWithIndex ItO( this->GetOutput(), this->GetOutput()->GetRequestedRegion() ); ItO.GoToBegin(); LabelIterator it = classifier->GetOutput()->Begin(); while( it != classifier->GetOutput()->End() ) { if( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItO.GetIndex() ) == this->m_MaskLabel ) { ItO.Set( it.GetClassLabel() ); ++it; } else { ItO.Set( NumericTraits::Zero ); } ++ItO; } /** * If there are more than one intensity images, use the results from the first * kmeans grouping to seed a second invoking of the algorithm using both * the input image and the auxiliary images. */ if( this->m_NumberOfIntensityImages > 1 ) { VariableSizeMatrix classMeanValues; classMeanValues.SetSize( this->m_NumberOfIntensityImages, this->m_NumberOfClasses ); for( unsigned int i = 0; i < this->m_NumberOfIntensityImages; i++ ) { typedef LabelStatisticsImageFilter StatsType; typename StatsType::Pointer stats = StatsType::New(); stats->SetInput( this->GetIntensityImage( i ) ); stats->SetLabelInput( this->GetOutput() ); stats->UseHistogramsOff(); stats->Update(); for( unsigned int j = 0; j < this->m_NumberOfClasses; j++ ) { classMeanValues( i, j ) = stats->GetMean( j + 1 ); } } typedef itk::Statistics::WeightedCentroidKdTreeGenerator TreeGeneratorType; typedef typename TreeGeneratorType::KdTreeType TreeType; typedef itk::Statistics::KdTreeBasedKmeansEstimator EstimatorType; typedef typename EstimatorType::ParametersType ParametersType; typename EstimatorType::Pointer estimator = EstimatorType::New(); ParametersType initialMeans( this->m_NumberOfClasses * ( this->m_NumberOfIntensityImages ) ); initialMeans.Fill( 0.0 ); for( unsigned int n = 0; n < this->m_NumberOfClasses; n++ ) { for( unsigned int i = 0; i < this->m_NumberOfIntensityImages; i++ ) { initialMeans[this->m_NumberOfIntensityImages * n + i] = classMeanValues( i, n ); } } typename SampleType::Pointer sample = SampleType::New(); sample->SetMeasurementVectorSize( this->m_NumberOfIntensityImages ); for( ItO.GoToBegin(); !ItO.IsAtEnd(); ++ItO ) { if( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItO.GetIndex() ) == this->m_MaskLabel ) { typename SampleType::MeasurementVectorType measurement; measurement.SetSize( this->m_NumberOfIntensityImages ); for( unsigned int i = 0; i < this->m_NumberOfIntensityImages; i++ ) { measurement[i] = this->GetIntensityImage( i )->GetPixel( ItO.GetIndex() ); } sample->PushBack( measurement ); } } typename TreeGeneratorType::Pointer treeGenerator = TreeGeneratorType::New(); treeGenerator->SetSample( sample ); treeGenerator->SetBucketSize( 16 ); treeGenerator->Update(); estimator->SetParameters( initialMeans ); estimator->SetKdTree( treeGenerator->GetOutput() ); estimator->SetMaximumIteration( 200 ); estimator->SetCentroidPositionChangesThreshold( 0.0 ); estimator->StartOptimization(); /** * Classify the samples */ typedef itk::Statistics::MinimumDecisionRule2 DecisionRuleType; typename DecisionRuleType::Pointer decisionRule = DecisionRuleType::New(); typedef itk::Statistics::SampleClassifierFilter ClassifierType; typename ClassifierType::Pointer classifier = ClassifierType::New(); classifier->SetDecisionRule( decisionRule ); classifier->SetInput( sample ); classifier->SetNumberOfClasses( this->m_NumberOfClasses ); typename ClassifierType::ClassLabelVectorObjectType::Pointer classLabels = ClassifierType::ClassLabelVectorObjectType::New(); classifier->SetClassLabels( classLabels ); typename ClassifierType::ClassLabelVectorType &classLabelVector = classLabels->Get(); /** * Order the cluster means so that the lowest mean of the input image * corresponds to label '1', the second lowest to label '2', etc. */ std::vector estimatorParameters; for( unsigned int n = 0; n < this->m_NumberOfClasses; n++ ) { estimatorParameters.push_back( estimator->GetParameters()[n] ); } std::sort( estimatorParameters.begin(), estimatorParameters.end() ); typedef itk::Statistics::DistanceToCentroidMembershipFunction MembershipFunctionType; typename ClassifierType::MembershipFunctionVectorObjectType::Pointer membershipFunctions = ClassifierType::MembershipFunctionVectorObjectType::New(); typename ClassifierType::MembershipFunctionVectorType &membershipFunctionsVector = membershipFunctions->Get(); classifier->SetMembershipFunctions( membershipFunctions ); for( unsigned int n = 0; n < this->m_NumberOfClasses; n++ ) { typename MembershipFunctionType::Pointer membershipFunction = MembershipFunctionType::New(); membershipFunction->SetMeasurementVectorSize( sample->GetMeasurementVectorSize() ); typename MembershipFunctionType::CentroidType centroid; ::itk::Statistics::MeasurementVectorTraits::SetLength( centroid, sample->GetMeasurementVectorSize() ); for( unsigned int i = 0; i < this->m_NumberOfIntensityImages; i++ ) { centroid[i] = estimator->GetParameters()[ ( this->m_NumberOfIntensityImages ) * n + i]; } membershipFunction->SetCentroid( centroid ); membershipFunctionsVector.push_back( membershipFunction.GetPointer() ); classLabelVector.push_back( static_cast( n + 1 ) ); } classifier->Update(); /** * Classify the voxels */ typedef typename ClassifierType::MembershipSampleType ClassifierOutputType; typedef typename ClassifierOutputType::ConstIterator LabelIterator; ItO.GoToBegin(); LabelIterator it = classifier->GetOutput()->Begin(); while( it != classifier->GetOutput()->End() ) { if( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItO.GetIndex() ) == this->m_MaskLabel ) { ItO.Set( it.GetClassLabel() ); ++it; } else { ItO.Set( NumericTraits::Zero ); } ++ItO; } } } template typename AtroposSegmentationImageFilter ::RealType AtroposSegmentationImageFilter ::UpdateClassLabeling() { typename RealImageType::Pointer maxPosteriorProbabilityImage = RealImageType::New(); maxPosteriorProbabilityImage->SetOrigin( this->GetOutput()->GetOrigin() ); maxPosteriorProbabilityImage->SetSpacing( this->GetOutput()->GetSpacing() ); maxPosteriorProbabilityImage->SetDirection( this->GetOutput()->GetDirection() ); maxPosteriorProbabilityImage->SetRegions( this->GetOutput()->GetRequestedRegion() ); maxPosteriorProbabilityImage->Allocate(); maxPosteriorProbabilityImage->FillBuffer( NumericTraits::Zero ); typename ClassifiedImageType::Pointer maxLabels = ClassifiedImageType::New(); maxLabels->SetRegions( this->GetOutput()->GetRequestedRegion() ); maxLabels->SetOrigin( this->GetOutput()->GetOrigin() ); maxLabels->SetSpacing( this->GetOutput()->GetSpacing() ); maxLabels->SetDirection( this->GetOutput()->GetDirection() ); maxLabels->Allocate(); maxLabels->FillBuffer( NumericTraits::Zero ); typename RealImageType::Pointer weightedPriorProbabilityImage = RealImageType::New(); weightedPriorProbabilityImage->SetRegions( this->GetOutput()->GetRequestedRegion() ); weightedPriorProbabilityImage->SetOrigin( this->GetOutput()->GetOrigin() ); weightedPriorProbabilityImage->SetSpacing( this->GetOutput()->GetSpacing() ); weightedPriorProbabilityImage->SetDirection( this->GetOutput()->GetDirection() ); weightedPriorProbabilityImage->Allocate(); weightedPriorProbabilityImage->FillBuffer( NumericTraits::Zero ); Array sumPosteriors( this->m_NumberOfClasses ); sumPosteriors.Fill( 0.0 ); typename SampleType::Pointer sample = SampleType::New(); sample = this->GetScalarSamples(); unsigned long totalSampleSize = sample->Size(); for( unsigned int n = 0; n < this->m_NumberOfClasses; n++ ) { typename RealImageType::Pointer posteriorProbabilityImage = this->GetPosteriorProbabilityImage( n + 1 ); ImageRegionIteratorWithIndex ItO( maxLabels, maxLabels->GetRequestedRegion() ); ImageRegionConstIterator ItP( posteriorProbabilityImage, posteriorProbabilityImage->GetRequestedRegion() ); ImageRegionIterator ItM( maxPosteriorProbabilityImage, maxPosteriorProbabilityImage->GetRequestedRegion() ); WeightArrayType weights( totalSampleSize ); unsigned long count = 0; ItP.GoToBegin(); ItM.GoToBegin(); ItO.GoToBegin(); while( !ItP.IsAtEnd() ) { if( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItO.GetIndex() ) == this->m_MaskLabel ) { RealType posteriorProbability = ItP.Get(); weights.SetElement( count++, posteriorProbability ); if( posteriorProbability > this->m_ProbabilityThreshold && posteriorProbability >= ItM.Get() ) { ItM.Set( posteriorProbability ); ItO.Set( static_cast( n + 1 ) ); } sumPosteriors[n] += posteriorProbability; } ++ItP; ++ItM; ++ItO; } this->m_MixtureModelComponents[n]->SetWeights( &weights ); this->m_MixtureModelComponents[n]->SetInputListSample( sample ); this->m_MixtureModelComponents[n]->ClearInputListSample(); if( this->m_UseMixtureModelProportions ) { /** * Perform the following calculation as a preprocessing step to update the * class proportions. */ typename RealImageType::Pointer distancePriorProbabilityImage = this->GetDistancePriorProbabilityImage( n + 1 ); typename RealImageType::Pointer priorProbabilityImage = this->GetPriorProbabilityImage( n + 1 ); ImageRegionIteratorWithIndex ItW( weightedPriorProbabilityImage, weightedPriorProbabilityImage->GetRequestedRegion() ); for( ItW.GoToBegin(); !ItW.IsAtEnd(); ++ItW ) { if( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItW.GetIndex() ) == this->m_MaskLabel ) { RealType priorProbability = 0.0; if( this->m_InitializationStrategy == PriorLabelImage || this->m_InitializationStrategy == PriorProbabilityImages ) { if( priorProbabilityImage ) { priorProbability = priorProbabilityImage->GetPixel( ItW.GetIndex() ); } if( priorProbability <= this->m_ProbabilityThreshold && distancePriorProbabilityImage ) { priorProbability = distancePriorProbabilityImage->GetPixel( ItW.GetIndex() ); } } else { priorProbability = 1.0; } ItW.Set( ItW.Get() + this->m_MixtureModelProportions[n] * priorProbability ); } } } } this->SetNthOutput( 0, maxLabels ); if( this->m_UseMixtureModelProportions ) { /** * Update the class proportions */ for( unsigned int n = 0; n < this->m_NumberOfClasses; n++ ) { RealType denominator = 0.0; typename RealImageType::Pointer distancePriorProbabilityImage = this->GetDistancePriorProbabilityImage( n + 1 ); typename RealImageType::Pointer priorProbabilityImage = this->GetPriorProbabilityImage( n + 1 ); ImageRegionIteratorWithIndex ItW( weightedPriorProbabilityImage, weightedPriorProbabilityImage->GetRequestedRegion() ); for( ItW.GoToBegin(); !ItW.IsAtEnd(); ++ItW ) { if( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItW.GetIndex() ) == this->m_MaskLabel ) { RealType priorProbability = 0.0; if( this->m_InitializationStrategy == PriorLabelImage || this->m_InitializationStrategy == PriorProbabilityImages ) { if( priorProbabilityImage ) { priorProbability = priorProbabilityImage->GetPixel( ItW.GetIndex() ); } if( priorProbability <= this->m_ProbabilityThreshold && distancePriorProbabilityImage ) { priorProbability = distancePriorProbabilityImage->GetPixel( ItW.GetIndex() ); } } else { priorProbability = 1.0; } if( ItW.Get() > 0.0 ) { denominator += ( priorProbability / ItW.Get() ); } } } if( denominator > 0.0 ) { this->m_MixtureModelProportions[n] = sumPosteriors[n] / denominator; } else { this->m_MixtureModelProportions[n] = 0.0; } } } /** * Calculate the maximum posterior probability sum over the region of * interest. This quantity should increase at each iteration. */ RealType maxPosteriorSum = 0.0; ImageRegionIteratorWithIndex ItM( maxPosteriorProbabilityImage, maxPosteriorProbabilityImage->GetRequestedRegion() ); for( ItM.GoToBegin(); !ItM.IsAtEnd(); ++ItM ) { if( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItM.GetIndex() ) == this->m_MaskLabel ) { maxPosteriorSum += ItM.Get(); } } return maxPosteriorSum / static_cast( totalSampleSize ); } template typename AtroposSegmentationImageFilter ::SamplePointer AtroposSegmentationImageFilter ::GetScalarSamples() { /** * This function returns a set of samples for each class such that each * measurement vector of the returned SampleType corresponds to a single * voxel across the set of auxiliary and input images. */ std::vector samples; /** * Accumulate the samples in individual SampleTypes. This allows us to * "filter" the samples of each auxiliary/input image. This filtering * could including outlier winsorization, log transformation, and/or * converting vector/tensor auxiliary images to scalar data for * modeling. */ for( unsigned int i = 0; i < this->m_NumberOfIntensityImages; i++ ) { typename SampleType::Pointer sample = SampleType::New(); sample->SetMeasurementVectorSize( 1 ); samples.push_back( sample ); } ImageRegionIteratorWithIndex ItO( this->GetOutput(), this->GetOutput()->GetRequestedRegion() ); for( ItO.GoToBegin(); !ItO.IsAtEnd(); ++ItO ) { if( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItO.GetIndex() ) == this->m_MaskLabel ) { for( unsigned int i = 0; i < this->m_NumberOfIntensityImages; i++ ) { typename SampleType::MeasurementVectorType measurement; ::itk::Statistics::MeasurementVectorTraits::SetLength( measurement, 1 ); measurement.SetSize( 1 ); measurement[0] = this->GetIntensityImage( i )->GetPixel( ItO.GetIndex() ); samples[i]->PushBack( measurement ); } } } /** * Simultaneously filter the samples and accumulate for return. */ typename SampleType::Pointer scalarSamples = SampleType::New(); scalarSamples->SetMeasurementVectorSize( this->m_NumberOfIntensityImages ); for( unsigned int i = 0; i < this->m_NumberOfIntensityImages; i++ ) { typename SampleType::Pointer univariateSamples = SampleType::New(); univariateSamples->SetMeasurementVectorSize( 1 ); if( this->m_OutlierHandlingFilter ) { this->m_OutlierHandlingFilter->SetInput( samples[i] ); this->m_OutlierHandlingFilter->Update(); univariateSamples = this->m_OutlierHandlingFilter->GetOutput(); } else { univariateSamples = samples[i]; } if( i == 0 ) { typename SampleType::ConstIterator It = univariateSamples->Begin(); while( It != univariateSamples->End() ) { typename SampleType::MeasurementVectorType measurement; measurement.SetSize( this->m_NumberOfIntensityImages ); ::itk::Statistics::MeasurementVectorTraits::SetLength( measurement, this->m_NumberOfIntensityImages ); measurement[0] = It.GetMeasurementVector()[0]; scalarSamples->PushBack( measurement ); ++It; } } else { typename SampleType::Iterator ItS = scalarSamples->Begin(); typename SampleType::ConstIterator It = univariateSamples->Begin(); while( ItS != scalarSamples->End() ) { scalarSamples->SetMeasurement( ItS.GetInstanceIdentifier(), i, It.GetMeasurementVector()[0] ); ++It; ++ItS; } } } return scalarSamples; } template typename AtroposSegmentationImageFilter ::RealType AtroposSegmentationImageFilter ::CalculateLocalPosteriorProbability( RealType mixtureModelProportion, RealType spatialPriorProbability, RealType distancePriorProbability, RealType mrfPriorProbability, RealType likelihood ) { RealType posteriorProbability = 0.0; switch( this->m_PosteriorProbabilityFormulation ) { case Socrates: default: { posteriorProbability = vcl_pow( static_cast( spatialPriorProbability ), static_cast( this->m_PriorProbabilityWeight ) ) * vcl_pow( static_cast( likelihood * mrfPriorProbability ), static_cast( 1.0 - this->m_PriorProbabilityWeight ) ); break; } case Plato: { posteriorProbability = 1.0; break; } case Aristotle: { posteriorProbability = 1.0; break; } // case Zeno: // { // posteriorProbability = 1.0; // break; // } // case Diogenes: // { // posteriorProbability = 1.0; // break; // } // case Thales: // { // posteriorProbability = 1.0; // break; // } // case Democritus: // { // posteriorProbability = 1.0; // break; // } } return posteriorProbability; } template typename AtroposSegmentationImageFilter ::RealImageType::Pointer AtroposSegmentationImageFilter ::GetPosteriorProbabilityImage( unsigned int whichClass ) { if( whichClass > this->m_NumberOfClasses ) { itkExceptionMacro( "Requested class is greater than the number of classes." ); } /** * If memory minimization is turned off and if the posterior probability * images have already been calculated, simply return the probability * image for the requested class. Otherwise, calculate the probability * image. */ if( whichClass <= this->m_PosteriorProbabilityImages.size() ) { return this->m_PosteriorProbabilityImages[whichClass-1]; } else { /** * Here we assume that the calling function is invoked in order such * that GetPosteriorProbabilityImage( 1 ) is called before * GetPosteriorProbabilityImage( 2 ), etc. As such, when this part of * the code is reached and the class requested is '1', we assume that * the sum of the posterior probability images needs to be calculated * for normalization purposes. This sum is then saved for subsequent calls. */ typename RealImageType::Pointer posteriorProbabilityImage = RealImageType::New(); posteriorProbabilityImage->SetRegions( this->GetOutput()->GetRequestedRegion() ); posteriorProbabilityImage->SetOrigin( this->GetOutput()->GetOrigin() ); posteriorProbabilityImage->SetSpacing( this->GetOutput()->GetSpacing() ); posteriorProbabilityImage->SetDirection( this->GetOutput()->GetDirection() ); posteriorProbabilityImage->Allocate(); posteriorProbabilityImage->FillBuffer( 0 ); /** * Calculate the sum of the probability images. Also, store the * posterior probability images if m_MinimizeMemoryUsage == false. */ if( whichClass == 1 ) { this->m_SumPosteriorProbabilityImage = RealImageType::New(); this->m_SumPosteriorProbabilityImage->SetRegions( this->GetOutput()->GetRequestedRegion() ); this->m_SumPosteriorProbabilityImage->SetOrigin( this->GetOutput()->GetOrigin() ); this->m_SumPosteriorProbabilityImage->SetSpacing( this->GetOutput()->GetSpacing() ); this->m_SumPosteriorProbabilityImage->SetDirection( this->GetOutput()->GetDirection() ); this->m_SumPosteriorProbabilityImage->Allocate(); this->m_SumPosteriorProbabilityImage->FillBuffer( 0 ); typename RealImageType::Pointer sumPriorProbabilityImage = NULL; if( this->m_InitializationStrategy == PriorLabelImage || this->m_InitializationStrategy == PriorProbabilityImages ) { sumPriorProbabilityImage = RealImageType::New(); sumPriorProbabilityImage->SetRegions( this->GetOutput()->GetRequestedRegion() ); sumPriorProbabilityImage->SetOrigin( this->GetOutput()->GetOrigin() ); sumPriorProbabilityImage->SetSpacing( this->GetOutput()->GetSpacing() ); sumPriorProbabilityImage->SetDirection( this->GetOutput()->GetDirection() ); sumPriorProbabilityImage->Allocate(); sumPriorProbabilityImage->FillBuffer( 0 ); for( unsigned int c = 0; c < this->m_NumberOfClasses; c++ ) { typename RealImageType::Pointer priorProbabilityImage = this->GetPriorProbabilityImage( c + 1 ); ImageRegionIteratorWithIndex ItS( sumPriorProbabilityImage, sumPriorProbabilityImage->GetLargestPossibleRegion() ); for( ItS.GoToBegin(); !ItS.IsAtEnd(); ++ItS ) { RealType priorProbability = 0.0; if( priorProbabilityImage ) { priorProbability = priorProbabilityImage->GetPixel( ItS.GetIndex() ); } ItS.Set( ItS.Get() + this->m_MixtureModelProportions[c] * priorProbability ); } } } for( unsigned int c = 0; c < this->m_NumberOfClasses; c++ ) { std::vector smoothImages; if( this->m_InitializationStrategy == PriorProbabilityImages || this->m_InitializationStrategy == PriorLabelImage ) { for( unsigned int i = 0; i < this->m_NumberOfIntensityImages; i++ ) { if( this->m_AdaptiveSmoothingWeights.size() > i && this->m_AdaptiveSmoothingWeights[i] > 0.0 ) { smoothImages.push_back( this->GetSmoothIntensityImageFromPriorImage( i, c + 1 ) ); } else { smoothImages.push_back( NULL ); } } } typename RealImageType::Pointer distancePriorProbabilityImage = this->GetDistancePriorProbabilityImage( c + 1 ); typename RealImageType::Pointer priorProbabilityImage = this->GetPriorProbabilityImage( c + 1 ); typename NeighborhoodIterator::RadiusType radius; unsigned int neighborhoodSize = 1; for( unsigned int d = 0; d < ImageDimension; d++ ) { neighborhoodSize *= ( 2 * this->m_MRFRadius[d] + 1 ); radius[d] = this->m_MRFRadius[d]; } ConstNeighborhoodIterator ItO( radius, this->GetOutput(), this->GetOutput()->GetRequestedRegion() ); ImageRegionIterator ItS( this->m_SumPosteriorProbabilityImage, this->m_SumPosteriorProbabilityImage->GetRequestedRegion() ); for( ItO.GoToBegin(), ItS.GoToBegin(); !ItO.IsAtEnd(); ++ItO, ++ItS ) { if( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItO.GetIndex() ) == this->m_MaskLabel ) { /** * Perform mrf prior calculation */ RealType mrfPriorProbability = 1.0; if( this->m_MRFSmoothingFactor > 0.0 && neighborhoodSize > 1 ) { Array weightedNumberOfClassNeighbors; weightedNumberOfClassNeighbors.SetSize( this->m_NumberOfClasses ); weightedNumberOfClassNeighbors.Fill( 0.0 ); for( unsigned int n = 0; n < neighborhoodSize; n++ ) { if( n == static_cast( 0.5 * neighborhoodSize ) ) { continue; } bool isInBounds = false; LabelType label = ItO.GetPixel( n, isInBounds ); if( !isInBounds || label == 0 ) { continue; } typename ClassifiedImageType::OffsetType offset = ItO.GetOffset( n ); double distance = 0.0; for( unsigned int d = 0; d < ImageDimension; d++ ) { distance += vnl_math_sqr( offset[d] * this->GetOutput()->GetSpacing()[d] ); } distance = vcl_sqrt( distance ); weightedNumberOfClassNeighbors[label-1] += ( 1.0 / distance ); } RealType numerator = vcl_exp( this->m_MRFSmoothingFactor * weightedNumberOfClassNeighbors[c] ); RealType denominator = 0.0; for( unsigned int n = 0; n < this->m_NumberOfClasses; n++ ) { denominator += vcl_exp( this->m_MRFSmoothingFactor * weightedNumberOfClassNeighbors[n] ); } if( denominator > 0.0 ) { mrfPriorProbability = numerator / denominator; } } /** * Perform prior calculation using both the mixing proportions * and template-based prior images (if available) */ RealType priorProbability = 0.0; RealType distancePriorProbability = 0.0; if( this->m_InitializationStrategy == PriorLabelImage || this->m_InitializationStrategy == PriorProbabilityImages ) { if( distancePriorProbabilityImage ) { distancePriorProbability = distancePriorProbabilityImage->GetPixel( ItO.GetIndex() ); } if( priorProbabilityImage ) { priorProbability = priorProbabilityImage->GetPixel( ItO.GetIndex() ); } RealType sumPriorProbability = sumPriorProbabilityImage->GetPixel( ItO.GetIndex() ); if( priorProbability > this->m_ProbabilityThreshold ) { priorProbability *= ( this->m_MixtureModelProportions[c] / sumPriorProbability ); } else if( sumPriorProbability <= this->m_ProbabilityThreshold && distancePriorProbabilityImage ) { priorProbability = distancePriorProbability; } } MeasurementVectorType measurement; measurement.SetSize( this->m_NumberOfIntensityImages ); for( unsigned int i = 0; i < this->m_NumberOfIntensityImages; i++ ) { measurement[i] = this->GetIntensityImage( i )->GetPixel( ItO.GetIndex() ); if( ( this->m_InitializationStrategy == PriorProbabilityImages || this->m_InitializationStrategy == PriorLabelImage ) && smoothImages[i] ) { measurement[i] = ( 1.0 - this->m_AdaptiveSmoothingWeights[i] ) * measurement[i] + this->m_AdaptiveSmoothingWeights[i] * smoothImages[i]->GetPixel( ItO.GetIndex() ); } } /** * Calculate likelihood probability from the model */ RealType likelihood = this->m_MixtureModelComponents[c]->Evaluate( measurement ); /** * Calculate the local posterior probability. Given that the * algorithm is meant to maximize the posterior probability of the * labeling configuration, this is the critical energy minimization * equation. */ RealType posteriorProbability = CalculateLocalPosteriorProbability( this->m_MixtureModelProportions[c], priorProbability, distancePriorProbability, mrfPriorProbability, likelihood ); if( vnl_math_isnan( posteriorProbability ) || vnl_math_isinf( posteriorProbability ) ) { posteriorProbability = 0.0; } if( ( c == 0 ) || !this->m_MinimizeMemoryUsage ) { posteriorProbabilityImage->SetPixel( ItO.GetIndex(), posteriorProbability ); } /** * Calculate a running total of the posterior probabilities. */ ItS.Set( ItS.Get() + posteriorProbability ); } } if( !this->m_MinimizeMemoryUsage ) { typedef ImageDuplicator DuplicatorType; typename DuplicatorType::Pointer duplicator = DuplicatorType::New(); duplicator->SetInputImage( posteriorProbabilityImage ); duplicator->Update(); this->m_PosteriorProbabilityImages.push_back( duplicator->GetOutput() ); } } /** * Normalize the posterior probability image(s). */ ImageRegionIterator ItS( this->m_SumPosteriorProbabilityImage, this->m_SumPosteriorProbabilityImage->GetRequestedRegion() ); if( this->m_MinimizeMemoryUsage ) { ImageRegionIterator ItP( posteriorProbabilityImage, posteriorProbabilityImage->GetRequestedRegion() ); for( ItP.GoToBegin(), ItS.GoToBegin(); !ItS.IsAtEnd(); ++ItP, ++ItS ) { if( ItS.Get() > 0 ) { ItP.Set( ItP.Get() / ItS.Get() ); } } return posteriorProbabilityImage; } else { for( unsigned int n = 0; n < this->m_NumberOfClasses; n++ ) { ImageRegionIterator ItP( this->m_PosteriorProbabilityImages[n], this->m_PosteriorProbabilityImages[n]->GetRequestedRegion() ); for( ItP.GoToBegin(), ItS.GoToBegin(); !ItS.IsAtEnd(); ++ItP, ++ItS ) { if( ItS.Get() > 0 ) { ItP.Set( ItP.Get() / ItS.Get() ); } } } return this->m_PosteriorProbabilityImages[0]; } } else // whichClass > 1 { typename RealImageType::Pointer sumPriorProbabilityImage = NULL; if( this->m_InitializationStrategy == PriorLabelImage || this->m_InitializationStrategy == PriorProbabilityImages ) { sumPriorProbabilityImage = RealImageType::New(); sumPriorProbabilityImage->SetRegions( this->GetOutput()->GetRequestedRegion() ); sumPriorProbabilityImage->SetOrigin( this->GetOutput()->GetOrigin() ); sumPriorProbabilityImage->SetSpacing( this->GetOutput()->GetSpacing() ); sumPriorProbabilityImage->SetDirection( this->GetOutput()->GetDirection() ); sumPriorProbabilityImage->Allocate(); sumPriorProbabilityImage->FillBuffer( 0 ); for( unsigned int c = 0; c < this->m_NumberOfClasses; c++ ) { typename RealImageType::Pointer priorProbabilityImage = this->GetPriorProbabilityImage( c + 1 ); ImageRegionIteratorWithIndex ItS( sumPriorProbabilityImage, sumPriorProbabilityImage->GetLargestPossibleRegion() ); for( ItS.GoToBegin(); !ItS.IsAtEnd(); ++ItS ) { RealType priorProbability = 0.0; if( priorProbabilityImage ) { priorProbability = priorProbabilityImage->GetPixel( ItS.GetIndex() ); } ItS.Set( ItS.Get() + this->m_MixtureModelProportions[c] * priorProbability ); } } } std::vector smoothImages; if( this->m_InitializationStrategy == PriorProbabilityImages || this->m_InitializationStrategy == PriorLabelImage ) { for( unsigned int i = 0; i < this->m_NumberOfIntensityImages; i++ ) { if( this->m_AdaptiveSmoothingWeights.size() > i && this->m_AdaptiveSmoothingWeights[i] > 0.0 ) { smoothImages.push_back( this->GetSmoothIntensityImageFromPriorImage( i, whichClass ) ); } else { smoothImages.push_back( NULL ); } } } typename RealImageType::Pointer distancePriorProbabilityImage = this->GetDistancePriorProbabilityImage( whichClass ); typename RealImageType::Pointer priorProbabilityImage = this->GetPriorProbabilityImage( whichClass ); typename NeighborhoodIterator::RadiusType radius; unsigned int neighborhoodSize = 1; for( unsigned int d = 0; d < ImageDimension; d++ ) { neighborhoodSize *= ( 2 * this->m_MRFRadius[d] + 1 ); radius[d] = this->m_MRFRadius[d]; } ConstNeighborhoodIterator ItO( radius, this->GetOutput(), this->GetOutput()->GetRequestedRegion() ); for( ItO.GoToBegin(); !ItO.IsAtEnd(); ++ItO ) { if( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItO.GetIndex() ) == this->m_MaskLabel ) { /** * Perform mrf prior calculation */ RealType mrfPriorProbability = 1.0; if( this->m_MRFSmoothingFactor > 0.0 && neighborhoodSize > 1 ) { Array weightedNumberOfClassNeighbors; weightedNumberOfClassNeighbors.SetSize( this->m_NumberOfClasses ); weightedNumberOfClassNeighbors.Fill( 0.0 ); for( unsigned int n = 0; n < neighborhoodSize; n++ ) { if( n == static_cast( 0.5 * neighborhoodSize ) ) { continue; } bool isInBounds = false; LabelType label = ItO.GetPixel( n, isInBounds ); if( !isInBounds || label == 0 ) { continue; } typename ClassifiedImageType::OffsetType offset = ItO.GetOffset( n ); double distance = 0.0; for( unsigned int d = 0; d < ImageDimension; d++ ) { distance += vnl_math_sqr( offset[d] * this->GetOutput()->GetSpacing()[d] ); } distance = vcl_sqrt( distance ); weightedNumberOfClassNeighbors[label-1] += ( 1.0 / distance ); } RealType numerator = vcl_exp( this->m_MRFSmoothingFactor * weightedNumberOfClassNeighbors[whichClass-1] ); RealType denominator = 0.0; for( unsigned int n = 0; n < this->m_NumberOfClasses; n++ ) { denominator += vcl_exp( this->m_MRFSmoothingFactor * weightedNumberOfClassNeighbors[n] ); } mrfPriorProbability = numerator / denominator; if( vnl_math_isinf( mrfPriorProbability ) || vnl_math_isnan( mrfPriorProbability ) ) { mrfPriorProbability = 1.0; } } /** * Perform prior calculation using both the mixing proportions * and template-based prior images (if available) */ RealType priorProbability = 0.0; RealType distancePriorProbability = 0.0; if( this->m_InitializationStrategy == PriorLabelImage || this->m_InitializationStrategy == PriorProbabilityImages ) { if( distancePriorProbabilityImage ) { distancePriorProbability = distancePriorProbabilityImage->GetPixel( ItO.GetIndex() ); } if( priorProbabilityImage ) { priorProbability = priorProbabilityImage->GetPixel( ItO.GetIndex() ); } RealType sumPriorProbability = sumPriorProbabilityImage->GetPixel( ItO.GetIndex() ); if( priorProbability > this->m_ProbabilityThreshold ) { priorProbability *= ( this->m_MixtureModelProportions[whichClass-1] / sumPriorProbability ); } else if( sumPriorProbability <= this->m_ProbabilityThreshold && distancePriorProbabilityImage ) { priorProbability = distancePriorProbability; } } MeasurementVectorType measurement; measurement.SetSize( this->m_NumberOfIntensityImages ); for( unsigned int i = 0; i < this->m_NumberOfIntensityImages; i++ ) { measurement[i] = this->GetIntensityImage( i )->GetPixel( ItO.GetIndex() ); if( ( this->m_InitializationStrategy == PriorProbabilityImages || this->m_InitializationStrategy == PriorLabelImage ) && smoothImages[i] ) { measurement[i] = ( 1.0 - this->m_AdaptiveSmoothingWeights[i] ) * measurement[i] + this->m_AdaptiveSmoothingWeights[i] * smoothImages[i]->GetPixel( ItO.GetIndex() ); } } /** * Calculate likelihood probability from the model */ RealType likelihood = this->m_MixtureModelComponents[whichClass-1]->Evaluate( measurement ); /** * Calculate the local posterior probability. Given that the * algorithm is meant to maximize the posterior probability of the * labeling configuration, this is the critical energy minimization * equation. */ RealType posteriorProbability = CalculateLocalPosteriorProbability( this->m_MixtureModelProportions[whichClass-1], priorProbability, distancePriorProbability, mrfPriorProbability, likelihood ); if( vnl_math_isnan( posteriorProbability ) || vnl_math_isinf( posteriorProbability ) ) { posteriorProbability = 0.0; } posteriorProbabilityImage->SetPixel( ItO.GetIndex(), posteriorProbability ); } } /** * Normalize the posterior probability image(s). */ ImageRegionIterator ItS( this->m_SumPosteriorProbabilityImage, this->m_SumPosteriorProbabilityImage->GetRequestedRegion() ); ImageRegionIterator ItP( posteriorProbabilityImage, posteriorProbabilityImage->GetRequestedRegion() ); for( ItP.GoToBegin(), ItS.GoToBegin(); !ItS.IsAtEnd(); ++ItP, ++ItS ) { if( ItS.Get() > 0 ) { ItP.Set( ItP.Get() / ItS.Get() ); } } return posteriorProbabilityImage; } } } template typename AtroposSegmentationImageFilter ::RealImageType::Pointer AtroposSegmentationImageFilter ::GetDistancePriorProbabilityImage( unsigned int whichClass ) { if( this->m_InitializationStrategy != PriorLabelImage && this->m_InitializationStrategy != PriorProbabilityImages ) { return NULL; } if( whichClass > this->m_NumberOfClasses ) { itkExceptionMacro( "Requested class is greater than the number of classes." ); } /** * If memory minimization is turned off and if the distance prior probability * images have already been calculated, simply return the probability * image for the requested class. Otherwise, calculate the probability * image. */ if( whichClass <= this->m_DistancePriorProbabilityImages.size() ) { return this->m_DistancePriorProbabilityImages[whichClass-1]; } else { /** * Here we assume that the calling function is invoked in order such * that GetDistancePriorImage( 1 ) is called before * GetDistancePriorImage( 2 ), etc. As such, when this part of * the code is reached and the class requested is '1', we assume that * the sum of the distance prior probability images needs to be calculated * for normalization purposes. This sum is then saved for subsequent calls. */ typename RealImageType::Pointer distancePriorProbabilityImage = NULL; /** * Calculate the sum of the distance probability images. Also, store the * distance probability images if m_MinimizeMemoryUsage == false. */ if( whichClass == 1 ) { this->m_SumDistancePriorProbabilityImage = RealImageType::New(); this->m_SumDistancePriorProbabilityImage->SetRegions( this->GetOutput()->GetRequestedRegion() ); this->m_SumDistancePriorProbabilityImage->SetOrigin( this->GetOutput()->GetOrigin() ); this->m_SumDistancePriorProbabilityImage->SetSpacing( this->GetOutput()->GetSpacing() ); this->m_SumDistancePriorProbabilityImage->SetDirection( this->GetOutput()->GetDirection() ); this->m_SumDistancePriorProbabilityImage->Allocate(); this->m_SumDistancePriorProbabilityImage->FillBuffer( 0 ); for( unsigned int c = 0; c < this->m_NumberOfClasses; c++ ) { typedef BinaryThresholdImageFilter ThresholderType; typename ThresholderType::Pointer thresholder = ThresholderType::New(); if( this->m_InitializationStrategy == PriorLabelImage ) { thresholder->SetInput( const_cast( this->GetPriorLabelImage() ) ); } else { thresholder->SetInput( this->GetOutput() ); } thresholder->SetInsideValue( 1 ); thresholder->SetOutsideValue( 0 ); thresholder->SetLowerThreshold( static_cast( c + 1 ) ); thresholder->SetUpperThreshold( static_cast( c + 1 ) ); thresholder->Update(); typename RealImageType::Pointer distanceImage = RealImageType::New(); if( this->m_UseEuclideanDistanceForPriorLabels ) { typedef SignedMaurerDistanceMapImageFilter DistancerType; typename DistancerType::Pointer distancer = DistancerType::New(); distancer->SetInput( thresholder->GetOutput() ); distancer->SetSquaredDistance( false ); distancer->SetUseImageSpacing( true ); distancer->SetInsideIsPositive( false ); distancer->Update(); distanceImage = distancer->GetOutput(); } else { typedef BinaryContourImageFilter ContourFilterType; typename ContourFilterType::Pointer contour = ContourFilterType::New(); contour->SetInput( thresholder->GetOutput() ); contour->FullyConnectedOff(); contour->SetBackgroundValue( 0 ); contour->SetForegroundValue( 1 ); contour->Update(); typedef FastMarchingImageFilter FastMarchingFilterType; typename FastMarchingFilterType::Pointer fastMarching = FastMarchingFilterType::New(); typedef CastImageFilter CasterType; typename CasterType::Pointer caster = CasterType::New(); if( this->GetMaskImage() ) { caster->SetInput( const_cast( this->GetMaskImage() ) ); caster->Update(); fastMarching->SetInput( caster->GetOutput() ); } else { fastMarching->SetSpeedConstant( 1.0 ); fastMarching->SetOverrideOutputInformation( true ); fastMarching->SetOutputOrigin( this->GetOutput()->GetOrigin() ); fastMarching->SetOutputSpacing( this->GetOutput()->GetSpacing() ); fastMarching->SetOutputRegion( this->GetOutput()->GetRequestedRegion() ); fastMarching->SetOutputDirection( this->GetOutput()->GetDirection() ); } typedef typename FastMarchingFilterType::NodeContainer NodeContainer; typedef typename FastMarchingFilterType::NodeType NodeType; typename NodeContainer::Pointer trialPoints = NodeContainer::New(); trialPoints->Initialize(); unsigned long trialCount = 0; ImageRegionIteratorWithIndex ItC( contour->GetOutput(), contour->GetOutput()->GetRequestedRegion() ); for( ItC.GoToBegin(); !ItC.IsAtEnd(); ++ItC ) { if( ItC.Get() == contour->GetForegroundValue() ) { NodeType node; node.SetValue( 0.0 ); node.SetIndex( ItC.GetIndex() ); trialPoints->InsertElement( trialCount++, node ); } } fastMarching->SetTrialPoints( trialPoints ); fastMarching->SetStoppingValue( NumericTraits::max() ); // fastMarching->SetTopologyCheck( FastMarchingFilterType::None ); fastMarching->Update(); ImageRegionIterator ItT( thresholder->GetOutput(), thresholder->GetOutput()->GetRequestedRegion() ); ImageRegionIterator ItF( fastMarching->GetOutput(), fastMarching->GetOutput()->GetRequestedRegion() ); for( ItT.GoToBegin(), ItF.GoToBegin(); !ItT.IsAtEnd(); ++ItT, ++ItF ) { if( ItT.Get() == 1 ) { ItF.Set( -ItF.Get() ); } } distanceImage = fastMarching->GetOutput(); } RealType maximumInteriorDistance = 0.0; ImageRegionIterator ItD( distanceImage, distanceImage->GetRequestedRegion() ); for( ItD.GoToBegin(); !ItD.IsAtEnd(); ++ItD ) { if( ItD.Get() < 0 && maximumInteriorDistance < vnl_math_abs( ItD.Get() ) ) { maximumInteriorDistance = vnl_math_abs( ItD.Get() ); } } RealType labelLambda = 0.0; RealType labelBoundaryProbability = 1.0; typename LabelParameterMapType::iterator it = this->m_PriorLabelParameterMap.find( c + 1 ); if( it != this->m_PriorLabelParameterMap.end() ) { labelLambda = ( it->second ).first; labelBoundaryProbability = ( it->second ).second; } for( ItD.GoToBegin(); !ItD.IsAtEnd(); ++ItD ) { if( labelLambda == 0 ) { if( ItD.Get() <= 0 ) { ItD.Set( labelBoundaryProbability ); } else { ItD.Set( 0.0 ); } } else if( ItD.Get() >= 0 ) { ItD.Set( labelBoundaryProbability * vcl_exp( -labelLambda * ItD.Get() ) ); } else if( ItD.Get() < 0 ) { ItD.Set( 1.0 - ( 1.0 - labelBoundaryProbability ) * ( maximumInteriorDistance - vnl_math_abs( ItD.Get() ) ) / ( maximumInteriorDistance ) ); } } typedef AddImageFilter AdderType; typename AdderType::Pointer adder = AdderType::New(); adder->SetInput1( this->m_SumDistancePriorProbabilityImage ); adder->SetInput2( distanceImage ); adder->Update(); this->m_SumDistancePriorProbabilityImage = adder->GetOutput(); if( ( c == 0 ) && this->m_MinimizeMemoryUsage ) { distancePriorProbabilityImage = distanceImage; } if( !this->m_MinimizeMemoryUsage ) { this->m_DistancePriorProbabilityImages.push_back( distanceImage ); } } /** * Normalize the distance prior probability image(s). */ ImageRegionIterator ItS( this->m_SumDistancePriorProbabilityImage, this->m_SumDistancePriorProbabilityImage->GetRequestedRegion() ); if( this->m_MinimizeMemoryUsage ) { ImageRegionIteratorWithIndex ItD( distancePriorProbabilityImage, distancePriorProbabilityImage->GetRequestedRegion() ); for( ItD.GoToBegin(), ItS.GoToBegin(); !ItS.IsAtEnd(); ++ItD, ++ItS ) { if( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItD.GetIndex() ) == this->m_MaskLabel ) { if( ItS.Get() <= this->m_ProbabilityThreshold ) { ItD.Set( NumericTraits::Zero ); } else { ItD.Set( ItD.Get() / ItS.Get() ); } } } return distancePriorProbabilityImage; } else { for( unsigned int c = 0; c < this->m_NumberOfClasses; c++ ) { ImageRegionIteratorWithIndex ItD( this->m_DistancePriorProbabilityImages[c], this->m_DistancePriorProbabilityImages[c]->GetRequestedRegion() ); for( ItD.GoToBegin(), ItS.GoToBegin(); !ItS.IsAtEnd(); ++ItD, ++ItS ) { if( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItD.GetIndex() ) == this->m_MaskLabel ) { if( ItS.Get() <= this->m_ProbabilityThreshold ) { ItD.Set( NumericTraits::Zero ); } else { ItD.Set( ItD.Get() / ItS.Get() ); } } } } return this->m_DistancePriorProbabilityImages[0]; } } else // whichClass > 1 { typedef BinaryThresholdImageFilter ThresholderType; typename ThresholderType::Pointer thresholder = ThresholderType::New(); if( this->m_InitializationStrategy == PriorLabelImage ) { thresholder->SetInput( const_cast( this->GetPriorLabelImage() ) ); } else { thresholder->SetInput( this->GetOutput() ); } thresholder->SetInsideValue( 1 ); thresholder->SetOutsideValue( 0 ); thresholder->SetLowerThreshold( static_cast( whichClass ) ); thresholder->SetUpperThreshold( static_cast( whichClass ) ); thresholder->Update(); typename RealImageType::Pointer distanceImage = RealImageType::New(); if( this->m_UseEuclideanDistanceForPriorLabels ) { typedef SignedMaurerDistanceMapImageFilter DistancerType; typename DistancerType::Pointer distancer = DistancerType::New(); distancer->SetInput( thresholder->GetOutput() ); distancer->SetSquaredDistance( false ); distancer->SetUseImageSpacing( true ); distancer->SetInsideIsPositive( false ); distancer->Update(); distanceImage = distancer->GetOutput(); } else { typedef BinaryContourImageFilter ContourFilterType; typename ContourFilterType::Pointer contour = ContourFilterType::New(); contour->SetInput( thresholder->GetOutput() ); contour->FullyConnectedOff(); contour->SetBackgroundValue( 0 ); contour->SetForegroundValue( 1 ); contour->Update(); typedef FastMarchingImageFilter FastMarchingFilterType; typename FastMarchingFilterType::Pointer fastMarching = FastMarchingFilterType::New(); typedef CastImageFilter CasterType; typename CasterType::Pointer caster = CasterType::New(); if( this->GetMaskImage() ) { caster->SetInput( const_cast( this->GetMaskImage() ) ); caster->Update(); fastMarching->SetInput( caster->GetOutput() ); } else { fastMarching->SetSpeedConstant( 1.0 ); fastMarching->SetOverrideOutputInformation( true ); fastMarching->SetOutputOrigin( this->GetOutput()->GetOrigin() ); fastMarching->SetOutputSpacing( this->GetOutput()->GetSpacing() ); fastMarching->SetOutputRegion( this->GetOutput()->GetRequestedRegion() ); fastMarching->SetOutputDirection( this->GetOutput()->GetDirection() ); } typedef typename FastMarchingFilterType::NodeContainer NodeContainer; typedef typename FastMarchingFilterType::NodeType NodeType; typename NodeContainer::Pointer trialPoints = NodeContainer::New(); trialPoints->Initialize(); unsigned long trialCount = 0; ImageRegionIteratorWithIndex ItC( contour->GetOutput(), contour->GetOutput()->GetRequestedRegion() ); for( ItC.GoToBegin(); !ItC.IsAtEnd(); ++ItC ) { if( ItC.Get() == contour->GetForegroundValue() ) { NodeType node; node.SetValue( 0.0 ); node.SetIndex( ItC.GetIndex() ); trialPoints->InsertElement( trialCount++, node ); } } fastMarching->SetTrialPoints( trialPoints ); fastMarching->SetStoppingValue( NumericTraits::max() ); // fastMarching->SetTopologyCheck( FastMarchingFilterType::None ); fastMarching->Update(); ImageRegionIterator ItT( thresholder->GetOutput(), thresholder->GetOutput()->GetRequestedRegion() ); ImageRegionIterator ItF( fastMarching->GetOutput(), fastMarching->GetOutput()->GetRequestedRegion() ); for( ItT.GoToBegin(), ItF.GoToBegin(); !ItT.IsAtEnd(); ++ItT, ++ItF ) { if( ItT.Get() == 1 ) { ItF.Set( -ItF.Get() ); } } distanceImage = fastMarching->GetOutput(); } distancePriorProbabilityImage = distanceImage; RealType maximumInteriorDistance = 0.0; ImageRegionIterator ItD( distancePriorProbabilityImage, distancePriorProbabilityImage->GetRequestedRegion() ); for( ItD.GoToBegin(); !ItD.IsAtEnd(); ++ItD ) { if( ItD.Get() < 0 && maximumInteriorDistance < vnl_math_abs( ItD.Get() ) ) { maximumInteriorDistance = vnl_math_abs( ItD.Get() ); } } RealType labelLambda = 0.0; RealType labelBoundaryProbability = 1.0; typename LabelParameterMapType::iterator it = this->m_PriorLabelParameterMap.find( whichClass ); if( it != this->m_PriorLabelParameterMap.end() ) { labelLambda = ( it->second ).first; labelBoundaryProbability = ( it->second ).second; } for( ItD.GoToBegin(); !ItD.IsAtEnd(); ++ItD ) { if( labelLambda == 0 ) { if( ItD.Get() <= 0 ) { ItD.Set( labelBoundaryProbability ); } else { ItD.Set( 0.0 ); } } else if( ItD.Get() >= 0 ) { ItD.Set( labelBoundaryProbability * vcl_exp( -labelLambda * ItD.Get() ) ); } else if( ItD.Get() < 0 ) { ItD.Set( 1.0 - ( 1.0 - labelBoundaryProbability ) * ( maximumInteriorDistance - vnl_math_abs( ItD.Get() ) ) / ( maximumInteriorDistance ) ); } } /** * Normalize the distance prior probability image(s). */ ImageRegionIterator ItS( this->m_SumDistancePriorProbabilityImage, this->m_SumDistancePriorProbabilityImage->GetRequestedRegion() ); for( ItD.GoToBegin(), ItS.GoToBegin(); !ItS.IsAtEnd(); ++ItD, ++ItS ) { if( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItD.GetIndex() ) == this->m_MaskLabel ) { if( ItS.Get() <= this->m_ProbabilityThreshold ) { ItD.Set( NumericTraits::Zero ); } else { ItD.Set( ItD.Get() / ItS.Get() ); } } } return distancePriorProbabilityImage; } } } template typename AtroposSegmentationImageFilter ::RealImageType::Pointer AtroposSegmentationImageFilter ::GetSmoothIntensityImageFromPriorImage( unsigned int whichImage, unsigned int whichClass ) { typename ScalarImageType::Pointer bsplineImage; if( this->m_ControlPointLattices[whichImage][whichClass-1].GetPointer() != NULL ) { typedef BSplineControlPointImageFilter BSplineReconstructorType; typename BSplineReconstructorType::Pointer bspliner = BSplineReconstructorType::New(); bspliner->SetInput( this->m_ControlPointLattices[whichImage][whichClass-1] ); bspliner->SetSize( this->GetInput()->GetRequestedRegion().GetSize() ); bspliner->SetSpacing( this->GetInput()->GetSpacing() ); bspliner->SetOrigin( this->GetInput()->GetOrigin() ); bspliner->SetDirection( this->GetInput()->GetDirection() ); bspliner->SetSplineOrder( this->m_SplineOrder ); bspliner->Update(); bsplineImage = bspliner->GetOutput(); } else { typename PointSetType::Pointer points = PointSetType::New(); points->Initialize(); typedef typename BSplineFilterType::WeightsContainerType WeightsType; typename WeightsType::Pointer weights = WeightsType::New(); weights->Initialize(); typename RealImageType::Pointer probabilityImage; if( this->m_InitializationStrategy == PriorProbabilityImages ) { probabilityImage = this->GetPriorProbabilityImage( whichClass ); } else { typedef BinaryThresholdImageFilter ThresholderType; typename ThresholderType::Pointer thresholder = ThresholderType::New(); thresholder->SetInput( const_cast( this->GetPriorLabelImage() ) ); thresholder->SetInsideValue( 1 ); thresholder->SetOutsideValue( 0 ); thresholder->SetLowerThreshold( static_cast( whichClass ) ); thresholder->SetUpperThreshold( static_cast( whichClass ) ); thresholder->Update(); probabilityImage = thresholder->GetOutput(); } typename RealImageType::DirectionType originalDirection = probabilityImage->GetDirection(); typename RealImageType::DirectionType identity; identity.SetIdentity(); probabilityImage->SetDirection( identity ); unsigned long count = 0; ImageRegionIteratorWithIndex ItP( probabilityImage, probabilityImage->GetBufferedRegion() ); for( ItP.GoToBegin(); !ItP.IsAtEnd(); ++ItP ) { if( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItP.GetIndex() ) == this->m_MaskLabel ) { if( ItP.Get() >= 0.5 ) { typename RealImageType::PointType imagePoint; probabilityImage->TransformIndexToPhysicalPoint( ItP.GetIndex(), imagePoint ); typename PointSetType::PointType bsplinePoint; bsplinePoint.CastFrom( imagePoint ); ScalarType intensity; intensity[0] = this->GetIntensityImage( whichImage )->GetPixel( ItP.GetIndex() ); points->SetPoint( count, bsplinePoint ); points->SetPointData( count, intensity ); weights->InsertElement( count, ItP.Get() ); count++; } } } probabilityImage->SetDirection( originalDirection ); typename BSplineFilterType::ArrayType numberOfControlPoints; typename BSplineFilterType::ArrayType numberOfLevels; for( unsigned int d = 0; d < ImageDimension; d++ ) { numberOfControlPoints[d] = this->m_NumberOfControlPoints[d]; numberOfLevels[d] = this->m_NumberOfLevels[d]; } typename BSplineFilterType::Pointer bspliner = BSplineFilterType::New(); bspliner->SetInput( points ); bspliner->SetPointWeights( weights ); bspliner->SetNumberOfLevels( numberOfLevels ); bspliner->SetSplineOrder( this->m_SplineOrder ); bspliner->SetNumberOfControlPoints( numberOfControlPoints ); bspliner->SetSize( this->GetOutput()->GetLargestPossibleRegion().GetSize() ); bspliner->SetOrigin( this->GetOutput()->GetOrigin() ); bspliner->SetDirection( this->GetOutput()->GetDirection() ); bspliner->SetSpacing( this->GetOutput()->GetSpacing() ); bspliner->SetGenerateOutputImage( true ); bspliner->Update(); bsplineImage = bspliner->GetOutput(); this->m_ControlPointLattices[whichImage][whichClass-1] = bspliner->GetPhiLattice(); } typedef VectorIndexSelectionCastImageFilter CasterType; typename CasterType::Pointer caster = CasterType::New(); caster->SetInput( bsplineImage ); caster->SetIndex( 0 ); caster->Update(); return caster->GetOutput(); } template typename AtroposSegmentationImageFilter ::RealImageType::Pointer AtroposSegmentationImageFilter ::GetLikelihoodImage( unsigned int whichClass ) { typename RealImageType::Pointer likelihoodImage = RealImageType::New(); likelihoodImage->SetSpacing( this->GetInput()->GetSpacing() ); likelihoodImage->SetOrigin( this->GetInput()->GetOrigin() ); likelihoodImage->SetDirection( this->GetInput()->GetDirection() ); likelihoodImage->SetRegions( this->GetInput()->GetRequestedRegion() ); likelihoodImage->Allocate(); likelihoodImage->FillBuffer( 0 ); std::vector smoothImages; if( this->m_InitializationStrategy == PriorProbabilityImages || this->m_InitializationStrategy == PriorLabelImage ) { for( unsigned int i = 0; i < this->m_NumberOfIntensityImages; i++ ) { if( this->m_AdaptiveSmoothingWeights.size() > i && this->m_AdaptiveSmoothingWeights[i] > 0.0 ) { smoothImages.push_back( this->GetSmoothIntensityImageFromPriorImage( i, whichClass ) ); } else { smoothImages.push_back( NULL ); } } } ImageRegionIteratorWithIndex It( likelihoodImage, likelihoodImage->GetRequestedRegion() ); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { if( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( It.GetIndex() ) == this->m_MaskLabel ) { MeasurementVectorType measurement; measurement.SetSize( this->m_NumberOfIntensityImages ); for( unsigned int i = 0; i < this->m_NumberOfIntensityImages; i++ ) { measurement[i] = this->GetIntensityImage( i )->GetPixel( It.GetIndex() ); if( ( this->m_InitializationStrategy == PriorProbabilityImages || this->m_InitializationStrategy == PriorLabelImage ) && smoothImages[i] ) { measurement[i] = ( 1.0 - this->m_AdaptiveSmoothingWeights[i] ) * measurement[i] + this->m_AdaptiveSmoothingWeights[i] * smoothImages[i]->GetPixel( It.GetIndex() ); } } RealType likelihood = this->m_MixtureModelComponents[whichClass-1]->Evaluate( measurement ); It.Set( likelihood ); } } return likelihoodImage; } template void AtroposSegmentationImageFilter ::PrintSelf( std::ostream& os, Indent indent ) const { Superclass::PrintSelf( os, indent ); os << indent << "Maximum number of iterations: " << this->m_MaximumNumberOfIterations << std::endl; os << indent << "Convergence threshold: " << this->m_ConvergenceThreshold << std::endl; os << indent << "Mask label: " << static_cast::PrintType> ( this->m_MaskLabel ) << std::endl; os << indent << "Number of classes: " << this->m_NumberOfClasses << std::endl; os << indent << "Minimize memory usage:"; if( this->m_MinimizeMemoryUsage ) { os << " true"; if( this->m_MinimizeMemoryUsage && this->m_InitializationStrategy == PriorProbabilityImages ) { os << " (prior probability threshold = " << this->m_ProbabilityThreshold << ")" << std::endl; } os << std::endl; } else { os << " false" << std::endl; } os << indent << "Initialization strategy: "; switch( this->m_InitializationStrategy ) { case Random: { os << "Random" << std::endl; break; } case KMeans: { os << "K means clustering" << std::endl; break; } case Otsu: { os << "Otsu thresholding" << std::endl; break; } case PriorProbabilityImages: { os << "Prior probability images" << std::endl; os << indent << " Use Euclidean distance for prior labels:"; if( this->m_UseEuclideanDistanceForPriorLabels ) { os << " true" << std::endl; } else { os << " false" << std::endl; } if( this->m_PriorLabelParameterMap.size() > 0 ) { os << indent << " Specified prior label parameters:" << std::endl; typename LabelParameterMapType::const_iterator it; for( it = this->m_PriorLabelParameterMap.begin(); it != this->m_PriorLabelParameterMap.end(); ++it ) { RealType label = it->first; RealType lambda = ( it->second ).first; RealType boundaryProbability = ( it->second ).second; os << indent << " Class " << label << ": lambda = " << lambda << ", boundary probability = " << boundaryProbability << std::endl; } } break; } case PriorLabelImage: { os << "Prior label image" << std::endl; os << indent << " Use Euclidean distance for prior labels:"; if( this->m_UseEuclideanDistanceForPriorLabels ) { os << " true" << std::endl; } else { os << " false" << std::endl; } os << indent << " Specified prior label parameters:" << std::endl; typename LabelParameterMapType::const_iterator it; for( it = this->m_PriorLabelParameterMap.begin(); it != this->m_PriorLabelParameterMap.end(); ++it ) { RealType label = it->first; RealType lambda = ( it->second ).first; RealType boundaryProbability = ( it->second ).second; os << indent << " Class " << label << ": lambda = " << lambda << ", boundary probability = " << boundaryProbability << std::endl; } break; } } os << indent << "Posterior probability formulation: "; switch( this->m_PosteriorProbabilityFormulation ) { case Socrates: { os << "Socrates" << std::endl; break; } case Plato: { os << "Plato" << std::endl; break; } case Aristotle: { os << "Aristotle" << std::endl; break; } // case Zeno: // { // os << "Zeno" << std::endl; // break; // } // case Diogenes: // { // os << "Diogenes" << std::endl; // break; // } // case Thales: // { // os << "Thales" << std::endl; // break; // } // case Democritus: // { // os << "Democritus" << std::endl; // break; // } } os << indent << "MRF parameters" << std::endl; os << indent << " MRF smoothing factor: " << this->m_MRFSmoothingFactor << std::endl; os << indent << " MRF radius: " << this->m_MRFRadius << std::endl; if( this->m_OutlierHandlingFilter ) { os << indent << "Outlier handling " << std::endl; this->m_OutlierHandlingFilter->Print( os, indent.GetNextIndent() ); } else { os << indent << "No outlier handling." << std::endl; } if( ( this->m_InitializationStrategy == PriorProbabilityImages || this->m_InitializationStrategy == PriorLabelImage ) && this->m_AdaptiveSmoothingWeights.size() > 0 ) { os << indent << "Adaptive smoothing weights: ["; for( unsigned int i = 0; i < this->m_AdaptiveSmoothingWeights.size()-1; i++ ) { os << this->m_AdaptiveSmoothingWeights[i] << ", "; } os << this->m_AdaptiveSmoothingWeights[ this->m_AdaptiveSmoothingWeights.size() - 1] << "]" << std::endl; os << indent << "BSpline smoothing" << std::endl; os << indent << " Spline order: " << this->m_SplineOrder << std::endl; os << indent << " Number of levels: " << this->m_NumberOfLevels << std::endl; os << indent << " Number of initial control points: " << this->m_NumberOfControlPoints << std::endl; } for( unsigned int n = 0; n < this->m_NumberOfClasses; n++ ) { if( this->m_MixtureModelProportions.size() > n ) { os << indent << "Class " << n + 1 << ": proportion = " << this->m_MixtureModelProportions[n] << std::endl; } if( this->m_MixtureModelComponents.size() > n ) { this->m_MixtureModelComponents[n]->Print( os, indent.GetNextIndent() ); } } } } // namespace ants } // namespace itk #endif ants-1.9.2+svn680.dfsg/ImageSegmentation/antsBoxPlotQuantileListSampleFilter.h000066400000000000000000000100021147325206600273460ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: antsBoxPlotQuantileListSampleFilter.h,v $ Language: C++ Date: $Date: $ Version: $Revision: $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __antsBoxPlotQuantileListSampleFilter_h #define __antsBoxPlotQuantileListSampleFilter_h #include "antsListSampleToListSampleFilter.h" #include namespace itk { namespace ants { namespace Statistics { /** \class BoxPlotQuantileListSampleFilter * \brief Base class of filters intended to generate scalar samples from * intensity samples. * */ template class ITK_EXPORT BoxPlotQuantileListSampleFilter : public ListSampleToListSampleFilter { public: /** * Standard class typedefs. */ typedef BoxPlotQuantileListSampleFilter Self; typedef ListSampleToListSampleFilter Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** * Standard macros */ itkTypeMacro( BoxPlotQuantileListSampleFilter, ListSampleToScalarListSampleFilter ); /** * Method for creation through the object factory. */ itkNewMacro( Self ); /** * Conveneient typedefs */ typedef double RealType; typedef TScalarListSample ScalarListSampleType; typedef typename ScalarListSampleType ::MeasurementVectorType MeasurementVectorType; typedef typename ScalarListSampleType ::InstanceIdentifier InstanceIdentifierType; typedef std::vector InstanceIdentifierContainerType; enum OutlierHandlingType { None, Trim, Winsorize }; itkSetMacro( OutlierHandling, OutlierHandlingType ); itkGetConstMacro( OutlierHandling, OutlierHandlingType ); itkSetMacro( WhiskerScalingFactor, RealType ); itkGetConstMacro( WhiskerScalingFactor, RealType ); itkSetClampMacro( UpperPercentile, RealType, 0, 1 ); itkGetConstMacro( UpperPercentile, RealType ); itkSetClampMacro( LowerPercentile, RealType, 0, 1 ); itkGetConstMacro( LowerPercentile, RealType ); InstanceIdentifierContainerType GetOutlierInstanceIdentifiers() { return this->m_OutlierInstanceIdentifiers; } // itkGetConstMacro( Outliers, InstanceIdentifierContainerType ); protected: BoxPlotQuantileListSampleFilter(); virtual ~BoxPlotQuantileListSampleFilter(); void PrintSelf( std::ostream& os, Indent indent ) const; virtual void GenerateData(); private: BoxPlotQuantileListSampleFilter( const Self& ); //purposely not implemented void operator=( const Self& ); //purposely not implemented InstanceIdentifierType FindMaximumNonOutlierDeviationValue( RealType, RealType ); bool IsMeasurementAnOutlier( RealType, RealType, RealType, unsigned long ); OutlierHandlingType m_OutlierHandling; InstanceIdentifierContainerType m_OutlierInstanceIdentifiers; RealType m_WhiskerScalingFactor; RealType m_LowerPercentile; RealType m_UpperPercentile; }; // end of class } // end of namespace Statistics } // end of namespace ants4443 } // end of namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "antsBoxPlotQuantileListSampleFilter.txx" #endif #endif ants-1.9.2+svn680.dfsg/ImageSegmentation/antsBoxPlotQuantileListSampleFilter.txx000066400000000000000000000141161147325206600277540ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: antsBoxPlotQuantileListSampleFilter.txx,v $ Language: C++ Date: $Date: $ Version: $Revision: $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __antsBoxPlotQuantileListSampleFilter_txx #define __antsBoxPlotQuantileListSampleFilter_txx #include "antsBoxPlotQuantileListSampleFilter.h" #include "itkDenseFrequencyContainer2.h" #include "itkHistogram.h" #include "itkSampleToHistogramFilter.h" namespace itk { namespace ants { namespace Statistics { template BoxPlotQuantileListSampleFilter ::BoxPlotQuantileListSampleFilter() { this->AllocateOutput(); this->GetOutput()->SetMeasurementVectorSize( 1 ); this->m_OutlierHandling = Winsorize; this->m_WhiskerScalingFactor = 1.5; this->m_LowerPercentile = 0.25; this->m_UpperPercentile = 0.75; } template BoxPlotQuantileListSampleFilter ::~BoxPlotQuantileListSampleFilter() { } template void BoxPlotQuantileListSampleFilter ::GenerateData() { if( this->GetInput()->GetMeasurementVectorSize() != 1 ) { itkExceptionMacro( "The input sample must be univariate." ); } if( this->m_LowerPercentile >= this->m_UpperPercentile ) { itkExceptionMacro( "Lower percentile must be less than upper percentile." ); } const unsigned int scalarMeasurementVectorSize = this->GetInput()->GetMeasurementVectorSize(); this->GetOutput()->SetMeasurementVectorSize( scalarMeasurementVectorSize ); /** * Initialize the histogram in preparation */ typedef itk::Statistics:: Histogram HistogramType; typedef itk::Statistics:: SampleToHistogramFilter SampleFilterType; typename SampleFilterType::HistogramSizeType histogramSize( 1 ); histogramSize.Fill( 200 ); typename SampleFilterType::Pointer sampleFilter = SampleFilterType::New(); sampleFilter->SetInput( this->GetInput() ); sampleFilter->SetHistogramSize( histogramSize ); sampleFilter->Update(); RealType lowerQuantile = sampleFilter->GetOutput()-> Quantile( 0, this->m_LowerPercentile ); RealType upperQuantile = sampleFilter->GetOutput()-> Quantile( 0, this->m_UpperPercentile ); RealType upperBound = upperQuantile + this->m_WhiskerScalingFactor * ( upperQuantile - lowerQuantile ); RealType lowerBound = lowerQuantile - this->m_WhiskerScalingFactor * ( upperQuantile - lowerQuantile ); typename ScalarListSampleType::ConstIterator It = this->GetInput()->Begin(); It = this->GetInput()->Begin(); while( It != this->GetInput()->End() ) { MeasurementVectorType inputMeasurement = It.GetMeasurementVector(); typename ScalarListSampleType::MeasurementVectorType outputMeasurement; outputMeasurement.SetSize( scalarMeasurementVectorSize ); if( inputMeasurement[0] < lowerBound || inputMeasurement[0] > upperBound ) { this->m_OutlierInstanceIdentifiers.push_back( It.GetInstanceIdentifier() ); if( this->m_OutlierHandling == None ) { outputMeasurement[0] = inputMeasurement[0]; this->GetOutput()->PushBack( outputMeasurement ); } // else trim from the output } else { outputMeasurement[0] = inputMeasurement[0]; this->GetOutput()->PushBack( outputMeasurement ); } ++It; } if( this->m_OutlierHandling == Winsorize ) { /** Retabulate the histogram with the outliers removed */ typename SampleFilterType::Pointer sampleFilter2 = SampleFilterType::New(); sampleFilter2->SetInput( this->GetOutput() ); sampleFilter2->SetHistogramSize( histogramSize ); sampleFilter2->Update(); RealType lowerQuantile2 = sampleFilter2->GetOutput()-> Quantile( 0, this->m_LowerPercentile ); RealType upperQuantile2 = sampleFilter2->GetOutput()-> Quantile( 0, this->m_UpperPercentile ); RealType upperBound2 = upperQuantile2 + this->m_WhiskerScalingFactor * ( upperQuantile2 - lowerQuantile2 ); RealType lowerBound2 = lowerQuantile2 - this->m_WhiskerScalingFactor * ( upperQuantile2 - lowerQuantile2 ); this->GetOutput()->Clear(); It = this->GetInput()->Begin(); while( It != this->GetInput()->End() ) { MeasurementVectorType inputMeasurement = It.GetMeasurementVector(); typename ScalarListSampleType::MeasurementVectorType outputMeasurement; outputMeasurement.SetSize( scalarMeasurementVectorSize ); outputMeasurement[0] = inputMeasurement[0]; if( inputMeasurement[0] < lowerBound ) { outputMeasurement[0] = lowerBound2; } else if( inputMeasurement[0] > upperBound ) { outputMeasurement[0] = upperBound2; } this->GetOutput()->PushBack( outputMeasurement ); ++It; } } } template void BoxPlotQuantileListSampleFilter ::PrintSelf( std::ostream& os, Indent indent ) const { os << indent << "Percentile Bounds: [" << this->m_LowerPercentile << ", " << this->m_UpperPercentile << "]" << std::endl; os << indent << "Whisker scaling factor: " << this->m_WhiskerScalingFactor << std::endl; os << indent << "Outlier handling: "; if( this->m_OutlierHandling == None ) { os << "None" << std::endl; } if( this->m_OutlierHandling == Trim ) { os << "Trim" << std::endl; } if( this->m_OutlierHandling == Winsorize ) { os << "Winsorize" << std::endl; } } } // end of namespace Statistics } // end of namespace ants } // end of namespace itk #endif ants-1.9.2+svn680.dfsg/ImageSegmentation/antsGaussianListSampleFunction.h000066400000000000000000000060531147325206600264010ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: antsGaussianListSampleFunction.h,v $ Language: C++ Date: $Date: $ Version: $Revision: $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __antsGaussianListSampleFunction_h #define __antsGaussianListSampleFunction_h #include "antsListSampleFunction.h" #include "itkGaussianMembershipFunction.h" namespace itk { namespace ants { namespace Statistics { /** \class GaussianListSampleFunction.h * \brief point set filter. */ template class ITK_EXPORT GaussianListSampleFunction : public ListSampleFunction { public: typedef GaussianListSampleFunction Self; typedef ListSampleFunction Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); /** Run-time type information (and related methods). */ itkTypeMacro( GaussianListSampleFunction, ListSampleFunction ); typedef typename Superclass::InputListSampleType InputListSampleType; typedef typename Superclass::InputMeasurementVectorType InputMeasurementVectorType; typedef typename Superclass::InputMeasurementType InputMeasurementType; /** List sample typedef support. */ typedef TListSample ListSampleType; /** Gaussian typedefs */ typedef typename itk::Statistics::GaussianMembershipFunction GaussianType; /** Other typedef */ typedef TOutput RealType; typedef TOutput OutputType; /** Helper functions */ virtual void SetInputListSample( const InputListSampleType * ptr ); virtual TOutput Evaluate( const InputMeasurementVectorType& measurement ) const; protected: GaussianListSampleFunction(); virtual ~GaussianListSampleFunction(); void PrintSelf( std::ostream& os, Indent indent ) const; void GenerateData(); private: //purposely not implemented GaussianListSampleFunction( const Self& ); void operator=( const Self& ); typename GaussianType::Pointer m_Gaussian; }; } // end of namespace Statistics } // end of namespace ants } // end of namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "antsGaussianListSampleFunction.txx" #endif #endif ants-1.9.2+svn680.dfsg/ImageSegmentation/antsGaussianListSampleFunction.txx000066400000000000000000000113361147325206600267750ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: antsGaussianListSampleFunction.txx,v $ Language: C++ Date: $Date: $ Version: $Revision: $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __antsGaussianListSampleFunction_txx #define __antsGaussianListSampleFunction_txx #include "antsGaussianListSampleFunction.h" #include "itkCovarianceSampleFilter.h" #include "itkMeanSampleFilter.h" #include "itkWeightedCovarianceSampleFilter.h" #include "itkWeightedMeanSampleFilter.h" namespace itk { namespace ants { namespace Statistics { template GaussianListSampleFunction ::GaussianListSampleFunction() { this->m_Gaussian = GaussianType::New(); } template GaussianListSampleFunction ::~GaussianListSampleFunction() { } template void GaussianListSampleFunction ::SetInputListSample( const InputListSampleType * ptr ) { this->m_ListSample = ptr; if( !this->m_ListSample ) { return; } if( this->m_ListSample->Size() > 1 ) { if( this->m_Weights.Size() == this->m_ListSample->Size() ) { typedef typename itk::Statistics:: WeightedCovarianceSampleFilter CovarianceCalculatorType; typename CovarianceCalculatorType::Pointer covarianceCalculator = CovarianceCalculatorType::New(); covarianceCalculator->SetWeights( this->m_Weights ); covarianceCalculator->SetInput( this->m_ListSample ); covarianceCalculator->Update(); typename GaussianType::MeanType mean; ::itk::Statistics::MeasurementVectorTraits::SetLength( mean, this->m_ListSample->GetMeasurementVectorSize() ); for( unsigned int d = 0; d < this->m_ListSample->GetMeasurementVectorSize(); d++ ) { mean[d] = covarianceCalculator->GetMean()[d]; } this->m_Gaussian->SetMean( mean ); this->m_Gaussian->SetCovariance( covarianceCalculator->GetCovarianceMatrix() ); } else { typedef itk::Statistics::CovarianceSampleFilter CovarianceCalculatorType; typename CovarianceCalculatorType::Pointer covarianceCalculator = CovarianceCalculatorType::New(); covarianceCalculator->SetInput( this->m_ListSample ); covarianceCalculator->Update(); typename GaussianType::MeanType mean; ::itk::Statistics::MeasurementVectorTraits::SetLength( mean, this->m_ListSample->GetMeasurementVectorSize() ); for( unsigned int d = 0; d < this->m_ListSample->GetMeasurementVectorSize(); d++ ) { mean[d] = covarianceCalculator->GetMean()[d]; } this->m_Gaussian->SetMean( mean ); this->m_Gaussian->SetCovariance( covarianceCalculator->GetCovarianceMatrix() ); } } else { itkWarningMacro( "The input list sample has <= 1 element." << "Function evaluations will be equal to 0." ); } } template TOutput GaussianListSampleFunction ::Evaluate( const InputMeasurementVectorType &measurement ) const { try { return this->m_Gaussian->Evaluate( measurement ); } catch(...) { return 0.0; } } /** * Standard "PrintSelf" method */ template void GaussianListSampleFunction ::PrintSelf( std::ostream& os, Indent indent) const { os << indent << "mean = " << this->m_Gaussian->GetMean() << ", "; typename GaussianType::CovarianceType covariance = this->m_Gaussian->GetCovariance(); os << "covariance = ["; for( unsigned int r = 0; r < covariance.Rows(); r++ ) { for( unsigned int c = 0; c < covariance.Cols() - 1; c++ ) { os << covariance( r, c ) << ", "; } if( r == covariance.Rows() - 1 ) { os << covariance( r, covariance.Cols() - 1 ) << "]" << std::endl; } else { os << covariance( r, covariance.Cols() - 1 ) << "; "; } } } } // end of namespace Statistics } // end of namespace ants } // end of namespace itk #endif ants-1.9.2+svn680.dfsg/ImageSegmentation/antsGrubbsRosnerListSampleFilter.h000066400000000000000000000074351147325206600267110ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: antsGrubbsRosnerListSampleFilter.h,v $ Language: C++ Date: $Date: $ Version: $Revision: $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __antsGrubbsRosnerListSampleFilter_h #define __antsGrubbsRosnerListSampleFilter_h #include "antsListSampleToListSampleFilter.h" #include namespace itk { namespace ants { namespace Statistics { /** \class GrubbsRosnerListSampleFilter * \brief Base class of filters intended to generate scalar samples from * intensity samples. * */ template class ITK_EXPORT GrubbsRosnerListSampleFilter : public ListSampleToListSampleFilter { public: /** * Standard class typedefs. */ typedef GrubbsRosnerListSampleFilter Self; typedef ListSampleToListSampleFilter Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** * Standard macros */ itkTypeMacro( GrubbsRosnerListSampleFilter, ListSampleToScalarListSampleFilter ); /** * Method for creation through the object factory. */ itkNewMacro( Self ); /** * Conveneient typedefs */ typedef double RealType; typedef TScalarListSample ScalarListSampleType; typedef typename ScalarListSampleType ::MeasurementVectorType MeasurementVectorType; typedef typename ScalarListSampleType ::InstanceIdentifier InstanceIdentifierType; typedef std::vector InstanceIdentifierContainerType; enum OutlierHandlingType { None, Trim, Winsorize }; itkSetMacro( OutlierHandling, OutlierHandlingType ); itkGetConstMacro( OutlierHandling, OutlierHandlingType ); itkSetMacro( WinsorizingLevel, RealType ); itkGetConstMacro( WinsorizingLevel, RealType ); itkSetMacro( SignificanceLevel, RealType ); itkGetConstMacro( SignificanceLevel, RealType ); InstanceIdentifierContainerType GetOutlierInstanceIdentifiers() { return this->m_OutlierInstanceIdentifiers; } // itkGetConstMacro( Outliers, InstanceIdentifierContainerType ); protected: GrubbsRosnerListSampleFilter(); virtual ~GrubbsRosnerListSampleFilter(); void PrintSelf( std::ostream& os, Indent indent ) const; virtual void GenerateData(); private: GrubbsRosnerListSampleFilter( const Self& ); //purposely not implemented void operator=( const Self& ); //purposely not implemented InstanceIdentifierType FindMaximumNonOutlierDeviationValue( RealType, RealType ); bool IsMeasurementAnOutlier( RealType, RealType, RealType, unsigned long ); OutlierHandlingType m_OutlierHandling; RealType m_WinsorizingLevel; InstanceIdentifierContainerType m_OutlierInstanceIdentifiers; RealType m_SignificanceLevel; }; // end of class } // end of namespace Statistics } // end of namespace ants } // end of namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "antsGrubbsRosnerListSampleFilter.txx" #endif #endif ants-1.9.2+svn680.dfsg/ImageSegmentation/antsGrubbsRosnerListSampleFilter.txx000066400000000000000000000210111147325206600272670ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: antsGrubbsRosnerListSampleFilter.txx,v $ Language: C++ Date: $Date: $ Version: $Revision: $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __antsGrubbsRosnerListSampleFilter_txx #define __antsGrubbsRosnerListSampleFilter_txx #include "antsGrubbsRosnerListSampleFilter.h" #include "itkTDistribution.h" namespace itk { namespace ants { namespace Statistics { template GrubbsRosnerListSampleFilter ::GrubbsRosnerListSampleFilter() { this->AllocateOutput(); this->GetOutput()->SetMeasurementVectorSize( 1 ); this->m_OutlierHandling = Winsorize; this->m_WinsorizingLevel = 0.10; this->m_SignificanceLevel = 0.05; } template GrubbsRosnerListSampleFilter ::~GrubbsRosnerListSampleFilter() { } template void GrubbsRosnerListSampleFilter ::GenerateData() { if( this->GetInput()->GetMeasurementVectorSize() != 1 ) { itkExceptionMacro( "The input sample must be univariate." ); } const unsigned int scalarMeasurementVectorSize = this->GetOutput()->GetMeasurementVectorSize(); this->GetOutput()->SetMeasurementVectorSize( scalarMeasurementVectorSize ); /** * A common hueristic is that Grubbs-Rosner outlier removal does not work for * sample sizes less than or equal to 6. */ if( this->GetInput()->Size() <= 6 ) { typename ScalarListSampleType::ConstIterator It = this->GetInput()->Begin(); while( It != this->GetInput()->End() ) { MeasurementVectorType inputMeasurement = It.GetMeasurementVector(); MeasurementVectorType outputMeasurement; outputMeasurement.SetSize( scalarMeasurementVectorSize ); for( unsigned int d = 0; d < scalarMeasurementVectorSize; d++ ) { outputMeasurement[d] = inputMeasurement[d]; } this->GetOutput()->PushBack( outputMeasurement ); ++It; } return; } /** * Otherwise, iterate through the input list, removing t */ RealType mean = 0.0; RealType variance = 0.0; RealType count = 0.0; typename ScalarListSampleType::ConstIterator It = this->GetInput()->Begin(); while( It != this->GetInput()->End() ) { MeasurementVectorType inputMeasurement = It.GetMeasurementVector(); count += 1.0; variance += ( count - 1.0 ) * vnl_math_sqr( inputMeasurement[0] - mean ) / count; mean = mean + ( inputMeasurement[0] - mean ) / count; ++It; } variance /= ( count - 1.0 ); bool outlierFound = true; this->m_OutlierInstanceIdentifiers.clear(); while( outlierFound == true && ( this->GetInput()->Size() - this->m_OutlierInstanceIdentifiers.size() > 6 ) ) { outlierFound = false; InstanceIdentifierType id = this->FindMaximumNonOutlierDeviationValue( mean, variance ); if( this->GetInput()->GetFrequency( id ) > 0 ) { MeasurementVectorType measurement = this->GetInput()->GetMeasurementVector( id ); outlierFound = this->IsMeasurementAnOutlier( measurement[0], mean, variance, this->GetInput()->Size() - this->m_OutlierInstanceIdentifiers.size() ); if( outlierFound ) { /** Retabulate the variance and mean by removing the previous estimate */ RealType count = this->GetInput()->Size() - this->m_OutlierInstanceIdentifiers.size(); mean = ( mean * count - measurement[0] ) / ( count - 1.0 ); variance = ( count - 1.0 ) * variance - ( count - 1.0 ) * vnl_math_sqr( measurement[0] - mean ) / count; variance /= ( count - 2.0 ); this->m_OutlierInstanceIdentifiers.push_back( id ); } } } RealType lowerWinsorBound = 0.0; RealType upperWinsorBound = 0.0; if( this->m_OutlierHandling == Winsorize ) { typename itk::Statistics::TDistribution::Pointer tdistribution = itk::Statistics::TDistribution::New(); RealType t = tdistribution->EvaluateInverseCDF( 1.0 - 0.5 * this->m_WinsorizingLevel, this->GetInput()->Size() - this->m_OutlierInstanceIdentifiers.size() ); lowerWinsorBound = mean - t * vcl_sqrt( variance ); upperWinsorBound = mean + t * vcl_sqrt( variance ); } It = this->GetInput()->Begin(); while( It != this->GetInput()->End() ) { MeasurementVectorType inputMeasurement = It.GetMeasurementVector(); MeasurementVectorType outputMeasurement; outputMeasurement.SetSize( scalarMeasurementVectorSize ); if( this->m_OutlierHandling == None || std::find( this->m_OutlierInstanceIdentifiers.begin(), this->m_OutlierInstanceIdentifiers.end(), It.GetInstanceIdentifier() ) == this->m_OutlierInstanceIdentifiers.end() ) { outputMeasurement[0] = inputMeasurement[0]; this->GetOutput()->PushBack( outputMeasurement ); } else if( this->m_OutlierHandling == Winsorize ) { if( inputMeasurement[0] < lowerWinsorBound ) { outputMeasurement[0] = lowerWinsorBound; } else { outputMeasurement[0] = upperWinsorBound; } this->GetOutput()->PushBack( outputMeasurement ); } ++It; } } template typename GrubbsRosnerListSampleFilter ::InstanceIdentifierType GrubbsRosnerListSampleFilter ::FindMaximumNonOutlierDeviationValue( RealType mean, RealType variance ) { RealType maximumDeviation = 0.0; InstanceIdentifierType maximumID = NumericTraits::max(); typename ScalarListSampleType::ConstIterator It = this->GetInput()->Begin(); while( It != this->GetInput()->End() ) { MeasurementVectorType inputMeasurement = It.GetMeasurementVector(); InstanceIdentifierType inputID = It.GetInstanceIdentifier(); if( std::find( this->m_OutlierInstanceIdentifiers.begin(), this->m_OutlierInstanceIdentifiers.end(), inputID ) == this->m_OutlierInstanceIdentifiers.end() ) { if( vnl_math_abs( inputMeasurement[0] - mean ) > maximumDeviation ) { maximumDeviation = vnl_math_abs( inputMeasurement[0] - mean ); maximumID = inputID; } } ++It; } return maximumID; } template bool GrubbsRosnerListSampleFilter ::IsMeasurementAnOutlier( RealType x, RealType mean, RealType variance, unsigned long N ) { /** * The Grubb critical two-sided value is defined to be * (N-1)/sqrt(N)*sqrt( t*t / (N-2+t*t) ) where t is at the * (alpha / (2N)) signficance level with N-2 degrees of freedom. */ RealType sig = this->m_SignificanceLevel / ( 2.0 * static_cast( N ) ); typename itk::Statistics::TDistribution::Pointer tdistribution = itk::Statistics::TDistribution::New(); RealType t = tdistribution->EvaluateInverseCDF( 1.0 - sig, N - 2 ); RealType nu = static_cast( N - 1 ); RealType g = nu / vcl_sqrt( nu + 1.0 ) * vcl_sqrt( t*t / ( nu - 1 + t*t ) ); return g < ( vnl_math_abs( x - mean ) / vcl_sqrt( variance ) ); } template void GrubbsRosnerListSampleFilter ::PrintSelf( std::ostream& os, Indent indent ) const { os << indent << "Significance level: " << this->m_SignificanceLevel << std::endl; os << indent << "Outlier handling: "; if( this->m_OutlierHandling == None ) { os << "None" << std::endl; } if( this->m_OutlierHandling == Trim ) { os << "Trim" << std::endl; } if( this->m_OutlierHandling == Winsorize ) { os << "Winsorize"; os << " (level = " << this->m_WinsorizingLevel << ")" << std::endl; } if( this->m_OutlierInstanceIdentifiers.size() > 0 ) { os << indent << "Outlier Identifiers: " << std::endl; for( unsigned int d = 0; d < this->m_OutlierInstanceIdentifiers.size(); d++ ) { os << indent << " " << this->m_OutlierInstanceIdentifiers[d] << std::endl; } } else { os << indent << "There are no outliers." << std::endl; } } } // end of namespace Statistics } // end of namespace ants } // end of namespace itk #endif ants-1.9.2+svn680.dfsg/ImageSegmentation/antsHistogramParzenWindowsListSampleFunction.h000066400000000000000000000066531147325206600313250ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: antsHistogramParzenWindowsListSampleFunction.h,v $ Language: C++ Date: $Date: $ Version: $Revision: $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __antsHistogramParzenWindowsListSampleFunction_h #define __antsHistogramParzenWindowsListSampleFunction_h #include "antsListSampleFunction.h" #include "itkImage.h" namespace itk { namespace ants { namespace Statistics { /** \class HistogramParzenWindowsListSampleFunction.h * \brief point set filter. */ template class ITK_EXPORT HistogramParzenWindowsListSampleFunction : public ListSampleFunction { public: typedef HistogramParzenWindowsListSampleFunction Self; typedef ListSampleFunction Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); /** Run-time type information (and related methods). */ itkTypeMacro( HistogramParzenWindowsListSampleFunction, ListSampleFunction ); typedef typename Superclass::InputListSampleType InputListSampleType; typedef typename Superclass::InputMeasurementVectorType InputMeasurementVectorType; typedef typename Superclass::InputMeasurementType InputMeasurementType; /** List sample typedef support. */ typedef TListSample ListSampleType; /** Other typedef */ typedef TOutput RealType; typedef TOutput OutputType; typedef Image HistogramImageType; /** Helper functions */ itkSetMacro( Sigma, RealType ); itkGetConstMacro( Sigma, RealType ); itkSetMacro( NumberOfHistogramBins, unsigned int ); itkGetConstMacro( NumberOfHistogramBins, unsigned int ); virtual void SetInputListSample( const InputListSampleType * ptr ); virtual TOutput Evaluate( const InputMeasurementVectorType& measurement ) const; protected: HistogramParzenWindowsListSampleFunction(); virtual ~HistogramParzenWindowsListSampleFunction(); void PrintSelf( std::ostream& os, Indent indent ) const; void GenerateData(); private: //purposely not implemented HistogramParzenWindowsListSampleFunction( const Self& ); void operator=( const Self& ); unsigned int m_NumberOfHistogramBins; RealType m_Sigma; std::vector m_HistogramImages; }; } // end of namespace Statistics } // end of namespace ants } // end of namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "antsHistogramParzenWindowsListSampleFunction.txx" #endif #endif ants-1.9.2+svn680.dfsg/ImageSegmentation/antsHistogramParzenWindowsListSampleFunction.txx000066400000000000000000000172311147325206600317130ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: antsHistogramParzenWindowsListSampleFunction.txx,v $ Language: C++ Date: $Date: $ Version: $Revision: $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __antsHistogramParzenWindowsListSampleFunction_txx #define __antsHistogramParzenWindowsListSampleFunction_txx #include "antsHistogramParzenWindowsListSampleFunction.h" #include "itkArray.h" #include "itkBSplineInterpolateImageFunction.h" #include "itkContinuousIndex.h" #include "itkDiscreteGaussianImageFilter.h" #include "itkDivideByConstantImageFilter.h" #include "itkStatisticsImageFilter.h" namespace itk { namespace ants { namespace Statistics { template HistogramParzenWindowsListSampleFunction ::HistogramParzenWindowsListSampleFunction() { this->m_NumberOfHistogramBins = 32; this->m_Sigma = 1.0; } template HistogramParzenWindowsListSampleFunction ::~HistogramParzenWindowsListSampleFunction() { } template void HistogramParzenWindowsListSampleFunction ::SetInputListSample( const InputListSampleType * ptr ) { this->m_ListSample = ptr; if( !this->m_ListSample ) { return; } if( this->m_ListSample->Size() <= 1 ) { itkWarningMacro( "The input list sample has <= 1 element." << "Function evaluations will be equal to 0." ); return; } const unsigned int Dimension = this->m_ListSample->GetMeasurementVectorSize(); /** * Find the min/max values to define the histogram domain */ Array minValues( Dimension ); minValues.Fill( NumericTraits::max() ); Array maxValues( Dimension ); maxValues.Fill( NumericTraits::NonpositiveMin() ); typename InputListSampleType::ConstIterator It = this->m_ListSample->Begin(); while( It != this->m_ListSample->End() ) { InputMeasurementVectorType inputMeasurement = It.GetMeasurementVector(); for( unsigned int d = 0; d < Dimension; d++ ) { if( inputMeasurement[d] < minValues[d] ) { minValues[d] = inputMeasurement[d]; } if( inputMeasurement[d] > maxValues[d] ) { maxValues[d] = inputMeasurement[d]; } } ++It; } this->m_HistogramImages.clear(); for( unsigned int d = 0; d < Dimension; d++ ) { this->m_HistogramImages.push_back( HistogramImageType::New() ); typename HistogramImageType::SpacingType spacing; spacing[0] = ( maxValues[d] - minValues[d] ) / static_cast( this->m_NumberOfHistogramBins - 1 ); typename HistogramImageType::PointType origin; origin[0] = minValues[d] - 3.0 * ( this->m_Sigma * spacing[0] ); typename HistogramImageType::SizeType size; size[0] = static_cast( vcl_ceil( ( maxValues[d] + 3.0 * ( this->m_Sigma * spacing[0] ) - ( minValues[d] - 3.0 * ( this->m_Sigma * spacing[0] ) ) ) / spacing[0] ) ); this->m_HistogramImages[d]->SetOrigin( origin ); this->m_HistogramImages[d]->SetSpacing( spacing ); this->m_HistogramImages[d]->SetRegions( size ); this->m_HistogramImages[d]->Allocate(); this->m_HistogramImages[d]->FillBuffer( 0 ); } unsigned long count = 0; It = this->m_ListSample->Begin(); while( It != this->m_ListSample->End() ) { InputMeasurementVectorType inputMeasurement = It.GetMeasurementVector(); RealType newWeight = 1.0; if( this->m_Weights.Size() == this->m_ListSample->Size() ) { newWeight = this->m_Weights[count]; } for( unsigned int d = 0; d < Dimension; d++ ) { typename HistogramImageType::PointType point; point[0] = inputMeasurement[d]; ContinuousIndex cidx; this->m_HistogramImages[d]->TransformPhysicalPointToContinuousIndex( point, cidx ); typename HistogramImageType::IndexType idx; idx[0] = static_cast( vcl_floor( cidx[0] ) ); if( this->m_HistogramImages[d]->GetLargestPossibleRegion().IsInside( idx ) ) { RealType oldWeight = this->m_HistogramImages[d]->GetPixel( idx ); this->m_HistogramImages[d]->SetPixel( idx, ( 1.0 - ( cidx[0] - idx[0] ) ) * newWeight + oldWeight ); } idx[0]++; if( this->m_HistogramImages[d]->GetLargestPossibleRegion().IsInside( idx ) ) { RealType oldWeight = this->m_HistogramImages[d]->GetPixel( idx ); this->m_HistogramImages[d]->SetPixel( idx, ( 1.0 - ( idx[0] - cidx[0] ) ) * newWeight + oldWeight ); } } ++count; ++It; } for( unsigned int d = 0; d < Dimension; d++ ) { typedef DiscreteGaussianImageFilter GaussianFilterType; typename GaussianFilterType::Pointer gaussian = GaussianFilterType::New(); gaussian->SetInput( this->m_HistogramImages[d] ); gaussian->SetVariance( this->m_Sigma * this->m_Sigma ); gaussian->SetMaximumError( 0.01 ); gaussian->SetUseImageSpacing( false ); gaussian->Update(); typedef StatisticsImageFilter StatsFilterType; typename StatsFilterType::Pointer stats = StatsFilterType::New(); stats->SetInput( gaussian->GetOutput() ); stats->Update(); typedef DivideByConstantImageFilter DividerType; typename DividerType::Pointer divider = DividerType::New(); divider->SetInput( gaussian->GetOutput() ); divider->SetConstant( stats->GetSum() ); divider->Update(); this->m_HistogramImages[d] = divider->GetOutput(); } } template TOutput HistogramParzenWindowsListSampleFunction ::Evaluate( const InputMeasurementVectorType &measurement ) const { try { typedef BSplineInterpolateImageFunction InterpolatorType; RealType probability = 1.0; for( unsigned int d = 0; d < this->m_HistogramImages.size(); d++ ) { typename HistogramImageType::PointType point; point[0] = measurement[d]; typename InterpolatorType::Pointer interpolator = InterpolatorType::New(); interpolator->SetSplineOrder( 3 ); interpolator->SetInputImage( this->m_HistogramImages[d] ); if( interpolator->IsInsideBuffer( point ) ) { probability *= interpolator->Evaluate( point ); } else { return 0; } } return probability; } catch(...) { return 0; } } /** * Standard "PrintSelf" method */ template void HistogramParzenWindowsListSampleFunction ::PrintSelf( std::ostream& os, Indent indent) const { os << indent << "Sigma: " << this->m_Sigma << std::endl; os << indent << "Number of histogram bins: " << this->m_NumberOfHistogramBins << std::endl; } } // end of namespace Statistics } // end of namespace ants } // end of namespace itk #endif ants-1.9.2+svn680.dfsg/ImageSegmentation/antsListSampleFunction.h000066400000000000000000000107141147325206600247050ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: antsListSampleFunction.h,v $ Language: C++ Date: $Date: 2008/10/18 00:20:04 $ Version: $Revision: 1.1.1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __antsListSampleFunction_h #define __antsListSampleFunction_h #include "itkFunctionBase.h" #include "itkArray.h" namespace itk { namespace ants { namespace Statistics { /** \class ListSampleFunction * \brief Evaluates a function of an image at specified position. * * ListSampleFunction is a baseclass for all objects that evaluates * a function of a list sample at a measurement * This class is templated over the input list type, the type * of the function output and the coordinate representation type * (e.g. float or double). * * The input list sample is set via method SetInputListSample(). * The methods Evaluate() evaluates the function at a measurement vector. * * \ingroup ListSampleFunctions */ template class ITK_EXPORT ListSampleFunction : public FunctionBase { public: /** Standard class typedefs. */ typedef ListSampleFunction Self; typedef FunctionBase Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Run-time type information (and related methods). */ itkTypeMacro( ListSampleFunction, FunctionBase ); /** InputListSampleType typedef support. */ typedef TInputListSample InputListSampleType; /** Array typedef for weights */ typedef Array WeightArrayType; /** InputPixel typedef support */ typedef typename InputListSampleType::MeasurementVectorType InputMeasurementVectorType; typedef typename InputListSampleType::MeasurementType InputMeasurementType; /** OutputType typedef support. */ typedef TOutput OutputType; /** CoordRepType typedef support. */ typedef TCoordRep CoordRepType; /** Set the input point set. * \warning this method caches BufferedRegion information. * If the BufferedRegion has changed, user must call * SetInputListSample again to update cached values. */ virtual void SetInputListSample( const InputListSampleType * ptr ); /** Get the input image. */ const InputListSampleType * GetInputListSample() const { return m_ListSample.GetPointer(); } /** Clear the input list sample to free memory */ virtual void ClearInputListSample() { this->SetInputListSample( NULL ); } /** Sets the weights using an array */ virtual void SetWeights( WeightArrayType* array ); /** Gets the weights array */ WeightArrayType* GetWeights(); /** Evaluate the function at specified Point position. * Subclasses must provide this method. */ virtual TOutput Evaluate( const InputMeasurementVectorType& measurement ) const = 0; protected: ListSampleFunction(); ~ListSampleFunction() {} void PrintSelf(std::ostream& os, Indent indent) const; /** Const pointer to the input image. */ typename InputListSampleType::ConstPointer m_ListSample; WeightArrayType m_Weights; private: ListSampleFunction(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end of namespace Statistics } // end of namespace ants } // end of namespace itk // Define instantiation macro for this template. #define ITK_TEMPLATE_ListSampleFunction(_, EXPORT, x, y) namespace itk { \ _(3(class EXPORT ListSampleFunction< ITK_TEMPLATE_3 x >)) \ namespace Templates { typedef ListSampleFunction< ITK_TEMPLATE_3 x > ListSampleFunction##y; } \ } #if ITK_TEMPLATE_EXPLICIT # include "Templates/antsListSampleFunction+-.h" #endif #if ITK_TEMPLATE_TXX # include "antsListSampleFunction.txx" #endif #endif ants-1.9.2+svn680.dfsg/ImageSegmentation/antsListSampleFunction.txx000066400000000000000000000043301147325206600252760ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: antsListSampleFunction.txx,v $ Language: C++ Date: $Date: 2008/10/18 00:20:04 $ Version: $Revision: 1.1.1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __antsListSampleFunction_txx #define __antsListSampleFunction_txx #include "antsListSampleFunction.h" namespace itk { namespace ants { namespace Statistics { /** * Constructor */ template ListSampleFunction ::ListSampleFunction() { this->m_ListSample = NULL; } /** * Standard "PrintSelf" method */ template void ListSampleFunction ::PrintSelf( std::ostream& os, Indent indent) const { os << indent << "InputListSample: " << m_ListSample.GetPointer() << std::endl; } template void ListSampleFunction ::SetWeights( WeightArrayType* array ) { this->m_Weights = *array; this->Modified(); } template typename ListSampleFunction::WeightArrayType* ListSampleFunction ::GetWeights() { return &this->m_Weights; } /** * Initialize by setting the input point set */ template void ListSampleFunction ::SetInputListSample( const InputListSampleType * ptr ) { // set the input image m_ListSample = ptr; } } // end of namespace Statistics } // end of namespace ants } // end of namespace itk #endif ants-1.9.2+svn680.dfsg/ImageSegmentation/antsListSampleToListSampleFilter.h000066400000000000000000000055751147325206600266570ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: antsListSampleToListSampleFilter.h,v $ Language: C++ Date: $$ Version: $Revision: $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __antsListSampleToListSampleFilter_h #define __antsListSampleToListSampleFilter_h #include "itkProcessObject.h" namespace itk { namespace ants { namespace Statistics { /** \class ListSampleToListSampleFilter * \brief Base class for filters that take a list sample as an input and output * another list sample. * * ListSampleToListSampleFilter is the base class for all process objects that output * list sample data, and require list sample data as input. Specifically, this class * defines the SetInput() method for defining the input to a filter. * * \ingroup ListSampleFilters * */ template class ITK_EXPORT ListSampleToListSampleFilter : public ProcessObject { public: /** Standard class typedefs. */ typedef ListSampleToListSampleFilter Self; typedef ProcessObject Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Run-time type information (and related methods). */ itkTypeMacro( ListSampleToListSampleFilter, ProcessObject ); /** Some convenient typedefs. */ typedef TInputListSample InputListSampleType; typedef TOutputListSample OutputListSampleType; /** Set the list sample input of this object. */ void SetInput( const InputListSampleType *input ); /** Get the list sample input of this object. */ InputListSampleType * GetInput(); /** Get the list sample output of this object. */ OutputListSampleType * GetOutput(); virtual void Update() { this->GenerateData(); } protected: ListSampleToListSampleFilter(); ~ListSampleToListSampleFilter() {}; virtual void GenerateData() = 0; void AllocateOutput(); private: ListSampleToListSampleFilter( const Self& ); //purposely not implemented void operator=( const Self& ); //purposely not implemented // typename InputListSampleType::ConstPointer m_InputListSample; // typename OutputListSampleType::Pointer m_OutputListSample; }; } // end namespace Statistics } // end namespace ants } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "antsListSampleToListSampleFilter.txx" #endif #endif ants-1.9.2+svn680.dfsg/ImageSegmentation/antsListSampleToListSampleFilter.txx000066400000000000000000000060311147325206600272370ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: antsListSampleToListSampleFilter.txx,v $ Language: C++ Date: $Date: $ Version: $Revision: $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkListSampleToListSampleFilter_txx #define _itkListSampleToListSampleFilter_txx #include "antsListSampleToListSampleFilter.h" namespace itk { namespace ants { namespace Statistics { /** * */ template ListSampleToListSampleFilter ::ListSampleToListSampleFilter() { // Modify superclass default values, can be overridden by subclasses this->SetNumberOfRequiredInputs( 1 ); this->SetNumberOfRequiredOutputs( 1 ); } template void ListSampleToListSampleFilter ::SetInput( const TInputListSample *input ) { // this->m_InputListSample = const_cast( input ); this->ProcessObject::SetNthInput( 0, reinterpret_cast( const_cast( input ) ) ); } template void ListSampleToListSampleFilter ::AllocateOutput() { typename DataObject::Pointer obj = reinterpret_cast(TOutputListSample::New().GetPointer()); // typename TOutputListSample::Pointer output // = reinterpret_cast(obj.GetPointer()); this->ProcessObject::SetNumberOfRequiredOutputs( 1 ); this->ProcessObject::SetNthOutput( 0, obj.GetPointer() ); // this->m_OutputListSample = OutputListSampleType::New(); } /** * */ template typename ListSampleToListSampleFilter::InputListSampleType * ListSampleToListSampleFilter ::GetInput() { return reinterpret_cast( this->ProcessObject::GetInput( 0 ) ); } template typename ListSampleToListSampleFilter::OutputListSampleType * ListSampleToListSampleFilter ::GetOutput() { if (this->GetNumberOfOutputs() < 1) { return 0; } // we assume that the first output is of the templated type return reinterpret_cast( this->ProcessObject::GetOutput( 0 ) ); // return this->m_OutputListSample.GetPointer(); } } // end of namespace Statistics } // end of namespace ants } // end of namespace itk #endif ants-1.9.2+svn680.dfsg/ImageSegmentation/antsLogEuclideanGaussianListSampleFunction.h000066400000000000000000000065241147325206600306600ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: antsLogEuclideanGaussianListSampleFunction.h,v $ Language: C++ Date: $Date: $ Version: $Revision: $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __antsLogEuclideanGaussianListSampleFunction_h #define __antsLogEuclideanGaussianListSampleFunction_h #include "antsListSampleFunction.h" #include "itkVariableSizeMatrix.h" namespace itk { namespace ants { namespace Statistics { /** \class LogEuclideanGaussianListSampleFunction.h * \brief */ template class ITK_EXPORT LogEuclideanGaussianListSampleFunction : public ListSampleFunction { public: typedef LogEuclideanGaussianListSampleFunction Self; typedef ListSampleFunction Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); /** Run-time type information (and related methods). */ itkTypeMacro( LogEuclideanGaussianListSampleFunction, ListSampleFunction ); typedef typename Superclass::InputListSampleType InputListSampleType; typedef typename Superclass::InputMeasurementVectorType InputMeasurementVectorType; typedef typename Superclass::InputMeasurementType InputMeasurementType; /** List sample typedef support. */ typedef TListSample ListSampleType; /** Other typedef */ typedef TOutput RealType; typedef TOutput OutputType; typedef VariableSizeMatrix TensorType; /** Helper functions */ virtual void SetInputListSample( const InputListSampleType * ptr ); virtual TOutput Evaluate( const InputMeasurementVectorType& measurement ) const; protected: LogEuclideanGaussianListSampleFunction(); virtual ~LogEuclideanGaussianListSampleFunction(); void PrintSelf( std::ostream& os, Indent indent ) const; void GenerateData(); TensorType LogTensorTransform( const TensorType & ) const; TensorType ExpTensorTransform( const TensorType & ) const; RealType CalculateTensorDistance( const TensorType &, const TensorType & ) const; TensorType m_MeanTensor; RealType m_Dispersion; private: //purposely not implemented LogEuclideanGaussianListSampleFunction( const Self& ); void operator=( const Self& ); }; } // end of namespace Statistics } // end of namespace ants } // end of namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "antsLogEuclideanGaussianListSampleFunction.txx" #endif #endif ants-1.9.2+svn680.dfsg/ImageSegmentation/antsLogEuclideanGaussianListSampleFunction.txx000066400000000000000000000172511147325206600312530ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: antsLogEuclideanGaussianListSampleFunction.txx,v $ Language: C++ Date: $Date: $ Version: $Revision: $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __antsLogEuclideanGaussianListSampleFunction_txx #define __antsLogEuclideanGaussianListSampleFunction_txx #include "antsLogEuclideanGaussianListSampleFunction.h" #include "itkDecomposeTensorFunction.h" #include "vnl/vnl_trace.h" namespace itk { namespace ants { namespace Statistics { template LogEuclideanGaussianListSampleFunction ::LogEuclideanGaussianListSampleFunction() { } template LogEuclideanGaussianListSampleFunction ::~LogEuclideanGaussianListSampleFunction() { } template void LogEuclideanGaussianListSampleFunction ::SetInputListSample( const InputListSampleType * ptr ) { this->m_ListSample = ptr; if( !this->m_ListSample ) { return; } if( this->m_ListSample->Size() > 1 ) { RealType L = static_cast( this->m_ListSample->GetMeasurementVectorSize() ); unsigned int D = static_cast( 0.5 * ( -1 + vcl_sqrt( 1.0 + 8.0 * L ) ) ); this->m_MeanTensor.SetSize( D, D ); this->m_MeanTensor.Fill( 0.0 ); unsigned long N = 0; RealType totalWeight = 0.0; typename InputListSampleType::ConstIterator It = this->m_ListSample->Begin(); while( It != this->m_ListSample->End() ) { InputMeasurementVectorType measurement = It.GetMeasurementVector(); TensorType T( D, D ); unsigned int index = 0; for( unsigned int i = 0; i < D; i++ ) { for( unsigned int j = i; j < D; j++ ) { T( i, j ) = measurement( index++ ); T( j, i ) = T( i, j ); } } T = this->LogTensorTransform( T ); RealType weight = 1.0; if( this->m_Weights.Size() == this->m_ListSample->Size() ) { weight = this->m_Weights[N++]; } totalWeight += weight; this->m_MeanTensor += ( T * weight ); ++It; } if( totalWeight > 0.0 ) { this->m_MeanTensor /= totalWeight; } this->m_MeanTensor = this->ExpTensorTransform( this->m_MeanTensor ); /** * Now calculate the dispersion (i.e. variance) */ this->m_Dispersion = 0.0; N = 0; It = this->m_ListSample->Begin(); while( It != this->m_ListSample->End() ) { InputMeasurementVectorType measurement = It.GetMeasurementVector(); TensorType T( D, D ); unsigned int index = 0; for( unsigned int i = 0; i < D; i++ ) { for( unsigned int j = i; j < D; j++ ) { T( i, j ) = measurement( index++ ); T( j, i ) = T( i, j ); } } RealType distance = this->CalculateTensorDistance( T, this->m_MeanTensor ); RealType weight = 1.0; if( this->m_Weights.Size() == this->m_ListSample->Size() ) { weight = this->m_Weights[N++]; } this->m_Dispersion += ( weight * vnl_math_sqr( distance ) ); ++It; } this->m_Dispersion /= static_cast( N ); } else { itkWarningMacro( "The input list sample has <= 1 element." << "Function evaluations will be equal to 0." ); } } template typename LogEuclideanGaussianListSampleFunction ::TensorType LogEuclideanGaussianListSampleFunction ::LogTensorTransform( const TensorType &T ) const { TensorType V; TensorType W; TensorType Tc = T; typedef DecomposeTensorFunction DecomposerType; typename DecomposerType::Pointer decomposer = DecomposerType::New(); decomposer->EvaluateSymmetricEigenDecomposition( Tc, W, V ); for( unsigned int i = 0; i < W.Rows(); i++ ) { if( W( i, i ) > 0.0 ) { W( i, i ) = vcl_log( W( i, i ) ); } else { W( i, i ) = 0.0; } } W *= V.GetTranspose(); TensorType logT = V * W; return logT; } template typename LogEuclideanGaussianListSampleFunction ::TensorType LogEuclideanGaussianListSampleFunction ::ExpTensorTransform( const TensorType &T ) const { TensorType V; TensorType W; TensorType Tc = T; typedef DecomposeTensorFunction DecomposerType; typename DecomposerType::Pointer decomposer = DecomposerType::New(); decomposer->EvaluateSymmetricEigenDecomposition( Tc, W, V ); for( unsigned int i = 0; i < W.Rows(); i++ ) { W( i, i ) = vcl_exp( W( i, i ) ); } W *= V.GetTranspose(); TensorType expT = V * W; return expT; } template typename LogEuclideanGaussianListSampleFunction ::RealType LogEuclideanGaussianListSampleFunction ::CalculateTensorDistance( const TensorType &S, const TensorType &T ) const { TensorType logS = this->LogTensorTransform( S ); TensorType logT = this->LogTensorTransform( T ); TensorType diff = logS - logT; TensorType diffSq = diff * diff; RealType distance = vcl_sqrt( vnl_trace( ( diffSq ).GetVnlMatrix() ) ); // RealType distance = ( ( logS - logT ).GetVnlMatrix() ).frobenius_norm(); return distance; } template TOutput LogEuclideanGaussianListSampleFunction ::Evaluate( const InputMeasurementVectorType &measurement ) const { unsigned int D = this->m_MeanTensor.Rows(); TensorType T( D, D ); unsigned int index = 0; for( unsigned int i = 0; i < D; i++ ) { for( unsigned int j = i; j < D; j++ ) { T( i, j ) = measurement( index++ ); T( j, i ) = T( i, j ); } } RealType distance = this->CalculateTensorDistance( T, this->m_MeanTensor ); RealType preFactor = 1.0 / ( vcl_sqrt( 2.0 * vnl_math::pi * this->m_Dispersion ) ); RealType probability = preFactor * vcl_exp( -0.5 * vnl_math_sqr( distance ) / this->m_Dispersion ); return probability; } /** * Standard "PrintSelf" method */ template void LogEuclideanGaussianListSampleFunction ::PrintSelf( std::ostream& os, Indent indent) const { os << indent << "Mean tensor = ["; for( unsigned int r = 0; r < this->m_MeanTensor.Rows(); r++ ) { for( unsigned int c = 0; c < this->m_MeanTensor.Cols() - 1; c++ ) { os << this->m_MeanTensor( r, c ) << ", "; } if( r == this->m_MeanTensor.Rows() - 1 ) { os << this->m_MeanTensor( r, this->m_MeanTensor.Cols() - 1 ) << "], "; } else { os << this->m_MeanTensor( r, this->m_MeanTensor.Cols() - 1 ) << "; "; } } os << "Dispersion (variance) = " << this->m_Dispersion << std::endl; } } // end of namespace Statistics } // end of namespace ants } // end of namespace itk #endif ants-1.9.2+svn680.dfsg/ImageSegmentation/antsManifoldParzenWindowsListSampleFunction.h000066400000000000000000000110011147325206600311000ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: antsManifoldParzenWindowsListSampleFunction.h,v $ Language: C++ Date: $Date: $ Version: $Revision: $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __antsManifoldParzenWindowsListSampleFunction_h #define __antsManifoldParzenWindowsListSampleFunction_h #include "antsListSampleFunction.h" #include "itkGaussianMembershipFunction.h" #include "itkWeightedCentroidKdTreeGenerator.h" #include namespace itk { namespace ants { namespace Statistics { /** \class ManifoldParzenWindowsListSampleFunction.h * \brief point set filter. */ template class ITK_EXPORT ManifoldParzenWindowsListSampleFunction : public ListSampleFunction { public: typedef ManifoldParzenWindowsListSampleFunction Self; typedef ListSampleFunction Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); /** Run-time type information (and related methods). */ itkTypeMacro( ManifoldParzenWindowsListSampleFunction, ListSampleFunction ); typedef typename Superclass::InputListSampleType InputListSampleType; typedef typename Superclass::InputMeasurementVectorType InputMeasurementVectorType; typedef typename Superclass::InputMeasurementType InputMeasurementType; /** List sample typedef support. */ typedef TListSample ListSampleType; /** Kd tree typedefs */ typedef typename itk::Statistics:: WeightedCentroidKdTreeGenerator TreeGeneratorType; typedef typename TreeGeneratorType::KdTreeType KdTreeType; typedef typename KdTreeType ::InstanceIdentifierVectorType NeighborhoodIdentifierType; /** Other typedef */ typedef TOutput RealType; typedef TOutput OutputType; typedef typename itk::Statistics::GaussianMembershipFunction GaussianType; typedef std::vector GaussianContainerType; typedef typename GaussianType::CovarianceType CovarianceMatrixType; /** Helper functions */ itkSetMacro( EvaluationKNeighborhood, unsigned int ); itkGetConstMacro( EvaluationKNeighborhood, unsigned int ); itkSetMacro( RegularizationSigma, RealType ); itkGetConstMacro( RegularizationSigma, RealType ); itkSetMacro( CovarianceKNeighborhood, unsigned int ); itkGetConstMacro( CovarianceKNeighborhood, unsigned int ); itkSetMacro( KernelSigma, RealType ); itkGetConstMacro( KernelSigma, RealType ); virtual void SetInputListSample( const InputListSampleType * ptr ); virtual TOutput Evaluate( const InputMeasurementVectorType& measurement ) const; protected: ManifoldParzenWindowsListSampleFunction(); virtual ~ManifoldParzenWindowsListSampleFunction(); void PrintSelf( std::ostream& os, Indent indent ) const; void GenerateData(); private: //purposely not implemented ManifoldParzenWindowsListSampleFunction( const Self& ); void operator=( const Self& ); unsigned int m_CovarianceKNeighborhood; unsigned int m_EvaluationKNeighborhood; RealType m_RegularizationSigma; RealType m_KernelSigma; RealType m_NormalizationFactor; typename TreeGeneratorType::Pointer m_KdTreeGenerator; GaussianContainerType m_Gaussians; }; } // end of namespace Statistics } // end of namespace ants } // end of namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "antsManifoldParzenWindowsListSampleFunction.txx" #endif #endif ants-1.9.2+svn680.dfsg/ImageSegmentation/antsManifoldParzenWindowsListSampleFunction.txx000066400000000000000000000170351147325206600315110ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: antsManifoldParzenWindowsListSampleFunction.txx,v $ Language: C++ Date: $Date: $ Version: $Revision: $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __antsManifoldParzenWindowsListSampleFunction_txx #define __antsManifoldParzenWindowsListSampleFunction_txx #include "antsManifoldParzenWindowsListSampleFunction.h" namespace itk { namespace ants { namespace Statistics { template ManifoldParzenWindowsListSampleFunction ::ManifoldParzenWindowsListSampleFunction() { this->m_KdTreeGenerator = NULL; this->m_EvaluationKNeighborhood = 50; this->m_RegularizationSigma = 1.0; this->m_CovarianceKNeighborhood = 0; this->m_KernelSigma = 0.0; } template ManifoldParzenWindowsListSampleFunction ::~ManifoldParzenWindowsListSampleFunction() { } template void ManifoldParzenWindowsListSampleFunction ::SetInputListSample( const InputListSampleType * ptr ) { this->m_ListSample = ptr; if( !this->m_ListSample ) { return; } if( this->m_ListSample->Size() <= 1 ) { itkWarningMacro( "The input list sample has <= 1 element." << "Function evaluations will be equal to 0." ); return; } /** * Generate KdTree and create set of gaussians from input point set */ this->m_KdTreeGenerator = TreeGeneratorType::New(); this->m_KdTreeGenerator->SetSample( const_cast( this->m_ListSample.GetPointer() ) ); this->m_KdTreeGenerator->SetBucketSize( 16 ); this->m_KdTreeGenerator->Update(); /** * Calculate covariance matrices */ this->m_Gaussians.resize( this->m_ListSample->Size() ); const unsigned int Dimension = this->m_ListSample->GetMeasurementVectorSize(); unsigned long count = 0; typename InputListSampleType::ConstIterator It = this->m_ListSample->Begin(); while( It != this->m_ListSample->End() ) { InputMeasurementVectorType inputMeasurement = It.GetMeasurementVector(); typename GaussianType::MeanType mean( Dimension ); for( unsigned int d = 0; d < Dimension; d++ ) { mean[d] = inputMeasurement[d]; } this->m_Gaussians[count] = GaussianType::New(); this->m_Gaussians[count]->SetMean( mean ); if( this->m_CovarianceKNeighborhood > 0 ) { /** Temporarily set the covariance */ CovarianceMatrixType Cov( Dimension, Dimension ); Cov.SetIdentity(); Cov *= this->m_KernelSigma; this->m_Gaussians[count]->SetCovariance( Cov ); Cov.Fill( 0 ); typename TreeGeneratorType::KdTreeType ::InstanceIdentifierVectorType neighbors; unsigned int numberOfNeighbors = vnl_math_min( this->m_CovarianceKNeighborhood, static_cast( this->m_ListSample->Size() ) ); this->m_KdTreeGenerator->GetOutput()->Search( inputMeasurement, numberOfNeighbors, neighbors ); RealType denominator = 0.0; for( unsigned int j = 0; j < numberOfNeighbors; j++ ) { if( neighbors[j] != count && neighbors[j] < this->m_ListSample->Size() ) { InputMeasurementVectorType neighbor = this->m_KdTreeGenerator->GetOutput()->GetMeasurementVector( neighbors[j] ); RealType kernelValue = this->m_Gaussians[count]->Evaluate( neighbor ); if( this->m_Weights.Size() == this->m_Gaussians.size() ) { kernelValue *= this->m_Weights[count]; } denominator += kernelValue; if( kernelValue > 0.0 ) { for( unsigned int m = 0; m < Dimension; m++ ) { for( unsigned int n = m; n < Dimension; n++ ) { RealType covariance = kernelValue * ( neighbor[m] - inputMeasurement[m] ) * ( neighbor[n] - inputMeasurement[n] ); Cov( m, n ) += covariance; Cov( n, m ) += covariance; } } } } } if( denominator > 0.0 ) { Cov /= denominator; } for( unsigned int m = 0; m < Dimension; m++ ) { Cov( m, m ) += ( this->m_RegularizationSigma * this->m_RegularizationSigma ); } this->m_Gaussians[count]->SetCovariance( Cov ); } else { CovarianceMatrixType Cov( Dimension, Dimension ); Cov.SetIdentity(); Cov *= this->m_RegularizationSigma; this->m_Gaussians[count]->SetCovariance( Cov ); } ++It; ++count; } /** * Calculate normalization factor */ this->m_NormalizationFactor = 0.0; for( unsigned int i = 0; i < this->m_Gaussians.size(); i++ ) { if( this->m_Weights.Size() == this->m_Gaussians.size() ) { this->m_NormalizationFactor += this->m_Weights[i]; } else { this->m_NormalizationFactor += 1.0; } } } template TOutput ManifoldParzenWindowsListSampleFunction ::Evaluate( const InputMeasurementVectorType &measurement ) const { try { unsigned int numberOfNeighbors = vnl_math_min( this->m_EvaluationKNeighborhood, static_cast( this->m_Gaussians.size() ) ); OutputType sum = 0.0; if( numberOfNeighbors == this->m_Gaussians.size() ) { for( unsigned int j = 0; j < this->m_Gaussians.size(); j++ ) { sum += static_cast( this->m_Gaussians[j]->Evaluate( measurement ) ); } } else { typename TreeGeneratorType::KdTreeType ::InstanceIdentifierVectorType neighbors; this->m_KdTreeGenerator->GetOutput()->Search( measurement, numberOfNeighbors, neighbors ); for( unsigned int j = 0; j < numberOfNeighbors; j++ ) { sum += static_cast( this->m_Gaussians[neighbors[j]]->Evaluate( measurement ) ); } } return static_cast( sum / this->m_NormalizationFactor ); } catch(...) { return 0; } } /** * Standard "PrintSelf" method */ template void ManifoldParzenWindowsListSampleFunction ::PrintSelf( std::ostream& os, Indent indent) const { os << indent << "Regularization sigma: " << this->m_RegularizationSigma << std::endl; os << indent << "Evaluation K neighborhood: " << this->m_EvaluationKNeighborhood << std::endl; if( this->m_CovarianceKNeighborhood > 0 ) { os << indent << "Covariance K neighborhood: " << this->m_CovarianceKNeighborhood << std::endl; os << indent << "Kernel sigma: " << this->m_KernelSigma << std::endl; } } } // end of namespace Statistics } // end of namespace ants } // end of namespace itk #endif ants-1.9.2+svn680.dfsg/ImageSegmentation/antsPassThroughListSampleFilter.h000066400000000000000000000050151147325206600265330ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: antsPassThroughListSampleFilter.h,v $ Language: C++ Date: $Date: $ Version: $Revision: $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __antsPassThroughListSampleFilter_h #define __antsPassThroughListSampleFilter_h #include "antsListSampleToListSampleFilter.h" namespace itk { namespace ants { namespace Statistics { /** \class PassThroughListSampleFilter * \brief Simple class which pass the input to the output. * */ template class ITK_EXPORT PassThroughListSampleFilter : public ListSampleToListSampleFilter { public: /** * Standard class typedefs. */ typedef PassThroughListSampleFilter Self; typedef ListSampleToListSampleFilter Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** * Standard macros */ itkTypeMacro( PassThroughListSampleFilter, ListSampleToScalarListSampleFilter ); /** * Method for creation through the object factory. */ itkNewMacro( Self ); /** * Conveneient typedefs */ typedef TListSample ListSampleType; typedef ListSampleType InputType; typedef typename ListSampleType::MeasurementVectorType MeasurementVectorType; typedef typename ListSampleType::MeasurementType MeasurementType; protected: PassThroughListSampleFilter(); virtual ~PassThroughListSampleFilter(); void PrintSelf( std::ostream& os, Indent indent ) const; virtual void GenerateData(); private: PassThroughListSampleFilter( const Self& ); //purposely not implemented void operator=( const Self& ); //purposely not implemented }; // end of class } // end of namespace Statistics } // end of namespace ants } // end of namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "antsPassThroughListSampleFilter.txx" #endif #endif ants-1.9.2+svn680.dfsg/ImageSegmentation/antsPassThroughListSampleFilter.txx000066400000000000000000000036441147325206600271350ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: antsPassThroughListSampleFilter.txx,v $ Language: C++ Date: $Date: $ Version: $Revision: $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __antsPassThroughListSampleFilter_txx #define __antsPassThroughListSampleFilter_txx #include "antsPassThroughListSampleFilter.h" namespace itk { namespace ants { namespace Statistics { template PassThroughListSampleFilter ::PassThroughListSampleFilter() { this->AllocateOutput(); this->GetOutput()->SetMeasurementVectorSize( this->GetInput()->GetMeasurementVectorSize() ); } template PassThroughListSampleFilter ::~PassThroughListSampleFilter() { } template void PassThroughListSampleFilter ::GenerateData() { const unsigned int measurementVectorSize = this->GetOutput()->GetMeasurementVectorSize(); /** * Simply pass the input to the output. */ typename ListSampleType::ConstIterator It = this->GetInput()->Begin(); while( It != this->GetInput()->End() ) { this->GetOutput()->PushBack( It.GetMeasurementVector() ); ++It; } } template void PassThroughListSampleFilter ::PrintSelf( std::ostream& os, Indent indent ) const { this->Superclass::PrintSelf( os, indent ); } } // end of namespace Statistics } // end of namespace ants } // end of namespace itk #endif ants-1.9.2+svn680.dfsg/README.txt000066400000000000000000000143441147325206600161640ustar00rootroot00000000000000Release 1.9.2 --- svn release 674++ Homepage: http://www.picsl.upenn.edu/ANTS/ Introduction -- ANTS is a tool for computational neuroanatomy based on medical images. ANTS reads any image type that can be read by ITK (www.itk.org), that is, jpg, tiff, hdr, nii, nii.gz, mha/d and more image types as well. For the most part, ANTS will output float images which you can convert to other types with the ANTS ConvertImagePixelType tool. ImageMath has a bunch of basic utilities such as multiplication, inversion and many more advanced tools such as computation of the Lipschitz norm of a deformation field. ANTS programs may be called from the command line on almost any platform .... you can compile the code yourself or use the precompiled binaries for Windows (Vista), OSX (Darwin) or linux (32 bit or 64 bit). Download the binaries that are correct for you. If you are a naive user (not from a medical imaging background) then you might still find the tools here useful. Many of the operations available, for instance, in PhotoShop are available in ANTS and many more are available as well. For instance, ANTS tools may be useful in face mapping / morphing and also in generating animations from two different images, for instance, interpolating between frames in a movie. But, mainly, ANTS is useful for brain mapping, segmentation, measuring cortical thickness and in generating automated or semi-automated labeling of three-dimensional imagery (e.g. labeling hippocampus or cortical regions or lobes of the lung). Many prior-based segmentation possibilities are available in the Atropos tool, including three tissue segmentation, structure-specific segmentation and brain extracton. The ants.pdf file has more details and examples. New Stuff 1.9.2 : New atropos interface + ROIStatistics in ImageMath New Stuff 1.9.1 : Atropos refactored , vtk dependencies allowed , additional tools for surface-based mapping (not much tested), augmented warping for vtk files Must compile ITK with USE_REVIEW_STATISTICS ON if you want Atropos functionality Should compile ITK with USE_REVIEW ON Should compile ITK with USE_OPTIMIZED_REGISTRATION ON Should have ITK v 3.20 or greater. New Stuff 1.9 : Atropos revisions + various utilities. New Stuff 1.8 : Sped up CC metric -- comparable to PR but faster. Fixed the MI metric -- fast and functional. WarpTimeSeries --- for deforming 4D or vector images. New Stuff 1.7 : Now using SymmetricSecondOrderPixelType -- fixes some DT bugs with Nifti I/O etc. This means our nii tensors have SYMMATRIX as intent (as with standard) Update in parameters for convergence and inversion -- aids performance. TensorToVector coloring --- also preliminary integration of vector field Speed up number one for the CC metric (number two coming later). Atropos ! new tool for segmentation. Nick's N4 bias correction tool (publication on the way). Updates to buildtemplateparallel that allow parallel use on multicore machines. Various utilities and a few improvements in usage. New Stuff 1.6 : Check DT tensor ordering in DTI Read/Write HistogramMatching in ImageMath ConvertImagePixelType utility Affine averaging in buildtemplateparallel Updated time-dependent diffeomorphic mapping (option --geodesic 1 / 2 ). Updated with a greedy exponential mapping diffeomorphic approach akin to DiffeomorphicDemons. Bug fix in checking ANTS convergence. Other miscellaneous including minor Apocrita changes and allowing spaces in command line interface. # directory guide: Documentation -- pdf / tex describing ANTS Examples -- the executable programs and test data in Examples/Data Scripts -- user-friendly scripts for template building and running studies Utilities --- basic utilities ImageRegistration -- base code for ImageRegistration Temporary -- where temporary code lives Tensor -- base code for diffusion tensor operations Use cmake (cmake.org) to set up compilation. To build ANTS, do the following: 1. get ANTS svn checkout https://advants.svn.sourceforge.net/svnroot/advants ANTS 2. get itk : cvs -d :pserver:anoncvs@www.itk.org:/cvsroot/Insight co Insight 3. compile itk and ANTS -- link ANTS to itk build directory ccmake ANTS/Examples/ 4. call ctest in the compile directory and verify that the tests pass 5. in general, to perform a mapping : # include the mask, if desired. mask is inclusive. ANTS 3 -m PR[tp22_s1.nii,template.nii.gz,1,4] -i 50x20x10 -o tp22map -t SyN[0.25] -x mask.nii.gz -r Gauss[3,0] # The ANTS executable reflects the variational optimization problem # which balances regularization of the transformation model's parameters # and the quality of matchins as driven by a similarity (or data) term # # explanation : -m PR -- the similarity metric => PR[fixed.nii,moving,nii,weight,metric-radius] # : -i 50x20 -- the number of iterations and number of resolution levels # : -o tp22map -- the output naming convention (can add an extension) # : -t SyN/Elast/Exp/Syn[time] --- transformation model # : -r Gauss/Bspline -- the regularization models # Gauss[gradient-regularize,deformation-regularize] # : -x mask -- an inclusive mask -- dictates what information to use in registration # -- defined in the fixed domain but works on both domains # : -m other metrics : PSE MSQ MI etc -- some are label-image (or point-set) metrics # and some are intensity metrics # # Call ANTS with no params to get detailed help # # warp the tp22 to template image WarpImageMultiTransform 3 tp22_s1.nii tp22totemplate.nii -R template.nii.gz -i tp22mapAffine.txt tp22mapInverseWarp.nii # warp the template image to tp22 -- note reversal of order from above WarpImageMultiTransform 3 template.nii.gz templatetotp22.nii -R tp22_s1.nii tp22mapWarp.nii tp22mapAffine.txt # or call ants.sh for a standard approach. # # use CreateJacobianDeterminantImage to get log-Jacobian (volumetric change) images # and programs StudentsTestOnImages or GLM to peform a statistical study # one might also use SurfaceCurvature to do a curvature study # or LaplacianThickness to do a thickness study # ants-1.9.2+svn680.dfsg/Scripts/000077500000000000000000000000001147325206600161075ustar00rootroot00000000000000ants-1.9.2+svn680.dfsg/Scripts/ANTSAverage2DAffine.sh000077500000000000000000000043411147325206600220070ustar00rootroot00000000000000#!/bin/sh NUMPARAM=$# if [ $NUMPARAM -lt 2 ] ; then echo " Usage " echo " $0 OUTNameAffine.txt *Affine.txt " echo " assumes close to idetity affine transforms " exit fi OUTNM=$1 shift 1 FLIST=$* NFILES=0 PARAM1=0 PARAM2=0 PARAM3=0 PARAM4=0 PARAM5=0 PARAM6=0 PARAM7=0 PARAM8=0 LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 2 ` for x in $LL ; do PARAM1=` awk -v a=$PARAM1 -v b=$x 'BEGIN{print (a + b)}' ` ; let NFILES=$NFILES+1 ; done PARAM1=` awk -v a=$PARAM1 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 3 ` for x in $LL ; do PARAM2=` awk -v a=$PARAM2 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM2=` awk -v a=$PARAM2 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 4 ` for x in $LL ; do PARAM3=` awk -v a=$PARAM3 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM3=` awk -v a=$PARAM3 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 5 ` for x in $LL ; do PARAM4=` awk -v a=$PARAM4 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM4=` awk -v a=$PARAM4 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 6 ` for x in $LL ; do PARAM5=` awk -v a=$PARAM5 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM5=` awk -v a=$PARAM5 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 7 ` for x in $LL ; do PARAM6=` awk -v a=$PARAM6 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM6=` awk -v a=$PARAM6 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` cat $FLIST | grep FixedParamet | cut -d ' ' -f 2 ` for x in $LL ; do PARAM7=` awk -v a=$PARAM7 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM7=` awk -v a=$PARAM7 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` cat $FLIST | grep FixedParamet | cut -d ' ' -f 3 ` for x in $LL ; do PARAM8=` awk -v a=$PARAM8 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM8=` awk -v a=$PARAM8 -v b=$NFILES 'BEGIN{print (a / b)}' ` echo "#Insight Transform File V1.0 " > $OUTNM echo "# Transform 0 " >> $OUTNM echo "Transform: MatrixOffsetTransformBase_double_2_2 " >> $OUTNM echo "Parameters: $PARAM1 $PARAM2 $PARAM3 $PARAM4 $PARAM5 $PARAM6 " >> $OUTNM echo "FixedParameters: $PARAM7 $PARAM8 " >> $OUTNM exit ants-1.9.2+svn680.dfsg/Scripts/ANTSAverage3DAffine.sh000077500000000000000000000075421147325206600220160ustar00rootroot00000000000000#!/bin/sh NUMPARAM=$# if [ $NUMPARAM -lt 2 ] ; then echo " Usage " echo " $0 OUTNameAffine.txt *Affine.txt " echo " assumes close to idetity affine transforms " exit fi OUTNM=$1 shift 1 FLIST=$* NFILES=0 PARAM1=0 PARAM2=0 PARAM3=0 PARAM4=0 PARAM5=0 PARAM6=0 PARAM7=0 PARAM8=0 PARAM9=0 PARAM10=0 PARAM11=0 PARAM12=0 PARAM13=0 PARAM14=0 PARAM15=0 LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 2 ` for x in $LL ; do PARAM1=` awk -v a=$PARAM1 -v b=$x 'BEGIN{print (a + b)}' ` ; let NFILES=$NFILES+1 ; done PARAM1=` awk -v a=$PARAM1 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 3 ` for x in $LL ; do PARAM2=` awk -v a=$PARAM2 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM2=` awk -v a=$PARAM2 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 4 ` for x in $LL ; do PARAM3=` awk -v a=$PARAM3 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM3=` awk -v a=$PARAM3 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 5 ` for x in $LL ; do PARAM4=` awk -v a=$PARAM4 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM4=` awk -v a=$PARAM4 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 6 ` for x in $LL ; do PARAM5=` awk -v a=$PARAM5 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM5=` awk -v a=$PARAM5 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 7 ` for x in $LL ; do PARAM6=` awk -v a=$PARAM6 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM6=` awk -v a=$PARAM6 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 8 ` for x in $LL ; do PARAM7=` awk -v a=$PARAM7 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM7=` awk -v a=$PARAM7 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 9 ` for x in $LL ; do PARAM8=` awk -v a=$PARAM8 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM8=` awk -v a=$PARAM8 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 10 ` for x in $LL ; do PARAM9=` awk -v a=$PARAM9 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM9=` awk -v a=$PARAM9 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 11 ` for x in $LL ; do PARAM10=` awk -v a=$PARAM10 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM10=` awk -v a=$PARAM10 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 12 ` for x in $LL ; do PARAM11=` awk -v a=$PARAM11 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM11=` awk -v a=$PARAM11 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 13 ` for x in $LL ; do PARAM12=` awk -v a=$PARAM12 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM12=` awk -v a=$PARAM12 -v b=$NFILES 'BEGIN{print (a / b)}' ` # translation params below LL=` cat $FLIST | grep FixedParamet | cut -d ' ' -f 2 ` for x in $LL ; do PARAM13=` awk -v a=$PARAM13 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM13=` awk -v a=$PARAM13 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` cat $FLIST | grep FixedParamet | cut -d ' ' -f 3 ` for x in $LL ; do PARAM14=` awk -v a=$PARAM14 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM14=` awk -v a=$PARAM14 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` cat $FLIST | grep FixedParamet | cut -d ' ' -f 4 ` for x in $LL ; do PARAM15=` awk -v a=$PARAM15 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM15=` awk -v a=$PARAM15 -v b=$NFILES 'BEGIN{print (a / b)}' ` echo "#Insight Transform File V1.0 " > $OUTNM echo "# Transform 0 " >> $OUTNM echo "Transform: MatrixOffsetTransformBase_double_3_3 " >> $OUTNM echo "Parameters: $PARAM1 $PARAM2 $PARAM3 $PARAM4 $PARAM5 $PARAM6 $PARAM7 $PARAM8 $PARAM9 $PARAM10 $PARAM11 $PARAM12 " >> $OUTNM echo "FixedParameters: $PARAM13 $PARAM14 $PARAM15 " >> $OUTNM exit ants-1.9.2+svn680.dfsg/Scripts/ANTSpexec.sh000077500000000000000000000104361147325206600202440ustar00rootroot00000000000000#!/bin/bash VERSION="0.0.1" function Usage { cat <&2 fi while getopts j:rh OPT; do # "j:" waits for an argument "h" doesnt case $OPT in h) Help ;; j) MAX_NPROC=$OPTARG ;; r) REPLACE_CMD=1 ;; \?) Usage >&2 ;; esac done # Main program echo Using max $MAX_NPROC parallel threads if [ $MAX_NPROC -eq 1 ] ; then echo " Dont use pexec to run 1 process at a time. " echo " In this case, just run in series. " exit fi shift `expr $OPTIND - 1` # shift input args, ignore processed args COMMAND=$1 shift # keep list of started processes echo "#!/bin/bash" >> ${here}/killme.sh chmod +x ${here}/killme.sh for INS in $* # for the rest of the arguments do # DEFINE COMMAND if [ $REPLACE_CMD -eq 1 ]; then CMD=${COMMAND//"*"/$INS} else CMD="$COMMAND $INS" #append args fi echo "Running $CMD" eval "$CMD &" # DEFINE COMMAND END PID=$! echo "kill $PID" >> ${here}/killme.sh queue $PID osmac=0 osmac="` uname -a | grep Darwin `" oslin=0 oslin="`uname -a | grep Linux`" if [ ${#osmac} -ne 0 ] then while [ $NUM -ge $MAX_NPROC ]; do checkqueuemac sleep 0.5 done elif [ ${#oslin} -ne 0 ] then while [ $NUM -ge $MAX_NPROC ]; do checkqueuelinux sleep 0.5 done fi done wait # wait for all processes to finish before exit rm ${here}/killme.sh exit 0 ants-1.9.2+svn680.dfsg/Scripts/ants.sh000077500000000000000000000117161147325206600174210ustar00rootroot00000000000000#!/bin/bash NUMPARAMS=$# MAXITERATIONS=30x90x20 export ANTSPATH=${ANTSPATH:="$HOME/bin/ants/"} # EDIT THIS if [ $NUMPARAMS -lt 3 ] then echo " USAGE :: " echo " sh ants.sh ImageDimension fixed.ext moving.ext OPTIONAL-OUTPREFIX OPTIONAL-max-iterations OPTIONAL-Labels-In-Fixed-Image-Space-To-Deform-To-Moving-Image Option-DoANTSQC " echo " be sure to set ANTSPATH environment variable " echo " Max-Iterations in form : JxKxL where " echo " J = max iterations at coarsest resolution (here, reduce by power of 2^2) " echo " K = middle resolution iterations ( here, reduce by power of 2 ) " echo " L = fine resolution iterations ( here, full resolution ) -- this level takes much more time per iteration " echo " an extra Ix before JxKxL would add another level " echo " Default ierations is $MAXITERATIONS -- you can often get away with fewer for many apps " echo " Other parameters are updates of the defaults used in the A. Klein evaluation paper in Neuroimage, 2009 " exit fi #ANTSPATH=YOURANTSPATH if [ ${#ANTSPATH} -le 0 ] then echo " Please set ANTSPATH=LocationOfYourAntsBinaries " echo " Either set this in your profile or directly, here in the script. " echo " For example : " echo " ANTSPATH=/home/yourname/bin/ants/ " exit else echo " ANTSPATH is $ANTSPATH " fi #initialization, here, is unbiased DIM=$1 if [ ${#DIM} -gt 1 ] then echo " Problem with specified ImageDimension => User Specified Value = $DIM " exit fi FIXED=$2 if [ ${#FIXED} -lt 1 -o ! -f $FIXED ] then echo " Problem with specified Fixed Image => User Specified Value = $FIXED " exit fi MOVING=$3 if [ ${#MOVING} -lt 1 -o ! -f $MOVING ] then echo " Problem with specified Moving Image => User Specified Value = $MOVING " exit fi OUTPUTNAME=` echo $MOVING | cut -d '.' -f 1 ` if [ $NUMPARAMS -gt 4 ] then MAXITERATIONS=$5 fi LABELIMAGE=0 if [ $NUMPARAMS -gt 5 ] then LABELIMAGE=$6 fi DoANTSQC=0 if [ $NUMPARAMS -gt 6 ] then DoANTSQC=$7 fi if [ $NUMPARAMS -gt 3 ] then OUTPUTNAME=${4} fi # Mapping Parameters TRANSFORMATION=SyN[0.25] ITERATLEVEL=(`echo $MAXITERATIONS | tr 'x' ' '`) NUMLEVELS=${#ITERATLEVEL[@]} echo $NUMLEVELS REGULARIZATION=Gauss[3,0] METRIC=CC[ METRICPARAMS=1,4] # echo " $METRICPARAMS & $METRIC " # exit echo " ANTSPATH is $ANTSPATH " echo " Mapping Parameters :: " echo " Transformation is: $TRANSFORMATION " echo " MaxIterations : $MAXITERATIONS " echo " Number Of MultiResolution Levels $NUMLEVELS " echo " Regularization : $REGULARIZATION " echo " Metric : ${METRIC} " echo " OutputName : $OUTPUTNAME " echo " " echo " if the files and parameters are all ok then uncomment the exit call below this line " echo " " #exit if [[ ! -s ${OUTPUTNAME}repaired.nii.gz ]] ; then ${ANTSPATH}N3BiasFieldCorrection $DIM $MOVING ${OUTPUTNAME}repaired.nii.gz 4 fi exe=" ${ANTSPATH}ANTS $DIM -m ${METRIC}${FIXED},${OUTPUTNAME}repaired.nii.gz,${METRICPARAMS} -t $TRANSFORMATION -r $REGULARIZATION -o ${OUTPUTNAME} -i $MAXITERATIONS --use-Histogram-Matching --number-of-affine-iterations 10000x10000x10000x10000x10000 --MI-option 32x16000 " echo " $exe " $exe #below, some affine options #--MI-option 16x8000 #-a InitAffine.txt --continue-affine 0 ${ANTSPATH}WarpImageMultiTransform $DIM ${OUTPUTNAME}repaired.nii.gz ${OUTPUTNAME}deformed.nii.gz ${OUTPUTNAME}Warp.nii.gz ${OUTPUTNAME}Affine.txt -R ${FIXED} if [ ${#LABELIMAGE} -gt 3 ] then ${ANTSPATH}WarpImageMultiTransform $DIM $LABELIMAGE ${OUTPUTNAME}labeled.nii.gz -i ${OUTPUTNAME}Affine.txt ${OUTPUTNAME}InverseWarp.nii.gz -R ${MOVING} --use-NN fi exit if [ $DoANTSQC -eq 1 ] ; then # measure image similarity for SIM in 0 1 2 ; do ${ANTSPATH}MeasureImageSimilarity $DIM $SIM $FIXED ${OUTPUTNAME}deformed.nii.gz done # measure dice overlap and mds ${ANTSPATH}ThresholdImage $DIM $FIXED ${OUTPUTNAME}fixthresh.nii.gz Otsu 4 ${ANTSPATH}ThresholdImage $DIM $MOVING ${OUTPUTNAME}movthresh.nii.gz Otsu 4 ${ANTSPATH}WarpImageMultiTransform $DIM ${OUTPUTNAME}movthresh.nii.gz ${OUTPUTNAME}defthresh.nii.gz ${OUTPUTNAME}Warp.nii.gz ${OUTPUTNAME}Affine.txt -R ${FIXED} --use-NN ${ANTSPATH}ImageMath $DIM ${OUTPUTNAME}dicestats.txt DiceAndMinDistSum ${OUTPUTNAME}fixthresh.nii.gz ${OUTPUTNAME}movthresh.nii.gz ${OUTPUTNAME}mindistsum.nii.gz # labelstats for jacobian wrt segmenation # below, to compose # ${ANTSPATH}ComposeMultiTransform $DIM ${OUTPUTNAME}CompWarp.nii.gz -R $FIXED ${OUTPUTNAME}Warp.nii.gz ${OUTPUTNAME}Affine.txt # ${ANTSPATH}CreateJacobianDeterminantImage $DIM ${OUTPUTNAME}CompWarp.nii.gz ${OUTPUTNAME}jacobian.nii.gz 0 # ${ANTSPATH}ImageMath $DIM ${OUTPUTNAME}movlabstat.txt LabelStats ${OUTPUTNAME}movthresh.nii.gz ${OUTPUTNAME}movthresh.nii.gz # ${ANTSPATH}ImageMath $DIM ${OUTPUTNAME}jaclabstat.txt LabelStats ${OUTPUTNAME}defthresh.nii.gz ${OUTPUTNAME}jacobian.nii.gz # we compare the output of these last two lines: # the Volume of the movlabstat computation vs. the mass of the jaclabstat fi ants-1.9.2+svn680.dfsg/Scripts/antsIntroduction.sh000077500000000000000000000615071147325206600220260ustar00rootroot00000000000000#!/bin/bash VERSION="0.0.7" # Uncomment the line below in case you have not set the ANTSPATH variable in your environment. export ANTSPATH=${ANTSPATH:="$HOME/bin/ants/"} function Usage { cat <&2 elif [ $nargs -lt 6 ] then Usage >&2 fi # reading command line arguments while getopts "d:f:r:i:h:o:m:n:l:q:s:t:" OPT do case $OPT in h) #help echo "$Help" exit 0 ;; d) #dimensions DIM=$OPTARG ;; f) #ignore header warnings IGNORE_HDR_WARNING=$OPTARG ;; i) #input or moving image MOVING=$OPTARG OUTPUTNAME=` echo $MOVING | cut -d '.' -f 1 ` ;; l) #use label image LABELIMAGE=$OPTARG ;; m) #max iterations other than default MAXITERATIONS=$OPTARG ;; n) #apply bias field correction N3CORRECT=$OPTARG ;; o) #output name prefix OUTPUTNAME=$OPTARG ;; q) #perform quality check after running ANTS DoANTSQC=$OPTARG ;; r) #ref image FIXED=$OPTARG ;; s) #similarity model METRICTYPE=$OPTARG ;; t) #transformation model TRANSFORMATIONTYPE=$OPTARG ;; \?) # getopts issues an error message echo "$USAGE" >&2 exit 1 ;; esac done #ANTSPATH=YOURANTSPATH if [ ${#ANTSPATH} -le 0 ] then setPath >&2 fi # test input parameter before starting if [ ${#DIM} -gt 1 ] then echo "Problem with specified ImageDimension => User Specified Value = $DIM" exit fi if [ ${#FIXED} -lt 1 -o ! -f $FIXED ] then echo "Problem with specified Fixed Image => User Specified Value = $FIXED" exit fi if [ ${#MOVING} -lt 1 -o ! -f $MOVING ] then echo "Problem with specified Moving Image => User Specified Value = $MOVING" exit fi # check the image headers echo "input files: checking" compareheaders=`${ANTSPATH}ImageMath $DIM ${OUTPUTNAME}repaired.nii.gz CompareHeadersAndImages $FIXED $MOVING | grep FailureState | cut -d ' ' -f 4 ` if [ $compareheaders -ne 0 ] then dataCheck if [ $IGNORE_HDR_WARNING -eq 0 ] then exit 1 fi else echo "input files: check passed" fi ITERATLEVEL=(`echo $MAXITERATIONS | tr 'x' ' '`) NUMLEVELS=${#ITERATLEVEL[@]} # Transformation model if [ "${TRANSFORMATIONTYPE}" == "RI" ] then RIGID=1 RIGIDTRANSF=" --do-rigid: true " elif [ "${TRANSFORMATIONTYPE}" == "RA" ] then RIGID=1 RIGIDTRANSF=" --rigid-affine true " elif [ "${TRANSFORMATIONTYPE}" == "EL" ] then # Mapping Parameters TRANSFORMATION=Elast[1] REGULARIZATION=Gauss[3,0.5] # Gauss[3,x] is usually the best option. x is usually 0 for SyN --- if you want to reduce flexibility/increase mapping smoothness, the set x > 0. # We did a large scale evaluation of SyN gradient parameters in normal brains and found 0.25 => 0.5 to perform best when # combined with default Gauss[3,0] regularization. You would increase the gradient step in some cases, though, to make # the registration converge faster --- though oscillations occur if the step is too high and other instability might happen too. elif [ "${TRANSFORMATIONTYPE}" == "S2" ] then # Mapping Parameters for the LDDMM style SyN --- the params are SyN[GradientStepLength,NTimeDiscretizationPoints,IntegrationTimeStep] # increasing IntegrationTimeStep increases accuracy in the diffeomorphism integration and takes more computation time. # NTimeDiscretizationPoints is set to 2 here TRANSFORMATION=SyN[1,2,0.05] REGULARIZATION=Gauss[3,0.] elif [ "${TRANSFORMATIONTYPE}" == "SY" ] then # Mapping Parameters for the LDDMM style SyN --- the params are SyN[GradientStepLength,NTimeDiscretizationPoints,IntegrationTimeStep] # increasing IntegrationTimeStep increases accuracy in the diffeomorphism integration and takes more computation time. # NTimeDiscretizationPoints is the number of spatial indices in the time dimension (the 4th dim when doing 3D registration) # increasing NTimeDiscretizationPoints increases flexibility and takes more computation time. # the --geodesic option enables either 1 asymmetric gradient estimation or 2 symmetric gradient estimation (the default here ) TRANSFORMATION=" SyN[1,2,0.05] --geodesic 2 " REGULARIZATION=Gauss[3,0.] elif [ "${TRANSFORMATIONTYPE}" == "LDDMM" ] then # Mapping Parameters for the LDDMM style SyN --- the params are SyN[GradientStepLength,NTimeDiscretizationPoints,IntegrationTimeStep] # increasing IntegrationTimeStep increases accuracy in the diffeomorphism integration and takes more computation time. # NTimeDiscretizationPoints is the number of spatial indices in the time dimension (the 4th dim when doing 3D registration) # increasing NTimeDiscretizationPoints increases flexibility and takes more computation time. # the --geodesic option enables either 1 asymmetric gradient estimation or 2 symmetric gradient estimation (the default here ) TRANSFORMATION=" SyN[1,2,0.05] --geodesic 1 " REGULARIZATION=Gauss[3,0.] elif [ "${TRANSFORMATIONTYPE}" == "GR" ] then # Mapping Parameters for the greedy gradient descent (fast) version of SyN -- only needs GradientStepLength TRANSFORMATION=SyN[0.25] REGULARIZATION=Gauss[3,0] elif [ "${TRANSFORMATIONTYPE}" == "EX" ] then # Mapping Parameters TRANSFORMATION=Exp[0.5,10] REGULARIZATION=Gauss[3,0.5] elif [ "${TRANSFORMATIONTYPE}" == "DD" ] then # Mapping Parameters for diffemorphic demons style optimization Exp[GradientStepLength,NTimePointsInIntegration] # NTimePointsInIntegration controls the number of compositions in the transformation update , see the DD paper TRANSFORMATION=GreedyExp[0.5,10] REGULARIZATION=Gauss[3,0.5] else echo "Invalid transformation metric. Use RI, RA, EL, SY, S2, GR , DD or EX or type sh $0 -h." exit 1 fi # Similarity Measures if [ "${METRICTYPE}" == "PR" ] then # Mapping Parameters METRIC=PR[ METRICPARAMS=1,4] elif [ "${METRICTYPE}" == "CC" ] then # Mapping Parameters METRIC=CC[ METRICPARAMS=1,5] elif [ "${METRICTYPE}" == "MI" ] then # Mapping Parameters METRIC=MI[ METRICPARAMS=1,32] elif [ "${METRICTYPE}" == "MSQ" ] then # Mapping Parameters METRIC=MSQ[ METRICPARAMS=1,0] else echo "Invalid similarity metric. Use CC, MI, MSQ or PR or type sh $0 -h." exit 1 fi # main script reportMappingParameters # write config file MOVINGBASE=` echo ${MOVING} | cut -d '.' -f 1 ` if [ -f ${MOVINGBASE}.cfg ] ; then rm -f ${MOVINGBASE}.cfg fi echo "REGDIR=${currentdir}" >> ${MOVINGBASE}.cfg echo "DIM=$DIM" >> ${MOVINGBASE}.cfg echo "FIXED=$FIXED" >> ${MOVINGBASE}.cfg echo "MOVING=$MOVING" >> ${MOVINGBASE}.cfg echo "LABELIMAGE=$LABELIMAGE" >> ${MOVINGBASE}.cfg echo "N3CORRECT=$N3CORRECT" >> ${MOVINGBASE}.cfg echo "DoANTSQC=$DoANTSQC" >> ${MOVINGBASE}.cfg echo "METRICTYPE=$METRICTYPE" >> ${MOVINGBASE}.cfg echo "TRANSFORMATIONTYPE=$TRANSFORMATIONTYPE" >> ${MOVINGBASE}.cfg echo "REGULARIZATION=$REGULARIZATION" >> ${MOVINGBASE}.cfg echo "MAXITERATIONS=$MAXITERATIONS" >> ${MOVINGBASE}.cfg echo "NUMLEVELS=$NUMLEVELS" >> ${MOVINGBASE}.cfg echo "OUTPUTNAME=$OUTPUTNAME" >> ${MOVINGBASE}.cfg if [ ${N3CORRECT} -eq 0 ] && [ ${RIGID} -eq 0 ] then # Apply ANTS mapping command without N3BiasFieldCorrection exe="${ANTSPATH}ANTS $DIM -m ${METRIC}${FIXED},${MOVING},${METRICPARAMS} -t $TRANSFORMATION -r $REGULARIZATION -o ${OUTPUTNAME} -i $MAXITERATIONS --use-Histogram-Matching --number-of-affine-iterations 10000x10000x10000x10000x10000 --MI-option 32x16000 " echo echo "--------------------------------------------------------------------------------------" echo "ANTS command:" echo "$exe " echo "--------------------------------------------------------------------------------------" $exe echo "execants=$exe" >> ${MOVINGBASE}.cfg # Apply forward transformation to MOVING echo echo "--------------------------------------------------------------------------------------" echo "Applying forward transformation to ${MOVING}" echo "--------------------------------------------------------------------------------------" ${ANTSPATH}WarpImageMultiTransform $DIM ${MOVING} ${OUTPUTNAME}deformed.nii.gz ${OUTPUTNAME}Warp.nii.gz ${OUTPUTNAME}Affine.txt -R ${FIXED} echo "warpfw=${ANTSPATH}WarpImageMultiTransform $DIM ${MOVING} ${OUTPUTNAME}deformed.nii.gz ${OUTPUTNAME}Warp.nii.gz ${OUTPUTNAME}Affine.txt -R ${FIXED}" >> ${MOVINGBASE}.cfg # Apply inverse transformation to FIXED if [ "${TRANSFORMATIONTYPE}" == "EL" ] then echo echo "--------------------------------------------------------------------------------------" echo "Applying inverse transformation to ${FIXED} is not possible for elastic registration." echo "--------------------------------------------------------------------------------------" else echo echo "--------------------------------------------------------------------------------------" echo "Applying inverse transformation to ${FIXED}" echo "--------------------------------------------------------------------------------------" FIXEDBASE=` echo ${FIXED} | cut -d '.' -f 1 ` ${ANTSPATH}WarpImageMultiTransform $DIM ${FIXED} ${FIXEDBASE}_InverseWarp.nii.gz -R ${MOVING} -i ${OUTPUTNAME}Affine.txt ${OUTPUTNAME}InverseWarp.nii.gz echo "warpinv=${ANTSPATH}WarpImageMultiTransform $DIM ${FIXED} ${FIXEDBASE}_InverseWarp.nii.gz -R ${MOVING} -i ${OUTPUTNAME}Affine.txt ${OUTPUTNAME}InverseWarp.nii.gz" >> ${MOVINGBASE}.cfg fi elif [ ${N3CORRECT} -eq 1 ] && [ ${RIGID} -eq 0 ] then # Apply N3BiasFieldCorrection exe="${ANTSPATH}N3BiasFieldCorrection $DIM $MOVING ${OUTPUTNAME}.nii.gz 4" echo echo "--------------------------------------------------------------------------------------" echo "N3BiasFieldCorrection command:" echo "$exe " echo "--------------------------------------------------------------------------------------" $exe echo "execN3=$exe" >> ${MOVINGBASE}.cfg # Apply ANTS mapping command on N3 corrected image exe="${ANTSPATH}ANTS $DIM -m ${METRIC}${FIXED},${OUTPUTNAME}.nii.gz,${METRICPARAMS} -t $TRANSFORMATION -r $REGULARIZATION -o ${OUTPUTNAME} -i $MAXITERATIONS --use-Histogram-Matching --number-of-affine-iterations 10000x10000x10000x10000x10000 --MI-option 32x16000 " echo echo "--------------------------------------------------------------------------------------" echo "ANTS command:" echo "$exe " echo "--------------------------------------------------------------------------------------" $exe echo "execants=$exe" >> ${MOVINGBASE}.cfg # Apply forward transformation to MOVING echo echo "--------------------------------------------------------------------------------------" echo "Applying forward transformation to ${MOVING}" echo "--------------------------------------------------------------------------------------" ${ANTSPATH}WarpImageMultiTransform $DIM ${OUTPUTNAME}.nii.gz ${OUTPUTNAME}deformed.nii.gz ${OUTPUTNAME}Warp.nii.gz ${OUTPUTNAME}Affine.txt -R ${FIXED} echo "warpfw=${ANTSPATH}WarpImageMultiTransform $DIM ${OUTPUTNAME}.nii.gz ${OUTPUTNAME}deformed.nii.gz ${OUTPUTNAME}Warp.nii.gz ${OUTPUTNAME}Affine.txt -R ${FIXED}" >> ${MOVINGBASE}.cfg # Apply inverse transformation to FIXED if [ "${TRANSFORMATIONTYPE}" == "EL" ] then echo echo "--------------------------------------------------------------------------------------" echo "Applying inverse transformation to ${FIXED} is not possible for elastic registration." echo "--------------------------------------------------------------------------------------" else echo echo "--------------------------------------------------------------------------------------" echo "Applying inverse transformation to ${FIXED}" echo "--------------------------------------------------------------------------------------" FIXEDBASE=` echo ${FIXED} | cut -d '.' -f 1 ` ${ANTSPATH}WarpImageMultiTransform $DIM ${FIXED} ${FIXEDBASE}_InverseWarp.nii.gz -i ${OUTPUTNAME}Affine.txt ${OUTPUTNAME}InverseWarp.nii.gz -R ${OUTPUTNAME}.nii.gz echo "warpinv=${ANTSPATH}WarpImageMultiTransform $DIM ${FIXED} ${FIXEDBASE}_InverseWarp.nii.gz -R ${OUTPUTNAME}.nii.gz -i ${OUTPUTNAME}Affine.txt ${OUTPUTNAME}InverseWarp.nii.gz" >> ${MOVINGBASE}.cfg fi elif [ ${N3CORRECT} -eq 0 ] && [ ${RIGID} -eq 1 ] then exe=" ${ANTSPATH}ANTS $DIM -m MI[${FIXED},${MOVING},1,32] -o ${OUTPUTNAME}.nii.gz -i 0 --use-Histogram-Matching --number-of-affine-iterations 10000x10000x10000x10000x10000 --MI-option 32x16000 ${RIGIDTRANSF} " echo echo "--------------------------------------------------------------------------------------" echo "ANTS command:" echo "$exe " echo "--------------------------------------------------------------------------------------" $exe echo "execants=$exe" >> ${MOVINGBASE}.cfg # Apply forward transformation to MOVING echo echo "--------------------------------------------------------------------------------------" echo "Applying rigid transformation to ${MOVING}" echo "--------------------------------------------------------------------------------------" ${ANTSPATH}WarpImageMultiTransform $DIM ${MOVING} ${OUTPUTNAME}deformed.nii.gz ${OUTPUTNAME}Affine.txt -R ${FIXED} elif [ ${N3CORRECT} -eq 1 ] && [ ${RIGID} -eq 1 ] then # Apply N3BiasFieldCorrection exe="${ANTSPATH}N3BiasFieldCorrection $DIM $MOVING ${OUTPUTNAME}.nii.gz 4" echo echo "--------------------------------------------------------------------------------------" echo "N3BiasFieldCorrection command:" echo "$exe " echo "--------------------------------------------------------------------------------------" $exe echo "execN3=$exe" >> ${MOVINGBASE}.cfg exe=" ${ANTSPATH}ANTS $DIM -m MI[${FIXED},${MOVING},1,32] -o ${OUTPUTNAME}.nii.gz -i 0 --use-Histogram-Matching --number-of-affine-iterations 10000x10000x10000x10000x10000 --MI-option 32x16000 ${RIGIDTRANSF} " echo echo "--------------------------------------------------------------------------------------" echo "ANTS command:" echo "$exe " echo "--------------------------------------------------------------------------------------" $exe echo "execants=$exe" >> ${MOVINGBASE}.cfg # Apply forward transformation to MOVING echo echo "--------------------------------------------------------------------------------------" echo "Applying rigid transformation to ${MOVING}" echo "--------------------------------------------------------------------------------------" ${ANTSPATH}WarpImageMultiTransform $DIM ${MOVING} ${OUTPUTNAME}deformed.nii.gz ${OUTPUTNAME}Affine.txt -R ${FIXED} fi # Apply transformation on labeled image? if [ ${#LABELIMAGE} -gt 3 ] then ${ANTSPATH}WarpImageMultiTransform $DIM $LABELIMAGE ${OUTPUTNAME}labeled.nii.gz -i ${OUTPUTNAME}Affine.txt ${OUTPUTNAME}InverseWarp.nii.gz -R ${MOVING} --use-NN fi if [ $DoANTSQC -eq 1 ] then # measure image similarity - 0 = intensity difference, 1 = correlation , 2 = mutual information for SIM in 0 1 2 ; do ${ANTSPATH}MeasureImageSimilarity $DIM $SIM $FIXED ${OUTPUTNAME}deformed.nii.gz done # the lines below measure dice overlap and min-dist-sum from the approximately segmented brain (3 tissues and background) ${ANTSPATH}ThresholdImage $DIM $FIXED ${OUTPUTNAME}fixthresh.nii.gz Otsu 4 ${ANTSPATH}ThresholdImage $DIM $MOVING ${OUTPUTNAME}movthresh.nii.gz Otsu 4 ${ANTSPATH}WarpImageMultiTransform $DIM ${OUTPUTNAME}movthresh.nii.gz ${OUTPUTNAME}defthresh.nii.gz ${OUTPUTNAME}Warp.nii.gz ${OUTPUTNAME}Affine.txt -R ${FIXED} --use-NN ${ANTSPATH}ImageMath $DIM ${OUTPUTNAME}dicestats.txt DiceAndMinDistSum ${OUTPUTNAME}fixthresh.nii.gz ${OUTPUTNAME}movthresh.nii.gz ${OUTPUTNAME}mindistsum.nii.gz # below not used unless you want to compare segmentation volumes to those estimated by the jacobian # labelstats for jacobian wrt segmenation below, to compose # ${ANTSPATH}ComposeMultiTransform $DIM ${OUTPUTNAME}CompWarp.nii.gz -R $FIXED ${OUTPUTNAME}Warp.nii.gz ${OUTPUTNAME}Affine.txt # ${ANTSPATH}CreateJacobianDeterminantImage $DIM ${OUTPUTNAME}CompWarp.nii.gz ${OUTPUTNAME}jacobian.nii.gz 0 # ${ANTSPATH}ImageMath $DIM ${OUTPUTNAME}movlabstat.txt LabelStats ${OUTPUTNAME}movthresh.nii.gz ${OUTPUTNAME}movthresh.nii.gz # ${ANTSPATH}ImageMath $DIM ${OUTPUTNAME}jaclabstat.txt LabelStats ${OUTPUTNAME}defthresh.nii.gz ${OUTPUTNAME}jacobian.nii.gz # we compare the output of these last two lines: # the Volume of the movlabstat computation vs. the mass of the jaclabstat fi time_end=`date +%s` time_elapsed=$((time_end - time_start)) echo " ">> ${MOVINGBASE}.cfg echo " Script executed in $time_elapsed seconds" >> ${MOVINGBASE}.cfg echo " $(( time_elapsed / 3600 ))h $(( time_elapsed %3600 / 60 ))m $(( time_elapsed % 60 ))s" >> ${MOVINGBASE}.cfg exit ants-1.9.2+svn680.dfsg/Scripts/antsaffine.sh000077500000000000000000000035031147325206600205650ustar00rootroot00000000000000#!/bin/sh export ANTSPATH=${ANTSPATH:="$HOME/bin/ants/"} NUMPARAMS=$# if [ $NUMPARAMS -lt 3 ] then echo " USAGE :: " echo " sh ants.sh ImageDimension fixed.ext moving.ext OPTIONAL-OUTPREFIX PURELY-RIGID " echo " be sure to set ANTSPATH environment variable " echo " affine only registration " exit fi #ANTSPATH=YOURANTSPATH if [ ${#ANTSPATH} -le 0 ] then echo " Please set ANTSPATH=LocationOfYourAntsBinaries " echo " Either set this in your profile or directly, here in the script. " echo " For example : " echo $ANTSPATH echo " ANTSPATH=/home/yourname/bin/ants/ " exit else echo " ANTSPATH is $ANTSPATH " fi #initialization, here, is unbiased DIM=$1 if [ ${#DIM} -gt 1 ] then echo " Problem with specified ImageDimension => User Specified Value = $DIM " exit fi FIXED=$2 if [ ${#FIXED} -lt 1 -o ! -f $FIXED ] then echo " Problem with specified Fixed Image => User Specified Value = $FIXED " exit fi MOVING=$3 if [ ${#MOVING} -lt 1 -o ! -f $MOVING ] then echo " Problem with specified Moving Image => User Specified Value = $MOVING " exit fi OUTPUTNAME=` echo $MOVING | cut -d '.' -f 1 ` if [ $NUMPARAMS -gt 3 ] then OUTPUTNAME=${4} fi RIGID=" --rigid-affine false " if [ $NUMPARAMS -gt 4 ] then RIGID=" --rigid-affine true --affine-gradient-descent-option 0.5x0.95x1.e-4x1.e-4 " fi echo " Will this mapping be purely rigid? $RIGID " echo " ANTSPATH is $ANTSPATH " #below, some affine options #--MI-option 16x8000 #-a InitAffine.txt --continue-affine 0 exe=" ${ANTSPATH}ANTS $DIM -m MI[${FIXED},${MOVING},1,32] -o ${OUTPUTNAME} -i 0 --use-Histogram-Matching --number-of-affine-iterations 10000x10000x10000x10000x10000 $RIGID " echo " $exe " $exe ${ANTSPATH}WarpImageMultiTransform $DIM $MOVING ${OUTPUTNAME}deformed.nii.gz ${OUTPUTNAME}Affine.txt -R ${FIXED} ants-1.9.2+svn680.dfsg/Scripts/antsbashstats.sh000077500000000000000000000077021147325206600213360ustar00rootroot00000000000000#!/bin/bash # sttdev.sh: Standard Deviation # Original version obtained from: http://tldp.org/LDP/abs/html/contributed-scripts.html#STDDEV # 2009-03-09: Panagiotis Kritikakos # Function stat_median() added for calculating the median value # of the data set # ------------------------------------------------------------ # The Standard Deviation indicates how consistent a set of data is. # It shows to what extent the individual data points deviate from the #+ arithmetic mean, i.e., how much they "bounce around" (or cluster). # It is essentially the average deviation-distance of the #+ data points from the mean. # =========================================================== # # To calculate the Standard Deviation: # # 1 Find the arithmetic mean (average) of all the data points. # 2 Subtract each data point from the arithmetic mean, # and square that difference. # 3 Add all of the individual difference-squares in # 2. # 4 Divide the sum in # 3 by the number of data points. # This is known as the "variance." # 5 The square root of # 4 gives the Standard Deviation. # =========================================================== # count=0 # Number of data points; global. SC=9 # Scale to be used by bc. Nine decimal places. E_DATAFILE=90 # Data file error. # ----------------- Set data file --------------------- if [ ! -z "$1" ] # Specify filename as cmd-line arg? then datafile="$1" # ASCII text file, else #+ one (numerical) data point per line! datafile=sample.dat fi # See example data file, below. if [ ! -e "$datafile" ] then echo "\""$datafile"\" does not exist!" echo " Usage : " echo " sh $0 textdatafile.txt " exit $E_DATAFILE fi # ----------------------------------------------------- arith_mean () { local rt=0 # Running total. local am=0 # Arithmetic mean. local ct=0 # Number of data points. while read value # Read one data point at a time. do echo $value rt=$(echo "scale=$SC; $rt + $value" | bc) (( ct++ )) done am=$(echo "scale=$SC; $rt / $ct" | bc) echo $am; return $ct # This function "returns" TWO values! # Caution: This little trick will not work if $ct > 255! # To handle a larger number of data points, #+ simply comment out the "return $ct" above. } <"$datafile" # Feed in data file. sd () { mean1=$1 # Arithmetic mean (passed to function). n=$2 # How many data points. sum2=0 # Sum of squared differences ("variance"). avg2=0 # Average of $sum2. sdev=0 # Standard Deviation. while read value # Read one line at a time. do diff=$(echo "scale=$SC; $mean1 - $value" | bc) # Difference between arith. mean and data point. dif2=$(echo "scale=$SC; $diff * $diff" | bc) # Squared. sum2=$(echo "scale=$SC; $sum2 + $dif2" | bc) # Sum of squares. done avg2=$(echo "scale=$SC; $sum2 / $n" | bc) # Avg. of sum of squares. sdev=$(echo "scale=$SC; sqrt($avg2)" | bc) # Square root = echo $sdev # Standard Deviation. } <"$datafile" # Rewinds data file. stat_median() { NUMS=(`sort -n $1`) TOTALNUMS=${#NUMS[*]} MOD=$(($TOTALNUMS % 2)) if [ $MOD -eq 0 ]; then ARRAYMIDDLE=$(echo "($TOTALNUMS / 2)-1" | bc) ARRAYNEXTMIDDLE=$(($ARRAYMIDDLE + 1)) MEDIAN=$(echo "scale=$SC; ((${NUMS[$ARRAYMIDDLE]})+(${NUMS[$ARRAYNEXTMIDDLE]})) / 2" | bc) elif [ $MOD -eq 1 ]; then ARRAYMIDDLE=$(echo "($TOTALNUMS / 2)" | bc) MEDIAN=${NUMS[$ARRAYMIDDLE]} fi echo $MEDIAN } # ======================================================= # mean=$(arith_mean); count=$? # Two returns from function! std_dev=$(sd $mean $count) median=$(stat_median $1) echo " Mean : $mean SD: $std_dev " #echo #echo "Number of data points in \""$datafile"\" = $count" #echo "Arithmetic mean (average) = $mean" #echo "Standard Deviation = $std_dev" #echo "Median number (middle) = $median" #echo # ======================================================= # exit ants-1.9.2+svn680.dfsg/Scripts/antsdeformationmag.sh000077500000000000000000000037441147325206600223400ustar00rootroot00000000000000#!/bin/bash NUMPARAMS=$# if [ $NUMPARAMS -lt 4 ] then echo " USAGE :: " echo " $0 inWarp WhichTypeOfMagnitude outputmagnitudeimage.nii ANTSPATH " echo " WhichTypeOfMagnitude == Laplacian Grad Euclidean " echo " the numbers that come out of this depend on some itk filters being dimensionally correct and consistent. " echo " this may not be the case -- however, relative values should be ok , e.g comparing Grad values to Grad values etc " exit fi inwarp=$1 whichmag=$2 magimage=$3 ANTSPATH=$4 dimarr=(`${ANTSPATH}PrintHeader ${inwarp}xvec.nii.gz | grep Spacing | grep , | wc `) DIM=${dimarr[0]} let DIM=${DIM}+1 # multiply some image of the right size by 0 to make the initial magnitude image ${ANTSPATH}ImageMath $DIM $magimage m ${inwarp}xvec.nii.gz 0 ${ANTSPATH}ImageMath $DIM ${magimage}temp.nii.gz m ${inwarp}xvec.nii.gz 0 ok=0 for x in x y z do if [ -s ${inwarp}${x}vec.nii.gz ] then # compute the magnitude of the x-component deformation if [ $whichmag == Laplacian ] then echo " Laplacian $x " ok=1 ${ANTSPATH}ImageMath $DIM ${magimage}temp.nii.gz Laplacian ${inwarp}${x}vec.nii.gz 1.0 0 elif [ $whichmag == Grad ] then echo " Grad $x " ok=1 ${ANTSPATH}ImageMath $DIM ${magimage}temp.nii.gz Grad ${inwarp}${x}vec.nii.gz 1.0 0 #ImageMath $DIM temp.nii.gz m temp.nii.gz temp.nii.gz elif [ $whichmag == Euclidean ] then echo " Euclidean $x " ok=1 ${ANTSPATH}ImageMath $DIM ${magimage}temp.nii.gz m ${inwarp}${x}vec.nii.gz ${inwarp}${x}vec.nii.gz fi # add the xvec magnitude to the magimage ${ANTSPATH}ImageMath $DIM $magimage + $magimage ${magimage}temp.nii.gz fi # ifz done #loop if [ $ok -eq 1 ] then # take the square root ${ANTSPATH}ImageMath $DIM $magimage ^ $magimage 0.5 ${ANTSPATH}MeasureMinMaxMean $DIM $magimage #thus, #magimage=sqrt( x*x + y*y + z*z ) else echo " User Requested deformation measure :: $whichmag " echo " that is not available -- user needs to choose another type of deformation measure " fi rm -f ${magimage}temp.nii.gz ants-1.9.2+svn680.dfsg/Scripts/antsqsub.sh000077500000000000000000000017771147325206600203220ustar00rootroot00000000000000#!/bin/bash #$ -S /bin/bash dir=$1 cmd=$2 para=$3 naming=$4 fixname=$5 movingname=$6 echo $6 opta=$7 echo $7 optb=$8 echo $8 optc=$9 echo $9 optd=${10} echo ${10} opte=${11} echo ${11} optf=${12} echo ${12} optg=${13} echo ${13} opth=${14} echo ${14} opti=${15} echo ${15} optj=${16} echo ${16} optk=${17} echo ${17} optl=${18} echo ${18} optm=${19} echo ${19} optn=${20} echo ${20} opto=${21} echo ${21} optp=${22} echo ${22} optq=${23} echo ${23} optr=${24} echo ${24} opts=${25} echo ${25} optt=${26} echo ${26} echo " dir " echo $1 cd $1 echo $cmd $para $naming $fixname $movingname $opta $optb $optc $optd $opte $optf $optg $opth $opti $optj $optk $optl $optm $optn $opto $optp $optq $optr $opts $optt $cmd $para $naming $fixname $movingname $opta $optb $optc $optd $opte $optf $optg $opth $opti $optj $optk $optl $optm $optn $opto $optp $optq $optr $opts $optt ${27} ${28} ${29} ${30} ${31} ${32} ${33} ${34} ${35} ${36} ${37} ${38} ${39} ${40} ${41} ${42} ${43} ${44} ${45} ${46} ${47} ${48} ${49} cd - ants-1.9.2+svn680.dfsg/Scripts/antswithdt.sh000077500000000000000000000106661147325206600206500ustar00rootroot00000000000000#!/bin/bash NUMPARAMS=$# MAXITERATIONS=30x90x20 export ANTSPATH=${ANTSPATH:="$HOME/bin/ants/"} if [ $NUMPARAMS -lt 3 ] then echo " USAGE :: " echo " sh ants.sh ImageDimension fixed.ext moving.ext Subject/Moving-DT-To-Deform-To-Fixed-Image OPTIONAL-Subject/Moving-BZero-To-DistortionCorrect-To-Moving-T1-Image " echo " be sure to set ANTSPATH environment variable " echo " Max-Iterations in form : JxKxL where " echo " J = max iterations at coarsest resolution (here, reduce by power of 2^2) " echo " K = middle resolution iterations ( here, reduce by power of 2 ) " echo " L = fine resolution iterations ( here, full resolution ) -- this level takes much more time per iteration " echo " an extra Ix before JxKxL would add another level " echo " Default ierations is $MAXITERATIONS -- you can often get away with fewer for many apps " echo " Other parameters are defaults used in the A. Klein evaluation paper in Neuroimage, 2009 " echo " " echo " The DT component is distortion corrected to the T1 image either by the B-Zero Image / Average-DWI Image " echo " or whatever is passed as the last parameter --- Alternatively, we compute the FA from the DTI and then " echo" distortion correct to it. One should test the validity of this approach on your data before widely applying ." echo " We use the cross-correlation to perform the distortion correction. " echo " Some parameter tuning may be required, depending on the distortions present in your acquisition. " exit fi #initialization, here, is unbiased DIM=$1 if [ ${#DIM} -gt 1 ] then echo " Problem with specified ImageDimension => User Specified Value = $DIM " exit fi FIXED=$2 if [ ${#FIXED} -lt 1 -o ! -f $FIXED ] then echo " Problem with specified Fixed Image => User Specified Value = $FIXED " exit fi MOVING=$3 if [ ${#MOVING} -lt 1 -o ! -f $MOVING ] then echo " Problem with specified Moving Image => User Specified Value = $MOVING " exit fi OUTPUTNAME=${MOVING%.*.*} if [ ${#OUTPUTNAME} -eq ${#MOVING} ] then OUTPUTNAME=${MOVING%.*} fi MOVINGDT=0 if [ $NUMPARAMS -gt 3 ] then MOVINGDT=$4 if [ ! -f $MOVINGDT ] then echo " THIS DTI DOES NOT EXIST : $MOVINGDT " MOVINGDT="" fi fi MOVINGBZ=0 if [ $NUMPARAMS -gt 4 ] then MOVINGBZ=$5 if [ ! -f $MOVINGBZ ] then echo " THIS BZero DOES NOT EXIST : $MOVINGBZ " MOVINGBZ=0 fi fi if [ ${#MOVINGDT} -gt 3 ] then echo " The BZero DOES NOT EXIST : Using FA Instead!!" ${ANTSPATH}ImageMath 3 ${OUTPUTNAME}_fa.nii TensorFA $MOVINGDT MOVINGBZ=${OUTPUTNAME}_fa.nii fi # Mapping Parameters TRANSFORMATION=SyN[0.25] ITERATLEVEL=(`echo $MAXITERATIONS | tr 'x' ' '`) NUMLEVELS=${#ITERATLEVEL[@]} echo $NUMLEVELS REGULARIZATION=Gauss[3,0] METRIC=PR[ METRICPARAMS=1,2] #echo " $METRICPARAMS & $METRIC " #exit #ANTSPATH=/mnt/aibs1/avants/bin/ants/ #ANTSPATH=YOURANTSPATH echo " ANTSPATH $ANTSPATH " echo " Mapping Parameters :: " echo " Transformation is: $TRANSFORMATION " echo " MaxIterations : $MAXITERATIONS " echo " Number Of MultiResolution Levels $NUMLEVELS " echo " Regularization : $REGULARIZATION " echo " Metric : ${METRIC} " echo " OutputName : $OUTPUTNAME " echo " " echo " if the files and parameters are all ok then uncomment the exit call below this line " echo " " #exit # first, do distortion correction of MOVINGDT to MOVING # use the B0 image ${ANTSPATH}ANTS 3 -m PR[${MOVINGBZ},${MOVING},1,2] -o ${OUTPUTNAME}distcorr -r Gauss[3,0] -t SyN[0.25] -i 25x20x0 --do-rigid 1 ${ANTSPATH}WarpImageMultiTransform 3 $MOVING ${OUTPUTNAME}distcorr.nii ${OUTPUTNAME}distcorrWarp.nii ${OUTPUTNAME}distcorrAffine.txt -R $MOVINGBZ #exit if [[ ! -s ${OUTPUTNAME}Affine.txt ]] ; then sh ${ANTSPATH}/ants.sh $DIM $FIXED $MOVING ${OUTPUTNAME} fi if [[ -s ${MOVINGDT} ]] && [[ -s ${OUTPUTNAME}Affine.txt ]] ; then DTDEF=${OUTPUTNAME}DTdeformed.nii FIXEDSUB=${OUTPUTNAME}fixedsub.nii ${ANTSPATH}ResampleImageBySpacing 3 $FIXED $FIXEDSUB 2 2 2 echo " Warp DT " ${ANTSPATH}WarpTensorImageMultiTransform $DIM $MOVINGDT $DTDEF ${OUTPUTNAME}Warp.nii ${OUTPUTNAME}Affine.txt -i ${OUTPUTNAME}distcorrAffine.txt ${OUTPUTNAME}distcorrInverseWarp.nii -R $FIXEDSUB COMPWARP=${OUTPUTNAME}DTwarp.nii ${ANTSPATH}ComposeMultiTransform $DIM $COMPWARP -R ${FIXEDSUB} ${OUTPUTNAME}Warp.nii ${OUTPUTNAME}Affine.txt -i ${OUTPUTNAME}distcorrAffine.txt ${OUTPUTNAME}distcorrInverseWarp.nii ${ANTSPATH}ReorientTensorImage 3 $DTDEF $DTDEF $COMPWARP fi exit ants-1.9.2+svn680.dfsg/Scripts/buildtemplateparallel.sh000077500000000000000000001155001147325206600230200ustar00rootroot00000000000000#!/bin/bash VERSION="0.0.13" # trap keyboard interrupt (control-c) trap control_c SIGINT # Uncomment the line below in case you have not set the ANTSPATH variable in your environment. export ANTSPATH=${ANTSPATH:="$HOME/bin/ants/"} #assuming .nii.gz as default file type. This is the case for ANTS 1.7 and up function Usage { cat < Example Case: echo " bash $0 -d 3 -m 30x50x20 -t GR -s CC -c 1 -o MY -z InitialTemplate.nii.gz *RF*T1x.nii.gz " echo " in this case you use 30x50x20 iterations per registration " echo " 4 iterations over template creation (that is the default) " echo " with Greedy-SyN and CC metrics to guide the mapping. " echo " Output is prepended with MY and the initial template is InitialTemplate.nii.gz (optional). " echo " the -c option is set to 1 which will try to use SGE to distribute the computation. " echo " if you do not have SGE, use -c 0 or -c 2 --- read the help." Compulsory arguments (minimal command line requires SGE cluster, otherwise use -c & -j options): -d: ImageDimension: 2 or 3 (for 2 or 3 dimensional registration of single volume) ImageDimension: 4 (for 3 dimensional registration of time-series; requires FSL) -o: OUTPREFIX; A prefix that is prepended to all output files. List of images in the current directory, eg *_t1.nii.gz. Should be at the end of the command. NB: All images to be added to the template should be in the same directory, and this script should be invoked from that directory. Optional arguments: -c: Control for parallel computation (default 1) -- 0 == run serially, 1 == SGE qsub, 2 == use PEXEC (localhost) -g: Gradient step size (default 0.25) -- smaller in magnitude results in more cautious steps -i: Iteration limit (default 4) -- iterations of the template construction (Iteration limit)*NumImages registrations. -j: Number of cpu cores to use (default: 2; -- requires "-c 2" -m: Max-iterations in each registration -n: N3BiasFieldCorrection of moving image (default 1) -- 0 == off, 1 == on -r: Do rigid-body registration of inputs before creating template (default 0) -- 0 == off 1 == on. Only useful when you do not have an initial template -s: Type of similarity metric used for registration. -t: Type of transformation model used for registration. -z: Use this this volume as the target of all inputs. When not used, the script will create an unbiased starting point by averaging all inputs. Use the full path! -------------------------------------------------------------------------------------- ANTS was created by: -------------------------------------------------------------------------------------- Brian B. Avants, Nick Tustison and Gang Song Penn Image Computing And Science Laboratory University of Pennsylvania -------------------------------------------------------------------------------------- script adapted by N.M. van Strien, http://www.mri-tutorial.com | NTNU MR-Center -------------------------------------------------------------------------------------- USAGE exit 1 } function Help { cat < Compulsory arguments (minimal command line requires SGE cluster, otherwise use -c & -j options):: -d: ImageDimension: 2 or 3 (for 2 or 3 dimensional registration of single volume) ImageDimension: 4 (for 3 dimensional registration of time-series; requires FSL) -o: OUTPREFIX; A prefix that is prepended to all output files. List of images in the current directory, eg *_t1.nii.gz. Should be at the end of the command. NB: All files to be added to the template should be in the same directory. Optional arguments: -c: Control for parallel computation --- if set to zero, run serially, if set to 2 , use PEXEC (localhost), if set to 1 , use SGE qsub. -g: Gradient step size; smaller in magnitude results in more cautious steps (default 0.25) -i: Iteration limit (default = 4) for template construction. requires 4*NumImages registrations. -j: Number of cpu cores to use (default: 2; --- set -c option to 2 to use this . The optimal number of cpu cores to use for template generation depends on the availability of cores, the amount of free working memory (RAM) and the resolution of the data. High resolution datasets typically require more RAM during processing. Running out of RAM during a calculation will slow down all processing on your computer. -m: Max-iterations Max-Iterations in form: JxKxL where J = max iterations at coarsest resolution (here, reduce by power of 2^2) K = middle resolution iterations (here,reduce by power of 2) L = fine resolution iterations (here, full resolution) !!this level takes much more time per iteration!! Adding an extra value before JxKxL (i.e. resulting in IxJxKxL) would add another iteration level. -n: N3BiasFieldCorrection of moving image ( 0 = off; 1 = on (default) ) -r: Do rigid-body registration of inputs before creating template (default 0) -- 0 == off 1 == on. Only useful when you do not have an initial template In case a template is specified (-z option), all inputs are registered to that template. If no template is specified, the inputs will be registered to the averaged input. -s: Type of similarity metric used for registration. For intramodal image registration, use: CC = cross-correlation MI = mutual information PR = probability mapping (default) MSQ = mean square difference For intermodal image registration, use: MI = mutual information PR = probability mapping (default) -t: Type of transformation model used for registration. For elastic image registration, use: EL = elastic transformation model (less deformation possible) For diffeomorphic image registration, use: SY = SyN with time (default) with arbitrary number of time points in time discretization S2 = SyN with time optimized specifically for 2 time points in the time discretization GR = Greedy SyN EX = Exponential DD = Diffeomorphic Demons style exponential mapping -z: Use this this volume as the target of all inputs. When not used, the script will create an unbiased starting point by averaging all inputs. Use the full path! Requirements: This scripts relies on the following scripts in your $ANTSPATH directory. The script will terminate prematurely if these files are not present or are not executable. - antsIntroduction.sh - pexec.sh - waitForSGEQJobs.pl (only for use with Sun Grid Engine) For 4D template building FSL is also needed -------------------------------------------------------------------------------------- Get the latest ANTS version at: -------------------------------------------------------------------------------------- http://sourceforge.net/projects/advants/ -------------------------------------------------------------------------------------- Read the ANTS documentation at: -------------------------------------------------------------------------------------- http://picsl.upenn.edu/ANTS/ -------------------------------------------------------------------------------------- ANTS was created by: -------------------------------------------------------------------------------------- Brian B. Avants, Nick Tustison and Gang Song Penn Image Computing And Science Laboratory University of Pennsylvania -------------------------------------------------------------------------------------- script adapted by N.M. van Strien, http://www.mri-tutorial.com | NTNU MR-Center -------------------------------------------------------------------------------------- HELP exit 1 } function setPath { cat < $OUTNM echo "# Transform 0 " >> $OUTNM echo "Transform: MatrixOffsetTransformBase_double_2_2 " >> $OUTNM echo "Parameters: $PARAM1 $PARAM2 $PARAM3 $PARAM4 $PARAM5 $PARAM6 " >> $OUTNM echo "FixedParameters: $PARAM7 $PARAM8 " >> $OUTNM } function ANTSAverage3DAffine { OUTNM=${templatename}Affine.txt FLIST=${outputname}*Affine.txt NFILES=0 PARAM1=0 PARAM2=0 PARAM3=0 PARAM4=0 PARAM5=0 PARAM6=0 PARAM7=0 PARAM8=0 PARAM9=0 PARAM10=0 PARAM11=0 PARAM12=0 PARAM13=0 PARAM14=0 PARAM15=0 LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 2 ` for x in $LL ; do PARAM1=` awk -v a=$PARAM1 -v b=$x 'BEGIN{print (a + b)}' ` ; let NFILES=$NFILES+1 ; done PARAM1=` awk -v a=$PARAM1 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 3 ` for x in $LL ; do PARAM2=` awk -v a=$PARAM2 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM2=` awk -v a=$PARAM2 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 4 ` for x in $LL ; do PARAM3=` awk -v a=$PARAM3 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM3=` awk -v a=$PARAM3 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 5 ` for x in $LL ; do PARAM4=` awk -v a=$PARAM4 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM4=` awk -v a=$PARAM4 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 6 ` for x in $LL ; do PARAM5=` awk -v a=$PARAM5 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM5=` awk -v a=$PARAM5 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 7 ` for x in $LL ; do PARAM6=` awk -v a=$PARAM6 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM6=` awk -v a=$PARAM6 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 8 ` for x in $LL ; do PARAM7=` awk -v a=$PARAM7 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM7=` awk -v a=$PARAM7 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 9 ` for x in $LL ; do PARAM8=` awk -v a=$PARAM8 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM8=` awk -v a=$PARAM8 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 10 ` for x in $LL ; do PARAM9=` awk -v a=$PARAM9 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM9=` awk -v a=$PARAM9 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 11 ` for x in $LL ; do PARAM10=` awk -v a=$PARAM10 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM10=0 # ` awk -v a=$PARAM10 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 12 ` for x in $LL ; do PARAM11=` awk -v a=$PARAM11 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM11=0 # ` awk -v a=$PARAM11 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 13 ` for x in $LL ; do PARAM12=` awk -v a=$PARAM12 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM12=0 # ` awk -v a=$PARAM12 -v b=$NFILES 'BEGIN{print (a / b)}' ` # origin params below LL=` cat $FLIST | grep FixedParamet | cut -d ' ' -f 2 ` for x in $LL ; do PARAM13=` awk -v a=$PARAM13 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM13=` awk -v a=$PARAM13 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` cat $FLIST | grep FixedParamet | cut -d ' ' -f 3 ` for x in $LL ; do PARAM14=` awk -v a=$PARAM14 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM14=` awk -v a=$PARAM14 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` cat $FLIST | grep FixedParamet | cut -d ' ' -f 4 ` for x in $LL ; do PARAM15=` awk -v a=$PARAM15 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM15=` awk -v a=$PARAM15 -v b=$NFILES 'BEGIN{print (a / b)}' ` echo "# Insight Transform File V1.0 " > $OUTNM echo "# Transform 0 " >> $OUTNM echo "Transform: MatrixOffsetTransformBase_double_3_3 " >> $OUTNM echo "Parameters: $PARAM1 $PARAM2 $PARAM3 $PARAM4 $PARAM5 $PARAM6 $PARAM7 $PARAM8 $PARAM9 $PARAM10 $PARAM11 $PARAM12 " >> $OUTNM echo "FixedParameters: $PARAM13 $PARAM14 $PARAM15 " >> $OUTNM } function jobfnamepadding { files=`ls job*.sh` BASENAME1=`echo $files[1] | cut -d 'b' -f 1` for file in ${files} do if [ "${#file}" -eq "9" ] then BASENAME2=`echo $file | cut -d 'b' -f 2 ` mv "$file" "${BASENAME1}b_000${BASENAME2}" elif [ "${#file}" -eq "10" ] then BASENAME2=`echo $file | cut -d 'b' -f 2 ` mv "$file" "${BASENAME1}b_00${BASENAME2}" elif [ "${#file}" -eq "11" ] then BASENAME2=`echo $file | cut -d 'b' -f 2 ` mv "$file" "${BASENAME1}b_0${BASENAME2}" fi done } cleanup() # example cleanup function { cd ${currentdir}/ echo -en "\n*** Performing cleanup, please wait ***\n" # 1st attempt to kill all remaining processes # put all related processes in array runningANTSpids=( `ps -C ANTS -C N3BiasFieldCorrection -C fslsplit | awk '{ printf "%s\n", $1 ; }'` ) # debug only #echo list 1: ${runningANTSpids[@]} # kill these processes, skip the first since it is text and not a PID for ((i = 1; i < ${#runningANTSpids[@]} ; i++)) do echo "killing: ${runningANTSpids[${i}]}" kill ${runningANTSpids[${i}]} done return $? } control_c() # run if user hits control-c { echo -en "\n*** User pressed CTRL + C ***\n" cleanup exit $? echo -en "\n*** Script cancelled by user ***\n" } #initializing variables with global scope time_start=`date +%s` currentdir=`pwd` nargs=$# MAXITERATIONS=30x90x20 LABELIMAGE=0 # initialize optional parameter METRICTYPE=CC # initialize optional parameter TRANSFORMATIONTYPE="GR" # initialize optional parameter N3CORRECT=1 # initialize optional parameter DOQSUB=1 # By default, buildtemplateparallel tries to do things in parallel GRADIENTSTEP=0.25 # Gradient step size, smaller in magnitude means more smaller (more cautious) steps ITERATIONLIMIT=4 CORES=2 TDIM=0 RIGID=0 RIGIDTYPE=" --do-rigid" # set to an empty string to use affine initialization range=0 REGTEMPLATE=target cpu_count=`cat /proc/cpuinfo | grep processor | wc -l` ##Getting system info from linux can be done with these variables. # RAM=`cat /proc/meminfo | sed -n -e '/MemTotal/p' | awk '{ printf "%s %s\n", $2, $3 ; }' | cut -d " " -f 1` # RAMfree=`cat /proc/meminfo | sed -n -e '/MemFree/p' | awk '{ printf "%s %s\n", $2, $3 ; }' | cut -d " " -f 1` # cpu_free_ram=$((${RAMfree}/${cpu_count})) # Provide output for Help if [ "$1" == "-h" ] then Help >&2 fi OUTPUTNAME=antsBTP # reading command line arguments while getopts "c:d:g:i:j:h:m:n:o:s:r:t:z:" OPT do case $OPT in h) #help echo "$USAGE" exit 0 ;; c) #use SGE cluster DOQSUB=$OPTARG if [[ ${#DOQSUB} -gt 2 ]] ; then echo " DOQSUB must be an integer value (0=serial, 1=SGE qsub, 2=try pexec ) you passed -c $DOQSUB " exit 1 fi ;; d) #dimensions DIM=$OPTARG if [[ ${DIM} -eq 4 ]] ; then DIM=3 TDIM=4 fi ;; g) #gradient stepsize (default = 0.25) GRADIENTSTEP=$OPTARG ;; i) #iteration limit (default = 3) ITERATIONLIMIT=$OPTARG ;; j) #number of cpu cores to use (default = 2) CORES=$OPTARG ;; m) #max iterations other than default MAXITERATIONS=$OPTARG ;; n) #apply bias field correction N3CORRECT=$OPTARG ;; o) #output name prefix OUTPUTNAME=$OPTARG TEMPLATENAME=${OUTPUTNAME}template TEMPLATE=${TEMPLATENAME}.nii.gz ;; s) #similarity model METRICTYPE=$OPTARG ;; r) #start with rigid-body registration RIGID=$OPTARG ;; t) #transformation model TRANSFORMATIONTYPE=$OPTARG ;; z) #initialization template REGTEMPLATE=$OPTARG ;; \?) # getopts issues an error message echo "$USAGE" >&2 exit 1 ;; esac done if [ $DOQSUB -eq 1 ] ; then qq=`which qsub` if [ ${#qq} -lt 1 ] ; then echo do you have qsub? if not, then choose another c option ... if so, then check where the qsub alias points ... exit fi fi # Provide different output for Usage and Help if [ ${TDIM} -eq 4 ] && [ $nargs -lt 5 ] then Usage >&2 elif [ ${TDIM} -eq 4 ] && [ $nargs -eq 5 ] then echo "" # This option is required to run 4D template creation on SGE with a minimal command line elif [ $nargs -lt 6 ] then Usage >&2 fi #ANTSPATH=YOURANTSPATH if [ ${#ANTSPATH} -le 0 ] then setPath >&2 fi # Creating the file list of images to make a template from. # Shiftsize is calculated because a variable amount of arguments can be used on the command line. # The shiftsize variable will give the correct number of arguments to skip. Issuing shift $shiftsize will # result in skipping that number of arguments on the command line, so that only the input images remain. shiftsize=$(($OPTIND - 1)) shift $shiftsize # The invocation of $* will now read all remaining arguments into the variable IMAGESETVARIABLE IMAGESETVARIABLE=$* NINFILES=$(($nargs - $shiftsize)) #test if FSL is available in case of 4D, exit if not if [ ${TDIM} -eq 4 ] && [ ${#FSLDIR} -le 0 ] then setFSLPath >&2 fi if [ ${NINFILES} -eq 0 ] then echo "Please provide at least 2 filenames for the template." echo "Use `basename $0` -h for help" exit 1 elif [[ ${NINFILES} -eq 1 ]] && [[ -s ${FSLDIR}/bin/fslnvols ]] then range=`fslnvols ${IMAGESETVARIABLE}` if [ ${range} -eq 1 ] && [ ${TDIM} -ne 4 ] then echo "Please provide at least 2 filenames for the template." echo "Use `basename $0` -h for help" exit 1 elif [ ${range} -gt 1 ] && [ ${TDIM} -ne 4 ] then echo "This is a multivolume file. Use -d 4" echo "Use `basename $0` -h for help" exit 1 elif [ ${range} -gt 1 ] && [ ${TDIM} -eq 4 ] then echo "--------------------------------------------------------------------------------------" echo " Creating template of 4D input. " echo "--------------------------------------------------------------------------------------" #splitting volume #setting up working dirs mkdir tmp mkdir tmp/selection #split the 4D file into 3D elements cp ${IMAGESETVARIABLE} tmp/ cd tmp/ fslsplit ${IMAGESETVARIABLE} rm -f ${IMAGESETVARIABLE} # selecting 16 volumes randomly from the timeseries for averaging, placing them in tmp/selection. folder for ((i = 0; i < 16 ; i++)) do number=$RANDOM let "number %= $range" if [ ${number} -lt 10 ] then cp vol000${number}.nii.gz selection/ elif [ ${number} -ge 10 ] && [ ${number} -lt 100 ] then cp vol00${number}.nii.gz selection/ elif [ ${number} -ge 100 ] && [ ${number} -lt 1000 ] then cp vol0${number}.nii.gz selection/ fi done # set filelist variable cd selection/ IMAGESETVARIABLE=`ls *.nii.gz` fi fi # System specific queue options, eg "-q name" to submit to a specific queue # It can be set to an empty string if you do not need any special cluster options QSUBOPTS="" # EDIT THIS # Test availability of helper scripts. # No need to test this more than once. Can reside outside of the main loop. ANTSSCRIPTNAME=${ANTSPATH}antsIntroduction.sh PEXEC=${ANTSPATH}ANTSpexec.sh SGE=${ANTSPATH}waitForSGEQJobs.pl for FLE in $ANTSSCRIPTNAME $PEXEC $SGE do if [ ! -x $FLE ] ; then echo echo "--------------------------------------------------------------------------------------" echo " FILE $FLE DOES NOT EXIST -- OR -- IS NOT EXECUTABLE !!! $0 will terminate." echo "--------------------------------------------------------------------------------------" echo " if the file is not executable, please change its permissions. " exit 1 fi done # exit # check for an initial template image and perform rigid body registration if requested if [ ! -s $REGTEMPLATE ] then echo echo "--------------------------------------------------------------------------------------" echo " No initial template exists. Creating a population average image from the inputs." echo "--------------------------------------------------------------------------------------" ${ANTSPATH}AverageImages $DIM $TEMPLATE 1 $IMAGESETVARIABLE else echo echo "--------------------------------------------------------------------------------------" echo " Initial template found. This will be used for guiding the registration. use : $REGTEMPLATE and $TEMPLATE " echo "--------------------------------------------------------------------------------------" # now move the initial registration template to OUTPUTNAME, otherwise this input gets overwritten. cp ${REGTEMPLATE} ${TEMPLATE} fi if [ ! -s $TEMPLATE ] ; then echo Your template : $TEMPLATE was not created. This indicates trouble! You may want to check correctness of your input parameters. exiting. exit fi # remove old job bash scripts rm -f job*.sh if [ "$RIGID" -eq 1 ] ; then count=0 jobIDs="" RIGID_IMAGESET="" for IMG in $IMAGESETVARIABLE do RIGID_IMAGESET="$RIGID_IMAGESET rigid_${IMG}" BASENAME=` echo ${IMG} | cut -d '.' -f 1 ` exe=" ${ANTSPATH}ANTS $DIM -m MI[${TEMPLATE},${IMG},1,32] -o rigid_${IMG} -i 0 --use-Histogram-Matching --number-of-affine-iterations 10000x10000x10000x10000x10000 $RIGIDTYPE" exe2="${ANTSPATH}WarpImageMultiTransform $DIM ${IMG} rigid_${IMG} rigid_${BASENAME}Affine.txt -R ${TEMPLATE}" pexe=" $exe >> job_${count}_metriclog.txt " qscript="job_${count}_qsub.sh" echo "$exe" > $qscript echo "$exe2" >> $qscript if [ $DOQSUB -eq 1 ] ; then id=`qsub -cwd -S /bin/bash -N antsBuildTemplate_rigid -v ANTSPATH=$ANTSPATH $QSUBOPTS $qscript | awk '{print $3}'` jobIDs="$jobIDs $id" sleep 0.5 elif [ $DOQSUB -eq 2 ] ; then # Send pexe and exe2 to same job file so that they execute in series echo $pexe >> job${count}_r.sh echo $exe2 >> job${count}_r.sh elif [ $DOQSUB -eq 0 ] ; then # execute jobs in series $exe $exe2 fi ((count++)) done if [ $DOQSUB -eq 1 ]; then # Run jobs on SGE and wait to finish echo echo "--------------------------------------------------------------------------------------" echo " Starting ANTS rigid registration on cluster. Submitted $count jobs " echo "--------------------------------------------------------------------------------------" # now wait for the jobs to finish. Rigid registration is quick, so poll queue every 60 seconds ${ANTSPATH}waitForSGEQJobs.pl 1 60 $jobIDs # Returns 1 if there are errors if [ ! $? -eq 0 ]; then echo "qsub submission failed - jobs went into error state" exit 1; fi fi # Run jobs on localhost and wait to finish if [ $DOQSUB -eq 2 ]; then echo echo "--------------------------------------------------------------------------------------" echo " Starting ANTS rigid registration on max ${CORES} cpucores. " echo " Progress can be viewed in job*_metriclog.txt" echo "--------------------------------------------------------------------------------------" jobfnamepadding #adds leading zeros to the jobnames, so they are carried out chronologically chmod +x job*.sh $PEXEC -j ${CORES} "sh" job*.sh fi # Update template ${ANTSPATH}AverageImages $DIM $TEMPLATE 1 $RIGID_IMAGESET # cleanup and save output in seperate folder mkdir rigid mv *.cfg rigid_*.nii.gz *Affine.txt rigid/ # backup logs if [ $DOQSUB -eq 1 ]; then mv antsBuildTemplate_rigid* rigid/ # Remove qsub scripts rm -f job_${count}_qsub.sh elif [ $DOQSUB -eq 2 ]; then mv job*.txt rigid/ fi fi # endif RIGID # Begin Main Loop ITERATLEVEL=(` echo $MAXITERATIONS | tr 'x' ' ' `) NUMLEVELS=${#ITERATLEVEL[@]} # debugging only #echo $ITERATLEVEL #echo $NUMLEVELS #echo ${ITERATIONLIMIT} echo "--------------------------------------------------------------------------------------" echo " Start to build template: ${TEMPLATE}" echo "--------------------------------------------------------------------------------------" reportMappingParameters i=0 while [ $i -lt ${ITERATIONLIMIT} ] do itdisplay=$((i+1)) rm -f ${OUTPUTNAME}*Warp*.nii* rm -f job*.sh # Used to save time by only running coarse registration for the first couple of iterations # But with decent initialization, this is probably not worthwhile. # If you uncomment this, replace MAXITERATIONS with ITERATIONS in the call to ants below # # # For the first couple of iterations, use high-level registration only # # eg if MAXITERATIONS=30x90x20, then for iteration 0, do 30x0x0 # # for iteration 1 do 30x90x0, then do 30x90x20 on subsequent iterations # if [ $i -gt $((NUMLEVELS - 1)) ] # then # ITERATIONS=$MAXITERATIONS # else # # ITERATIONS=${ITERATLEVEL[0]} # # for (( n = 1 ; n < ${NUMLEVELS}; n++ )) # do # ITERATIONS=${ITERATIONS}x$((${ITERATLEVEL[n]} * $((n <= i)) )) # done # fi # Job IDs of jobs submitted to queue in loop below jobIDs="" # Reinitialize count to 0 count=0 # Submit registration of each input to volume template to SGE or run locally. for IMG in $IMAGESETVARIABLE do # 1 determine working dir dir=`pwd` # 2 determine new filename POO=${OUTPUTNAME}${IMG} # 3 Make variable OUTFILENAME and remove anything behind . ; for example .nii.gz.gz OUTFN=${POO%.*.*} # 4 Test if outputfilename has only a single extention and remove that if [ ${#OUTFN} -eq ${#POO} ] then OUTFN=${OUTPUTNAME}${IMG%.*} fi # 5 prepare registration command exe="${ANTSSCRIPTNAME} -d ${DIM} -r ${dir}/${TEMPLATE} -i ${dir}/${IMG} -o ${dir}/${OUTFN} -m ${MAXITERATIONS} -n ${N3CORRECT} -s ${METRICTYPE} -t ${TRANSFORMATIONTYPE} " pexe=" $exe >> job_${count}_${i}_metriclog.txt " # 6 submit to SGE or else run locally if [ $DOQSUB -eq 1 ]; then id=`qsub -cwd -N antsBuildTemplate_deformable_${i} -S /bin/bash -v ANTSPATH=$ANTSPATH $QSUBOPTS $exe | awk '{print $3}'` jobIDs="$jobIDs $id" sleep 0.5 elif [ $DOQSUB -eq 2 ] ; then echo $pexe echo $pexe >> job${count}_${i}.sh elif [ $DOQSUB -eq 0 ] ; then bash $exe fi # counter updated, but not directly used in this loop count=`expr $count + 1`; # echo " submitting job number $count " # for debugging only done # SGE wait for script to finish if [ $DOQSUB -eq 1 ]; then echo echo "--------------------------------------------------------------------------------------" echo " Starting ANTS registration on SGE cluster. Iteration: $itdisplay of $ITERATIONLIMIT" echo "--------------------------------------------------------------------------------------" # now wait for the stuff to finish - this will take a while so poll queue every 10 mins ${ANTSPATH}waitForSGEQJobs.pl 1 600 $jobIDs if [ ! $? -eq 0 ]; then echo "qsub submission failed - jobs went into error state" exit 1; fi fi # Run jobs on localhost and wait to finish if [ $DOQSUB -eq 2 ]; then echo echo "--------------------------------------------------------------------------------------" echo " Starting ANTS registration on max ${CORES} cpucores. Iteration: $itdisplay of $ITERATIONLIMIT" echo " Progress can be viewed in job*_${i}_metriclog.txt" echo "--------------------------------------------------------------------------------------" jobfnamepadding #adds leading zeros to the jobnames, so they are carried out chronologically chmod +x job*.sh $PEXEC -j ${CORES} sh job*.sh fi shapeupdatetotemplate ${DIM} ${TEMPLATE} ${TEMPLATENAME} ${OUTPUTNAME} ${GRADIENTSTEP} if [ $DIM -eq 2 ] then ${ANTSPATH}MeasureMinMaxMean $DIM ${TEMPLATENAME}warpxvec.nii.gz ${TEMPLATENAME}warpxlog.txt 1 ${ANTSPATH}MeasureMinMaxMean $DIM ${TEMPLATENAME}warpyvec.nii.gz ${TEMPLATENAME}warpylog.txt 1 elif [ $DIM -eq 3 ] then ${ANTSPATH}MeasureMinMaxMean $DIM ${TEMPLATENAME}warpxvec.nii.gz ${TEMPLATENAME}warpxlog.txt 1 ${ANTSPATH}MeasureMinMaxMean $DIM ${TEMPLATENAME}warpyvec.nii.gz ${TEMPLATENAME}warpylog.txt 1 ${ANTSPATH}MeasureMinMaxMean $DIM ${TEMPLATENAME}warpzvec.nii.gz ${TEMPLATENAME}warpzlog.txt 1 fi echo echo "--------------------------------------------------------------------------------------" echo " Backing up results from iteration $itdisplay" echo "--------------------------------------------------------------------------------------" mkdir ${TRANSFORMATIONTYPE}_iteration_${i} cp ${TEMPLATENAME}warp*log.txt *.cfg *${OUTPUTNAME}*.nii.gz ${TRANSFORMATIONTYPE}_iteration_${i}/ # backup logs if [ $DOQSUB -eq 1 ]; then mv antsBuildTemplate_deformable_* ${TRANSFORMATIONTYPE}_iteration_${i} elif [ $DOQSUB -eq 2 ]; then mv job*.txt ${TRANSFORMATIONTYPE}_iteration_${i} fi ((i++)) done # end main loop rm -f job*.sh #cleanup of 4D files if [ "${range}" -gt 1 ] && [ "${TDIM}" -eq 4 ] then mv ${currentdir}/tmp/selection/${TEMPLATE} ${currentdir}/ cd ${currentdir} rm -rf ${currentdir}/tmp/ fi time_end=`date +%s` time_elapsed=$((time_end - time_start)) echo echo "--------------------------------------------------------------------------------------" echo " Done creating: ${TEMPLATE}" echo " Script executed in $time_elapsed seconds" echo " $(( time_elapsed / 3600 ))h $(( time_elapsed %3600 / 60 ))m $(( time_elapsed % 60 ))s" echo "--------------------------------------------------------------------------------------" exit 0 ants-1.9.2+svn680.dfsg/Scripts/geodesicinterpolation.sh000077500000000000000000000155441147325206600230510ustar00rootroot00000000000000#!/bin/bash NUMPARAMS=$# if [ $NUMPARAMS -lt 2 ] then echo " USAGE :: " echo " sh geodesicinterpolation.sh image1 image2 N-interpolation-points N-step-size Use-Affine-Initialization? Mask-Image Auxiliary-Template " echo " N-interpolation-points -- if you choose 10 points and step-size of 1 then you will get 8 interpolated/morphed images -- end-points are the original images " echo " if you choose 10 points and step-size of 2 then you will get 4 interpolated/morphed images -- end-points are the original images " echo " if you use affine initialization - sometimes it could cause unpleasantness, if the affine mapping is large -- sometimes it may be needed, though " echo " the mask image restricts matching to an ROI " echo " The Auxiliary-Template is another image through which the morph images (image1 and image2) are mapped -- this often helps, if the auxiliary template is an average face image " echo " Be sure to set the ANTSPATH path variable " echo " " echo " You may need to tune the ANTS parameters coded within this script for your application " exit fi # should we use the generative model to find landmarks then # drive individual mappings by the LMs? TEMPLATE=$1 TARGET=$2 NUMSTEPS=10 STEP=1 OUTNAME=ANTSMORPH if [ $NUMPARAMS -gt 2 ] then NUMSTEPS=$3 fi if [ $NUMPARAMS -gt 3 ] then STEP=$4 fi USEAFF=0 if [ $NUMPARAMS -gt 4 ] then USEAFF=$5 fi MASK=0 if [ $NUMPARAMS -gt 5 ] then MASK=$6 fi TEMPLATEB=0 if [ $NUMPARAMS -gt 6 ] then TEMPLATEB=$7 fi echo " Morphing in total $NUMSTEPS time-points by $STEP steps -- end-points are original images " echo " Use-Affine ? $USEAFF " echo " templateB $TEMPLATEB " for (( n = 0 ; n <= ${NUMSTEPS}; n=n+${STEP} )) do BLENDINGA=$(echo "scale=2; ${n}/${NUMSTEPS}" | bc ) BLENDINGB=$(echo "scale=2; 1-$BLENDINGA" | bc ) BLENDNAME=$(echo "scale=2; 100*$BLENDINGA" | bc ) echo " Blending values: $BLENDINGA and $BLENDINGB" done ITS=100x100x10 TRAN=SyN[0.5] REG=Gauss[3,1] RADIUS=6 if [ $NUMPARAMS -le 5 ] then if [ $USEAFF -eq 0 ] then ${ANTSPATH}ANTS 2 -m PR[$TEMPLATE,$TARGET,1,${RADIUS}] -t $TRAN -r $REG -o $OUTNAME -i $ITS --number-of-affine-iterations 0 -x $MASK fi if [ $USEAFF -ne 0 ] then ${ANTSPATH}ANTS 2 -m PR[$TEMPLATE,$TARGET,1,${RADIUS}] -t $TRAN -r $REG -o $OUTNAME -i $ITS -x $MASK #--MI-option 16x2000 fi ${ANTSPATH}ComposeMultiTransform 2 ${OUTNAME}Warp.nii -R $TEMPLATE ${OUTNAME}Warp.nii ${OUTNAME}Affine.txt ${ANTSPATH}ComposeMultiTransform 2 ${OUTNAME}InverseWarp.nii -R $TARGET -i ${OUTNAME}Affine.txt ${OUTNAME}InverseWarp.nii fi if [ $NUMPARAMS -gt 5 ] then if [ $USEAFF -eq 0 ] then #echo " Pseudo-Morphing " #echo " method 1 " ${ANTSPATH}ANTS 2 -m PR[$TEMPLATEB,$TARGET,1,${RADIUS}] -t $TRAN -r $REG -o ${OUTNAME}B -i $ITS --number-of-affine-iterations 0 -x $MASK ${ANTSPATH}ANTS 2 -m PR[$TEMPLATE,$TEMPLATEB,1,${RADIUS}] -t $TRAN -r $REG -o ${OUTNAME}A -i $ITS --number-of-affine-iterations 0 -x $MASK ${ANTSPATH}ComposeMultiTransform 2 ${OUTNAME}AWarp.nii -R $TEMPLATE ${OUTNAME}AWarp.nii ${OUTNAME}AAffine.txt ${ANTSPATH}ComposeMultiTransform 2 ${OUTNAME}AInverseWarp.nii -R $TEMPLATEB -i ${OUTNAME}AAffine.txt ${OUTNAME}AInverseWarp.nii ${ANTSPATH}ComposeMultiTransform 2 ${OUTNAME}BWarp.nii -R $TEMPLATEB ${OUTNAME}BWarp.nii ${OUTNAME}BAffine.txt ${ANTSPATH}ComposeMultiTransform 2 ${OUTNAME}BInverseWarp.nii -R $TARGET -i ${OUTNAME}BAffine.txt ${OUTNAME}BInverseWarp.nii ${ANTSPATH}ComposeMultiTransform 2 ${OUTNAME}Warp.nii -R $TEMPLATE ${OUTNAME}AWarp.nii ${OUTNAME}BWarp.nii ${ANTSPATH}ComposeMultiTransform 2 ${OUTNAME}InverseWarp.nii -R $TARGET ${OUTNAME}BInverseWarp.nii ${OUTNAME}AInverseWarp.nii # #echo " method 2 " #${ANTSPATH}ComposeMultiTransform 2 ${OUTNAME}Warp.nii -R $TEMPLATE ${OUTNAME}AWarp.nii ${OUTNAME}AAffine.txt ${OUTNAME}BWarp.nii ${OUTNAME}BAffine.txt #${ANTSPATH}ComposeMultiTransform 2 ${OUTNAME}InverseWarp.nii -R $TARGET -i ${OUTNAME}BAffine.txt ${OUTNAME}BInverseWarp.nii -i ${OUTNAME}AAffine.txt ${OUTNAME}AInverseWarp.nii # fi if [ $USEAFF -ne 0 ] then #echo " Pseudo-Morphing " #echo " method 1 " ${ANTSPATH}ANTS 2 -m PR[$TEMPLATEB,$TARGET,1,${RADIUS}] -t $TRAN -r $REG -o ${OUTNAME}B -i $ITS -x $MASK ${ANTSPATH}ANTS 2 -m PR[$TEMPLATE,$TEMPLATEB,1,${RADIUS}] -t $TRAN -r $REG -o ${OUTNAME}A -i $ITS -x $MASK ${ANTSPATH}ComposeMultiTransform 2 ${OUTNAME}AWarp.nii -R $TEMPLATE ${OUTNAME}AWarp.nii ${OUTNAME}AAffine.txt ${ANTSPATH}ComposeMultiTransform 2 ${OUTNAME}AInverseWarp.nii -R $TEMPLATEB -i ${OUTNAME}AAffine.txt ${OUTNAME}AInverseWarp.nii ${ANTSPATH}ComposeMultiTransform 2 ${OUTNAME}BWarp.nii -R $TEMPLATEB ${OUTNAME}BWarp.nii ${OUTNAME}BAffine.txt ${ANTSPATH}ComposeMultiTransform 2 ${OUTNAME}BInverseWarp.nii -R $TARGET -i ${OUTNAME}BAffine.txt ${OUTNAME}BInverseWarp.nii ${ANTSPATH}ComposeMultiTransform 2 ${OUTNAME}Warp.nii -R $TEMPLATE ${OUTNAME}AWarp.nii ${OUTNAME}BWarp.nii ${ANTSPATH}ComposeMultiTransform 2 ${OUTNAME}InverseWarp.nii -R $TARGET ${OUTNAME}BInverseWarp.nii ${OUTNAME}AInverseWarp.nii # #echo " method 2 " #${ANTSPATH}ComposeMultiTransform 2 ${OUTNAME}Warp.nii -R $TEMPLATE ${OUTNAME}AWarp.nii ${OUTNAME}AAffine.txt ${OUTNAME}BWarp.nii ${OUTNAME}BAffine.txt #${ANTSPATH}ComposeMultiTransform 2 ${OUTNAME}InverseWarp.nii -R $TARGET -i ${OUTNAME}BAffine.txt ${OUTNAME}BInverseWarp.nii -i ${OUTNAME}AAffine.txt ${OUTNAME}AInverseWarp.nii # fi fi for (( n = 0 ; n <= ${NUMSTEPS}; n=n+${STEP} )) do BLENDINGA=$(echo "scale=2; ${n}/${NUMSTEPS}" | bc ) BLENDINGB=$(echo "scale=2; 1-$BLENDINGA" | bc ) BLENDNAME=$(echo "scale=2; 100*$BLENDINGA" | bc ) echo " Blending values: $BLENDINGA and $BLENDINGB" BASEA=${TEMPLATE%.*} BASEB=${TARGET%.*} ${ANTSPATH}MultiplyImages 2 ${OUTNAME}InverseWarpxvec.nii $BLENDINGA SM${OUTNAME}InverseWarpxvec.nii ${ANTSPATH}MultiplyImages 2 ${OUTNAME}InverseWarpyvec.nii $BLENDINGA SM${OUTNAME}InverseWarpyvec.nii ${ANTSPATH}MultiplyImages 2 ${OUTNAME}Warpxvec.nii $BLENDINGB SM${OUTNAME}Warpxvec.nii ${ANTSPATH}MultiplyImages 2 ${OUTNAME}Warpyvec.nii $BLENDINGB SM${OUTNAME}Warpyvec.nii ${ANTSPATH}WarpImageMultiTransform 2 $TARGET temp.nii SM${OUTNAME}Warp.nii -R $TEMPLATE ${ANTSPATH}ImageMath 2 temp.nii Normalize temp.nii 1 ${ANTSPATH}ImageMath 2 temp.nii m temp.nii 1. #$BLENDINGA ${ANTSPATH}WarpImageMultiTransform 2 $TEMPLATE temp2.nii SM${OUTNAME}InverseWarp.nii -R $TEMPLATE ${ANTSPATH}ImageMath 2 temp2.nii Normalize temp2.nii 1 ${ANTSPATH}ImageMath 2 temp2.nii m temp2.nii 0 #$BLENDINGB echo " ImageMath 2 ${BASEA}${BASEB}${BLENDNAME}morph.nii + temp2.nii temp.nii " ${ANTSPATH}ImageMath 2 ${BASEA}${BASEB}${BLENDNAME}morph.nii + temp2.nii temp.nii ${ANTSPATH}ConvertToJpg ${BASEA}${BASEB}${BLENDNAME}morph.nii ${BASEA}${BASEB}${BLENDNAME}morph.jpg rm -f ${BASEA}${BASEB}${BLENDNAME}morph.nii done rm -f ${OUTNAME}* SM${OUTNAME}* exit ants-1.9.2+svn680.dfsg/Scripts/guidedregistration.sh000077500000000000000000000040371147325206600223460ustar00rootroot00000000000000#!/bin/sh if [ $# -lt 7 ] then echo " USAGE \n sh command.sh fixed.nii fixedhipp.nii moving.nii movinghipp.nii outputname iterations DIM " echo " the template = fixed.nii , the individual = moving.nii " echo " iterations should be of the form 100x100x10 " exit fi FIX=$1 FIXH=$2 MOV=$3 MOVH=$4 OUT=$5 ITS=$6 DIM=$7 #ANTSPATH='/Users/stnava/Code/bin/ants/' if [ ${#FIX} -lt 1 -o ! -f $FIX ] then echo " Problem with specified Fixed Image => User Specified Value = $FIX " exit fi if [ ${#MOV} -lt 1 -o ! -f $MOV ] then echo " Problem with specified Moving Image => User Specified Value = $MOV " exit fi if [ ${#FIXH} -lt 1 -o ! -f $FIXH ] then echo " Problem with specified Fixed Label Image => User Specified Value = $FIXH " exit fi if [ ${#MOVH} -lt 1 -o ! -f $MOVH ] then echo " Problem with specified Moving Label Image => User Specified Value = $MOVH " exit fi # == Important Parameters Begin == LMWT=$9 # weight on landmarks INTWT=$8 # weight on intensity -- twice the landmarks # PSE/point-set-expectation/PointSetExpectation[fixedImage,movingImage,fixedPoints,movingPoints,weight,pointSetPercentage,pointSetSigma,boundaryPointsOnly,kNeighborhood, PartialMatchingIterations=100000] # the partial matching option assumes the complete labeling is in the first set of label parameters ... # more iterations leads to more symmetry in the matching - 0 iterations means full asymmetry PCT=0.1 # percent of labeled voxels to use PARZ=100 # PARZEN sigma LM=PSE[${FIX},${MOV},$FIXH,$MOVH,${LMWT},${PCT},${PARZ},0,25,100] INTENSITY=PR[$FIX,${MOV},${INTWT},4] # == Important Parameters end? == ${ANTSPATH}ANTS $DIM -o $OUT -i $ITS -t SyN[0.25] -r Gauss[3,0] -m $INTENSITY -m $LM ${ANTSPATH}WarpImageMultiTransform $DIM $MOV ${OUT}toTemplate.nii ${OUT}Warp.nii ${OUT}Affine.txt -R $FIX ${ANTSPATH}WarpImageMultiTransform $DIM $FIX ${OUT}toMov.nii -i ${OUT}Affine.txt ${OUT}InverseWarp.nii -R $MOV ${ANTSPATH}WarpImageMultiTransform $DIM $FIXH ${OUT}hipp.nii -i ${OUT}Affine.txt ${OUT}InverseWarp.nii -R $MOV --UseNN ants-1.9.2+svn680.dfsg/Scripts/landmarkmatch.sh000077500000000000000000000051031147325206600212530ustar00rootroot00000000000000#!/bin/sh if [ $# -lt 6 ] then echo " USAGE \n sh $0 fixed.nii fixedhipp.nii moving.nii movinghipp.nii ITERATIONS LandmarkWeight " echo " you should set LandmarkWeight yourself --- the intensity weight is 1. so the landmark weight should be in that range (e.g. 0.5 => 1 ) " echo " the usage indicates .nii but you can use whatever image type you like (as long as itk can read it). also, the fixedhipp.nii can contain either the full hippocampus or the landmarks for the hippocampus --- this would be your template. the moving.nii can contain either one as well. if the moving contains the landmarks and the fixed contains the full hippocampus, then this is known as "partial matching" as in Pluta, et al Hippocampus 2009. " exit fi #ANTSPATH="/mnt/aibs1/avants/bin/ants/" ITS=$5 LMWT=$6 INTWT=1 FIX=$1 FIXH=$2 MOV=$3 MOVH=$4 OUT=${MOV%.*} NN=${#OUT} MM=${#MOV} let DD=MM-NN if [ $DD -eq 3 ] then OUT=${MOV%.*.*} fi OUT2=${FIX%.*} NN=${#OUT2} MM=${#FIX} let DD=MM-NN if [ $DD -eq 3 ] then OUT2=${FIX%.*.*} fi dothis=0 if [ $dothis -eq 1 ] then # here, we select a subset of the labeled regions FIXHMOD=${OUT2}locmod.nii.gz MOVHMOD=${OUT}locmod.nii.gz for i in $MOVH do PRE=$OUT TOUT=${PRE}locmod.nii.gz echo " DOING $TOUT " ${ANTSPATH}ThresholdImage 3 $i ${PRE}tempb.nii.gz 30.5 32.5 ${ANTSPATH}MultiplyImages 3 $i ${PRE}tempb.nii.gz $TOUT ${ANTSPATH}ThresholdImage 3 $i ${PRE}tempb.nii.gz 20.5 22.5 ${ANTSPATH}MultiplyImages 3 $i ${PRE}tempb.nii.gz ${PRE}tempb.nii ${ANTSPATH}ImageMath 3 $TOUT + $TOUT ${PRE}tempb.nii.gz ${ANTSPATH}ThresholdImage 3 $i ${PRE}tempb.nii.gz 8.5 10.5 ${ANTSPATH}MultiplyImages 3 $i ${PRE}tempb.nii.gz ${PRE}tempb.nii.gz ${ANTSPATH}ImageMath 3 $TOUT + $TOUT ${PRE}tempb.nii.gz done else FIXHMOD=$FIXH MOVHMOD=$MOVH fi KNN=10 PERCENTtoUSE=0.5 PARZSIG=10 LM=PSE[${FIX},${MOV},${FIXHMOD},${MOVHMOD},${LMWT},${PERCENTtoUSE},${PARZSIG},0,${KNN}] echo $LM if [ $# -gt 6 ] then OUT=$7 fi STEPL=0.25 INTENSITY=PR[$FIX,${MOV},${INTWT},4] if [ $LMWT -le 0 ] then exe="${ANTSPATH}ANTS 3 -o $OUT -i $ITS -t SyN[${STEPL}] -r Gauss[3,0] -m $INTENSITY " else exe="${ANTSPATH}ANTS 3 -o $OUT -i $ITS -t SyN[${STEPL}] -r Gauss[3,0] -m $LM -m $INTENSITY " fi echo " $exe " $exe ${ANTSPATH}WarpImageMultiTransform 3 $MOV ${OUT}deformed.nii.gz ${OUT}Warp.nii.gz ${OUT}Affine.txt -R $FIX ${ANTSPATH}WarpImageMultiTransform 3 $MOVH ${OUT}label.nii.gz ${OUT}Warp.nii.gz ${OUT}Affine.txt -R $FIX --use-NN ${ANTSPATH}WarpImageMultiTransform 3 $FIXH ${OUT}labelinv.nii.gz -i ${OUT}Affine.txt ${OUT}InverseWarp.nii.gz -R $MOV --use-NN ants-1.9.2+svn680.dfsg/Scripts/lohmann.sh000077500000000000000000000056331147325206600201110ustar00rootroot00000000000000#!/bin/sh NUMPARAMS=$# MAXITERATIONS=30x90x20 if [ $NUMPARAMS -lt 2 ] then echo " USAGE :: " echo " sh $0 ImageDimension image.ext " echo " be sure to set ANTSPATH environment variable " exit fi ANTSPATH=/mnt/aibs1/avants/bin/ants/ if [ ${#ANTSPATH} -le 0 ] then echo " Please set ANTSPATH=LocationOfYourAntsBinaries " echo " Either set this in your profile or directly, here in the script. " echo " For example : " echo " ANTSPATH=/home/yourname/bin/ants/ " exit else echo " ANTSPATH is $ANTSPATH " fi #initialization, here, is unbiased DIM=$1 if [ ${#DIM} -gt 1 ] then echo " Problem with specified ImageDimension => User Specified Value = $DIM " exit fi FIXED=$2 if [ ${#FIXED} -lt 1 -o ! -f $FIXED ] then echo " Problem with specified Fixed Image => User Specified Value = $FIXED " exit fi OUTPUTNAME=` echo $FIXED | cut -d '.' -f 1 ` if [[ ! -s ${OUTPUTNAME}repaired.nii.gz ]] ; then ${ANTSPATH}N3BiasFieldCorrection $DIM $FIXED ${OUTPUTNAME}repaired.nii.gz 4 fi ${ANTSPATH}ThresholdImage $DIM ${OUTPUTNAME}repaired.nii.gz ${OUTPUTNAME}mask.nii.gz 0.1 99999 WM=${OUTPUTNAME}seg.nii.gz if [[ ! -s $WM ]] ; then ${ANTSPATH}Apocrita $DIM -x ${OUTPUTNAME}mask.nii.gz -m [0.5,1,0,0] -o [${OUTPUTNAME}seg.nii.gz] -i Otsu[${OUTPUTNAME}repaired.nii.gz,3] -h 0 ${ANTSPATH}ThresholdImage $DIM $WM $WM 3 3 ${ANTSPATH}ImageMath $DIM $WM GetLargestComponent $WM fi OUT=${OUTPUTNAME} if [[ ! -s ${OUT}max.nii.gz ]] ; then # the closing operation - arachnoid surface ${ANTSPATH}ImageMath 3 ${OUT}max.nii.gz MD $WM 15 ${ANTSPATH}ImageMath 3 ${OUT}max.nii.gz ME ${OUT}max.nii.gz 15 # unfolded or lissencephalic surface # we would prefer to do this in a topology preserving way ${ANTSPATH}ImageMath 3 ${OUT}min.nii.gz ME $WM 5 ${ANTSPATH}ImageMath 3 ${OUT}min.nii.gz MD ${OUT}min.nii.gz 5 # distance transform from arachnoid surface to inside of brain # perhaps should be a geodesic distance transform ${ANTSPATH}ImageMath 3 ${OUT}max.nii.gz Neg ${OUT}max.nii ${ANTSPATH}ImageMath 3 ${OUT}dist.nii.gz D ${OUT}max.nii fi # multiply the distance transform by the "unfolded" surface # this gives the "feature" surface/image that could be input # to an image registration similarity metric ${ANTSPATH}ImageMath 3 ${OUT}lohmann.nii.gz m ${OUT}dist.nii.gz ${OUT}min.nii.gz # surface stuff for visualization ${ANTSPATH}ImageMath 3 ${OUT}surf.nii.gz ME ${OUT}min.nii.gz 2 ${ANTSPATH}ImageMath 3 ${OUT}surf.nii.gz - ${OUT}min.nii.gz ${OUT}surf.nii.gz ${ANTSPATH}ImageMath 3 ${OUT}maxvals.nii.gz m ${OUT}lohmann.nii.gz ${OUT}surf.nii.gz # find sulci using curvature of the WM/lohmann surface ${ANTSPATH}SurfaceCurvature ${OUT}lohmann.nii.gz ${OUT}lohmannk.nii.gz 1.5 ${ANTSPATH}ThresholdImage 3 ${OUT}lohmann.nii.gz ${OUT}lohmann.nii.gz 0.001 9999 ${ANTSPATH}ImageMath 3 ${OUT}lohmannk.nii.gz Normalize ${OUT}lohmannk.nii.gz ${ANTSPATH}ImageMath 3 ${OUT}lohmannk.nii.gz m ${OUT}lohmannk.nii.gz ${OUT}lohmann.nii.gz exit ants-1.9.2+svn680.dfsg/Scripts/optimalsmooth.sh000077500000000000000000000006111147325206600213430ustar00rootroot00000000000000#!/bin/sh echo " sh $0 outputprefix " if [ $# -lt 1 ] ; then exit fi export ANTSPATH=${ANTSPATH:="$HOME/bin/ants/"} SUB=$1 TH=${SUB}thicknorm.nii.gz JA=${SUB}logjacobian.nii MK=${SUB}mask.nii.gz OUTT=${SUB}smooththick.nii.gz OUTJ=${SUB}smoothjac.nii.gz REPS=5 ${ANTSPATH}SurfaceBasedSmoothing $TH 1.5 $MK $OUTT $REPS ${ANTSPATH}SurfaceBasedSmoothing $JA 1.5 $MK $OUTJ $REPS ants-1.9.2+svn680.dfsg/Scripts/phantomstudy.sh000077500000000000000000000027351147325206600212140ustar00rootroot00000000000000#!/bin/sh ANTSPATH="/Users/stnava/Code/bin/ants/" #initialization, here, is unbiased count=0 for x in phantomAwmgm.jpg phantomBwmgm.jpg phantomCwmgm.jpg phantomDwmgm.jpg phantomEwmgm.jpg phantomFwmgm.jpg phantomGwmgm.jpg phantomHwmgm.jpg do count=`expr $count + 1` # Increment the counter echo " count is $count and file is $x " ${ANTSPATH}ANTS 2 -m PR[phantomtemplate.jpg,$x,1,4] -i 20x161x161x161 -o TEST{$count} -t SyN[2] -r Gauss[3,0.] # # -t SyN[1,2,0.05] -r Gauss[3,0.25] ${ANTSPATH}WarpImageMultiTransform 2 $x TEST{$count}registered.nii TEST{$count}Warp.nii TEST{$count}Affine.txt done # to look at the stack of results, do this: ${ANTSPATH}StackSlices volo.hdr -1 -1 0 *$1 ${ANTSPATH}StackSlices volr.hdr -1 -1 0 *registered.nii count=0 for x in phantomAwmgm.jpg phantomBwmgm.jpg phantomCwmgm.jpg phantomDwmgm.jpg phantomEwmgm.jpg phantomFwmgm.jpg phantomGwmgm.jpg phantomHwmgm.jpg do count=`expr $count + 1` # Increment the counter echo " count is $count and file is $x " echo " CreateJacobianDeterminantImage 2 TEST{$count}Warp.nii TEST{$count}logjac.nii 1 " ${ANTSPATH}CreateJacobianDeterminantImage 2 TEST{$count}Warp.nii TEST{$count}logjac.nii 1 # ${ANTSPATH}SmoothImage 2 TEST{$count}logjac.nii 1 TEST{$count}logjac.nii done ${ANTSPATH}ThresholdImage 2 phantomtemplate.jpg mask.nii 100 200 # use the GLM with mask ${ANTSPATH}GLM 2 mask.nii designmat.txt contrast.txt Result.nii 1000 TEST*logjac.nii ants-1.9.2+svn680.dfsg/Scripts/registerimages.pl000077500000000000000000000033441147325206600214650ustar00rootroot00000000000000#!/usr/bin/perl -w use File::Basename; if ($#ARGV >= 0) { $who = join(' ', $ARGV[0]); } if ($#ARGV >= 1) { $template = join(' ', $ARGV[1]); } if ($#ARGV >= 2) { $params = join(' ', $ARGV[2]); } else { $params=0; } if ($#ARGV >= 3) { $outnaming = join(' ', $ARGV[3]); } else { $outnaming=0; } if ($#ARGV >= 4) { $metric= join(' ', $ARGV[4]); } else { $metric="PR[".$template; } if ($#ARGV >= 5) { $metricpar= join(' ', $ARGV[5]); } else { $metricpar=",1,4]"; } opendir(DIR, $who); @filelist = glob("$who"); # @filelist2 = glob("$who2"); $ct=100; print " Expecting Normalization params ".$params." \n \n \n doing \n $who \n "; $dir = `pwd`; $dir=substr($dir,0,length($dir)-1)."/"; $pathpre=""; $qsname="superfresh.sh"; foreach $name (@filelist) { $pre=$outnaming.$name; $pre=~s/\.[^.]*$//; print $pre." \n "; $prog=$params; $exe=$prog." -m ".$metric.",".$name.",".$metricpar." -o ".$pre." \n "; $exe2 = "/mnt/pkg/sge-root/bin/lx24-x86/qsub -q mac ".$qsname." ".$dir." ".$exe." "; print $exe."\n"; system $exe2; # USE qstat TO VERIFY THE JOB IS SUBMITTED $ct=$ct+1; } print " DONE WITH LOOP \n "; $user=`whoami`; chomp($user); print " you are ".$user." = surf-king "; $atcfn=0; if ($atcfn) { $waitcmd="vq -s | grep ".$user; } else { $waitcmd=" qstat -u ".$user." | grep ".substr($qsname,0,4); } $continuewaiting=1; if ($continuewaiting) { system "sleep 40"; } # Now Wait for the jobs to complete while($continuewaiting) { system $waitcmd; $output = `$waitcmd`; if ( $output eq "" ) { $continuewaiting=0; print "Jobs all finished!\n"; } else { print " wait ";system "sleep 30"; } } closedir(DIR); ants-1.9.2+svn680.dfsg/Scripts/runprogramonimageset.pl000077500000000000000000000012541147325206600227210ustar00rootroot00000000000000#!/usr/bin/perl -w if ($#ARGV >= 0) { $who = join(' ', $ARGV[0]); } opendir(DIR, $who); @filelist = glob("$who"); # @filelist2 = glob("$who2"); $ct=0; $dir = `pwd`; $dir=substr($dir,0,length($dir)-1)."/"; $pathpre="/mnt/aibs1/avants/bin/ants/"; foreach $name (@filelist) { $exe = $pathpre."ANTS 3 -m PR[templatecontrol.nii,".$name.",1,2] -o ".$name." -t SyN[3] -r Gauss[2,0] -i 100x100x30 "; $exe2 = "/mnt/pkg/sge-root/bin/lx24-x86/qsub -q mac /mnt/data1/avants/Data/code/qsub3.sh ".$dir." ".$exe." "; print "$exe\n"; system $exe2; # USE qstat TO VERIFY THE JOB IS SUBMITTED $ct=$ct+1; } closedir(DIR); ants-1.9.2+svn680.dfsg/Scripts/shapeupdatetotemplate.sh000077500000000000000000000274331147325206600230610ustar00rootroot00000000000000#!/bin/bash function shapeupdatetotemplate { # local declaration of values dim=${DIM} template=${TEMPLATE} templatename=${TEMPLATENAME} outputname=${OUTPUTNAME} gradientstep=-${GRADIENTSTEP} # debug only # echo $dim # echo ${template} # echo ${templatename} # echo ${outputname} # echo ${outputname}*formed.nii* # echo ${gradientstep} # We find the average warp to the template and apply its inverse to the template image # This keeps the template shape stable over multiple iterations of template building echo echo "--------------------------------------------------------------------------------------" echo " shapeupdatetotemplate 1" echo "--------------------------------------------------------------------------------------" ${ANTSPATH}AverageImages $dim ${template} 1 ${outputname}*formed.nii.gz echo echo "--------------------------------------------------------------------------------------" echo " shapeupdatetotemplate 2" echo "--------------------------------------------------------------------------------------" if [ $dim -eq 2 ] then ${ANTSPATH}AverageImages $dim ${templatename}warpxvec.nii.gz 0 `ls ${outputname}*Warpxvec.nii.gz | grep -v "InverseWarpxvec"` ${ANTSPATH}AverageImages $dim ${templatename}warpyvec.nii.gz 0 `ls ${outputname}*Warpyvec.nii.gz | grep -v "InverseWarpyvec"` elif [ $dim -eq 3 ] then ${ANTSPATH}AverageImages $dim ${templatename}warpxvec.nii.gz 0 `ls ${outputname}*Warpxvec.nii.gz | grep -v "InverseWarpxvec"` ${ANTSPATH}AverageImages $dim ${templatename}warpyvec.nii.gz 0 `ls ${outputname}*Warpyvec.nii.gz | grep -v "InverseWarpyvec"` ${ANTSPATH}AverageImages $dim ${templatename}warpzvec.nii.gz 0 `ls ${outputname}*Warpzvec.nii.gz | grep -v "InverseWarpzvec"` fi echo echo "--------------------------------------------------------------------------------------" echo " shapeupdatetotemplate 3" echo "--------------------------------------------------------------------------------------" if [ $dim -eq 2 ] then ${ANTSPATH}MultiplyImages $dim ${templatename}warpxvec.nii.gz ${gradientstep} ${templatename}warpxvec.nii.gz ${ANTSPATH}MultiplyImages $dim ${templatename}warpyvec.nii.gz ${gradientstep} ${templatename}warpyvec.nii.gz elif [ $dim -eq 3 ] then ${ANTSPATH}MultiplyImages $dim ${templatename}warpxvec.nii.gz ${gradientstep} ${templatename}warpxvec.nii.gz ${ANTSPATH}MultiplyImages $dim ${templatename}warpyvec.nii.gz ${gradientstep} ${templatename}warpyvec.nii.gz ${ANTSPATH}MultiplyImages $dim ${templatename}warpzvec.nii.gz ${gradientstep} ${templatename}warpzvec.nii.gz fi echo echo "--------------------------------------------------------------------------------------" echo " shapeupdatetotemplate 4" echo "--------------------------------------------------------------------------------------" rm -f ${templatename}Affine.txt echo echo "--------------------------------------------------------------------------------------" echo " shapeupdatetotemplate 5" echo "--------------------------------------------------------------------------------------" # Averaging and inversion code if [ ${dim} -eq 2 ] then ANTSAverage2DAffine ${templatename}Affine.txt ${outputname}*Affine.txt ${ANTSPATH}WarpImageMultiTransform ${dim} ${templatename}warpxvec.nii.gz ${templatename}warpxvec.nii.gz -i ${templatename}Affine.txt -R ${template} ${ANTSPATH}WarpImageMultiTransform ${dim} ${templatename}warpyvec.nii.gz ${templatename}warpyvec.nii.gz -i ${templatename}Affine.txt -R ${template} ${ANTSPATH}WarpImageMultiTransform ${dim} ${template} ${template} -i ${templatename}Affine.txt ${templatename}warp.nii.gz ${templatename}warp.nii.gz ${templatename}warp.nii.gz ${templatename}warp.nii.gz -R ${template} elif [ ${dim} -eq 3 ] then ANTSAverage3DAffine ${templatename}Affine.txt ${outputname}*Affine.txt ${ANTSPATH}WarpImageMultiTransform ${dim} ${templatename}warpxvec.nii.gz ${templatename}warpxvec.nii.gz -i ${templatename}Affine.txt -R ${template} ${ANTSPATH}WarpImageMultiTransform ${dim} ${templatename}warpyvec.nii.gz ${templatename}warpyvec.nii.gz -i ${templatename}Affine.txt -R ${template} ${ANTSPATH}WarpImageMultiTransform ${dim} ${templatename}warpzvec.nii.gz ${templatename}warpzvec.nii.gz -i ${templatename}Affine.txt -R ${template} ${ANTSPATH}WarpImageMultiTransform ${dim} ${template} ${template} -i ${templatename}Affine.txt ${templatename}warp.nii.gz ${templatename}warp.nii.gz ${templatename}warp.nii.gz ${templatename}warp.nii.gz -R ${template} fi echo echo "--------------------------------------------------------------------------------------" echo " shapeupdatetotemplate 6" echo "--------------------------------------------------------------------------------------" if [ ${dim} -eq 2 ] then ${ANTSPATH}MeasureMinMaxMean ${dim} ${templatename}warpxvec.nii.gz ${templatename}warpxlog.txt 1 ${ANTSPATH}MeasureMinMaxMean ${dim} ${templatename}warpyvec.nii.gz ${templatename}warpylog.txt 1 elif [ ${dim} -eq 3 ] then ${ANTSPATH}MeasureMinMaxMean ${dim} ${templatename}warpxvec.nii.gz ${templatename}warpxlog.txt 1 ${ANTSPATH}MeasureMinMaxMean ${dim} ${templatename}warpyvec.nii.gz ${templatename}warpylog.txt 1 ${ANTSPATH}MeasureMinMaxMean ${dim} ${templatename}warpzvec.nii.gz ${templatename}warpzlog.txt 1 fi } function ANTSAverage2DAffine { OUTNM=${templatename}Affine.txt FLIST=${outputname}*Affine.txt NFILES=0 PARAM1=0 PARAM2=0 PARAM3=0 PARAM4=0 PARAM5=0 PARAM6=0 PARAM7=0 PARAM8=0 LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 2 ` for x in $LL ; do PARAM1=` awk -v a=$PARAM1 -v b=$x 'BEGIN{print (a + b)}' ` ; let NFILES=$NFILES+1 ; done PARAM1=` awk -v a=$PARAM1 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 3 ` for x in $LL ; do PARAM2=` awk -v a=$PARAM2 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM2=` awk -v a=$PARAM2 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 4 ` for x in $LL ; do PARAM3=` awk -v a=$PARAM3 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM3=` awk -v a=$PARAM3 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 5 ` for x in $LL ; do PARAM4=` awk -v a=$PARAM4 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM4=` awk -v a=$PARAM4 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 6 ` for x in $LL ; do PARAM5=` awk -v a=$PARAM5 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM5=0 # ` awk -v a=$PARAM5 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 7 ` for x in $LL ; do PARAM6=` awk -v a=$PARAM6 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM6=0 # ` awk -v a=$PARAM6 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` cat $FLIST | grep FixedParamet | cut -d ' ' -f 2 ` for x in $LL ; do PARAM7=` awk -v a=$PARAM7 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM7=` awk -v a=$PARAM7 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` cat $FLIST | grep FixedParamet | cut -d ' ' -f 3 ` for x in $LL ; do PARAM8=` awk -v a=$PARAM8 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM8=` awk -v a=$PARAM8 -v b=$NFILES 'BEGIN{print (a / b)}' ` echo "# Insight Transform File V1.0 " > $OUTNM echo "# Transform 0 " >> $OUTNM echo "Transform: MatrixOffsetTransformBase_double_2_2 " >> $OUTNM echo "Parameters: $PARAM1 $PARAM2 $PARAM3 $PARAM4 $PARAM5 $PARAM6 " >> $OUTNM echo "FixedParameters: $PARAM7 $PARAM8 " >> $OUTNM } function ANTSAverage3DAffine { OUTNM=${templatename}Affine.txt FLIST=${outputname}*Affine.txt NFILES=0 PARAM1=0 PARAM2=0 PARAM3=0 PARAM4=0 PARAM5=0 PARAM6=0 PARAM7=0 PARAM8=0 PARAM9=0 PARAM10=0 PARAM11=0 PARAM12=0 PARAM13=0 PARAM14=0 PARAM15=0 LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 2 ` for x in $LL ; do PARAM1=` awk -v a=$PARAM1 -v b=$x 'BEGIN{print (a + b)}' ` ; let NFILES=$NFILES+1 ; done PARAM1=` awk -v a=$PARAM1 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 3 ` for x in $LL ; do PARAM2=` awk -v a=$PARAM2 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM2=` awk -v a=$PARAM2 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 4 ` for x in $LL ; do PARAM3=` awk -v a=$PARAM3 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM3=` awk -v a=$PARAM3 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 5 ` for x in $LL ; do PARAM4=` awk -v a=$PARAM4 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM4=` awk -v a=$PARAM4 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 6 ` for x in $LL ; do PARAM5=` awk -v a=$PARAM5 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM5=` awk -v a=$PARAM5 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 7 ` for x in $LL ; do PARAM6=` awk -v a=$PARAM6 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM6=` awk -v a=$PARAM6 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 8 ` for x in $LL ; do PARAM7=` awk -v a=$PARAM7 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM7=` awk -v a=$PARAM7 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 9 ` for x in $LL ; do PARAM8=` awk -v a=$PARAM8 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM8=` awk -v a=$PARAM8 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 10 ` for x in $LL ; do PARAM9=` awk -v a=$PARAM9 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM9=` awk -v a=$PARAM9 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 11 ` for x in $LL ; do PARAM10=` awk -v a=$PARAM10 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM10=0 # ` awk -v a=$PARAM10 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 12 ` for x in $LL ; do PARAM11=` awk -v a=$PARAM11 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM11=0 # ` awk -v a=$PARAM11 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` head -n 4 $FLIST | grep Paramet | cut -d ' ' -f 13 ` for x in $LL ; do PARAM12=` awk -v a=$PARAM12 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM12=0 # ` awk -v a=$PARAM12 -v b=$NFILES 'BEGIN{print (a / b)}' ` # origin params below LL=` cat $FLIST | grep FixedParamet | cut -d ' ' -f 2 ` for x in $LL ; do PARAM13=` awk -v a=$PARAM13 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM13=` awk -v a=$PARAM13 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` cat $FLIST | grep FixedParamet | cut -d ' ' -f 3 ` for x in $LL ; do PARAM14=` awk -v a=$PARAM14 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM14=` awk -v a=$PARAM14 -v b=$NFILES 'BEGIN{print (a / b)}' ` LL=` cat $FLIST | grep FixedParamet | cut -d ' ' -f 4 ` for x in $LL ; do PARAM15=` awk -v a=$PARAM15 -v b=$x 'BEGIN{print (a + b)}' ` ; done PARAM15=` awk -v a=$PARAM15 -v b=$NFILES 'BEGIN{print (a / b)}' ` echo "# Insight Transform File V1.0 " > $OUTNM echo "# Transform 0 " >> $OUTNM echo "Transform: MatrixOffsetTransformBase_double_3_3 " >> $OUTNM echo "Parameters: $PARAM1 $PARAM2 $PARAM3 $PARAM4 $PARAM5 $PARAM6 $PARAM7 $PARAM8 $PARAM9 $PARAM10 $PARAM11 $PARAM12 " >> $OUTNM echo "FixedParameters: $PARAM13 $PARAM14 $PARAM15 " >> $OUTNM } DIM=3 TEMPLATE=t2template.nii.gz TEMPLATENAME=t2template OUTPUTNAME=t2 GRADIENTSTEP=0.1 shapeupdatetotemplate ${DIM} ${TEMPLATE} ${TEMPLATENAME} ${OUTPUTNAME} ${GRADIENTSTEP} ants-1.9.2+svn680.dfsg/Scripts/submitexperimentalbuild.sh000077500000000000000000000005211147325206600234050ustar00rootroot00000000000000#!/bin/sh # request Bourne shell as shell for job ctest -D ExperimentalStart ctest -D ExperimentalUpdate ctest -D ExperimentalConfigure ctest -D ExperimentalBuild ctest -D ExperimentalSubmit ctest -D ExperimentalTest #ctest -D ExperimentalCoverage ctest -D ExperimentalSubmit #ctest -D ExperimentalMemCheck #ctest -D ExperimentalSubmit ants-1.9.2+svn680.dfsg/Scripts/sygnccavg.sh000077500000000000000000000010141147325206600204260ustar00rootroot00000000000000#!/bin/sh TNAME=$1 GRADSTEP=$2 shift 2 FLIST2=$* ImageMath 2 $TNAME Normalize $TNAME for x in $(seq 1 5 ) ; do for y in $FLIST2 ; do ImageMath 2 turdx${y} Normalize $y ImageMath 2 turdx${y} CorrelationUpdate $TNAME turdx${y} 4 # MeasureImageSimilarity 2 1 $y $TNAME SmoothImage 2 turdx${y} 0.5 turdx${y} # MeasureMinMaxMean 2 turdx$y done AverageImages 2 turdu.nii.gz 0 turdx** MultiplyImages 2 turdu.nii.gz $GRADSTEP turdu.nii.gz ImageMath 2 $TNAME + $TNAME turdu.nii.gz done ants-1.9.2+svn680.dfsg/Scripts/thickstudy.sh000077500000000000000000000017471147325206600206520ustar00rootroot00000000000000#!/bin/sh count=0 for x in phantomA phantomB phantomC phantomD phantomE phantomF phantomG phantomH do count=`expr $count + 1` # Increment the counter echo " $x " echo " LaplacianThickness ${x}wm.hdr ${x}gm.hdr 15 TEST{$count}thick.hdr 1 0.5 " ThresholdImage 2 ${x}wmgm.jpg ${x}wm.nii 245 290 ThresholdImage 2 ${x}wmgm.jpg ${x}gm.nii 111 133 LaplacianThickness ${x}wm.nii ${x}gm.nii 15 TEST{$count}thick.hdr 5 3 WarpImageMultiTransform 2 TEST{$count}thick.hdr TEST{$count}thickreg.hdr TEST{$count}Warp.nii TEST{$count}Affine.txt # SmoothImage 2 TEST{$count}thickreg.hdr 1 TEST{$count}thickreg.hdr done StudentsTestOnImages 2 THICK2.hdr 4 4 ~/Code/bin/ants/GLM 2 mask.nii designmat.txt contrastvec.txt temp.hdr 1000 TEST{1}thickreg.hdr TEST{2}thickreg.hdr TEST{3}thickreg.hdr TEST{4}thickreg.hdr TEST{5}thickreg.hdr TEST{6}thickreg.hdr TEST{7}thickreg.hdr TEST{8}thickreg.hdr MultiplyImages 2 THICK2.hdr phantomtemplate.jpg THICK2.hdr ants-1.9.2+svn680.dfsg/Scripts/waitForSGEQJobs.pl000077500000000000000000000062731147325206600213700ustar00rootroot00000000000000#!/usr/bin/perl -w use strict; # Usage: waitForSGEQJobs.pl [job IDs] # # # Takes as args a string of qsub job IDs and periodically monitors them. Once they all finish, it returns 0 # # If any of the jobs go into error state, an error is printed to stderr and the program waits for the non-error # jobs to finish, then returns 1 # # Usual qstat format - check this at run time # job-ID prior name user state submit/start at queue slots ja-task-ID # First thing to do is parse our input my ($verbose, $delay, @jobIDs) = @ARGV; # Check for user stupidity if ($delay < 10) { print STDERR "Sleep period is too short, will poll queue once every 10 seconds\n"; $delay = 10; } elsif ($delay > 3600) { print STDERR "Sleep period is too long, will poll queue once every 60 minutes\n"; $delay = 3600; } print " Waiting for " . scalar(@jobIDs) . " jobs: @jobIDs\n"; my $user=`whoami`; my $qstatOutput = `qstat -u $user`; if (!scalar(@jobIDs) || !$qstatOutput) { # Nothing to do exit 0; } my @qstatLines = split("\n", $qstatOutput); my @header = split('\s+', trim($qstatLines[0])); # Position in qstat output of tokens we want my $jobID_Pos = -1; my $statePos = -1; foreach my $i (0..$#header) { if ($header[$i] eq "job-ID") { $jobID_Pos = $i; } elsif ($header[$i] eq "state") { $statePos = $i; } } # If we can't parse the job IDs, something is very wrong if ($jobID_Pos < 0 || $statePos < 0) { die "Cannot find job-ID and state field in qstat output, cannot monitor jobs\n"; } # Now check on all of our jobs my $jobsIncomplete = 1; # Set to 1 for any job in an error state my $haveErrors = 0; while ($jobsIncomplete) { # Jobs that are still showing up in qstat $jobsIncomplete = 0; foreach my $job (@jobIDs) { # iterate over all user jobs in the queue qstatLine: foreach my $line (@qstatLines) { # trim string for trailing white space so that the tokens are in the correct sequence # We are being paranoid by matching tokens to job-IDs this way. Less elegant than a # match but also less chance of a false-positive match my @tokens = split('\s+', trim($line)); if ( $tokens[$jobID_Pos] eq $job) { # Check status if ($tokens[$statePos] =~ m/E/) { $haveErrors = 1; } else { $jobsIncomplete = $jobsIncomplete + 1; } if ($verbose) { print " Job $job is in state $tokens[$statePos]\n"; } } last qstatLine if ( $tokens[$jobID_Pos] eq $job ); } } if ($jobsIncomplete) { if ($verbose) { my $timestamp = `date`; chomp $timestamp; print " ($timestamp) Still waiting for $jobsIncomplete jobs\n\n"; } # Use of backticks rather than system permits a ctrl+c to work `sleep $delay`; $qstatOutput = `qstat -u $user`; @qstatLines = split("\n", $qstatOutput); } } if ($haveErrors) { print " No more jobs to run - some jobs had errors\n\n"; exit 1; } else { print " No more jobs in queue\n\n"; exit 0; } sub trim { my ($string) = @_; $string =~ s/^\s+//; $string =~ s/\s+$//; return $string; } ants-1.9.2+svn680.dfsg/Scripts/warpimages.pl000077500000000000000000000030131147325206600206030ustar00rootroot00000000000000#!/usr/bin/perl -w if ($#ARGV >= 0) { $who = join(' ', $ARGV[0]); } if ($#ARGV >= 1) { $template = join(' ', $ARGV[1]); } if ($#ARGV >= 2) { $dim = join(' ', $ARGV[2]); } else { $dim=0; } if ($#ARGV >= 3) { $outnaming = join(' ', $ARGV[3]); } else { $outnaming=""; } opendir(DIR, $who); @filelist = glob("$who"); # @filelist2 = glob("$who2"); $ct=0; print " EXPECTING IMAGE DIMENSION ".$dim." \n \n \n "; $dir = `pwd`; $dir=substr($dir,0,length($dir)-1)."/"; $pathpre="/mnt/aibs1/avants/bin/ants/"; $qsname="superfresh.sh"; foreach $name (@filelist) { $pre=$outnaming.$name; $pre=~s/\.[^.]*$//; print $pre." \n "; $prog=$pathpre."WarpImageMultiTransform ".$dim; $exe=$prog." ".$name." ".$pre."deformed.nii ".$pre."Warp.nii ".$pre."Affine.txt -R ".$template; $exe2 = "/mnt/pkg/sge-root/bin/lx24-x86/qsub -q mac ".$qsname." ".$dir." ".$exe." "; print $exe."\n"; system $exe2; # USE qstat TO VERIFY THE JOB IS SUBMITTED $ct=$ct+1; } $user=`whoami`; chomp($user); print " you are ".$user." = a jingle-jangle-jungle "; $atcfn=0; if ($atcfn) { $waitcmd="vq -s | grep ".$user; } else { $waitcmd=" qstat -u ".$user." | grep ".substr($qsname,0,4); } $continuewaiting=1; if ($continuewaiting) { system "sleep 40"; } while($continuewaiting) { system $waitcmd; $output = `$waitcmd`; if ( $output eq "" ) { $continuewaiting=0; print "Jobs all finished!\n"; } else { print " wait ";system "sleep 30"; } } closedir(DIR); ants-1.9.2+svn680.dfsg/Scripts/weightedaverage.pl000077500000000000000000000021421147325206600216010ustar00rootroot00000000000000#!/usr/bin/perl -w if ($#ARGV >= 0) { $who = join(' ', $ARGV[0]); } if ($#ARGV >= 1) { $imagedimension = join(' ', $ARGV[1]); } if ($#ARGV >= 2) { $outputname = join(' ', $ARGV[2]); } if ($#ARGV < 2) { print " usage: \n perl weightedaverage.pl *files.jpg imagedimension weight1 ... weightN \n "; exit(1); } opendir(DIR, $who); @filelist = glob("$who"); $basect=3; $ct=$basect; $dir = `pwd`; $dir=substr($dir,0,length($dir)-1)."/"; foreach $name (@filelist) { $weight=0; if ($#ARGV >= $ct) { $weight = join(' ', $ARGV[$ct]); } print " count ".$ct." & w= ".$weight." \n "; $tempname=" temp.nii "; $exe = " ImageMath ".$imagedimension." ".$tempname." m ".$name." ".$weight." \n "; system $exe; $exe = " ImageMath ".$imagedimension." ".$outputname." + ".$tempname." ".$outputname." \n "; if ($ct > $basect ) { system $exe; } else { $exe=" ImageMath ".$imagedimension." ".$outputname." m ".$name." ".$weight." \n "; print " YEE \n"; system $exe; } $ct=$ct+1; } closedir(DIR); ants-1.9.2+svn680.dfsg/Scripts/weightedaverage.sh000077500000000000000000000015651147325206600216100ustar00rootroot00000000000000#!/bin/sh echo "Usage: \n sh weightedaverage.sh \"Faces*tiff\" \n " count=9 for x in `ls -tr $1 ` do count=`expr $count + 1` # Increment the counter echo " count is $count and file is $x at it $i " ANTS 2 -m PR[template.nii,$x,1,8,-0.9] -t SyN[3] -r Gauss[3,1.5] -o ROBU{$count} -i 22x21x10 --MI-option 16x8000 #-a InitAffine.txt --continue-affine 0 WarpImageMultiTransform 2 $x ROBU{$count}{$i}registered.nii ROBU{$count}Warp.nii ROBU{$count}Affine.txt ComposeMultiTransform 2 ROBU{$count}Warp.nii -R template.nii ROBU{$count}Warp.nii ROBU{$count}Affine.txt ComposeMaps 2 ROBU{$count}Warp.nii ROBU{$count}Affine.txt ROBU{$count}Warp.nii 2 ConvertToJpg ROBU{$count}{$i}registered.nii ROBU{$count}{$i}registered.jpg MeasureImageSimilarity 2 2 template.nii ROBU{$count}{$i}registered.nii metriclog.txt done # end loop ants-1.9.2+svn680.dfsg/Temporary/000077500000000000000000000000001147325206600164425ustar00rootroot00000000000000ants-1.9.2+svn680.dfsg/Temporary/itkAddConstantToImageFilter.h000066400000000000000000000105121147325206600241400ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkAddConstantToImageFilter.h,v $ Language: C++ Date: $Date: 2009-02-24 19:03:14 $ Version: $Revision: 1.6 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkAddConstantToImageFilter_h #define __itkAddConstantToImageFilter_h #include "itkUnaryFunctorImageFilter.h" #include "itkNumericTraits.h" namespace itk { /** \class AddConstantToImageFilter * * \brief Add a constant to all input pixels. * * This filter is templated over the input image type * and the output image type. * * \author Tom Vercauteren, INRIA & Mauna Kea Technologies * * Based on filters from the Insight Journal paper: * http://hdl.handle.net/1926/510 * * \ingroup IntensityImageFilters Multithreaded * \sa UnaryFunctorImageFilter */ namespace Functor { template< class TInput, class TConstant, class TOutput> class AddConstantTo { public: AddConstantTo() : m_Constant(NumericTraits::One) {}; ~AddConstantTo() {}; bool operator!=( const AddConstantTo & other ) const { return !(*this == other); } bool operator==( const AddConstantTo & other ) const { return other.m_Constant == m_Constant; } inline TOutput operator()( const TInput & A ) const { // Because the user has to specify the constant we don't // check if the cte is not 0; return static_cast( A + m_Constant ); } void SetConstant(TConstant ct) {this->m_Constant = ct; } const TConstant & GetConstant() const { return m_Constant; } TConstant m_Constant; }; } template class ITK_EXPORT AddConstantToImageFilter : public UnaryFunctorImageFilter > { public: /** Standard class typedefs. */ typedef AddConstantToImageFilter Self; typedef UnaryFunctorImageFilter< TInputImage,TOutputImage, Functor::AddConstantTo< typename TInputImage::PixelType, TConstant, typename TOutputImage::PixelType> > Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Run-time type information (and related methods). */ itkTypeMacro(AddConstantToImageFilter, UnaryFunctorImageFilter); /** Set the constant that will be used to multiply all the image * pixels */ void SetConstant(TConstant ct) { if( ct != this->GetFunctor().GetConstant() ) { this->GetFunctor().SetConstant(ct); this->Modified(); } } const TConstant & GetConstant() const { return this->GetFunctor().GetConstant(); } #ifdef ITK_USE_CONCEPT_CHECKING /** Begin concept checking */ itkConceptMacro(InputConvertibleToOutputCheck, (Concept::Convertible)); itkConceptMacro(Input1Input2OutputAddOperatorCheck, (Concept::AdditiveOperators)); /** End concept checking */ #endif protected: AddConstantToImageFilter() {}; virtual ~AddConstantToImageFilter() {}; void PrintSelf(std::ostream &os, Indent indent) const { Superclass::PrintSelf(os, indent); os << indent << "Constant: " << static_cast::PrintType>(this->GetConstant()) << std::endl; } private: AddConstantToImageFilter(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkAutumnColormapFunctor.h000066400000000000000000000045221147325206600236350ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkAutumnColormapFunctor.h,v $ Language: C++ Date: $Date: 2009-05-15 19:22:31 $ Version: $Revision: 1.3 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkAutumnColormapFunctor_h #define __itkAutumnColormapFunctor_h #include "itkColormapFunctor.h" namespace itk { namespace Functor { /** * \class AutumnColormapFunctor * \brief Function object which maps a scalar value into an RGB colormap value. * * \author Nicholas Tustison, Hui Zhang, Gaetan Lehmann, Paul Yushkevich and James C. Gee * * This code was contributed in the Insight Journal paper: * * "Meeting Andy Warhol Somewhere Over the Rainbow: RGB Colormapping and ITK" * http://www.insight-journal.org/browse/publication/285 * http://hdl.handle.net/1926/1452 * */ template< class TScalar, class TRGBPixel > class ITK_EXPORT AutumnColormapFunctor : public ColormapFunctor { public: typedef AutumnColormapFunctor Self; typedef ColormapFunctor Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); typedef typename Superclass::RGBPixelType RGBPixelType; typedef typename Superclass::ScalarType ScalarType; typedef typename Superclass::RealType RealType; virtual RGBPixelType operator()( const TScalar & ) const; protected: AutumnColormapFunctor() {}; ~AutumnColormapFunctor() {}; private: AutumnColormapFunctor(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace functor } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkAutumnColormapFunctor.txx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkAutumnColormapFunctor.txx000066400000000000000000000030641147325206600242310ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkAutumnColormapFunctor.txx,v $ Language: C++ Date: $Date: 2009-05-15 02:47:59 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkAutumnColormapFunctor_txx #define __itkAutumnColormapFunctor_txx #include "itkAutumnColormapFunctor.h" namespace itk { namespace Functor { template typename AutumnColormapFunctor::RGBPixelType AutumnColormapFunctor ::operator()( const TScalar & v ) const { // Map the input scalar between [0, 1]. RealType value = this->RescaleInputValue( v ); // Apply the color mapping. RealType red = 1.0; RealType green = value; RealType blue = 0.0; // Set the rgb components after rescaling the values. RGBPixelType pixel; pixel[0] = this->RescaleRGBComponentValue( red ); pixel[1] = this->RescaleRGBComponentValue( green ); pixel[2] = this->RescaleRGBComponentValue( blue ); return pixel; } } // end namespace Functor } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkBinaryContourImageFilter.h000066400000000000000000000155171147325206600242430ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkBinaryContourImageFilter.h,v $ Language: C++ Date: $Date: 2009/03/10 17:30:41 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkBinaryContourImageFilter_h #define __itkBinaryContourImageFilter_h #include "itkInPlaceImageFilter.h" #include "itkImage.h" #include "itkConceptChecking.h" #include #include #include "itkProgressReporter.h" #include "itkBarrier.h" namespace itk { /** * \class BinaryContourImageFilter * \brief Give the pixels on the border of an object. * * BinaryContourImageFilter labels the pixels on the borders * of the objects in a binary image. * * \sa ImageToImageFilter */ template class ITK_EXPORT BinaryContourImageFilter : public InPlaceImageFilter< TInputImage, TOutputImage > { public: /** * Standard "Self" & Superclass typedef. */ typedef BinaryContourImageFilter Self; typedef InPlaceImageFilter< TInputImage, TOutputImage > Superclass; /** * Types from the Superclass */ typedef typename Superclass::InputImagePointer InputImagePointer; /** * Extract some information from the image types. Dimensionality * of the two images is assumed to be the same. */ typedef typename TOutputImage::PixelType OutputPixelType; typedef typename TOutputImage::InternalPixelType OutputInternalPixelType; typedef typename TInputImage::PixelType InputPixelType; typedef typename TInputImage::InternalPixelType InputInternalPixelType; itkStaticConstMacro(ImageDimension, unsigned int, TOutputImage::ImageDimension); itkStaticConstMacro(OutputImageDimension, unsigned int, TOutputImage::ImageDimension); itkStaticConstMacro(InputImageDimension, unsigned int, TInputImage::ImageDimension); /** * Image typedef support */ typedef TInputImage InputImageType; typedef typename TInputImage::IndexType IndexType; typedef typename TInputImage::SizeType SizeType; typedef typename TInputImage::OffsetType OffsetType; typedef typename TInputImage::PixelType InputImagePixelType; typedef TOutputImage OutputImageType; typedef typename TOutputImage::RegionType RegionType; typedef typename TOutputImage::IndexType OutputIndexType; typedef typename TOutputImage::SizeType OutputSizeType; typedef typename TOutputImage::OffsetType OutputOffsetType; typedef typename TOutputImage::PixelType OutputImagePixelType; typedef std::list ListType; /** * Smart pointer typedef support */ typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** * Run-time type information (and related methods) */ itkTypeMacro(BinaryContourImageFilter, ImageToImageFilter); /** * Method for creation through the object factory. */ itkNewMacro(Self); /** * Set/Get whether the connected components are defined strictly by * face connectivity or by face+edge+vertex connectivity. Default is * FullyConnectedOff. For objects that are 1 pixel wide, use * FullyConnectedOn. */ itkSetMacro(FullyConnected, bool); itkGetConstReferenceMacro(FullyConnected, bool); itkBooleanMacro(FullyConnected); // Concept checking -- input and output dimensions must be the same itkConceptMacro(SameDimension, (Concept::SameDimension)); /** */ itkSetMacro(BackgroundValue, OutputImagePixelType); itkGetMacro(BackgroundValue, OutputImagePixelType); /** */ itkSetMacro(ForegroundValue, InputImagePixelType); itkGetMacro(ForegroundValue, InputImagePixelType); protected: BinaryContourImageFilter() { m_FullyConnected = false; m_ForegroundValue = NumericTraits< InputImagePixelType >::max(); m_BackgroundValue = NumericTraits< OutputImagePixelType >::Zero; m_NumberOfThreads = 0; this->SetInPlace( false ); } virtual ~BinaryContourImageFilter() {} BinaryContourImageFilter(const Self&) {} void PrintSelf(std::ostream& os, Indent indent) const; /** * Standard pipeline methods. */ void BeforeThreadedGenerateData (); void AfterThreadedGenerateData (); void ThreadedGenerateData (const RegionType& outputRegionForThread, int threadId); /** BinaryContourImageFilter needs the entire input. Therefore * it must provide an implementation GenerateInputRequestedRegion(). * \sa ProcessObject::GenerateInputRequestedRegion(). */ void GenerateInputRequestedRegion(); /** BinaryContourImageFilter will produce all of the output. * Therefore it must provide an implementation of * EnlargeOutputRequestedRegion(). * \sa ProcessObject::EnlargeOutputRequestedRegion() */ void EnlargeOutputRequestedRegion(DataObject *itkNotUsed(output)); private: InputImagePixelType m_ForegroundValue; OutputImagePixelType m_BackgroundValue; bool m_FullyConnected; // some additional types typedef typename TOutputImage::RegionType::SizeType OutSizeType; // types to support the run length encoding of lines class runLength { public: // run length information - may be a more type safe way of doing this long int length; typename InputImageType::IndexType where; // Index of the start of the run }; typedef std::vector lineEncoding; // the map storing lines typedef std::vector LineMapType; typedef std::vector OffsetVec; // the types to support union-find operations typedef std::vector UnionFindType; bool CheckNeighbors(const OutputIndexType &A, const OutputIndexType &B); void CompareLines(lineEncoding ¤t, const lineEncoding &Neighbour); void SetupLineOffsets(OffsetVec &LineOffsets); void Wait() { if( m_NumberOfThreads > 1 ) { m_Barrier->Wait(); } } typename Barrier::Pointer m_Barrier; LineMapType m_ForegroundLineMap; LineMapType m_BackgroundLineMap; long m_NumberOfThreads; }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkBinaryContourImageFilter.txx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkBinaryContourImageFilter.txx000066400000000000000000000361131147325206600246320ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkBinaryContourImageFilter.txx,v $ Language: C++ Date: $Date: 2009/03/10 17:30:41 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkBinaryContourImageFilter_txx #define __itkBinaryContourImageFilter_txx #include "itkBinaryContourImageFilter.h" #include "itkNumericTraits.h" // don't think we need the indexed version as we only compute the // index at the start of each run, but there isn't a choice #include "itkImageLinearConstIteratorWithIndex.h" #include "itkImageLinearIteratorWithIndex.h" #include "itkConstShapedNeighborhoodIterator.h" #include "itkImageRegionIterator.h" #include "itkMaskImageFilter.h" #include "itkConnectedComponentAlgorithm.h" namespace itk { template< class TInputImage, class TOutputImage> void BinaryContourImageFilter< TInputImage, TOutputImage> ::GenerateInputRequestedRegion() { // call the superclass' implementation of this method Superclass::GenerateInputRequestedRegion(); // We need all the input. InputImagePointer input = const_cast(this->GetInput()); if( !input ) { return; } input->SetRequestedRegion( input->GetLargestPossibleRegion() ); } template< class TInputImage, class TOutputImage> void BinaryContourImageFilter< TInputImage, TOutputImage> ::EnlargeOutputRequestedRegion(DataObject *) { this->GetOutput() ->SetRequestedRegion( this->GetOutput()->GetLargestPossibleRegion() ); } template< class TInputImage, class TOutputImage> void BinaryContourImageFilter< TInputImage, TOutputImage> ::BeforeThreadedGenerateData() { typename TOutputImage::Pointer output = this->GetOutput(); typename TInputImage::ConstPointer input = this->GetInput(); long nbOfThreads = this->GetNumberOfThreads(); if( itk::MultiThreader::GetGlobalMaximumNumberOfThreads() != 0 ) { nbOfThreads = vnl_math_min( this->GetNumberOfThreads(), itk::MultiThreader::GetGlobalMaximumNumberOfThreads() ); } // number of threads can be constrained by the region size, so call the SplitRequestedRegion // to get the real number of threads which will be used typename TOutputImage::RegionType splitRegion; // dummy region - just to call the following method nbOfThreads = this->SplitRequestedRegion(0, nbOfThreads, splitRegion); // std::cout << "nbOfThreads: " << nbOfThreads << std::endl; m_Barrier = Barrier::New(); m_Barrier->Initialize( nbOfThreads ); long pixelcount = output->GetRequestedRegion().GetNumberOfPixels(); long xsize = output->GetRequestedRegion().GetSize()[0]; long linecount = pixelcount/xsize; m_ForegroundLineMap.clear(); m_ForegroundLineMap.resize( linecount ); m_BackgroundLineMap.clear(); m_BackgroundLineMap.resize( linecount ); m_NumberOfThreads = nbOfThreads; } template< class TInputImage, class TOutputImage> void BinaryContourImageFilter< TInputImage, TOutputImage> ::ThreadedGenerateData(const RegionType& outputRegionForThread, int threadId) { typename TOutputImage::Pointer output = this->GetOutput(); typename TInputImage::ConstPointer input = this->GetInput(); // create a line iterator typedef itk::ImageLinearConstIteratorWithIndex InputLineIteratorType; InputLineIteratorType inLineIt(input, outputRegionForThread); inLineIt.SetDirection(0); typedef itk::ImageLinearIteratorWithIndex OutputLineIteratorType; OutputLineIteratorType outLineIt(output, outputRegionForThread); outLineIt.SetDirection(0); // set the progress reporter to deal with the number of lines long pixelcountForThread = outputRegionForThread.GetNumberOfPixels(); long xsizeForThread = outputRegionForThread.GetSize()[0]; long linecountForThread = pixelcountForThread/xsizeForThread; ProgressReporter progress(this, threadId, linecountForThread * 2); // find the split axis IndexType outputRegionIdx = output->GetRequestedRegion().GetIndex(); IndexType outputRegionForThreadIdx = outputRegionForThread.GetIndex(); unsigned int splitAxis = 0; for( unsigned int i=0; iGetRequestedRegion().GetSize(); outputRegionSize[splitAxis] = outputRegionForThreadIdx[splitAxis] - outputRegionIdx[splitAxis]; long firstLineIdForThread = RegionType( outputRegionIdx, outputRegionSize ).GetNumberOfPixels() / xsizeForThread; long lineId = firstLineIdForThread; OffsetVec LineOffsets; SetupLineOffsets(LineOffsets); outLineIt.GoToBegin(); for( inLineIt.GoToBegin(); !inLineIt.IsAtEnd(); inLineIt.NextLine(), outLineIt.NextLine() ) { inLineIt.GoToBeginOfLine(); outLineIt.GoToBeginOfLine(); lineEncoding fgLine; lineEncoding bgLine; while (! inLineIt.IsAtEndOfLine()) { InputPixelType PVal = inLineIt.Get(); //std::cout << inLineIt.GetIndex() << std::endl; if (PVal == m_ForegroundValue) { // We've hit the start of a run runLength thisRun; long length=0; IndexType thisIndex; thisIndex = inLineIt.GetIndex(); //std::cout << thisIndex << std::endl; outLineIt.Set( m_BackgroundValue ); ++length; ++inLineIt; ++outLineIt; while( !inLineIt.IsAtEndOfLine() && inLineIt.Get() == m_ForegroundValue ) { outLineIt.Set( m_BackgroundValue ); ++length; ++inLineIt; ++outLineIt; } // create the run length object to go in the vector thisRun.length=length; thisRun.where = thisIndex; fgLine.push_back(thisRun); // std::cout << "fg " << thisIndex << " " << length << std::endl; } else { // We've hit the start of a run runLength thisRun; long length=0; IndexType thisIndex; thisIndex = inLineIt.GetIndex(); //std::cout << thisIndex << std::endl; outLineIt.Set( PVal ); ++length; ++inLineIt; ++outLineIt; while( !inLineIt.IsAtEndOfLine() && inLineIt.Get() != m_ForegroundValue ) { outLineIt.Set( inLineIt.Get() ); ++length; ++inLineIt; ++outLineIt; } // create the run length object to go in the vector thisRun.length=length; thisRun.where = thisIndex; bgLine.push_back(thisRun); // std::cout << "bg " << thisIndex << " " << length << std::endl; } } // std::cout << std::endl; m_ForegroundLineMap[lineId] = fgLine; m_BackgroundLineMap[lineId] = bgLine; lineId++; progress.CompletedPixel(); } // wait for the other threads to complete that part this->Wait(); // now process the map and make appropriate entries in an equivalence // table // assert( linecount == m_ForegroundLineMap.size() ); long pixelcount = output->GetRequestedRegion().GetNumberOfPixels(); long xsize = output->GetRequestedRegion().GetSize()[0]; long linecount = pixelcount/xsize; long lastLineIdForThread = linecount; if( threadId != m_NumberOfThreads - 1 ) { lastLineIdForThread = firstLineIdForThread + RegionType( outputRegionIdx, outputRegionForThread.GetSize() ).GetNumberOfPixels() / xsizeForThread; } for(long ThisIdx = firstLineIdForThread; ThisIdx < lastLineIdForThread; ++ThisIdx) { if( !m_ForegroundLineMap[ThisIdx].empty() ) { for (OffsetVec::const_iterator I = LineOffsets.begin(); I != LineOffsets.end(); ++I) { long NeighIdx = ThisIdx + (*I); // check if the neighbor is in the map if ( NeighIdx >= 0 && NeighIdx < linecount && !m_BackgroundLineMap[NeighIdx].empty() ) { // Now check whether they are really neighbors bool areNeighbors = CheckNeighbors(m_ForegroundLineMap[ThisIdx][0].where, m_BackgroundLineMap[NeighIdx][0].where); if (areNeighbors) { // Compare the two lines CompareLines(m_ForegroundLineMap[ThisIdx], m_BackgroundLineMap[NeighIdx]); } } } } progress.CompletedPixel(); } } template< class TInputImage, class TOutputImage> void BinaryContourImageFilter< TInputImage, TOutputImage> ::AfterThreadedGenerateData() { m_Barrier = NULL; m_ForegroundLineMap.clear(); m_BackgroundLineMap.clear(); } template< class TInputImage, class TOutputImage> void BinaryContourImageFilter< TInputImage, TOutputImage> ::SetupLineOffsets(OffsetVec &LineOffsets) { // Create a neighborhood so that we can generate a table of offsets // to "previous" line indexes // We are going to mis-use the neighborhood iterators to compute the // offset for us. All this messing around produces an array of // offsets that will be used to index the map typename TOutputImage::Pointer output = this->GetOutput(); typedef Image PretendImageType; typedef typename PretendImageType::RegionType::SizeType PretendSizeType; typedef typename PretendImageType::RegionType::IndexType PretendIndexType; typedef ConstShapedNeighborhoodIterator LineNeighborhoodType; typename PretendImageType::Pointer fakeImage; fakeImage = PretendImageType::New(); typename PretendImageType::RegionType LineRegion; //LineRegion = PretendImageType::RegionType::New(); OutSizeType OutSize = output->GetRequestedRegion().GetSize(); PretendSizeType PretendSize; // The first dimension has been collapsed for (unsigned int i = 0; iSetRegions( LineRegion ); PretendSizeType kernelRadius; kernelRadius.Fill(1); LineNeighborhoodType lnit(kernelRadius, fakeImage, LineRegion); setConnectivity( &lnit, m_FullyConnected ); typename LineNeighborhoodType::IndexListType ActiveIndexes; ActiveIndexes = lnit.GetActiveIndexList(); typename LineNeighborhoodType::IndexListType::const_iterator LI; PretendIndexType idx = LineRegion.GetIndex(); long offset = fakeImage->ComputeOffset( idx ); for (LI=ActiveIndexes.begin(); LI != ActiveIndexes.end(); LI++) { LineOffsets.push_back( fakeImage->ComputeOffset( idx + lnit.GetOffset( *LI ) ) - offset ); } LineOffsets.push_back( 0 ); // LineOffsets is the thing we wanted. } template< class TInputImage, class TOutputImage> bool BinaryContourImageFilter< TInputImage, TOutputImage> ::CheckNeighbors(const OutputIndexType &A, const OutputIndexType &B) { // this checks whether the line encodings are really neighbors. The // first dimension gets ignored because the encodings are along that // axis OutputOffsetType Off = A - B; for (unsigned i = 1; i < OutputImageDimension; i++) { if (abs(Off[i]) > 1) { return(false); } } return(true); } template< class TInputImage, class TOutputImage> void BinaryContourImageFilter< TInputImage, TOutputImage> ::CompareLines(lineEncoding ¤t, const lineEncoding &Neighbour) { bool sameLine = true; OutputOffsetType Off = current[0].where - Neighbour[0].where; for (unsigned i = 1; i < OutputImageDimension; i++) { if (Off[i] != 0) { sameLine = false; } } long offset = 0; if (m_FullyConnected || sameLine) { offset = 1; } // std::cout << "offset: " << offset << std::endl; typename TOutputImage::Pointer output = this->GetOutput(); typename lineEncoding::const_iterator nIt, mIt; typename lineEncoding::iterator cIt; mIt = Neighbour.begin(); // out marker iterator for (cIt = current.begin();cIt != current.end();++cIt) { //runLength cL = *cIt; long cStart = cIt->where[0]; // the start x position long cLast = cStart + cIt->length - 1; bool lineCompleted = false; for (nIt=mIt; nIt != Neighbour.end() && !lineCompleted; ++nIt) { //runLength nL = *nIt; long nStart = nIt->where[0]; long nLast = nStart + nIt->length - 1; // there are a few ways that neighbouring lines might overlap // neighbor S------------------E // current S------------------------E //------------- // neighbor S------------------E // current S----------------E //------------- // neighbor S------------------E // current S------------------E //------------- // neighbor S------------------E // current S-------E //------------- long ss1 = nStart - offset; // long ss2 = nStart + offset; // long ee1 = nLast - offset; long ee2 = nLast + offset; bool eq = false; long oStart = 0; long oLast = 0; // the logic here can probably be improved a lot if ((ss1 >= cStart) && (ee2 <= cLast)) { // case 1 // std::cout << "case 1" << std::endl; eq = true; oStart = ss1; oLast = ee2; } else if ((ss1 <= cStart) && (ee2 >= cLast)) { // case 4 // std::cout << "case 4" << std::endl; eq = true; oStart = cStart; oLast = cLast; } else if ((ss1 <= cLast) && (ee2 >= cLast)) { // case 2 // std::cout << "case 2" << std::endl; eq = true; oStart = ss1; oLast = cLast; } else if ((ss1 <= cStart) && (ee2 >= cStart)) { // case 3 // std::cout << "case 3" << std::endl; eq = true; oStart = cStart; oLast = ee2; } if (eq) { // std::cout << oStart << " " << oLast << std::endl; assert( oStart <= oLast ); IndexType idx = cIt->where; for( int x=oStart; x<=oLast; x++ ) { // std::cout << idx << std::endl; idx[0] = x; output->SetPixel( idx, m_ForegroundValue ); } if( oStart == cStart && oLast == cLast ) { lineCompleted = true; } } } } } template< class TInputImage, class TOutputImage> void BinaryContourImageFilter< TInputImage, TOutputImage> ::PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf(os,indent); os << indent << "FullyConnected: " << m_FullyConnected << std::endl; os << indent << "ForegroundValue: " << static_cast::PrintType>(m_ForegroundValue) << std::endl; os << indent << "BackgroundValue: " << static_cast::PrintType>(m_BackgroundValue) << std::endl; } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkBlueColormapFunctor.h000066400000000000000000000045031147325206600232520ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkBlueColormapFunctor.h,v $ Language: C++ Date: $Date: 2009-05-15 19:08:09 $ Version: $Revision: 1.2 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkBlueColormapFunctor_h #define __itkBlueColormapFunctor_h #include "itkColormapFunctor.h" namespace itk { namespace Functor { /** * \class BlueColormapFunctor * \brief Function object which maps a scalar value into an RGB colormap value. * * \author Nicholas Tustison, Hui Zhang, Gaetan Lehmann, Paul Yushkevich and ̈James C. Gee * * This code was contributed in the Insight Journal paper: * * "Meeting Andy Warhol Somewhere Over the Rainbow: RGB Colormapping and ITK" * http://www.insight-journal.org/browse/publication/285 * http://hdl.handle.net/1926/1452 * */ template< class TScalar, class TRGBPixel > class ITK_EXPORT BlueColormapFunctor : public ColormapFunctor { public: typedef BlueColormapFunctor Self; typedef ColormapFunctor Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); typedef typename Superclass::RGBPixelType RGBPixelType; typedef typename Superclass::ScalarType ScalarType; typedef typename Superclass::RealType RealType; virtual RGBPixelType operator()( const TScalar & ) const; protected: BlueColormapFunctor() {}; ~BlueColormapFunctor() {}; private: BlueColormapFunctor(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace functor } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkBlueColormapFunctor.txx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkBlueColormapFunctor.txx000066400000000000000000000026511147325206600236500ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkBlueColormapFunctor.txx,v $ Language: C++ Date: $Date: 2009-05-15 02:47:59 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkBlueColormapFunctor_txx #define __itkBlueColormapFunctor_txx #include "itkBlueColormapFunctor.h" namespace itk { namespace Functor { template typename BlueColormapFunctor::RGBPixelType BlueColormapFunctor ::operator()( const TScalar & v ) const { // Map the input scalar between [0, 1]. RealType value = this->RescaleInputValue( v ); // Set the rgb components after rescaling the values. RGBPixelType pixel; pixel[0] = 0; pixel[1] = 0; pixel[2] = this->RescaleRGBComponentValue( value ); return pixel; } } // end namespace Functor } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkColormapFunctor.h000066400000000000000000000121051147325206600224370ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkColormapFunctor.h,v $ Language: C++ Date: $Date: 2009-05-21 20:16:56 $ Version: $Revision: 1.6 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkColormapFunctor_h #define __itkColormapFunctor_h #include "itkLightObject.h" #include "itkNumericTraits.h" #include "itkRGBPixel.h" namespace itk { namespace Functor { /** * \class ColormapFunctor * \brief Function object which maps a scalar value into an RGB colormap value. * * * \author Nicholas Tustison, Hui Zhang, Gaetan Lehmann, Paul Yushkevich and ̈James C. Gee * * This code was contributed in the Insight Journal paper: * * "Meeting Andy Warhol Somewhere Over the Rainbow: RGB Colormapping and ITK" * http://www.insight-journal.org/browse/publication/285 * http://hdl.handle.net/1926/1452 * */ template< class TScalar, class TRGBPixel > class ITK_EXPORT ColormapFunctor : public Object { public: typedef ColormapFunctor Self; typedef Object Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Run-time type information (and related methods). */ itkTypeMacro(ColormapFunctor, Object); typedef TRGBPixel RGBPixelType; typedef typename TRGBPixel::ComponentType RGBComponentType; typedef TScalar ScalarType; typedef typename NumericTraits::RealType RealType; itkSetMacro( MinimumRGBComponentValue, RGBComponentType ); itkGetConstMacro( MinimumRGBComponentValue, RGBComponentType ); itkSetMacro( MaximumRGBComponentValue, RGBComponentType ); itkGetConstMacro( MaximumRGBComponentValue, RGBComponentType ); itkSetMacro( MinimumInputValue, ScalarType ); itkGetConstMacro( MinimumInputValue, ScalarType ); itkSetMacro( MaximumInputValue, ScalarType ); itkGetConstMacro( MaximumInputValue, ScalarType ); virtual bool operator!=( const ColormapFunctor & ) const { return false; } virtual bool operator==( const ColormapFunctor & other ) const { return !(*this != other); } virtual RGBPixelType operator()( const ScalarType & ) const = 0; protected: ColormapFunctor() { this->m_MinimumInputValue = NumericTraits::min(); this->m_MaximumInputValue = NumericTraits::max(); this->m_MinimumRGBComponentValue = NumericTraits::min(); this->m_MaximumRGBComponentValue = NumericTraits::max(); } ~ColormapFunctor() {}; /** * Map [min, max] input values to [0, 1]. */ RealType RescaleInputValue( ScalarType v ) const { RealType d = static_cast( this->m_MaximumInputValue - this->m_MinimumInputValue ); RealType value = ( static_cast( v ) - static_cast( this->m_MinimumInputValue ) ) / d; value = vnl_math_max( 0.0, value ); value = vnl_math_min( 1.0, value ); return value; } /** * Map [0, 1] value to [min, max] rgb component values. */ RGBComponentType RescaleRGBComponentValue( RealType v ) const { RealType d = static_cast( m_MaximumRGBComponentValue - m_MinimumRGBComponentValue ); const RGBComponentType rescaled = static_cast( d * v ) + this->m_MinimumRGBComponentValue; return rescaled; } void PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf(os, indent); os << indent << "Minimum RGB Component Value: " << static_cast::PrintType>( this->GetMinimumRGBComponentValue()) << std::endl; os << indent << "Maximum RGB Component Value: " << static_cast::PrintType>( this->GetMaximumRGBComponentValue()) << std::endl; os << indent << "Minimum Input Value: " << static_cast::PrintType>( this->GetMinimumInputValue()) << std::endl; os << indent << "Maximum Input Value: " << static_cast::PrintType>( this->GetMaximumInputValue()) << std::endl; } private: ColormapFunctor(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented ScalarType m_MinimumInputValue; ScalarType m_MaximumInputValue; RGBComponentType m_MinimumRGBComponentValue; RGBComponentType m_MaximumRGBComponentValue; }; } // end namespace functor } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkContourDirectedMeanDistanceImageFilter.h000066400000000000000000000146371147325206600270200ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkContourDirectedMeanDistanceImageFilter.h,v $ Language: C++ Date: $Date: 2009-04-25 12:27:20 $ Version: $Revision: 1.7 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkContourDirectedMeanDistanceImageFilter_h #define __itkContourDirectedMeanDistanceImageFilter_h #include "itkImageToImageFilter.h" #include "itkNumericTraits.h" #include "itkArray.h" #include "itkImage.h" namespace itk { /** \class ContourDirectedMeanDistanceImageFilter * \brief Computes the directed Mean distance between the boundaries of * non-zero pixel regions of two images. * * ContourDirectedMeanDistanceImageFilter computes the distance between the set * non-zero pixels of two images using the following formula: * \f[ h(A,B) = \mathrm{mean}_{a \in A} \min_{b \in B} \| a - b\| \f] * where \f$A\f$ and \f$B\f$ are respectively the set of non-zero pixels * in the first and second input images. It identifies the point \f$ a \in A \f$ * that is farthest from any point of \f$B\f$ and measures the distance from \f$a\f$ * to the nearest neighbor in \f$B\f$. Note that this function is not * is not symmetric and hence is not a true distance. * * In particular, this filter uses the SignedDanielssonDistanceMapImageFilter inside * to compute distance map from all non-zero pixels in the second image. It * then computes the mean distance (in pixels) within the boundary pixels of * non-zero regions in the first image. * * Use MeanDistanceImageFilter to compute the full Mean distance. * * This filter requires the largest possible region of the first image and the * same corresponding region in the second image. It behaves as filter with * two input and one output. Thus it can be inserted in a pipeline with other * filters. The filter passes the first input through unmodified. * * This filter is templated over the two input image type. It assume * both image have the same number of dimensions. * * \sa SignedDanielssonDistanceMapImageFilter * \sa MeanDistanceImageFilter * * \ingroup MultiThreaded * * \author Teo Popa, ISIS Center, Georgetown University * */ template class ITK_EXPORT ContourDirectedMeanDistanceImageFilter : public ImageToImageFilter { public: /** Standard Self typedef */ typedef ContourDirectedMeanDistanceImageFilter Self; typedef ImageToImageFilter Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Runtime information support. */ itkTypeMacro(ContourDirectedMeanDistanceImageFilter, ImageToImageFilter); /** Image related typedefs. */ typedef TInputImage1 InputImage1Type; typedef TInputImage2 InputImage2Type; typedef typename TInputImage1::Pointer InputImage1Pointer; typedef typename TInputImage2::Pointer InputImage2Pointer; typedef typename TInputImage1::ConstPointer InputImage1ConstPointer; typedef typename TInputImage2::ConstPointer InputImage2ConstPointer; typedef typename TInputImage1::RegionType RegionType; typedef typename TInputImage1::SizeType SizeType; typedef typename TInputImage1::IndexType IndexType; typedef typename TInputImage1::PixelType InputImage1PixelType; typedef typename TInputImage2::PixelType InputImage2PixelType; /** Image related typedefs. */ itkStaticConstMacro(ImageDimension, unsigned int, TInputImage1::ImageDimension); /** Type to use form computations. */ typedef typename NumericTraits::RealType RealType; /** Set the first input. */ void SetInput1( const InputImage1Type * image ) { this->SetInput( image ); } /** Set the second input. */ void SetInput2( const InputImage2Type * image ); /** Get the first input. */ const InputImage1Type * GetInput1(void) { return this->GetInput(); } /** Get the second input. */ const InputImage2Type * GetInput2(void); /** Return the computed directed Mean distance. */ itkGetConstMacro(ContourDirectedMeanDistance,RealType); #ifdef ITK_USE_CONCEPT_CHECKING /** Begin concept checking */ itkConceptMacro(InputHasNumericTraitsCheck, (Concept::HasNumericTraits)); /** End concept checking */ #endif protected: ContourDirectedMeanDistanceImageFilter(); ~ContourDirectedMeanDistanceImageFilter(){}; void PrintSelf(std::ostream& os, Indent indent) const; /** Pass the input through unmodified. Do this by Grafting in the AllocateOutputs method. */ void AllocateOutputs(); /** Initialize some accumulators before the threads run. */ void BeforeThreadedGenerateData (); /** Do final mean and variance computation from data accumulated in threads. */ void AfterThreadedGenerateData (); /** Multi-thread version GenerateData. */ void ThreadedGenerateData (const RegionType& outputRegionForThread, int threadId); // Override since the filter needs all the data for the algorithm void GenerateInputRequestedRegion(); // Override since the filter produces all of its output void EnlargeOutputRequestedRegion(DataObject *data); private: ContourDirectedMeanDistanceImageFilter(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented typedef Image DistanceMapType; typename DistanceMapType::Pointer m_DistanceMap; Array m_MeanDistance; Array m_Count; RealType m_ContourDirectedMeanDistance; }; // end of class } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkContourDirectedMeanDistanceImageFilter.txx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkContourDirectedMeanDistanceImageFilter.txx000066400000000000000000000171341147325206600274070ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkContourDirectedMeanDistanceImageFilter.txx,v $ Language: C++ Date: $Date: 2008-10-13 18:54:27 $ Version: $Revision: 1.9 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkContourDirectedMeanDistanceImageFilter_txx #define __itkContourDirectedMeanDistanceImageFilter_txx #include "itkContourDirectedMeanDistanceImageFilter.h" #include "itkConstNeighborhoodIterator.h" #include "itkNeighborhoodInnerProduct.h" #include "itkNeighborhoodAlgorithm.h" #include "itkZeroFluxNeumannBoundaryCondition.h" #include "itkOffset.h" #include "itkImageRegionIterator.h" #include "itkImageRegionConstIterator.h" #include "itkNumericTraits.h" #include "itkSignedMaurerDistanceMapImageFilter.h" #include "itkProgressReporter.h" namespace itk { template ContourDirectedMeanDistanceImageFilter ::ContourDirectedMeanDistanceImageFilter(): m_MeanDistance(1),m_Count(1) { // this filter requires two input images this->SetNumberOfRequiredInputs( 2 ); m_DistanceMap = NULL; m_ContourDirectedMeanDistance = NumericTraits::Zero; } template void ContourDirectedMeanDistanceImageFilter ::SetInput2( const TInputImage2 * image ) { this->SetNthInput(1, const_cast( image ) ); } template const typename ContourDirectedMeanDistanceImageFilter ::InputImage2Type * ContourDirectedMeanDistanceImageFilter ::GetInput2() { return static_cast< const TInputImage2 * > (this->ProcessObject::GetInput(1)); } template void ContourDirectedMeanDistanceImageFilter ::GenerateInputRequestedRegion() { Superclass::GenerateInputRequestedRegion(); // this filter requires: // - the largeset possible region of the first image // - the corresponding region of the second image if ( this->GetInput1() ) { InputImage1Pointer image1 = const_cast< InputImage1Type * >( this->GetInput1() ); image1->SetRequestedRegionToLargestPossibleRegion(); if ( this->GetInput2() ) { InputImage2Pointer image2 = const_cast< InputImage2Type * >( this->GetInput2() ); image2->SetRequestedRegion( this->GetInput1()->GetRequestedRegion() ); } } } template void ContourDirectedMeanDistanceImageFilter ::EnlargeOutputRequestedRegion(DataObject *data) { Superclass::EnlargeOutputRequestedRegion(data); data->SetRequestedRegionToLargestPossibleRegion(); } template void ContourDirectedMeanDistanceImageFilter ::AllocateOutputs() { // Pass the first input through as the output InputImage1Pointer image = const_cast< TInputImage1 * >( this->GetInput1() ); this->GraftOutput( image ); } template void ContourDirectedMeanDistanceImageFilter ::BeforeThreadedGenerateData() { int numberOfThreads = this->GetNumberOfThreads(); // Resize the thread temporaries m_MeanDistance.SetSize(numberOfThreads); m_Count.SetSize(numberOfThreads); // Initialize the temporaries m_MeanDistance.Fill(NumericTraits::Zero); m_Count.Fill(0); // Compute SignedMaurer distance from non-zero pixels in the second image typedef SignedMaurerDistanceMapImageFilter FilterType; typename FilterType::Pointer filter = FilterType::New(); filter->SetInput( this->GetInput2() ); filter->Update(); m_DistanceMap = filter->GetOutput(); } template void ContourDirectedMeanDistanceImageFilter ::AfterThreadedGenerateData() { int i; int numberOfThreads = this->GetNumberOfThreads(); // find mean over all threads int count = 0; RealType sum = NumericTraits::Zero; for( i = 0; i < numberOfThreads; i++) { sum += m_MeanDistance[i]; count += m_Count[i]; } if (count != 0) { m_ContourDirectedMeanDistance= sum/static_cast(count); } else { m_ContourDirectedMeanDistance = NumericTraits::Zero; } } template void ContourDirectedMeanDistanceImageFilter ::ThreadedGenerateData(const RegionType& outputRegionForThread, int threadId) { unsigned int i; ZeroFluxNeumannBoundaryCondition nbc; ConstNeighborhoodIterator bit; typename InputImage1Type::ConstPointer input = this->GetInput(); // Find the data-set boundary "faces" typedef typename InputImage1Type::SizeType InputSizeType; InputSizeType radius; radius.Fill(1); typename NeighborhoodAlgorithm::ImageBoundaryFacesCalculator::FaceListType faceList; NeighborhoodAlgorithm::ImageBoundaryFacesCalculator bC; faceList = bC(input, outputRegionForThread, radius); typename NeighborhoodAlgorithm::ImageBoundaryFacesCalculator::FaceListType::iterator fit; typedef typename InputImage1Type::PixelType InputPixelType; // support progress methods/callbacks ProgressReporter progress(this, threadId, outputRegionForThread.GetNumberOfPixels()); // Process each of the boundary faces. These are N-d regions which border // the edge of the buffer. for (fit=faceList.begin(); fit != faceList.end(); ++fit) { ImageRegionConstIterator it2 (m_DistanceMap, *fit); bit = ConstNeighborhoodIterator(radius, input, *fit); unsigned int neighborhoodSize = bit.Size(); bit.OverrideBoundaryCondition(&nbc); bit.GoToBegin(); bool bIsOnContour; while ( ! bit.IsAtEnd() ) { // first test // if current pixel is not on, let's continue if( bit.GetCenterPixel() != NumericTraits< InputPixelType >::Zero ) { bIsOnContour = false; for (i = 0; i < neighborhoodSize; ++i) { // second test if at least one neighbour pixel is off // the center pixel belongs to contour if( bit.GetPixel(i) == NumericTraits< InputPixelType >::Zero ) { bIsOnContour = true; break; } } // set pixel center pixel value whether it is or not on contour if( bIsOnContour ) { const RealType value = it2.Get(); m_MeanDistance[threadId] += vnl_math_abs( value ); m_Count[threadId]++; } } ++bit; ++it2; progress.CompletedPixel(); } } } template void ContourDirectedMeanDistanceImageFilter ::PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf(os,indent); os << indent << "ContourDirectedMeanDistance: " << m_ContourDirectedMeanDistance << std::endl; } }// end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkCoolColormapFunctor.h000066400000000000000000000045071147325206600232630ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkCoolColormapFunctor.h,v $ Language: C++ Date: $Date: 2009-05-15 19:08:09 $ Version: $Revision: 1.2 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkCoolColormapFunctor_h #define __itkCoolColormapFunctor_h #include "itkColormapFunctor.h" namespace itk { namespace Functor { /** * \class CoolColormapFunctor * \brief Function object which maps a scalar value into an RGB colormap value. * * * \author Nicholas Tustison, Hui Zhang, Gaetan Lehmann, Paul Yushkevich and ̈James C. Gee * * This code was contributed in the Insight Journal paper: * * "Meeting Andy Warhol Somewhere Over the Rainbow: RGB Colormapping and ITK" * http://www.insight-journal.org/browse/publication/285 * http://hdl.handle.net/1926/1452 * */ template< class TScalar, class TRGBPixel > class ITK_EXPORT CoolColormapFunctor : public ColormapFunctor { public: typedef CoolColormapFunctor Self; typedef ColormapFunctor Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); typedef typename Superclass::RGBPixelType RGBPixelType; typedef typename Superclass::ScalarType ScalarType; typedef typename Superclass::RealType RealType; virtual RGBPixelType operator()( const TScalar & ) const; protected: CoolColormapFunctor() {}; ~CoolColormapFunctor() {}; private: CoolColormapFunctor(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace functor } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkCoolColormapFunctor.txx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkCoolColormapFunctor.txx000066400000000000000000000030601147325206600236500ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkCoolColormapFunctor.txx,v $ Language: C++ Date: $Date: 2009-05-15 02:47:59 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkCoolColormapFunctor_txx #define __itkCoolColormapFunctor_txx #include "itkCoolColormapFunctor.h" namespace itk { namespace Functor { template typename CoolColormapFunctor::RGBPixelType CoolColormapFunctor ::operator()( const TScalar & v ) const { // Map the input scalar between [0, 1]. RealType value = this->RescaleInputValue( v ); // Apply the color mapping. RealType red = value; RealType green = 1.0 - value; RealType blue = 1.0; // Set the rgb components after rescaling the values. RGBPixelType pixel; pixel[0] = this->RescaleRGBComponentValue( red ); pixel[1] = this->RescaleRGBComponentValue( green ); pixel[2] = this->RescaleRGBComponentValue( blue ); return pixel; } } // end namespace Functor } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkCopperColormapFunctor.h000066400000000000000000000045301147325206600236130ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkCopperColormapFunctor.h,v $ Language: C++ Date: $Date: 2009-05-15 19:08:09 $ Version: $Revision: 1.2 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkCopperColormapFunctor_h #define __itkCopperColormapFunctor_h #include "itkColormapFunctor.h" namespace itk { namespace Functor { /** * \class CopperColormapFunctor * \brief Function object which maps a scalar value into an RGB colormap value. * * * \author Nicholas Tustison, Hui Zhang, Gaetan Lehmann, Paul Yushkevich and ̈James C. Gee * * This code was contributed in the Insight Journal paper: * * "Meeting Andy Warhol Somewhere Over the Rainbow: RGB Colormapping and ITK" * http://www.insight-journal.org/browse/publication/285 * http://hdl.handle.net/1926/1452 * */ template< class TScalar, class TRGBPixel > class ITK_EXPORT CopperColormapFunctor : public ColormapFunctor { public: typedef CopperColormapFunctor Self; typedef ColormapFunctor Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); typedef typename Superclass::RGBPixelType RGBPixelType; typedef typename Superclass::ScalarType ScalarType; typedef typename Superclass::RealType RealType; virtual RGBPixelType operator()( const TScalar & ) const; protected: CopperColormapFunctor() {}; ~CopperColormapFunctor() {}; private: CopperColormapFunctor(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace functor } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkCopperColormapFunctor.txx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkCopperColormapFunctor.txx000066400000000000000000000032421147325206600242060ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkCopperColormapFunctor.txx,v $ Language: C++ Date: $Date: 2009-05-15 02:47:59 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkCopperColormapFunctor_txx #define __itkCopperColormapFunctor_txx #include "itkCopperColormapFunctor.h" namespace itk { namespace Functor { template typename CopperColormapFunctor::RGBPixelType CopperColormapFunctor ::operator()( const TScalar & v ) const { // Map the input scalar between [0, 1]. RealType value = this->RescaleInputValue( v ); // Apply the color map. RealType red = 1.2 * value; red = vnl_math_min( 1.0, red ); RealType green = 0.8 * value; RealType blue = 0.5 * value; // Set the rgb components after rescaling the values. RGBPixelType pixel; pixel[0] = this->RescaleRGBComponentValue( red ); pixel[1] = this->RescaleRGBComponentValue( green ); pixel[2] = this->RescaleRGBComponentValue( blue ); return pixel; } } // end namespace Functor } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkCustomColormapFunctor.h000066400000000000000000000057401147325206600236410ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkCustomColormapFunctor.h,v $ Language: C++ Date: $Date: 2009-05-15 19:22:31 $ Version: $Revision: 1.2 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkCustomColormapFunctor_h #define __itkCustomColormapFunctor_h #include "itkColormapFunctor.h" #include namespace itk { namespace Functor { /** * \class CustomColormapFunctor * \brief Function object which maps a scalar value into an RGB colormap value. * * * \author Nicholas Tustison, Hui Zhang, Gaetan Lehmann, Paul Yushkevich and ̈James C. Gee * * This code was contributed in the Insight Journal paper: * * "Meeting Andy Warhol Somewhere Over the Rainbow: RGB Colormapping and ITK" * http://www.insight-journal.org/browse/publication/285 * http://hdl.handle.net/1926/1452 * */ template< class TScalar, class TRGBPixel > class ITK_EXPORT CustomColormapFunctor : public ColormapFunctor { public: typedef CustomColormapFunctor Self; typedef ColormapFunctor Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); typedef typename Superclass::RGBPixelType RGBPixelType; typedef typename Superclass::ScalarType ScalarType; typedef typename Superclass::RealType RealType; typedef std::vector ChannelType; virtual RGBPixelType operator()( const TScalar & ) const; void SetRedChannel( ChannelType red ) { m_RedChannel = red; } ChannelType GetRedChannel() const { return m_RedChannel; } void SetGreenChannel( ChannelType green ) { m_GreenChannel = green; } ChannelType GetGreenChannel() const { return m_GreenChannel; } void SetBlueChannel( ChannelType blue ) { m_BlueChannel = blue; } ChannelType GetBlueChannel() const { return m_BlueChannel; } protected: CustomColormapFunctor() {}; ~CustomColormapFunctor() {}; private: CustomColormapFunctor(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented ChannelType m_RedChannel; ChannelType m_GreenChannel; ChannelType m_BlueChannel; }; } // end namespace functor } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkCustomColormapFunctor.txx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkCustomColormapFunctor.txx000066400000000000000000000062661147325206600242410ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkCustomColormapFunctor.txx,v $ Language: C++ Date: $Date: 2009-05-15 02:47:59 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkCustomColormapFunctor_txx #define __itkCustomColormapFunctor_txx #include "itkCustomColormapFunctor.h" namespace itk { namespace Functor { template typename CustomColormapFunctor::RGBPixelType CustomColormapFunctor ::operator()( const TScalar & v ) const { // Map the input scalar between [0, 1]. RealType value = this->RescaleInputValue( v ); // Apply the color mapping. RealType red = 0.0; if ( this->m_RedChannel.size() == 1 || value == 0.0 ) { red = this->m_RedChannel[0]; } else if ( this->m_RedChannel.size() > 1 ) { RealType size = static_cast( this->m_RedChannel.size() ); RealType index = vcl_ceil( value * ( size - 1.0 ) ); RealType p1 = this->m_RedChannel[static_cast( index )]; RealType m1 = this->m_RedChannel[static_cast( index-1 )]; RealType d = p1 - m1; red = d * ( size - 1.0 ) * ( value - ( index - 1.0 )/( size - 1.0 ) ) + m1; } RealType green = 0.0; if ( this->m_GreenChannel.size() == 1 || value == 0.0 ) { green = this->m_GreenChannel[0]; } else if ( this->m_GreenChannel.size() > 1 ) { RealType size = static_cast( this->m_GreenChannel.size() ); RealType index = vcl_ceil( value * ( size - 1.0 ) ); RealType p1 = this->m_GreenChannel[static_cast( index )]; RealType m1 = this->m_GreenChannel[static_cast( index-1 )]; RealType d = p1 - m1; green = d * ( size - 1.0 ) * ( value - ( index - 1.0 )/( size - 1.0 ) ) + m1; } RealType blue = 0.0; if ( this->m_BlueChannel.size() == 1 || value == 0.0 ) { blue = this->m_BlueChannel[0]; } else if ( this->m_BlueChannel.size() > 1 ) { RealType size = static_cast( this->m_BlueChannel.size() ); RealType index = vcl_ceil( value * ( size - 1.0 ) ); RealType p1 = this->m_BlueChannel[static_cast( index )]; RealType m1 = this->m_BlueChannel[static_cast( index-1 )]; RealType d = p1 - m1; blue = d * ( size - 1.0 ) * ( value - ( index - 1.0 )/( size - 1.0 ) ) + m1; } // Set the rgb components after rescaling the values. RGBPixelType pixel; pixel[0] = this->RescaleRGBComponentValue( red ); pixel[1] = this->RescaleRGBComponentValue( green ); pixel[2] = this->RescaleRGBComponentValue( blue ); return pixel; } } // end namespace Functor } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkDensityFunction.h000066400000000000000000000043671147325206600224620ustar00rootroot00000000000000/*========================================================================= Program: Insight Segmentation & Registration Toolkit Module: $RCSfile: itkDensityFunction.h,v $ Language: C++ Date: $Date: 2009-03-04 15:23:46 $ Version: $Revision: 1.11 $ Copyright (c) Insight Software Consortium. All rights reserved. See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkDensityFunction_h #define __itkDensityFunction_h #include "itkMembershipFunctionBase.h" #include "itkMeasurementVectorTraits.h" namespace itk { namespace Statistics { /** \class DensityFunction * \brief DensityFunction class defines common interfaces for * density functions. * * The Evaluate method returns density value for the input measurement vector. * The length of the measurement vector must be set using * \c SetMeasurementVectorSize() before using the class. */ template< class TMeasurementVector > class ITK_EXPORT DensityFunction : public MembershipFunctionBase< TMeasurementVector > { public: /** Standard class typedefs */ typedef DensityFunction Self; typedef MembershipFunctionBase< TMeasurementVector > Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Strandard macros */ itkTypeMacro(DensityFunction, MembershipFunctionBase); /** Length of each measurement vector */ typedef unsigned int MeasurementVectorSizeType; /** Method to get probability of an instance. The return value is the * value of the density function, not probability. */ virtual double Evaluate(const TMeasurementVector &measurement) const = 0; protected: DensityFunction() {} virtual ~DensityFunction() {} void PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf(os,indent); } private: }; // end of class } // end of namespace Statistics } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkDijkstrasAlgorithm.cxx000077500000000000000000000200071147325206600235050ustar00rootroot00000000000000/*========================================================================= Program: Insight Segmentation & Registration Toolkit (ITK) Module: $RCSfile: itkDijkstrasAlgorithm.cxx,v $ Language: C++ Date: $Date: 2008/06/05 18:39:14 $ Version: $Revision: 1.8 $ =========================================================================*/ #ifndef _itkDijkstrasAlgorithm_cxx_ #define _itkDijkstrasAlgorithm_cxx_ #include "itkDijkstrasAlgorithm.h" namespace itk { template DijkstrasAlgorithm::DijkstrasAlgorithm() { m_Graph = GraphType::New(); m_QS = DijkstrasAlgorithmQueue::New(); m_MaxCost=vnl_huge_val(m_MaxCost); // not defined for unsigned char this->m_TotalCost=0; }; template void DijkstrasAlgorithm::SetGraphSize(GraphSizeType Sz) { for (int i=0; i < GraphDimension; i++) { m_GraphSize[i]=Sz[i]; } } template void DijkstrasAlgorithm::InitializeGraph() { bool InBorder=false; m_GraphRegion.SetSize( m_GraphSize ); m_Graph->SetLargestPossibleRegion( m_GraphRegion ); m_Graph->SetRequestedRegion( m_GraphRegion ); m_Graph->SetBufferedRegion( m_GraphRegion ); m_Graph->Allocate(); GraphIteratorType GraphIterator( m_Graph, m_GraphRegion ); GraphIterator.GoToBegin(); NodeLocationType loc; while( !GraphIterator.IsAtEnd() ) { typename GraphSearchNode::Pointer G=NULL; GraphIterator.Set(G); ++GraphIterator; /* m_GraphIndex = GraphIterator.GetIndex(); //std::cout << " allocating " << m_GraphIndex << std::endl; ///GraphSearchNode::Pointer G= G=TGraphSearchNode::New(); G->SetUnVisited(); G->SetTotalCost(m_MaxCost); for (int i=0; iSetLocation(loc); G->SetPredecessor(NULL); m_Graph->SetPixel(m_GraphIndex,G);*/ m_Graph->SetPixel( GraphIterator.GetIndex(),NULL); // USE IF POINTER IMAGE defines visited } m_SearchFinished=false; } template void DijkstrasAlgorithm::InitializeQueue() { int n = m_QS->m_SourceNodes.size(); GraphIteratorType GraphIterator( m_Graph, m_GraphRegion ); m_GraphSize=m_Graph->GetLargestPossibleRegion().GetSize(); GraphIterator.GoToBegin(); m_GraphIndex = GraphIterator.GetIndex(); NodeLocationType loc; // make sure the graph contains the right pointers for (int i=0; i::Pointer G = m_QS->m_SourceNodes[i]; G->SetPredecessor(G); m_QS->m_Q.push(G); loc=G->GetLocation(); for (int d=0;dSetPixel(m_GraphIndex,G); } for (int i=0; im_SinkNodes.size(); i++) { typename GraphSearchNode::Pointer G = m_QS->m_SinkNodes[i]; G->SetPredecessor(NULL); loc=G->GetLocation(); for (int d=0;dSetPixel(m_GraphIndex,G); } m_SearchFinished=false; if (m_EdgeTemplate.empty()) InitializeEdgeTemplate(); } template void DijkstrasAlgorithm::InitializeEdgeTemplate (vector < unsigned int > UserEdgeTemplate,unsigned int R) { for (int i=0; i void DijkstrasAlgorithm::InitializeEdgeTemplate() { int MaxIndex=1; for (int i=0; i typename DijkstrasAlgorithm:: PixelType DijkstrasAlgorithm::LocalCost() { return 1.0; // manhattan distance }; template bool DijkstrasAlgorithm::TerminationCondition() { if (!m_QS->m_SinkNodes.empty()) { if (m_NeighborNode == m_QS->m_SinkNodes[0] && !m_SearchFinished ) { m_SearchFinished=true; m_NeighborNode->SetPredecessor(m_CurrentNode); } } else m_SearchFinished=true; return m_SearchFinished; } template void DijkstrasAlgorithm::SearchEdgeSet() { //std::cout << " SES 0 " << std::endl; int i=0,j=0; GraphNeighborhoodIteratorType GHood(m_Radius, m_Graph,m_Graph->GetRequestedRegion()); GraphNeighborhoodIndexType GNI; //std::cout << " SES 1 " << std::endl; for (i=0; i < GraphDimension; i++) { //std::cout << " SES 2 " << std::endl; GNI[i]=(long)(m_CurrentNode->GetLocation()[i]+0.5); } //std::cout << " SES 3 " << std::endl; GHood.SetLocation(GNI); bool inimage=true; for (unsigned int dd=0; dd (unsigned long)(m_GraphSize[dd]-2)) return; } for (i = 0; i < m_EdgeTemplate.size(); i++) { //std::cout << " SES 4 " << std::endl; //std::cout << " ET " << m_EdgeTemplate[i] << " RAD " << m_Radius << " ind " << GHood.GetIndex(m_EdgeTemplate[i]) << std::endl; if (!GHood.GetPixel(m_EdgeTemplate[i])) //std::cout << " OK " << std::endl; ///else { // std::cout << " NOT OK " <SetUnVisited(); G->SetTotalCost(m_MaxCost); NodeLocationType loc; for (int i=0; iSetLocation(loc); G->SetPredecessor(m_CurrentNode); m_Graph->SetPixel(ind,G); } m_NeighborNode=GHood.GetPixel(m_EdgeTemplate[i]); //std::cout << "DA i " << i << " position " << m_NeighborNode->GetLocation() << endl; this->TerminationCondition(); if (!m_SearchFinished && m_CurrentNode != m_NeighborNode && !m_NeighborNode->GetDelivered()) { m_NewCost = m_CurrentCost + LocalCost(); CheckNodeStatus(); } } } template void DijkstrasAlgorithm::CheckNodeStatus() // checks a graph neighbor's status { if (!m_NeighborNode->GetVisited() ) { // set the cost and put into the queue m_NeighborNode->SetTotalCost(m_NewCost); m_NeighborNode->SetPredecessor(m_CurrentNode); m_NeighborNode->SetVisited(); m_QS->m_Q.push(m_NeighborNode); } else if (m_NewCost < m_NeighborNode->GetTotalCost() ) { m_NeighborNode->SetTotalCost(m_NewCost); m_NeighborNode->SetPredecessor(m_CurrentNode); m_QS->m_Q.push(m_NeighborNode); } } template void DijkstrasAlgorithm::FindPath() { if (m_QS->m_SourceNodes.empty()) { std::cout << "ERROR !! DID NOT SET SOURCE!!\n"; return; } //std::cout << "DA start find path " << " Q size " << m_QS->m_Q.size() << " \n"; while ( !m_SearchFinished && !m_QS->m_Q.empty() ) { m_CurrentNode=m_QS->m_Q.top(); m_CurrentCost=m_CurrentNode->GetTotalCost(); m_QS->m_Q.pop(); if (!m_CurrentNode->GetDelivered()) { m_QS->IncrementTimer(); //std::cout << " searching " << m_CurrentNode->GetLocation() << " \n"; this->SearchEdgeSet(); this->m_TotalCost+=m_CurrentNode->GetTotalCost(); //if ( (m_CurrentNode->GetTimer() % 1.e5 ) == 0) // std::cout << " searched " << m_CurrentNode->GetTimer() << " \n"; } m_CurrentNode->SetDelivered(); } // end of while m_NumberSearched = (unsigned long) m_QS->GetTimer(); std::cout << "Done with find path " << " Q size " << m_QS->m_Q.size() << " num searched " << m_NumberSearched << " \n"; return; } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkDijkstrasAlgorithm.h000077500000000000000000000435401147325206600231410ustar00rootroot00000000000000/*========================================================================= Program: Insight Segmentation & Registration Toolkit (ITK) Module: $RCSfile: itkDijkstrasAlgorithm.h,v $ Language: C++ Date: $Date: 2008/06/05 18:39:14 $ Version: $Revision: 1.8 $ Copyright (c) 2001 Insight Consortium All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * The name of the Insight Consortium, nor the names of any consortium members, nor of any contributors, may be used to endorse or promote products derived from this software without specific prior written permission. * Modified source versions must be plainly marked as such, and must not be misrepresented as being the original software. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDER AND CONTRIBUTORS ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHORS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. =========================================================================*/ #ifndef _itkDijkstrasAlgorithm_h_ #define _itkDijkstrasAlgorithm_h_ #include #include #include #include #include #include #include #include "vnl/vnl_math.h" //#include "vnl/vnl_matrix_fixed.h" //#include "vnl/vnl_vector_fixed.h" #include "itkImage.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkNeighborhoodIterator.h" #include "itkVector.h" using namespace std; namespace itk { /** The GraphSearchNode class defines a general shortest path graph node. * The algorithm requires * the node to have a pointer to itself and entry for the cumulative cost. * We also define an index to its location and a couple of booleans * for keeping track of the graph node's state. * We assume the connectivity between nodes is defined externally. * The class will also be useful for minimum spanning trees and * other graph search algorithms. Connectivity is defined externally. * May be worthwhile to implement arbitrary connectivity e.g. for random graphs. * One way to do this is to include a list of pointers which define * the neighbors of this node, similar to how the predecessor is defined. */ template class GraphSearchNode : public itk::LightObject { public: /* Standard typedefs.*/ typedef GraphSearchNode Self; typedef LightObject Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; itkTypeMacro(GraphSearchNode,LightObject); itkNewMacro(Self); /** Method for creation through the object factory. */ enum StateType {UnVisitedState, VisitedState, DeliveredState, UnVisitableState }; enum{GraphDimension=NGraphDimension}; typedef TPixelType PixelType; /** defines the cost data type */ typedef TCoordRep CoordRep; /** defines the location data type */ typedef itk::Vector NodeLocationType; // typedef typename itk::Image::IndexType NodeLocationType; typedef vector < Pointer > NodeListType; // typedef itk::Image::IndexType NodeLocationType; inline void SetLocation(NodeLocationType loc) { m_Location=loc; } inline NodeLocationType GetLocation() { return m_Location; } inline void SetTotalCost(TPixelType cost) { m_TotalCost=cost; } inline void SetValue(TPixelType cost,int which=0) { if (which <= 0) m_Value1=cost; if (which == 1) m_Value2=cost; if (which == 2) m_Value3=cost; if (which >= 3) m_Value4=cost; } inline TPixelType GetValue(int which=0) { if (which <= 0) return m_Value1; if (which == 1) return m_Value2; if (which == 2) return m_Value3; if (which >= 3) return m_Value4; return m_Value1; } inline void SetUnVisited() { m_State=UnVisitedState; } inline void SetUnVisitable() { m_State=UnVisitableState; } inline void SetVisited() { m_State=VisitedState; } inline void SetDelivered() { m_State=DeliveredState; } inline bool IsInQueue() { if ( m_State == VisitedState ) return true; else return false; } inline bool WasVisited() { if ( m_State == VisitedState ) return true; else if ( m_State == DeliveredState ) return true; else return false; } inline TPixelType GetTotalCost() { return m_TotalCost; } inline void SetPredecessor(Pointer address) { m_PredecessorAddress=address; } inline Pointer GetPredecessor() { return m_PredecessorAddress; } inline void SetAncestor(Pointer address) { m_AncestorAddress=address; } inline Pointer GetAncestor() { return m_AncestorAddress; } inline bool GetUnVisited() { if (m_State==UnVisitedState) return true; else return false; } inline bool GetUnVisitable() { if (m_State==UnVisitableState) return true; else return false; } inline bool GetVisited() { if (m_State==VisitedState) return true; else return false; } inline bool GetDelivered() { if (m_State==DeliveredState) return true; else return false; } inline void SetState(StateType S) { m_State=S; } inline StateType GetState() { return m_State; } inline void SetIdentity(unsigned int i) { m_Identity=i; } inline unsigned int GetIdentity() { return m_Identity; } inline int GetNumberOfNeighbors() { return m_Neighbors.size(); } inline Pointer GetNeighbor(int i) { return m_Neighbors[i]; } void SetNeighborSize(int i){m_Neighbors.resize(i);} NodeListType m_Neighbors; unsigned short m_NumberOfNeighbors; unsigned int m_Identity; protected: GraphSearchNode() { m_TotalCost=0.0; m_Value1=0.0; m_Value2=0.0; m_Value3=0.0; m_Value4=0.0; m_PredecessorAddress=NULL; m_AncestorAddress=NULL; m_State=UnVisitedState; m_NumberOfNeighbors=0; m_Identity=0; } ~GraphSearchNode(){} private: TPixelType m_TotalCost; /** keeps track of the minimum accumulated cost. */ TPixelType m_Value1; /** keeps track of the minimum accumulated cost. */ TPixelType m_Value2; /** keeps track of the minimum accumulated cost. */ TPixelType m_Value3; /** keeps track of the minimum accumulated cost. */ TPixelType m_Value4; /** keeps track of the minimum accumulated cost. */ StateType m_State; NodeLocationType m_Location; /** provides the location in the graph. */ Pointer m_PredecessorAddress;/** provides the best predecessor address */ Pointer m_AncestorAddress;/** provides the best predecessor address */ GraphSearchNode(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; // Forward declaration of DijkstrasAlgorithm so it can be declared a friend template class DijkstrasAlgorithm; template class DijkstrasAlgorithmQueue : public itk::LightObject /** \class DijkstrasAlgorithmQueue the class containing the priority queue and associated data. */ { private: template class GraphSearchNodePriority /* defines the comparison operator for the prioritiy queue */ { public: bool operator() ( typename G::Pointer N1, typename G::Pointer N2) { return N1->GetTotalCost() > N2->GetTotalCost(); } }; public: /* Standard typedefs.*/ typedef DijkstrasAlgorithmQueue Self; typedef LightObject Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; itkTypeMacro(DijkstrasAlgorithmQueue,LightObject); itkNewMacro(Self); /** Method for creation through the object factory. */ typedef typename TGraphSearchNode::Pointer TGraphSearchNodePointer; typedef typename TGraphSearchNode::PixelType PixelType; /** pixel type for the cost */ typedef typename TGraphSearchNode::CoordRep CoordRep; /** type for coordinates */ typedef typename std::priority_queue< typename TGraphSearchNode::Pointer,std::vector< typename TGraphSearchNode::Pointer>, GraphSearchNodePriority >QType; /** the queue we are using */ typedef vector < typename TGraphSearchNode::Pointer > NodeListType; inline QType GetQ() { return m_Q; } void AddToPath(TGraphSearchNodePointer G) { this->m_Path.push_back(G); } inline NodeListType GetPath() { return m_Path; } void EmptyPath() { m_Path.clear(); } inline NodeListType GetSourceNodes() { return m_SourceNodes; } inline NodeListType GetSinkNodes() { return m_SinkNodes; } inline void IncrementTimer() { m_timer++;} inline long GetTimer() { return m_timer; } inline void EmptyQ() { while (!m_Q.empty()) m_Q.pop(); m_timer=0; m_SourceNodes.clear(); m_SinkNodes.clear(); } NodeListType m_SinkNodes; NodeListType m_SourceNodes; QType m_Q; NodeListType m_Path; protected: friend class DijkstrasAlgorithm; // so it can access this data easily DijkstrasAlgorithmQueue() { m_timer=0; } ~DijkstrasAlgorithmQueue() {} private: unsigned long m_timer; DijkstrasAlgorithmQueue(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; /** * \class DijkstrasAlgorithm * \brief General shortest path / greedy dynamic programming solver. * * This class uses Dijkstra's algorithm to solve the shortest path problem. * We use the stl priority queue which is not optimal for this problem, but which * works o.k. It's implemented to be used for general regularly connected * graphs with fixed costs, or for the variational solution of an integral * curve matching energy. * Note: we assume all edge weights are positive. * The class is implemented as a an abstract base class, with virtual functions for * LocalCost, SearchEdgeSet and FindPath. LocalCost must be implemented by derived classes. * The connectivity of the graph defined * here is always regular and is controlled by a set of neighborhood indices. * the default is a radius 1 neighborhood with all entries used. However, the * user may also supply her own regular connectivity by selecting the size of * the neighborhood and a subset of the indices which define the edges. If * the GraphSearchNode contains its edges, they may be used with minor modification * to the function SearchEdgeSet. * Another improvement would be to make the LocalCost function a pointer * to a function which could be set. */ template class DijkstrasAlgorithm : public itk::LightObject { public: typedef DijkstrasAlgorithm Self; typedef LightObject Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; itkTypeMacro(DijkstrasAlgorithm,LightObject); itkNewMacro(Self); // Computation Data typedef TGraphSearchNode SearchNode; /** dimension of the graph */ typedef typename SearchNode::Pointer SearchNodePointer; enum{GraphDimension=SearchNode::GraphDimension}; /** dimension of the graph */ typedef typename SearchNode::PixelType PixelType; /** pixel type for the cost */ typedef typename SearchNode::CoordRep CoordRep; /** coordinate type */ typedef Image GraphType; typedef typename GraphType::SizeType GraphSizeType; typedef ImageRegionIteratorWithIndex GraphIteratorType; typedef typename GraphType::RegionType GraphRegionType; typedef typename DijkstrasAlgorithmQueue::Pointer QType; typedef typename DijkstrasAlgorithmQueue::NodeListType NodeListType; typedef itk::NeighborhoodIterator GraphNeighborhoodIteratorType; typedef typename GraphNeighborhoodIteratorType::IndexType GraphNeighborhoodIndexType; typedef typename GraphNeighborhoodIteratorType::RadiusType RadiusType; typedef typename TGraphSearchNode::NodeLocationType NodeLocationType; typedef typename GraphType::IndexType IndexType; // FUNCTIONS void InitializeGraph(); /** initializes all graph values appropriately */ void InitializeQueue(); /** initializes all queue values appropriately call AFTER source and sink are set*/ void InitializeEdgeTemplate(); /** helper function initializes edge set appropriately */ void InitializeEdgeTemplate(vector < unsigned int >,unsigned int); /** user supplied edge template */ void SetGraphSize(typename GraphType::SizeType Sz); /** the rectangular size of the graph */ inline void EmptyQ() { m_QS->EmptyQ(); this->m_TotalCost=0; } /* adds a source to the source set */ void SetSource(typename TGraphSearchNode::Pointer G) { m_QS->m_SourceNodes.push_back(G); for (int i=0; iGetLocation()[i]+0.5); // std::cout << " mgi " << m_GraphIndex[i]; } m_Graph->SetPixel(m_GraphIndex,G); }; typename TGraphSearchNode::Pointer GetGraphNode( IndexType index) { // std::cout << " get node " << index << std::endl; return m_Graph->GetPixel(index); }; // adds a sink to the sink set void SetSink(typename TGraphSearchNode::Pointer G) { m_QS->m_SinkNodes.push_back(G); } // Backtracks from the given node to its source node; void BackTrack(typename TGraphSearchNode::Pointer SinkNode) { m_QS->m_Path.clear(); typename TGraphSearchNode::Pointer G=SinkNode; typename TGraphSearchNode::Pointer P=SinkNode->GetPredecessor(); if (!P || !G) return; float highcost=G->GetValue(); if (G->GetTotalCost() > P->GetValue()) { P->SetAncestor(G); P->SetValue(G->GetTotalCost()); highcost=G->GetTotalCost(); } while(P && G != P) { m_QS->m_Path.push_back(G); G=P; P=G->GetPredecessor(); if (P->GetValue() < highcost) { P->SetValue(highcost); P->SetAncestor(G); } } if (!P) cout << " null pred "; //else cout << " pred == self \n"; return; } // Inverse of backtrack - from the given node to its sink node; void ForwardTrack(typename TGraphSearchNode::Pointer SinkNode) { typename TGraphSearchNode::Pointer G=SinkNode; typename TGraphSearchNode::Pointer P=SinkNode->GetAncestor(); while(P && G != P && G) { if (P->GetValue() > G->GetValue()) G->SetValue(P->GetValue()); if (G->GetValue() > P->GetValue()) P->SetValue(G->GetValue()); G=P; P=G->GetAncestor(); } return; } virtual bool TerminationCondition(); /** decides when the algorithm stops */ virtual void SearchEdgeSet(); /** loops over the neighbors in the graph */ void CheckNodeStatus(); /** checks if the node has been explored already, its cost, etc. */ virtual PixelType LocalCost(); /* computes the local cost */ /* alternatively, we could pass the function as a template parameter or set a function pointer. the latter method is used in dijkstrasegment. */ virtual void FindPath(); /* runs the algorithm */ inline unsigned int GetPathSize() { return m_QS->m_Path.size(); } inline typename TGraphSearchNode::Pointer GetPathAtIndex(unsigned int i) { return m_QS->m_Path[i]; } inline typename TGraphSearchNode::Pointer GetNeighborNode() { return m_NeighborNode; } inline typename TGraphSearchNode::Pointer GetCurrentNode() { return m_CurrentNode; } void SetMaxCost(PixelType m){ m_MaxCost=m;} double GetTotalCost() { return m_TotalCost; } void SetSearchFinished(bool m){ m_SearchFinished=m;} /** sets the boolean that indicates if the algorithm is done */ protected: QType m_QS; vector < unsigned int > m_EdgeTemplate;/** defines neighborhood connectivity */ RadiusType m_Radius; /** used by the neighborhood iterator */ typename TGraphSearchNode::Pointer m_PredecessorNode; /** holds the predecessor node */ typename TGraphSearchNode::Pointer m_CurrentNode; /** holds the current node */ typename TGraphSearchNode::Pointer m_NeighborNode; /** holds the current neighbor node */ typename GraphType::Pointer m_Graph; /** holds all the graph information */ GraphRegionType m_GraphRegion; GraphSizeType m_GraphSize; /** rectangular size of graph */ typename GraphType::IndexType m_GraphIndex; bool m_SearchFinished; PixelType m_NewCost; PixelType m_CurrentCost; PixelType m_MaxCost; // This creates an insurmountable barrier unless all costs are max double m_TotalCost; unsigned long m_NumberSearched; DijkstrasAlgorithm(); ~DijkstrasAlgorithm(){}; private: DijkstrasAlgorithm(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkDijkstrasAlgorithm.cxx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkDirectedHausdorffDistanceImageFilter.h000066400000000000000000000147251147325206600265050ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkDirectedHausdorffDistanceImageFilter.h,v $ Language: C++ Date: $Date: 2009-04-25 12:27:21 $ Version: $Revision: 1.11 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkDirectedHausdorffDistanceImageFilter_h #define __itkDirectedHausdorffDistanceImageFilter_h #include "itkImageToImageFilter.h" #include "itkNumericTraits.h" #include "itkArray.h" #include "itkImage.h" namespace itk { /** \class DirectedHausdorffDistanceImageFilter * \brief Computes the directed Hausdorff distance between the set of * non-zero pixels of two images. * * DirectedHausdorffDistanceImageFilter computes the distance between the set * non-zero pixels of two images using the following formula: * \f[ h(A,B) = \max_{a \in A} \min_{b \in B} \| a - b\| \f] * where \f$A\f$ and \f$B\f$ are respectively the set of non-zero pixels * in the first and second input images. It identifies the point \f$ a \in A \f$ * that is farthest from any point of \f$B\f$ and measures the distance from \f$a\f$ * to the nearest neighbor in \f$B\f$. Note that this function is not * is not symmetric and hence is not a true distance. * * In particular, this filter uses the DanielssonDistanceMapImageFilter inside to * compute distance map from all non-zero pixels in the second image. It then * find the largest distance (in pixels) within the set of all non-zero pixels in the first * image. * * Use HausdorffDistanceImageFilter to compute the full Hausdorff distance. * * This filter requires the largest possible region of the first image * and the same corresponding region in the second image. * It behaves as filter with * two input and one output. Thus it can be inserted in a pipeline with * other filters. The filter passes the first input through unmodified. * * This filter is templated over the two input image type. It assume * both image have the same number of dimensions. * * \sa DanielssonDistanceMapImageFilter * \sa HausdorffDistanceImageFilter * * \ingroup MultiThreaded */ template class ITK_EXPORT DirectedHausdorffDistanceImageFilter : public ImageToImageFilter { public: /** Standard Self typedef */ typedef DirectedHausdorffDistanceImageFilter Self; typedef ImageToImageFilter Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Runtime information support. */ itkTypeMacro(DirectedHausdorffDistanceImageFilter, ImageToImageFilter); /** Image related typedefs. */ typedef TInputImage1 InputImage1Type; typedef TInputImage2 InputImage2Type; typedef typename TInputImage1::Pointer InputImage1Pointer; typedef typename TInputImage2::Pointer InputImage2Pointer; typedef typename TInputImage1::ConstPointer InputImage1ConstPointer; typedef typename TInputImage2::ConstPointer InputImage2ConstPointer; typedef typename TInputImage1::RegionType RegionType; typedef typename TInputImage1::SizeType SizeType; typedef typename TInputImage1::IndexType IndexType; typedef typename TInputImage1::PixelType InputImage1PixelType; typedef typename TInputImage2::PixelType InputImage2PixelType; /** Image related typedefs. */ itkStaticConstMacro(ImageDimension, unsigned int, TInputImage1::ImageDimension); /** Type to use form computations. */ typedef typename NumericTraits::RealType RealType; /** Set the first input. */ void SetInput1( const InputImage1Type * image ) { this->SetInput( image ); } /** Set the second input. */ void SetInput2( const InputImage2Type * image ); /** Get the first input. */ const InputImage1Type * GetInput1(void) { return this->GetInput(); } /** Get the second input. */ const InputImage2Type * GetInput2(void); /** Return the computed directed Hausdorff distance. */ itkGetConstMacro(DirectedHausdorffDistance,RealType); itkGetConstMacro(AverageHausdorffDistance,RealType); #ifdef ITK_USE_CONCEPT_CHECKING /** Begin concept checking */ itkConceptMacro(InputHasNumericTraitsCheck, (Concept::HasNumericTraits)); /** End concept checking */ #endif protected: DirectedHausdorffDistanceImageFilter(); ~DirectedHausdorffDistanceImageFilter(){}; void PrintSelf(std::ostream& os, Indent indent) const; /** Pass the input through unmodified. Do this by Grafting in the * AllocateOutputs method. */ void AllocateOutputs(); /** Initialize some accumulators before the threads run. */ void BeforeThreadedGenerateData (); /** Do final mean and variance computation from data accumulated in threads. */ void AfterThreadedGenerateData (); /** Multi-thread version GenerateData. */ void ThreadedGenerateData (const RegionType& outputRegionForThread, int threadId); // Override since the filter needs all the data for the algorithm void GenerateInputRequestedRegion(); // Override since the filter produces all of its output void EnlargeOutputRequestedRegion(DataObject *data); private: DirectedHausdorffDistanceImageFilter(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented typedef Image DistanceMapType; typename DistanceMapType::Pointer m_DistanceMap; Array m_MaxDistance; Array m_PixelCount; Array m_Sum; RealType m_DirectedHausdorffDistance; RealType m_AverageHausdorffDistance; }; // end of class } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkDirectedHausdorffDistanceImageFilter.txx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkDirectedHausdorffDistanceImageFilter.txx000066400000000000000000000143151147325206600270740ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkDirectedHausdorffDistanceImageFilter.txx,v $ Language: C++ Date: $Date: 2008-10-14 19:20:33 $ Version: $Revision: 1.14 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkDirectedHausdorffDistanceImageFilter_txx #define __itkDirectedHausdorffDistanceImageFilter_txx #include "itkDirectedHausdorffDistanceImageFilter.h" #include "itkImageRegionIterator.h" #include "itkImageRegionConstIterator.h" #include "itkNumericTraits.h" #include "itkSignedMaurerDistanceMapImageFilter.h" #include "itkProgressReporter.h" namespace itk { template DirectedHausdorffDistanceImageFilter ::DirectedHausdorffDistanceImageFilter(): m_MaxDistance(1) { // this filter requires two input images this->SetNumberOfRequiredInputs( 2 ); m_DistanceMap = NULL; m_DirectedHausdorffDistance = NumericTraits::Zero; m_AverageHausdorffDistance = NumericTraits::Zero; } template void DirectedHausdorffDistanceImageFilter ::SetInput2( const TInputImage2 * image ) { this->SetNthInput(1, const_cast( image ) ); } template const typename DirectedHausdorffDistanceImageFilter ::InputImage2Type * DirectedHausdorffDistanceImageFilter ::GetInput2() { return static_cast< const TInputImage2 * > (this->ProcessObject::GetInput(1)); } template void DirectedHausdorffDistanceImageFilter ::GenerateInputRequestedRegion() { Superclass::GenerateInputRequestedRegion(); // this filter requires: // - the largeset possible region of the first image // - the corresponding region of the second image if ( this->GetInput1() ) { InputImage1Pointer image1 = const_cast< InputImage1Type * >( this->GetInput1() ); image1->SetRequestedRegionToLargestPossibleRegion(); if ( this->GetInput2() ) { InputImage2Pointer image2 = const_cast< InputImage2Type * >( this->GetInput2() ); image2->SetRequestedRegion( this->GetInput1()->GetRequestedRegion() ); } } } template void DirectedHausdorffDistanceImageFilter ::EnlargeOutputRequestedRegion(DataObject *data) { Superclass::EnlargeOutputRequestedRegion(data); data->SetRequestedRegionToLargestPossibleRegion(); } template void DirectedHausdorffDistanceImageFilter ::AllocateOutputs() { // Pass the first input through as the output InputImage1Pointer image = const_cast< TInputImage1 * >( this->GetInput1() ); this->GraftOutput( image ); } template void DirectedHausdorffDistanceImageFilter ::BeforeThreadedGenerateData() { int numberOfThreads = this->GetNumberOfThreads(); // Resize the thread temporaries m_MaxDistance.SetSize(numberOfThreads); m_PixelCount.SetSize(numberOfThreads); m_Sum.SetSize(numberOfThreads); // Initialize the temporaries m_MaxDistance.Fill(NumericTraits::Zero); m_PixelCount.Fill(0); m_Sum.Fill(NumericTraits::Zero); // Compute distance map from non-zero pixels in the second image typedef itk::SignedMaurerDistanceMapImageFilter FilterType; typename FilterType::Pointer filter = FilterType::New(); filter->SetInput( this->GetInput2() ); filter->SetUseImageSpacing( false ); filter->Update(); m_DistanceMap = filter->GetOutput(); } template void DirectedHausdorffDistanceImageFilter ::AfterThreadedGenerateData() { int i; int numberOfThreads = this->GetNumberOfThreads(); m_DirectedHausdorffDistance = NumericTraits::Zero; RealType sum = NumericTraits::Zero; unsigned int pixelcount = 0; // find max over all threads for( i = 0; i < numberOfThreads; i++) { if ( m_MaxDistance[i] > m_DirectedHausdorffDistance ) { m_DirectedHausdorffDistance = m_MaxDistance[i]; } pixelcount += m_PixelCount[i]; sum += m_Sum[i]; } m_AverageHausdorffDistance = sum / (RealType) pixelcount; // clean up m_DistanceMap = NULL; } template void DirectedHausdorffDistanceImageFilter ::ThreadedGenerateData(const RegionType& regionForThread, int threadId) { ImageRegionConstIterator it1 (this->GetInput1(), regionForThread); ImageRegionConstIterator it2 (m_DistanceMap, regionForThread); // support progress methods/callbacks ProgressReporter progress(this, threadId, regionForThread.GetNumberOfPixels()); // do the work while (!it1.IsAtEnd()) { if( it1.Get() != NumericTraits::Zero ) { if ( it2.Get() > m_MaxDistance[threadId] ) { m_MaxDistance[threadId] = it2.Get(); } m_PixelCount[threadId]++; m_Sum[threadId] += it2.Get(); } ++it1; ++it2; progress.CompletedPixel(); } } template void DirectedHausdorffDistanceImageFilter ::PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf(os,indent); os << indent << "DirectedHausdorffDistance: " << m_DirectedHausdorffDistance << std::endl; os << indent << "AverageHausdorffDistance: " << m_AverageHausdorffDistance << std::endl; } }// end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkDivideByConstantImageFilter.h000066400000000000000000000113411147325206600246450ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkDivideByConstantImageFilter.h,v $ Language: C++ Date: $Date: 2009-02-24 19:03:15 $ Version: $Revision: 1.4 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkDivideByConstantImageFilter_h #define __itkDivideByConstantImageFilter_h #include "itkUnaryFunctorImageFilter.h" #include "itkNumericTraits.h" namespace itk { /** \class DivideByConstantImageFilter * * \brief Divide input pixels by a constant. * * This filter is templated over the input image type * and the output image type. * * \author Tom Vercauteren, INRIA & Mauna Kea Technologies * * This implementation was taken from the Insight Journal paper: * http://hdl.handle.net/1926/510 * * \ingroup IntensityImageFilters Multithreaded * \sa UnaryFunctorImageFilter */ namespace Functor { template< class TInput, class TConstant, class TOutput> class DivideByConstant { public: DivideByConstant() : m_Constant(NumericTraits::One) {}; ~DivideByConstant() {}; bool operator!=( const DivideByConstant & other ) const { return !(*this == other); } bool operator==( const DivideByConstant & other ) const { return other.m_Constant == m_Constant; } inline TOutput operator()( const TInput & A ) const { // Because the user has to specify the constant we don't // check if the constant is not too small (i.e. almost equal to zero); return static_cast( A / m_Constant ); } void SetConstant(TConstant ct) { if( ct == NumericTraits::Zero ) { itkGenericExceptionMacro( <<"The constant value used as denominator should not be set to zero"); } this->m_Constant = ct; } const TConstant & GetConstant() const { return m_Constant; } TConstant m_Constant; }; } template class ITK_EXPORT DivideByConstantImageFilter : public UnaryFunctorImageFilter > { public: /** Standard class typedefs. */ typedef DivideByConstantImageFilter Self; typedef UnaryFunctorImageFilter< TInputImage,TOutputImage, Functor::DivideByConstant< typename TInputImage::PixelType, TConstant, typename TOutputImage::PixelType> > Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Run-time type information (and related methods). */ itkTypeMacro(DivideByConstantImageFilter, UnaryFunctorImageFilter); /** Set the constant value that will be used for dividing all the image * pixels */ void SetConstant(TConstant ct) { if( ct != this->GetFunctor().GetConstant() ) { this->GetFunctor().SetConstant(ct); this->Modified(); } } const TConstant & GetConstant() const { return this->GetFunctor().GetConstant(); } #ifdef ITK_USE_CONCEPT_CHECKING /** Begin concept checking */ itkConceptMacro(InputConvertibleToOutputCheck, (Concept::Convertible)); // The following concept check doesn't seem to work with vector immages //itkConceptMacro(Input1Input2OutputDivisionOperatorsCheck, // (Concept::DivisionOperators)); /** End concept checking */ #endif protected: DivideByConstantImageFilter() {}; virtual ~DivideByConstantImageFilter() {}; void PrintSelf(std::ostream &os, Indent indent) const { Superclass::PrintSelf(os, indent); os << indent << "Constant: " << static_cast::PrintType>(this->GetConstant()) << std::endl; } private: DivideByConstantImageFilter(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkFEMConformalMap.cxx000066400000000000000000001053701147325206600226120ustar00rootroot00000000000000/*========================================================================= Program: Insight Segmentation & Registration Toolkit Module: $RCSfile: itkFEMConformalMap.cxx,v $ Language: C++ Date: $Date: 2005/11/08 15:34:16 $ Version: $Revision: 1.1.1.1 $ Copyright (c) 2002 Insight Consortium. All rights reserved. See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for detailm_Solver. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _FEMConformalMap_txx #define _FEMConformalMap_txx #include "vtkFeatureEdges.h" #include "vtkPointLocator.h" #include "vtkCellLocator.h" #include "vtkTriangleFilter.h" #include "vtkCleanPolyData.h" #include "vtkPolyDataConnectivityFilter.h" #include #include #include #include #include #include #include "itkFEMConformalMap.h" #include "itkFEMLoadNode.h" namespace itk { vtkPolyData* vtkGenSmoothMesh(vtkPolyData* vds) { float fai=45; vtkSmoothPolyDataFilter *polySmoother = vtkSmoothPolyDataFilter::New(); // polySmoother->SetInput(decimator->GetOutput()); polySmoother->SetInput(vds); polySmoother->SetNumberOfIterations(100); polySmoother->SetRelaxationFactor(0.01); polySmoother->SetFeatureAngle(fai); polySmoother->FeatureEdgeSmoothingOff(); polySmoother->BoundarySmoothingOff(); polySmoother->Update(); return polySmoother->GetOutput(); } template FEMConformalMap ::FEMConformalMap() { m_Sigma=2.0e-4; m_ParameterFileName = ""; m_NorthPole=0; m_SouthPole=1; m_VtkSurfaceMesh=NULL; m_Pi= 3.14159265358979323846; m_ReadFromFile=true; m_Debug=false; m_FindingRealSolution=true; m_SurfaceMesh=NULL; m_Image=NULL; m_SphereImage=NULL; for (int i=0; i<7; i++) m_PoleElementsGN[i]=0; } template bool FEMConformalMap ::GenerateSystemFromVtkSurfaceMesh() { if (!m_VtkSurfaceMesh) { std::cout << " NO MESH"; return false;} std::cout << " Generate system from surface mesh " << std::endl; vtkTriangleFilter* fltTriangle = vtkTriangleFilter::New(); fltTriangle->SetInput(m_VtkSurfaceMesh); cout << " converting mesh to triangles " << endl; fltTriangle->Update(); // Clean the data vtkCleanPolyData* fltCleaner = vtkCleanPolyData::New(); fltCleaner->SetInput(fltTriangle->GetOutput()); fltCleaner->SetTolerance(0); fltCleaner->ConvertPolysToLinesOn(); cout << " cleaning up triangle mesh " << endl; fltCleaner->Update(); // Go through and delete the cells that are of the wrong type vtkPolyData* clean= fltCleaner->GetOutput(); for(vtkIdType i = clean->GetNumberOfCells();i > 0;i--) { if(clean->GetCellType(i-1) != VTK_TRIANGLE) clean->DeleteCell(i-1); } clean->BuildCells(); m_VtkSurfaceMesh=clean; // create a material // Choose the material properties typename MaterialType::Pointer m; m=MaterialType::New(); m->GN=0; // Global number of the material /// m->E=m_Sigma; // Young modulus -- used in the membrane /// m->A=0.0; // Crossection area /// m->h=0.0; // Crossection area /// m->I=0.0; // Moment of inertia /// m->nu=0.; //.0; // poissons -- DONT CHOOSE 1.0!!/// m->RhoC=0.0; // Create the element type ElementType::Pointer e1=ElementType::New(); e1->m_mat=dynamic_cast( m ); vtkPoints* vtkpoints = m_VtkSurfaceMesh->GetPoints(); int numPoints = vtkpoints->GetNumberOfPoints(); int foundnum=0; int boundsz=0; for(int i =0; i < numPoints; i++) { double* pt = vtkpoints->GetPoint(i); typename NodeType::Pointer n; n=new NodeType(pt[0],pt[1],pt[2]); n->GN=i; m_Solver.node.push_back(itk::fem::FEMP(n)); foundnum++; } std::cout << " foundnum " << foundnum << std::endl; typename NodeType::Pointer* narr = new NodeType::Pointer[numPoints]; for(int i =0; i < numPoints; i++) { narr[i]=m_Solver.node.Find( i ); } vtkCellArray* vtkcells = m_VtkSurfaceMesh->GetPolys(); vtkIdType npts; vtkIdType* pts; unsigned long i = 0; unsigned long toti = vtkcells->GetNumberOfCells(); unsigned long rate = toti/50; // std::cout << " progress "; std::cout << " DONE: NUMBER OF CELLS start " << toti << std::endl; for(vtkcells->InitTraversal(); vtkcells->GetNextCell(npts, pts); ) { bool eltok=true; ElementType::Pointer e; e=dynamic_cast(e1->Clone()); e->SetNode(2,narr[pts[0]]); e->SetNode(1,narr[pts[1]]); e->SetNode(0,narr[pts[2]]); e->GN=i; m_Solver.el.push_back(itk::fem::FEMP(e)); i++; } std::cout << " DONE: NUMBER OF CELLS " << i << std::endl; return true; } template bool FEMConformalMap ::GenerateSystemFromSurfaceMesh() { if (!m_SurfaceMesh) return false; // create a material // Choose the material properties typename MaterialType::Pointer m; m=MaterialType::New(); m->GN=0; // Global number of the material /// m->E=m_Sigma; // Young modulus -- used in the membrane /// m->A=0.0; // Crossection area /// m->h=0.0; // Crossection area /// m->I=0.0; // Moment of inertia /// m->nu=0.; //.0; // poissons -- DONT CHOOSE 1.0!!/// m->RhoC=0.0; // 3.e-4 prolly good // std::cout << " input E "; std::cin >> m->E; // Create the element type ElementType::Pointer e1=ElementType::New(); e1->m_mat=dynamic_cast( m ); PointType* pt_ptr; PointType pt; pt_ptr = &pt; for(int i=0; iGetNumberOfPoints(); i++) { m_SurfaceMesh->GetPoint(i, pt_ptr); if (m_Debug) std::cout << "Point: " << i << " coords " << pt[0] << ", " << pt[1] << ", "<< pt[2] << std::endl; // turn the point into a node { // Create nodes typename NodeType::Pointer n; n=new NodeType(pt[0],pt[1],pt[2]); n->GN=i; m_Solver.node.push_back(itk::fem::FEMP(n)); } } InputCellsContainerPointer cellList = m_SurfaceMesh->GetCells(); InputCellsContainerIterator cells = cellList->Begin(); for(int i=0; iGetNumberOfCells(); i++) { const unsigned long *tp; typename SurfaceType::CellType * cellPtr = cells.Value(); tp = cellPtr->GetPointIds(); if (m_Debug) std::cout << " pt 1 id " << tp[0] << " pt 2 id " << tp[1] <<" pt 3 id " << tp[2] << std::endl; cells++; // turn the cell into an element { // Create elements ElementType::Pointer e; e=dynamic_cast(e1->Clone()); e->SetNode(0,m_Solver.node.Find( tp[0] )); e->SetNode(1,m_Solver.node.Find( tp[1] )); e->SetNode(2,m_Solver.node.Find( tp[2] )); e->GN=i; m_Solver.el.push_back(itk::fem::FEMP(e)); } } return true; } template void FEMConformalMap ::MapImageToSphere(TImage* img, float rad ) { typename ImageType::SizeType imageSize=img->GetLargestPossibleRegion().GetSize(); vnl_vector Pos; // solution at the point vnl_vector Sol; // solution at the local point vnl_vector Gpt; // global position given by local point Sol.set_size(ImageDimension); Gpt.set_size(ImageDimension); Pos.set_size(ImageDimension); VectorType disp; disp.set_size(3); disp.fill(0.0); float solval,posval; float WIDTH = rad*2.1; float HEIGHT = rad*2.1; float DEPTH = rad*2.1; float frad=(float)rad; int i, j, k; typename ImageType::SizeType size; size[0] = WIDTH; size[1] = HEIGHT; size[2] = DEPTH; typename ImageType::IndexType start; start.Fill(0); typename ImageType::RegionType region; region.SetSize( size ); region.SetIndex( start ); m_SphereImage = ImageType::New(); m_SphereImage->SetRegions( region ); m_SphereImage->Allocate(); int center=(int)WIDTH/2; typename ImageType::PixelType backgroundValue=0; itk::ImageRegionIteratorWithIndex it( m_SphereImage, region ); it.GoToBegin(); typename ImageType::IndexType rindex = it.GetIndex(); typename ImageType::IndexType sindex = it.GetIndex(); while( !it.IsAtEnd() ) { rindex=it.GetIndex(); float dist=0; for (int ii=0; ii < ImageDimension; ii++) dist+=(float)(rindex[ii]-center)*(float)(rindex[ii]-center); dist = sqrt(dist); if ( dist > rad) it.Set( backgroundValue ); else it.Set( 1 ); ++it; } PointType* pt_ptr; PointType pt; pt_ptr = &pt; bool inimage; for( itk::fem::Element::ArrayType::iterator elt=m_Solver.el.begin(); elt!=m_Solver.el.end(); elt++) { unsigned int Nnodes= (*elt)->GetNumberOfNodes(); inimage=true; for (unsigned int nd=0; nd< Nnodes; nd++) { for(unsigned int f=0; fGetNumberOfDegreesOfFreedomPerNode() == 3) { Sol[f]= m_Solver.GetLinearSystemWrapper()->GetSolutionValue( (*elt)->GetNode(nd)->GetDegreeOfFreedom(f) , 0); x=(*elt)->GetNode(nd)->GetCoordinates()[f]; } else { Sol[f]= (*elt)->GetNode(nd)->GetCoordinates()(f); m_SurfaceMesh->GetPoint((*elt)->GetNode(nd)->GN, pt_ptr); x=pt[f]; } // get img index long int temp; if (x !=0) temp=(long int) ((x)+0.5); else temp=0;// round rindex[f]=temp; // get sphr index disp[f] =(float) ((float)center)+rad*Sol[f]; x=disp[f]; if (x !=0) temp=(long int) ((x)+0.5); else temp=0;// round sindex[f]=temp; } if (inimage ) m_SphereImage->SetPixel(sindex, img->GetPixel(rindex) + 1.0);//*100. +110.0); } } // end of elt array loop } template void FEMConformalMap ::MapCheckerboardToImage( float increment ) { typedef float Float; if (increment < 0) increment=0; if (increment > 1) increment=1; float phiinc=m_Pi*increment; float thetainc=m_Pi*increment; vnl_vector Sol; // solution at the local point typename ImageType::SpacingType spacing=m_Image->GetSpacing(); typename ImageType::SizeType imageSize=m_Image->GetLargestPossibleRegion().GetSize(); std::cout << " size " << imageSize << std::endl; VectorType disp; disp.set_size(3); disp.fill(0.0); // Float solval,posval; bool inimage; float phi; float theta; float s; float t; float gridval; ImageRegionIteratorWithIndex wimIter( m_Image, m_Image->GetLargestPossibleRegion() ); wimIter.GoToBegin(); for( ; !wimIter.IsAtEnd(); ++wimIter ) wimIter.Set(0.0); typename ImageType::IndexType rindex = wimIter.GetIndex(); Sol.set_size(ImageDimension); PointType* pt_ptr; PointType pt; pt_ptr = &pt; for( itk::fem::Element::ArrayType::iterator elt=m_Solver.el.begin(); elt!=m_Solver.el.end(); elt++) { unsigned int Nnodes= (*elt)->GetNumberOfNodes(); inimage=true; for (unsigned int nd=0; nd< Nnodes; nd++) { for(unsigned int f=0; fGetNode(nd)->GetCoordinates()(f); m_SurfaceMesh->GetPoint((*elt)->GetNode(nd)->GN, pt_ptr); x=pt[f]; long int temp; if (x !=0) temp=(long int) ((x)+0.5); else temp=0;// round rindex[f]=temp/spacing[f]; disp[f] =(Float) 1.0*Sol[f]; if ( temp < 0 || temp > (long int) imageSize[f]-1) inimage=false; } // convert the cartesian coordinates to theta and phi spherical coords phi=acos(Sol[2]); theta = acos(Sol[0]/sin(phi)); s=phi/phiinc; t=theta/thetainc; gridval; if ( ( ((int)t) + ((int)s)) % 2 == 0) gridval = 100; else gridval=200; if (inimage ) m_Image->SetPixel(rindex, gridval);//*100. +110.0); } } // end of elt array loop } template void FEMConformalMap ::MapStereographicCoordinatesToImage(int cdim) { typedef float Float; Float rstep,sstep,tstep; vnl_vector Pos; // solution at the point vnl_vector Sol; // solution at the local point vnl_vector Gpt; // global position given by local point typename ImageType::SpacingType spacing=m_Image->GetSpacing(); typename ImageType::SizeType imageSize=m_Image->GetLargestPossibleRegion().GetSize(); VectorType disp; disp.set_size(3); disp.fill(0.0); Float solval,posval; bool inimage; ImageRegionIteratorWithIndex wimIter( m_Image, m_Image->GetLargestPossibleRegion() ); wimIter.GoToBegin(); for( ; !wimIter.IsAtEnd(); ++wimIter ) wimIter.Set(0.0); typename ImageType::IndexType rindex = wimIter.GetIndex(); Sol.set_size(ImageDimension); Gpt.set_size(ImageDimension); Pos.set_size(ImageDimension); PointType* pt_ptr; PointType pt; pt_ptr = &pt; for( itk::fem::Element::ArrayType::iterator elt=m_Solver.el.begin(); elt!=m_Solver.el.end(); elt++) { unsigned int Nnodes= (*elt)->GetNumberOfNodes(); inimage=true; for (unsigned int nd=0; nd< Nnodes; nd++) { for(unsigned int f=0; fGetNumberOfDegreesOfFreedomPerNode() == 3) { Sol[f]= m_Solver.GetLinearSystemWrapper()->GetSolutionValue( (*elt)->GetNode(nd)->GetDegreeOfFreedom(f) , 0); x=(*elt)->GetNode(nd)->GetCoordinates()[f]; } else { Sol[f]= (*elt)->GetNode(nd)->GetCoordinates()(f); m_SurfaceMesh->GetPoint((*elt)->GetNode(nd)->GN, pt_ptr); x=pt[f]; } long int temp; if (x !=0) temp=(long int) ((x)+0.5); else temp=0;// round rindex[f]=temp/spacing[f]; disp[f] =(Float) 1.0*Sol[f]; if ( temp < 0 || temp > (long int) imageSize[f]-1) inimage=false; } if (inimage ) m_Image->SetPixel(rindex, disp[cdim]+2.0);//*100. +110.0); } } // end of elt array loop } template void FEMConformalMap ::FindPoles(int dim) { // find node with min x coord and its elt -> use as north pole (apply forces there) // find node with max x coord and its elt -> use as south pole (fix solution to zero) if (dim < 0) return; vnl_vector minx(3,9.e9); vnl_vector maxx(3,0.0); itk::fem::Element::VectorType minv; itk::fem::Element::VectorType maxv; unsigned int dofsperelt; itk::fem::Element::ArrayType::iterator elt=m_Solver.el.begin(); unsigned int Nnodes= (*elt)->GetNumberOfNodes(); dofsperelt=(*elt)->GetNumberOfDegreesOfFreedomPerNode()*Nnodes; for( ; elt!=m_Solver.el.end(); elt++) { for (unsigned int nd=0; nd< Nnodes; nd++) { for (unsigned int dd=0; dd< ImageDimension; dd++) { float x=((*elt)->GetNode(nd)->GetCoordinates()[dd]); if (x <= minx[dd]) { // std::cout << " x " << x << " minx " << minx << std::endl; m_PoleElementsGN[dd*2]=(*elt)->GN; minv=(*elt)->GetNode(nd)->GetCoordinates(); minx[dd]=x; } else if (x >= maxx[dd]) { // std::cout << " x " << x << " maxx " << maxx << std::endl; m_PoleElementsGN[dd*2+1]=(*elt)->GN; maxv=(*elt)->GetNode(nd)->GetCoordinates(); maxx[dd]=x; } } } } for (int jj=0; jj<3; jj++){ std::cout << " Element " << m_PoleElementsGN[jj*2] << " is north " << jj << std::endl; std::cout << " Element " << m_PoleElementsGN[jj*2+1] << " is south " << jj << std::endl; } } template void FEMConformalMap ::FixPoles(int dim) { // if (dim > 3 || dim < -3 || dim == 0 ) return; if (dim == m_NorthPole) return; unsigned int dofsperelt; itk::fem::Element::ArrayType::iterator elt=m_Solver.el.begin(); unsigned int Nnodes= (*elt)->GetNumberOfNodes(); unsigned int dofs=(*elt)->GetNumberOfDegreesOfFreedomPerNode(); dofsperelt=dofs*Nnodes; { float fixval; itk::fem::LoadBC::Pointer l1; itk::fem::LoadBC::Pointer l2; itk::fem::LoadBC::Pointer l3; itk::fem::LoadBC::Pointer l4; itk::fem::LoadBC::Pointer l5; // itk::fem::LoadBC::Pointer l6; int absdim=abs(dim); float oc=2.0; std::cout << " Fixing BC " << absdim << std::endl; switch (absdim) { case 0: for (int i=0; im_element=m_Solver.el[m_PoleElementsGN[0]]; l1->m_dof=i; l1->m_value=vnl_vector(1,fixval); m_Solver.load.push_back( itk::fem::FEMP(&*l1) );} break; case 1: for (int i=0; im_element=m_Solver.el[m_PoleElementsGN[1]]; l1->m_dof=i; l1->m_value=vnl_vector(1,fixval); m_Solver.load.push_back( itk::fem::FEMP(&*l1) );} break; case 2: for (int i=0; im_element=m_Solver.el[m_PoleElementsGN[2]]; l2->m_dof=i; l2->m_value=vnl_vector(1,fixval); m_Solver.load.push_back( itk::fem::FEMP(&*l2) );} break; case 3: for (int i=0; im_element=m_Solver.el[m_PoleElementsGN[3]]; l3->m_dof=i; l3->m_value=vnl_vector(1,fixval); m_Solver.load.push_back( itk::fem::FEMP(&*l3) );} break; case 4: for (int i=0; im_element=m_Solver.el[m_PoleElementsGN[4]]; l4->m_dof=i; l4->m_value=vnl_vector(1,fixval); m_Solver.load.push_back( itk::fem::FEMP(&*l4) );} break; case 5: for (int i=0; im_element=m_Solver.el[m_PoleElementsGN[5]]; l5->m_dof=i; l5->m_value=vnl_vector(1,fixval); m_Solver.load.push_back( itk::fem::FEMP(&*l5) );} break; } } } template void FEMConformalMap ::ApplyRealForces(int dim) { int ct=0; if (dim > 6) dim=6; // loop over all elements, applying forces to the nodes 0 and 1 (real forces are zero on the 3rd node) // for( ::itk::fem::Solver::ElementArray::iterator e = m_Solver.el.begin(); e!=m_Solver.el.end(); e++) { itk::fem::Element::Pointer e = m_Solver.el[m_PoleElementsGN[dim]]; int na=0; int nb=1; int nc=2; { VectorType A=(e)->GetNode(na)->GetCoordinates(); VectorType B=(e)->GetNode(nb)->GetCoordinates(); VectorType C=(e)->GetNode(nc)->GetCoordinates(); VectorType BA =B-A; VectorType AC =A-C; VectorType CB =C-B; float bamag=BA.magnitude(); float cbmag=CB.magnitude(); float acmag=AC.magnitude(); if (bamag > cbmag && bamag > acmag) { na=0; nb=1; nc=2; } if (cbmag > bamag && cbmag > acmag) { na=1; nb=2; nc=0; } if (acmag > bamag && acmag > cbmag) { na=2; nb=0; nc=1; } } VectorType A=(e)->GetNode(na)->GetCoordinates(); VectorType B=(e)->GetNode(nb)->GetCoordinates(); VectorType C=(e)->GetNode(nc)->GetCoordinates(); VectorType BA =B-A; VectorType CA =C-A; VectorType CB =C-B; float bamag=BA.magnitude(); float cbmag=CB.magnitude(); float acmag=CA.magnitude(); float theta=(CA[0]*BA[0]+CA[1]*BA[1]+CA[2]*BA[2])/bamag*bamag; if (theta > 1) std::cout << " theta " << theta << std::endl; VectorType E = A+BA*theta; VectorType CE =C-E; float cemag=CE.magnitude(); float mag=bamag; // load node 0 { itk::fem::LoadNode::Pointer load=itk::fem::LoadNode::New(); load->m_pt=na; load->F.set_size(3); load->F.fill(-1.0/bamag); load->m_element=(e); m_Solver.load.push_back( itk::fem::FEMP(&*load) ); } // load node 1 { itk::fem::LoadNode::Pointer load=itk::fem::LoadNode::New(); load->m_pt=nb; load->F.set_size(3); load->F.fill(1.0/bamag); load->m_element=(e); m_Solver.load.push_back( itk::fem::FEMP(&*load) ); } ct++; if (ct > 0) return; } } template void FEMConformalMap ::ApplyImaginaryForces(int dim) { unsigned int ct=0; if (dim > 6) dim=6; // loop over all elements, applying forces to the nodes 0 and 1 (real forces are zero on the 3rd node) // for( ::itk::fem::Solver::ElementArray::iterator e = m_Solver.el.begin(); e!=m_Solver.el.end(); e++) { itk::fem::Element::Pointer e = m_Solver.el[m_PoleElementsGN[dim]]; /* VectorType A=(e)->GetNode(0)->GetCoordinates(); VectorType B=(e)->GetNode(1)->GetCoordinates(); VectorType C=(e)->GetNode(2)->GetCoordinates(); VectorType BA =B-A; VectorType CA =C-A; float bamag=BA.magnitude(); float theta=(CA[0]*BA[0]+CA[1]*BA[1]+CA[2]*BA[2])/bamag*bamag; if (theta > 1) std::cout << " theta " << theta << std::endl; VectorType E = A+BA*theta; VectorType CE =C-E; float cemag=CE.magnitude(); */ int na=0; int nb=1; int nc=2; { VectorType A=(e)->GetNode(na)->GetCoordinates(); VectorType B=(e)->GetNode(nb)->GetCoordinates(); VectorType C=(e)->GetNode(nc)->GetCoordinates(); VectorType BA =B-A; VectorType AC =A-C; VectorType CB =C-B; float bamag=BA.magnitude(); float cbmag=CB.magnitude(); float acmag=AC.magnitude(); if (bamag > cbmag && bamag > acmag) { na=0; nb=1; nc=2; } if (cbmag > bamag && cbmag > acmag) { na=1; nb=2; nc=0; } if (acmag > bamag && acmag > cbmag) { na=2; nb=0; nc=1; } } VectorType A=(e)->GetNode(na)->GetCoordinates(); VectorType B=(e)->GetNode(nb)->GetCoordinates(); VectorType C=(e)->GetNode(nc)->GetCoordinates(); VectorType BA =B-A; VectorType CA =C-A; VectorType CB =C-B; float bamag=BA.magnitude(); float cbmag=CB.magnitude(); float acmag=CA.magnitude(); float theta=(CA[0]*BA[0]+CA[1]*BA[1]+CA[2]*BA[2])/bamag*bamag; if (theta > 1 || theta < 0) { // std::cout << " ERROR : theta from non-acute angle " << theta << std::endl; // throw; // return; } VectorType E = A+BA*theta; VectorType CE =C-E; float cemag=CE.magnitude(); // load node 0 { itk::fem::LoadNode::Pointer load=itk::fem::LoadNode::New(); load->m_pt=na; load->F.set_size(3); load->F.fill((1.0-theta)/cemag); load->m_element=(e); m_Solver.load.push_back( itk::fem::FEMP(&*load) ); } // load node 1 { itk::fem::LoadNode::Pointer load=itk::fem::LoadNode::New(); load->m_pt=nb; load->F.set_size(3); load->F.fill((theta)/cemag); load->m_element=(e); m_Solver.load.push_back( itk::fem::FEMP(&*load) ); } // load node 2 { itk::fem::LoadNode::Pointer load=itk::fem::LoadNode::New(); load->m_pt=nc; load->F.set_size(3); load->F.fill(-1.0/cemag); load->m_element=(e); m_Solver.load.push_back( itk::fem::FEMP(&*load) ); } ct++; if (ct > 0) return; } } template void FEMConformalMap ::ComputeStereographicCoordinates() { std::cout << " coords on unit sphere : " << std::endl; float radsq; // radius squared float c1,c2,c3; // stereographic coordinates unsigned int ngfn=0,dof; // ::itk::fem::Solver::NodeArray::iterator n = m_Solver.node.begin(); for( ::itk::fem::Solver::NodeArray::iterator n = m_Solver.node.begin(); n!=m_Solver.node.end(); n++) { dof=(*n)->GetDegreeOfFreedom(0); float x=m_RealSolution[dof]; float y=m_ImagSolution[dof]; radsq=x*x+y*y; c1=2.0*x/(1.0+radsq); c2=2.0*y/(1.0+radsq); c3=2.0*radsq/(1.0+radsq)-1.0; // std::cout << c1 << " " << c2 << " " << c3 << " mag " << sqrt(c1*c1+c2*c2+c3*c3) << std::endl; VectorType coord=(*n)->GetCoordinates(); coord[0]=c1; coord[1]=c2; coord[2]=c3; /// (*n)->SetCoordinates(coord); } std::cout << " coords on unit sphere done " << std::endl; } template void FEMConformalMap ::ConformalMap() { m_Solver.load.clear(); m_Solver.node.clear(); m_Solver.el.clear(); /** * Open the file and assign it to stream object f */ if (m_ReadFromFile) { const char* filename=m_ParameterFileName.c_str(); std::cout<<"Reading FEM problem from file: "<GenerateSystemFromVtkSurfaceMesh(); } // else this->GenerateSystemFromSurfaceMesh(); int dir=m_NorthPole; // std::cout << " input dir to fix "; std::cin >> dir; this->FindPoles(dir); if (m_NorthPole != m_SouthPole) this->FixPoles(m_SouthPole); // for (int oo=0; oo<6; oo++) // this->FixPoles(oo); /** * Assign a unique id (global freedom number - GFN) * to every degree of freedom (DOF) in a system. */ m_Solver.GenerateGFN(); m_ImagSolution.set_size(m_Solver.GetNumberOfDegreesOfFreedom()); m_RealSolution.set_size(m_Solver.GetNumberOfDegreesOfFreedom()); m_RealSolution.fill(0); m_ImagSolution.fill(0); if (m_Debug) { for( ::itk::fem::Solver::NodeArray::iterator n = m_Solver.node.begin(); n!=m_Solver.node.end(); n++) { std::cout<<"Node#: "<<(*n)->GN<<": "; std::cout << " coord " << (*n)->GetCoordinates() << std::endl; } for( ::itk::fem::Solver::ElementArray::iterator n = m_Solver.el.begin(); n!=m_Solver.el.end(); n++) { std::cout<<"Elt#: "<<(*n)->GN<<": has " << (*n)->GetNumberOfNodes() << " nodes "; for (int i=0; i<(*n)->GetNumberOfNodes(); i++) std::cout << " coord " << (*n)->GetNode(i)->GetCoordinates() << std::endl; } } unsigned int maxits=m_Solver.GetNumberOfDegreesOfFreedom(); // should be > twice ndofs //if (m_Debug) std::cout << " ndof " << maxits << std::endl; itpackWrapper.SetMaximumNumberIterations(maxits*4); itpackWrapper.SetTolerance(1.e-5); itpackWrapper.JacobianConjugateGradient(); itpackWrapper.SetMaximumNonZeroValuesInMatrix(maxits*100); m_Solver.SetLinearSystemWrapper(&itpackWrapper); /** * Assemble the master stiffness matrix. In order to do this * the GFN's should already be assigned to every DOF. */ std::cout << " assemble k " << std::endl; m_Solver.AssembleK(); std::cout << " assemble k done " << std::endl; if (m_Debug) { for (int i=0; iGetMatrixValue(i,j); if ( i != j) sum+=val; // if (val != 0) std::cout << " entry i,j " << i << " , " << j << " = " << m_Solver.GetLinearSystemWrapper()->GetMatrixValue(i,j)<GN<<": "; if (m_Debug) std::cout << " coord " << (*n)->GetCoordinates() << std::endl; // For each DOF in the node... */ for( unsigned int d=0, dof; (dof=(*n)->GetDegreeOfFreedom(d))!=::itk::fem::Element::InvalidDegreeOfFreedomID; d++ ) { if (m_Debug) std::cout< void FEMConformalMap ::BuildOutputMeshes(typename TImage::Pointer image) { // Get the number of points in the mesh int numPoints = m_Solver.node.size(); // Create vtk polydata vtkPolyData* polydata1 = vtkPolyData::New(); vtkPolyData* polydata2 = vtkPolyData::New(); // Create the vtkPoints object and set the number of points vtkPoints* vpoints1 = vtkPoints::New(); vtkPoints* vpoints2 = vtkPoints::New(); vpoints1->SetNumberOfPoints(numPoints); vpoints2->SetNumberOfPoints(numPoints); vtkFloatArray* param = vtkFloatArray::New(); param->SetName("angle"); std::cout <<" start pts "; int idx=0; vtkDataArray* scs=NULL; if (m_VtkSurfaceMesh) { vtkPointData *pd=m_VtkSurfaceMesh->GetPointData(); scs = pd->GetScalars(); } typename TImage::SpacingType spacing = image->GetSpacing(); for( ::itk::fem::Solver::NodeArray::iterator n = m_Solver.node.begin(); n!=m_Solver.node.end(); n++) { // extrinisic coords VectorType loc = (*n)->GetCoordinates(); // spherical coords unsigned int dof=(*n)->GetDegreeOfFreedom(0); float x=m_RealSolution[dof]; float y=m_ImagSolution[dof]; float radsq=x*x+y*y; float c1=2.0*x/(1.0+radsq); float c2=2.0*y/(1.0+radsq); float c3=2.0*radsq/(1.0+radsq)-1.0; if (idx % 1000 == 0) std::cout << c1 << " " << c2 << " " << c3 << " mag " << sqrt(c1*c1+c2*c2+c3*c3) << std::endl; // float pt1[3]; pt1[0]=c1; pt1[1]=c2; pt1[2]=c3; float pt2[3]; pt2[0]=loc[0]; pt2[1]=loc[1]; pt2[2]=loc[2]; vpoints1->SetPoint(idx,pt1); vpoints2->SetPoint(idx,pt2); typename TImage::IndexType index; index[0]=(long int) loc[0]/spacing[0]; index[1]=(long int) loc[1]/spacing[1]; index[2]=(long int) loc[2]/spacing[2]; float temp; if (m_VtkSurfaceMesh) temp = scs->GetTuple1(idx); else temp = (float) image->GetPixel(index); param->InsertNextValue(temp); (*n)->GN=idx; idx++; } std::cout << " done with pts " << std::endl; vtkCellArray* tris1 = vtkCellArray::New(); vtkCellArray* tris2 = vtkCellArray::New(); std::cout << " start with tris " << std::endl; for( ::itk::fem::Solver::ElementArray::iterator n = m_Solver.el.begin(); n!=m_Solver.el.end(); n++) { tris1->InsertNextCell(3); tris2->InsertNextCell(3); for (int i=0; i<(*n)->GetNumberOfNodes(); i++) { tris1->InsertCellPoint((*n)->GetNode(i)->GN); tris2->InsertCellPoint((*n)->GetNode(i)->GN); } } std::cout << " done with tris " << std::endl; // Assign points and cells polydata1->SetPoints(vpoints1); polydata2->SetPoints(vpoints2); polydata1->SetPolys(tris1); polydata2->SetPolys(tris2); polydata1->GetPointData()->SetScalars(param); polydata2->GetPointData()->SetScalars(param); // vtkDelaunay2D* delny1=vtkDelaunay2D::New(); // delny1->SetInput(polydata1); //m_ExtractedSurfaceMesh=delny1->GetOutput();//polydata1;// m_ExtractedSurfaceMesh=vtkGenSmoothMesh(polydata1); // vtkDelaunay2D* delny2=vtkDelaunay2D::New(); // delny2->SetInput(polydata2); // m_DiskSurfaceMesh=delny2->GetOutput(); if (!m_VtkSurfaceMesh) m_VtkSurfaceMesh=vtkGenSmoothMesh(polydata2);//polydata2; return; } } // namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkFEMConformalMap.h000077500000000000000000000153001147325206600222330ustar00rootroot00000000000000/*========================================================================= Program: Insight Segmentation & Registration Toolkit Module: $RCSfile: itkFEMConformalMap.h,v $ Language: C++ Date: $Date: 2006/10/13 19:20:46 $ Version: $Revision: 1.2 $ Copyright (c) 2002 Insight Consortium. All rights reserved. See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _FEMConformalMap_h #define _FEMConformalMap_h #include #include #include #include "vtkDataSetWriter.h" #include "vtkDataSetMapper.h" #include "vtkRenderer.h" #include "vtkRenderWindow.h" #include "vtkActor.h" #include "vtkRenderWindowInteractor.h" #include "vtkDataSetReader.h" #include "vtkUnstructuredGrid.h" #include "vtkDataSet.h" #include "vtkCellArray.h" #include "vtkVolume16Reader.h" #include "vtkImageReader2.h" #include "vtkPolyDataMapper.h" #include "vtkActor.h" #include "vtkOutlineFilter.h" #include "vtkCamera.h" #include "vtkProperty.h" #include "vtkPolyData.h" #include "vtkPolyVertex.h" #include "vtkPointData.h" #include "vtkExtractEdges.h" #include "vtkPolyDataNormals.h" #include "vtkMarchingCubes.h" #include "vtkImageGaussianSmooth.h" #include "vtkDecimatePro.h" #include "vtkContourFilter.h" #include "vtkPolyDataConnectivityFilter.h" //#include "vtkKitwareContourFilter.h" #include "vtkSmoothPolyDataFilter.h" #include "vtkSTLWriter.h" #include "vtkUnstructuredGridToPolyDataFilter.h" //#include "itkImageToVTKImageFilter.h" #include "vtkDelaunay2D.h" #include "vtkFloatArray.h" #include "itkObject.h" #include "itkProcessObject.h" #include "itkVectorContainer.h" #include "itkCastImageFilter.h" #include "itkFEM.h" #include "itkFEMLinearSystemWrapperItpack.h" #include "itkFEMElement3DC0LinearTriangularLaplaceBeltrami.h" #include "itkFEMElement3DC0LinearTriangularMembrane.h" namespace itk { /** \class FEMConformalMap * Angenent, Haker conformal mapping algorithm using FEM. * * \note The origin of a neighborhood is always taken to be * the first point entered into and the * last point stored in the list. */ template < typename TSurface, typename TImage, unsigned int TDimension=3 > class FEMConformalMap : public ProcessObject { public: /** Standard class typedefs. */ typedef FEMConformalMap Self; typedef ProcessObject Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Run-time type information (and related methods). */ itkTypeMacro(FEMConformalMap,ProcessObject); /** Method for creation through the object factory. */ itkNewMacro(Self); /** Surface (mesh) types. */ typedef TImage ImageType; typedef typename TImage::Pointer ImageTypePointer; /** Surface (mesh) types. */ typedef TSurface SurfaceType; typedef typename SurfaceType::Pointer SurfaceTypePointer; typedef typename SurfaceType::PointType PointType; typedef typename SurfaceType::CellsContainerPointer InputCellsContainerPointer; typedef typename SurfaceType::CellsContainer::Iterator InputCellsContainerIterator; /** Image dimension. */ // itkStaticConstMacro(ImageDimension, unsigned int, TImage::ImageDimension); itkStaticConstMacro(ImageDimension, unsigned int, TDimension); itkStaticConstMacro(SurfaceDimension, unsigned int, TDimension); typedef double RealType; typedef vnl_vector VectorType; typedef vnl_vector_fixed FixedVectorType; typedef vnl_matrix MatrixType; /** FEM types */ typedef itk::fem::MaterialLinearElasticity MaterialType; typedef itk::fem::Node NodeType; typedef itk::fem::LoadNode LoadType; typedef itk::fem::Element3DC0LinearTriangularLaplaceBeltrami ElementType; typedef itk::fem::Element3DC0LinearTriangularMembrane ElementType1; /** Set input parameter file */ itkSetStringMacro( ParameterFileName ); /** Set input parameter file */ itkGetStringMacro( ParameterFileName ); itkGetMacro(Sigma, RealType); itkSetMacro(Sigma, RealType); itkGetMacro(SurfaceMesh, SurfaceTypePointer); itkSetMacro(SurfaceMesh, SurfaceTypePointer); itkGetMacro(Image, ImageTypePointer); itkSetMacro(Image, ImageTypePointer); itkGetMacro(SphereImage, ImageTypePointer); itkSetMacro(SphereImage, ImageTypePointer); itkSetMacro(SouthPole, int); void SetNorthPole(int p) { if (p % 2 == 0 ) { m_NorthPole=p; m_SouthPole=p+1; } else { m_NorthPole=p-1; m_SouthPole=p-1; } } void SetDebug(bool b) {m_Debug=b;} void SetReadFromFile(bool b) {m_ReadFromFile=b;} void FindPoles(int dim); void FixPoles(int dim); void ConformalParameterize(); void ConformalMap(); void ComputeStereographicCoordinates(); void MapStereographicCoordinatesToImage(int dim); void MapImageToSphere(ImageType* img, float rad ); void MapCheckerboardToImage(float increment); void BuildOutputMeshes(typename TImage::Pointer image); vtkPolyData* m_ExtractedSurfaceMesh; vtkPolyData* m_VtkSurfaceMesh; protected: bool GenerateSystemFromSurfaceMesh(); bool GenerateSystemFromVtkSurfaceMesh(); void ApplyRealForces(int dim); void ApplyImaginaryForces(int dim); FEMConformalMap(); virtual ~FEMConformalMap(){}; private: RealType m_Sigma; RealType m_Pi; std::string m_ParameterFileName; int m_NorthPole; int m_SouthPole; itk::fem::Solver m_Solver; bool m_ReadFromFile; bool m_Debug; bool m_FindingRealSolution; VectorType m_RealSolution; VectorType m_ImagSolution; ImageTypePointer m_Image; ImageTypePointer m_SphereImage; SurfaceTypePointer m_SurfaceMesh; itk::fem::LinearSystemWrapperItpack itpackWrapper; unsigned long m_PoleElementsGN[7]; }; } // namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkFEMConformalMap.cxx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkFEMDiscConformalMap.cxx000066400000000000000000001504341147325206600234160ustar00rootroot00000000000000/*========================================================================= Program: Insight Segmentation & Registration Toolkit Module: $RCSfile: itkFEMDiscConformalMap.cxx,v $ Language: C++ Date: $Date: 2007/08/07 15:30:39 $ Version: $Revision: 1.3 $ Copyright (c) 2002 Insight Consortium. All rights reserved. See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for detailm_Solver. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _FEMDiscConformalMap_txx #define _FEMDiscConformalMap_txx #include #include #include #include #include #include #include "vtkDelaunay2D.h" #include "vtkSelectPolyData.h" #include "vtkFloatArray.h" #include "itkDiscreteGaussianImageFilter.h" #include "itkFEMDiscConformalMap.h" #include "itkFEMLoadNode.h" #include "itkSurfaceMeshCurvature.h" #include "vtkClipPolyData.h" #include "vtkContourFilter.h" #include "vtkSmartPointer.h" namespace itk { template FEMDiscConformalMap ::FEMDiscConformalMap() { m_Sigma=2.0e-4; m_ParameterFileName = ""; m_NorthPole=0; m_SourceNodeNumber=1; this->m_DistanceCostWeight=1; this->m_LabelCostWeight=0; m_Pi= 3.14159265358979323846; m_ReadFromFile=false; m_Debug=false; m_FindingRealSolution=true; this->m_SurfaceMesh=NULL; for (int i=0; i<7; i++) m_PoleElementsGN[i]=0; this->m_MapToCircle=true; this->m_MapToSquare=false; this->m_ParamWhileSearching=true; m_Smooth=2.0; this->m_Label_to_Flatten=0; this->m_FlatImage = NULL; manifoldIntegrator=ManifoldIntegratorType::New(); } template bool FEMDiscConformalMap ::InBorder(typename FEMDiscConformalMap::GraphSearchNodePointer g) { if ( this->m_HelpFindLoop[ g->GetIdentity() ] > 0 ) return true; else return false; float dist=g->GetTotalCost(); if (dist > m_MaxCost && dist < m_MaxCost + 1.0) return true; return false; } template bool FEMDiscConformalMap ::InDisc(typename FEMDiscConformalMap::GraphSearchNodePointer g) { // if ( this->m_HelpFindLoop[ g->GetIdentity() ] != 0 ) return true; // else return false; // if ( g->WasVisited() ) return true; float d=g->GetTotalCost(); for (unsigned int i=0; im_DiscBoundaryList.size(); i++) { if ( d <= this->m_DiscBoundaryList[i]->GetTotalCost() ) return true; } return false; if (g->GetPredecessor()) return true; else return false; } template void FEMDiscConformalMap ::FindSource(IndexType index) { vtkPoints* vtkpoints = this->m_SurfaceMesh->GetPoints(); int numPoints = vtkpoints->GetNumberOfPoints(); float mindist= 1.e9; for(int i =0; i < numPoints; i++) { double* pt = vtkpoints->GetPoint(i); float dist = 0.0; for (int j=0;j void FEMDiscConformalMap ::FindMeanSourceInLabel(unsigned int label ) { vtkPoints* vtkpoints = this->m_SurfaceMesh->GetPoints(); int numPoints = vtkpoints->GetNumberOfPoints(); float mindist= 1.e9; float meanx=0.,meany=0,meanz=0; unsigned long ct=0; vtkDataArray* labels=this->m_SurfaceMesh->GetPointData()->GetArray("Label"); vtkDataArray* features=NULL; if ( this->m_SurfaceFeatureMesh ) if ( this->m_SurfaceFeatureMesh->GetPointData()->GetArray("Feature") ) features=this->m_SurfaceFeatureMesh->GetPointData()->GetArray("Feature"); else features=labels; if ( !labels ) { std::cout << " cant get Labels --- need an array named Label in the mesh ... " << std::endl; exit(1); } else std::cout << " got Labels " << std::endl; for(int i =0; i < numPoints; i++) { double* pt = vtkpoints->GetPoint(i); manifoldIntegrator->GetGraphNode(i)->SetValue(labels->GetTuple1(i),3); // std::cout << " label " << labels->GetTuple1(i) << " & " << label <GetTuple1(i) - label ) < 0.5 ) { // std::cout << " choose " << labels->GetTuple1(i) << " & " << label << std::endl; meanx+=pt[0]; meany+=pt[1]; meanz+=pt[2]; ct++; // i=numPoints+1; } else { // ct+=0; manifoldIntegrator->GetGraphNode(i)->SetUnVisitable(); } } meanx/=(float)ct; meany/=(float)ct; meanz/=(float)ct; for(int i =0; i < numPoints; i++) { double* pt = vtkpoints->GetPoint(i); float dist = 0.0; dist+=(pt[0]-meanx)*(pt[0]-meanx); dist+=(pt[1]-meany)*(pt[1]-meany); dist+=(pt[2]-meanz)*(pt[2]-meanz); dist=sqrt(dist); if (dist < mindist && fabs( label - labels->GetTuple1(i) ) < 0.5 ) { mindist=dist; m_SourceNodeNumber = i; // std::cout << " label " << label << " chose " << labels->GetTuple1(i) << std::endl; } } if ( this->FindLoopAroundNode( this->m_SourceNodeNumber ) == 2 ) { std::cout << " found loop " << std::endl; } if ( ct > 0 ) std::cout << meanx << " " << meany << " " << meanz << std::endl; else { std::cout << " no label " << label << " exiting " << std::endl; exit(1); } } template float FEMDiscConformalMap ::AssessNodeDistanceCost( unsigned int nodeid ) { return manifoldIntegrator->GetGraphNode(nodeid)->GetTotalCost(); /* diff=G1->GetLocation()-G3->GetLocation(); tangentlength=0; for (unsigned int d=0; dGetLocation()-G3->GetLocation(); tangentlength=0; for (unsigned int d=0; d float FEMDiscConformalMap ::GetBoundaryParameterForSquare( unsigned int nodeid, unsigned int whichParam ) { // compute loop length and check validity float tangentlength=0,totallength=0,nodeparam=0,x=0,y=0; typename GraphSearchNodeType::NodeLocationType diff; for (unsigned int i=0; im_DiscBoundaryList.size(); i++) { unsigned int nxt=( ( i+1 ) % this->m_DiscBoundaryList.size() ); diff=this->m_DiscBoundaryList[i]->GetLocation()-this->m_DiscBoundaryList[nxt]->GetLocation(); if ( this->m_DiscBoundaryList[i]->GetIdentity() == nodeid ) nodeparam=totallength; tangentlength=0; for (unsigned int d=0; d 1 */ } else if ( arclength <= 0.5 ) { x=(0.5-arclength)/0.25; /* 0 => 1 */ y=1; } else if ( arclength <= 0.75 ) { x=1; y=(0.75-arclength)/0.25; /* 1 => 0 */ } else if ( arclength <= 1 ) { x=(1-arclength)/0.25; /* 1 => 0 */ y=0; } // std::cout <<" x " << x << " y " << y << " al " << arclength << std::endl; if ( whichParam == 0 ) return x; else if ( whichParam == 1 ) return y; else return arclength; } template float FEMDiscConformalMap ::GetBoundaryParameterForCircle( unsigned int nodeid, unsigned int whichParam ) { // compute loop length and check validity float tangentlength=0,totallength=0,nodeparam=0; typename GraphSearchNodeType::NodeLocationType diff; for (unsigned int i=0; im_DiscBoundaryList.size(); i++) { unsigned int nxt=( ( i+1 ) % this->m_DiscBoundaryList.size() ); diff=this->m_DiscBoundaryList[i]->GetLocation()-this->m_DiscBoundaryList[nxt]->GetLocation(); if ( this->m_DiscBoundaryList[i]->GetIdentity() == nodeid ) nodeparam=totallength; tangentlength=0; for (unsigned int d=0; d unsigned int FEMDiscConformalMap ::AddVertexToLoop( ) { // compute loop length and check validity float tangentlength=0,totallength=0; bool isvalid=true; typename GraphSearchNodeType::NodeLocationType diff; for (unsigned int i=0; im_DiscBoundaryList.size(); i++) { unsigned int nxt=( ( i+1 ) % this->m_DiscBoundaryList.size() ); // std::cout << " this->m_DiscBoundaryList[i]->GetLocation() " << this->m_DiscBoundaryList[i]->GetLocation() << std::endl; diff=this->m_DiscBoundaryList[i]->GetLocation()-this->m_DiscBoundaryList[nxt]->GetLocation(); tangentlength=0; for (unsigned int d=0; dm_DiscBoundaryList[i]->m_NumberOfNeighbors; n++ ) { if ( this->m_DiscBoundaryList[i]->m_Neighbors[n] == this->m_DiscBoundaryList[nxt] ) isvalid=true; } } std::cout << " length " << totallength << " valid? " << isvalid << " entries " << this->m_DiscBoundaryList.size() << " gsz " << this->m_HelpFindLoop.size() << std::endl; /** now find a node with HelpFindLoop value == 0 that minimally changes the length ... and that has a root neighbor and next neighbor consistent with the original edge */ unsigned int newnodeid=0; unsigned int nodeparam=0; // the position in the curve parameter float newedgelength=1.e9; // minimize this GraphSearchNodePointer G1,G2,G3,BestG=NULL; for (unsigned int i=0; im_DiscBoundaryList.size(); i++) { unsigned int nxt=( ( i+1 ) % this->m_DiscBoundaryList.size() ); G1=this->m_DiscBoundaryList[i]; G2=this->m_DiscBoundaryList[nxt]; G3=NULL; /** 3 conditions : (1) HelpFindLoop == 0 (2) am neighbor of curnode (3) am neighbor of next node (4) minlength */ for ( unsigned int n=0; n < G1->m_NumberOfNeighbors; n++ ) { for ( unsigned int m=0; m < G2->m_NumberOfNeighbors; m++ ) { long hfl=abs(this->m_HelpFindLoop[G1->m_Neighbors[n]->GetIdentity()]); hfl+=abs(this->m_HelpFindLoop[G2->m_Neighbors[m]->GetIdentity()]); if ( G1->m_Neighbors[n] == G2->m_Neighbors[m] && hfl == 0 ){ G3=G1->m_Neighbors[n]; n=G1->m_NumberOfNeighbors; m=G2->m_NumberOfNeighbors; float elementlength=this->AssessNodeDistanceCost( G3->GetIdentity() ); if ( elementlength < newedgelength ) { newedgelength=elementlength; BestG=G3; newnodeid=G3->GetIdentity(); nodeparam=i; } } } } } if ( !BestG ) { std::cout << " does not exist " << std::endl; return false; } if ( this->m_HelpFindLoop[ BestG->GetIdentity() ] != 0 ) { std::cout << " already done " << std::endl; return false; } std::vector neighborlist; long paramct=0; totallength=0; for (unsigned int i=0; im_DiscBoundaryList.size(); i++) { paramct++; neighborlist.push_back( this->m_DiscBoundaryList[i] ); this->m_HelpFindLoop[this->m_DiscBoundaryList[i]->GetIdentity()]=paramct; if ( i == nodeparam ) { paramct++; neighborlist.push_back( BestG ); this->m_HelpFindLoop[BestG->GetIdentity()]=paramct; } } // std::cout << " len1 " << this->m_DiscBoundaryList.size() << " len2 " << neighborlist.size() << std::endl; this->SetDiscBoundaryList( neighborlist ); // this->m_DiscBoundaryList.assign(neighborlist.begin(),neighborlist.end()); /* for (unsigned int i=0; im_DiscBoundaryList.size(); i++) { unsigned int nxt=( ( i+1 ) % this->m_DiscBoundaryList.size() ); diff=this->m_DiscBoundaryList[i]->GetLocation()-this->m_DiscBoundaryList[nxt]->GetLocation(); tangentlength=0; for (unsigned int d=0; dm_DiscBoundaryList.size(); i++) { paramct++; neighborlist.push_back( this->m_DiscBoundaryList[i] ); this->m_HelpFindLoop[ this->m_DiscBoundaryList[i]->GetIdentity() ]=paramct; if ( this->m_DiscBoundaryList[i]->GetIdentity() == newnodeid ) { // find the parameter of any of this fellows neighors ... // if it's greater than i+1 then skip ahead long bestparam= this->m_HelpFindLoop[ this->m_DiscBoundaryList[i]->GetIdentity()] +1; for ( unsigned int n=0; n < this->m_DiscBoundaryList[i]->m_NumberOfNeighbors; n++ ) { if ( this->m_HelpFindLoop[ this->m_DiscBoundaryList[i]->m_Neighbors[n]->GetIdentity()] > bestparam ) { bestparam=this->m_HelpFindLoop[ this->m_DiscBoundaryList[i]->m_Neighbors[n]->GetIdentity()]; BestG=this->m_DiscBoundaryList[i]->m_Neighbors[n]; } } // neighborlist.push_back( BestG ); for ( unsigned int j=i+1; j<(unsigned int) bestparam-1; j++) this->m_HelpFindLoop[ this->m_DiscBoundaryList[j]->GetIdentity() ]=-1; i=(unsigned int) (bestparam-2); } } this->SetDiscBoundaryList( neighborlist ); // this->m_DiscBoundaryList.assign(neighborlist.begin(),neighborlist.end()); float newtotallength=0; for (unsigned int i=0; im_DiscBoundaryList.size(); i++) { unsigned int nxt=( ( i+1 ) % this->m_DiscBoundaryList.size() ); diff=this->m_DiscBoundaryList[i]->GetLocation()-this->m_DiscBoundaryList[nxt]->GetLocation(); tangentlength=0; for (unsigned int d=0; dm_DiscBoundaryList.size(); i++) { for (unsigned int j=0; jm_DiscBoundaryList.size(); j++) { if ( i != j && this->m_DiscBoundaryList[i]->GetIdentity() == this->m_DiscBoundaryList[j]->GetIdentity() ) isvalid=false; } } for ( unsigned long g = 0 ; g < this->m_HelpFindLoop.size(); g ++ ) { if ( this->m_HelpFindLoop[g] != 0 ) this->m_HelpFindLoop[g]=-1; } for ( unsigned int j = 0 ; j < this->m_DiscBoundaryList.size(); j ++ ) { m_HelpFindLoop[this->m_DiscBoundaryList[j]->GetIdentity()]=j+1; } unsigned long MAXLIST=200; if ( this->m_DiscBoundaryList.size() > MAXLIST ) { std::cout << this->m_RootNode->GetLocation() < unsigned int FEMDiscConformalMap ::FindLoopAroundNode( unsigned int j_in ) { vtkDataArray* labels=this->m_SurfaceMesh->GetPointData()->GetArray("Label"); vtkDataArray* features=NULL; if ( this->m_SurfaceFeatureMesh ) if ( this->m_SurfaceFeatureMesh->GetPointData()->GetArray("Feature") ) features=this->m_SurfaceFeatureMesh->GetPointData()->GetArray("Feature"); else features=labels; this->m_DiscBoundaryList.clear(); unsigned int gsz=manifoldIntegrator->GetGraphSize(); // get distance from this node to all others // now measure the length distortion of the given solution this->m_RootNode=manifoldIntegrator->GetGraphNode(j_in); for (int i=0; iGetGraphSize(); i++) { manifoldIntegrator->GetGraphNode(i)->SetTotalCost(vnl_huge_val(manifoldIntegrator->GetMaxCost())); manifoldIntegrator->GetGraphNode(i)->SetUnVisited(); manifoldIntegrator->GetGraphNode(i)->SetValue(0.0,1); manifoldIntegrator->GetGraphNode(i)->SetValue(0.0,2); float labval=labels->GetTuple1(i); manifoldIntegrator->GetGraphNode(i)->SetValue(labval,3); manifoldIntegrator->GetGraphNode(i)->SetPredecessor(NULL); } manifoldIntegrator->EmptyQ(); manifoldIntegrator->SetSearchFinished( false ); manifoldIntegrator->SetSource(this->m_RootNode); manifoldIntegrator->InitializeQueue(); manifoldIntegrator->SetParamWhileSearching( this->m_ParamWhileSearching ); manifoldIntegrator->SetWeights(this->m_MaxCost,this->m_DistanceCostWeight,this->m_LabelCostWeight); manifoldIntegrator->PrintWeights(); manifoldIntegrator->FindPath(); /** at this point, we should have extracted the disc now we want to find a boundary point and an instant parameterization via FEM */ this->SetDiscBoundaryList( manifoldIntegrator->m_BoundaryList ); // this->m_DiscBoundaryList.assign(manifoldIntegrator->m_BoundaryList.begin(), // manifoldIntegrator->m_BoundaryList.end()); this->m_HelpFindLoop.clear(); this->m_HelpFindLoop.resize(gsz, 0); this->m_HelpFindLoop[j_in]=-1; unsigned int furthestnode=0; float maxdist=0; GraphSearchNodePointer farnode1=NULL; for ( unsigned int j = 0 ; j < this->m_DiscBoundaryList.size(); j ++ ) { if ( this->m_DiscBoundaryList[j]->GetTotalCost() > maxdist ) { maxdist= this->m_DiscBoundaryList[j]->GetTotalCost(); furthestnode=this->m_DiscBoundaryList[j]->GetIdentity(); farnode1=this->m_DiscBoundaryList[j]; } m_HelpFindLoop[this->m_DiscBoundaryList[j]->GetIdentity()]=j+1; } if ( this->m_ParamWhileSearching ) return 2; // butt ManifoldIntegratorTypePointer discParameterizer=ManifoldIntegratorType::New(); discParameterizer->SetSurfaceMesh(this->m_SurfaceMesh); discParameterizer->InitializeGraph3(); discParameterizer->SetMaxCost(1.e9); std::cout << " dcz "<< discParameterizer->GetGraphSize() << std::endl; for (int i=0; iGetGraphSize(); i++) { discParameterizer->GetGraphNode(i)->SetTotalCost(vnl_huge_val(discParameterizer->GetMaxCost())); discParameterizer->GetGraphNode(i)->SetUnVisitable(); if ( this->m_HelpFindLoop[i] > 0 ) discParameterizer->GetGraphNode(i)->SetUnVisited(); discParameterizer->GetGraphNode(i)->SetValue(0.0,1); discParameterizer->GetGraphNode(i)->SetValue(0.0,2); discParameterizer->GetGraphNode(i)->SetPredecessor(NULL); } discParameterizer->EmptyQ(); discParameterizer->SetSearchFinished( false ); discParameterizer->SetSource(discParameterizer->GetGraphNode(furthestnode)); discParameterizer->InitializeQueue(); discParameterizer->SetWeights(1.e9,1,0); discParameterizer->FindPath(); float temp=0; GraphSearchNodePointer farnode2=NULL; unsigned int vct=0; for (int i=0; iGetGraphSize(); i++) { if ( discParameterizer->GetGraphNode(i)->WasVisited() ) { float t=discParameterizer->GetGraphNode(i)->GetTotalCost() ; if ( t > temp ) { temp=t; farnode2=discParameterizer->GetGraphNode(i); } vct++; //std::cout << " dist " << t << " vct " << vct << std::endl; } } discParameterizer->BackTrack(farnode2); // butt2 ManifoldIntegratorTypePointer lastlooper=ManifoldIntegratorType::New(); lastlooper->SetSurfaceMesh(this->m_SurfaceMesh); lastlooper->InitializeGraph3(); lastlooper->SetMaxCost(1.e9); // std::cout << " dcz "<< lastlooper->GetGraphSize() << std::endl; for (int i=0; iGetGraphSize(); i++) { lastlooper->GetGraphNode(i)->SetTotalCost(vnl_huge_val(lastlooper->GetMaxCost())); lastlooper->GetGraphNode(i)->SetUnVisitable(); if ( this->m_HelpFindLoop[i] > 0 ) lastlooper->GetGraphNode(i)->SetUnVisited(); lastlooper->GetGraphNode(i)->SetValue(0.0,1); lastlooper->GetGraphNode(i)->SetValue(0.0,2); lastlooper->GetGraphNode(i)->SetPredecessor(NULL); } lastlooper->EmptyQ(); lastlooper->SetSearchFinished( false ); for ( unsigned int pp=0; ppGetPathSize(); pp++) { unsigned int id=discParameterizer->GetPathAtIndex(pp)->GetIdentity(); lastlooper->SetSource( lastlooper->GetGraphNode(id) ); } lastlooper->InitializeQueue(); lastlooper->SetWeights(1.e9,1,0); lastlooper->FindPath(); GraphSearchNodePointer farnode3=NULL; vct=0; temp=0; for (int i=0; iGetGraphSize(); i++) { if ( lastlooper->GetGraphNode(i)->WasVisited() ) { float t=lastlooper->GetGraphNode(i)->GetTotalCost() ; if ( t > temp ) { temp=t; farnode3=lastlooper->GetGraphNode(i); } vct++; // std::cout << " dist " << t << " vct " << vct << std::endl; } } // exit(1); // finally reuse lastlooper with farnode3 as root ... for (int i=0; iGetGraphSize(); i++) { lastlooper->GetGraphNode(i)->SetTotalCost(vnl_huge_val(lastlooper->GetMaxCost())); lastlooper->GetGraphNode(i)->SetUnVisitable(); if ( this->m_HelpFindLoop[i] > 0 ) lastlooper->GetGraphNode(i)->SetUnVisited(); lastlooper->GetGraphNode(i)->SetValue(0.0,1); lastlooper->GetGraphNode(i)->SetValue(0.0,2); lastlooper->GetGraphNode(i)->SetPredecessor(NULL); } lastlooper->EmptyQ(); lastlooper->SetSearchFinished( false ); lastlooper->SetSource( lastlooper->GetGraphNode(farnode3->GetIdentity()) ); lastlooper->InitializeQueue(); lastlooper->SetWeights(1.e9,1,0); lastlooper->FindPath(); // now assemble the parameterization... // this->m_DiscBoundaryList.clear(); // add both the above to the list // std::cout << " part 1 " << std::endl; for ( unsigned int i = 0; i< discParameterizer->GetPathSize(); i++) { unsigned int id=discParameterizer->GetPathAtIndex(i)->GetIdentity(); // std::cout << manifoldIntegrator->GetGraphNode(id)->GetLocation() << std::endl; this->m_DiscBoundaryList.push_back( manifoldIntegrator->GetGraphNode(id) ); } // std::cout << " part 2 " << std::endl; lastlooper->BackTrack( lastlooper->GetGraphNode( farnode1->GetIdentity() ) ); for ( unsigned int i = 0; i< lastlooper->GetPathSize(); i++) { unsigned int id=lastlooper->GetPathAtIndex(i)->GetIdentity(); // std::cout << manifoldIntegrator->GetGraphNode(id)->GetLocation() << std::endl; this->m_DiscBoundaryList.push_back( manifoldIntegrator->GetGraphNode(id) ); } // std::cout << farnode1->GetLocation() << std::endl; // std::cout << farnode2->GetLocation() << std::endl; // std::cout << farnode3->GetLocation() << std::endl; // std::cout << " another idea --- get two points far apart then solve a minimization problem across the graph that gives the average value in 0 => 1 ... " << std::endl; // std::cout << " path 1 sz " << discParameterizer->GetPathSize() << std::endl; // finally do but add in reverse order // std::cout << " part 3 " <GetIdentity() << " and " << farnode1->GetIdentity() << std::endl; lastlooper->EmptyPath(); lastlooper->BackTrack( lastlooper->GetGraphNode( farnode2->GetIdentity()) ); for ( unsigned int i = lastlooper->GetPathSize()-1; i > 0 ; i--) { unsigned int id=lastlooper->GetPathAtIndex(i)->GetIdentity(); // std::cout << manifoldIntegrator->GetGraphNode(id)->GetLocation() << std::endl; this->m_DiscBoundaryList.push_back( manifoldIntegrator->GetGraphNode(id) ); } std::cout<< " Almost ... " << std::endl; this->m_HelpFindLoop.clear(); this->m_HelpFindLoop.resize(gsz, 0); for ( unsigned int j = 0 ; j < this->m_DiscBoundaryList.size(); j ++ ) { m_HelpFindLoop[this->m_DiscBoundaryList[j]->GetIdentity()]=j+1; } std::cout<< " Achievement!! " << std::endl; return 2; } template void FEMDiscConformalMap ::LocateAndParameterizeDiscBoundary( unsigned int label , bool CheckCost ) { float effectivemaxcost=0; this->m_DiscBoundaryList.clear(); unsigned int gsz=manifoldIntegrator->GetGraphSize(); std::vector alreadyfound(gsz, false); this->m_DiscBoundarySorter.clear(); this->m_DiscBoundarySorter.resize(gsz,0); for ( unsigned int j=0; jGetGraphNode(j) ) { float cost=manifoldIntegrator->GetGraphNode(j)->GetTotalCost(); if ( ! CheckCost ) cost=0; if ( fabs( manifoldIntegrator->GetGraphNode(j)->GetValue(3) - label ) < 0.5 && cost <= this->m_MaxCost ) { for (unsigned int i = 0; i < manifoldIntegrator->GetGraphNode(j)->m_NumberOfNeighbors; i++) { if ( fabs( manifoldIntegrator->GetGraphNode(j)->m_Neighbors[i]->GetValue(3) - label ) > 0.5 ) { // CurrentNode is in the boundary inb=1; } } // neighborhood if ( inb > 0 ) { // std::cout << " Node is in boundary " << std::endl; inb=0; for (unsigned int i = 0; i < manifoldIntegrator->GetGraphNode(j)->m_NumberOfNeighbors; i++) { if ( fabs( manifoldIntegrator->GetGraphNode(j)->m_Neighbors[i]->GetValue(3) - label ) < 0.5 && cost <= this->m_MaxCost ) { // CurrentNode is in the boundary inb+=1; } } // neighborhood if ( inb >= 2 && alreadyfound[j] == false ) { // need at least two neighbors with same label alreadyfound[j]=true; this->m_DiscBoundaryList.push_back(manifoldIntegrator->GetGraphNode(j)); if ( cost > effectivemaxcost ) effectivemaxcost=cost; } } } // less than max cost } // if node exists } // gsz std::cout << " Boundary has " << this->m_DiscBoundaryList.size() << " elements with eff. max cost " << effectivemaxcost << std::endl; if ( CheckCost ) { // very inefficient way to parameterize boundary .... unsigned int bsz=this->m_DiscBoundaryList.size(); std::vector alreadyfound(bsz, false); this->m_DiscBoundarySorter.resize(bsz,0); unsigned int boundcount=0; unsigned int rootind=0,lastroot=0; this->m_DiscBoundarySorter[rootind]=boundcount; alreadyfound[rootind]=true; bool paramdone=false; while ( ! paramdone ) { std::cout << " start param " << bsz << std::endl; if ( bsz == 0 ) exit(0); for (unsigned int myi = 0; myi < bsz ; myi++) { if ( this->m_DiscBoundaryList[myi] != this->m_DiscBoundaryList[rootind] ) { std::cout <<" myi " << myi << " root " << rootind << " bc " << boundcount << std::endl; for (unsigned int n = 0; n < this->m_DiscBoundaryList[rootind]->m_NumberOfNeighbors; n++) { if ( this->m_DiscBoundaryList[myi] == this->m_DiscBoundaryList[rootind]->m_Neighbors[n] && !alreadyfound[myi] ) { // its in the bndry // check that it's not an isolated bastard bool oknode=true; if ( oknode ) { boundcount++; alreadyfound[myi]=true; this->m_DiscBoundarySorter[myi]=boundcount; n=this->m_DiscBoundaryList[rootind]->m_NumberOfNeighbors+1; std::cout <<" cur " << this->m_DiscBoundaryList[rootind]->GetLocation() << " next " << this->m_DiscBoundaryList[myi]->GetLocation() << " boundcount " << boundcount << " of " << bsz << " curroot " << rootind << std::endl; lastroot=rootind; rootind=myi; myi=bsz; } } // is in boundary } // neighborhood loop } // not currootnode if ( boundcount >= bsz-1 ) paramdone=true; else if ( myi == (bsz-1) ) { boundcount--; rootind=lastroot; this->m_DiscBoundarySorter[rootind]=-1; std::cout <<" failure " << std::endl; exit(0); } } // all boundary nodes } // while // exit(0); }// param boundary if } template void FEMDiscConformalMap ::ExtractSurfaceDisc(unsigned int label) { std::cout << " set surface mesh " << std::endl; manifoldIntegrator->SetSurfaceMesh(this->m_SurfaceMesh); std::cout << " begin initializing graph " << std::endl; manifoldIntegrator->InitializeGraph3(); this->m_SurfaceMesh=manifoldIntegrator->GetSurfaceMesh(); // float frac=0; // IndexType index; if ( this->m_Label_to_Flatten == 0 ) { std::cout <<" enter LabelToExtract "; std::cin >> m_Label_to_Flatten; float mc=0; std::cout << " Enter max cost "; std::cin >> mc; m_MaxCost=mc; } manifoldIntegrator->SetMaxCost(this->m_MaxCost); this->FindMeanSourceInLabel( this->m_Label_to_Flatten ); // this->LocateAndParameterizeDiscBoundary( m_Label_to_Flatten , false ); // this->LocateAndParameterizeDiscBoundary( m_Label_to_Flatten , true ); // std::cout << " findpath in extractsurfacedisk done "; // assign scalars to the original surface mesh // typedef itk::SurfaceMeshCurvature surfktype; // typename surfktype::Pointer surfk=surfktype::New(); vtkPoints* vtkpoints = this->m_SurfaceMesh->GetPoints(); int numPoints = vtkpoints->GetNumberOfPoints(); vtkFloatArray* param = vtkFloatArray::New(); param->SetName("angle"); for(int i =0; i < numPoints; i++) { float temp=fabs(manifoldIntegrator->GetGraphNode(i)->GetTotalCost()); if (temp > m_MaxCost) temp=m_MaxCost; param->InsertNextValue(temp*255./m_MaxCost); } std::cout << " extractsurfacedisk done "; // m_SurfaceMesh->GetPointData()->SetScalars(param); } template bool FEMDiscConformalMap ::GenerateSystemFromSurfaceMesh() { if (!this->m_SurfaceMesh) { std::cout << " NO MESH"; return false;} std::cout << " Generate system from surface mesh " << std::endl; m_Smooth=m_Sigma; // create a material // Choose the material properties typename MaterialType::Pointer m; m=MaterialType::New(); m->GN=0; // Global number of the material /// m->E=4.0; // Young modulus -- used in the membrane /// m->A=0.0; // Crossection area /// m->h=0.0; // Crossection area /// m->I=0.0; // Moment of inertia /// m->nu=0.; //.0; // poissons -- DONT CHOOSE 1.0!!/// m->RhoC=0.0; // Create the element type ElementType::Pointer e1=ElementType::New(); e1->m_mat=dynamic_cast( m ); vtkPoints* vtkpoints = this->m_SurfaceMesh->GetPoints(); int numPoints = vtkpoints->GetNumberOfPoints(); int foundnum=0; int boundsz=0; for(int i =0; i < numPoints; i++) { double* pt = vtkpoints->GetPoint(i); typename NodeType::Pointer n; n=new NodeType(pt[0],pt[1],pt[2]); if (this->InDisc(manifoldIntegrator->GetGraphNode(i))) { n->GN=i; m_Solver.node.push_back(itk::fem::FEMP(n)); foundnum++; } if (this->InBorder(manifoldIntegrator->GetGraphNode(i))) boundsz++; } typename NodeType::Pointer* narr = new NodeType::Pointer[numPoints]; for(int i =0; i < numPoints; i++) { if (this->InDisc(manifoldIntegrator->GetGraphNode(i)) || this->InBorder(manifoldIntegrator->GetGraphNode(i))) { narr[i]=m_Solver.node.Find( i ); } } std::cout << " Found " << foundnum << " nodes " << std::endl; std::cout << " bound " << boundsz << std::endl; vtkCellArray* vtkcells = this->m_SurfaceMesh->GetPolys(); vtkIdType npts; vtkIdType* pts; unsigned long i = 0; // unsigned long toti = vtkcells->GetNumberOfCells(); // unsigned long rate = toti/50; // std::cout << " progress "; for(vtkcells->InitTraversal(); vtkcells->GetNextCell(npts, pts); ) { // if ( i % rate == 0 && i > rate ) std::cout << " " << (float) i / (float) toti << " "; // turn the cell into an element // std::cout << " points ids a " << pts[0] << " b " << pts[1] << " c " << pts[2] << std::endl; bool eltok=true; ElementType::Pointer e; e=dynamic_cast(e1->Clone()); if (this->InDisc(manifoldIntegrator->GetGraphNode(pts[0]))) e->SetNode(2,narr[pts[0]]); // e->SetNode(2,m_Solver.node.Find( pts[0] )); else eltok=false; if (this->InDisc(manifoldIntegrator->GetGraphNode(pts[1]))) e->SetNode(1,narr[pts[1]]); // e->SetNode(1,m_Solver.node.Find( pts[1] )); else eltok=false; if (this->InDisc(manifoldIntegrator->GetGraphNode(pts[2]))) e->SetNode(0,narr[pts[2]]); // e->SetNode(0,m_Solver.node.Find( pts[2] )); else eltok=false; if (eltok) { e->GN=i; m_Solver.el.push_back(itk::fem::FEMP(e)); i++; }//else std::cout <<" cannot find elt " << std::endl; } std::cout << " DONE: NUMBER OF CELLS " << i << std::endl; return true; } template void FEMDiscConformalMap ::FixBoundaryPoints(unsigned int option) { // if (dim > 3 || dim < -3 || dim == 0 ) return; unsigned int dofsperelt; itk::fem::Element::ArrayType::iterator elt=m_Solver.el.begin(); unsigned int Nnodes= (*elt)->GetNumberOfNodes(); unsigned int dofs=(*elt)->GetNumberOfDegreesOfFreedomPerNode(); dofsperelt=dofs*Nnodes; int fixct=0; int eltct=0; while(elt!=m_Solver.el.end()) { for (unsigned int i=0; iGetNode(i)->GN; // if( manifoldIntegrator->GetGraphNode(nodeid)->GetTotalCost() > 222 ) if( this->InBorder(manifoldIntegrator->GetGraphNode(nodeid)) ) { itk::fem::LoadBC::Pointer l1; l1=itk::fem::LoadBC::New(); l1->m_element=(*elt); l1->m_dof=i; l1->m_value=vnl_vector(1,0.0); // for exp float fixvalue=0.0; if ( this->m_MapToCircle) fixvalue=this->GetBoundaryParameterForCircle(nodeid,option); else fixvalue=this->GetBoundaryParameterForSquare(nodeid,option); l1->m_value=vnl_vector(1,fixvalue); // for direct rad m_Solver.load.push_back( itk::fem::FEMP(&*l1) ); /* itk::fem::LoadNode::Pointer ln1=itk::fem::LoadNode::New(); ln1->m_pt=0; ln1->F.set_size(1); ln1->F.fill(fixvalue); ln1->m_element=(*elt); m_Solver.load.push_back( itk::fem::FEMP(&*ln1) ); itk::fem::LoadNode::Pointer ln2=itk::fem::LoadNode::New(); ln2->m_pt=1; ln2->F.set_size(1); ln2->F.fill(fixvalue); ln2->m_element=(*elt); m_Solver.load.push_back( itk::fem::FEMP(&*ln2) ); itk::fem::LoadNode::Pointer ln3=itk::fem::LoadNode::New(); ln3->m_pt=2; ln3->F.set_size(1); ln3->F.fill(fixvalue); ln3->m_element=(*elt); m_Solver.load.push_back( itk::fem::FEMP(&*ln3) ); */ if (i==0) fixct++; // std::cout << " bord coord "<< manifoldIntegrator->GetGraphNode(nodeid)->GetLocation() << std::endl; } } elt++; eltct++; } std::cout << " Fixed elt number " << fixct << " of " << eltct << std::endl; } template void FEMDiscConformalMap ::ApplyRealForces() { /* itk::fem::Element::Pointer e = m_Solver.el[m_PoleElementsGN[0]]; // load node 0 { itk::fem::LoadNode::Pointer ln1=itk::fem::LoadNode::New(); ln1->m_pt=0; ln1->F.set_size(1); ln1->F.fill(-4.0*m_Pi); ln1->m_element=(e); m_Solver.load.push_back( itk::fem::FEMP(&*ln1) ); itk::fem::LoadNode::Pointer ln2=itk::fem::LoadNode::New(); ln2->m_pt=1; ln2->F.set_size(1); ln2->F.fill(-4.0*m_Pi); ln2->m_element=(e); m_Solver.load.push_back( itk::fem::FEMP(&*ln2) ); itk::fem::LoadNode::Pointer ln3=itk::fem::LoadNode::New(); ln3->m_pt=2; ln3->F.set_size(1); ln3->F.fill(-4.0*m_Pi); ln3->m_element=(e); m_Solver.load.push_back( itk::fem::FEMP(&*ln3) ); } */ } template void FEMDiscConformalMap ::ApplyImaginaryForces() { itk::fem::Element::Pointer e = m_Solver.el[m_PoleElementsGN[0]]; itk::fem::LoadBC::Pointer l1; l1=itk::fem::LoadBC::New(); l1->m_element=(e); l1->m_dof=0; l1->m_value=vnl_vector(1,-1./3.);// for exp m_Solver.load.push_back( itk::fem::FEMP(&*l1) ); itk::fem::LoadBC::Pointer l2; l2=itk::fem::LoadBC::New(); l2->m_element=(e); l2->m_dof=1; l2->m_value=vnl_vector(1,-1./3.);// for exp m_Solver.load.push_back( itk::fem::FEMP(&*l2) ); itk::fem::LoadBC::Pointer l3; l3=itk::fem::LoadBC::New(); l3->m_element=(e); l3->m_dof=2; l3->m_value=vnl_vector(1,2./3.);// for exp m_Solver.load.push_back( itk::fem::FEMP(&*l3) ); } template void FEMDiscConformalMap::MakeFlatImage() { // first declare the flat image typename FlatImageType::RegionType region; typename FlatImageType::SizeType size; int sz=256; size[0]=sz; size[1]=sz; region.SetSize( size ); m_FlatImage = FlatImageType::New(); m_FlatImage->SetRegions( region ); m_FlatImage->Allocate(); typename FlatImageType::IndexType index; std::cout << " Making flat image " << std::endl; int maxits=100; for ( int its =0; its <= maxits; its++) { for( ::itk::fem::Solver::NodeArray::iterator n = m_Solver.node.begin(); n!=m_Solver.node.end(); n++) { float temp=255.0 - manifoldIntegrator->GetGraphNode((*n)->GN)->GetValue(3); // curvature // float temp=255.0*manifoldIntegrator->GetGraphNode((*n)->GN)->GetValue(2); // extrinsic dist index[0]=(long int)(0.5+( 1.0+manifoldIntegrator->GetGraphNode((*n)->GN)->GetValue(0) )*(float)(sz-1)/2.); index[1]=(long int)(0.5+( 1.0+manifoldIntegrator->GetGraphNode((*n)->GN)->GetValue(1) )*(float)(sz-1)/2.); //std::cout << " ind " << index << std::endl; m_FlatImage->SetPixel(index, temp); } typedef itk::DiscreteGaussianImageFilter dgf; typename dgf::Pointer filter = dgf::New(); filter->SetVariance(1.0); filter->SetUseImageSpacingOff(); filter->SetMaximumError(.01f); filter->SetInput(m_FlatImage); filter->Update(); m_FlatImage=filter->GetOutput(); if ( its < maxits) { int center=(int)sz/2; itk::ImageRegionIteratorWithIndex it( m_FlatImage, region ); it.GoToBegin(); typename FlatImageType::IndexType index; while( !it.IsAtEnd() ) { index=it.GetIndex(); float x=(float)index[0]-(float)sz/2.0; float y=(float)index[1]-(float)sz/2.0; float dist=sqrt(x*x+y*y); // std::cout << "center " << center << " index " << index << "dist " << dist ; if ( dist > center ) it.Set( 0.0 ); ++it; } } } } template void FEMDiscConformalMap::BuildOutputMeshes(float tval) { std::cout << " build output mesh " << std::endl; typedef GraphSearchNodeType::NodeLocationType loctype; // Get the number of points in the mesh int numPoints = m_Solver.node.size(); vtkDataArray* labels=this->m_SurfaceMesh->GetPointData()->GetArray("Label"); vtkDataArray* features=NULL; if ( this->m_SurfaceFeatureMesh ) if ( this->m_SurfaceFeatureMesh->GetPointData()->GetArray("Feature") ) features=this->m_SurfaceFeatureMesh->GetPointData()->GetArray("Feature"); else features=labels; else features=labels; // Create vtk polydata vtkPolyData* polydata1 = vtkPolyData::New(); vtkPolyData* polydata2 = vtkPolyData::New(); // Create the vtkPoints object and set the number of points vtkPoints* vpoints1 = vtkPoints::New(); vtkPoints* vpoints2 = vtkPoints::New(); vpoints1->SetNumberOfPoints(numPoints); vpoints2->SetNumberOfPoints(numPoints); std::cout <<" start pts "; int idx=0; vtkFloatArray* param = vtkFloatArray::New(); param->SetName("feature"); vtkFloatArray* paramAngle = vtkFloatArray::New(); paramAngle->SetName("angle"); vtkFloatArray* paramDistance = vtkFloatArray::New(); paramDistance->SetName("distance"); vtkIdTypeArray* paramPoints = vtkIdTypeArray::New(); paramPoints->SetName("points"); for( ::itk::fem::Solver::NodeArray::iterator n = m_Solver.node.begin(); n!=m_Solver.node.end(); n++) { loctype loc = manifoldIntegrator->GetGraphNode((*n)->GN)->GetLocation(); float pt1[3]; pt1[0]=loc[0]; pt1[1]=loc[1]; pt1[2]=loc[2]; float pt2[3]; pt2[0]=manifoldIntegrator->GetGraphNode((*n)->GN)->GetValue(0)*100.* (1.0 - tval) + tval * loc[0]; pt2[1]=manifoldIntegrator->GetGraphNode((*n)->GN)->GetValue(1)*100.* (1.0 - tval) + tval * loc[1]; pt2[2]=1. * (1.0 - tval) + tval * loc[2]; vpoints1->SetPoint(idx,pt1); vpoints2->SetPoint(idx,pt2); // temp=m_RealSolution[(*n)->GN]*255.0; // temp=( (unsigned int) this->InBorder(manifoldIntegrator->GetGraphNode((*n)->GN)) )*255; float temp=manifoldIntegrator->GetGraphNode((*n)->GN)->GetValue(3); // for curvature temp=features->GetTuple1((*n)->GN); // float temp=manifoldIntegrator->GetGraphNode((*n)->GN)->GetValue(1)*255; // for curvature float temp2=manifoldIntegrator->GetGraphNode((*n)->GN)->GetValue(0)*255;// for length param->InsertNextValue(temp); paramDistance->InsertNextValue(temp2); paramPoints->InsertNextValue((*n)->GN); paramAngle->InsertNextValue(temp); // curvature // param->InsertNextValue(temp*255./m_MaxCost); (*n)->GN=idx; idx++; } std::cout << " done with pts " << std::endl; vtkCellArray* tris1 = vtkCellArray::New(); vtkCellArray* tris2 = vtkCellArray::New(); std::cout << " start with tris " << std::endl; for( ::itk::fem::Solver::ElementArray::iterator n = m_Solver.el.begin(); n!=m_Solver.el.end(); n++) { tris1->InsertNextCell(3); tris2->InsertNextCell(3); for (unsigned int i=0; i<(*n)->GetNumberOfNodes(); i++) { tris1->InsertCellPoint((*n)->GetNode(i)->GN); tris2->InsertCellPoint((*n)->GetNode(i)->GN); } } std::cout << " done with tris " << std::endl; // Assign points and cells polydata1->SetPoints(vpoints1); polydata2->SetPoints(vpoints2); polydata1->SetPolys(tris1); polydata2->SetPolys(tris2); polydata1->GetPointData()->SetScalars(param); polydata2->GetPointData()->SetScalars(param); polydata1->GetPointData()->AddArray(paramAngle); polydata2->GetPointData()->AddArray(paramAngle); polydata1->GetPointData()->AddArray(paramDistance); polydata2->GetPointData()->AddArray(paramDistance); polydata1->GetPointData()->AddArray(paramPoints); polydata2->GetPointData()->AddArray(paramPoints); m_ExtractedSurfaceMesh=polydata1;// vtkDelaunay2D* delny2=vtkDelaunay2D::New(); delny2->SetInput(polydata2); m_DiskSurfaceMesh=delny2->GetOutput(); //m_DiskSurfaceMesh=polydata2; return; } template void FEMDiscConformalMap ::ConformalMap() { m_Solver.load.clear(); m_Solver.node.clear(); m_Solver.el.clear(); /** * Open the file and assign it to stream object f */ if (m_ReadFromFile) { const char* filename=m_ParameterFileName.c_str(); std::cout<<"Reading FEM problem from file: "<GenerateSystemFromSurfaceMesh()); else return; /** * Assign a unique id (global freedom number - GFN) * to every degree of freedom (DOF) in a system. */ m_Solver.GenerateGFN(); m_ImagSolution.set_size(m_Solver.GetNumberOfDegreesOfFreedom()); m_RealSolution.set_size(m_Solver.GetNumberOfDegreesOfFreedom()); m_Radius.set_size(m_Solver.GetNumberOfDegreesOfFreedom()); m_RealSolution.fill(0); m_ImagSolution.fill(0); m_Radius.fill(0); m_Debug=false; if (m_Debug) { for( ::itk::fem::Solver::NodeArray::iterator n = m_Solver.node.begin(); n!=m_Solver.node.end(); n++) { std::cout<<"Node#: "<<(*n)->GN<<": "; std::cout << " coord " << (*n)->GetCoordinates() << " coord2 " << manifoldIntegrator->GetGraphNode((*n)->GN)->GetLocation() << std::endl; } for( ::itk::fem::Solver::ElementArray::iterator n = m_Solver.el.begin(); n!=m_Solver.el.end(); n++) { std::cout<<"Elt#: "<<(*n)->GN<<": has " << (*n)->GetNumberOfNodes() << " nodes "; for (unsigned int i=0; i<(*n)->GetNumberOfNodes(); i++) std::cout << " coord " << (*n)->GetNode(i)->GetCoordinates() << std::endl; } } unsigned int maxits=m_Solver.GetNumberOfDegreesOfFreedom(); // should be > twice ndofs //if (m_Debug) std::cout << " ndof " << maxits << std::endl; itpackWrapper.SetMaximumNumberIterations(maxits*5); itpackWrapper.SetTolerance(1.e-4); itpackWrapper.SuccessiveOverrelaxation(); // itpackWrapper.JacobianConjugateGradient(); itpackWrapper.SetMaximumNonZeroValuesInMatrix(maxits*50); m_Solver.SetLinearSystemWrapper(&itpackWrapper); this->FixBoundaryPoints(0); m_Solver.AssembleK(); m_Solver.DecomposeK(); // this->ApplyRealForces(); std::cout << " appl force "; m_Solver.AssembleF(); // for (int i=0; iGetDegreeOfFreedom(d))!=::itk::fem::Element::InvalidDegreeOfFreedomID; d++ ) { m_RealSolution[dof]=m_Solver.GetSolution(dof); // if ( ct % 10 == 0) std::cout << " mrdof " << m_RealSolution[dof] << " dof " << dof << std::endl; } ct++; } this->ConformalMap2(); // this->ConformalMap3(); this->ConjugateHarmonic(); } template void FEMDiscConformalMap ::ConformalMap2() { m_Solver.load.clear(); this->FixBoundaryPoints(1); m_Solver.AssembleK(); // need to reassemble b/c LoadBC's affect K m_Solver.AssembleF(); m_Solver.Solve(); m_Solver.UpdateDisplacements();// copies solution to nodes unsigned long ct = 0; for( ::itk::fem::Solver::NodeArray::iterator n = m_Solver.node.begin(); n!=m_Solver.node.end(); n++) { for( unsigned int d=0, dof; (dof=(*n)->GetDegreeOfFreedom(d))!=::itk::fem::Element::InvalidDegreeOfFreedomID; d++ ) { m_ImagSolution[dof]=m_Solver.GetSolution(dof); // if (ct % 10 == 0) std::cout << " midof " << m_ImagSolution[dof] << " dof " << dof << std::endl; } ct++; } } template void FEMDiscConformalMap ::MeasureLengthDistortion() { // now measure the length distortion of the given solution manifoldIntegrator->EmptyQ(); manifoldIntegrator->SetSearchFinished( false ); for (int i=0; iGetGraphSize(); i++) { manifoldIntegrator->GetGraphNode(i)->SetTotalCost(vnl_huge_val(manifoldIntegrator->GetMaxCost())); manifoldIntegrator->GetGraphNode(i)->SetUnVisited(); manifoldIntegrator->GetGraphNode(i)->SetValue(0.0,1); manifoldIntegrator->GetGraphNode(i)->SetValue(0.0,2); manifoldIntegrator->GetGraphNode(i)->SetPredecessor(NULL); } manifoldIntegrator->SetSource(manifoldIntegrator->GetGraphNode(this->m_SourceNodeNumber)); manifoldIntegrator->InitializeQueue(); float mchere = 1.2; manifoldIntegrator->SetMaxCost(mchere); manifoldIntegrator->SetWeights(this->m_MaxCost,this->m_DistanceCostWeight,this->m_LabelCostWeight); manifoldIntegrator->FindPath(); // backtrack everywhere to set up forweard tracking float maxmanifolddist=0; float meanmanifolddist=0; float distDistortion=0; unsigned int ct = 0; for (int i=0; iGetGraphSize(); i++) { if (manifoldIntegrator->GetGraphNode(i)) if (manifoldIntegrator->GetGraphNode(i)->GetTotalCost() <= manifoldIntegrator->GetMaxCost() ) { float ttt=manifoldIntegrator->GetGraphNode(i)->GetValue(2); meanmanifolddist+=ttt; if ( ttt > maxmanifolddist) maxmanifolddist=ttt; ct++; } } meanmanifolddist/=(float)ct; ct=0; for (int i=0; iGetGraphSize(); i++) { float manifolddist; float rad; if (manifoldIntegrator->GetGraphNode(i)) if (manifoldIntegrator->GetGraphNode(i)->GetTotalCost() < manifoldIntegrator->GetMaxCost()) { rad=manifoldIntegrator->GetGraphNode(i)->GetValue(0); manifolddist=manifoldIntegrator->GetGraphNode(i)->GetValue(2)/maxmanifolddist; manifoldIntegrator->GetGraphNode(i)->SetValue(manifolddist,2); distDistortion+=fabs(rad-manifolddist); ct++; } } std::cout << " distDistortion/ct " << distDistortion/(float)ct << " maxmfd " << maxmanifolddist <GetOutput(); float total=0.0; for (int i=0; im_SurfaceMesh->GetPoints()->GetNumberOfPoints(); i++) { float length1=0; float length2=0; float pt1[3]; for (int ne=0; neGetGraphNode(i)->GetNumberOfNeighbors(); ne++) { } } */ } template void FEMDiscConformalMap ::ConjugateHarmonic() { std::cout << " Conformal coordinates " << std::endl; unsigned long ct = 0; for( ::itk::fem::Solver::NodeArray::iterator n = m_Solver.node.begin(); n!=m_Solver.node.end(); n++) { ct++; unsigned long dof=(*n)->GetDegreeOfFreedom(0); if ( dof < m_RealSolution.size() ) { // std::cout << " dof " << dof << std::endl; float U = m_RealSolution[dof]; // std::cout << " U " << U << std::endl; float V = m_ImagSolution[dof]; // std::cout << " V " << V << " ngn " << (*n)->GN << std::endl; manifoldIntegrator->GetGraphNode( (*n)->GN )->SetValue(U,0); manifoldIntegrator->GetGraphNode( (*n)->GN )->SetValue(V,1); // if (ct % 100 == 0) std::cout << " U " << U<< " V " << V << std::endl; } } // this->MakeFlatImage(); this->BuildOutputMeshes(); return; } } // namespace itk /* vtkSelectPolyData *loop = vtkSelectPolyData::New(); loop->SetInput(this->m_SurfaceMesh); // put points inside ... vtkPoints* points = vtkPoints::New(); points->SetNumberOfPoints( this->m_DiscBoundaryList.size()); unsigned int idx=0; for ( unsigned int j = 0 ; j < this->m_DiscBoundaryList.size(); j ++ ) { float pt1[3]; typename GraphSearchNodeType::NodeLocationType loc=this->m_DiscBoundaryList[j]->GetLocation(); pt1[0]=loc[0]; pt1[1]=loc[1]; pt1[2]=loc[2]; // unsigned int idx=this->m_DiscBoundaryList[j]->GetIdentity(); points->SetPoint(idx,pt); idx++; } loop->GenerateSelectionScalarsOff(); loop->SetSelectionModeToClosestPointRegion(); //negative scalars inside loop->SetSelectionModeToSmallestRegion(); //negative scalars inside loop->SetLoop(points); loop->Modified(); vtkClipPolyData *clip = vtkClipPolyData::New(); //clips out positive region clip->SetInput(loop->GetOutput()); vtkPolyDataMapper *clipMapper = vtkPolyDataMapper::New(); clipMapper->SetInput(clip->GetOutput()); vtkActor *clipActor = vtkActor::New(); clipActor->SetMapper(clipMapper); clipActor->AddPosition(1, 0, 0); // clipActor->GetProperty()->SetColor(0, 0, 1); //Set colour blue vtkRenderer *ren1 = vtkRenderer::New(); vtkRenderWindow* renWin = vtkRenderWindow::New(); renWin->AddRenderer(ren1); vtkRenderWindowInteractor* inter = vtkRenderWindowInteractor::New(); inter->SetRenderWindow(renWin); ren1->SetViewport(0.0, 0.0, 1.0, 1.0); ren1->AddActor(clipActor); renWin->Render(); inter->Start(); ren1->Delete(); renWin->Delete(); points-> Delete(); loop->Delete(); clip->Delete(); clipMapper->Delete(); clipActor->Delete(); */ #endif ants-1.9.2+svn680.dfsg/Temporary/itkFEMDiscConformalMap.h000077500000000000000000000220161147325206600230400ustar00rootroot00000000000000/*========================================================================= Program: Insight Segmentation & Registration Toolkit Module: $RCSfile: itkFEMDiscConformalMap.h,v $ Language: C++ Date: $Date: 2006/10/13 19:20:46 $ Version: $Revision: 1.2 $ Copyright (c) 2002 Insight Consortium. All rights reserved. See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _FEMDiscConformalMap_h #define _FEMDiscConformalMap_h #include #include #include #include "vtkDataSetWriter.h" #include "vtkDataSetMapper.h" #include "vtkRenderer.h" #include "vtkRenderWindow.h" #include "vtkActor.h" #include "vtkRenderWindowInteractor.h" #include "vtkDataSetReader.h" #include "vtkUnstructuredGrid.h" #include "vtkDataSet.h" #include "vtkCellArray.h" #include "vtkVolume16Reader.h" #include "vtkImageReader2.h" #include "vtkPolyDataMapper.h" #include "vtkActor.h" #include "vtkOutlineFilter.h" #include "vtkCamera.h" #include "vtkProperty.h" #include "vtkPolyData.h" #include "vtkPolyVertex.h" #include "vtkPointData.h" #include "vtkExtractEdges.h" #include "vtkPolyDataNormals.h" #include "vtkMarchingCubes.h" #include "vtkImageGaussianSmooth.h" #include "vtkDecimatePro.h" #include "vtkContourFilter.h" #include "vtkPolyDataConnectivityFilter.h" //#include "vtkKitwareContourFilter.h" #include "vtkSmoothPolyDataFilter.h" #include "vtkSTLWriter.h" #include "vtkUnstructuredGridToPolyDataFilter.h" //#include "itkImageToVTKImageFilter.h" #include "itkDijkstrasAlgorithm.h" #include "itkManifoldIntegrationAlgorithm.h" //#include "itkTriangulatedDijkstrasAlgorithm.h" #include "itkObject.h" #include "itkProcessObject.h" #include "itkVectorContainer.h" #include "itkCastImageFilter.h" #include "itkFEM.h" #include "itkFEMLinearSystemWrapperItpack.h" #include "itkMesh.h" namespace itk { /** \class FEMDiscConformalMap * Avants Epstein conformal mapping algorithm using FEM. * * \note The origin of a neighborhood is always taken to be * the first point entered into and the * last point stored in the list. */ template < typename TSurface, typename TImage, unsigned int TDimension=3 > class FEMDiscConformalMap : public ProcessObject { public: /** Standard class typedefs. */ typedef FEMDiscConformalMap Self; typedef ProcessObject Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Run-time type information (and related methods). */ itkTypeMacro(FEMDiscConformalMap,ProcessObject); /** Method for creation through the object factory. */ itkNewMacro(Self); /** Surface (mesh) types. */ typedef TImage ImageType; typedef typename TImage::Pointer ImageTypePointer; typedef typename TImage::IndexType IndexType; /** Surface (mesh) types. */ typedef TSurface SurfaceType; typedef SurfaceType* SurfaceTypePointer; // typedef typename SurfaceType::PointType PointType; // typedef typename SurfaceType::CellsContainerPointer InputCellsContainerPointer; // typedef typename SurfaceType::CellsContainer::Iterator InputCellsContainerIterator; /** Image dimension. */ // itkStaticConstMacro(ImageDimension, unsigned int, TImage::ImageDimension); itkStaticConstMacro(ImageDimension, unsigned int, TDimension); itkStaticConstMacro(SurfaceDimension, unsigned int, TDimension); typedef float RealType; typedef vnl_vector VectorType; typedef vnl_vector_fixed FixedVectorType; typedef vnl_matrix MatrixType; typedef Image FlatImageType; typedef typename FlatImageType::Pointer FlatImageTypePointer; typedef GraphSearchNode GraphSearchNodeType; typedef typename GraphSearchNodeType::Pointer GraphSearchNodePointer; typedef typename GraphSearchNodeType::NodeLocationType NodeLocationType; typedef ManifoldIntegrationAlgorithm ManifoldIntegratorType; // typedef TriangulatedDijkstrasAlgorithm ManifoldIntegratorType; typedef typename ManifoldIntegratorType::Pointer ManifoldIntegratorTypePointer; /** FEM types */ typedef itk::fem::MaterialLinearElasticity MaterialType; typedef itk::fem::Node NodeType; typedef itk::fem::LoadNode LoadType; typedef itk::fem::Element3DC0LinearTriangularLaplaceBeltrami ElementType; typedef itk::fem::Element3DC0LinearTriangularMembrane ElementType1; /** Set input parameter file */ itkSetStringMacro( ParameterFileName ); /** Set input parameter file */ itkGetStringMacro( ParameterFileName ); itkGetMacro(Sigma, RealType); itkSetMacro(Sigma, RealType); itkGetMacro(SurfaceMesh, SurfaceTypePointer); itkSetMacro(SurfaceMesh, SurfaceTypePointer); itkGetMacro(SurfaceFeatureMesh, SurfaceTypePointer); itkSetMacro(SurfaceFeatureMesh, SurfaceTypePointer); void SetDebug(bool b) {m_Debug=b;} void SetReadFromFile(bool b) {m_ReadFromFile=b;} float AssessNodeDistanceCost( unsigned int ); float GetBoundaryParameterForSquare(unsigned int, unsigned int); float GetBoundaryParameterForCircle(unsigned int, unsigned int); unsigned int FindLoopAroundNode( unsigned int j ); unsigned int AddVertexToLoop(); void LocateAndParameterizeDiscBoundary(unsigned int,bool) ; void FixBoundaryPoints( unsigned int option ); void ConformalMap(); void ConformalMap2(); void ConjugateHarmonic(); bool InBorder(GraphSearchNodePointer); bool InDisc(GraphSearchNodePointer); void ExtractSurfaceDisc( unsigned int label = 0 ); void BuildOutputMeshes(float tval = 0.0); SurfaceTypePointer m_ExtractedSurfaceMesh; SurfaceTypePointer m_DiskSurfaceMesh; void SetSmooth(float i){ m_Smooth=i;} void MeasureLengthDistortion(); void SetParamWhileSearching( bool b ) { this->m_ParamWhileSearching=b; } void FindSource(IndexType); void FindMeanSourceInLabel(unsigned int); void MakeFlatImage(); FlatImageTypePointer m_FlatImage; inline void SetLabelToFlatten( unsigned int b ) { this->m_Label_to_Flatten=b; } inline void SetMaxCost( float f ) { this->m_MaxCost=f; } inline void SetDistanceCostWeight(float d) { this->m_DistanceCostWeight=d; } inline void SetLabelCostWeight(float d) { this->m_LabelCostWeight=d; } inline void SetMapToSquare( ) { this->m_MapToSquare=true; this->m_MapToCircle=false; } inline void SetMapToCircle( ) { this->m_MapToSquare=false; this->m_MapToCircle=true; } inline void SetDiscBoundaryList( std::vector b ) { this->m_DiscBoundaryList.assign(b.begin(),b.end()); }; protected: bool GenerateSystemFromSurfaceMesh(); void ApplyRealForces(); void ApplyImaginaryForces(); FEMDiscConformalMap(); virtual ~FEMDiscConformalMap(){}; private: std::vector m_DiscBoundaryList; // contains ids of nodes at boundary std::vector m_HelpFindLoop; // 0 = not found, 2 = already done , 1 = in loop std::vector m_DiscBoundarySorter; // contains ids of nodes at boundary std::vector m_DiscBoundaryParameter; // contains ids of nodes at boundary RealType m_Sigma; RealType m_Pi; std::string m_ParameterFileName; int m_NorthPole; int m_SourceNodeNumber; float m_DistanceCostWeight; float m_LabelCostWeight; itk::fem::Solver m_Solver; bool m_ReadFromFile; bool m_Debug; bool m_FindingRealSolution; bool m_MapToCircle; bool m_MapToSquare; bool m_ParamWhileSearching; VectorType m_RealSolution; VectorType m_ImagSolution; VectorType m_Radius; SurfaceTypePointer m_SurfaceMesh; SurfaceTypePointer m_SurfaceFeatureMesh; float m_MaxCost; itk::fem::LinearSystemWrapperItpack itpackWrapper; unsigned long m_PoleElementsGN[7]; ManifoldIntegratorTypePointer manifoldIntegrator; float m_Smooth; unsigned int m_Label_to_Flatten; GraphSearchNodePointer m_RootNode; }; } // namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkFEMDiscConformalMap.cxx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkFEMElement3DC0LinearTriangular.cxx000066400000000000000000000231671147325206600253660ustar00rootroot00000000000000/*========================================================================= Program: Insight Segmentation & Registration Toolkit Module: $RCSfile: itkFEMElement3DC0LinearTriangular.cxx,v $ Language: C++ Date: $Date: 2005/11/08 15:34:16 $ Version: $Revision: 1.1.1.1 $ Copyright (c) Insight Software Consortium. All rights reserved. See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ // disable debug warnings in MS compiler #ifdef _MSC_VER #pragma warning(disable: 4786) #endif #include "itkFEMElement3DC0LinearTriangular.h" #include "vnl/vnl_math.h" #include "vnl/algo/vnl_svd.h" #include "vnl/algo/vnl_qr.h" namespace itk { namespace fem { const Element3DC0LinearTriangular::Float Element3DC0LinearTriangular ::trigGaussRuleInfo[6][7][4] = { { // order=0, never used { 0.0 } }, { // order=1 //<-------------------------- point ---------------------------> <-------weight-----> { 0.33333333333333333, 0.33333333333333333, 0.33333333333333333, 1.00000000000000000} }, { // order=2 { 0.66666666666666667, 0.16666666666666667, 0.16666666666666667, 0.33333333333333333}, { 0.16666666666666667, 0.66666666666666667, 0.16666666666666667, 0.33333333333333333}, { 0.16666666666666667, 0.16666666666666667, 0.66666666666666667, 0.33333333333333333} }, { // order=3, p=-3 in the book { 0.00000000000000000, 0.50000000000000000, 0.50000000000000000, 0.33333333333333333}, { 0.50000000000000000, 0.00000000000000000, 0.50000000000000000, 0.33333333333333333}, { 0.50000000000000000, 0.50000000000000000, 0.00000000000000000, 0.33333333333333333} }, { // order=4, p=6 in the book { 0.10810301816807023, 0.44594849091596489, 0.44594849091596489, 0.22338158967801147}, { 0.44594849091596489, 0.10810301816807023, 0.44594849091596489, 0.22338158967801147}, { 0.44594849091596489, 0.44594849091596489, 0.10810301816807023, 0.22338158967801147}, { 0.81684757298045851, 0.09157621350977074, 0.09157621350977074, 0.10995174365532187}, { 0.09157621350977074, 0.81684757298045851, 0.09157621350977074, 0.10995174365532187}, { 0.09157621350977074, 0.09157621350977074, 0.81684757298045851, 0.10995174365532187} }, { // order=5, p=7 in the book { 0.33333333333333333, 0.33333333333333333, 0.33333333333333333, 0.22500000000000000}, { 0.79742698535308732, 0.10128650732345634, 0.10128650732345634, 0.12593918054482715}, { 0.10128650732345634, 0.79742698535308732, 0.10128650732345634, 0.12593918054482715}, { 0.10128650732345634, 0.10128650732345634, 0.79742698535308732, 0.12593918054482715}, { 0.05971587178976982, 0.47014206410511509, 0.47014206410511509, 0.13239415278850618}, { 0.47014206410511509, 0.05971587178976982, 0.47014206410511509, 0.13239415278850618}, { 0.47014206410511509, 0.47014206410511509, 0.05971587178976982, 0.13239415278850618} } }; const unsigned int Element3DC0LinearTriangular ::Nip[6]= { 0,1,3,3,6,7 }; void Element3DC0LinearTriangular ::GetIntegrationPointAndWeight(unsigned int i, VectorType& pt, Float& w, unsigned int order) const { // FIXME: range checking // default integration order if (order==0 || order>5) { order=DefaultIntegrationOrder; } pt.set_size(3); /* * We provide implementation for 5 different integration rules * as defined in chapter 24 - Implementation of Iso-P Truangular * Elements, of http://titan.colorado.edu/courses.d/IFEM.d/. * * Note that the order parameter here does not correspond to the * actual order of integration, but rather the degree of polynomials * that are exactly integrated. In addition, there are two integration * rules for polynomials of 2nd degree. In order to allow using both of * them, we assign the index number 3 to the second one. Note that this * does not mean that the rule is capable of integrating the polynomials * of 3rd degree. It's just an index of a rule. */ pt.copy_in(trigGaussRuleInfo[order][i]); // We scale the weight by 0.5, to take into account // the factor that must be applied when integrating. w=0.5*trigGaussRuleInfo[order][i][3]; } unsigned int Element3DC0LinearTriangular ::GetNumberOfIntegrationPoints(unsigned int order) const { // FIXME: range checking // default integration order if (order==0) { order=DefaultIntegrationOrder; } return Nip[order]; } Element3DC0LinearTriangular::VectorType Element3DC0LinearTriangular ::ShapeFunctions( const VectorType& pt ) const { // Linear triangular element has 3 shape functions VectorType shapeF(3); // Shape functions are equal to coordinates shapeF=pt; return shapeF; } void Element3DC0LinearTriangular ::ShapeFunctionDerivatives( const VectorType&, MatrixType& shapeD ) const { // Matrix of shape functions derivatives is an // identity matrix for linear triangular element. shapeD.set_size(3,3); shapeD.fill(0.0); shapeD[0][0]=1.0; shapeD[1][1]=1.0; shapeD[2][2]=1.0; } bool Element3DC0LinearTriangular ::GetLocalFromGlobalCoordinates( const VectorType& globalPt , VectorType& localPt) const { Float x, x1, x2, x3, y, y1, y2, y3, z, z1, z2, z3, A; localPt.set_size(3); x=globalPt[0]; y=globalPt[1]; z=globalPt[2]; x1 = this->m_node[0]->GetCoordinates()[0]; y1 = this->m_node[0]->GetCoordinates()[1]; x2 = this->m_node[1]->GetCoordinates()[0]; y2 = this->m_node[1]->GetCoordinates()[1]; x3 = this->m_node[2]->GetCoordinates()[0]; y3 = this->m_node[2]->GetCoordinates()[1]; z1 = this->m_node[0]->GetCoordinates()[2]; z2 = this->m_node[1]->GetCoordinates()[2]; z3 = this->m_node[2]->GetCoordinates()[2]; //FIXME! A=x1*y2 - x2*y1 + x3*y1 - x1*y3 + x2*y3 - x3*y2; // localPt[0]=((y2 - y3)*x + (x3 - x2)*y + x2*y3 - x3*y2)/A; // localPt[1]=((y3 - y1)*x + (x1 - x3)*y + x3*y1 - x1*y3)/A; // localPt[2]=((y1 - y2)*x + (x2 - x1)*y + x1*y2 - x2*y1)/A; if (localPt[0] < 0.0 || localPt[0] > 1.0 || localPt[1] < 0.0 || localPt[1] > 1.0 || localPt[2] < 0.0 || localPt[2] > 1.0 ) { return false; } else { return true; } } Element3DC0LinearTriangular::Float Element3DC0LinearTriangular ::JacobianDeterminant( const VectorType& pt, const MatrixType* pJ ) const { // use heron's formula int na=0; int nb=1; int nc=2; VectorType A=this->GetNode(na)->GetCoordinates(); VectorType B=this->GetNode(nb)->GetCoordinates(); VectorType C=this->GetNode(nc)->GetCoordinates(); VectorType BA =B-A; VectorType CA =C-A; VectorType CB =C-B; float L1=CB.magnitude(); float L2=CA.magnitude(); float L3=BA.magnitude(); float s=(L1+L2+L3)*.5; Float det = sqrt( s*(s-L1)*(s-L2)*(s-L3) ); /* // use the formula for tri pqr, area is mag( vec(pq) cross vec(pr) ) VectorType a=this->GetNode(2)->GetCoordinates()-this->GetNode(0)->GetCoordinates(); VectorType b=this->GetNode(1)->GetCoordinates()-this->GetNode(0)->GetCoordinates(); VectorType c; c.set_size(3); c[0] = a[1] * b[2] - a[2] * b[1]; c[1] = a[2] * b[0] - a[0] * b[2]; c[2] = a[0] * b[1] - a[1] * b[0]; Float det=0.5*c.magnitude(); */ // std::cout << " area " << det << std::endl; return det; } void Element3DC0LinearTriangular ::JacobianInverse( const VectorType& pt, MatrixType& invJ, const MatrixType* pJ ) const { MatrixType* pJlocal=0; // If Jacobian was not provided, we // need to compute it here if(pJ==0) { pJlocal=new MatrixType(); this->Jacobian( pt, *pJlocal ); pJ=pJlocal; } // invJ=vnl_svd_inverse(*pJ); invJ=vnl_qr(*pJ).inverse(); /* // Note that inverse of Jacobian is not quadratic matrix MatrixType invJ2; invJ2.set_size(3,3); invJ2.fill(0); Float idet=1.0/this->JacobianDeterminant( pt, pJ ); invJ2[0][0]=idet*((*pJ)[1][1]-(*pJ)[2][1]); invJ2[0][1]=idet*((*pJ)[2][1]-(*pJ)[0][1]); invJ2[0][2]=idet*((*pJ)[0][1]-(*pJ)[1][1]); invJ2[1][0]=idet*((*pJ)[2][0]-(*pJ)[1][0]); invJ2[1][1]=idet*((*pJ)[0][0]-(*pJ)[2][0]); invJ2[1][2]=idet*((*pJ)[1][0]-(*pJ)[0][0]); std::cout << " pJ " << std::endl; std::cout << (*pJ) << std::endl; std::cout << " invJ " << std::endl; std::cout << (invJ) << std::endl; std::cout << " invJ2 " << std::endl; std::cout << (invJ2) << std::endl;*/ delete pJlocal; } /* * Draw the element on device context pDC. */ #ifdef FEM_BUILD_VISUALIZATION void Element3DC0LinearTriangular ::Draw(CDC* pDC, Solution::ConstPointer sol) const { int x1=m_node[0]->GetCoordinates()[0]*DC_Scale; int y1=m_node[0]->GetCoordinates()[1]*DC_Scale; int x2=m_node[1]->GetCoordinates()[0]*DC_Scale; int y2=m_node[1]->GetCoordinates()[1]*DC_Scale; int x3=m_node[2]->GetCoordinates()[0]*DC_Scale; int y3=m_node[2]->GetCoordinates()[1]*DC_Scale; x1+=sol->GetSolutionValue(this->m_node[0]->GetDegreeOfFreedom(0))*DC_Scale; y1+=sol->GetSolutionValue(this->m_node[0]->GetDegreeOfFreedom(1))*DC_Scale; x2+=sol->GetSolutionValue(this->m_node[1]->GetDegreeOfFreedom(0))*DC_Scale; y2+=sol->GetSolutionValue(this->m_node[1]->GetDegreeOfFreedom(1))*DC_Scale; x3+=sol->GetSolutionValue(this->m_node[2]->GetDegreeOfFreedom(0))*DC_Scale; y3+=sol->GetSolutionValue(this->m_node[2]->GetDegreeOfFreedom(1))*DC_Scale; pDC->MoveTo(x1,y1); pDC->LineTo(x2,y2); pDC->LineTo(x3,y3); pDC->LineTo(x1,y1); } #endif }} // end namespace itk::fem ants-1.9.2+svn680.dfsg/Temporary/itkFEMElement3DC0LinearTriangular.h000077500000000000000000000052161147325206600250110ustar00rootroot00000000000000/*========================================================================= Program: Insight Segmentation & Registration Toolkit Module: $RCSfile: itkFEMElement3DC0LinearTriangular.h,v $ Language: C++ Date: $Date: 2005/11/08 15:34:16 $ Version: $Revision: 1.1.1.1 $ Copyright (c) Insight Software Consortium. All rights reserved. See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkFEMElement3DC0LinearTriangular_h #define __itkFEMElement3DC0LinearTriangular_h #include "itkFEMElementStd.h" namespace itk { namespace fem { /** * \class Element3DC0LinearTriangular * \brief 3-noded, linear, C0 continuous finite element in 2D space. */ class Element3DC0LinearTriangular : public ElementStd<3,3> { typedef ElementStd<3,3> TemplatedParentClass; FEM_ABSTRACT_CLASS( Element3DC0LinearTriangular, TemplatedParentClass ) public: ////////////////////////////////////////////////////////////////////////// /* * Methods related to numeric integration */ enum { DefaultIntegrationOrder = 1 }; virtual void GetIntegrationPointAndWeight(unsigned int i, VectorType& pt, Float& w, unsigned int order) const; virtual unsigned int GetNumberOfIntegrationPoints(unsigned int order) const; ////////////////////////////////////////////////////////////////////////// /* * Methods related to the geometry of an element */ virtual VectorType ShapeFunctions( const VectorType& pt ) const; virtual void ShapeFunctionDerivatives( const VectorType& pt, MatrixType& shapeD ) const; // FIXME: Write a proper implementation virtual bool GetLocalFromGlobalCoordinates( const VectorType& globalPt , VectorType& localPt) const; virtual Float JacobianDeterminant( const VectorType& pt, const MatrixType* pJ = 0 ) const; virtual void JacobianInverse( const VectorType& pt, MatrixType& invJ, const MatrixType* pJ = 0 ) const; /** * Draw the element on the specified device context */ #ifdef FEM_BUILD_VISUALIZATION void Draw(CDC* pDC, Solution::ConstPointer sol) const; #endif /** * Constants for integration rules. */ static const Float trigGaussRuleInfo[6][7][4]; /** * Array that holds number of integration point for each order * of numerical integration. */ static const unsigned int Nip[6]; }; }} // end namespace itk::fem #endif // #ifndef __itkFEMElement3DC0LinearTriangular_h ants-1.9.2+svn680.dfsg/Temporary/itkFEMElement3DC0LinearTriangularLaplaceBeltrami.cxx000066400000000000000000000170401147325206600303210ustar00rootroot00000000000000/*========================================================================= Program: Insight Segmentation & Registration Toolkit Module: $RCSfile: itkFEMElement3DC0LinearTriangularLaplaceBeltrami.cxx,v $ Language: C++ Date: $Date: 2005/11/08 15:34:16 $ Version: $Revision: 1.1.1.1 $ Copyright (c) Insight Software Consortium. All rights reserved. See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ // disable debug warnings in MS compiler #ifdef _MSC_VER #pragma warning(disable: 4786) #endif #include #include "itkFEMElement3DC0LinearTriangularLaplaceBeltrami.h" namespace itk { namespace fem { Element3DC0LinearTriangularLaplaceBeltrami ::Element3DC0LinearTriangularLaplaceBeltrami() : Superclass() {} Element3DC0LinearTriangularLaplaceBeltrami ::Element3DC0LinearTriangularLaplaceBeltrami( NodeIDType n1_, NodeIDType n2_, NodeIDType n3_, Material::ConstPointer m_) : Superclass() { // Set the geometrical points this->SetNode( 0, n1_ ); this->SetNode( 1, n2_ ); this->SetNode( 2, n3_ ); /* * Initialize the pointer to material object and check that * we were given the pointer to the right class. * If the material class was incorrect an exception is thrown. */ if( (m_mat=dynamic_cast(&*m_)) == 0 ) { throw FEMExceptionWrongClass(__FILE__,__LINE__,"Element3DC0LinearTriangularLaplaceBeltrami::Element3DC0LinearTriangularLaplaceBeltrami()"); } } void Element3DC0LinearTriangularLaplaceBeltrami::GetStiffnessMatrix(MatrixType& Ke) const { MatrixType cot,D,BB; this->GetMaterialMatrix( D ); // //std::cout<< " Nip " << Nip << " w " << w << std::endl; this->GetMaterialMatrix(D); cot.set_size(3,3); cot.fill(0.); int na=0; int nb=1; int nc=2; VectorType A=this->GetNode(na)->GetCoordinates(); VectorType B=this->GetNode(nb)->GetCoordinates(); VectorType C=this->GetNode(nc)->GetCoordinates(); VectorType BA =B-A; VectorType CA =C-A; VectorType CB =C-B; float L1=CB.magnitude(); float L2=CA.magnitude(); float L3=BA.magnitude(); float s=(L1+L2+L3)*.5; Float Area = sqrt( s*(s-L1)*(s-L2)*(s-L3) ); cot[0][0]=(2.0*L1*L1)*D[0][0]; cot[1][1]=(2.0*L2*L2)*D[0][0]; cot[2][2]=(2.0*L3*L3)*D[0][0]; cot[0][1]=(L3*L3-L1*L1-L2*L2)*D[0][0]; cot[0][2]=(L2*L2-L1*L1-L3*L3)*D[0][0]; cot[1][2]=(L1*L1-L3*L3-L2*L2)*D[0][0]; cot[1][0]=(L3*L3-L1*L1-L2*L2)*D[0][0]; cot[2][0]=(L2*L2-L1*L1-L3*L3)*D[0][0]; cot[2][1]=(L1*L1-L3*L3-L2*L2)*D[0][0]; cot=cot*1.0/(8.0*Area); /* if ( this->GetNode(0)->GetDegreeOfFreedom(0)==53 || this->GetNode(1)->GetDegreeOfFreedom(0)==53 || this->GetNode(2)->GetDegreeOfFreedom(0)==53 ) { std::cout << " cot " << this->GetNode(0)->GetDegreeOfFreedom(0) << " " <GetNode(1)->GetDegreeOfFreedom(0) << " " <GetNode(2)->GetDegreeOfFreedom(0) <GetMaterialMatrix( D ); VectorType ip; Float w; MatrixType J; MatrixType shapeDgl; MatrixType shapeD; // //std::cout<< " Nip " << Nip << " w " << w << std::endl; this->GetMaterialMatrix(D); cot.set_size(3,3); cot.fill(0.); int na=0; int nb=1; int nc=2; { VectorType A=this->GetNode(na)->GetCoordinates(); VectorType B=this->GetNode(nb)->GetCoordinates(); VectorType C=this->GetNode(nc)->GetCoordinates(); VectorType BA =B-A; VectorType AC =A-C; VectorType CB =C-B; float bamag=BA.magnitude(); float cbmag=CB.magnitude(); float acmag=AC.magnitude(); if (bamag > cbmag && bamag > acmag) { na=0; nb=1; nc=2; } if (cbmag > bamag && cbmag > acmag) { na=1; nb=2; nc=0; } if (acmag > bamag && acmag > cbmag) { na=2; nb=0; nc=1; } } VectorType A=this->GetNode(na)->GetCoordinates(); VectorType B=this->GetNode(nb)->GetCoordinates(); VectorType C=this->GetNode(nc)->GetCoordinates(); VectorType BA =B-A; VectorType CA =C-A; VectorType CB =C-B; float bamag=BA.magnitude(); float cbmag=CB.magnitude(); float acmag=CA.magnitude(); float t=(CA[0]*BA[0]+CA[1]*BA[1]+CA[2]*BA[2])/bamag*bamag; VectorType E = A+BA*t; VectorType CE =C-E; VectorType BE =B-E; VectorType AE =A-E; float cemag=CE.magnitude(); float bemag=CE.magnitude(); float aemag=AE.magnitude(); float h1; if (acmag > aemag) h1=acmag; else h1=aemag; float theta1=asin(cemag/h1); float h2; if (cbmag > bemag) h2=cbmag; else h2=bemag; float theta2=asin(cemag/h2); float theta3=acos(-1.0)-theta1-theta2; float cottheta1=cemag/aemag; // if (aemag == 0) cottheta1=1.0/tan(3.14159/2.0); float cottheta2=cemag/bemag; float cottheta3=1.0/tan(theta3); // if (fabs(cottheta1-1) < 1.e-6 && fabs(cottheta2-1) < 1.e-6) cottheta3=1.0; // std::cout <<" ct0 " << cottheta1 <<" ct1 " << cottheta2 <<" ct2 " << cottheta3 << std::endl; cot[na][na]=(cottheta3+cottheta2)*D[0][0]; cot[nb][nb]=(cottheta3+cottheta1)*D[0][0]; cot[nc][nc]=(cottheta1+cottheta2)*D[0][0]; cot[na][na]=-cottheta3*D[0][0]; cot[na][nc]=-cottheta2*D[0][0]; cot[nb][nc]=-cottheta1*D[0][0]; cot[nc][nb]=cot[nb][nc]*D[0][0]; cot[nc][na]=cot[na][nc]*D[0][0]; cot[nb][na]=cot[na][nb]*D[0][0]; cot=cot*0.5; Ke=cot; if ( this->GetNode(0)->GetDegreeOfFreedom(0)==909 || this->GetNode(1)->GetDegreeOfFreedom(0)==909 || this->GetNode(2)->GetDegreeOfFreedom(0)==909 ) { std::cout << " cot " << std::endl; std::cout << cot < { FEM_CLASS(Element3DC0LinearTriangularLaplaceBeltrami,Element3DMembrane1DOF) public: HANDLE_ELEMENT_LOADS(); /** * Default constructor only clears the internal storage */ Element3DC0LinearTriangularLaplaceBeltrami(); /** * Construct an element by specifying pointers to * 3 points and a material. */ Element3DC0LinearTriangularLaplaceBeltrami( NodeIDType n1_, NodeIDType n2_, NodeIDType n3_, Material::ConstPointer p_ ); virtual unsigned int GetNumberOfDegreesOfFreedomPerNode( void ) const { return 1; } virtual void GetStiffnessMatrix( MatrixType& Ke ) const; // void Read( std::istream&, void* info ); }; // class Element3DC0LinearTriangularLaplaceBeltrami FEM_CLASS_INIT(Element3DC0LinearTriangularLaplaceBeltrami) }} // end namespace itk::fem #endif // #ifndef __itkFEMElement3DC0LinearTriangularLaplaceBeltrami_h ants-1.9.2+svn680.dfsg/Temporary/itkFEMElement3DC0LinearTriangularMembrane.cxx000066400000000000000000000102551147325206600270270ustar00rootroot00000000000000/*========================================================================= Program: Insight Segmentation & Registration Toolkit Module: $RCSfile: itkFEMElement3DC0LinearTriangularMembrane.cxx,v $ Language: C++ Date: $Date: 2005/11/08 15:34:16 $ Version: $Revision: 1.1.1.1 $ Copyright (c) Insight Software Consortium. All rights reserved. See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ // disable debug warnings in MS compiler #ifdef _MSC_VER #pragma warning(disable: 4786) #endif #include #include "itkFEMElement3DC0LinearTriangularMembrane.h" namespace itk { namespace fem { Element3DC0LinearTriangularMembrane ::Element3DC0LinearTriangularMembrane() : Superclass() {} Element3DC0LinearTriangularMembrane ::Element3DC0LinearTriangularMembrane( NodeIDType n1_, NodeIDType n2_, NodeIDType n3_, Material::ConstPointer m_) : Superclass() { // Set the geometrical points this->SetNode( 0, n1_ ); this->SetNode( 1, n2_ ); this->SetNode( 2, n3_ ); /* * Initialize the pointer to material object and check that * we were given the pointer to the right class. * If the material class was incorrect an exception is thrown. */ if( (m_mat=dynamic_cast(&*m_)) == 0 ) { throw FEMExceptionWrongClass(__FILE__,__LINE__,"Element3DC0LinearTriangularMembrane::Element3DC0LinearTriangularMembrane()"); } } /* void Element3DC0LinearTriangularMembrane::GetStiffnessMatrix(MatrixType& Ke) const { MatrixType D; unsigned int Nip=this->GetNumberOfIntegrationPoints(0); VectorType ip; Float w; this->GetIntegrationPointAndWeight(0,ip,w,0); // //std::cout<< " Nip " << Nip << " w " << w << std::endl; this->GetMaterialMatrix(D); Ke.set_size(3,3); int na=0; int nb=1; int nc=2; { VectorType A=this->GetNode(na)->GetCoordinates(); VectorType B=this->GetNode(nb)->GetCoordinates(); VectorType C=this->GetNode(nc)->GetCoordinates(); VectorType BA =B-A; VectorType AC =A-C; VectorType CB =C-B; float bamag=BA.magnitude(); float cbmag=CB.magnitude(); float acmag=AC.magnitude(); if (bamag > cbmag && bamag > acmag) { na=0; nb=1; nc=2; } if (cbmag > bamag && cbmag > acmag) { na=1; nb=2; nc=0; } if (acmag > bamag && acmag > cbmag) { na=2; nb=0; nc=1; } } VectorType A=this->GetNode(na)->GetCoordinates(); VectorType B=this->GetNode(nb)->GetCoordinates(); VectorType C=this->GetNode(nc)->GetCoordinates(); VectorType BA =B-A; VectorType CA =C-A; VectorType CB =C-B; float bamag=BA.magnitude(); float cbmag=CB.magnitude(); float acmag=CA.magnitude(); float t=(CA[0]*BA[0]+CA[1]*BA[1]+CA[2]*BA[2])/bamag*bamag; VectorType E = A+BA*t; VectorType CE =C-E; VectorType BE =B-E; VectorType AE =A-E; float cemag=CE.magnitude(); float bemag=CE.magnitude(); float aemag=AE.magnitude(); float h1; if (acmag > aemag) h1=acmag; else h1=aemag; float theta1=asin(cemag/h1); float h2; if (cbmag > bemag) h2=cbmag; else h2=bemag; float theta2=asin(cemag/h2); float theta3=acos(-1.0)-theta1-theta2; float cottheta1=atan(theta1); float cottheta2=atan(theta2); float cottheta3=atan(theta3); Ke[0][0]=(cottheta3+cottheta2)*D[0][0]; Ke[1][1]=(cottheta3+cottheta1)*D[0][0]; Ke[2][2]=(cottheta1+cottheta2)*D[0][0]; Ke[0][1]=-cottheta3*D[0][0]; Ke[0][2]=-cottheta2*D[0][0]; Ke[1][2]=-cottheta1*D[0][0]; Ke[2][1]=Ke[1][2]*D[0][0]; Ke[2][0]=Ke[0][2]*D[0][0]; Ke[1][0]=Ke[0][1]*D[0][0]; // std::cout << " lapl belt " << std::endl; // std::cout << Ke << std::endl; } */ FEM_CLASS_REGISTER(Element3DC0LinearTriangularMembrane) }} // end namespace itk::fem ants-1.9.2+svn680.dfsg/Temporary/itkFEMElement3DC0LinearTriangularMembrane.h000077500000000000000000000042421147325206600264560ustar00rootroot00000000000000/*========================================================================= Program: Insight Segmentation & Registration Toolkit Module: $RCSfile: itkFEMElement3DC0LinearTriangularMembrane.h,v $ Language: C++ Date: $Date: 2005/11/08 15:34:16 $ Version: $Revision: 1.1.1.1 $ Copyright (c) Insight Software Consortium. All rights reserved. See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkFEMElement3DC0LinearTriangularMembrane_h #define __itkFEMElement3DC0LinearTriangularMembrane_h #include "itkFEMElement3DC0LinearTriangular.h" #include "itkFEMElement3DMembrane.h" #include "itkFEMElement3DMembrane1DOF.h" namespace itk { namespace fem { /** * \class Element3DC0LinearTriangularMembrane * \brief 3-noded finite element class in 3D space for surface membrane problem. * * This element is combined from Element3DC0LinearTriangular and Element3DMembrane. */ class Element3DC0LinearTriangularMembrane : public Element3DMembrane { FEM_CLASS(Element3DC0LinearTriangularMembrane,Element3DMembrane) public: HANDLE_ELEMENT_LOADS(); /** * Default constructor only clears the internal storage */ Element3DC0LinearTriangularMembrane(); /** * Construct an element by specifying pointers to * 3 points and a material. */ Element3DC0LinearTriangularMembrane( NodeIDType n1_, NodeIDType n2_, NodeIDType n3_, Material::ConstPointer p_ ); // virtual unsigned int GetNumberOfDegreesOfFreedomPerNode( void ) const // { return 1; } // virtual void GetStiffnessMatrix( MatrixType& Ke ) const; // void Read( std::istream&, void* info ); }; // class Element3DC0LinearTriangularMembrane FEM_CLASS_INIT(Element3DC0LinearTriangularMembrane) }} // end namespace itk::fem #endif // #ifndef __itkFEMElement3DC0LinearTriangularMembrane_h ants-1.9.2+svn680.dfsg/Temporary/itkFEMElement3DMembrane1DOF.h000066400000000000000000000072651147325206600235260ustar00rootroot00000000000000/*========================================================================= Program: Insight Segmentation & Registration Toolkit Module: $RCSfile: itkFEMElement3DMembrane1DOF.h,v $ Language: C++ Date: $Date: 2005/11/08 15:34:16 $ Version: $Revision: 1.1.1.1 $ Copyright (c) 2002 Insight Consortium. All rights reserved. See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkFEMElement3DMembrane1DOF_h #define __itkFEMElement3DMembrane1DOF_h #include "itkFEMElementBase.h" #include "itkFEMMaterialLinearElasticity.h" namespace itk { namespace fem { /** * \class Element3DMembrane1DOF * \brief Class that is used to define a membrane energy problem in 3D space. * * This class only defines the physics of the problem. Use his class together * with element classes that specify the geometry to fully define the element. * * You can specify one template parameter: * * TBaseClass - Class from which Element3DMembrane1DOF is derived. TBaseClass must * be derived from the Element base class. This enables you * to use this class at any level of element definition. * If not specified, it defaults to the Element base class. */ template class Element3DMembrane1DOF : public TBaseClass { FEM_ABSTRACT_CLASS(Element3DMembrane1DOF,TBaseClass) public: // Repeat the required typedefs and enums from parent class typedef typename Superclass::Float Float; typedef typename Superclass::MatrixType MatrixType; typedef typename Superclass::VectorType VectorType; /** * Read data for this class from input stream */ virtual void Read( std::istream&, void* info ); /** * Write this class to output stream */ virtual void Write( std::ostream& f ) const; /** * Default constructor only clears the internal storage */ Element3DMembrane1DOF(); ////////////////////////////////////////////////////////////////////////// /* * Methods related to the physics of the problem. */ /** * Compute the B matrix. */ virtual void GetStrainDisplacementMatrix(MatrixType& B, const MatrixType& shapeDgl) const; /** * Compute the D matrix. */ virtual void GetMaterialMatrix(MatrixType& D) const; /** * Compute the mass matrix specific for 3D membrane problems. */ void GetMassMatrix(MatrixType& Me) const; /** * 3D membrane elements have 3 DOFs per node. */ virtual unsigned int GetNumberOfDegreesOfFreedomPerNode( void ) const { return 3; } virtual void GetStiffnessMatrix( MatrixType& Ke ) const; public: /** * Pointer to material properties of the element */ MaterialLinearElasticity::ConstPointer m_mat; virtual Material::ConstPointer GetMaterial(void) const { return m_mat; } virtual void SetMaterial(Material::ConstPointer mat_ ) { m_mat=dynamic_cast(&*mat_); } }; // class Element3DMembrane1DOF #ifdef _MSC_VER // Declare a static dummy function to prevent a MSVC 6.0 SP5 from crashing. // I have no idea why things don't work when this is not declared, but it // looks like this declaration makes compiler forget about some of the // troubles it has with templates. static void Dummy( void ); #endif // #ifdef _MSC_VER }} // end namespace itk::fem #ifndef ITK_MANUAL_INSTANTIATION #include "itkFEMElement3DMembrane1DOF.txx" #endif #endif // #ifndef __itkFEMElement3DMembrane1DOF_h ants-1.9.2+svn680.dfsg/Temporary/itkFEMElement3DMembrane1DOF.txx000066400000000000000000000100551147325206600241110ustar00rootroot00000000000000/*========================================================================= Program: Insight Segmentation & Registration Toolkit Module: $RCSfile: itkFEMElement3DMembrane1DOF.txx,v $ Language: C++ Date: $Date: 2006/10/16 16:30:27 $ Version: $Revision: 1.2 $ Copyright (c) 2002 Insight Consortium. All rights reserved. See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkFEMElement3DMembrane1DOF_txx #define __itkFEMElement3DMembrane1DOF_txx #include "itkFEMElement3DMembrane1DOF.h" namespace itk { namespace fem { template Element3DMembrane1DOF ::Element3DMembrane1DOF() : Superclass(), m_mat(0) {} ////////////////////////////////////////////////////////////////////////// /* * Methods related to the physics of the problem. */ template void Element3DMembrane1DOF ::GetStrainDisplacementMatrix(MatrixType& B, const MatrixType& shapeDgl) const { } template void Element3DMembrane1DOF ::GetMassMatrix(MatrixType& Me) const { // Call the parent's get matrix function Superclass::GetMassMatrix(Me); // Since parent class doesn't have the material properties, // we need to adjust Me matrix here for the density of the element. Me=Me*m_mat->RhoC; } template void Element3DMembrane1DOF ::GetMaterialMatrix(MatrixType& D) const { unsigned int d=3; D.set_size(d,d); D.fill(0.0); // This is the main difference from the linear elasticity problem. /* Material properties matrix. Simpler than linear elasticity. */ Float disot = m_mat->E; for (unsigned int i=0; i void Element3DMembrane1DOF::GetStiffnessMatrix(MatrixType& Ke) const { Superclass::GetStiffnessMatrix(Ke); } template void Element3DMembrane1DOF ::Read( std::istream& f, void* info ) { int n; /* * Convert the info pointer to a usable objects */ ReadInfoType::MaterialArrayPointer mats=static_cast(info)->m_mat; /* first call the parent's read function */ Superclass::Read(f,info); try { /* * Read and set the material pointer */ FEMLightObject::SkipWhiteSpace(f); f>>n; if(!f) goto out; m_mat=dynamic_cast( &*mats->Find(n)); } catch ( FEMExceptionObjectNotFound e ) { throw FEMExceptionObjectNotFound(__FILE__,__LINE__,"Element3DMembrane1DOF::Read()",e.m_baseClassName,e.m_GN); } // Check if the material object was of correct class if(!m_mat) { throw FEMExceptionWrongClass(__FILE__,__LINE__,"Element3DMembrane1DOF::Read()"); } out: if( !f ) { throw FEMExceptionIO(__FILE__,__LINE__,"Element3DMembrane1DOF::Read()","Error reading FEM element!"); } } /* * Write the element to the output stream. */ template void Element3DMembrane1DOF ::Write( std::ostream& f ) const { // First call the parent's write function Superclass::Write(f); /* * then write the actual data (material number) * We also add some comments in the output file */ f<<"\t"<GN<<"\t% MaterialLinearElasticity ID\n"; // check for errors if (!f) { throw FEMExceptionIO(__FILE__,__LINE__,"Element3DMembrane1DOF::Write()","Error writing FEM element!"); } } #ifdef _MSC_VER // Declare a static dummy function to prevent a MSVC 6.0 SP5 from crashing. // I have no idea why things don't work when this is not declared, but it // looks like this declaration makes compiler forget about some of the // troubles it has with templates. static void Dummy( void ); #endif // #ifdef _MSC_VER }} // end namespace itk::fem #endif // #ifndef __itkFEMElement3DMembrane1DOF_txx ants-1.9.2+svn680.dfsg/Temporary/itkFastMarchingImageFilter.h000066400000000000000000000422401147325206600240040ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkFastMarchingImageFilter.h,v $ Language: C++ Date: $Date: 2009/03/10 17:30:41 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkFastMarchingImageFilter_h #define __itkFastMarchingImageFilter_h #include "itkArray.h" #include "itkImageToImageFilter.h" #include "itkIndex.h" #include "itkLevelSet.h" #include "itkNeighborhoodIterator.h" #include "vnl/vnl_math.h" #include #include namespace itk { /** \class FastMarchingImageFilter * \brief Solve an Eikonal equation using Fast Marching * * Fast marching solves an Eikonal equation where the speed is always * non-negative and depends on the position only. Starting from an * initial position on the front, fast marching systematically moves the * front forward one grid point at a time. * * Updates are preformed using an entropy satisfy scheme where only * "upwind" neighborhoods are used. This implementation of Fast Marching * uses a std::priority_queue to locate the next proper grid position to * update. * * Fast Marching sweeps through N grid points in (N log N) steps to obtain * the arrival time value as the front propagates through the grid. * * Implementation of this class is based on Chapter 8 of * "Level Set Methods and Fast Marching Methods", J.A. Sethian, * Cambridge Press, Second edition, 1999. * * This class is templated over the level set image type and the speed * image type. The initial front is specified by two containers: one * containing the known points and one containing the trial * points. Alive points are those that are already part of the * object, and trial points are considered for inclusion. * In order for the filter to evolve, at least some trial * points must be specified. These can for instance be specified as the layer of * pixels around the alive points. * The speed function can be specified as a speed image or a * speed constant. The speed image is set using the method * SetInput(). If the speed image is NULL, a constant speed function * is used and is specified using method the SetSpeedConstant(). * * If the speed function is constant and of value one, fast marching results * in an approximate distance function from the initial alive points. * FastMarchingImageFilter is used in the ReinitializeLevelSetImageFilter * object to create a signed distance function from the zero level set. * * The algorithm can be terminated early by setting an appropriate stopping * value. The algorithm terminates when the current arrival time being * processed is greater than the stopping value. * * There are two ways to specify the output image information * ( LargestPossibleRegion, Spacing, Origin): (a) it is copied directly from * the input speed image or (b) it is specified by the user. Default values * are used if the user does not specify all the information. * * The output information is computed as follows. * If the speed image is NULL or if the OverrideOutputInformation is set to * true, the output information is set from user specified parameters. These * parameters can be specified using methods SetOutputRegion(), SetOutputSpacing(), SetOutputDirection(), * and SetOutputOrigin(). Else if the speed image is not NULL, the output information * is copied from the input speed image. * * Possible Improvements: * In the current implemenation, std::priority_queue only allows * taking nodes out from the front and putting nodes in from the back. * To update a value already on the heap, a new node is added to the heap. * The defunct old node is left on the heap. When it is removed from the * top, it will be recognized as invalid and not used. * Future implementations can implement the heap in a different way * allowing the values to be updated. This will generally require * some sift-up and sift-down functions and * an image of back-pointers going from the image to heap in order * to locate the node which is to be updated. * * \sa LevelSetTypeDefault * \ingroup LevelSetSegmentation */ template < class TLevelSet, class TSpeedImage = Image::ImageDimension> > class ITK_EXPORT FastMarchingImageFilter : public ImageToImageFilter { public: /** Standard class typdedefs. */ typedef FastMarchingImageFilter Self; typedef ImageSource Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Run-time type information (and related methods). */ itkTypeMacro(FastMarchingImageFilter, ImageSource); /** Typedef support of level set method types. */ typedef LevelSetTypeDefault LevelSetType; typedef typename LevelSetType::LevelSetImageType LevelSetImageType; typedef typename LevelSetType::LevelSetPointer LevelSetPointer; typedef typename LevelSetType::PixelType PixelType; typedef typename LevelSetType::NodeType NodeType; typedef typename LevelSetType::NodeContainer NodeContainer; typedef typename LevelSetType::NodeContainerPointer NodeContainerPointer; typedef typename LevelSetImageType::SizeType OutputSizeType; typedef typename LevelSetImageType::RegionType OutputRegionType; typedef typename LevelSetImageType::SpacingType OutputSpacingType; typedef typename LevelSetImageType::DirectionType OutputDirectionType; typedef typename LevelSetImageType::PointType OutputPointType; class AxisNodeType : public NodeType { public: int GetAxis() const { return m_Axis; } void SetAxis( int axis ) { m_Axis = axis; } const AxisNodeType & operator=(const NodeType & node) { this->NodeType::operator=(node); return *this; } private: int m_Axis; }; /** SpeedImage typedef support. */ typedef TSpeedImage SpeedImageType; /** SpeedImagePointer typedef support. */ typedef typename SpeedImageType::Pointer SpeedImagePointer; typedef typename SpeedImageType::ConstPointer SpeedImageConstPointer; /** Dimension of the level set and the speed image. */ itkStaticConstMacro(SetDimension, unsigned int, LevelSetType::SetDimension); itkStaticConstMacro(SpeedImageDimension, unsigned int, SpeedImageType::ImageDimension); /** Index typedef support. */ typedef Index IndexType; /** Enum of Fast Marching algorithm point types. FarPoints represent far * away points; TrialPoints represent points within a narrowband of the * propagating front; and AlivePoints represent points which have already * been processed. Topology points were trial points but their inclusion * would have violated topology checks. */ enum LabelType { FarPoint, AlivePoint, TrialPoint, InitialTrialPoint, TopologyPoint }; /** LabelImage typedef support. */ typedef Image LabelImageType; typedef NeighborhoodIterator NeighborhoodIteratorType; /** LabelImagePointer typedef support. */ typedef typename LabelImageType::Pointer LabelImagePointer; /** ConnectedComponentImage typedef support. */ typedef Image ConnectedComponentImageType; /** ConnectedComponentImagePointer typedef support. */ typedef typename ConnectedComponentImageType::Pointer ConnectedComponentImagePointer; /** Set the container of Alive Points representing the initial front. * Alive points are represented as a VectorContainer of LevelSetNodes. */ void SetAlivePoints( NodeContainer * points ) { m_AlivePoints = points; this->Modified(); } /** Get the container of Alive Points representing the initial front. */ NodeContainerPointer GetAlivePoints( ) { return m_AlivePoints; } /** Set the container of Trial Points representing the initial front. * Trial points are represented as a VectorContainer of LevelSetNodes. */ void SetTrialPoints( NodeContainer * points ) { m_TrialPoints = points; this->Modified(); } /** Get the container of Trial Points representing the initial front. */ NodeContainerPointer GetTrialPoints( ) { return m_TrialPoints; } /** Get the point type label image. */ LabelImagePointer GetLabelImage() const { return m_LabelImage; } /** Get the point type label image. */ ConnectedComponentImagePointer GetConnectedComponentImage() const { return m_ConnectedComponentImage; } /** Set the Speed Constant. If the Speed Image is NULL, * the SpeedConstant value is used for the whole level set. * By default, the SpeedConstant is set to 1.0. */ void SetSpeedConstant( double value ) { m_SpeedConstant = value; m_InverseSpeed = -1.0 * vnl_math_sqr( 1.0 / m_SpeedConstant ); this->Modified(); } /** Get the Speed Constant. */ itkGetConstReferenceMacro( SpeedConstant, double ); /** Set/Get the Normalization Factor for the Speed Image. The values in the Speed Image is divided by this factor. This allows the use of images with integer pixel types to represent the speed. */ itkSetMacro( NormalizationFactor, double ); itkGetConstMacro( NormalizationFactor, double ); /** Set the Fast Marching algorithm Stopping Value. The Fast Marching * algorithm is terminated when the value of the smallest trial point * is greater than the stopping value. */ itkSetMacro( StoppingValue, double ); /** Get the Fast Marching algorithm Stopping Value. */ itkGetConstReferenceMacro( StoppingValue, double ); /** Set the Collect Points flag. Instrument the algorithm to collect * a container of all nodes which it has visited. Useful for * creating Narrowbands for level set algorithms that supports * narrow banding. */ itkSetMacro( CollectPoints, bool ); /** Get thConste Collect Points flag. */ itkGetConstReferenceMacro( CollectPoints, bool ); itkBooleanMacro( CollectPoints ); enum TopologyCheckType { None, NoHandles, Strict }; /** Set/Get boolean macro indicating whether the user wants to check topology. */ itkSetMacro( TopologyCheck, TopologyCheckType ); itkGetConstReferenceMacro( TopologyCheck, TopologyCheckType ); /** Get the container of Processed Points. If the CollectPoints flag * is set, the algorithm collects a container of all processed nodes. * This is useful for defining creating Narrowbands for level * set algorithms that supports narrow banding. */ NodeContainerPointer GetProcessedPoints() const { return m_ProcessedPoints; } /** The output largeset possible, spacing and origin is computed as follows. * If the speed image is NULL or if the OverrideOutputInformation is true, * the output information is set from user specified parameters. These * parameters can be specified using methods SetOutputRegion(), SetOutputSpacing(), SetOutputDirection(), * and SetOutputOrigin(). Else if the speed image is not NULL, the output information * is copied from the input speed image. */ virtual void SetOutputSize( const OutputSizeType& size ) { m_OutputRegion = size; } virtual OutputSizeType GetOutputSize() const { return m_OutputRegion.GetSize(); } itkSetMacro( OutputRegion, OutputRegionType ); itkGetConstReferenceMacro( OutputRegion, OutputRegionType ); itkSetMacro( OutputSpacing, OutputSpacingType ); itkGetConstReferenceMacro( OutputSpacing, OutputSpacingType ); itkSetMacro( OutputDirection, OutputDirectionType ); itkGetConstReferenceMacro( OutputDirection, OutputDirectionType ); itkSetMacro( OutputOrigin, OutputPointType ); itkGetConstReferenceMacro( OutputOrigin, OutputPointType ); itkSetMacro( OverrideOutputInformation, bool ); itkGetConstReferenceMacro( OverrideOutputInformation, bool ); itkBooleanMacro( OverrideOutputInformation ); #ifdef ITK_USE_CONCEPT_CHECKING /** Begin concept checking */ itkConceptMacro(SameDimensionCheck, (Concept::SameDimension)); itkConceptMacro(SpeedConvertibleToDoubleCheck, (Concept::Convertible)); itkConceptMacro(DoubleConvertibleToLevelSetCheck, (Concept::Convertible)); itkConceptMacro(LevelSetOStreamWritableCheck, (Concept::OStreamWritable)); /** End concept checking */ #endif protected: FastMarchingImageFilter(); ~FastMarchingImageFilter(){}; void PrintSelf( std::ostream& os, Indent indent ) const; virtual void Initialize( LevelSetImageType * ); virtual void UpdateNeighbors( const IndexType& index, const SpeedImageType *, LevelSetImageType * ); virtual double UpdateValue( const IndexType& index, const SpeedImageType *, LevelSetImageType * ); const AxisNodeType& GetNodeUsedInCalculation(unsigned int idx) const { return m_NodesUsed[idx]; } void GenerateData(); /** Generate the output image meta information. */ virtual void GenerateOutputInformation(); virtual void EnlargeOutputRequestedRegion(DataObject *output); /** Get Large Value. This value is used to represent the concept of infinity for the time assigned to pixels that have not been visited. This value is set by default to half the max() of the pixel type used to represent the time-crossing map. */ itkGetConstReferenceMacro( LargeValue, PixelType ); OutputRegionType m_BufferedRegion; typedef typename LevelSetImageType::IndexType LevelSetIndexType; LevelSetIndexType m_StartIndex; LevelSetIndexType m_LastIndex; itkGetConstReferenceMacro( StartIndex, LevelSetIndexType ); itkGetConstReferenceMacro( LastIndex, LevelSetIndexType ); private: FastMarchingImageFilter(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented NodeContainerPointer m_AlivePoints; NodeContainerPointer m_TrialPoints; LabelImagePointer m_LabelImage; ConnectedComponentImagePointer m_ConnectedComponentImage; double m_SpeedConstant; double m_InverseSpeed; double m_StoppingValue; bool m_CollectPoints; NodeContainerPointer m_ProcessedPoints; OutputRegionType m_OutputRegion; OutputPointType m_OutputOrigin; OutputSpacingType m_OutputSpacing; OutputDirectionType m_OutputDirection; bool m_OverrideOutputInformation; typename LevelSetImageType::PixelType m_LargeValue; AxisNodeType m_NodesUsed[SetDimension]; /** Trial points are stored in a min-heap. This allow efficient access * to the trial point with minimum value which is the next grid point * the algorithm processes. */ typedef std::vector HeapContainer; typedef std::greater NodeComparer; typedef std::priority_queue< AxisNodeType, HeapContainer, NodeComparer > HeapType; HeapType m_TrialHeap; double m_NormalizationFactor; /** * Functions and variables to check for topology changes (2D/3D only). */ TopologyCheckType m_TopologyCheck; // Functions/data for the 2-D case void InitializeIndices2D(); bool IsChangeWellComposed2D( IndexType ); bool IsCriticalC1Configuration2D( Array ); bool IsCriticalC2Configuration2D( Array ); bool IsCriticalC3Configuration2D( Array ); bool IsCriticalC4Configuration2D( Array ); bool IsSpecialCaseOfC4Configuration2D( PixelType, IndexType, IndexType, IndexType ); Array m_RotationIndices[4]; Array m_ReflectionIndices[2]; // Functions/data for the 3-D case void InitializeIndices3D(); bool IsCriticalC1Configuration3D( Array ); unsigned int IsCriticalC2Configuration3D( Array ); bool IsChangeWellComposed3D( IndexType ); Array m_C1Indices[12]; Array m_C2Indices[8]; // Functions for both 2D/3D cases bool DoesVoxelChangeViolateWellComposedness( IndexType ); bool DoesVoxelChangeViolateStrictTopology( IndexType ); }; } // namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkFastMarchingImageFilter.txx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkFastMarchingImageFilter.txx000066400000000000000000001007201147325206600243760ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkFastMarchingImageFilter.txx,v $ Language: C++ Date: $Date: 2009/03/10 17:30:41 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkFastMarchingImageFilter_txx #define __itkFastMarchingImageFilter_txx #include "itkFastMarchingImageFilter.h" #include "itkConnectedComponentImageFilter.h" #include "itkImageRegionIterator.h" #include "itkNumericTraits.h" #include "itkRelabelComponentImageFilter.h" #include "vnl/vnl_math.h" #include namespace itk { template FastMarchingImageFilter ::FastMarchingImageFilter() : m_TrialHeap( ) { this->ProcessObject::SetNumberOfRequiredInputs(0); OutputSizeType outputSize; outputSize.Fill( 16 ); typename LevelSetImageType::IndexType outputIndex; outputIndex.Fill( 0 ); this->m_OutputRegion.SetSize( outputSize ); this->m_OutputRegion.SetIndex( outputIndex ); this->m_OutputOrigin.Fill( 0.0 ); this->m_OutputSpacing.Fill( 1.0 ); this->m_OutputDirection.SetIdentity(); this->m_OverrideOutputInformation = false; this->m_AlivePoints = NULL; this->m_TrialPoints = NULL; this->m_ProcessedPoints = NULL; this->m_SpeedConstant = 1.0; this->m_InverseSpeed = -1.0; this->m_LabelImage = LabelImageType::New(); this->m_LargeValue = static_cast( NumericTraits::max() / 2.0 ); this->m_StoppingValue = static_cast( this->m_LargeValue ); this->m_CollectPoints = false; this->m_NormalizationFactor = 1.0; this->m_TopologyCheck = None; } template void FastMarchingImageFilter ::PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf(os,indent); os << indent << "Alive points: " << this->m_AlivePoints.GetPointer() << std::endl; os << indent << "Trial points: " << this->m_TrialPoints.GetPointer() << std::endl; os << indent << "Speed constant: " << this->m_SpeedConstant << std::endl; os << indent << "Stopping value: " << this->m_StoppingValue << std::endl; os << indent << "Large Value: " << static_cast::PrintType>( this->m_LargeValue ) << std::endl; os << indent << "Normalization Factor: " << this->m_NormalizationFactor << std::endl; os << indent << "Topology check: "; switch( this->m_TopologyCheck ) { case None: { os << "None" << std::endl; } case NoHandles: { os << "No handles" << std::endl; } case Strict: { os << "Strict" << std::endl; } } os << indent << "Collect points: " << this->m_CollectPoints << std::endl; os << indent << "OverrideOutputInformation: "; os << this->m_OverrideOutputInformation << std::endl; os << indent << "OutputRegion: " << this->m_OutputRegion << std::endl; os << indent << "OutputOrigin: " << this->m_OutputOrigin << std::endl; os << indent << "OutputSpacing: " << this->m_OutputSpacing << std::endl; os << indent << "OutputDirection: " << this->m_OutputDirection << std::endl; } template void FastMarchingImageFilter ::GenerateOutputInformation() { // copy output information from input image Superclass::GenerateOutputInformation(); // use user-specified output information if ( this->GetInput() == NULL || this->m_OverrideOutputInformation ) { LevelSetPointer output = this->GetOutput(); output->SetLargestPossibleRegion( this->m_OutputRegion ); output->SetOrigin( this->m_OutputOrigin ); output->SetSpacing( this->m_OutputSpacing ); output->SetDirection( this->m_OutputDirection ); } } template void FastMarchingImageFilter ::EnlargeOutputRequestedRegion( DataObject *output ) { // enlarge the requested region of the output // to the whole data set TLevelSet * imgData; imgData = dynamic_cast( output ); if ( imgData ) { imgData->SetRequestedRegionToLargestPossibleRegion(); } else { // Pointer could not be cast to TLevelSet * itkWarningMacro(<< "itk::FastMarchingImageFilter" << "::EnlargeOutputRequestedRegion cannot cast " << typeid(output).name() << " to " << typeid(TLevelSet*).name() ); } } template void FastMarchingImageFilter ::Initialize( LevelSetImageType * output ) { // allocate memory for the output buffer output->SetBufferedRegion( output->GetRequestedRegion() ); output->Allocate(); // cache some buffered region information this->m_BufferedRegion = output->GetBufferedRegion(); this->m_StartIndex = this->m_BufferedRegion.GetIndex(); this->m_LastIndex = this->m_StartIndex + this->m_BufferedRegion.GetSize(); typename LevelSetImageType::OffsetType offset; offset.Fill( 1 ); this->m_LastIndex -= offset; // allocate memory for the LabelImage this->m_LabelImage->CopyInformation( output ); this->m_LabelImage->SetBufferedRegion( output->GetBufferedRegion() ); this->m_LabelImage->Allocate(); // Checking for handles only requires an image to keep track of // connected components. if( this->m_TopologyCheck == NoHandles ) { this->m_ConnectedComponentImage = ConnectedComponentImageType::New(); this->m_ConnectedComponentImage->SetOrigin( output->GetOrigin() ); this->m_ConnectedComponentImage->SetSpacing( output->GetSpacing() ); this->m_ConnectedComponentImage->SetRegions( output->GetBufferedRegion() ); this->m_ConnectedComponentImage->SetDirection( output->GetDirection() ); this->m_ConnectedComponentImage->Allocate(); this->m_ConnectedComponentImage->FillBuffer( 0 ); } // set all output value to infinity typedef ImageRegionIterator OutputIterator; OutputIterator outIt ( output, output->GetBufferedRegion() ); PixelType outputPixel; outputPixel = this->m_LargeValue; for ( outIt.GoToBegin(); !outIt.IsAtEnd(); ++outIt ) { outIt.Set( outputPixel ); } // set all points type to FarPoint typedef ImageRegionIterator< LabelImageType > LabelIterator; LabelIterator typeIt( this->m_LabelImage, this->m_LabelImage->GetBufferedRegion() ); for ( typeIt.GoToBegin(); !typeIt.IsAtEnd(); ++typeIt ) { typeIt.Set( FarPoint ); } // process input alive points AxisNodeType node; if ( this->m_AlivePoints ) { typename NodeContainer::ConstIterator pointsIter = this->m_AlivePoints->Begin(); typename NodeContainer::ConstIterator pointsEnd = this->m_AlivePoints->End(); for (; pointsIter != pointsEnd; ++pointsIter ) { // get node from alive points container node = pointsIter.Value(); // check if node index is within the output level set if ( !this->m_BufferedRegion.IsInside( node.GetIndex() ) ) { continue; } // make this an alive point this->m_LabelImage->SetPixel( node.GetIndex(), AlivePoint ); // if( this->m_TopologyCheck == NoHandles ) { this->m_ConnectedComponentImage->SetPixel( node.GetIndex(), NumericTraits::One ); } outputPixel = node.GetValue(); output->SetPixel( node.GetIndex(), outputPixel ); } } if( this->m_TopologyCheck == NoHandles ) { // Now create the connected component image and relabel such that labels // are 1, 2, 3, ... typedef ConnectedComponentImageFilter ConnectedComponentFilterType; typename ConnectedComponentFilterType::Pointer connecter = ConnectedComponentFilterType::New(); connecter->SetInput( this->m_ConnectedComponentImage ); typedef RelabelComponentImageFilter RelabelerType; typename RelabelerType::Pointer relabeler = RelabelerType::New(); relabeler->SetInput( connecter->GetOutput() ); relabeler->Update(); this->m_ConnectedComponentImage = relabeler->GetOutput(); } // make sure the heap is empty while ( !this->m_TrialHeap.empty() ) { this->m_TrialHeap.pop(); } // process the input trial points if ( this->m_TrialPoints ) { typename NodeContainer::ConstIterator pointsIter = this->m_TrialPoints->Begin(); typename NodeContainer::ConstIterator pointsEnd = this->m_TrialPoints->End(); for (; pointsIter != pointsEnd; ++pointsIter ) { // get node from trial points container node = pointsIter.Value(); // check if node index is within the output level set if ( !this->m_BufferedRegion.IsInside( node.GetIndex() ) ) { continue; } #ifdef ITK_USE_DEPRECATED_FAST_MARCHING // make this a trial point m_LabelImage->SetPixel( node.GetIndex(), TrialPoint ); #else // make this an initial trial point m_LabelImage->SetPixel( node.GetIndex(), InitialTrialPoint ); #endif outputPixel = node.GetValue(); output->SetPixel( node.GetIndex(), outputPixel ); this->m_TrialHeap.push( node ); } } // initialize indices if this->m_TopologyCheck is activated if( this->m_TopologyCheck != None ) { if( SetDimension == 2 ) { this->InitializeIndices2D(); } else if( SetDimension == 3 ) { this->InitializeIndices3D(); } else { itkExceptionMacro( "Topology checking is only valid for level set dimensions of 2 and 3" ); } } } template void FastMarchingImageFilter ::GenerateData() { LevelSetPointer output = this->GetOutput(); SpeedImageConstPointer speedImage = this->GetInput(); this->Initialize( output ); if ( this->m_CollectPoints ) { this->m_ProcessedPoints = NodeContainer::New(); } // process points on the heap AxisNodeType node; double currentValue; double oldProgress = 0; this->UpdateProgress( 0.0 ); // Send first progress event while ( !this->m_TrialHeap.empty() ) { // get the node with the smallest value node = this->m_TrialHeap.top(); this->m_TrialHeap.pop(); // does this node contain the current value ? currentValue = (double) output->GetPixel( node.GetIndex() ); if ( node.GetValue() != currentValue ) { continue; } // is this node already alive ? #ifdef ITK_USE_DEPRECATED_FAST_MARCHING if ( m_LabelImage->GetPixel( node.GetIndex() ) != TrialPoint ) #else if ( m_LabelImage->GetPixel( node.GetIndex() ) == AlivePoint ) #endif { continue; } if( this->m_TopologyCheck != None ) { bool wellComposednessViolation = this->DoesVoxelChangeViolateWellComposedness( node.GetIndex() ); bool strictTopologyViolation = this->DoesVoxelChangeViolateStrictTopology( node.GetIndex() ); if( this->m_TopologyCheck == Strict && ( wellComposednessViolation || strictTopologyViolation ) ) { output->SetPixel( node.GetIndex(), -0.00000001 ); this->m_LabelImage->SetPixel( node.GetIndex(), TopologyPoint ); continue; } if( this->m_TopologyCheck == NoHandles ) { if( wellComposednessViolation ) { output->SetPixel( node.GetIndex(), -0.00000001 ); this->m_LabelImage->SetPixel( node.GetIndex(), TopologyPoint ); continue; } if( strictTopologyViolation ) { // check for handles typename NeighborhoodIteratorType::RadiusType radius; radius.Fill( 1 ); NeighborhoodIteratorType ItL( radius, this->m_LabelImage, this->m_LabelImage->GetBufferedRegion() ); ItL.SetLocation( node.GetIndex() ); NeighborhoodIterator ItC( radius, this->m_ConnectedComponentImage, this->m_ConnectedComponentImage->GetBufferedRegion() ); ItC.SetLocation( node.GetIndex() ); typename ConnectedComponentImageType::PixelType minLabel = NumericTraits::Zero; typename ConnectedComponentImageType::PixelType otherLabel = NumericTraits::Zero; bool doesChangeCreateHandle = false; for( unsigned int d = 0; d < SetDimension; d++ ) { if( ItL.GetNext( d ) == AlivePoint && ItL.GetPrevious( d ) == AlivePoint ) { if( ItC.GetNext( d ) == ItC.GetPrevious( d ) ) { doesChangeCreateHandle = true; } else { minLabel = vnl_math_min( ItC.GetNext( d ), ItC.GetPrevious( d ) ); otherLabel = vnl_math_max( ItC.GetNext( d ), ItC.GetPrevious( d ) ); } break; } } if( doesChangeCreateHandle ) { output->SetPixel( node.GetIndex(), -0.0001 ); this->m_LabelImage->SetPixel( node.GetIndex(), TopologyPoint ); continue; } else { for( ItC.GoToBegin(); !ItC.IsAtEnd(); ++ItC ) { if( ItC.GetCenterPixel() == otherLabel ) { ItC.SetCenterPixel( minLabel ); } } } } } } if ( currentValue > this->m_StoppingValue ) { break; } if ( this->m_CollectPoints ) { this->m_ProcessedPoints->InsertElement( this->m_ProcessedPoints->Size(), node ); } // set this node as alive this->m_LabelImage->SetPixel( node.GetIndex(), AlivePoint ); // for topology handle checks, we need to update the connected // component image at the current node with the appropriate label. if( this->m_TopologyCheck == NoHandles ) { typename ConnectedComponentImageType::PixelType neighborhoodLabel = NumericTraits::Zero; typename NeighborhoodIteratorType::RadiusType radius; radius.Fill( 1 ); NeighborhoodIterator ItC( radius, this->m_ConnectedComponentImage, this->m_ConnectedComponentImage->GetBufferedRegion() ); ItC.SetLocation( node.GetIndex() ); for( unsigned int n = 0; n < ItC.Size(); n++ ) { if( n == static_cast( 0.5 * ItC.Size() ) ) { continue; } typename ConnectedComponentImageType::PixelType c = ItC.GetPixel( n ); if( c > 0 ) { neighborhoodLabel = c; break; } } if( neighborhoodLabel > 0 ) { ItC.SetCenterPixel( neighborhoodLabel ); } } // update its neighbors this->UpdateNeighbors( node.GetIndex(), speedImage, output ); // Send events every certain number of points. const double newProgress = currentValue / this->m_StoppingValue; if( newProgress - oldProgress > 0.01 ) // update every 1% { this->UpdateProgress( newProgress ); oldProgress = newProgress; if( this->GetAbortGenerateData() ) { this->InvokeEvent( AbortEvent() ); this->ResetPipeline(); ProcessAborted e(__FILE__,__LINE__); e.SetDescription("Process aborted."); e.SetLocation(ITK_LOCATION); throw e; } } } } template void FastMarchingImageFilter ::UpdateNeighbors( const IndexType& index, const SpeedImageType * speedImage, LevelSetImageType * output ) { IndexType neighIndex = index; for ( unsigned int j = 0; j < SetDimension; j++ ) { // update left neighbor if( index[j] > this->m_StartIndex[j] ) { neighIndex[j] = index[j] - 1; } #ifdef ITK_USE_DEPRECATED_FAST_MARCHING if ( m_LabelImage->GetPixel( neighIndex ) != AlivePoint ) #else unsigned char label = m_LabelImage->GetPixel( neighIndex ); if ( label != AlivePoint && label != InitialTrialPoint ) #endif { this->UpdateValue( neighIndex, speedImage, output ); } // update right neighbor if ( index[j] < this->m_LastIndex[j] ) { neighIndex[j] = index[j] + 1; } #ifdef ITK_USE_DEPRECATED_FAST_MARCHING if ( m_LabelImage->GetPixel( neighIndex ) != AlivePoint ) #else label = m_LabelImage->GetPixel( neighIndex ); if ( label != AlivePoint && label != InitialTrialPoint ) #endif { this->UpdateValue( neighIndex, speedImage, output ); } //reset neighIndex neighIndex[j] = index[j]; } } template double FastMarchingImageFilter ::UpdateValue( const IndexType& index, const SpeedImageType * speedImage, LevelSetImageType * output ) { IndexType neighIndex = index; typename TLevelSet::PixelType neighValue; PixelType outputPixel; AxisNodeType node; for ( unsigned int j = 0; j < SetDimension; j++ ) { node.SetValue( this->m_LargeValue ); // find smallest valued neighbor in this dimension for( int s = -1; s < 2; s = s + 2 ) { neighIndex[j] = index[j] + s; if( neighIndex[j] > this->m_LastIndex[j] || neighIndex[j] < this->m_StartIndex[j] ) { continue; } if ( this->m_LabelImage->GetPixel( neighIndex ) == AlivePoint ) { outputPixel = output->GetPixel( neighIndex ); neighValue = outputPixel; if( node.GetValue() > neighValue ) { node.SetValue( neighValue ); node.SetIndex( neighIndex ); } } } // put the minimum neighbor onto the heap this->m_NodesUsed[j] = node; this->m_NodesUsed[j].SetAxis(j); // reset neighIndex neighIndex[j] = index[j]; } // sort the local list std::sort( this->m_NodesUsed, this->m_NodesUsed + SetDimension ); // solve quadratic equation double aa, bb, cc; double solution = this->m_LargeValue; aa = 0.0; bb = 0.0; if ( speedImage ) { typedef typename SpeedImageType::PixelType SpeedPixelType; cc = (double) speedImage->GetPixel( index ) / this->m_NormalizationFactor; cc = -1.0 * vnl_math_sqr( 1.0 / cc ); } else { cc = this->m_InverseSpeed; } OutputSpacingType spacing = this->GetOutput()->GetSpacing(); double discrim; for ( unsigned int j = 0; j < SetDimension; j++ ) { node = this->m_NodesUsed[j]; if ( solution >= node.GetValue() ) { const int axis = node.GetAxis(); const double spaceFactor = vnl_math_sqr( 1.0 / spacing[axis] ); const double value = double(node.GetValue()); aa += spaceFactor; bb += value * spaceFactor; cc += vnl_math_sqr( value ) * spaceFactor; discrim = vnl_math_sqr( bb ) - aa * cc; if ( discrim < 0.0 ) { // Discriminant of quadratic eqn. is negative ExceptionObject err(__FILE__, __LINE__); err.SetLocation( ITK_LOCATION ); err.SetDescription( "Discriminant of quadratic equation is negative" ); throw err; } solution = ( vcl_sqrt( discrim ) + bb ) / aa; } else { break; } } if ( solution < this->m_LargeValue ) { // write solution to this->m_OutputLevelSet outputPixel = static_cast( solution ); output->SetPixel( index, outputPixel ); // insert point into trial heap this->m_LabelImage->SetPixel( index, TrialPoint ); node.SetValue( static_cast( solution ) ); node.SetIndex( index ); this->m_TrialHeap.push( node ); } return solution; } /** * Topology check functions */ template bool FastMarchingImageFilter ::DoesVoxelChangeViolateWellComposedness( IndexType idx ) { bool isChangeWellComposed = false; if( SetDimension == 2 ) { isChangeWellComposed = this->IsChangeWellComposed2D( idx ); } else // SetDimension == 3 { isChangeWellComposed = this->IsChangeWellComposed3D( idx ); } return !isChangeWellComposed; } template bool FastMarchingImageFilter ::DoesVoxelChangeViolateStrictTopology( IndexType idx ) { typename NeighborhoodIteratorType::RadiusType radius; radius.Fill( 1 ); NeighborhoodIteratorType It( radius, this->m_LabelImage, this->m_LabelImage->GetBufferedRegion() ); It.SetLocation( idx ); unsigned int numberOfCriticalC3Configurations = 0; unsigned int numberOfFaces = 0; for( unsigned int d = 0; d < SetDimension; d++ ) { if( It.GetNext( d ) == AlivePoint ) { numberOfFaces++; } if( It.GetPrevious( d ) == AlivePoint ) { numberOfFaces++; } if( It.GetNext( d ) == AlivePoint && It.GetPrevious( d ) == AlivePoint ) { numberOfCriticalC3Configurations++; } } if( numberOfCriticalC3Configurations > 0 && numberOfFaces % 2 == 0 && numberOfCriticalC3Configurations * 2 == numberOfFaces ) { return true; } return false; } template bool FastMarchingImageFilter ::IsChangeWellComposed2D( IndexType idx ) { typename NeighborhoodIteratorType::RadiusType radius; radius.Fill( 1 ); NeighborhoodIteratorType It( radius, this->m_LabelImage, this->m_LabelImage->GetBufferedRegion() ); It.SetLocation( idx ); Array neighborhoodPixels( 9 ); // Check for critical configurations: 4 90-degree rotations for ( unsigned int i = 0; i < 4; i++ ) { for ( unsigned int j = 0; j < 9; j++ ) { neighborhoodPixels[j] = ( It.GetPixel( this->m_RotationIndices[i][j] ) != AlivePoint ); if( this->m_RotationIndices[i][j] == 4 ) { neighborhoodPixels[j] = !neighborhoodPixels[j]; } } if( this->IsCriticalC1Configuration2D( neighborhoodPixels ) || this->IsCriticalC2Configuration2D( neighborhoodPixels ) || this->IsCriticalC3Configuration2D( neighborhoodPixels ) || this->IsCriticalC4Configuration2D( neighborhoodPixels ) ) { return false; } } // Check for critical configurations: 2 reflections // Note that the reflections for the C1 and C2 cases // are covered by the rotation cases above (except // in the case of FullInvariance == false. for ( unsigned int i = 0; i < 2; i++ ) { for ( unsigned int j = 0; j < 9; j++ ) { neighborhoodPixels[j] = ( It.GetPixel( this->m_ReflectionIndices[i][j] ) != AlivePoint ); if( this->m_ReflectionIndices[i][j] == 4 ) { neighborhoodPixels[j] = !neighborhoodPixels[j]; } } // if( !this->m_FullInvariance // && ( this->IsCriticalC1Configuration2D( neighborhoodPixels ) // || this->IsCriticalC2Configuration2D( neighborhoodPixels ) ) ) // { // return false; // } if( this->IsCriticalC3Configuration2D( neighborhoodPixels ) || this->IsCriticalC4Configuration2D( neighborhoodPixels ) ) { return false; } } return true; } template bool FastMarchingImageFilter ::IsCriticalC1Configuration2D( Array neighborhood ) { return ( !neighborhood[0] && neighborhood[1] && neighborhood[3] && !neighborhood[4] && !neighborhood[8] ); } template bool FastMarchingImageFilter ::IsCriticalC2Configuration2D( Array neighborhood ) { return ( !neighborhood[0] && neighborhood[1] && neighborhood[3] && !neighborhood[4] && neighborhood[8] && ( neighborhood[5] || neighborhood[7] ) ); } template bool FastMarchingImageFilter ::IsCriticalC3Configuration2D( Array neighborhood ) { return ( !neighborhood[0] && neighborhood[1] && neighborhood[3] && !neighborhood[4] && !neighborhood[5] && neighborhood[6] && !neighborhood[7] && neighborhood[8] ); } template bool FastMarchingImageFilter ::IsCriticalC4Configuration2D( Array neighborhood ) { return ( !neighborhood[0] && neighborhood[1] && neighborhood[3] && !neighborhood[4] && !neighborhood[5] && !neighborhood[6] && !neighborhood[7] && neighborhood[8] ); } template void FastMarchingImageFilter ::InitializeIndices2D() { this->m_RotationIndices[0].SetSize( 9 ); this->m_RotationIndices[1].SetSize( 9 ); this->m_RotationIndices[2].SetSize( 9 ); this->m_RotationIndices[3].SetSize( 9 ); this->m_RotationIndices[0][0] = 0; this->m_RotationIndices[0][1] = 1; this->m_RotationIndices[0][2] = 2; this->m_RotationIndices[0][3] = 3; this->m_RotationIndices[0][4] = 4; this->m_RotationIndices[0][5] = 5; this->m_RotationIndices[0][6] = 6; this->m_RotationIndices[0][7] = 7; this->m_RotationIndices[0][8] = 8; this->m_RotationIndices[1][0] = 2; this->m_RotationIndices[1][1] = 5; this->m_RotationIndices[1][2] = 8; this->m_RotationIndices[1][3] = 1; this->m_RotationIndices[1][4] = 4; this->m_RotationIndices[1][5] = 7; this->m_RotationIndices[1][6] = 0; this->m_RotationIndices[1][7] = 3; this->m_RotationIndices[1][8] = 6; this->m_RotationIndices[2][0] = 8; this->m_RotationIndices[2][1] = 7; this->m_RotationIndices[2][2] = 6; this->m_RotationIndices[2][3] = 5; this->m_RotationIndices[2][4] = 4; this->m_RotationIndices[2][5] = 3; this->m_RotationIndices[2][6] = 2; this->m_RotationIndices[2][7] = 1; this->m_RotationIndices[2][8] = 0; this->m_RotationIndices[3][0] = 6; this->m_RotationIndices[3][1] = 3; this->m_RotationIndices[3][2] = 0; this->m_RotationIndices[3][3] = 7; this->m_RotationIndices[3][4] = 4; this->m_RotationIndices[3][5] = 1; this->m_RotationIndices[3][6] = 8; this->m_RotationIndices[3][7] = 5; this->m_RotationIndices[3][8] = 2; this->m_ReflectionIndices[0].SetSize( 9 ); this->m_ReflectionIndices[1].SetSize( 9 ); this->m_ReflectionIndices[0][0] = 6; this->m_ReflectionIndices[0][1] = 7; this->m_ReflectionIndices[0][2] = 8; this->m_ReflectionIndices[0][3] = 3; this->m_ReflectionIndices[0][4] = 4; this->m_ReflectionIndices[0][5] = 5; this->m_ReflectionIndices[0][6] = 0; this->m_ReflectionIndices[0][7] = 1; this->m_ReflectionIndices[0][8] = 2; this->m_ReflectionIndices[1][0] = 2; this->m_ReflectionIndices[1][1] = 1; this->m_ReflectionIndices[1][2] = 0; this->m_ReflectionIndices[1][3] = 5; this->m_ReflectionIndices[1][4] = 4; this->m_ReflectionIndices[1][5] = 3; this->m_ReflectionIndices[1][6] = 8; this->m_ReflectionIndices[1][7] = 7; this->m_ReflectionIndices[1][8] = 6; } template bool FastMarchingImageFilter ::IsChangeWellComposed3D( IndexType idx ) { Array neighborhoodPixels( 8 ); typename NeighborhoodIteratorType::RadiusType radius; radius.Fill( 1 ); NeighborhoodIteratorType It( radius, this->m_LabelImage, this->m_LabelImage->GetRequestedRegion() ); It.SetLocation( idx ); // Check for C1 critical configurations for ( unsigned int i = 0; i < 12; i++ ) { for ( unsigned int j = 0; j < 4; j++ ) { neighborhoodPixels[j] = ( It.GetPixel( this->m_C1Indices[i][j] ) == AlivePoint ); if( this->m_C1Indices[i][j] == 13 ) { neighborhoodPixels[j] = !neighborhoodPixels[j]; } } if( this->IsCriticalC1Configuration3D( neighborhoodPixels ) ) { return false; } } // Check for C2 critical configurations for ( unsigned int i = 0; i < 8; i++ ) { for ( unsigned int j = 0; j < 8; j++ ) { neighborhoodPixels[j] = ( It.GetPixel( this->m_C2Indices[i][j] ) == AlivePoint ); if( this->m_C2Indices[i][j] == 13 ) { neighborhoodPixels[j] = !neighborhoodPixels[j]; } } if( this->IsCriticalC2Configuration3D( neighborhoodPixels ) ) { return false; } } return true; } template bool FastMarchingImageFilter ::IsCriticalC1Configuration3D( Array neighborhood ) { return ( ( neighborhood[0] && neighborhood[1] && !neighborhood[2] && !neighborhood[3] ) || ( !neighborhood[0] && !neighborhood[1] && neighborhood[2] && neighborhood[3] ) ); } template unsigned int FastMarchingImageFilter ::IsCriticalC2Configuration3D( Array neighborhood ) { // Check if Type 1 or Type 2 for ( unsigned int i = 0; i < 4; i++ ) { bool isC2 = false; if( neighborhood[2*i] == neighborhood[2*i+1] ) { isC2 = true; for ( unsigned int j = 0; j < 8; j++ ) { if( neighborhood[j] == neighborhood[2*i] && j != 2*i && j != 2*i+1 ) { isC2 = false; } } } if( isC2 ) { if( neighborhood[2*i] ) { return 1; } else { return 2; } } } return 0; } template void FastMarchingImageFilter ::InitializeIndices3D() { for ( unsigned int i = 0; i < 12; i++ ) { this->m_C1Indices[i].SetSize( 4 ); } for ( unsigned int i = 0; i < 8; i++ ) { this->m_C2Indices[i].SetSize( 8 ); } this->m_C1Indices[0][0] = 1; this->m_C1Indices[0][1] = 13; this->m_C1Indices[0][2] = 4; this->m_C1Indices[0][3] = 10; this->m_C1Indices[1][0] = 9; this->m_C1Indices[1][1] = 13; this->m_C1Indices[1][2] = 10; this->m_C1Indices[1][3] = 12; this->m_C1Indices[2][0] = 3; this->m_C1Indices[2][1] = 13; this->m_C1Indices[2][2] = 4; this->m_C1Indices[2][3] = 12; this->m_C1Indices[3][0] = 4; this->m_C1Indices[3][1] = 14; this->m_C1Indices[3][2] = 5; this->m_C1Indices[3][3] = 13; this->m_C1Indices[4][0] = 12; this->m_C1Indices[4][1] = 22; this->m_C1Indices[4][2] = 13; this->m_C1Indices[4][3] = 21; this->m_C1Indices[5][0] = 13; this->m_C1Indices[5][1] = 23; this->m_C1Indices[5][2] = 14; this->m_C1Indices[5][3] = 22; this->m_C1Indices[6][0] = 4; this->m_C1Indices[6][1] = 16; this->m_C1Indices[6][2] = 7; this->m_C1Indices[6][3] = 13; this->m_C1Indices[7][0] = 13; this->m_C1Indices[7][1] = 25; this->m_C1Indices[7][2] = 16; this->m_C1Indices[7][3] = 22; this->m_C1Indices[8][0] = 10; this->m_C1Indices[8][1] = 22; this->m_C1Indices[8][2] = 13; this->m_C1Indices[8][3] = 19; this->m_C1Indices[9][0] = 12; this->m_C1Indices[9][1] = 16; this->m_C1Indices[9][2] = 13; this->m_C1Indices[9][3] = 15; this->m_C1Indices[10][0] = 13; this->m_C1Indices[10][1] = 17; this->m_C1Indices[10][2] = 14; this->m_C1Indices[10][3] = 16; this->m_C1Indices[11][0] = 10; this->m_C1Indices[11][1] = 14; this->m_C1Indices[11][2] = 11; this->m_C1Indices[11][3] = 13; this->m_C2Indices[0][0] = 0; this->m_C2Indices[0][1] = 13; this->m_C2Indices[0][2] = 1; this->m_C2Indices[0][3] = 12; this->m_C2Indices[0][4] = 3; this->m_C2Indices[0][5] = 10; this->m_C2Indices[0][6] = 4; this->m_C2Indices[0][7] = 9; this->m_C2Indices[4][0] = 9; this->m_C2Indices[4][1] = 22; this->m_C2Indices[4][2] = 10; this->m_C2Indices[4][3] = 21; this->m_C2Indices[4][4] = 12; this->m_C2Indices[4][5] = 19; this->m_C2Indices[4][6] = 13; this->m_C2Indices[4][7] = 18; for ( unsigned int i = 1; i < 4; i++ ) { int addend; if( i == 2 ) { addend = 2; } else { addend = 1; } for ( unsigned int j = 0; j < 8; j++ ) { this->m_C2Indices[i ][j] = this->m_C2Indices[i-1][j] + addend; this->m_C2Indices[i+4][j] = this->m_C2Indices[i+3][j] + addend; } } } } // namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkGreenColormapFunctor.h000066400000000000000000000045141147325206600234250ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkGreenColormapFunctor.h,v $ Language: C++ Date: $Date: 2009-05-15 19:08:09 $ Version: $Revision: 1.2 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkGreenColormapFunctor_h #define __itkGreenColormapFunctor_h #include "itkColormapFunctor.h" namespace itk { namespace Functor { /** * \class GreenColormapFunctor * \brief Function object which maps a scalar value into an RGB colormap value. * * \author Nicholas Tustison, Hui Zhang, Gaetan Lehmann, Paul Yushkevich and ̈James C. Gee * * This code was contributed in the Insight Journal paper: * * "Meeting Andy Warhol Somewhere Over the Rainbow: RGB Colormapping and ITK" * http://www.insight-journal.org/browse/publication/285 * http://hdl.handle.net/1926/1452 * */ template< class TScalar, class TRGBPixel > class ITK_EXPORT GreenColormapFunctor : public ColormapFunctor { public: typedef GreenColormapFunctor Self; typedef ColormapFunctor Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); typedef typename Superclass::RGBPixelType RGBPixelType; typedef typename Superclass::ScalarType ScalarType; typedef typename Superclass::RealType RealType; virtual RGBPixelType operator()( const TScalar & ) const; protected: GreenColormapFunctor() {}; ~GreenColormapFunctor() {}; private: GreenColormapFunctor(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace functor } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkGreenColormapFunctor.txx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkGreenColormapFunctor.txx000066400000000000000000000026571147325206600240270ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkGreenColormapFunctor.txx,v $ Language: C++ Date: $Date: 2009-05-15 02:47:59 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkGreenColormapFunctor_txx #define __itkGreenColormapFunctor_txx #include "itkGreenColormapFunctor.h" namespace itk { namespace Functor { template typename GreenColormapFunctor::RGBPixelType GreenColormapFunctor ::operator()( const TScalar & v ) const { // Map the input scalar between [0, 1]. RealType value = this->RescaleInputValue( v ); // Set the rgb components after rescaling the values. RGBPixelType pixel; pixel[0] = 0; pixel[1] = this->RescaleRGBComponentValue( value ); pixel[2] = 0; return pixel; } } // end namespace Functor } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkGreyColormapFunctor.h000066400000000000000000000045031147325206600232710ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkGreyColormapFunctor.h,v $ Language: C++ Date: $Date: 2009-05-15 19:22:31 $ Version: $Revision: 1.2 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkGreyColormapFunctor_h #define __itkGreyColormapFunctor_h #include "itkColormapFunctor.h" namespace itk { namespace Functor { /** * \class GreyColormapFunctor * \brief Function object which maps a scalar value into an RGB colormap value. * * \author Nicholas Tustison, Hui Zhang, Gaetan Lehmann, Paul Yushkevich and ̈James C. Gee * * This code was contributed in the Insight Journal paper: * * "Meeting Andy Warhol Somewhere Over the Rainbow: RGB Colormapping and ITK" * http://www.insight-journal.org/browse/publication/285 * http://hdl.handle.net/1926/1452 * */ template< class TScalar, class TRGBPixel > class ITK_EXPORT GreyColormapFunctor : public ColormapFunctor { public: typedef GreyColormapFunctor Self; typedef ColormapFunctor Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); typedef typename Superclass::RGBPixelType RGBPixelType; typedef typename Superclass::ScalarType ScalarType; typedef typename Superclass::RealType RealType; virtual RGBPixelType operator()( const TScalar & ) const; protected: GreyColormapFunctor() {}; ~GreyColormapFunctor() {}; private: GreyColormapFunctor(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace functor } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkGreyColormapFunctor.txx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkGreyColormapFunctor.txx000066400000000000000000000026671147325206600236760ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkGreyColormapFunctor.txx,v $ Language: C++ Date: $Date: 2009-05-15 02:47:59 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkGreyColormapFunctor_txx #define __itkGreyColormapFunctor_txx #include "itkGreyColormapFunctor.h" namespace itk { namespace Functor { template typename GreyColormapFunctor::RGBPixelType GreyColormapFunctor ::operator()( const TScalar & v ) const { // Map the input scalar between [0, 1]. RealType value = this->RescaleInputValue( v ); // Set the rgb components after rescaling the values. RGBPixelType pixel; pixel[0] = this->RescaleRGBComponentValue( value ); pixel[1] = pixel[0]; pixel[2] = pixel[0]; return pixel; } } // end namespace Functor } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkHSVColormapFunctor.h000066400000000000000000000044721147325206600230300ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkHSVColormapFunctor.h,v $ Language: C++ Date: $Date: 2009-05-15 19:22:31 $ Version: $Revision: 1.2 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkHSVColormapFunctor_h #define __itkHSVColormapFunctor_h #include "itkColormapFunctor.h" namespace itk { namespace Functor { /** * \class HSVColormapFunctor * \brief Function object which maps a scalar value into an RGB colormap value. * * \author Nicholas Tustison, Hui Zhang, Gaetan Lehmann, Paul Yushkevich and ̈James C. Gee * * This code was contributed in the Insight Journal paper: * * "Meeting Andy Warhol Somewhere Over the Rainbow: RGB Colormapping and ITK" * http://www.insight-journal.org/browse/publication/285 * http://hdl.handle.net/1926/1452 * */ template< class TScalar, class TRGBPixel > class ITK_EXPORT HSVColormapFunctor : public ColormapFunctor { public: typedef HSVColormapFunctor Self; typedef ColormapFunctor Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); typedef typename Superclass::RGBPixelType RGBPixelType; typedef typename Superclass::ScalarType ScalarType; typedef typename Superclass::RealType RealType; virtual RGBPixelType operator()( const TScalar & ) const; protected: HSVColormapFunctor() {}; ~HSVColormapFunctor() {}; private: HSVColormapFunctor(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace functor } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkHSVColormapFunctor.txx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkHSVColormapFunctor.txx000066400000000000000000000036521147325206600234230ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkHSVColormapFunctor.txx,v $ Language: C++ Date: $Date: 2009-05-15 02:47:59 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkHSVColormapFunctor_txx #define __itkHSVColormapFunctor_txx #include "itkHSVColormapFunctor.h" namespace itk { namespace Functor { template typename HSVColormapFunctor::RGBPixelType HSVColormapFunctor ::operator()( const TScalar & v ) const { // Map the input scalar between [0, 1]. RealType value = this->RescaleInputValue( v ); // Apply the color mapping. // Apply the color mapping. RealType red = vnl_math_abs( 5.0 * ( value - 0.5 ) ) - 5.0/6.0; red = vnl_math_min( red, 1.0 ); red = vnl_math_max( 0.0, red ); RealType green = -vnl_math_abs( 5.0 * ( value - 11.0/30.0 ) ) + 11.0/6.0; green = vnl_math_min( green, 1.0 ); green = vnl_math_max( 0.0, green ); RealType blue = -vnl_math_abs( 5.0 * ( value - 19.0/30.0 ) ) + 11.0/6.0; blue = vnl_math_min( blue, 1.0 ); blue = vnl_math_max( 0.0, blue ); // Set the rgb components after rescaling the values. RGBPixelType pixel; pixel[0] = this->RescaleRGBComponentValue( red ); pixel[1] = this->RescaleRGBComponentValue( green ); pixel[2] = this->RescaleRGBComponentValue( blue ); return pixel; } } // end namespace Functor } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkHotColormapFunctor.h000066400000000000000000000044721147325206600231220ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkHotColormapFunctor.h,v $ Language: C++ Date: $Date: 2009-05-15 19:22:31 $ Version: $Revision: 1.2 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkHotColormapFunctor_h #define __itkHotColormapFunctor_h #include "itkColormapFunctor.h" namespace itk { namespace Functor { /** * \class HotColormapFunctor * \brief Function object which maps a scalar value into an RGB colormap value. * * \author Nicholas Tustison, Hui Zhang, Gaetan Lehmann, Paul Yushkevich and ̈James C. Gee * * This code was contributed in the Insight Journal paper: * * "Meeting Andy Warhol Somewhere Over the Rainbow: RGB Colormapping and ITK" * http://www.insight-journal.org/browse/publication/285 * http://hdl.handle.net/1926/1452 * */ template< class TScalar, class TRGBPixel > class ITK_EXPORT HotColormapFunctor : public ColormapFunctor { public: typedef HotColormapFunctor Self; typedef ColormapFunctor Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); typedef typename Superclass::RGBPixelType RGBPixelType; typedef typename Superclass::ScalarType ScalarType; typedef typename Superclass::RealType RealType; virtual RGBPixelType operator()( const TScalar & ) const; protected: HotColormapFunctor() {}; ~HotColormapFunctor() {}; private: HotColormapFunctor(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace functor } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkHotColormapFunctor.txx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkHotColormapFunctor.txx000066400000000000000000000034741147325206600235170ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkHotColormapFunctor.txx,v $ Language: C++ Date: $Date: 2009-05-15 02:47:59 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkHotColormapFunctor_txx #define __itkHotColormapFunctor_txx #include "itkHotColormapFunctor.h" namespace itk { namespace Functor { template typename HotColormapFunctor::RGBPixelType HotColormapFunctor ::operator()( const TScalar & v ) const { // Map the input scalar between [0, 1]. RealType value = this->RescaleInputValue( v ); // Apply the color mapping. RealType red = 63.0/26.0 * value - 1.0/13.0; red = vnl_math_max( 0.0, red ); red = vnl_math_min( 1.0, red ); RealType green = 63.0/26.0 * value - 11.0/13.0; green = vnl_math_max( 0.0, green ); green = vnl_math_min( 1.0, green ); RealType blue = 4.5 * value - 3.5; blue = vnl_math_max( 0.0, blue ); blue = vnl_math_min( 1.0, blue ); // Set the rgb components after rescaling the values. RGBPixelType pixel; pixel[0] = this->RescaleRGBComponentValue( red ); pixel[1] = this->RescaleRGBComponentValue( green ); pixel[2] = this->RescaleRGBComponentValue( blue ); return pixel; } } // end namespace Functor } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkJetColormapFunctor.h000066400000000000000000000044721147325206600231120ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkJetColormapFunctor.h,v $ Language: C++ Date: $Date: 2009-05-15 19:22:31 $ Version: $Revision: 1.2 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkJetColormapFunctor_h #define __itkJetColormapFunctor_h #include "itkColormapFunctor.h" namespace itk { namespace Functor { /** * \class JetColormapFunctor * \brief Function object which maps a scalar value into an RGB colormap value. * * \author Nicholas Tustison, Hui Zhang, Gaetan Lehmann, Paul Yushkevich and ̈James C. Gee * * This code was contributed in the Insight Journal paper: * * "Meeting Andy Warhol Somewhere Over the Rainbow: RGB Colormapping and ITK" * http://www.insight-journal.org/browse/publication/285 * http://hdl.handle.net/1926/1452 * */ template< class TScalar, class TRGBPixel > class ITK_EXPORT JetColormapFunctor : public ColormapFunctor { public: typedef JetColormapFunctor Self; typedef ColormapFunctor Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); typedef typename Superclass::RGBPixelType RGBPixelType; typedef typename Superclass::ScalarType ScalarType; typedef typename Superclass::RealType RealType; virtual RGBPixelType operator()( const TScalar & ) const; protected: JetColormapFunctor() {}; ~JetColormapFunctor() {}; private: JetColormapFunctor(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace functor } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkJetColormapFunctor.txx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkJetColormapFunctor.txx000066400000000000000000000035761147325206600235120ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkJetColormapFunctor.txx,v $ Language: C++ Date: $Date: 2009-05-15 02:47:59 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkJetColormapFunctor_txx #define __itkJetColormapFunctor_txx #include "itkJetColormapFunctor.h" namespace itk { namespace Functor { template typename JetColormapFunctor::RGBPixelType JetColormapFunctor ::operator()( const TScalar & v ) const { // Map the input scalar between [0, 1]. RealType value = this->RescaleInputValue( v ); // Apply the color mapping. RealType red = -vnl_math_abs( 3.75 * ( value - 0.75 ) ) + 1.625; red = vnl_math_min( red, 1.0 ); red = vnl_math_max( 0.0, red ); RealType green = -vnl_math_abs( 3.75 * ( value - 0.5 ) ) + 1.625; green = vnl_math_min( green, 1.0 ); green = vnl_math_max( 0.0, green ); RealType blue = -vnl_math_abs( 3.75 * ( value - 0.25 ) ) + 1.625; blue = vnl_math_min( blue, 1.0 ); blue = vnl_math_max( 0.0, blue ); // Set the rgb components after rescaling the values. RGBPixelType pixel; pixel[0] = this->RescaleRGBComponentValue( red ); pixel[1] = this->RescaleRGBComponentValue( green ); pixel[2] = this->RescaleRGBComponentValue( blue ); return pixel; } } // end namespace Functor } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkLabelContourImageFilter.h000066400000000000000000000150331147325206600240270ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkLabelContourImageFilter.h,v $ Language: C++ Date: $Date: 2009/03/10 17:30:41 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkLabelContourImageFilter_h #define __itkLabelContourImageFilter_h #include "itkInPlaceImageFilter.h" #include "itkImage.h" #include "itkConceptChecking.h" #include #include #include "itkProgressReporter.h" #include "itkBarrier.h" namespace itk { /** * \class LabelContourImageFilter * \brief Give the pixels on the border of an object. * * LabelContourImageFilter labels the pixels on the borders * of the objects in a binary image. * * \sa ImageToImageFilter */ template class ITK_EXPORT LabelContourImageFilter : public InPlaceImageFilter< TInputImage, TOutputImage > { public: /** * Standard "Self" & Superclass typedef. */ typedef LabelContourImageFilter Self; typedef InPlaceImageFilter< TInputImage, TOutputImage > Superclass; /** * Types from the Superclass */ typedef typename Superclass::InputImagePointer InputImagePointer; /** * Extract some information from the image types. Dimensionality * of the two images is assumed to be the same. */ typedef typename TOutputImage::PixelType OutputPixelType; typedef typename TOutputImage::InternalPixelType OutputInternalPixelType; typedef typename TInputImage::PixelType InputPixelType; typedef typename TInputImage::InternalPixelType InputInternalPixelType; itkStaticConstMacro(ImageDimension, unsigned int, TOutputImage::ImageDimension); itkStaticConstMacro(OutputImageDimension, unsigned int, TOutputImage::ImageDimension); itkStaticConstMacro(InputImageDimension, unsigned int, TInputImage::ImageDimension); /** * Image typedef support */ typedef TInputImage InputImageType; typedef typename TInputImage::IndexType IndexType; typedef typename TInputImage::SizeType SizeType; typedef typename TInputImage::OffsetType OffsetType; typedef typename TInputImage::PixelType InputImagePixelType; typedef TOutputImage OutputImageType; typedef typename TOutputImage::RegionType RegionType; typedef typename TOutputImage::IndexType OutputIndexType; typedef typename TOutputImage::SizeType OutputSizeType; typedef typename TOutputImage::OffsetType OutputOffsetType; typedef typename TOutputImage::PixelType OutputImagePixelType; typedef std::list ListType; /** * Smart pointer typedef support */ typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** * Run-time type information (and related methods) */ itkTypeMacro(LabelContourImageFilter, ImageToImageFilter); /** * Method for creation through the object factory. */ itkNewMacro(Self); /** * Set/Get whether the connected components are defined strictly by * face connectivity or by face+edge+vertex connectivity. Default is * FullyConnectedOff. For objects that are 1 pixel wide, use * FullyConnectedOn. */ itkSetMacro(FullyConnected, bool); itkGetConstReferenceMacro(FullyConnected, bool); itkBooleanMacro(FullyConnected); // Concept checking -- input and output dimensions must be the same itkConceptMacro(SameDimension, (Concept::SameDimension)); /** */ itkSetMacro(BackgroundValue, OutputImagePixelType); itkGetMacro(BackgroundValue, OutputImagePixelType); protected: LabelContourImageFilter() { m_FullyConnected = false; m_BackgroundValue = NumericTraits< OutputImagePixelType >::Zero; m_NumberOfThreads = 0; this->SetInPlace( false ); } virtual ~LabelContourImageFilter() {} LabelContourImageFilter(const Self&) {} void PrintSelf(std::ostream& os, Indent indent) const; /** * Standard pipeline methods. */ void BeforeThreadedGenerateData (); void AfterThreadedGenerateData (); void ThreadedGenerateData (const RegionType& outputRegionForThread, int threadId); /** LabelContourImageFilter needs the entire input. Therefore * it must provide an implementation GenerateInputRequestedRegion(). * \sa ProcessObject::GenerateInputRequestedRegion(). */ void GenerateInputRequestedRegion(); /** LabelContourImageFilter will produce all of the output. * Therefore it must provide an implementation of * EnlargeOutputRequestedRegion(). * \sa ProcessObject::EnlargeOutputRequestedRegion() */ void EnlargeOutputRequestedRegion(DataObject *itkNotUsed(output)); private: OutputImagePixelType m_BackgroundValue; bool m_FullyConnected; // some additional types typedef typename TOutputImage::RegionType::SizeType OutSizeType; // types to support the run length encoding of lines class runLength { public: // run length information - may be a more type safe way of doing this long int length; typename InputImageType::IndexType where; // Index of the start of the run InputImagePixelType label; }; typedef std::vector lineEncoding; // the map storing lines typedef std::vector LineMapType; typedef std::vector OffsetVec; // the types to support union-find operations typedef std::vector UnionFindType; bool CheckNeighbors(const OutputIndexType &A, const OutputIndexType &B); void CompareLines(lineEncoding ¤t, const lineEncoding &Neighbour); void SetupLineOffsets(OffsetVec &LineOffsets); void Wait() { if( m_NumberOfThreads > 1 ) { m_Barrier->Wait(); } } typename Barrier::Pointer m_Barrier; LineMapType m_LineMap; long m_NumberOfThreads; }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkLabelContourImageFilter.txx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkLabelContourImageFilter.txx000066400000000000000000000334371147325206600244330ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkLabelContourImageFilter.txx,v $ Language: C++ Date: $Date: 2009/03/10 17:30:41 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkLabelContourImageFilter_txx #define __itkLabelContourImageFilter_txx #include "itkLabelContourImageFilter.h" #include "itkNumericTraits.h" // don't think we need the indexed version as we only compute the // index at the start of each run, but there isn't a choice #include "itkImageLinearConstIteratorWithIndex.h" #include "itkImageLinearIteratorWithIndex.h" #include "itkConstShapedNeighborhoodIterator.h" #include "itkImageRegionIterator.h" #include "itkMaskImageFilter.h" #include "itkConnectedComponentAlgorithm.h" namespace itk { template< class TInputImage, class TOutputImage> void LabelContourImageFilter< TInputImage, TOutputImage> ::GenerateInputRequestedRegion() { // call the superclass' implementation of this method Superclass::GenerateInputRequestedRegion(); // We need all the input. InputImagePointer input = const_cast(this->GetInput()); if( !input ) { return; } input->SetRequestedRegion( input->GetLargestPossibleRegion() ); } template< class TInputImage, class TOutputImage> void LabelContourImageFilter< TInputImage, TOutputImage> ::EnlargeOutputRequestedRegion(DataObject *) { this->GetOutput() ->SetRequestedRegion( this->GetOutput()->GetLargestPossibleRegion() ); } template< class TInputImage, class TOutputImage> void LabelContourImageFilter< TInputImage, TOutputImage> ::BeforeThreadedGenerateData() { typename TOutputImage::Pointer output = this->GetOutput(); typename TInputImage::ConstPointer input = this->GetInput(); long nbOfThreads = this->GetNumberOfThreads(); if( itk::MultiThreader::GetGlobalMaximumNumberOfThreads() != 0 ) { nbOfThreads = vnl_math_min( this->GetNumberOfThreads(), itk::MultiThreader::GetGlobalMaximumNumberOfThreads() ); } // number of threads can be constrained by the region size, so call the SplitRequestedRegion // to get the real number of threads which will be used typename TOutputImage::RegionType splitRegion; // dummy region - just to call the following method nbOfThreads = this->SplitRequestedRegion(0, nbOfThreads, splitRegion); // std::cout << "nbOfThreads: " << nbOfThreads << std::endl; m_Barrier = Barrier::New(); m_Barrier->Initialize( nbOfThreads ); long pixelcount = output->GetRequestedRegion().GetNumberOfPixels(); long xsize = output->GetRequestedRegion().GetSize()[0]; long linecount = pixelcount/xsize; m_LineMap.clear(); m_LineMap.resize( linecount ); m_NumberOfThreads = nbOfThreads; } template< class TInputImage, class TOutputImage> void LabelContourImageFilter< TInputImage, TOutputImage> ::ThreadedGenerateData(const RegionType& outputRegionForThread, int threadId) { typename TOutputImage::Pointer output = this->GetOutput(); typename TInputImage::ConstPointer input = this->GetInput(); // create a line iterator typedef itk::ImageLinearConstIteratorWithIndex InputLineIteratorType; InputLineIteratorType inLineIt(input, outputRegionForThread); inLineIt.SetDirection(0); typedef itk::ImageLinearIteratorWithIndex OutputLineIteratorType; OutputLineIteratorType outLineIt(output, outputRegionForThread); outLineIt.SetDirection(0); // set the progress reporter to deal with the number of lines long pixelcountForThread = outputRegionForThread.GetNumberOfPixels(); long xsizeForThread = outputRegionForThread.GetSize()[0]; long linecountForThread = pixelcountForThread/xsizeForThread; ProgressReporter progress(this, threadId, linecountForThread * 2); // find the split axis IndexType outputRegionIdx = output->GetRequestedRegion().GetIndex(); IndexType outputRegionForThreadIdx = outputRegionForThread.GetIndex(); int splitAxis = 0; for( int i=0; iGetRequestedRegion().GetSize(); outputRegionSize[splitAxis] = outputRegionForThreadIdx[splitAxis] - outputRegionIdx[splitAxis]; long firstLineIdForThread = RegionType( outputRegionIdx, outputRegionSize ).GetNumberOfPixels() / xsizeForThread; long lineId = firstLineIdForThread; OffsetVec LineOffsets; SetupLineOffsets(LineOffsets); outLineIt.GoToBegin(); for( inLineIt.GoToBegin(); !inLineIt.IsAtEnd(); inLineIt.NextLine(), outLineIt.NextLine() ) { inLineIt.GoToBeginOfLine(); outLineIt.GoToBeginOfLine(); lineEncoding Line; while (! inLineIt.IsAtEndOfLine()) { InputPixelType PVal = inLineIt.Get(); runLength thisRun; long length=0; IndexType thisIndex; thisIndex = inLineIt.GetIndex(); //std::cout << thisIndex << std::endl; outLineIt.Set( m_BackgroundValue ); ++length; ++inLineIt; ++outLineIt; while( !inLineIt.IsAtEndOfLine() && inLineIt.Get() == PVal ) { outLineIt.Set( m_BackgroundValue ); ++length; ++inLineIt; ++outLineIt; } // create the run length object to go in the vector thisRun.length=length; thisRun.where = thisIndex; thisRun.label = PVal; Line.push_back(thisRun); } // std::cout << std::endl; m_LineMap[lineId] = Line; lineId++; progress.CompletedPixel(); } // wait for the other threads to complete that part this->Wait(); // now process the map and make appropriate entries in an equivalence // table // assert( linecount == m_ForegroundLineMap.size() ); long pixelcount = output->GetRequestedRegion().GetNumberOfPixels(); long xsize = output->GetRequestedRegion().GetSize()[0]; long linecount = pixelcount/xsize; long lastLineIdForThread = linecount; if( threadId != m_NumberOfThreads - 1 ) { lastLineIdForThread = firstLineIdForThread + RegionType( outputRegionIdx, outputRegionForThread.GetSize() ).GetNumberOfPixels() / xsizeForThread; } for(long ThisIdx = firstLineIdForThread; ThisIdx < lastLineIdForThread; ++ThisIdx) { if( !m_LineMap[ThisIdx].empty() ) { for (OffsetVec::const_iterator I = LineOffsets.begin(); I != LineOffsets.end(); ++I) { long NeighIdx = ThisIdx + (*I); // check if the neighbor is in the map if ( NeighIdx >= 0 && NeighIdx < linecount && !m_LineMap[NeighIdx].empty() ) { // Now check whether they are really neighbors bool areNeighbors = CheckNeighbors(m_LineMap[ThisIdx][0].where, m_LineMap[NeighIdx][0].where); if (areNeighbors) { // Compare the two lines CompareLines(m_LineMap[ThisIdx], m_LineMap[NeighIdx]); } } } } progress.CompletedPixel(); } } template< class TInputImage, class TOutputImage> void LabelContourImageFilter< TInputImage, TOutputImage> ::AfterThreadedGenerateData() { m_Barrier = NULL; m_LineMap.clear(); } template< class TInputImage, class TOutputImage> void LabelContourImageFilter< TInputImage, TOutputImage> ::SetupLineOffsets(OffsetVec &LineOffsets) { // Create a neighborhood so that we can generate a table of offsets // to "previous" line indexes // We are going to mis-use the neighborhood iterators to compute the // offset for us. All this messing around produces an array of // offsets that will be used to index the map typename TOutputImage::Pointer output = this->GetOutput(); typedef Image PretendImageType; typedef typename PretendImageType::RegionType::SizeType PretendSizeType; typedef typename PretendImageType::RegionType::IndexType PretendIndexType; typedef ConstShapedNeighborhoodIterator LineNeighborhoodType; typename PretendImageType::Pointer fakeImage; fakeImage = PretendImageType::New(); typename PretendImageType::RegionType LineRegion; //LineRegion = PretendImageType::RegionType::New(); OutSizeType OutSize = output->GetRequestedRegion().GetSize(); PretendSizeType PretendSize; // The first dimension has been collapsed for (unsigned int i = 0; iSetRegions( LineRegion ); PretendSizeType kernelRadius; kernelRadius.Fill(1); LineNeighborhoodType lnit(kernelRadius, fakeImage, LineRegion); setConnectivity( &lnit, m_FullyConnected ); typename LineNeighborhoodType::IndexListType ActiveIndexes; ActiveIndexes = lnit.GetActiveIndexList(); typename LineNeighborhoodType::IndexListType::const_iterator LI; PretendIndexType idx = LineRegion.GetIndex(); long offset = fakeImage->ComputeOffset( idx ); for (LI=ActiveIndexes.begin(); LI != ActiveIndexes.end(); LI++) { LineOffsets.push_back( fakeImage->ComputeOffset( idx + lnit.GetOffset( *LI ) ) - offset ); } LineOffsets.push_back( 0 ); // LineOffsets is the thing we wanted. } template< class TInputImage, class TOutputImage> bool LabelContourImageFilter< TInputImage, TOutputImage> ::CheckNeighbors(const OutputIndexType &A, const OutputIndexType &B) { // this checks whether the line encodings are really neighbors. The // first dimension gets ignored because the encodings are along that // axis OutputOffsetType Off = A - B; for (unsigned i = 1; i < OutputImageDimension; i++) { if (abs(Off[i]) > 1) { return(false); } } return(true); } template< class TInputImage, class TOutputImage> void LabelContourImageFilter< TInputImage, TOutputImage> ::CompareLines(lineEncoding ¤t, const lineEncoding &Neighbour) { bool sameLine = true; OutputOffsetType Off = current[0].where - Neighbour[0].where; for (unsigned i = 1; i < OutputImageDimension; i++) { if (Off[i] != 0) { sameLine = false; } } long offset = 0; if (m_FullyConnected || sameLine) { offset = 1; } // std::cout << "offset: " << offset << std::endl; typename TOutputImage::Pointer output = this->GetOutput(); typename lineEncoding::const_iterator nIt, mIt; typename lineEncoding::iterator cIt; mIt = Neighbour.begin(); // out marker iterator for (cIt = current.begin();cIt != current.end();++cIt) { if( cIt->label == m_BackgroundValue ) { continue; } //runLength cL = *cIt; long cStart = cIt->where[0]; // the start x position long cLast = cStart + cIt->length - 1; bool lineCompleted = false; for (nIt=mIt; nIt != Neighbour.end() && !lineCompleted; ++nIt) { if( nIt->label == cIt->label ) { continue; } //runLength nL = *nIt; long nStart = nIt->where[0]; long nLast = nStart + nIt->length - 1; // there are a few ways that neighbouring lines might overlap // neighbor S------------------E // current S------------------------E //------------- // neighbor S------------------E // current S----------------E //------------- // neighbor S------------------E // current S------------------E //------------- // neighbor S------------------E // current S-------E //------------- long ss1 = nStart - offset; // long ss2 = nStart + offset; // long ee1 = nLast - offset; long ee2 = nLast + offset; bool eq = false; long oStart = 0; long oLast = 0; // the logic here can probably be improved a lot if ((ss1 >= cStart) && (ee2 <= cLast)) { // case 1 // std::cout << "case 1" << std::endl; eq = true; oStart = ss1; oLast = ee2; } else if ((ss1 <= cStart) && (ee2 >= cLast)) { // case 4 // std::cout << "case 4" << std::endl; eq = true; oStart = cStart; oLast = cLast; } else if ((ss1 <= cLast) && (ee2 >= cLast)) { // case 2 // std::cout << "case 2" << std::endl; eq = true; oStart = ss1; oLast = cLast; } else if ((ss1 <= cStart) && (ee2 >= cStart)) { // case 3 // std::cout << "case 3" << std::endl; eq = true; oStart = cStart; oLast = ee2; } if (eq) { // std::cout << oStart << " " << oLast << std::endl; assert( oStart <= oLast ); IndexType idx = cIt->where; for( int x=oStart; x<=oLast; x++ ) { // std::cout << idx << std::endl; idx[0] = x; output->SetPixel( idx, cIt->label ); } if( oStart == cStart && oLast == cLast ) { lineCompleted = true; } } } } } template< class TInputImage, class TOutputImage> void LabelContourImageFilter< TInputImage, TOutputImage> ::PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf(os,indent); os << indent << "FullyConnected: " << m_FullyConnected << std::endl; os << indent << "BackgroundValue: " << static_cast::PrintType>(m_BackgroundValue) << std::endl; } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkManifoldIntegrationAlgorithm.cxx000066400000000000000000000507271147325206600255150ustar00rootroot00000000000000/*========================================================================= Program: Insight Segmentation & Registration Toolkit (ITK) Module: $RCSfile: itkManifoldIntegrationAlgorithm.cxx,v $ Language: C++ Date: $Date: 2006/10/13 19:21:27 $ Version: $Revision: 1.3 $ =========================================================================*/ #ifndef _itkManifoldIntegrationAlgorithm_cxx_ #define _itkManifoldIntegrationAlgorithm_cxx_ #include "itkSurfaceMeshCurvature.h" #include "vtkFeatureEdges.h" #include "vtkPointLocator.h" #include "vtkCellLocator.h" #include "vtkTriangleFilter.h" #include "vtkCleanPolyData.h" #include "vtkPolyDataConnectivityFilter.h" namespace itk { template ManifoldIntegrationAlgorithm::ManifoldIntegrationAlgorithm() { m_SurfaceMesh=NULL; m_QS = DijkstrasAlgorithmQueue::New(); m_MaxCost=vnl_huge_val(m_MaxCost); m_PureDist=false; // m_LabelCost=0; m_ParamWhileSearching=false; this->m_DistanceCostWeight=1; this->m_LabelCostWeight=0; } template float ManifoldIntegrationAlgorithm::dstarUestimate( typename TGraphSearchNode::Pointer G) { typedef itk::SurfaceMeshCurvature surfktype; typename surfktype::Pointer surfk=surfktype::New(); surfk->SetSurfacePatch(G); surfk->FindNeighborhood(); float dsu=(float) surfk->dstarUestimate(); return dsu; } template void ManifoldIntegrationAlgorithm::InitializeGraph3() { if (!m_SurfaceMesh) return; // Construct simple triangles vtkTriangleFilter* fltTriangle = vtkTriangleFilter::New(); fltTriangle->SetInput(m_SurfaceMesh); cout << " converting mesh to triangles " << endl; fltTriangle->Update(); /* Clean the data vtkCleanPolyData* fltCleaner = vtkCleanPolyData::New(); fltCleaner->SetInput(fltTriangle->GetOutput()); fltCleaner->SetTolerance(0); fltCleaner->ConvertPolysToLinesOn(); cout << " cleaning up triangle mesh " << endl; fltCleaner->Update(); // Go through and delete the cells that are of the wrong type //m_SurfaceMesh vtkPolyData* clean= fltCleaner->GetOutput(); for(vtkIdType i = clean->GetNumberOfCells();i > 0;i--) { if(clean->GetCellType(i-1) != VTK_TRIANGLE) clean->DeleteCell(i-1); } clean->BuildCells(); m_SurfaceMesh=clean;*/ m_SurfaceMesh=fltTriangle->GetOutput(); typedef float labelType; typedef std::vector LabelSetType; LabelSetType myLabelSet; vtkPoints* vtkpoints = m_SurfaceMesh->GetPoints(); vtkPointData *pd=m_SurfaceMesh->GetPointData(); int numPoints = vtkpoints->GetNumberOfPoints(); vtkDataArray* scs=pd->GetScalars(); m_GraphX.resize(numPoints); for(int i =0; i < numPoints; i++) { NodeLocationType loc; double* pt = vtkpoints->GetPoint(i); typename GraphSearchNode::Pointer G= GraphSearchNode::New(); G->SetUnVisited(); G->SetTotalCost(m_MaxCost); G->SetValue(scs->GetTuple1(i),3); /** here we put the label value */ labelType label = scs->GetTuple1(i); if( find( myLabelSet.begin(), myLabelSet.end(), label ) == myLabelSet.end() ) myLabelSet.push_back( label ); // std::cout << " label " << scs->GetTuple1(i) << std::endl; // std::cout << " set3 " << scs->GetTuple3(i) << std::endl; // std::cout << " set4 " << scs->GetTuple4(i) << std::endl; for (int j=0; jSetLocation(loc); G->SetPredecessor(NULL); G->m_NumberOfNeighbors=0; G->SetIdentity(i); m_GraphX[i]=G; } std::cout << " you have " << myLabelSet.size() << " labels " << std::endl; for ( unsigned int i=0; i < myLabelSet.size(); i++) std::cout <<" label " << myLabelSet[i] << std::endl; std::cout << " allocation of graph done "; // now loop through the cells to get triangles and also edges vtkCellArray* vtkcells = m_SurfaceMesh->GetPolys(); vtkIdType npts; vtkIdType* pts; /* count possible neighbors ... */ for(vtkcells->InitTraversal(); vtkcells->GetNextCell(npts, pts); ) { m_GraphX[pts[0]]->m_NumberOfNeighbors+=2; m_GraphX[pts[1]]->m_NumberOfNeighbors+=2; m_GraphX[pts[2]]->m_NumberOfNeighbors+=2; } for(int i =0; i < numPoints; i++) { m_GraphX[i]->m_Neighbors.resize(m_GraphX[i]->m_NumberOfNeighbors); // std::cout <<" Num Neigh " << i << " is " << m_GraphX[i]->m_NumberOfNeighbors << std::endl; m_GraphX[i]->m_NumberOfNeighbors=0; } for(vtkcells->InitTraversal(); vtkcells->GetNextCell(npts, pts); ) { m_GraphX[pts[0]]->m_Neighbors[ m_GraphX[pts[0]]->m_NumberOfNeighbors ]=m_GraphX[pts[1]]; m_GraphX[pts[0]]->m_NumberOfNeighbors++; m_GraphX[pts[0]]->m_Neighbors[ m_GraphX[pts[0]]->m_NumberOfNeighbors ]=m_GraphX[pts[2]]; m_GraphX[pts[0]]->m_NumberOfNeighbors++; m_GraphX[pts[1]]->m_Neighbors[ m_GraphX[pts[1]]->m_NumberOfNeighbors ]=m_GraphX[pts[0]]; m_GraphX[pts[1]]->m_NumberOfNeighbors++; m_GraphX[pts[1]]->m_Neighbors[ m_GraphX[pts[1]]->m_NumberOfNeighbors ]=m_GraphX[pts[2]]; m_GraphX[pts[1]]->m_NumberOfNeighbors++; m_GraphX[pts[2]]->m_Neighbors[ m_GraphX[pts[2]]->m_NumberOfNeighbors ]=m_GraphX[pts[0]]; m_GraphX[pts[2]]->m_NumberOfNeighbors++; m_GraphX[pts[2]]->m_Neighbors[ m_GraphX[pts[2]]->m_NumberOfNeighbors ]=m_GraphX[pts[1]]; m_GraphX[pts[2]]->m_NumberOfNeighbors++; } // now go through each node and make its list of neighbors unique // had to do this b/c it's easier than fixing the junk above ... too tired! for(int i =0; i < numPoints; i++) { std::vector neighlist; for ( unsigned int n=0; nm_NumberOfNeighbors; n++) { neighlist.push_back(m_GraphX[i]->m_Neighbors[n]->GetIdentity()); } std::sort( neighlist.begin(), neighlist.end() ); std::vector::iterator new_end_pos; new_end_pos = std::unique( neighlist.begin(),neighlist.end() ); neighlist.erase( new_end_pos, neighlist.end() ); // std::cout << " new leng " << neighlist.size() << " old " << len1 << std::endl; } } template void ManifoldIntegrationAlgorithm::InitializeGraph2() { if (!m_SurfaceMesh) return; /* // Construct simple triangles vtkTriangleFilter* fltTriangle = vtkTriangleFilter::New(); fltTriangle->SetInput(m_SurfaceMesh); cout << " converting mesh to triangles " << endl; fltTriangle->Update(); cout << " this mesh has " << fltTriangle->GetOutput()->GetNumberOfPoints() << " points" << endl; cout << " this mesh has " << fltTriangle->GetOutput()->GetNumberOfCells() << " cells" << endl; // Clean the data vtkCleanPolyData* fltCleaner = vtkCleanPolyData::New(); fltCleaner->SetInput(fltTriangle->GetOutput()); fltCleaner->SetTolerance(0); fltCleaner->ConvertPolysToLinesOn(); cout << " cleaning up triangle mesh " << endl; fltCleaner->Update(); // Go through and delete the cells that are of the wrong type //m_SurfaceMesh vtkPolyData* clean= fltCleaner->GetOutput(); for(vtkIdType i = clean->GetNumberOfCells();i > 0;i--) { if(clean->GetCellType(i-1) != VTK_TRIANGLE) clean->DeleteCell(i-1); } clean->BuildCells(); */ vtkFeatureEdges* fltEdge = vtkFeatureEdges::New(); fltEdge->BoundaryEdgesOff(); fltEdge->FeatureEdgesOff(); fltEdge->NonManifoldEdgesOff(); fltEdge->ManifoldEdgesOn(); fltEdge->ColoringOff(); fltEdge->SetInput(m_SurfaceMesh); cout << " extracting edges from the mesh" << endl; fltEdge->Update(); // Got the new poly data vtkPolyData* m_EdgePolys = fltEdge->GetOutput(); m_EdgePolys->BuildCells(); m_EdgePolys->BuildLinks(); unsigned int nEdges = m_EdgePolys->GetNumberOfLines(); cout << " number of edges (lines) : " << nEdges << endl; cout << " number of cells : " << m_EdgePolys->GetNumberOfCells() << endl; cout << " number if points : " << m_EdgePolys->GetNumberOfPoints() << endl; vtkPoints* vtkpoints = m_EdgePolys->GetPoints(); int numPoints = vtkpoints->GetNumberOfPoints(); m_GraphX.resize(numPoints); for(int i =0; i < numPoints; i++) { NodeLocationType loc; double* pt = vtkpoints->GetPoint(i); typename GraphSearchNode::Pointer G= GraphSearchNode::New(); G->SetUnVisited(); G->SetTotalCost(m_MaxCost); for (int j=0; jSetLocation(loc); G->SetPredecessor(NULL); G->m_NumberOfNeighbors=0; m_GraphX[i]=G; } std::cout << " allocation of graph done "; vtkIdType nPoints = 0; vtkIdType *xPoints = NULL; for(unsigned int i=0;iGetCellPoints(i, nPoints, xPoints); // Place the edge into the Edge structure assert(nPoints == 2); // Place the edge into the Edge structure // std::cout << " nPoints " << nPoints << std::endl; // std::cout << " pt " << xPoints[0] << " connects " << xPoints[1] << std::endl; assert(nPoints == 2); m_GraphX[xPoints[0]]->m_NumberOfNeighbors++; } std::cout << " counting nhood done "; // second, resize the vector for each G for (int i=0; im_Neighbors.resize(m_GraphX[i]->m_NumberOfNeighbors); m_GraphX[i]->m_NumberOfNeighbors=0; } for(unsigned int i=0;iGetCellPoints(i, nPoints, xPoints); // Place the edge into the Edge structure assert(nPoints == 2); m_GraphX[xPoints[0]]->m_Neighbors[ m_GraphX[xPoints[0]]->m_NumberOfNeighbors ]=m_GraphX[xPoints[1]]; m_GraphX[xPoints[0]]->m_NumberOfNeighbors++; } // vtkPolyDataConnectivityFilter* con = vtkPolyDataConnectivityFilter::New(); // con->SetExtractionModeToLargestRegion(); // con->SetInput(m_EdgePolys); // m_SurfaceMesh=con->GetOutput(); m_SurfaceMesh=m_EdgePolys; } template void ManifoldIntegrationAlgorithm::InitializeGraph() { if (!m_SurfaceMesh) return; std::cout << " Generate graph from surface mesh " << std::endl; // get size of the surface mesh vtkExtractEdges* edgeex=vtkExtractEdges::New(); edgeex->SetInput(m_SurfaceMesh); edgeex->Update(); vtkPolyData* edg1=edgeex->GetOutput(); vtkIdType nedg=edg1->GetNumberOfCells(); vtkIdType vers = m_SurfaceMesh->GetNumberOfPoints(); int nfac = m_SurfaceMesh->GetNumberOfPolys(); float g = 0.5 * (2.0 - vers + nedg - nfac); std::cout << " Genus " << g << std::endl; edg1->BuildCells(); // now cruise through all edges and add to each node's neighbor list // first, count the num of edges for each node // m_SurfaceMesh=edg1; vtkPoints* vtkpoints = edg1->GetPoints(); int numPoints = vtkpoints->GetNumberOfPoints(); m_GraphX.resize(numPoints); for(int i =0; i < numPoints; i++) { NodeLocationType loc; double* pt = vtkpoints->GetPoint(i); typename GraphSearchNode::Pointer G= GraphSearchNode::New(); G->SetUnVisited(); G->SetTotalCost(m_MaxCost); for (int j=0; jSetLocation(loc); G->SetPredecessor(NULL); G->m_NumberOfNeighbors=0; m_GraphX[i]=G; } std::cout << " allocation of graph done "; std::cout << " begin edg iter "; vtkIdType nPoints = 0; vtkIdType *xPoints = NULL; for(unsigned int i=0;iGetCellPoints(i, nPoints, xPoints); // Place the edge into the Edge structure // std::cout << " nPoints " << nPoints << std::endl; // std::cout << " pt " << xPoints[0] << " connects " << xPoints[1] << std::endl; assert(nPoints == 2); m_GraphX[xPoints[0]]->m_NumberOfNeighbors++; } std::cout << " counting nhood done "; // second, resize the vector for each G for (int i=0; im_Neighbors.resize(m_GraphX[i]->m_NumberOfNeighbors); m_GraphX[i]->m_NumberOfNeighbors=0; } for(unsigned int i=0;iGetCellPoints(i, nPoints, xPoints); // Place the edge into the Edge structure assert(nPoints == 2); m_GraphX[xPoints[0]]->m_Neighbors[ m_GraphX[xPoints[0]]->m_NumberOfNeighbors ]=m_GraphX[xPoints[1]]; m_GraphX[xPoints[0]]->m_NumberOfNeighbors++; } m_SurfaceMesh=edg1; return; } template void ManifoldIntegrationAlgorithm ::ConvertGraphBackToMesh() {// this is a sanity check } template void ManifoldIntegrationAlgorithm::InitializeQueue() { int n = m_QS->m_SourceNodes.size(); // GraphIteratorType GraphIterator( m_Graph, m_GraphRegion ); // GraphIterator.GoToBegin(); // m_GraphIndex = GraphIterator.GetIndex(); NodeLocationType loc; // make sure the graph contains the right pointers for (int i=0; i::Pointer G = m_QS->m_SourceNodes[i]; G->SetPredecessor(G); m_QS->m_Q.push(G); loc=G->GetLocation(); // for (int d=0;dSetPixel(m_GraphIndex,G); } for (unsigned int i=0; im_SinkNodes.size(); i++) { typename GraphSearchNode::Pointer G = m_QS->m_SinkNodes[i]; G->SetPredecessor(NULL); loc=G->GetLocation(); // for (int d=0;dSetPixel(m_GraphIndex,G); } m_SearchFinished=false; } /** * parameterize the boundary --- an estimate */ template bool ManifoldIntegrationAlgorithm ::ParameterizeBoundary( ManifoldIntegrationAlgorithm::SearchNodePointer rootNode ) { std::vector neighborlist; bool I_Am_A_Neighbor=false; SearchNodePointer neighbor=NULL; SearchNodePointer curNode=rootNode; // unsigned int rootnn=rootNode>m_NumberOfNeighbors; unsigned int ct=0; bool canparam=false; unsigned int qsz= this->m_QS->m_Q.size(); while ( ! I_Am_A_Neighbor && ct <= qsz*3 ) { unsigned int limit = curNode->m_NumberOfNeighbors; for (unsigned int i = 0; i < limit; i++) { neighbor=curNode->m_Neighbors[i]; bool inb=false; for ( unsigned int q=0; q 2 ) { I_Am_A_Neighbor=true; canparam=true; } if ( neighbor->IsInQueue() && !inb ) { // add to border list neighborlist.push_back(neighbor); curNode=neighbor; i=limit; } // add to border } // neighborhood ct++; }// while if ( neighborlist.size() >= this->m_BoundaryList.size() && canparam ) { neighborlist.push_back(rootNode); this->m_BoundaryList.clear(); this->m_BoundaryList.assign(neighborlist.begin(),neighborlist.end()); } // if ( ct > 0 && canparam) std::cout <<" qfrac " << this->m_BoundaryList.size() << " canp "<< canparam << " qsz " << qsz << " cost " << m_CurrentCost << std::endl; return canparam; } /** * Compute the local cost using Manhattan distance. */ template typename ManifoldIntegrationAlgorithm:: PixelType ManifoldIntegrationAlgorithm::MyLocalCost() { NodeLocationType dif=m_CurrentNode->GetLocation()-m_NeighborNode->GetLocation(); float mag = 0.0; for (int jj=0; jjGetValue(3)-m_NeighborNode->GetValue(3)); if ( dL > 0.5 ) dL=this->m_MaxCost*this->m_LabelCostWeight; else dL=0; return (mag*this->m_DistanceCostWeight+dL); } } template bool ManifoldIntegrationAlgorithm::TerminationCondition() { if (!m_QS->m_SinkNodes.empty()) { if (m_NeighborNode == m_QS->m_SinkNodes[0] && !m_SearchFinished ) { // std::cout << " FOUND SINK "; m_SearchFinished=true; m_NeighborNode->SetTotalCost( m_CurrentCost + MyLocalCost()); m_NeighborNode->SetPredecessor(m_CurrentNode); } } if (m_CurrentCost>=m_MaxCost) m_SearchFinished=true; return m_SearchFinished; } template void ManifoldIntegrationAlgorithm::SearchEdgeSet() { int i=0;//,j=0; for (i = 0; i < m_CurrentNode->m_NumberOfNeighbors; i++) { m_NeighborNode=m_CurrentNode->m_Neighbors[i]; // std::cout << " i " << i << " position " << m_NeighborNode->GetLocation() << endl; // std::cout << " i " << i << " position " << m_NeighborNode->GetLocation() << " label " << m_CurrentNode->GetValue() << endl; TerminationCondition(); if (!m_SearchFinished && m_CurrentNode != m_NeighborNode && !m_NeighborNode->GetDelivered()) { m_NewCost = m_CurrentCost + MyLocalCost(); CheckNodeStatus(); } } } template void ManifoldIntegrationAlgorithm::GetSearchBoundary() { unsigned int gsz=this->GetGraphSize(); for ( unsigned int j=0; jm_CurrentNode=this->m_GraphX[j]; if ( this->m_CurrentNode ) { cost=m_CurrentNode->GetTotalCost(); if ( cost <= this->m_MaxCost && (cost >= this->m_MaxCost-4) ) { this->m_BoundaryList.push_back(this->m_CurrentNode); } } } } template void ManifoldIntegrationAlgorithm::CheckNodeStatus() // checks a graph neighbor's status { NodeLocationType dif=m_CurrentNode->GetLocation()-m_NeighborNode->GetLocation(); // std::cout << " visited? " << m_NeighborNode->GetVisited() << // " old cost " << m_NeighborNode->GetTotalCost() << " new cost " <GetVisited() && ! m_NeighborNode->GetUnVisitable() ) { // set the cost and put into the queue m_NeighborNode->SetTotalCost(m_NewCost); float delt=fabs(m_CurrentNode->GetValue()-m_NeighborNode->GetValue());//*dif.magnitude(); m_NeighborNode->SetValue(m_CurrentNode->GetValue()+delt); m_NeighborNode->SetPredecessor(m_CurrentNode); m_NeighborNode->SetVisited(); float mag = 0.0; for (int jj=0; jjSetValue(m_CurrentNode->GetValue(2)+mag,2); // the actual manifold distance travelled // if ( m_QS->m_Q.push(m_NeighborNode); // } // else { // m_NeighborNode->SetUnVisitable(); // } // std::cout << " Pushing new node on " << m_NewCost << std::endl; } else if (m_NewCost < m_NeighborNode->GetTotalCost()&& ! m_NeighborNode->GetUnVisitable() ) { // std::cout << " Updating " << std::endl; float delt=fabs(m_CurrentNode->GetValue()-m_NeighborNode->GetValue());//*dif.magnitude(); m_NeighborNode->SetValue(m_CurrentNode->GetValue()+delt); m_NeighborNode->SetTotalCost(m_NewCost); m_NeighborNode->SetPredecessor(m_CurrentNode); float mag = 0.0; for (int jj=0; jjSetValue(m_CurrentNode->GetValue(2)+mag,2); // the actual manifold distance travelled m_QS->m_Q.push(m_NeighborNode); } } template void ManifoldIntegrationAlgorithm::FindPath() { if (m_QS->m_SourceNodes.empty()) { std::cout << "ERROR !! DID NOT SET SOURCE!!\n"; return; } // std::cout << "MI start find path " << " Q size " << m_QS->m_Q.size() << " \n"; while ( !m_SearchFinished && !m_QS->m_Q.empty() ) { m_CurrentNode=m_QS->m_Q.top(); m_CurrentCost=m_CurrentNode->GetTotalCost(); if ( this->m_ParamWhileSearching ) this->ParameterizeBoundary( this->m_CurrentNode ); m_QS->m_Q.pop(); if (!m_CurrentNode->GetDelivered()) { m_QS->IncrementTimer(); ///std::cout << " searching " << m_CurrentNode->GetLocation() << " \n"; this->SearchEdgeSet(); //if ( (m_CurrentNode->GetTimer() % 1.e5 ) == 0) // std::cout << " searched " << m_CurrentNode->GetTimer() << " \n"; } m_CurrentNode->SetDelivered(); } // end of while m_NumberSearched = (unsigned long) m_QS->GetTimer(); // std::cout << "Done with find path " << " Q size " << m_QS->m_Q.size() << //" num searched " << m_NumberSearched << " \n"; // std::cout << " Max Distance " << m_CurrentCost << std::endl; if ( ! this->m_ParamWhileSearching ) this->GetSearchBoundary(); return; } } #endif ants-1.9.2+svn680.dfsg/Temporary/itkManifoldIntegrationAlgorithm.h000077500000000000000000000254541147325206600251440ustar00rootroot00000000000000/*========================================================================= Program: Insight Segmentation & Registration Toolkit (ITK) Module: $RCSfile: itkManifoldIntegrationAlgorithm.h,v $ Language: C++ Date: $Date: 2008/03/11 16:33:05 $ Version: $Revision: 1.2 $ Copyright (c) 2001 Insight Consortium All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * The name of the Insight Consortium, nor the names of any consortium members, nor of any contributors, may be used to endorse or promote products derived from this software without specific prior written permission. * Modified source versions must be plainly marked as such, and must not be misrepresented as being the original software. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDER AND CONTRIBUTORS ``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE AUTHORS OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. =========================================================================*/ #ifndef _itkManifoldIntegrationAlgorithm_h_ #define _itkManifoldIntegrationAlgorithm_h_ namespace itk { /** * \class ManifoldIntegrationAlgorithm * \brief General shortest path / greedy dynamic programming solver. */ template class ManifoldIntegrationAlgorithm : public itk::LightObject { public: typedef ManifoldIntegrationAlgorithm Self; typedef LightObject Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; itkTypeMacro(ManifoldIntegrationAlgorithm,LightObject); itkNewMacro(Self); // Computation Data typedef TGraphSearchNode SearchNode; /** dimension of the graph */ typedef typename SearchNode::Pointer SearchNodePointer; enum{GraphDimension=SearchNode::GraphDimension}; /** dimension of the graph */ typedef typename SearchNode::PixelType PixelType; /** pixel type for the cost */ typedef typename SearchNode::CoordRep CoordRep; /** coordinate type */ typedef typename DijkstrasAlgorithmQueue::Pointer QType; typedef typename DijkstrasAlgorithmQueue::NodeListType NodeListType; typedef typename TGraphSearchNode::NodeLocationType NodeLocationType; typedef vtkPolyData TriangulationType; typedef vtkPolyData* TriangulationTypePointer; typedef vector GraphType; // FUNCTIONS // estimate the metric tensor of the surface and also the (conjugate harmonic) function dstarU void GetSearchBoundary(); float dstarUestimate(SearchNodePointer G); void InitializeGraph3(); void InitializeGraph(); /** initializes all graph values appropriately */ void InitializeGraph2(); /** initializes all graph values appropriately */ void InitializeQueue(); /** initializes all queue values appropriately call AFTER source and sink are set*/ inline void EmptyQ() { m_QS->EmptyQ(); m_QS->EmptyPath(); } /* adds a source to the source set */ void SetSource(typename TGraphSearchNode::Pointer G) { G->SetTotalCost(0.0); m_QS->m_SourceNodes.push_back(G); }; // adds a sink to the sink set void SetSink(typename TGraphSearchNode::Pointer G) { m_QS->m_SinkNodes.push_back(G); } // Backtracks from the given node to its source node; float BackTrack(typename TGraphSearchNode::Pointer SinkNode) { m_QS->m_Path.clear(); typename TGraphSearchNode::Pointer G=SinkNode; typename TGraphSearchNode::Pointer P=SinkNode->GetPredecessor(); if (!P) {std::cout << " ERROR NO PRED TO SINK " << std::endl; return 1.;} m_QS->m_Path.push_back(G); // if (P->GetAncestor() && G) // { // if (P->GetAncestor()->GetTotalCost() < G->GetTotalCost() ) P->SetAncestor(G); // } // else if (G) P->SetAncestor(G); // if (P->GetValue(1) < G->GetValue(1) ) P->SetValue(G->GetValue(1),1); while(P && G != P) { // std::cout << " Backtrack " << G->GetValue(0) << std::endl; G=P; P=G->GetPredecessor(); // if (P->GetValue(1) < G->GetValue(1) ) P->SetValue(G->GetValue(1),1); P->SetAncestor(G); if (P) m_QS->m_Path.push_back(P); } // m_QS->m_Path.push_back(P); // std::cout << " final cost " << P->GetTotalCost() << " high " << highcost << std::endl; if (!P) cout << " null pred "; //else cout << " pred == self \n"; return P->GetValue(2); } // Backtracks from the given node to its source node performing integration void IntegrateBackward(typename TGraphSearchNode::Pointer SinkNode) { typename TGraphSearchNode::Pointer G=SinkNode; typename TGraphSearchNode::Pointer P=SinkNode->GetPredecessor(); float intval=0.0; G->SetTotalCost(intval); while(P && G != P) { // NodeLocationType dif=P->GetLocation()-G->GetLocation(); float dU = (P->GetValue()-G->GetValue()); intval+=dU; P->SetTotalCost(intval); G=P; P=G->GetPredecessor(); } // std::cout << " intval " << intval << " at " << G->GetLocation() << std::endl; if (!P) cout << " null pred "; //else cout << " pred == self \n"; return; } // Backtracks from the given node to its source node performing integration void IntegrateForward(typename TGraphSearchNode::Pointer SinkNode) { typename TGraphSearchNode::Pointer G=SinkNode; typename TGraphSearchNode::Pointer P=SinkNode->GetAncestor(); float intval=0.0; G->SetTotalCost(intval); while(P && G != P) { // NodeLocationType dif=P->GetLocation()-G->GetLocation(); float dU = (P->GetValue()-G->GetValue()); intval+=dU; P->SetTotalCost(intval); G=P; P=G->GetAncestor(); } // std::cout << " intval " << intval << " at " << G->GetLocation() << std::endl; if (!P) cout << " null pred "; //else cout << " pred == self \n"; return; } // Inverse of backtrack - from the given node to its sink node; float ForwardTrack(typename TGraphSearchNode::Pointer SinkNode) { typename TGraphSearchNode::Pointer G=SinkNode; typename TGraphSearchNode::Pointer P=SinkNode->GetAncestor(); if (!P) return G->GetValue(2); // float highcost=G->GetTotalCost(); while(P && G != P && G) { G=P; P=G->GetAncestor(); if (!P) return G->GetValue(2); // if (P->GetTotalCost() > highcost) highcost=P->GetTotalCost(); } // SinkNode->SetValue(highcost); // if (!P) cout << " null pred "; //else cout << " pred == self \n"; return P->GetValue(2); } bool ParameterizeBoundary( SearchNodePointer); bool TerminationCondition(); /** decides when the algorithm stops */ virtual void SearchEdgeSet(); /** loops over the neighbors in the graph */ void CheckNodeStatus(); /** checks if the node has been explored already, its cost, etc. */ virtual PixelType MyLocalCost(); /* computes the local cost */ /* alternatively, we could pass the function as a template parameter or set a function pointer. the latter method is used in dijkstrasegment. */ virtual void FindPath(); /* runs the algorithm */ inline unsigned int GetPathSize() { return m_QS->m_Path.size(); } inline void EmptyPath() { m_QS->m_Path.clear(); } inline typename TGraphSearchNode::Pointer GetPathAtIndex(unsigned int i) { return m_QS->m_Path[i]; } inline typename TGraphSearchNode::Pointer GetNeighborNode() { return m_NeighborNode; } inline typename TGraphSearchNode::Pointer GetCurrentNode() { return m_CurrentNode; } PixelType GetMaxCost(){ return this->m_MaxCost; } void SetMaxCost(PixelType m) { this->m_MaxCost=m; } void ResetMaxCost() { this->m_MaxCost=vnl_huge_val(this->m_MaxCost);} inline void SetDistanceCostWeight(float d) { this->m_DistanceCostWeight=d; } inline void SetLabelCostWeight(float d) { this->m_LabelCostWeight=d; } inline void SetWeights(float a, float b, float c) { this->m_MaxCost=a; this->m_DistanceCostWeight=b; this->m_LabelCostWeight=c; } inline void PrintWeights() { std::cout << this->m_MaxCost << " " << this->m_DistanceCostWeight << " " << this->m_LabelCostWeight << std::endl; } void SetSearchFinished(bool m){ m_SearchFinished=m;} /** sets the boolean that indicates if the algorithm is done */ void SetSurfaceMesh( TriangulationTypePointer mesh) { m_SurfaceMesh=mesh;} TriangulationTypePointer GetSurfaceMesh( ) { return m_SurfaceMesh;} SearchNodePointer GetGraphNode(int i) { return m_GraphX[i]; } int GetGraphSize() { return m_GraphX.size(); } // sanity check to see if mesh to graph conversion is ok // see if genus is the same void ConvertGraphBackToMesh(); void SetParamWhileSearching( bool b ) { this->m_ParamWhileSearching=b; } std::vector m_BoundaryList; protected: QType m_QS; vector < unsigned int > m_EdgeTemplate;/** defines neighborhood connectivity */ typename TGraphSearchNode::Pointer m_PredecessorNode; /** holds the predecessor node */ typename TGraphSearchNode::Pointer m_CurrentNode; /** holds the current node */ typename TGraphSearchNode::Pointer m_NeighborNode; /** holds the current neighbor node */ bool m_PureDist; bool m_SearchFinished; PixelType m_NewCost; PixelType m_CurrentCost; float m_MaxCost; // This creates an insurmountable barrier unless all costs are max float m_DistanceCostWeight; float m_LabelCostWeight; GraphType m_GraphX; unsigned long m_NumberSearched; TriangulationTypePointer m_SurfaceMesh; bool m_ParamWhileSearching; ManifoldIntegrationAlgorithm(); ~ManifoldIntegrationAlgorithm(){}; private: ManifoldIntegrationAlgorithm(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkManifoldIntegrationAlgorithm.cxx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkOverUnderColormapFunctor.h000066400000000000000000000045461147325206600243030ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkOverUnderColormapFunctor.h,v $ Language: C++ Date: $Date: 2009-05-15 19:22:31 $ Version: $Revision: 1.2 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkOverUnderColormapFunctor_h #define __itkOverUnderColormapFunctor_h #include "itkColormapFunctor.h" namespace itk { namespace Functor { /** * \class OverUnderColormapFunctor * \brief Function object which maps a scalar value into an RGB colormap value. * * \author Nicholas Tustison, Hui Zhang, Gaetan Lehmann, Paul Yushkevich and ̈James C. Gee * * This code was contributed in the Insight Journal paper: * * "Meeting Andy Warhol Somewhere Over the Rainbow: RGB Colormapping and ITK" * http://www.insight-journal.org/browse/publication/285 * http://hdl.handle.net/1926/1452 * */ template< class TScalar, class TRGBPixel > class ITK_EXPORT OverUnderColormapFunctor : public ColormapFunctor { public: typedef OverUnderColormapFunctor Self; typedef ColormapFunctor Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); typedef typename Superclass::RGBPixelType RGBPixelType; typedef typename Superclass::ScalarType ScalarType; typedef typename Superclass::RealType RealType; virtual RGBPixelType operator()( const TScalar & ) const; protected: OverUnderColormapFunctor() {}; ~OverUnderColormapFunctor() {}; private: OverUnderColormapFunctor(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace functor } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkOverUnderColormapFunctor.txx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkOverUnderColormapFunctor.txx000066400000000000000000000034751147325206600246770ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkOverUnderColormapFunctor.txx,v $ Language: C++ Date: $Date: 2009-05-15 02:47:59 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkOverUnderColormapFunctor_txx #define __itkOverUnderColormapFunctor_txx #include "itkOverUnderColormapFunctor.h" namespace itk { namespace Functor { template typename OverUnderColormapFunctor::RGBPixelType OverUnderColormapFunctor ::operator()( const TScalar & v ) const { // Map the input scalar between [0, 1]. RealType value = this->RescaleInputValue( v ); // Apply the color mapping. RealType red = value; RealType green = value; RealType blue = value; if( value == 0.0 ) { // pixel is saturated in the dark red = 0.0; green = 0.0; blue = 1.0; } else if( value == 1.0 ) { // pixel is saturated in the white red = 1.0; green = 0.0; blue = 0.0; } // Set the rgb components after rescaling the values. RGBPixelType pixel; pixel[0] = this->RescaleRGBComponentValue( red ); pixel[1] = this->RescaleRGBComponentValue( green ); pixel[2] = this->RescaleRGBComponentValue( blue ); return pixel; } } // end namespace Functor } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkRedColormapFunctor.h000066400000000000000000000044721147325206600231020ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkRedColormapFunctor.h,v $ Language: C++ Date: $Date: 2009-05-15 19:22:31 $ Version: $Revision: 1.2 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkRedColormapFunctor_h #define __itkRedColormapFunctor_h #include "itkColormapFunctor.h" namespace itk { namespace Functor { /** * \class RedColormapFunctor * \brief Function object which maps a scalar value into an RGB colormap value. * * \author Nicholas Tustison, Hui Zhang, Gaetan Lehmann, Paul Yushkevich and ̈James C. Gee * * This code was contributed in the Insight Journal paper: * * "Meeting Andy Warhol Somewhere Over the Rainbow: RGB Colormapping and ITK" * http://www.insight-journal.org/browse/publication/285 * http://hdl.handle.net/1926/1452 * */ template< class TScalar, class TRGBPixel > class ITK_EXPORT RedColormapFunctor : public ColormapFunctor { public: typedef RedColormapFunctor Self; typedef ColormapFunctor Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); typedef typename Superclass::RGBPixelType RGBPixelType; typedef typename Superclass::ScalarType ScalarType; typedef typename Superclass::RealType RealType; virtual RGBPixelType operator()( const TScalar & ) const; protected: RedColormapFunctor() {}; ~RedColormapFunctor() {}; private: RedColormapFunctor(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace functor } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkRedColormapFunctor.txx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkRedColormapFunctor.txx000066400000000000000000000026431147325206600234740ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkRedColormapFunctor.txx,v $ Language: C++ Date: $Date: 2009-05-15 02:47:59 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkRedColormapFunctor_txx #define __itkRedColormapFunctor_txx #include "itkRedColormapFunctor.h" namespace itk { namespace Functor { template typename RedColormapFunctor::RGBPixelType RedColormapFunctor ::operator()( const TScalar & v ) const { // Map the input scalar between [0, 1]. RealType value = this->RescaleInputValue( v ); // Set the rgb components after rescaling the values. RGBPixelType pixel; pixel[0] = this->RescaleRGBComponentValue( value ); pixel[1] = 0; pixel[2] = 0; return pixel; } } // end namespace Functor } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkScalarToRGBColormapImageFilter.h000066400000000000000000000116431147325206600252010ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkScalarToRGBColormapImageFilter.h,v $ Language: C++ Date: $Date: 2009-05-15 12:51:13 $ Version: $Revision: 1.2 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkScalarToRGBColormapImageFilter_h #define __itkScalarToRGBColormapImageFilter_h #include "itkImageToImageFilter.h" #include "itkColormapFunctor.h" namespace itk { /** \class ScalarToRGBColormapImageFilter * \brief Implements pixel-wise intensity->rgb mapping operation on one image. * * This class is parameterized over the type of the input image and * the type of the output image. * * ScalarToRGBColormapImageFilter * * \sa BinaryFunctorImageFilter TernaryFunctorImageFilter * * \ingroup IntensityImageFilters Multithreaded */ template class ITK_EXPORT ScalarToRGBColormapImageFilter : public ImageToImageFilter { public: /** Standard class typedefs. */ typedef ScalarToRGBColormapImageFilter Self; typedef ImageToImageFilter Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); /** Run-time type information (and related methods). */ itkTypeMacro( ScalarToRGBColormapImageFilter, ImageToImageFilter ); /** Some typedefs. */ typedef TInputImage InputImageType; typedef typename InputImageType::ConstPointer InputImagePointer; typedef typename InputImageType::RegionType InputImageRegionType; typedef typename InputImageType::PixelType InputImagePixelType; typedef TOutputImage OutputImageType; typedef typename OutputImageType::Pointer OutputImagePointer; typedef typename OutputImageType::RegionType OutputImageRegionType; typedef typename OutputImageType::PixelType OutputImagePixelType; typedef Functor::ColormapFunctor ColormapType; /** * Set/Get the colormap object. */ typename ColormapType::Pointer GetColormap() { return m_Colormap; } void SetColormap( ColormapType *colormap ) { if ( m_Colormap != colormap ) { m_Colormap = colormap; this->Modified(); } } /** * Enum type that provides for an easy interface to existing colormaps. */ typedef enum { Red, Green, Blue, Grey, Hot, Cool, Spring, Summer, Autumn, Winter, Copper, Jet, HSV, OverUnder } ColormapEnumType; void SetColormap( ColormapEnumType ); /** * Set/Get UseInputImageExtremaForScaling. If 'true', the colormap uses the * min and max values from the image to scale appropriately. Otherwise, * these values can be set in the colormap manually. */ itkSetMacro( UseInputImageExtremaForScaling, bool ); itkGetConstMacro( UseInputImageExtremaForScaling, bool ); itkBooleanMacro( UseInputImageExtremaForScaling ); protected: ScalarToRGBColormapImageFilter(); virtual ~ScalarToRGBColormapImageFilter() {}; void PrintSelf( std::ostream& os, Indent indent ) const; /** ScalarToRGBColormapImageFilter * can be implemented as a multithreaded filter. * Therefore, this implementation provides a ThreadedGenerateData() routine * which is called for each processing thread. The output image data is * allocated automatically by the superclass prior to calling * ThreadedGenerateData(). ThreadedGenerateData can only write to the * portion of the output image specified by the parameter * "outputRegionForThread" * * \sa ImageToImageFilter::ThreadedGenerateData(), * ImageToImageFilter::GenerateData() */ void ThreadedGenerateData( const OutputImageRegionType& outputRegionForThread, int threadId ); /** Process to execute before entering the multithreaded section */ void BeforeThreadedGenerateData(); private: ScalarToRGBColormapImageFilter(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented typename ColormapType::Pointer m_Colormap; bool m_UseInputImageExtremaForScaling; }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkScalarToRGBColormapImageFilter.txx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkScalarToRGBColormapImageFilter.txx000066400000000000000000000214471147325206600256000ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkScalarToRGBColormapImageFilter.txx,v $ Language: C++ Date: $Date: 2009-05-15 12:51:14 $ Version: $Revision: 1.2 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkScalarToRGBColormapImageFilter_txx #define __itkScalarToRGBColormapImageFilter_txx #include "itkScalarToRGBColormapImageFilter.h" #include "itkImageRegionIterator.h" #include "itkImageRegionConstIterator.h" #include "itkProgressReporter.h" #include "itkRedColormapFunctor.h" #include "itkGreenColormapFunctor.h" #include "itkBlueColormapFunctor.h" #include "itkGreyColormapFunctor.h" #include "itkHotColormapFunctor.h" #include "itkCoolColormapFunctor.h" #include "itkSpringColormapFunctor.h" #include "itkSummerColormapFunctor.h" #include "itkAutumnColormapFunctor.h" #include "itkWinterColormapFunctor.h" #include "itkCopperColormapFunctor.h" #include "itkHSVColormapFunctor.h" #include "itkJetColormapFunctor.h" #include "itkOverUnderColormapFunctor.h" namespace itk { /** * Constructor */ template ScalarToRGBColormapImageFilter ::ScalarToRGBColormapImageFilter() { this->SetNumberOfRequiredInputs( 1 ); this->m_UseInputImageExtremaForScaling = true; typedef Functor::GreyColormapFunctor< InputImagePixelType, OutputImagePixelType> DefaultColormapType; typename DefaultColormapType::Pointer greyColormap = DefaultColormapType::New(); this->SetColormap( greyColormap ); } /** * BeforeThreadedGenerateData */ template void ScalarToRGBColormapImageFilter ::BeforeThreadedGenerateData() { if( this->m_UseInputImageExtremaForScaling == true ) { ImageRegionConstIterator It( this->GetInput(), this->GetInput()->GetRequestedRegion() ); InputImagePixelType minimumValue = NumericTraits::max(); InputImagePixelType maximumValue = NumericTraits::min(); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { InputImagePixelType value = It.Get(); if( value < minimumValue ) { minimumValue = value; } if( value > maximumValue ) { maximumValue = value; } } this->GetColormap()->SetMinimumInputValue( minimumValue ); this->GetColormap()->SetMaximumInputValue( maximumValue ); } } /** * ThreadedGenerateData performs the pixel-wise mapping */ template void ScalarToRGBColormapImageFilter ::ThreadedGenerateData( const OutputImageRegionType &outputRegionForThread, int threadId ) { InputImagePointer inputPtr = this->GetInput(); OutputImagePointer outputPtr = this->GetOutput(); // Define the portion of the input to walk for this thread, using // the CallCopyOutputRegionToInputRegion method allows for the input // and output images to be different dimensions InputImageRegionType inputRegionForThread; this->CallCopyOutputRegionToInputRegion(inputRegionForThread, outputRegionForThread); // Define the iterators ImageRegionConstIterator inputIt(inputPtr, inputRegionForThread); ImageRegionIterator outputIt(outputPtr, outputRegionForThread); ProgressReporter progress( this, threadId, outputRegionForThread.GetNumberOfPixels()); inputIt.GoToBegin(); outputIt.GoToBegin(); while( !inputIt.IsAtEnd() ) { outputIt.Set( this->m_Colormap->operator()( inputIt.Get() ) ); ++inputIt; ++outputIt; progress.CompletedPixel(); // potential exception thrown here } } template void ScalarToRGBColormapImageFilter ::SetColormap( ColormapEnumType map ) { switch( map ) { case Red: { typedef Functor::RedColormapFunctor< InputImagePixelType, OutputImagePixelType> SpecificColormapType; typename SpecificColormapType::Pointer colormap = SpecificColormapType::New(); this->SetColormap( colormap ); break; } case Green: { typedef Functor::GreenColormapFunctor< InputImagePixelType, OutputImagePixelType> SpecificColormapType; typename SpecificColormapType::Pointer colormap = SpecificColormapType::New(); this->SetColormap( colormap ); break; } case Blue: { typedef Functor::BlueColormapFunctor< InputImagePixelType, OutputImagePixelType> SpecificColormapType; typename SpecificColormapType::Pointer colormap = SpecificColormapType::New(); this->SetColormap( colormap ); break; } case Grey: default: { typedef Functor::GreyColormapFunctor< InputImagePixelType, OutputImagePixelType> SpecificColormapType; typename SpecificColormapType::Pointer colormap = SpecificColormapType::New(); this->SetColormap( colormap ); break; } case Hot: { typedef Functor::HotColormapFunctor< InputImagePixelType, OutputImagePixelType> SpecificColormapType; typename SpecificColormapType::Pointer colormap = SpecificColormapType::New(); this->SetColormap( colormap ); break; } case Cool: { typedef Functor::CoolColormapFunctor< InputImagePixelType, OutputImagePixelType> SpecificColormapType; typename SpecificColormapType::Pointer colormap = SpecificColormapType::New(); this->SetColormap( colormap ); break; } case Spring: { typedef Functor::SpringColormapFunctor< InputImagePixelType, OutputImagePixelType> SpecificColormapType; typename SpecificColormapType::Pointer colormap = SpecificColormapType::New(); this->SetColormap( colormap ); break; } case Summer: { typedef Functor::SummerColormapFunctor< InputImagePixelType, OutputImagePixelType> SpecificColormapType; typename SpecificColormapType::Pointer colormap = SpecificColormapType::New(); this->SetColormap( colormap ); break; } case Autumn: { typedef Functor::AutumnColormapFunctor< InputImagePixelType, OutputImagePixelType> SpecificColormapType; typename SpecificColormapType::Pointer colormap = SpecificColormapType::New(); this->SetColormap( colormap ); break; } case Winter: { typedef Functor::WinterColormapFunctor< InputImagePixelType, OutputImagePixelType> SpecificColormapType; typename SpecificColormapType::Pointer colormap = SpecificColormapType::New(); this->SetColormap( colormap ); break; } case Copper: { typedef Functor::CopperColormapFunctor< InputImagePixelType, OutputImagePixelType> SpecificColormapType; typename SpecificColormapType::Pointer colormap = SpecificColormapType::New(); this->SetColormap( colormap ); break; } case Jet: { typedef Functor::JetColormapFunctor< InputImagePixelType, OutputImagePixelType> SpecificColormapType; typename SpecificColormapType::Pointer colormap = SpecificColormapType::New(); this->SetColormap( colormap ); break; } case HSV: { typedef Functor::HSVColormapFunctor< InputImagePixelType, OutputImagePixelType> SpecificColormapType; typename SpecificColormapType::Pointer colormap = SpecificColormapType::New(); this->SetColormap( colormap ); break; } case OverUnder: { typedef Functor::OverUnderColormapFunctor< InputImagePixelType, OutputImagePixelType> SpecificColormapType; typename SpecificColormapType::Pointer colormap = SpecificColormapType::New(); this->SetColormap( colormap ); break; } } } template void ScalarToRGBColormapImageFilter ::PrintSelf( std::ostream& os, Indent indent ) const { this->Superclass::PrintSelf(os, indent); os << indent << "Class Name: " << this->GetNameOfClass( ) << std::endl; if( this->m_Colormap.IsNotNull() ) { os << indent << "Colormap " << this->m_Colormap << std::endl; } else { os << indent << "Colormap is NULL " << std::endl; } os << indent << "Use Input Image Extrema for Scaling " << this->m_UseInputImageExtremaForScaling << std::endl; } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkSpringColormapFunctor.h000066400000000000000000000045251147325206600236310ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkSpringColormapFunctor.h,v $ Language: C++ Date: $Date: 2009-05-15 19:22:31 $ Version: $Revision: 1.2 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkSpringColormapFunctor_h #define __itkSpringColormapFunctor_h #include "itkColormapFunctor.h" namespace itk { namespace Functor { /** * \class SpringColormapFunctor * \brief Function object which maps a scalar value into an RGB colormap value. * * \author Nicholas Tustison, Hui Zhang, Gaetan Lehmann, Paul Yushkevich and ̈James C. Gee * * This code was contributed in the Insight Journal paper: * * "Meeting Andy Warhol Somewhere Over the Rainbow: RGB Colormapping and ITK" * http://www.insight-journal.org/browse/publication/285 * http://hdl.handle.net/1926/1452 * */ template< class TScalar, class TRGBPixel > class ITK_EXPORT SpringColormapFunctor : public ColormapFunctor { public: typedef SpringColormapFunctor Self; typedef ColormapFunctor Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); typedef typename Superclass::RGBPixelType RGBPixelType; typedef typename Superclass::ScalarType ScalarType; typedef typename Superclass::RealType RealType; virtual RGBPixelType operator()( const TScalar & ) const; protected: SpringColormapFunctor() {}; ~SpringColormapFunctor() {}; private: SpringColormapFunctor(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace functor } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkSpringColormapFunctor.txx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkSpringColormapFunctor.txx000066400000000000000000000030741147325206600242230ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkSpringColormapFunctor.txx,v $ Language: C++ Date: $Date: 2009-05-15 02:47:59 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkSpringColormapFunctor_txx #define __itkSpringColormapFunctor_txx #include "itkSpringColormapFunctor.h" namespace itk { namespace Functor { template typename SpringColormapFunctor::RGBPixelType SpringColormapFunctor ::operator()( const TScalar & v ) const { // Map the input scalar between [0, 1]. RealType value = this->RescaleInputValue( v ); // Apply the color mapping. RealType red = 1.0; RealType green = value; RealType blue = 1.0 - value; // Set the rgb components after rescaling the values. RGBPixelType pixel; pixel[0] = this->RescaleRGBComponentValue( red ); pixel[1] = this->RescaleRGBComponentValue( green ); pixel[2] = this->RescaleRGBComponentValue( blue ); return pixel; } } // end namespace Functor } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkSummerColormapFunctor.h000066400000000000000000000045251147325206600236370ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkSummerColormapFunctor.h,v $ Language: C++ Date: $Date: 2009-05-15 19:22:31 $ Version: $Revision: 1.2 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkSummerColormapFunctor_h #define __itkSummerColormapFunctor_h #include "itkColormapFunctor.h" namespace itk { namespace Functor { /** * \class SummerColormapFunctor * \brief Function object which maps a scalar value into an RGB colormap value. * * \author Nicholas Tustison, Hui Zhang, Gaetan Lehmann, Paul Yushkevich and ̈James C. Gee * * This code was contributed in the Insight Journal paper: * * "Meeting Andy Warhol Somewhere Over the Rainbow: RGB Colormapping and ITK" * http://www.insight-journal.org/browse/publication/285 * http://hdl.handle.net/1926/1452 * */ template< class TScalar, class TRGBPixel > class ITK_EXPORT SummerColormapFunctor : public ColormapFunctor { public: typedef SummerColormapFunctor Self; typedef ColormapFunctor Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); typedef typename Superclass::RGBPixelType RGBPixelType; typedef typename Superclass::ScalarType ScalarType; typedef typename Superclass::RealType RealType; virtual RGBPixelType operator()( const TScalar & ) const; protected: SummerColormapFunctor() {}; ~SummerColormapFunctor() {}; private: SummerColormapFunctor(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace functor } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkSummerColormapFunctor.txx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkSummerColormapFunctor.txx000066400000000000000000000031021147325206600242210ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkSummerColormapFunctor.txx,v $ Language: C++ Date: $Date: 2009-05-15 02:47:59 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkSummerColormapFunctor_txx #define __itkSummerColormapFunctor_txx #include "itkSummerColormapFunctor.h" namespace itk { namespace Functor { template typename SummerColormapFunctor::RGBPixelType SummerColormapFunctor ::operator()( const TScalar & v ) const { // Map the input scalar between [0, 1]. RealType value = this->RescaleInputValue( v ); // Apply the color mapping. RealType red = value; RealType green = 0.5 * value + 0.5; RealType blue = 0.4; // Set the rgb components after rescaling the values. RGBPixelType pixel; pixel[0] = this->RescaleRGBComponentValue( red ); pixel[1] = this->RescaleRGBComponentValue( green ); pixel[2] = this->RescaleRGBComponentValue( blue ); return pixel; } } // end namespace Functor } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/itkWinterColormapFunctor.h000066400000000000000000000045251147325206600236370ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkWinterColormapFunctor.h,v $ Language: C++ Date: $Date: 2009-05-15 19:22:31 $ Version: $Revision: 1.2 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkWinterColormapFunctor_h #define __itkWinterColormapFunctor_h #include "itkColormapFunctor.h" namespace itk { namespace Functor { /** * \class WinterColormapFunctor * \brief Function object which maps a scalar value into an RGB colormap value. * * \author Nicholas Tustison, Hui Zhang, Gaetan Lehmann, Paul Yushkevich and ̈James C. Gee * * This code was contributed in the Insight Journal paper: * * "Meeting Andy Warhol Somewhere Over the Rainbow: RGB Colormapping and ITK" * http://www.insight-journal.org/browse/publication/285 * http://hdl.handle.net/1926/1452 * */ template< class TScalar, class TRGBPixel > class ITK_EXPORT WinterColormapFunctor : public ColormapFunctor { public: typedef WinterColormapFunctor Self; typedef ColormapFunctor Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); typedef typename Superclass::RGBPixelType RGBPixelType; typedef typename Superclass::ScalarType ScalarType; typedef typename Superclass::RealType RealType; virtual RGBPixelType operator()( const TScalar & ) const; protected: WinterColormapFunctor() {}; ~WinterColormapFunctor() {}; private: WinterColormapFunctor(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace functor } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkWinterColormapFunctor.txx" #endif #endif ants-1.9.2+svn680.dfsg/Temporary/itkWinterColormapFunctor.txx000066400000000000000000000031701147325206600242260ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkWinterColormapFunctor.txx,v $ Language: C++ Date: $Date: 2009-05-15 02:47:59 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkWinterColormapFunctor_txx #define __itkWinterColormapFunctor_txx #include "itkWinterColormapFunctor.h" namespace itk { namespace Functor { template typename WinterColormapFunctor::RGBPixelType WinterColormapFunctor ::operator()( const TScalar & v ) const { // Map the input scalar between [0, 1]. RealType value = this->RescaleInputValue( v ); // Apply the color map. RealType red = 0.0; RealType green = value; RealType blue = 1.0 - 0.5 * value; // Set the rgb components after rescaling the values. RGBPixelType pixel; pixel[0] = this->RescaleRGBComponentValue( red ); pixel[1] = this->RescaleRGBComponentValue( green ); pixel[2] = this->RescaleRGBComponentValue( blue ); return pixel; } } // end namespace Functor } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Temporary/topological_numbers.h000066400000000000000000000300031147325206600226560ustar00rootroot00000000000000#ifndef TOPO_INCLUDED #define TOPO_INCLUDED /* This file provides some code to compute the topological numbers introduced by Giles Bertrand and Gregoire Malandin. This code is the result of my understanding of their paper, and is not optimized. In fact, it is possible to generate look-up tables to greatly speed-up computations... Given a binary 3*3*3 neighborhood ( the structure TOPOLOGICAL_NEIGHBORHOOD or NBH ) and a pair of consistent connectivities ( 1=(6+,18), 2=(18,6+), 3=(6,26), 4=(26,6) ), the function checkTn computes the topological number associated with the structure NBH and a specific connectivity. The fucntion checkSimple checks if the point is simple or not (see definitions below). The topological neighborhood NBH has to be initialized as a binary object 0-1, where 1 represents the Foreground Object and 0 the Background Object. The first connectivity in the pair of digital connectivities ( 1=(6+,18), 2=(18,6+), 3=(6,26), 4=(26,6) ) refers to the Foreground Object, the second one to the Background Object */ /* ////////////////////////////////////////////////////////////////////// // COMPATIBLE CONNECTIVITIES / TOPOLOGICAL NUMBERS // // TOPOLOGICAL CONVENTION // 0: No topological constraint // 1: (6+,18) // 2: (18,6+) // 3: (6,26) // 4: (26,6) // default: (6+,18) //////////////////////////////////////////////////////////////////// */ typedef unsigned char TOPOLOGICAL_NEIGHBORHOOD[3][3][3]; #define NBH TOPOLOGICAL_NEIGHBORHOOD #define TMP 100 #define MAXIMUM_NUMBER_OF_COMPONENTS 10 //maximum number of components in a NBH #define MAX_COMP MAXIMUM_NUMBER_OF_COMPONENTS /*The connectivity number is equal to the maximum allowed distance (||.||1 norm) used to generate the topological neighborhood*/ int connectivityNumber(int connectivity) { int con; switch(connectivity) { case 1: /*for the connectivity 1 [=(6+,18)], the topological neighborhood of a point x is defined by {x / ||a-x||1<=1 } -> the 6 neighborhood! */ con=1; break; case 2: /*the 18-neighborhood*/ con=2; break; case 3: /*the 6-heighborhood*/ con=1; break; case 4: /*the 26-neighborhood*/ con=3; break; default: con=1; break; } return con; } //Digital topology requires a pair of compatible connectivities int associatedConnectivity(int connectivity) { switch(connectivity) { case 1: return 2; break; case 2: return 1; break; case 3: return 4; break; case 4: return 3; break; default: return 2; break; } return 0; } NBH* reverseNBH(NBH* nbh_src,NBH *nbh_dst) { NBH *nbh; int i,j,k; if(nbh_dst) nbh=nbh_dst; else nbh=(NBH*)malloc(sizeof(NBH)); for(k=0;k<3;k++) for(j=0;j<3;j++) for(i=0;i<3;i++) if((*nbh_src)[i][j][k]) (*nbh)[i][j][k]=0; else (*nbh)[i][j][k]=1; return nbh; } NBH *N_6_1(NBH* nbh_src,NBH* nbh_dst) { int i,j,k; NBH* nbh; if(!nbh_dst) { nbh=(NBH*)calloc(1,sizeof(NBH)); if((*nbh_src)[0][1][1]) (*nbh)[0][1][1]=1; if((*nbh_src)[2][1][1]) (*nbh)[2][1][1]=1; if((*nbh_src)[1][0][1]) (*nbh)[1][0][1]=1; if((*nbh_src)[1][2][1]) (*nbh)[1][2][1]=1; if((*nbh_src)[1][1][0]) (*nbh)[1][1][0]=1; if((*nbh_src)[1][1][2]) (*nbh)[1][1][2]=1; return nbh; } else nbh=nbh_dst; if((*nbh_src)[0][1][1]) (*nbh)[0][1][1]=TMP; if((*nbh_src)[2][1][1]) (*nbh)[2][1][1]=TMP; if((*nbh_src)[1][0][1]) (*nbh)[1][0][1]=TMP; if((*nbh_src)[1][2][1]) (*nbh)[1][2][1]=TMP; if((*nbh_src)[1][1][0]) (*nbh)[1][1][0]=TMP; if((*nbh_src)[1][1][2]) (*nbh)[1][1][2]=TMP; for(k=0;k<3;k++) for(j=0;j<3;j++) for(i=0;i<3;i++) if((*nbh)[i][j][k]==TMP) (*nbh)[i][j][k]=1; else (*nbh)[i][j][k]=0; return nbh; } NBH* N_6_2(NBH* nbh_src,NBH* nbh_dst) { int i,j,k; NBH* nbh; nbh=N_6_1(nbh_src,NULL); for(i=0;i<3;i=i+2) if((*nbh)[i][1][1]) { if((*nbh_src)[i][0][1]) (*nbh)[i][0][1]=1; if((*nbh_src)[i][2][1]) (*nbh)[i][2][1]=1; if((*nbh_src)[i][1][0]) (*nbh)[i][1][0]=1; if((*nbh_src)[i][1][2]) (*nbh)[i][1][2]=1; } for(j=0;j<3;j=j+2) if((*nbh)[1][j][1]) { if((*nbh_src)[0][j][1]) (*nbh)[0][j][1]=1; if((*nbh_src)[2][j][1]) (*nbh)[2][j][1]=1; if((*nbh_src)[1][j][0]) (*nbh)[1][j][0]=1; if((*nbh_src)[1][j][2]) (*nbh)[1][j][2]=1; } for(k=0;k<3;k=k+2) if((*nbh)[1][1][k]) { if((*nbh_src)[0][1][k]) (*nbh)[0][1][k]=1; if((*nbh_src)[2][1][k]) (*nbh)[2][1][k]=1; if((*nbh_src)[1][0][k]) (*nbh)[1][0][k]=1; if((*nbh_src)[1][2][k]) (*nbh)[1][2][k]=1; } if(nbh_dst) { for(k=0;k<3;k++) for(j=0;j<3;j++) for(i=0;i<3;i++) (*nbh_dst)[i][j][k]=(*nbh)[i][j][k]; free(nbh); nbh=nbh_dst; } return nbh; } NBH* N_6_3(NBH* nbh_src,NBH* nbh_dst) { int i,j,k; NBH* nbh; nbh=N_6_2(nbh_src,NULL); i=0;j=0;k=0; if((*nbh_src)[i][j][k]) if((*nbh)[1][j][k]||(*nbh)[i][1][k]||(*nbh)[i][j][1]) (*nbh)[i][j][k]=1; i=0;j=0;k=2; if((*nbh_src)[i][j][k]) if((*nbh)[1][j][k]||(*nbh)[i][1][k]||(*nbh)[i][j][1]) (*nbh)[i][j][k]=1; i=0;j=2;k=0; if((*nbh_src)[i][j][k]) if((*nbh)[1][j][k]||(*nbh)[i][1][k]||(*nbh)[i][j][1]) (*nbh)[i][j][k]=1; i=0;j=2;k=2; if((*nbh_src)[i][j][k]) if((*nbh)[1][j][k]||(*nbh)[i][1][k]||(*nbh)[i][j][1]) (*nbh)[i][j][k]=1; i=2;j=0;k=0; if((*nbh_src)[i][j][k]) if((*nbh)[1][j][k]||(*nbh)[i][1][k]||(*nbh)[i][j][1]) (*nbh)[i][j][k]=1; i=2;j=0;k=2; if((*nbh_src)[i][j][k]) if((*nbh)[1][j][k]||(*nbh)[i][1][k]||(*nbh)[i][j][1]) (*nbh)[i][j][k]=1; i=2;j=2;k=0; if((*nbh_src)[i][j][k]) if((*nbh)[1][j][k]||(*nbh)[i][1][k]||(*nbh)[i][j][1]) (*nbh)[i][j][k]=1; i=2;j=2;k=2; if((*nbh_src)[i][j][k]) if((*nbh)[1][j][k]||(*nbh)[i][1][k]||(*nbh)[i][j][1]) (*nbh)[i][j][k]=1; if(nbh_dst) { for(k=0;k<3;k++) for(j=0;j<3;j++) for(i=0;i<3;i++) (*nbh_dst)[i][j][k]=(*nbh)[i][j][k]; free(nbh); nbh=nbh_dst; } return nbh; } NBH *N_18_1(NBH* nbh_src,NBH* nbh_dst) { int i,j,k; NBH* nbh; if(!nbh_dst) nbh=(NBH*)calloc(1,sizeof(NBH)); else nbh=nbh_dst; (*nbh)[0][0][0]=TMP;(*nbh)[2][0][0]=TMP; (*nbh)[0][0][2]=TMP;(*nbh)[2][0][2]=TMP; (*nbh)[0][2][0]=TMP;(*nbh)[2][2][0]=TMP; (*nbh)[0][2][2]=TMP;(*nbh)[2][2][2]=TMP; (*nbh)[1][1][1]=TMP; for(k=0;k<3;k++) for(j=0;j<3;j++) for(i=0;i<3;i++) if((*nbh)[i][j][k]!=TMP) { if((*nbh_src)[i][j][k]) (*nbh)[i][j][k]=1; else (*nbh)[i][j][k]=0; } else (*nbh)[i][j][k]=0; return nbh; } NBH* N_18_2(NBH* nbh_src,NBH* nbh_dst) { int i,j,k; NBH* nbh; nbh=N_18_1(nbh_src,NULL); i=0;j=0;k=0; if((*nbh_src)[i][j][k]) if((*nbh)[1][j][k]||(*nbh)[i][1][k]||(*nbh)[i][j][1] ||(*nbh)[1][1][k]||(*nbh)[1][j][1]||(*nbh)[i][1][1]) (*nbh)[i][j][k]=1; i=0;j=0;k=2; if((*nbh_src)[i][j][k]) if((*nbh)[1][j][k]||(*nbh)[i][1][k]||(*nbh)[i][j][1] ||(*nbh)[1][1][k]||(*nbh)[1][j][1]||(*nbh)[i][1][1]) (*nbh)[i][j][k]=1; i=0;j=2;k=0; if((*nbh_src)[i][j][k]) if((*nbh)[1][j][k]||(*nbh)[i][1][k]||(*nbh)[i][j][1] ||(*nbh)[1][1][k]||(*nbh)[1][j][1]||(*nbh)[i][1][1]) (*nbh)[i][j][k]=1; i=0;j=2;k=2; if((*nbh_src)[i][j][k]) if((*nbh)[1][j][k]||(*nbh)[i][1][k]||(*nbh)[i][j][1] ||(*nbh)[1][1][k]||(*nbh)[1][j][1]||(*nbh)[i][1][1]) (*nbh)[i][j][k]=1; i=2;j=0;k=0; if((*nbh_src)[i][j][k]) if((*nbh)[1][j][k]||(*nbh)[i][1][k]||(*nbh)[i][j][1] ||(*nbh)[1][1][k]||(*nbh)[1][j][1]||(*nbh)[i][1][1]) (*nbh)[i][j][k]=1; i=2;j=0;k=2; if((*nbh_src)[i][j][k]) if((*nbh)[1][j][k]||(*nbh)[i][1][k]||(*nbh)[i][j][1] ||(*nbh)[1][1][k]||(*nbh)[1][j][1]||(*nbh)[i][1][1]) (*nbh)[i][j][k]=1; i=2;j=2;k=0; if((*nbh_src)[i][j][k]) if((*nbh)[1][j][k]||(*nbh)[i][1][k]||(*nbh)[i][j][1] ||(*nbh)[1][1][k]||(*nbh)[1][j][1]||(*nbh)[i][1][1]) (*nbh)[i][j][k]=1; i=2;j=2;k=2; if((*nbh_src)[i][j][k]) if((*nbh)[1][j][k]||(*nbh)[i][1][k]||(*nbh)[i][j][1] ||(*nbh)[1][1][k]||(*nbh)[1][j][1]||(*nbh)[i][1][1]) (*nbh)[i][j][k]=1; if(nbh_dst) { for(k=0;k<3;k++) for(j=0;j<3;j++) for(i=0;i<3;i++) (*nbh_dst)[i][j][k]=(*nbh)[i][j][k]; free(nbh); nbh=nbh_dst; } return nbh; } NBH *N_26_1(NBH* nbh_src,NBH* nbh_dst) { int i,j,k; NBH* nbh; if(!nbh_dst) nbh=(NBH*)calloc(1,sizeof(NBH)); else nbh=nbh_dst; for(k=0;k<3;k++) for(j=0;j<3;j++) for(i=0;i<3;i++) if((*nbh_src)[i][j][k]) (*nbh)[i][j][k]=1; else (*nbh)[i][j][k]=0; (*nbh)[1][1][1]=0; return nbh; } NBH* Nnk(NBH* nbh_src,NBH *nbh_dst,int connectivity) { NBH* nbh; if(!nbh_dst) nbh=(NBH*)calloc(1,sizeof(NBH)); else nbh=nbh_dst; switch(connectivity) { case 1: //T6+(x,X)=#C6(N_6_3(x,X)) N_6_3(nbh_src,nbh); break; case 2: //T6+(x,X)=#C18(N_18_2) N_18_2(nbh_src,nbh); break; case 3: //T6(x,X)=#C6(N_6_2) N_6_2(nbh_src,nbh); break; case 4: //T26(x,X)=#C6(N_26_1) N_26_1(nbh_src,nbh); break; default: //T6+(x,X)=#C6(N_6_3(x,X)) N_6_3(nbh_src,nbh); break; } return nbh; } /*This function computes the topological number associated with NBH and a certain connectivity. This function is a "hack": a more elegant/efficient implementation should be done... NBH is a binary object (0-1) connectivity should be equal to 1,2,3, or 4 according to the convention above*/ int checkTn(NBH *nbh_src,NBH *nbh_dst,int connectivity) { int i,j,k,a,b,c,ik,jk,kk,ct; int con,nvox,label,sum; int comp_table[MAX_COMP]; int min_val; int x,y,z; NBH* nbh; if(!nbh_dst) nbh=(NBH*)calloc(1,sizeof(NBH)); else nbh=nbh_dst; con=connectivityNumber(connectivity); Nnk(nbh_src,nbh,connectivity); memset(comp_table,0,MAX_COMP*sizeof(int)); for(i=0;i<3;i++) for(j=0;j<3;j++) for(k=0;k<3;k++) if((*nbh)[i][j][k]) { for(nvox=0,ik=-1;ik<=1;ik++) { a=i+ik; if(a<0 || a>=3) continue; for(jk=-1;jk<=1;jk++) { b=j+jk; if(b<0 || b>=3) continue; for(kk=-1;kk<=1;kk++) { sum=abs(ik)+abs(jk)+abs(kk); if(sum>con || (!sum)) continue; c=k+kk; if(c<0 || c>=3) continue; label=(*nbh)[a][b][c]; if(label>1) { comp_table[label-1]=2; nvox++; } } } } if(!nvox) //find new basin! { for(ct=1;comp_table[ct] && ct float GetTensorFA( TensorType dtv) { // inner product float xx = dtv[0]; float xy = dtv[1]; float xz = dtv[2]; float yy = dtv[3]; float yz = dtv[4]; float zz = dtv[5]; float isp = ( xx*xx + yy*yy + zz*zz + 2.0*(xy*xy + xz*xz + yz*yz) ); // Computed as // FA = vcl_sqrt(1.5*sum(sum(N.*N))/sum((sum(D.*D)))) // where N = D - ((1/3)*trace(D)*eye(3,3)) // equation (28) in http://lmi.bwh.harvard.edu/papers/pdfs/2002/westinMEDIA02.pdf if( isp > 0.0 ) { float trace = dtv[0]; trace += dtv[3]; trace += dtv[5]; float anisotropy = 3.0 * isp - trace * trace; float fractionalAnisotropy =( vcl_sqrt(anisotropy / ( 2.0 * isp ) ) ); return fractionalAnisotropy; } return 0.0 ; } template float GetMetricTensorCost( TVectorType dpath, TTensorType dtv , unsigned int matrixpower) { typedef vnl_matrix MatrixType; MatrixType DT(3,3); DT.fill(0); DT(0,0)=dtv[0]; DT(1,1)=dtv[3]; DT(2,2)=dtv[5]; DT(1,0)=DT(0,1)=dtv[1]; DT(2,0)=DT(0,2)=dtv[2]; DT(2,1)=DT(1,2)=dtv[4]; vnl_symmetric_eigensystem< double > eig(DT); double e1 = (eig.D(0,0)); double e2 = (eig.D(1,1)); double e3 = (eig.D(2,2)); double etot=e1+e2+e3; if (etot==0) etot=1; MatrixType vec(3,1); vec(0,0)=dpath[0]; vec(1,0)=dpath[1]; vec(2,0)=dpath[2]; MatrixType inv = vnl_matrix_inverse(DT); for (unsigned int lo=1; lo TVectorType ChangeTensorByVector( TVectorType dpath, TTensorType dtv, float epsilon) { typedef TVectorType VectorType; typedef vnl_matrix MatrixType; MatrixType DT(3,3); DT.fill(0); DT(0,0)=dtv[0]; DT(1,1)=dtv[3]; DT(2,2)=dtv[5]; DT(1,0)=DT(0,1)=dtv[1]; DT(2,0)=DT(0,2)=dtv[2]; DT(2,1)=DT(1,2)=dtv[4]; vnl_symmetric_eigensystem< double > eig(DT); double e3 = (eig.D(0,0)); double e2 = (eig.D(1,1)); double e1 = (eig.D(2,2)); double etot=e1+e2+e3; if (etot==0) etot=1; MatrixType vec(3,1); vec(0,0)=dpath[0]; vec(1,0)=dpath[1]; vec(2,0)=dpath[2]; MatrixType evec1(3,1);//biggest evec1(0,0)=eig.V(0,2); evec1(1,0)=eig.V(1,2); evec1(2,0)=eig.V(2,2); MatrixType evec2(3,1);// middle evec2(0,0)=eig.V(0,1); evec2(1,0)=eig.V(1,1); evec2(2,0)=eig.V(2,1); MatrixType evec3(3,1);//smallest evec3(0,0)=eig.V(0,0); evec3(1,0)=eig.V(1,0); evec3(2,0)=eig.V(2,0); float temp; temp=(vec.transpose()*evec1)(0,0); temp=sqrt(temp*temp); e1 *=( 1.0 - epsilon*temp); if (e1 < 1.e-11) e1=1.e-11; temp=(vec.transpose()*evec2)(0,0); temp=sqrt(temp*temp); e2 *=( 1.0 - epsilon*temp); if (e2 < 1.e-11) e2=1.e-11; temp=(vec.transpose()*evec3)(0,0); temp=sqrt(temp*temp); e3 *=( 1.0 - epsilon*temp); if (e3 < 1.e-11) e3=1.e-11; DT = (evec3*evec3.transpose())*e3 + (evec2*evec2.transpose())*e2 + (evec1*evec1.transpose())*e1; itk::Vector newtens; newtens[0]=DT(0,0); newtens[3]=DT(1,1); newtens[5]=DT(2,2); newtens[1]=DT(0,1); newtens[2]=DT(0,2); newtens[4]=DT(2,1); return newtens; } template float GetTensorADC( TensorType dtv, unsigned int opt = 0) { float eps=1.e-9,mag=0; for (unsigned int jj=0; jj<6; jj++) { float ff=dtv[jj]; mag+=ff*ff; if ( vnl_math_isnan( ff ) || vnl_math_isinf(ff) ) { return 0; } } mag=sqrt(mag); if ( dtv[1]==0 && dtv[2] == 0 && dtv[4]==0) return 0; if (mag < eps) { return 0; } // typedef itk::Vector TensorTypeIn; typedef itk::Vector TensorType; typedef itk::Vector TensorTypeOut; typedef itk::Vector TensorTypeIn; const unsigned int ImageDimension = 3; typedef itk::Image InTensorImageType; typedef itk::Image OutTensorImageType; typedef itk::Image TensorImageType; typedef InTensorImageType::Pointer TensorImagePointer; typedef float PixelType; typedef itk::Vector VectorType1; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef ImageType::IndexType IndexType; typedef ImageType::SizeType SizeType; typedef ImageType::SpacingType SpacingType; // typedef itk::AffineTransform AffineTransformType; // typedef itk::LinearInterpolateImageFunction InterpolatorType1; // typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; itk::Vector dtv2; typedef vnl_matrix MatrixType; MatrixType DT(3,3); DT.fill(0); DT(0,0)=dtv[0]; DT(1,1)=dtv[3]; DT(2,2)=dtv[5]; DT(1,0)=DT(0,1)=dtv[1]; DT(2,0)=DT(0,2)=dtv[2]; DT(2,1)=DT(1,2)=dtv[4]; // if (takelog ) std::cout << " TAKING LOG " << std::endl; else std::cout << "TAKING EXP " << std::endl; // std::cout << " dtv " << dtv << std::endl; vnl_symmetric_eigensystem< double > eig(DT); double e1 = (eig.D(0,0)); double e2 = (eig.D(1,1)); double e3 = (eig.D(2,2)); if (opt <= 1 ) return (e1+e1+e3)/3.0; // else if (opt == 4 ) return e2; else if (opt == 3 ) return (e2+e1)/2.0; else if (opt == 2 ) return e3; } template itk::RGBPixel< unsigned char > GetTensorRGB( TTensorType dtv ) { itk::RGBPixel< unsigned char > zero; zero.Fill(0); float eps=1.e-9,mag=0; for (unsigned int jj=0; jj<6; jj++) { float ff=dtv[jj]; mag+=ff*ff; if ( vnl_math_isnan( ff ) || vnl_math_isinf(ff) ) { return zero; } } mag=sqrt(mag); if ( dtv[1]==0 && dtv[2] == 0 && dtv[4]==0) return zero; if (mag < eps) { return zero; } // typedef itk::Vector TensorTypeIn; typedef itk::Vector TensorType; typedef itk::Vector TensorTypeOut; typedef itk::Vector TensorTypeIn; const unsigned int ImageDimension = 3; typedef itk::Image InTensorImageType; typedef itk::Image OutTensorImageType; typedef itk::Image TensorImageType; typedef InTensorImageType::Pointer TensorImagePointer; typedef float PixelType; typedef itk::Vector VectorType1; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef ImageType::IndexType IndexType; typedef ImageType::SizeType SizeType; typedef ImageType::SpacingType SpacingType; // typedef itk::AffineTransform AffineTransformType; // typedef itk::LinearInterpolateImageFunction InterpolatorType1; // typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; itk::Vector dtv2; typedef vnl_matrix MatrixType; MatrixType DT(3,3); DT.fill(0); DT(0,0)=dtv[0]; DT(1,1)=dtv[3]; DT(2,2)=dtv[5]; DT(1,0)=DT(0,1)=dtv[1]; DT(2,0)=DT(0,2)=dtv[2]; DT(2,1)=DT(1,2)=dtv[4]; // if (takelog ) std::cout << " TAKING LOG " << std::endl; else std::cout << "TAKING EXP " << std::endl; // std::cout << " dtv " << dtv << std::endl; vnl_symmetric_eigensystem< double > eig(DT); double e1 = (eig.D(0,0)); double e2 = (eig.D(1,1)); double e3 = (eig.D(2,2)); itk::RGBPixel< float > rgb; float xx = dtv[0]; float xy = dtv[1]; float xz = dtv[2]; float yy = dtv[3]; float yz = dtv[4]; float zz = dtv[5]; float isp = ( xx*xx + yy*yy + zz*zz + 2.0*(xy*xy + xz*xz + yz*yz) ); float fa=0.0; if( isp > 0.0 ) { float trace = dtv[0]; trace += dtv[3]; trace += dtv[5]; float anisotropy = 3.0 * isp - trace * trace; fa=( vcl_sqrt(anisotropy / ( 2.0 * isp ) ) ); } //rgb[0]=eig.V(2,0)*fa*255;//+eig.V(1,0)*e2; //rgb[1]=eig.V(2,1)*fa*255;//+eig.V(1,1)*e2; // rgb[2]=eig.V(2,2)*fa*255;//+eig.V(1,2)*e2; rgb[0]=eig.V(0,2)*fa*255;//+eig.V(1,0)*e2; rgb[1]=eig.V(1,2)*fa*255;//+eig.V(1,1)*e2; rgb[2]=eig.V(2,2)*fa*255;//+eig.V(1,2)*e2; return rgb; } template itk::RGBPixel< float > GetTensorPrincipalEigenvector( TTensorType dtv ) { itk::RGBPixel< float > zero; zero.Fill(0); float eps=1.e-9,mag=0; for (unsigned int jj=0; jj<6; jj++) { float ff=dtv[jj]; mag+=ff*ff; if ( vnl_math_isnan( ff ) || vnl_math_isinf(ff) ) { return zero; } } mag=sqrt(mag); if ( dtv[1]==0 && dtv[2] == 0 && dtv[4]==0) return zero; if (mag < eps) { return zero; } // typedef itk::Vector TensorTypeIn; typedef itk::Vector TensorType; typedef itk::Vector TensorTypeOut; typedef itk::Vector TensorTypeIn; const unsigned int ImageDimension = 3; typedef itk::Image InTensorImageType; typedef itk::Image OutTensorImageType; typedef itk::Image TensorImageType; typedef InTensorImageType::Pointer TensorImagePointer; typedef float PixelType; typedef itk::Vector VectorType1; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef ImageType::IndexType IndexType; typedef ImageType::SizeType SizeType; typedef ImageType::SpacingType SpacingType; // typedef itk::AffineTransform AffineTransformType; // typedef itk::LinearInterpolateImageFunction InterpolatorType1; // typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; itk::Vector dtv2; typedef vnl_matrix MatrixType; MatrixType DT(3,3); DT.fill(0); DT(0,0)=dtv[0]; DT(1,1)=dtv[3]; DT(2,2)=dtv[5]; DT(1,0)=DT(0,1)=dtv[1]; DT(2,0)=DT(0,2)=dtv[2]; DT(2,1)=DT(1,2)=dtv[4]; // if (takelog ) std::cout << " TAKING LOG " << std::endl; else std::cout << "TAKING EXP " << std::endl; // std::cout << " dtv " << dtv << std::endl; vnl_symmetric_eigensystem< double > eig(DT); double e1 = (eig.D(0,0)); double e2 = (eig.D(1,1)); double e3 = (eig.D(2,2)); itk::RGBPixel< float > rgb; float xx = dtv[0]; float xy = dtv[1]; float xz = dtv[2]; float yy = dtv[3]; float yz = dtv[4]; float zz = dtv[5]; float isp = ( xx*xx + yy*yy + zz*zz + 2.0*(xy*xy + xz*xz + yz*yz) ); float fa=0.0; if( isp > 0.0 ) { float trace = dtv[0]; trace += dtv[3]; trace += dtv[5]; float anisotropy = 3.0 * isp - trace * trace; fa=( vcl_sqrt(anisotropy / ( 2.0 * isp ) ) ); } //rgb[0]=eig.V(2,0)*fa*255;//+eig.V(1,0)*e2; //rgb[1]=eig.V(2,1)*fa*255;//+eig.V(1,1)*e2; // rgb[2]=eig.V(2,2)*fa*255;//+eig.V(1,2)*e2; // biggest evec rgb[0]=eig.V(0,2);//+eig.V(1,0)*e2; rgb[1]=eig.V(1,2);//+eig.V(1,1)*e2; rgb[2]=eig.V(2,2);//+eig.V(1,2)*e2; return rgb; mag=rgb[0]*rgb[0]+rgb[1]*rgb[1]+rgb[2]*rgb[2]; mag=sqrt(mag); rgb[0]=rgb[0]/mag; rgb[1]=rgb[1]/mag; rgb[2]=rgb[2]/mag; return rgb; } template TTensorType TensorLogAndExp( TTensorType dtv, bool takelog , bool success=true) { float eps=1.e-9,mag=0; for (unsigned int jj=0; jj<6; jj++) { float ff=dtv[jj]; mag+=ff*ff; if ( vnl_math_isnan( ff ) || vnl_math_isinf(ff) ) { dtv.Fill(0); //dtv[0]=eps; dtv[3]=eps; dtv[5]=eps; return dtv; } } mag=sqrt(mag); if ( dtv[1]==0 && dtv[2] == 0 && dtv[4]==0) return dtv; if (mag < eps) { success = false; return dtv; } // typedef itk::Vector TensorTypeIn; typedef TTensorType TensorType; typedef TTensorType TensorTypeOut; typedef TTensorType TensorTypeIn; const unsigned int ImageDimension = 3; typedef itk::Image InTensorImageType; typedef itk::Image OutTensorImageType; typedef itk::Image TensorImageType; typedef InTensorImageType::Pointer TensorImagePointer; typedef float PixelType; typedef itk::Vector VectorType1; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef ImageType::IndexType IndexType; typedef ImageType::SizeType SizeType; typedef ImageType::SpacingType SpacingType; // typedef itk::AffineTransform AffineTransformType; // typedef itk::LinearInterpolateImageFunction InterpolatorType1; // typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; itk::Vector dtv2; typedef vnl_matrix MatrixType; MatrixType DT(3,3); DT.fill(0); DT(0,0)=dtv[0]; DT(1,1)=dtv[2]; DT(2,2)=dtv[5]; DT(1,0)=DT(0,1)=dtv[1]; DT(2,0)=DT(0,2)=dtv[3]; DT(2,1)=DT(1,2)=dtv[4]; // if (takelog ) std::cout << " TAKING LOG " << std::endl; else std::cout << "TAKING EXP " << std::endl; // std::cout << " dtv " << dtv << std::endl; vnl_symmetric_eigensystem< double > eig(DT); // std::cout << " eig.D " << eig.D << std::endl; // std::cout << " eig V " << eig.V << std::endl; double e1 = (eig.D(0,0)); double e2 = (eig.D(1,1)); double e3 = (eig.D(2,2)); float peigeps=1.e-2; float eigeps=1.e-6; float eigmx=10000; if ( e3 < peigeps ) { success=false; return dtv; } MatrixType eigmat(3,3); eigmat.fill(0); if (takelog) { eigmat(0,0)=log(e1); eigmat(1,1)=log(e2); eigmat(2,2)=log(e3); } else //take exp { eigmat(0,0)=exp(e1); eigmat(1,1)=exp(e2); eigmat(2,2)=exp(e3); } // std::cout << " e1 " << e1 << " e2 " << e2 << " e3 " << std::endl; MatrixType DTrec=eig.V*eigmat*eig.V.transpose(); dtv2[0]=DTrec(0,0); dtv2[2]=DTrec(1,1); dtv2[5]=DTrec(2,2); dtv2[1]=DTrec(0,1); dtv2[3]=DTrec(0,2); dtv2[4]=DTrec(1,2); return dtv2; } static float GetMetricTensorCost( itk::Vector dpath, itk::Vector dtv ) { typedef vnl_matrix MatrixType; MatrixType DT(3,3); DT.fill(0); DT(0,0)=dtv[0]; DT(1,1)=dtv[3]; DT(2,2)=dtv[5]; DT(1,0)=DT(0,1)=dtv[1]; DT(2,0)=DT(0,2)=dtv[2]; DT(2,1)=DT(1,2)=dtv[4]; vnl_symmetric_eigensystem< double > eig(DT); double e1 = (eig.D(0,0)); double e2 = (eig.D(1,1)); double e3 = (eig.D(2,2)); double etot=e1+e2+e3; if (etot==0) etot=1; MatrixType vec(3,1); vec(0,0)=dpath[0]; vec(1,0)=dpath[1]; vec(2,0)=dpath[2]; MatrixType inv = vnl_matrix_inverse(DT); MatrixType sol= vec.transpose()*inv*vec; float cost = sol(0,0)/etot; return cost; } */ #endif ants-1.9.2+svn680.dfsg/Tensor/TensorFunctions.h000066400000000000000000000510501147325206600212470ustar00rootroot00000000000000#ifndef _TensorFunctions_cxx_ #define _TensorFunctions_cxx_ //#include "itkSmallStrainDiffusionTensorReorientationImageFilter.h" //#include "itkVectorIndexSelectionCastImageFilter.h" #include "itkSymmetricSecondRankTensor.h" #include "itkVector.h" #include "itkVersor.h" #include "itkVariableSizeMatrix.h" #include "itkDecomposeTensorFunction2.h" #include "itkRotationMatrixFromVectors.h" #include "vnl/algo/vnl_matrix_inverse.h" #include "vnl/algo/vnl_symmetric_eigensystem.h" template MatrixType Vector2Matrix( TensorType dtv ) { MatrixType dtm(3,3); dtm(0,0) = dtv[0]; dtm(0,1) = dtm(1,0) = dtv[1]; dtm(0,2) = dtm(2,0) = dtv[2]; dtm(1,1) = dtv[3]; dtm(1,2) = dtm(2,1) = dtv[4]; dtm(2,2) = dtv[5]; return dtm; } template void Vector2Matrix( TensorType &dtv, MatrixType &dtm ) { dtm(0,0) = dtv[0]; dtm(0,1) = dtm(1,0) = dtv[1]; dtm(0,2) = dtm(2,0) = dtv[2]; dtm(1,1) = dtv[3]; dtm(1,2) = dtm(2,1) = dtv[4]; dtm(2,2) = dtv[5]; } template TensorType Matrix2Vector( MatrixType dtm ) { TensorType dtv; dtv[0] = dtm(0,0); dtv[1] = dtm(0,1); dtv[2] = dtm(0,2); dtv[3] = dtm(1,1); dtv[4] = dtm(1,2); dtv[5] = dtm(2,2); return dtv; } template void EigenAnalysis(TensorType dtv, MatrixType &evals, MatrixType &evecs) { MatrixType dtm = Vector2Matrix(dtv); itk::DecomposeTensorFunction2 decomposer; decomposer.EvaluateSymmetricEigenDecomposition( dtm, evals, evecs ); } template float DiffusionCoefficient( TensorType dtv, VectorType direction, bool normalized = false) { vnl_matrix tensor(3,3); tensor[0][0] = dtv[0]; tensor[1][0] = tensor[0][1] = dtv[1]; tensor[2][0] = tensor[0][2] = dtv[2]; tensor[1][1] = dtv[3]; tensor[1][2] = tensor[2][1] = dtv[4]; tensor[2][2] = dtv[5]; vnl_matrix tangentmat(3,1); tangentmat(0,0) = direction[0]; tangentmat(1,0) = direction[1]; tangentmat(2,0) = direction[2]; vnl_matrix fddmat = tangentmat.transpose() * tensor * tangentmat; float fdd = (float) fddmat(0,0); if (normalized > 0) { fdd = fdd/(tensor[0][0]+tensor[1][1]+tensor[2][2]); } return fdd; } template TensorType TensorLogAndExp( TensorType dtv, bool takelog , bool success=true) { float eps=1.e-12,mag=0; for (unsigned int jj=0; jj<6; jj++) { float ff=dtv[jj]; mag+=ff*ff; if ( vnl_math_isnan( ff ) || vnl_math_isinf(ff) ) { dtv.Fill(0); //dtv[0]=eps; dtv[3]=eps; dtv[5]=eps; success=false; return dtv; } } mag=sqrt(mag); if ( dtv[1]==0 && dtv[2] == 0 && dtv[4]==0) { success = false; return dtv; } if (mag < eps) { success = false; return dtv; } //typedef vnl_matrix MatrixType; typedef itk::VariableSizeMatrix MatrixType; //MatrixType DT = Vector2Matrix(dtv); MatrixType D; MatrixType V; EigenAnalysis(dtv,D,V); double e1 = D(0,0); double e2 = D(1,1); double e3 = D(2,2); //float peigeps=1.e-12; if ( fabs(e3) < eps ) { success=false; std::cout << "-4" << std::flush; return dtv; } MatrixType eigmat(3,3); eigmat.Fill(0); if (takelog) { eigmat(0,0)=log(fabs(e1)); eigmat(1,1)=log(fabs(e2)); eigmat(2,2)=log(fabs(e3)); } else //take exp { eigmat(0,0)=exp(e1); eigmat(1,1)=exp(e2); eigmat(2,2)=exp(e3); } if ( vnl_math_isnan(eigmat(0,0)) || vnl_math_isnan(eigmat(1,1)) || vnl_math_isnan(eigmat(2,2))) { dtv.Fill(0); success=false; return dtv; } typedef typename MatrixType::InternalMatrixType VnlMatrixType; VnlMatrixType DTrec=V.GetVnlMatrix()*eigmat.GetVnlMatrix()*V.GetTranspose(); TensorType dtv2 = Matrix2Vector(DTrec); return dtv2; } template TensorType TensorLog( TensorType dtv, bool success=true ) { return TensorLogAndExp( dtv, true, success ); } template TensorType TensorExp( TensorType dtv, bool takelog , bool success=true) { return TensorLogAndExp( dtv, false, success ); } template float GetTensorFA( TensorType dtv ) { // inner product float xx = dtv[0]; float xy = dtv[1]; float xz = dtv[2]; float yy = dtv[3]; float yz = dtv[4]; float zz = dtv[5]; float isp = ( xx*xx + yy*yy + zz*zz + 2.0*(xy*xy + xz*xz + yz*yz) ); // Computed as // FA = vcl_sqrt(1.5*sum(sum(N.*N))/sum((sum(D.*D)))) // where N = D - ((1/3)*trace(D)*eye(3,3)) // equation (28) in http://lmi.bwh.harvard.edu/papers/pdfs/2002/westinMEDIA02.pdf if( isp > 0.0 ) { float trace = dtv[0]; trace += dtv[3]; trace += dtv[5]; float anisotropy = 3.0 * isp - trace * trace; float fractionalAnisotropy =( vcl_sqrt(anisotropy / ( 2.0 * isp ) ) ); return fractionalAnisotropy; } return 0.0 ; /* // Computed as // FA = vcl_sqrt(1.5 * ( \sum_i ( lambda_i - lambda_mean )^2 ) / \sum_i ( lambda_i^2 ) ) // as in http://splweb.bwh.harvard.edu:8000/pages/papers/martha/DTI_Tech354.pdf // [lambda = eig(A)]. EigenValuesArrayType eigenValues; ComputeEigenValues( eigenValues ); eigenValues[0] = vnl_math_abs(eigenValues[0]); eigenValues[1] = vnl_math_abs(eigenValues[1]); eigenValues[2] = vnl_math_abs(eigenValues[2]); const RealValueType norm_E = vnl_math_sqr(eigenValues[0]) + vnl_math_sqr(eigenValues[1]) + vnl_math_sqr(eigenValues[2]); if( norm_E > 0.0 ) { const RealValueType anisotropy = vnl_math_sqr(eigenValues[0] - eigenValues[1]) + vnl_math_sqr(eigenValues[1] - eigenValues[2]) + vnl_math_sqr(eigenValues[2] - eigenValues[0]); const RealValueType fractionalAnisotropy = vcl_sqrt( 0.5 * anisotropy/norm_E); return fractionalAnisotropy; } return 0.0; */ } template float GetMetricTensorCost( TVectorType dpath, TTensorType dtv , unsigned int matrixpower) { typedef vnl_matrix MatrixType; MatrixType DT(3,3); DT.fill(0); DT(0,0)=dtv[0]; DT(1,1)=dtv[3]; DT(2,2)=dtv[5]; DT(1,0)=DT(0,1)=dtv[1]; DT(2,0)=DT(0,2)=dtv[2]; DT(2,1)=DT(1,2)=dtv[4]; vnl_symmetric_eigensystem< double > eig(DT); double e1 = (eig.D(0,0)); double e2 = (eig.D(1,1)); double e3 = (eig.D(2,2)); double etot=e1+e2+e3; if (etot==0) etot=1; MatrixType vec(3,1); vec(0,0)=dpath[0]; vec(1,0)=dpath[1]; vec(2,0)=dpath[2]; MatrixType inv = vnl_matrix_inverse(DT); for (unsigned int lo=1; lo TVectorType ChangeTensorByVector( TVectorType dpath, TTensorType dtv, float epsilon) { typedef TVectorType VectorType; typedef vnl_matrix MatrixType; MatrixType DT(3,3); DT.fill(0); DT(0,0)=dtv[0]; DT(1,1)=dtv[3]; DT(2,2)=dtv[5]; DT(1,0)=DT(0,1)=dtv[1]; DT(2,0)=DT(0,2)=dtv[2]; DT(2,1)=DT(1,2)=dtv[4]; vnl_symmetric_eigensystem< double > eig(DT); double e3 = (eig.D(0,0)); double e2 = (eig.D(1,1)); double e1 = (eig.D(2,2)); double etot=e1+e2+e3; if (etot==0) etot=1; MatrixType vec(3,1); vec(0,0)=dpath[0]; vec(1,0)=dpath[1]; vec(2,0)=dpath[2]; MatrixType evec1(3,1);//biggest evec1(0,0)=eig.V(0,2); evec1(1,0)=eig.V(1,2); evec1(2,0)=eig.V(2,2); MatrixType evec2(3,1);// middle evec2(0,0)=eig.V(0,1); evec2(1,0)=eig.V(1,1); evec2(2,0)=eig.V(2,1); MatrixType evec3(3,1);//smallest evec3(0,0)=eig.V(0,0); evec3(1,0)=eig.V(1,0); evec3(2,0)=eig.V(2,0); float temp; temp=(vec.transpose()*evec1)(0,0); temp=sqrt(temp*temp); e1 *=( 1.0 - epsilon*temp); if (e1 < 1.e-11) e1=1.e-11; temp=(vec.transpose()*evec2)(0,0); temp=sqrt(temp*temp); e2 *=( 1.0 - epsilon*temp); if (e2 < 1.e-11) e2=1.e-11; temp=(vec.transpose()*evec3)(0,0); temp=sqrt(temp*temp); e3 *=( 1.0 - epsilon*temp); if (e3 < 1.e-11) e3=1.e-11; DT = (evec3*evec3.transpose())*e3 + (evec2*evec2.transpose())*e2 + (evec1*evec1.transpose())*e1; itk::Vector newtens; newtens[0]=DT(0,0); newtens[3]=DT(1,1); newtens[5]=DT(2,2); newtens[1]=DT(0,1); newtens[2]=DT(0,2); newtens[4]=DT(2,1); return newtens; } template float GetTensorADC( TTensorType dtv, unsigned int opt = 0) { float eps=1.e-9,mag=0; for (unsigned int jj=0; jj<6; jj++) { float ff=dtv[jj]; mag+=ff*ff; if ( vnl_math_isnan( ff ) || vnl_math_isinf(ff) ) { return 0; } } mag=sqrt(mag); if ( dtv[1]==0 && dtv[2] == 0 && dtv[4]==0) return 0; if (mag < eps) { return 0; } // typedef itk::Vector TensorTypeIn; typedef itk::Vector TensorType; typedef itk::Vector TensorTypeOut; typedef itk::Vector TensorTypeIn; const unsigned int ImageDimension = 3; typedef itk::Image InTensorImageType; typedef itk::Image OutTensorImageType; typedef itk::Image TensorImageType; typedef InTensorImageType::Pointer TensorImagePointer; typedef float PixelType; typedef itk::Vector VectorType1; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef ImageType::IndexType IndexType; typedef ImageType::SizeType SizeType; typedef ImageType::SpacingType SpacingType; // typedef itk::AffineTransform AffineTransformType; // typedef itk::LinearInterpolateImageFunction InterpolatorType1; // typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; itk::Vector dtv2; typedef vnl_matrix MatrixType; MatrixType DT(3,3); DT.fill(0); DT(0,0)=dtv[0]; DT(1,1)=dtv[3]; DT(2,2)=dtv[5]; DT(1,0)=DT(0,1)=dtv[1]; DT(2,0)=DT(0,2)=dtv[2]; DT(2,1)=DT(1,2)=dtv[4]; // if (takelog ) std::cout << " TAKING LOG " << std::endl; else std::cout << "TAKING EXP " << std::endl; // std::cout << " dtv " << dtv << std::endl; vnl_symmetric_eigensystem< double > eig(DT); double e1 = (eig.D(0,0)); double e2 = (eig.D(1,1)); double e3 = (eig.D(2,2)); if (opt <= 1 ) return (e1+e1+e3)/3.0; // else if (opt == 4 ) return e2; else if (opt == 3 ) return (e2+e1)/2.0; else if (opt == 2 ) return e3; else return (e1+e1+e3)/3.0; } template itk::RGBPixel< unsigned char > GetTensorRGB( TTensorType dtv ) { typedef TTensorType TensorType; itk::RGBPixel< unsigned char > zero; zero.Fill(0); float eps=1.e-9,mag=0; for (unsigned int jj=0; jj<6; jj++) { float ff=dtv[jj]; mag+=ff*ff; if ( vnl_math_isnan( ff ) || vnl_math_isinf(ff) ) { return zero; } } mag=sqrt(mag); itk::RGBPixel< unsigned char > rgb; if ( dtv[1]==0 && dtv[2] == 0 && dtv[4]==0) return zero; if (mag < eps) { return zero; } typedef itk::VariableSizeMatrix EigenMatrixType; EigenMatrixType evals(3,3); EigenMatrixType evecs(3,3); EigenAnalysis(dtv,evals,evecs); float fa = GetTensorFA(dtv); rgb[0]=(unsigned char)(vcl_fabs(evecs(0,2))*fa*255); rgb[1]=(unsigned char)(vcl_fabs(evecs(1,2))*fa*255); rgb[2]=(unsigned char)(vcl_fabs(evecs(2,2))*fa*255); return rgb; } template itk::RGBPixel< float > GetTensorPrincipalEigenvector( TTensorType dtv ) { itk::RGBPixel< float > zero; zero.Fill(0); float eps=1.e-9,mag=0; for (unsigned int jj=0; jj<6; jj++) { float ff=dtv[jj]; mag+=ff*ff; if ( vnl_math_isnan( ff ) || vnl_math_isinf(ff) ) { return zero; } } mag=sqrt(mag); if ( dtv[1]==0 && dtv[2] == 0 && dtv[4]==0) return zero; if (mag < eps) { return zero; } // typedef itk::Vector TensorTypeIn; typedef itk::Vector TensorType; typedef itk::Vector TensorTypeOut; typedef itk::Vector TensorTypeIn; const unsigned int ImageDimension = 3; typedef itk::Image InTensorImageType; typedef itk::Image OutTensorImageType; typedef itk::Image TensorImageType; typedef InTensorImageType::Pointer TensorImagePointer; typedef float PixelType; typedef itk::Vector VectorType1; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef ImageType::IndexType IndexType; typedef ImageType::SizeType SizeType; typedef ImageType::SpacingType SpacingType; // typedef itk::AffineTransform AffineTransformType; // typedef itk::LinearInterpolateImageFunction InterpolatorType1; // typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; itk::Vector dtv2; typedef vnl_matrix MatrixType; MatrixType DT(3,3); DT.fill(0); DT(0,0)=dtv[0]; DT(1,1)=dtv[3]; DT(2,2)=dtv[5]; DT(1,0)=DT(0,1)=dtv[1]; DT(2,0)=DT(0,2)=dtv[2]; DT(2,1)=DT(1,2)=dtv[4]; // if (takelog ) std::cout << " TAKING LOG " << std::endl; else std::cout << "TAKING EXP " << std::endl; // std::cout << " dtv " << dtv << std::endl; vnl_symmetric_eigensystem< double > eig(DT); double e1 = (eig.D(0,0)); double e2 = (eig.D(1,1)); double e3 = (eig.D(2,2)); itk::RGBPixel< float > rgb; float xx = dtv[0]; float xy = dtv[1]; float xz = dtv[2]; float yy = dtv[3]; float yz = dtv[4]; float zz = dtv[5]; float isp = ( xx*xx + yy*yy + zz*zz + 2.0*(xy*xy + xz*xz + yz*yz) ); float fa=0.0; if( isp > 0.0 ) { float trace = dtv[0]; trace += dtv[3]; trace += dtv[5]; float anisotropy = 3.0 * isp - trace * trace; fa=( vcl_sqrt(anisotropy / ( 2.0 * isp ) ) ); } //rgb[0]=eig.V(2,0)*fa*255;//+eig.V(1,0)*e2; //rgb[1]=eig.V(2,1)*fa*255;//+eig.V(1,1)*e2; // rgb[2]=eig.V(2,2)*fa*255;//+eig.V(1,2)*e2; // biggest evec rgb[0]=eig.V(0,2);//+eig.V(1,0)*e2; rgb[1]=eig.V(1,2);//+eig.V(1,1)*e2; rgb[2]=eig.V(2,2);//+eig.V(1,2)*e2; return rgb; mag=rgb[0]*rgb[0]+rgb[1]*rgb[1]+rgb[2]*rgb[2]; mag=sqrt(mag); rgb[0]=rgb[0]/mag; rgb[1]=rgb[1]/mag; rgb[2]=rgb[2]/mag; return rgb; } template itk::Vector< float > GetTensorPrincipalEigenvector( TTensorType dtv , unsigned int whichvec) { itk::Vector< float,3 > zero; zero.Fill(0); float eps=1.e-9,mag=0; for (unsigned int jj=0; jj<6; jj++) { float ff=dtv[jj]; mag+=ff*ff; if ( vnl_math_isnan( ff ) || vnl_math_isinf(ff) ) { return zero; } } mag=sqrt(mag); if ( dtv[1]==0 && dtv[2] == 0 && dtv[4]==0) return zero; if (mag < eps) { return zero; } // typedef itk::Vector TensorTypeIn; typedef itk::Vector TensorType; typedef itk::Vector TensorTypeOut; typedef itk::Vector TensorTypeIn; const unsigned int ImageDimension = 3; typedef itk::Image InTensorImageType; typedef itk::Image OutTensorImageType; typedef itk::Image TensorImageType; typedef InTensorImageType::Pointer TensorImagePointer; typedef float PixelType; typedef itk::Vector VectorType1; typedef itk::Image FieldType; typedef itk::Image ImageType; typedef itk::ImageFileReader readertype; typedef itk::ImageFileWriter writertype; typedef ImageType::IndexType IndexType; typedef ImageType::SizeType SizeType; typedef ImageType::SpacingType SpacingType; // typedef itk::AffineTransform AffineTransformType; // typedef itk::LinearInterpolateImageFunction InterpolatorType1; // typedef itk::NearestNeighborInterpolateImageFunction InterpolatorType2; itk::Vector dtv2; typedef vnl_matrix MatrixType; MatrixType DT(3,3); DT.fill(0); DT(0,0)=dtv[0]; DT(1,1)=dtv[3]; DT(2,2)=dtv[5]; DT(1,0)=DT(0,1)=dtv[1]; DT(2,0)=DT(0,2)=dtv[2]; DT(2,1)=DT(1,2)=dtv[4]; // if (takelog ) std::cout << " TAKING LOG " << std::endl; else std::cout << "TAKING EXP " << std::endl; // std::cout << " dtv " << dtv << std::endl; vnl_symmetric_eigensystem< double > eig(DT); itk::Vector< float ,3 > rgb; float xx = dtv[0]; float xy = dtv[1]; float xz = dtv[2]; float yy = dtv[3]; float yz = dtv[4]; float zz = dtv[5]; float isp = ( xx*xx + yy*yy + zz*zz + 2.0*(xy*xy + xz*xz + yz*yz) ); float fa=0.0; if( isp > 0.0 ) { float trace = dtv[0]; trace += dtv[3]; trace += dtv[5]; float anisotropy = 3.0 * isp - trace * trace; fa=( vcl_sqrt(anisotropy / ( 2.0 * isp ) ) ); } rgb[0]=eig.V(0,whichvec);//+eig.V(1,0)*e2; rgb[1]=eig.V(1,whichvec);//+eig.V(1,1)*e2; rgb[2]=eig.V(2,whichvec);//+eig.V(1,2)*e2; return rgb; } template static float GetMetricTensorCost( itk::Vector dpath, TTensorType dtv ) { typedef vnl_matrix MatrixType; MatrixType DT(3,3); DT.fill(0); DT(0,0)=dtv[0]; DT(1,1)=dtv[3]; DT(2,2)=dtv[5]; DT(1,0)=DT(0,1)=dtv[1]; DT(2,0)=DT(0,2)=dtv[2]; DT(2,1)=DT(1,2)=dtv[4]; vnl_symmetric_eigensystem< double > eig(DT); double e1 = (eig.D(0,0)); double e2 = (eig.D(1,1)); double e3 = (eig.D(2,2)); double etot=e1+e2+e3; if (etot==0) etot=1; MatrixType vec(3,1); vec(0,0)=dpath[0]; vec(1,0)=dpath[1]; vec(2,0)=dpath[2]; MatrixType inv = vnl_matrix_inverse(DT); MatrixType sol= vec.transpose()*inv*vec; float cost = sol(0,0)/etot; return cost; } template VersorTensorType VersorTensor( TensorType dtv ) { typedef itk::VariableSizeMatrix EigenMatrixType; typedef itk::Vector VectorType; EigenMatrixType D; EigenMatrixType V; EigenAnalysis(dtv,D,V); VectorType e3; e3[0]=V(0,2); e3[1]=V(1,2); e3[2]=V(2,2); VectorType e2; e2[0]=V(0,1); e2[1]=V(1,1); e2[2]=V(2,1); VectorType xAxis; xAxis[0]=1; xAxis[1]=0; xAxis[2]=0; VectorType yAxis; yAxis[0]=0; yAxis[1]=1; yAxis[2]=0; MatrixType R1 = RotationMatrixFromVectors(e3,xAxis); e2 = R1*e2; MatrixType R2 = RotationMatrixFromVectors(e2,yAxis); MatrixType R = R2*R1; itk::Versor versor; versor.SetMatrix( R ); VersorTensorType dtq; dtq[0] = D(0,0); dtq[1] = D(1,1); dtq[2] = D(2,2); dtq[3] = versor[0]; dtq[4] = versor[1]; dtq[5] = versor[2]; return dtq; } template VersorTensorType VersorTensor( TensorType dtv, MatrixType frame ) { typedef itk::VariableSizeMatrix EigenMatrixType; typedef itk::Vector VectorType; EigenMatrixType D; EigenMatrixType V; EigenAnalysis(dtv,D,V); VectorType e3; e3[0]=V(0,2); e3[1]=V(1,2); e3[2]=V(2,2); VectorType e2; e2[0]=V(0,1); e2[1]=V(1,1); e2[2]=V(2,1); VectorType xAxis; xAxis[0]=frame(0,0); xAxis[1]=frame(1,0); xAxis[2]=frame(2,0); VectorType yAxis; yAxis[0]=frame(0,1); yAxis[1]=frame(1,1); yAxis[2]=frame(2,1); MatrixType R1 = RotationMatrixFromVectors(e3,xAxis); e2 = R1*e2; MatrixType R2 = RotationMatrixFromVectors(e2,yAxis); MatrixType R = R2*R1; itk::Versor versor; versor.SetMatrix( R ); VersorTensorType dtq; dtq[0] = D(0,0); dtq[1] = D(1,1); dtq[2] = D(2,2); dtq[3] = versor[0]; dtq[4] = versor[1]; dtq[5] = versor[2]; return dtq; } #endif ants-1.9.2+svn680.dfsg/Tensor/itkDecomposeTensorFunction2.h000066400000000000000000000056311147325206600235210ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkDecomposeTensorFunction2.h,v $ Language: C++ Date: $Date: 2009/01/29 00:15:47 $ Version: $Revision: 1.2 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkDecomposeTensorFunction2_h #define __itkDecomposeTensorFunction2_h #include "itkVariableSizeMatrix.h" namespace itk { /** \class DecomposeTensorFunction2 * */ template > class DecomposeTensorFunction2 { public: /** Standard class typedefs. */ typedef DecomposeTensorFunction2 Self; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Extract some information from the image types. Dimensionality * of the two images is assumed to be the same. */ typedef TInput InputMatrixType; typedef TOutput OutputMatrixType; /** Define the data type and the vector of data type used in calculations. */ typedef TRealType RealType; // Wrappers for vnl routines void EvaluateEigenDecomposition( InputMatrixType&, OutputMatrixType&, OutputMatrixType& ); void EvaluateSymmetricEigenDecomposition( InputMatrixType&, OutputMatrixType&, OutputMatrixType& ); void EvaluateQRDecomposition( InputMatrixType&, OutputMatrixType&, OutputMatrixType& ); void EvaluateSVDDecomposition( InputMatrixType&, OutputMatrixType&, OutputMatrixType&, OutputMatrixType& ); void EvaluateSVDEconomyDecomposition( InputMatrixType&, OutputMatrixType&, OutputMatrixType& ); void EvaluateLeftPolarDecomposition( InputMatrixType&, OutputMatrixType&, OutputMatrixType& ); void EvaluateRightPolarDecomposition( InputMatrixType&, OutputMatrixType&, OutputMatrixType& ); void EvaluateCholeskyDecomposition( InputMatrixType&, OutputMatrixType& ); RealType EvaluateDeterminant( InputMatrixType& ); DecomposeTensorFunction2(); virtual ~DecomposeTensorFunction2() {} protected: void PrintSelf ( std::ostream& os, Indent indent ) const; private: DecomposeTensorFunction2(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkDecomposeTensorFunction2.txx" #endif #endif ants-1.9.2+svn680.dfsg/Tensor/itkDecomposeTensorFunction2.txx000066400000000000000000000233211147325206600241110ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkDecomposeTensorFunction2.txx,v $ Language: C++ Date: $Date: 2009/01/29 00:15:47 $ Version: $Revision: 1.2 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkDecomposeTensorFunction2_txx #define _itkDecomposeTensorFunction2_txx #include "itkDecomposeTensorFunction2.h" #include "vnl/algo/vnl_cholesky.h" #include "vnl/algo/vnl_qr.h" #include "vnl/algo/vnl_real_eigensystem.h" #include "vnl/algo/vnl_svd.h" #include "vnl/algo/vnl_svd_economy.h" #include "vnl/algo/vnl_symmetric_eigensystem.h" #include "vnl/vnl_matrix.h" #include "vnl/vnl_matrix_fixed.h" #include "vnl/vnl_det.h" #include "vxl/vcl/vcl_complex.h" namespace itk { template DecomposeTensorFunction2 ::DecomposeTensorFunction2() {} template void DecomposeTensorFunction2 ::EvaluateEigenDecomposition( InputMatrixType &M, OutputMatrixType &D, OutputMatrixType &V ) { unsigned int RowDimensions = M.Rows(); unsigned int ColumnDimensions = M.Cols(); D.SetSize( RowDimensions, RowDimensions ); V.SetSize( RowDimensions, RowDimensions ); D.Fill( 0.0 ); vnl_matrix v( RowDimensions, ColumnDimensions ); for ( unsigned int j = 0; j < ColumnDimensions; j++ ) { for ( unsigned int i = 0; i < RowDimensions; i++ ) { v.put( i, j, M[i][j] ); } } vnl_real_eigensystem eig( v ); for ( unsigned int j = 0; j < ColumnDimensions; j++ ) { for ( unsigned int i = 0; i < RowDimensions; i++ ) { V[i][j] = static_cast( (eig.Vreal).get( i, j ) ); if ( i == j ) { D[i][j] = static_cast( vcl_real( eig.D(j) ) ); } } } } template void DecomposeTensorFunction2 ::EvaluateSymmetricEigenDecomposition( InputMatrixType &M, OutputMatrixType &D, OutputMatrixType &V ) { // The resulting eigenvectors and values are sorted in increasing order // so V.column(0) is the eigenvector corresponding to the // smallest eigenvalue. unsigned int RowDimensions = M.Rows(); unsigned int ColumnDimensions = M.Cols(); D.SetSize( RowDimensions, RowDimensions ); V.SetSize( RowDimensions, RowDimensions ); D.Fill( 0.0 ); vnl_symmetric_eigensystem eig( M.GetVnlMatrix() ); for ( unsigned int j = 0; j < ColumnDimensions; j++ ) { for ( unsigned int i = 0; i < RowDimensions; i++ ) { V[i][j] = (eig.V).get( i, j ); if ( i == j ) { D[i][j] = eig.D(j); } } } } template void DecomposeTensorFunction2 ::EvaluateRightPolarDecomposition( InputMatrixType &M, OutputMatrixType &R, OutputMatrixType &S ) { OutputMatrixType U; OutputMatrixType W; OutputMatrixType V; this->EvaluateSVDDecomposition( M, U, W, V ); R = U * V.GetTranspose(); S = V * W * V.GetTranspose(); } template void DecomposeTensorFunction2 ::EvaluateLeftPolarDecomposition( InputMatrixType &M, OutputMatrixType &S, OutputMatrixType &R ) { OutputMatrixType U; OutputMatrixType W; OutputMatrixType V; this->EvaluateSVDDecomposition( M, U, W, V ); R = U * V.GetTranspose(); S = U * W * U.GetTranspose(); } template void DecomposeTensorFunction2 ::EvaluateQRDecomposition( InputMatrixType &M, OutputMatrixType &Q, OutputMatrixType &R ) { unsigned int RowDimensions = M.Rows(); unsigned int ColumnDimensions = M.Cols(); Q.SetSize( RowDimensions, ColumnDimensions ); R.SetSize( ColumnDimensions, ColumnDimensions ); vnl_qr qr( M.GetVnlMatrix() ); for ( unsigned int i = 0; i < RowDimensions; i++ ) { for ( unsigned int j = 0; j < ColumnDimensions; j++ ) { Q[i][j] = qr.Q()(i, j); } } for ( unsigned int i = 0; i < ColumnDimensions; i++ ) { for ( unsigned int j = 0; j < ColumnDimensions; j++ ) { R[i][j] = qr.R()(i, j); } } } template void DecomposeTensorFunction2 ::EvaluateSVDDecomposition( InputMatrixType &M, OutputMatrixType &U, OutputMatrixType &W, OutputMatrixType &V ) { unsigned int RowDimensions = M.Rows(); unsigned int ColumnDimensions = M.Cols(); U.SetSize( RowDimensions, RowDimensions ); V.SetSize( ColumnDimensions, ColumnDimensions ); W.SetSize( RowDimensions, ColumnDimensions ); bool isidentity=true; float tol=1.e-12; for ( unsigned int i = 0; i < RowDimensions; i++ ) { for ( unsigned int j = 0; j < RowDimensions; j++ ) { if (i==j) { if (fabs(M[i][j]-1.0) > tol) isidentity=false; } else { if (fabs(M[i][j]) > tol) isidentity=false; } } } if (isidentity) { for ( unsigned int i = 0; i < RowDimensions; i++ ) { for ( unsigned int j = 0; j < RowDimensions; j++ ) { if (i==j) { U[i][j]=1.0; V[i][j]=1.0; W[i][i]=1; } else { U[i][j]=0.0; V[i][j]=0.0; } } } return; } vnl_svd svd( M.GetVnlMatrix() ); for ( unsigned int i = 0; i < RowDimensions; i++ ) { for ( unsigned int j = 0; j < RowDimensions; j++ ) { U[i][j] = svd.U(i, j); } } for ( unsigned int i = 0; i < ColumnDimensions; i++ ) { for ( unsigned int j = 0; j < ColumnDimensions; j++ ) { V[i][j] = svd.V(i, j); } } W.Fill( 0.0 ); unsigned int minDimensions = ColumnDimensions; if ( static_cast( RowDimensions ) ( ColumnDimensions ) ) { minDimensions = RowDimensions; } for ( unsigned int i = 0; i < minDimensions; i++ ) { W[i][i] = svd.W(i, i); } } template void DecomposeTensorFunction2 ::EvaluateSVDEconomyDecomposition( InputMatrixType &M, OutputMatrixType &W, OutputMatrixType &V ) { /** * Same as SVD except the routine does not return U --- * allows for faster computation. */ unsigned int RowDimensions = M.Rows(); unsigned int ColumnDimensions = M.Cols(); V.SetSize( ColumnDimensions, ColumnDimensions ); W.SetSize( RowDimensions, ColumnDimensions ); vnl_svd_economy svd( M.GetVnlMatrix() ); for ( unsigned int i = 0; i < ColumnDimensions; i++ ) { for ( unsigned int j = 0; j < ColumnDimensions; j++ ) { V[i][j] = svd.V()(i, j); } } W.Fill( 0.0 ); unsigned int minDimensions = ColumnDimensions; if ( static_cast( RowDimensions ) < static_cast( ColumnDimensions ) ) { minDimensions = RowDimensions; } for ( unsigned int i = 0; i < minDimensions; i++ ) { W[i][i] = svd.lambdas()[i]; } } template void DecomposeTensorFunction2 ::EvaluateCholeskyDecomposition( InputMatrixType &M, OutputMatrixType &L ) { // Assumes symmetric tensor of type double vnl_matrix m( M.Rows(), M.Cols() ); for ( unsigned int j = 0; j < M.Cols(); j++ ) { for ( unsigned int i = 0; i < M.Rows(); i++ ) { m.put( i, j, M[i][j] ); } } vnl_cholesky cholesky( m, vnl_cholesky::quiet ); L.SetSize( M.Rows(), M.Cols() ); for ( unsigned int i = 0; i < L.Rows(); i++ ) { for ( unsigned int j = 0; j < L.Cols(); j++ ) { L[i][j] = cholesky.L_badly_named_method().get(i, j); } } } template typename DecomposeTensorFunction2::RealType DecomposeTensorFunction2 ::EvaluateDeterminant( InputMatrixType &M ) { if ( M.Rows() == M.Cols() && M.Rows() >= 2 && M.Rows() <= 4 ) { vnl_matrix_fixed m2; vnl_matrix_fixed m3; vnl_matrix_fixed m4; for ( unsigned int i = 0; i < M.Rows(); i++ ) { for ( unsigned int j = 0; j < M.Cols(); j++ ) { switch( M.Rows() ) { case 2: m2.put( i, j, M[i][j] ); break; case 3: m3.put( i, j, M[i][j] ); break; case 4: m4.put( i, j, M[i][j] ); break; } } } switch( M.Rows() ) { case 2: return static_cast( vnl_det( m2 ) ); break; case 3: return static_cast( vnl_det( m3 ) ); break; case 4: return static_cast( vnl_det( m4 ) ); break; } } else { vnl_qr qr( M.GetVnlMatrix() ); return static_cast( qr.determinant() ); } } template void DecomposeTensorFunction2 ::PrintSelf( std::ostream& os, Indent indent ) const { } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Tensor/itkExpTensorImageFilter.h000066400000000000000000000107401147325206600226550ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkExpTensorImageFilter.h,v $ Language: C++ Date: $Date: 2009/01/27 19:56:28 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkExpTensorImageFilter_h #define __itkExpTensorImageFilter_h #include "itkImageToImageFilter.h" #include "itkImage.h" #include "itkNumericTraits.h" #include "itkDiffusionTensor3D.h" namespace itk { /** \class ExpTensorImageFilter * \brief Applies an averaging filter to an image * * Computes an image where a given pixel is the mean value of the * the pixels in a neighborhood about the corresponding input pixel. * * A mean filter is one of the family of linear filters. * * \sa Image * \sa Neighborhood * \sa NeighborhoodOperator * \sa NeighborhoodIterator * * \ingroup IntensityImageFilters */ template class ITK_EXPORT ExpTensorImageFilter : public ImageToImageFilter< TInputImage, TOutputImage > { public: /** Extract dimension from input and output image. */ itkStaticConstMacro(InputImageDimension, unsigned int, TInputImage::ImageDimension); itkStaticConstMacro(OutputImageDimension, unsigned int, TOutputImage::ImageDimension); /** Convenient typedefs for simplifying declarations. */ typedef TInputImage InputImageType; typedef TOutputImage OutputImageType; /** Standard class typedefs. */ typedef ExpTensorImageFilter Self; typedef ImageToImageFilter< InputImageType, OutputImageType> Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Run-time type information (and related methods). */ itkTypeMacro(ExpTensorImageFilter, ImageToImageFilter); /** Image typedef support. */ typedef typename InputImageType::ConstPointer InputImagePointer; typedef typename OutputImageType::Pointer OutputImagePointer; typedef typename InputImageType::PixelType InputPixelType; typedef typename OutputImageType::PixelType OutputPixelType; typedef typename InputPixelType::ValueType InputRealType; typedef typename OutputPixelType::ValueType OutputRealType; //typedef typename DiffusionTensor3D TensorType; //typedef typename TensorType::EigenValuesArrayType EigenValuesArrayType; //typedef typename TensorType::EigenVectorsMatrixType EigenVectorsMatrixType; typedef typename InputImageType::RegionType InputImageRegionType; typedef typename OutputImageType::RegionType OutputImageRegionType; typedef typename InputImageType::SizeType InputSizeType; typedef typename OutputImageType::SizeType OutputSizeType; typedef typename InputImageType::IndexType InputIndexType; typedef typename OutputImageType::IndexType OutputIndexType; /** Set the radius of the neighborhood used to compute the mean. */ //itkSetMacro(Radius, InputSizeType); /** Get the radius of the neighborhood used to compute the mean */ //itkGetConstReferenceMacro(Radius, InputSizeType); protected: ExpTensorImageFilter(); virtual ~ExpTensorImageFilter() {} void PrintSelf(std::ostream& os, Indent indent) const; /** ExpTensorImageFilter can be implemented as a multithreaded filter. * Therefore, this implementation provides a ThreadedGenerateData() * routine which is called for each processing thread. The output * image data is allocated automatically by the superclass prior to * calling ThreadedGenerateData(). ThreadedGenerateData can only * write to the portion of the output image specified by the * parameter "outputRegionForThread" * * \sa ImageToImageFilter::ThreadedGenerateData(), * ImageToImageFilter::GenerateData() */ void GenerateData( void ); private: ExpTensorImageFilter(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkExpTensorImageFilter.txx" #endif #endif ants-1.9.2+svn680.dfsg/Tensor/itkExpTensorImageFilter.txx000066400000000000000000000045751147325206600232620ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkExpTensorImageFilter.txx,v $ Language: C++ Date: $Date: 2009/01/27 19:56:28 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkExpTensorImageFilter_txx #define _itkExpTensorImageFilter_txx #include "itkConstNeighborhoodIterator.h" #include "itkNeighborhoodInnerProduct.h" #include "itkImageRegionIterator.h" #include "itkImageRegionConstIterator.h" #include "itkOffset.h" #include "itkProgressReporter.h" #include "itkObjectFactory.h" #include "vnl/vnl_matrix.h" #include "vnl/algo/vnl_symmetric_eigensystem.h" #include "itkExpTensorImageFilter.h" #include "TensorFunctions.h" namespace itk { template ExpTensorImageFilter ::ExpTensorImageFilter() { } template< class TInputImage, class TOutputImage> void ExpTensorImageFilter< TInputImage, TOutputImage > ::GenerateData() { InputImagePointer input = this->GetInput(); OutputImagePointer output = this->GetOutput(); ImageRegionConstIterator< InputImageType > inputIt( input, input->GetLargestPossibleRegion() ); InputSizeType inputSize = input->GetLargestPossibleRegion().GetSize(); output->SetRegions( input->GetLargestPossibleRegion() ); output->Allocate(); ImageRegionIterator< OutputImageType > outputIt( output, output->GetLargestPossibleRegion() ); for ( inputIt.GoToBegin(), outputIt.GoToBegin(); !inputIt.IsAtEnd() && !outputIt.IsAtEnd(); ++inputIt, ++outputIt) { InputPixelType result = TensorLogAndExp(inputIt.Value(), false); outputIt.Set( result ); } } /** * Standard "PrintSelf" method */ template void ExpTensorImageFilter ::PrintSelf( std::ostream& os, Indent indent) const { Superclass::PrintSelf( os, indent ); } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Tensor/itkLogTensorImageFilter.h000066400000000000000000000107341147325206600226450ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkLogTensorImageFilter.h,v $ Language: C++ Date: $Date: 2009/01/27 19:56:28 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkLogTensorImageFilter_h #define __itkLogTensorImageFilter_h #include "itkImageToImageFilter.h" #include "itkImage.h" #include "itkNumericTraits.h" #include "TensorFunctions.h" namespace itk { /** \class LogTensorImageFilter * \brief Applies an averaging filter to an image * * Computes an image where a given pixel is the mean value of the * the pixels in a neighborhood about the corresponding input pixel. * * A mean filter is one of the family of linear filters. * * \sa Image * \sa Neighborhood * \sa NeighborhoodOperator * \sa NeighborhoodIterator * * \ingroup IntensityImageFilters */ template class ITK_EXPORT LogTensorImageFilter : public ImageToImageFilter< TInputImage, TOutputImage > { public: /** Extract dimension from input and output image. */ itkStaticConstMacro(InputImageDimension, unsigned int, TInputImage::ImageDimension); itkStaticConstMacro(OutputImageDimension, unsigned int, TOutputImage::ImageDimension); /** Convenient typedefs for simplifying declarations. */ typedef TInputImage InputImageType; typedef TOutputImage OutputImageType; /** Standard class typedefs. */ typedef LogTensorImageFilter Self; typedef ImageToImageFilter< InputImageType, OutputImageType> Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Run-time type information (and related methods). */ itkTypeMacro(LogTensorImageFilter, ImageToImageFilter); /** Image typedef support. */ typedef typename InputImageType::ConstPointer InputImagePointer; typedef typename OutputImageType::Pointer OutputImagePointer; typedef typename InputImageType::PixelType InputPixelType; typedef typename OutputImageType::PixelType OutputPixelType; typedef typename InputPixelType::ValueType InputRealType; typedef typename OutputPixelType::ValueType OutputRealType; //typedef typename DiffusionTensor3D TensorType; //typedef typename TensorType::EigenValuesArrayType EigenValuesArrayType; //typedef typename TensorType::EigenVectorsMatrixType EigenVectorsMatrixType; typedef typename InputImageType::RegionType InputImageRegionType; typedef typename OutputImageType::RegionType OutputImageRegionType; typedef typename InputImageType::SizeType InputSizeType; typedef typename OutputImageType::SizeType OutputSizeType; typedef typename InputImageType::IndexType InputIndexType; typedef typename OutputImageType::IndexType OutputIndexType; /** Set the radius of the neighborhood used to compute the mean. */ //itkSetMacro(Radius, InputSizeType); /** Get the radius of the neighborhood used to compute the mean */ //itkGetConstReferenceMacro(Radius, InputSizeType); protected: LogTensorImageFilter(); virtual ~LogTensorImageFilter() {} void PrintSelf(std::ostream& os, Indent indent) const; /** LogTensorImageFilter can be implemented as a multithreaded filter. * Therefore, this implementation provides a ThreadedGenerateData() * routine which is called for each processing thread. The output * image data is allocated automatically by the superclass prior to * calling ThreadedGenerateData(). ThreadedGenerateData can only * write to the portion of the output image specified by the * parameter "outputRegionForThread" * * \sa ImageToImageFilter::ThreadedGenerateData(), * ImageToImageFilter::GenerateData() */ void GenerateData( void ); private: LogTensorImageFilter(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkLogTensorImageFilter.txx" #endif #endif ants-1.9.2+svn680.dfsg/Tensor/itkLogTensorImageFilter.txx000066400000000000000000000045741147325206600232460ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkLogTensorImageFilter.txx,v $ Language: C++ Date: $Date: 2009/03/17 18:59:48 $ Version: $Revision: 1.3 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkLogTensorImageFilter_txx #define _itkLogTensorImageFilter_txx #include "itkConstNeighborhoodIterator.h" #include "itkNeighborhoodInnerProduct.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkImageRegionConstIterator.h" #include "itkOffset.h" #include "itkProgressReporter.h" #include "itkObjectFactory.h" #include "vnl/vnl_matrix.h" #include "vnl/algo/vnl_symmetric_eigensystem.h" #include "itkLogTensorImageFilter.h" #include "TensorFunctions.h" namespace itk { template LogTensorImageFilter ::LogTensorImageFilter() { } template< class TInputImage, class TOutputImage> void LogTensorImageFilter< TInputImage, TOutputImage > ::GenerateData() { InputImagePointer input = this->GetInput(); OutputImagePointer output = this->GetOutput(); ImageRegionConstIterator< InputImageType > inputIt( input, input->GetLargestPossibleRegion() ); InputSizeType inputSize = input->GetLargestPossibleRegion().GetSize(); output->SetRegions( input->GetLargestPossibleRegion() ); output->Allocate(); ImageRegionIteratorWithIndex< OutputImageType > outputIt( output, output->GetLargestPossibleRegion() ); for ( inputIt.GoToBegin(), outputIt.GoToBegin(); !inputIt.IsAtEnd() && !outputIt.IsAtEnd(); ++inputIt, ++outputIt) { InputPixelType result = TensorLog(inputIt.Value()); outputIt.Set( result ); } } /** * Standard "PrintSelf" method */ template void LogTensorImageFilter ::PrintSelf( std::ostream& os, Indent indent) const { Superclass::PrintSelf( os, indent ); } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Tensor/itkPreservationOfPrincipalDirectionTensorReorientationImageFilter.cxx000066400000000000000000000465711147325206600337230ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkPreservationOfPrincipalDirectionTensorReorientationImageFilter.cxx,v $ Language: C++ Date: $Date: 2009/03/17 19:01:36 $ Version: $Revision: 1.2 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkPreservationOfPrincipalDirectionTensorReorientationImageFilter_txx #define _itkPreservationOfPrincipalDirectionTensorReorientationImageFilter_txx #include "itkConstNeighborhoodIterator.h" #include "itkNeighborhoodInnerProduct.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkImageRegionConstIterator.h" #include "itkNeighborhoodAlgorithm.h" #include "itkOffset.h" #include "itkProgressReporter.h" #include "itkObjectFactory.h" #include "itkVector.h" #include "itkPreservationOfPrincipalDirectionTensorReorientationImageFilter.h" #include "itkVectorLinearInterpolateImageFunction.h" #include "itkNumericTraitsFixedArrayPixel.h" #include "itkVariableSizeMatrix.h" #include "itkDecomposeTensorFunction.h" #include "itkSymmetricSecondRankTensor.h" #include #include #include "vnl/algo/vnl_qr.h" #include "vnl/algo/vnl_svd.h" //#include namespace itk { template PreservationOfPrincipalDirectionTensorReorientationImageFilter ::PreservationOfPrincipalDirectionTensorReorientationImageFilter() { m_DeformationField = NULL; m_ReferenceImage = NULL; } template vnl_matrix PreservationOfPrincipalDirectionTensorReorientationImageFilter ::FindRotation( vnl_vector from , vnl_vector to) { #define CROSS(dest,v1,v2){ \ dest[0]=v1[1]*v2[2]-v1[2]*v2[1]; \ dest[1]=v1[2]*v2[0]-v1[0]*v2[2]; \ dest[2]=v1[0]*v2[1]-v1[1]*v2[0];} #define DOT(v1,v2) (v1[0]*v2[0]+v1[1]*v2[1]+v1[2]*v2[2]) #define SUB(dest,v1,v2){ \ dest[0]=v1[0]-v2[0]; \ dest[1]=v1[1]-v2[1]; \ dest[2]=v1[2]-v2[2];} /* * A function for creating a rotation matrix that rotates a vector called * "f rom" into another vector called "to". * Input : from[3], to[3] which both must be *normalized* non-zero vectors * Output: mtx[3][3] -- a 3x3 matrix in colum-major form * Author: Tomas Moller, 1999 */ VnlMatrixType M(3,3); M.fill(0); float v[3]; float e,h; CROSS(v,from,to); e=DOT(from,to); float EPSILON=1.e-5; if(e>1.0-EPSILON) /* "from" almost or equal to "to"-vector? */ { /* return identity */ M(0, 0)=1.0; M(0, 1)=0.0; M(0, 2)=0.0; M(1, 0)=0.0; M(1, 1)=1.0; M(1, 2)=0.0; M(2, 0)=0.0; M(2, 1)=0.0; M(2, 2)=1.0; } else if(e<-1.0+EPSILON) /* "from" almost or equal to negated "to"? */ { float up[3],left[3]; float invlen; float fxx,fyy,fzz,fxy,fxz,fyz; float uxx,uyy,uzz,uxy,uxz,uyz; float lxx,lyy,lzz,lxy,lxz,lyz; /* left=CROSS(from, (1,0,0)) */ left[0]=0.0; left[1]=from[2]; left[2]=-from[1]; if(DOT(left,left) itk::SymmetricSecondRankTensor< float, 3 > PreservationOfPrincipalDirectionTensorReorientationImageFilter ::ReorientTensors( itk::SymmetricSecondRankTensor< float, 3 > fixedTens, itk::SymmetricSecondRankTensor< float, 3 > movingTens, typename TTensorImage::IndexType index) { // get from and to vectors VnlMatrixType fDT(3,3); fDT.fill(0); fDT(0,0)=fixedTens[0]; fDT(1,1)=fixedTens[3]; fDT(2,2)=fixedTens[5]; fDT(1,0)=fDT(0,1)=fixedTens[1]; fDT(2,0)=fDT(0,2)=fixedTens[2]; fDT(2,1)=fDT(1,2)=fixedTens[4]; VnlMatrixType mDT(3,3); mDT.fill(0); mDT(0,0)=movingTens[0]; mDT(1,1)=movingTens[3]; mDT(2,2)=movingTens[5]; mDT(1,0)=mDT(0,1)=movingTens[1]; mDT(2,0)=mDT(0,2)=movingTens[2]; mDT(2,1)=mDT(1,2)=movingTens[4]; // find rotation that maps 1st eigvecs - apply vnl_symmetric_eigensystem< float > eig(fDT); vnl_symmetric_eigensystem< float > meig(mDT); // get angle between vecs vvec from = meig.get_eigenvector(2); vvec to = eig.get_eigenvector(2) ; float dp = dot_product(from,to); float angletheta=0; if (dp >= 0.9999) angletheta = 0; else angletheta = acos(dp ) *180/3.1614 ; if ( angletheta > 90 ) { from = from * (-1.0); angletheta = 180 - 90; } // this->m_ThetaE+=theta; // if ( theta >= 45 ) { from = from * (90-theta)/90 + to * theta/90; from = from / sqrt(dot_product(from,from)); } //from = from * 0.5 + to * 0.5; from = from / sqrt(dot_product(from,from)); VnlMatrixType M = this->FindRotation(from, to ); // VnlMatrixType rDT = M*mDT*M.transpose(); vvec rvec = M*meig.get_eigenvector(1); // find rotation that maps 2nd eigvecs - apply from = rvec; to = eig.get_eigenvector(1) ; dp = dot_product(from,to); float anglepsi=0; if (dp >= 0.9999) anglepsi = 0; else anglepsi = acos(dp ) *180/3.1614 ; if ( anglepsi > 90 ) { from = from * (-1.0); anglepsi = 180 - 90; } if(this->m_AngleEnergyImage) { float mag=angletheta;//+anglepsi*anglepsi); this->m_AngleEnergyImage->SetPixel(index,mag); } VnlMatrixType M2 = this->FindRotation(from, to ); M2= M2*M; VnlMatrixType rDT = M2*mDT*M2.transpose(); TensorType rmovingTens; rmovingTens[0] = rDT[0][0]; rmovingTens[1] = rDT[1][0]; rmovingTens[2] = rDT[2][0]; rmovingTens[3] = rDT[1][1]; rmovingTens[4] = rDT[1][2]; rmovingTens[5] = rDT[2][2]; return rmovingTens; } template typename TTensorImage::PixelType PreservationOfPrincipalDirectionTensorReorientationImageFilter ::PPD(typename TTensorImage::IndexType index, vnl_matrix jMatrix ) { // typedef vnl_matrix VMatrixType; InputImagePointer input = this->GetInput(); InputPixelType dtv = input->GetPixel(index); itk::Vector dtv2; VnlMatrixType DT(3,3); DT.fill(0); DT(0,0)=dtv[0]; DT(1,1)=dtv[3]; DT(2,2)=dtv[5]; DT(1,0)=DT(0,1)=dtv[1]; DT(2,0)=DT(0,2)=dtv[2]; DT(2,1)=DT(1,2)=dtv[4]; vnl_symmetric_eigensystem< float > eig(DT); typedef vnl_vector vvec; vvec e1 = eig.get_eigenvector(2); vvec e1p = jMatrix*e1; e1p = e1p / sqrt(dot_product(e1p,e1p)); vvec e2 = jMatrix*eig.get_eigenvector(1); vvec e2p = e2 - dot_product( e2, e1p)*e1p; e2p = e2p / sqrt(dot_product(e2p,e2p)); vvec e3p = vnl_cross_3d(e1p,e2p); e3p = e3p / sqrt(dot_product(e3p,e3p)); VnlMatrixType DTrec = eig.get_eigenvalue(2) * outer_product(e1p,e1p) + eig.get_eigenvalue(1) * outer_product(e2p,e2p) + eig.get_eigenvalue(0) * outer_product(e3p,e3p); // if ( (eig.get_eigenvalue(2) - eig.get_eigenvalue(1)) > 0.5 ) { // std::cout << " DT IN " << DT << std::endl; // std::cout << " DT OU " << DTrec << std::endl; // std::cout << " eig1 " << eig.get_eigenvalue(0) << " 2 " << eig.get_eigenvalue(1) << std::endl; } dtv2[0]=DTrec(0,0); dtv2[3]=DTrec(1,1); dtv2[5]=DTrec(2,2); dtv2[1]=DTrec(0,1); dtv2[2]=DTrec(0,2); dtv2[4]=DTrec(1,2); return dtv2; } template void PreservationOfPrincipalDirectionTensorReorientationImageFilter ::GenerateData() { typedef VariableSizeMatrix MatrixType2; // get input and output images InputImagePointer input = this->GetInput(); OutputImagePointer output = this->GetOutput(); InputSizeType inputSize = input->GetLargestPossibleRegion().GetSize(); output->SetRegions( m_DeformationField->GetLargestPossibleRegion() ); output->SetSpacing( m_DeformationField->GetSpacing() ); output->SetOrigin( m_DeformationField->GetOrigin() ); output->SetDirection( m_DeformationField->GetDirection() ); output->Allocate(); this->m_AngleEnergyImage=FloatImageType::New(); this->m_AngleEnergyImage->SetRegions( m_DeformationField->GetLargestPossibleRegion() ); this->m_AngleEnergyImage->SetSpacing(m_DeformationField->GetSpacing()); this->m_AngleEnergyImage->SetOrigin(m_DeformationField->GetOrigin()); this->m_AngleEnergyImage->Allocate(); this->m_AngleEnergyImage->FillBuffer(0); ImageRegionConstIterator< InputImageType > inputIt( input, input->GetLargestPossibleRegion() ); ImageRegionIteratorWithIndex< OutputImageType > outputIt( output, output->GetLargestPossibleRegion() ); typedef itk::VectorLinearInterpolateImageFunction< InputImageType > VectorInterpType; typename VectorInterpType::Pointer inputInterp = VectorInterpType::New(); inputInterp->SetInputImage( input ); //DeformationFieldPointer tempField=DeformationFieldType::New(); // copy field to TempField //tempField->SetSpacing(m_DeformationField->GetSpacing() ); //tempField->SetOrigin(m_DeformationField->GetOrigin() ); //tempField->SetLargestPossibleRegion( // m_DeformationField->GetLargestPossibleRegion() ); //tempField->SetRequestedRegion( // m_DeformationField->GetRequestedRegion() ); //tempField->SetBufferedRegion(m_DeformationField->GetBufferedRegion() ); //tempField->Allocate(); // tempField->FillBuffer( ); //for ( inputIt.GoToBegin(); !inputIt.IsAtEnd(); ++inputIt ) //{ // tempField->SetPixel( inputIt.GetIndex(), m_DeformationField->GetPixel( inputIt.GetIndex())); //} ImageRegionIteratorWithIndex< DeformationFieldType > dispIt( m_DeformationField, m_DeformationField->GetLargestPossibleRegion() ); //double det=0.0; unsigned int posoff=1; //float difspace=1.0; float space=1.0; unsigned long ct = 0; // for all voxels for ( outputIt.GoToBegin(), dispIt.GoToBegin(); !outputIt.IsAtEnd(), !dispIt.IsAtEnd(); ++outputIt, ++dispIt ) { MatrixType2 jMatrix; jMatrix.SetSize(ImageDimension,ImageDimension); jMatrix.Fill(0.0); // Get Jacobian of displacement field typename DeformationFieldType::IndexType rindex=dispIt.GetIndex(); float mindist=1.0; bool oktosample=true; float dist=100.0; // ignore outer edges of warpfield for (unsigned int row=0; rowGetLargestPossibleRegion().GetSize()[row]-(float)rindex[row] ); if (dist < mindist) oktosample=false; } typename OutputImageType::PointType outputPt; output->TransformIndexToPhysicalPoint(outputIt.GetIndex(), outputPt); if (!inputInterp->IsInsideBuffer(outputPt)) oktosample=false; if (oktosample) { typename DeformationFieldType::IndexType temp = rindex; typename DeformationFieldType::PixelType cpix =this->TransformVector( m_DeformationField, rindex); typename DeformationFieldType::IndexType difIndex[ImageDimension][2]; for(unsigned int row=0; row< ImageDimension; row++) { difIndex[row][0]=rindex; difIndex[row][1]=rindex; typename DeformationFieldType::IndexType ddrindex=rindex; typename DeformationFieldType::IndexType ddlindex=rindex; if (rindex[row] < (int)m_DeformationField->GetLargestPossibleRegion().GetSize()[row]-2) { difIndex[row][0][row] = rindex[row]+posoff; ddrindex[row] = rindex[row]+posoff*2; } if (rindex[row] > 1 ) { difIndex[row][1][row] = rindex[row]-1; ddlindex[row] = rindex[row]-2; } float h=0.25; space=1.0; // should use image spacing here? typename DeformationFieldType::PixelType rpix = this->TransformVector( m_DeformationField,difIndex[row][1]); typename DeformationFieldType::PixelType lpix = this->TransformVector( m_DeformationField,difIndex[row][0]); typename DeformationFieldType::PixelType rrpix = this->TransformVector( m_DeformationField,ddrindex ); typename DeformationFieldType::PixelType llpix = this->TransformVector( m_DeformationField,ddlindex); rpix = rpix*h+cpix*(1.-h); lpix = lpix*h+cpix*(1.-h); rrpix = rrpix*h+rpix*(1.-h); llpix = llpix*h+lpix*(1.-h); //4th order centered difference typename DeformationFieldType::PixelType dPix; dPix= ( lpix*8.0 - rpix*8.0 + rrpix - llpix )*space/(12.0); //4th order centered differenceX for(unsigned int col=0; col< ImageDimension;col++) { float val; if (row == col) val=dPix[col]/m_DeformationField->GetSpacing()[col]+1.0; else val = dPix[col]/m_DeformationField->GetSpacing()[col]; jMatrix(col,row) = val; } } bool finitestrain = false; //finitestrain=true; bool ppd=true; if (finitestrain) ppd=false; InputPixelType outTensor; if (finitestrain) { // std::cout <<" use finite strain " << std::endl; // Copy tensor to matrix form //InputPixelType inTensor = inputIt.Value(); InputPixelType inTensor = input->GetPixel(rindex); //inputInterp->Evaluate(outputPt); MatrixType2 tensor; tensor.SetSize(3,3); tensor[0][0] = inTensor[0]; tensor[0][1] = tensor[1][0] = inTensor[1]; tensor[0][2] = tensor[2][0] = inTensor[2]; tensor[1][1] = inTensor[3]; tensor[1][2] = tensor[2][1] = inTensor[4]; tensor[2][2] = inTensor[5]; // replace with Polar Decomposition MatrixType2 rotationMatrix; MatrixType2 rotationMatrixT; VnlMatrixType M = jMatrix.GetVnlMatrix(); VnlMatrixType PQ = M; VnlMatrixType NQ = M; VnlMatrixType PQNQDiff; const unsigned int maximumIterations = 100; for(unsigned int ni = 0; ni < maximumIterations; ni++ ) { // Average current Qi with its inverse transpose NQ = ( PQ + vnl_inverse_transpose( PQ ) ) / 2.0; PQNQDiff = NQ - PQ; if( PQNQDiff.frobenius_norm() < 1e-4 ) { //std::cout << "Polar decomposition used " << ni << " iterations " << std::endl; break; } else { PQ = NQ; } } rotationMatrix = NQ; rotationMatrixT=rotationMatrix.GetTranspose(); // Rotate tensor MatrixType2 rTensor = rotationMatrixT * tensor * rotationMatrix; // Copy tensor back to vector form and update output image outTensor[0] = rTensor[0][0]; outTensor[1] = rTensor[1][0]; outTensor[2] = rTensor[2][0]; outTensor[3] = rTensor[1][1]; outTensor[4] = rTensor[1][2]; outTensor[5] = rTensor[2][2]; } else if (ppd) { // std::cout <<" use ppd reorientation " << std::endl; //InputPixelType inTensor = inputIt.Value(); InputPixelType inTensor = input->GetPixel(rindex); //inputInterp->Evaluate(outputPt); InputPixelType dtv = inTensor; // valid values? bool docompute=true; for (unsigned int jj=0; jj<6; jj++) { float ff=dtv[jj]; if ( vnl_math_isnan(ff)) { docompute=false; } } if (docompute) { TensorType dtv2; bool verbose=false; VnlMatrixType DT(3,3); DT.fill(0); DT(0,0)=dtv[0]; DT(1,1)=dtv[3]; DT(2,2)=dtv[5]; DT(1,0)=DT(0,1)=dtv[1]; DT(2,0)=DT(0,2)=dtv[2]; DT(2,1)=DT(1,2)=dtv[4]; if (verbose) std::cout << " DT " << DT << std::endl; if (verbose) std::cout << " jM " << jMatrix << std::endl; vnl_symmetric_eigensystem< float > eig(DT); typedef vnl_vector vvec; vvec e1 = eig.get_eigenvector(2); vvec e1p = jMatrix.GetTranspose() * e1; e1p = e1p / sqrt(dot_product(e1p,e1p)); vvec e2 = jMatrix.GetTranspose()* eig.get_eigenvector(1); vvec e2p = e2 - dot_product( e2, e1p)*e1p; e2p = e2p / sqrt(dot_product(e2p,e2p)); vvec e3p = vnl_cross_3d(e1p,e2p); e3p = e3p / sqrt(dot_product(e3p,e3p)); VnlMatrixType DTrec = eig.get_eigenvalue(2) * outer_product(e1p,e1p) + eig.get_eigenvalue(1) * outer_product(e2p,e2p) + eig.get_eigenvalue(0) * outer_product(e3p,e3p); if (verbose) std::cout << " DTrec " << DTrec << " ind " << rindex << std::endl; dtv2[0]=DTrec(0,0); dtv2[3]=DTrec(1,1); dtv2[5]=DTrec(2,2); dtv2[1]=DTrec(0,1); dtv2[2]=DTrec(0,2); dtv2[4]=DTrec(1,2); outTensor = dtv2; } else outTensor=inTensor; } if (this->m_ReferenceImage) { typename InputImageType::PixelType refTensor=this->m_ReferenceImage->GetPixel(outputIt.GetIndex()); outTensor=this->ReorientTensors(refTensor, outTensor,outputIt.GetIndex()); } outputIt.Set( outTensor ); //std::cout << inputIt.Value() << " - " << outputIt.Value() << " "; }//oktosample if else outputIt.Set( inputIt.Value() ); ct++; } //pixelIterations } /** * Standard "PrintSelf" method */ template void PreservationOfPrincipalDirectionTensorReorientationImageFilter ::PrintSelf( std::ostream& os, Indent indent) const { Superclass::PrintSelf( os, indent ); } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Tensor/itkPreservationOfPrincipalDirectionTensorReorientationImageFilter.h000066400000000000000000000142711147325206600333400ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkPreservationOfPrincipalDirectionTensorReorientationImageFilter.h,v $ Language: C++ Date: $Date: 2009/01/27 19:56:28 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkPreservationOfPrincipalDirectionTensorReorientationImageFilter_h #define __itkPreservationOfPrincipalDirectionTensorReorientationImageFilter_h #include "itkImageToImageFilter.h" #include "itkVectorInterpolateImageFunction.h" #include "itkImage.h" #include "itkMatrix.h" #include "itkNumericTraits.h" #include "itkVector.h" #include "itkSymmetricSecondRankTensor.h" namespace itk { /** \class PreservationOfPrincipalDirectionImageFilter * \brief Applies an averaging filter to an image * * Computes an image where a given pixel is the mean value of the * the pixels in a neighborhood about the corresponding input pixel. * * A mean filter is one of the family of linear filters. * * \sa Image * \sa Neighborhood * \sa NeighborhoodOperator * \sa NeighborhoodIterator * * \ingroup IntensityImageFilters */ template class ITK_EXPORT PreservationOfPrincipalDirectionTensorReorientationImageFilter : public ImageToImageFilter< TTensorImage, TTensorImage > { public: typedef TTensorImage InputImageType; typedef TTensorImage OutputImageType; typedef TVectorImage DeformationFieldType; typedef typename DeformationFieldType::Pointer DeformationFieldPointer; typedef Matrix MatrixType; typedef Vector VectorType; typedef Vector FakeVectorType; itkStaticConstMacro(ImageDimension, unsigned int, TTensorImage::ImageDimension); typedef itk::Image FloatImageType; // typedef Vector TensorType; typedef itk::SymmetricSecondRankTensor< float, 3 > TensorType; typedef Image TensorImageType; typedef typename TensorImageType::Pointer TensorImagePointer; typedef vnl_matrix VnlMatrixType; typedef vnl_vector vvec; /** Standard class typedefs. */ typedef PreservationOfPrincipalDirectionTensorReorientationImageFilter Self; typedef ImageToImageFilter< InputImageType, OutputImageType> Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); itkSetMacro(DeformationField, DeformationFieldPointer); itkGetMacro(DeformationField, DeformationFieldPointer); /** Run-time type information (and related methods). */ itkTypeMacro(PreservationOfPrincipalDirectionTensorReorientationImageFilter, ImageToImageFilter); /** Image typedef support. */ typedef typename InputImageType::ConstPointer InputImagePointer; typedef typename OutputImageType::Pointer OutputImagePointer; typedef typename InputImageType::PixelType InputPixelType; typedef typename OutputImageType::PixelType OutputPixelType; typedef typename InputPixelType::ValueType InputRealType; typedef typename InputImageType::RegionType InputImageRegionType; typedef typename OutputImageType::RegionType OutputImageRegionType; typedef typename InputImageType::SizeType InputSizeType; typedef typename OutputImageType::SizeType OutputSizeType; typedef typename InputImageType::IndexType InputIndexType; typedef typename OutputImageType::IndexType OutputIndexType; InputPixelType PPD(typename InputImageType::IndexType ind , VnlMatrixType jac); vnl_matrix FindRotation( vnl_vector , vnl_vector); TensorType ReorientTensors( TensorType fixedTens, TensorType movingTens, typename TTensorImage::IndexType index); // ::ReorientTensors( Vector fixedTens, Vector movingTens, typename TTensorImage::IndexType index) TensorImagePointer m_ReferenceImage; typename FloatImageType::Pointer m_ThetaImage; typename FloatImageType::Pointer m_PsiImage; typename FloatImageType::Pointer m_AngleEnergyImage; protected: PreservationOfPrincipalDirectionTensorReorientationImageFilter(); virtual ~PreservationOfPrincipalDirectionTensorReorientationImageFilter() {} void PrintSelf(std::ostream& os, Indent indent) const; /** PreservationOfPrincipalDirectionTensorReorientationImageFilter can be implemented as a multithreaded filter. * Therefore, this implementation provides a ThreadedGenerateData() * routine which is called for each processing thread. The output * image data is allocated automatically by the superclass prior to * calling ThreadedGenerateData(). ThreadedGenerateData can only * write to the portion of the output image specified by the * parameter "outputRegionForThread" * * \sa ImageToImageFilter::ThreadedGenerateData(), * ImageToImageFilter::GenerateData() */ void GenerateData( void ); typename DeformationFieldType::PixelType TransformVector(DeformationFieldType* field, typename FloatImageType::IndexType index ) { typename DeformationFieldType::PixelType vec=field->GetPixel(index); typename DeformationFieldType::PixelType newvec; newvec.Fill(0); for (unsigned int row=0; rowGetDirection()[row][col]; return newvec; } private: PreservationOfPrincipalDirectionTensorReorientationImageFilter(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented DeformationFieldPointer m_DeformationField; }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkPreservationOfPrincipalDirectionTensorReorientationImageFilter.cxx" #endif #endif ants-1.9.2+svn680.dfsg/Tensor/itkRotationMatrixFromVectors.h000066400000000000000000000133661147325206600240020ustar00rootroot00000000000000/* from * @article{MollerHughes99, author = "Tomas Mller and John F. Hughes", title = "Efficiently Building a Matrix to Rotate One Vector to Another", journal = "journal of graphics tools", volume = "4", number = "4", pages = "1-4", year = "1999", } http://jgt.akpeters.com/papers/MollerHughes99/code.html */ #include #include #include template MatrixType RotationMatrixFromVectors(VectorType from, VectorType to) { double EPSILON = 0.000001; typename VectorType::ValueType e,h,f; MatrixType mtx; VectorType v = CrossProduct(from, to); e = from*to; f = (e < 0) ? -e: e; if (f > 1.0 - EPSILON) /* "from" and "to"-vector almost parallel */ { VectorType u, v; /* temporary storage vectors */ VectorType x; /* vector most nearly orthogonal to "from" */ float c1, c2, c3; /* coefficients for later use */ int i, j; x[0] = (from[0] > 0.0)? from[0] : -from[0]; x[1] = (from[1] > 0.0)? from[1] : -from[1]; x[2] = (from[2] > 0.0)? from[2] : -from[2]; if (x[0] < x[1]) { if (x[0] < x[2]) { x[0] = 1.0; x[1] = x[2] = 0.0; } else { x[2] = 1.0; x[0] = x[1] = 0.0; } } else { if (x[1] < x[2]) { x[1] = 1.0; x[0] = x[2] = 0.0; } else { x[2] = 1.0; x[0] = x[1] = 0.0; } } u[0] = x[0] - from[0]; u[1] = x[1] - from[1]; u[2] = x[2] - from[2]; v[0] = x[0] - to[0]; v[1] = x[1] - to[1]; v[2] = x[2] - to[2]; c1 = 2.0 / (u*u); c2 = 2.0 / (v*v); c3 = c1 * c2 * (u*v); for (i = 0; i < 3; i++) { for (j = 0; j < 3; j++) { mtx[i][j] = - c1 * u[i] * u[j] - c2 * v[i] * v[j] + c3 * v[i] * u[j]; } mtx[i][i] += 1.0; } } else /* the most common case, unless "from"="to", or "from"=-"to" */ { /* ...otherwise use this hand optimized version (9 mults less) */ float hvx, hvz, hvxy, hvxz, hvyz; /* h = (1.0 - e)/DOT(v, v); old code */ h = 1.0/(1.0 + e); /* optimization by Gottfried Chen */ hvx = h * v[0]; hvz = h * v[2]; hvxy = hvx * v[1]; hvxz = hvx * v[2]; hvyz = hvz * v[1]; mtx[0][0] = e + hvx * v[0]; mtx[0][1] = hvxy - v[2]; mtx[0][2] = hvxz + v[1]; mtx[1][0] = hvxy + v[2]; mtx[1][1] = e + h * v[1] * v[1]; mtx[1][2] = hvyz - v[0]; mtx[2][0] = hvxz - v[1]; mtx[2][1] = hvyz + v[0]; mtx[2][2] = e + hvz * v[2]; } return mtx; } // /* // * A function for creating a rotation matrix that rotates a vector called // * "from" into another vector called "to". // * Input : from[3], to[3] which both must be *normalized* non-zero vectors // * Output: mtx[3][3] -- a 3x3 matrix in colum-major form // * Authors: Tomas Mller, John Hughes // * "Efficiently Building a Matrix to Rotate One Vector to Another" // * Journal of Graphics Tools, 4(4):1-4, 1999 // */ // void fromToRotation(float from[3], float to[3], float mtx[3][3]) { // float v[3]; // float e, h, f; // CROSS(v, from, to); // e = DOT(from, to); // f = (e < 0)? -e:e; // if (f > 1.0 - EPSILON) /* "from" and "to"-vector almost parallel */ // { // float u[3], v[3]; /* temporary storage vectors */ // float x[3]; /* vector most nearly orthogonal to "from" */ // float c1, c2, c3; /* coefficients for later use */ // int i, j; // x[0] = (from[0] > 0.0)? from[0] : -from[0]; // x[1] = (from[1] > 0.0)? from[1] : -from[1]; // x[2] = (from[2] > 0.0)? from[2] : -from[2]; // if (x[0] < x[1]) // { // if (x[0] < x[2]) // { // x[0] = 1.0; x[1] = x[2] = 0.0; // } // else // { // x[2] = 1.0; x[0] = x[1] = 0.0; // } // } // else // { // if (x[1] < x[2]) // { // x[1] = 1.0; x[0] = x[2] = 0.0; // } // else // { // x[2] = 1.0; x[0] = x[1] = 0.0; // } // } // u[0] = x[0] - from[0]; u[1] = x[1] - from[1]; u[2] = x[2] - from[2]; // v[0] = x[0] - to[0]; v[1] = x[1] - to[1]; v[2] = x[2] - to[2]; // c1 = 2.0 / DOT(u, u); // c2 = 2.0 / DOT(v, v); // c3 = c1 * c2 * DOT(u, v); // for (i = 0; i < 3; i++) { // for (j = 0; j < 3; j++) { // mtx[i][j] = - c1 * u[i] * u[j] // - c2 * v[i] * v[j] // + c3 * v[i] * u[j]; // } // mtx[i][i] += 1.0; // } // } // else /* the most common case, unless "from"="to", or "from"=-"to" */ // { // #if 0 // /* unoptimized version - a good compiler will optimize this. */ // /* h = (1.0 - e)/DOT(v, v); old code */ // h = 1.0/(1.0 + e); /* optimization by Gottfried Chen */ // mtx[0][0] = e + h * v[0] * v[0]; // mtx[0][1] = h * v[0] * v[1] - v[2]; // mtx[0][2] = h * v[0] * v[2] + v[1]; // mtx[1][0] = h * v[0] * v[1] + v[2]; // mtx[1][1] = e + h * v[1] * v[1]; // mtx[1][2] = h * v[1] * v[2] - v[0]; // mtx[2][0] = h * v[0] * v[2] - v[1]; // mtx[2][1] = h * v[1] * v[2] + v[0]; // mtx[2][2] = e + h * v[2] * v[2]; // #else // /* ...otherwise use this hand optimized version (9 mults less) */ // float hvx, hvz, hvxy, hvxz, hvyz; // /* h = (1.0 - e)/DOT(v, v); old code */ // h = 1.0/(1.0 + e); /* optimization by Gottfried Chen */ // hvx = h * v[0]; // hvz = h * v[2]; // hvxy = hvx * v[1]; // hvxz = hvx * v[2]; // hvyz = hvz * v[1]; // mtx[0][0] = e + hvx * v[0]; // mtx[0][1] = hvxy - v[2]; // mtx[0][2] = hvxz + v[1]; // mtx[1][0] = hvxy + v[2]; // mtx[1][1] = e + h * v[1] * v[1]; // mtx[1][2] = hvyz - v[0]; // mtx[2][0] = hvxz - v[1]; // mtx[2][1] = hvyz + v[0]; // mtx[2][2] = e + hvz * v[2]; // #endif // } // } ants-1.9.2+svn680.dfsg/Tensor/itkWarpTensorImageMultiTransformFilter.h000066400000000000000000000303651147325206600257460ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkWarpTensorImageMultiTransformFilter.h,v $ Language: C++ Date: $Date: 2009/01/27 19:56:28 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef ITKWarpTensorImageMultiTRANSFORMFILTER_H_ #define ITKWarpTensorImageMultiTRANSFORMFILTER_H_ #include "itkImageToImageFilter.h" #include "itkVectorInterpolateImageFunction.h" #include "itkLinearInterpolateImageFunction.h" #include "itkVectorLinearInterpolateImageFunction.h" #include "itkPoint.h" #include "itkFixedArray.h" #include "itkRecursiveGaussianImageFilter.h" #include #include namespace itk { /** \class WarpTensorImageMultiTransformFilter * \brief Warps a tensor image using an input deformation field. * * WarpTensorImageMultiTransformFilter warps an existing tensor image with respect to * a given deformation field. * * A deformation field is represented as a image whose pixel type is some * vector type with at least N elements, where N is the dimension of * the input image. The vector type must support element access via operator * []. * * The output image is produced by inverse mapping: the output pixels * are mapped back onto the input image. This scheme avoids the creation of * any holes and overlaps in the output image. * * Each vector in the deformation field represent the distance between * a geometric point in the input space and a point in the output space such * that: * * \f[ p_{in} = p_{out} + d \f] * * Typically the mapped position does not correspond to an integer pixel * position in the input image. Interpolation via an image function * is used to compute values at non-integer positions. The default * interpolation typed used is the LinearInterpolateImageFunction. * The user can specify a particular interpolation function via * SetInterpolator(). Note that the input interpolator must derive * from base class InterpolateImageFunction. * * Position mapped to outside of the input image buffer are assigned * a edge padding value. * * The LargetPossibleRegion for the output is inherited from the * input deformation field. The output image spacing and origin may be set * via SetOutputSpacing, SetOutputOrigin. The default are respectively a * vector of 1's and a vector of 0's. * * This class is templated over the type of the input image, the * type of the output image and the type of the deformation field. * * The input image is set via SetInput. The input deformation field * is set via SetDeformationField. * * This filter is implemented as a multithreaded filter. * * \warning This filter assumes that the input type, output type * and deformation field type all have the same number of dimensions. * * \ingroup GeometricTransforms MultiThreaded */ template < class TInputImage, class TOutputImage, class TDeformationField, class TTransform > class ITK_EXPORT WarpTensorImageMultiTransformFilter : public ImageToImageFilter { public: /** transform order type **/ // typedef enum _TransformOrderType {AffineFirst=0, AffineLast} TransformOrderType; /** transform type **/ typedef enum _SingleTransformType {EnumAffineType=0, EnumDeformationFieldType} SingleTransformType; /** Standard class typedefs. */ typedef WarpTensorImageMultiTransformFilter Self; typedef ImageToImageFilter Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Run-time type information (and related methods) */ itkTypeMacro( WarpTensorImageMultiTransformFilter, ImageToImageFilter ); /** Typedef to describe the output image region type. */ typedef typename TOutputImage::RegionType OutputImageRegionType; /** Inherit some types from the superclass. */ typedef typename Superclass::InputImageType InputImageType; typedef typename Superclass::InputImagePointer InputImagePointer; typedef typename Superclass::OutputImageType OutputImageType; typedef typename Superclass::OutputImagePointer OutputImagePointer; typedef typename Superclass::InputImageConstPointer InputImageConstPointer; typedef typename OutputImageType::IndexType IndexType; typedef typename OutputImageType::SizeType SizeType; typedef typename OutputImageType::PixelType PixelType; typedef typename OutputImageType::SpacingType SpacingType; typedef typename OutputImageType::DirectionType DirectionType; /** Determine the image dimension. */ itkStaticConstMacro(ImageDimension, unsigned int, TOutputImage::ImageDimension ); itkStaticConstMacro(InputImageDimension, unsigned int, TInputImage::ImageDimension ); itkStaticConstMacro(DeformationFieldDimension, unsigned int, TDeformationField::ImageDimension ); /** Deformation field typedef support. */ typedef TDeformationField DeformationFieldType; typedef typename DeformationFieldType::Pointer DeformationFieldPointer; typedef typename DeformationFieldType::PixelType DisplacementType; typedef typename DisplacementType::ValueType DisplacementScalarValueType; /** songgang: Affine transform typedef support. */ typedef TTransform TransformType; typedef typename TransformType::Pointer TransformTypePointer; /** Interpolator typedef support. */ typedef double CoordRepType; typedef VectorInterpolateImageFunction InterpolatorType; typedef typename InterpolatorType::Pointer InterpolatorPointer; typedef VectorLinearInterpolateImageFunction DefaultInterpolatorType; typedef VectorLinearInterpolateImageFunction DefaultVectorInterpolatorType; typedef typename DefaultVectorInterpolatorType::Pointer VectorInterpolatorPointer; /** Point type */ typedef Point PointType; typedef struct _DeformationTypeEx{ DeformationFieldPointer field; VectorInterpolatorPointer vinterp; } DeformationTypeEx; typedef struct _AffineTypeEx{ TransformTypePointer aff; } AffineTypeEx; typedef struct _VarTransformType{ AffineTypeEx aex; DeformationTypeEx dex; } VarTransformType; typedef std::pair SingleTransformItemType; typedef std::list TransformListType; /** Set the interpolator function. */ itkSetObjectMacro( Interpolator, InterpolatorType ); /** Get a pointer to the interpolator function. */ itkGetObjectMacro( Interpolator, InterpolatorType ); /** Set the output image spacing. */ itkSetMacro(OutputSpacing, SpacingType); virtual void SetOutputSpacing( const double* values); /** Get the output image spacing. */ itkGetConstReferenceMacro(OutputSpacing, SpacingType); /** Set the output image spacing. */ itkSetMacro(OutputDirection, DirectionType); /** Get the output image spacing. */ itkGetConstReferenceMacro(OutputDirection, DirectionType); /** Set the output image origin. */ itkSetMacro(OutputOrigin, PointType); virtual void SetOutputOrigin( const double* values); /** Get the output image origin. */ itkGetConstReferenceMacro(OutputOrigin, PointType); /** Set the output image size. */ itkSetMacro(OutputSize, SizeType); // virtual void SetOutputSize( const double *values); itkGetConstReferenceMacro(OutputSize, SizeType); /** Set the edge padding value */ itkSetMacro( EdgePaddingValue, PixelType ); /** Get the edge padding value */ itkGetMacro( EdgePaddingValue, PixelType ); TransformListType & GetTransformList(){ return m_TransformList; } void PrintTransformList(); /** WarpTensorImageMultiTransformFilter produces an image which is a different * size than its input image. As such, it needs to provide an * implemenation for GenerateOutputInformation() which set * the output information according the OutputSpacing, OutputOrigin * and the deformation field's LargestPossibleRegion. */ virtual void GenerateOutputInformation(); /** It is difficult to compute in advance the input image region * required to compute the requested output region. Thus the safest * thing to do is to request for the whole input image. * * For the deformation field, the input requested region * set to be the same as that of the output requested region. */ virtual void GenerateInputRequestedRegion(); /** This method is used to set the state of the filter before * multi-threading. */ virtual void BeforeThreadedGenerateData(); /** This method is used to set the state of the filter after * multi-threading. */ virtual void AfterThreadedGenerateData(); /** precompute the smoothed image if necessary **/ void SetSmoothScale(double scale); double GetSmoothScale() {return m_SmoothScale;}; // void UpdateSizeByScale(); void PushBackAffineTransform(const TransformType* t); void PushBackDeformationFieldTransform(const DeformationFieldType* t); bool MultiInverseAffineOnlySinglePoint(const PointType &point1, PointType &point2); bool MultiTransformSinglePoint(const PointType &point1, PointType &point2); bool MultiTransformPoint(const PointType &point1, PointType &point2, bool bFisrtDeformNoInterp, const IndexType &index); void DetermineFirstDeformNoInterp(); inline bool IsOutOfNumericBoundary(const PointType &p); // set interpolator from outside // virtual void SetInterpolator(InterpolatorPointer interp); #ifdef ITK_USE_CONCEPT_CHECKING /** Begin concept checking */ itkConceptMacro(SameDimensionCheck1, (Concept::SameDimension)); itkConceptMacro(SameDimensionCheck2, (Concept::SameDimension)); //removed to be compatible with vector form input image // itkConceptMacro(InputHasNumericTraitsCheck, // (Concept::HasNumericTraits)); itkConceptMacro(DeformationFieldHasNumericTraitsCheck, (Concept::HasNumericTraits)); /** End concept checking */ #endif bool m_bFirstDeformNoInterp; protected: WarpTensorImageMultiTransformFilter(); ~WarpTensorImageMultiTransformFilter() {}; void PrintSelf(std::ostream& os, Indent indent) const; /** WarpTensorImageMultiTransformFilter is implemented as a multi-threaded filter. * As such, it needs to provide and implementation for * ThreadedGenerateData(). */ void ThreadedGenerateData(const OutputImageRegionType& outputRegionForThread, int threadId ); InterpolatorPointer m_Interpolator; PixelType m_EdgePaddingValue; SpacingType m_OutputSpacing; PointType m_OutputOrigin; SizeType m_OutputSize; DirectionType m_OutputDirection; TransformListType m_TransformList; DeformationFieldPointer m_FullWarp; double m_SmoothScale; InputImagePointer m_CachedSmoothImage; private: WarpTensorImageMultiTransformFilter(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkWarpTensorImageMultiTransformFilter.txx" #endif #endif /*ITKWarpTensorImageMultiTRANSFORMFILTER_H_*/ ants-1.9.2+svn680.dfsg/Tensor/itkWarpTensorImageMultiTransformFilter.txx000066400000000000000000000532751147325206600263470ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkWarpTensorImageMultiTransformFilter.txx,v $ Language: C++ Date: $Date: 2009/01/27 19:56:28 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkWarpTensorImageMultiTransformFilter_txx #define __itkWarpTensorImageMultiTransformFilter_txx #include "itkWarpTensorImageMultiTransformFilter.h" #include "itkImageRegionIterator.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkNumericTraits.h" #include "itkProgressReporter.h" #include "itkVectorLinearInterpolateImageFunction.h" #include #include "vnl/vnl_matrix.h" #include "vnl/algo/vnl_symmetric_eigensystem.h" #include "TensorFunctions.h" namespace itk { template TTensorType LogExpTensor( TTensorType inTensor, bool doLog ) { TTensorType result; if ( (inTensor[0] != inTensor[0] ) || (inTensor[1] != inTensor[1] ) || (inTensor[2] != inTensor[2] ) || (inTensor[3] != inTensor[3] ) || (inTensor[4] != inTensor[4] ) || (inTensor[5] != inTensor[5] ) ) { result[0] = result[1] = result[2] = result[3] = result[4] = result[5] = 0; //std::cout << " - " << inTensor << std::endl; return result; } double sigma = 1.0e-15; if ( (inTensor[0] < sigma ) || (inTensor[1] < sigma ) || (inTensor[2] < sigma ) || (inTensor[3] < sigma ) || (inTensor[4] < sigma ) || (inTensor[5] < sigma ) ) { result[0] = result[1] = result[2] = result[3] = result[4] = result[5] = 0; //std::cout << " - " << inTensor << std::endl; return result; } vnl_matrix tensor(3,3); tensor[0][0] = inTensor[0]; tensor[1][0] = tensor[0][1] = inTensor[1]; tensor[1][1] = inTensor[2]; tensor[2][0] = tensor[0][2] = inTensor[3]; tensor[2][1] = tensor[1][2] = inTensor[4]; tensor[2][2] = inTensor[5]; //std::cout << " + " << inTensor << std::endl; if ( ((tensor[0][0] == 0) && (tensor[0][1] == 0) && (tensor[0][2] == 0) && (tensor[1][1] == 0) && (tensor[1][2] == 0) && (tensor[2][2] == 0)) || (tensor.has_nans()) || (!tensor.is_finite()) ) { result[0] = result[1] = result[2] = result[3] = result[4] = result[5] = 0; } else { vnl_symmetric_eigensystem eSystem(tensor); for (unsigned int i=0; i<3; i++) { if (doLog) { eSystem.D[i] = vcl_log(vcl_fabs(eSystem.D[i])); } else { eSystem.D[i] = vcl_exp(eSystem.D[i]); } } vnl_matrix leTensor = eSystem.recompose(); result[0] = leTensor[0][0]; result[1] = leTensor[1][0]; result[2] = leTensor[1][1]; result[3] = leTensor[2][0]; result[4] = leTensor[2][1]; result[5] = leTensor[2][2]; } return result; } /** * Default constructor. */ template WarpTensorImageMultiTransformFilter ::WarpTensorImageMultiTransformFilter() { // Setup the number of required inputs this->SetNumberOfRequiredInputs( 1 ); // Setup default values m_OutputSpacing.Fill( 1.0 ); m_OutputOrigin.Fill( 0.0 ); m_EdgePaddingValue = NumericTraits::Zero; // Setup default interpolator typename DefaultInterpolatorType::Pointer interp = DefaultInterpolatorType::New(); m_Interpolator = static_cast( interp.GetPointer() ); m_SmoothScale = -1; // m_bOutputDeformationField = false; // m_TransformOrder = AffineFirst; } //template //void //WarpTensorImageMultiTransformFilter //::SetInterpolator1(InterpolatorPointer interp) //{ // m_Interpolator = static_cast (interp.GetPointer()); // std::cout << "set interpolator in WarpImage:" << interp << std::endl; //} template void WarpTensorImageMultiTransformFilter ::PrintTransformList() { std::cout << "transform list: " << std::endl; typename TransformListType::iterator it = (m_TransformList.begin()); for(int ii=0; it != m_TransformList.end(); it++, ii++){ switch(it->first){ case EnumAffineType: std::cout << '[' << ii << "]: EnumAffineType" << std::endl; std::cout << it->second.aex.aff << std::endl; break; case EnumDeformationFieldType: std::cout << '[' << ii << "]: EnumDeformationFieldType: size" << it->second.dex.field->GetLargestPossibleRegion().GetSize() << std::endl; } } } /** * Standard PrintSelf method. */ template void WarpTensorImageMultiTransformFilter ::PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf(os, indent); os << indent << "OutputSpacing: " << m_OutputSpacing << std::endl;; os << indent << "OutputOrigin: " << m_OutputOrigin << std::endl; os << indent << "EdgePaddingValue: " << static_cast::PrintType>(m_EdgePaddingValue) << std::endl; os << indent << "Interpolator: " << m_Interpolator.GetPointer() << std::endl; os << indent << "m_bFirstDeformNoInterp = " << m_bFirstDeformNoInterp << std::endl; } /** * Set the output image spacing. * */ template void WarpTensorImageMultiTransformFilter ::SetOutputSpacing( const double* spacing) { SpacingType s(spacing); this->SetOutputSpacing( s ); } /** * Set the output image origin. * */ template void WarpTensorImageMultiTransformFilter ::SetOutputOrigin( const double* origin) { PointType p(origin); this->SetOutputOrigin(p); } /** * Setup state of filter before multi-threading. * InterpolatorType::SetInputImage is not thread-safe and hence * has to be setup before ThreadedGenerateData */ template void WarpTensorImageMultiTransformFilter ::BeforeThreadedGenerateData() { if( !m_Interpolator ) { itkExceptionMacro(<< "Interpolator not set"); } // Connect input image to interpolator // m_Interpolator->SetInputImage( this->GetInput() ); if (m_CachedSmoothImage.IsNull() && (this->GetInput() )){ m_CachedSmoothImage = const_cast (this->GetInput()); } m_Interpolator->SetInputImage( m_CachedSmoothImage ); } /** * Setup state of filter after multi-threading. */ template void WarpTensorImageMultiTransformFilter ::AfterThreadedGenerateData() { // Disconnect input image from interpolator m_Interpolator->SetInputImage( NULL ); } template void WarpTensorImageMultiTransformFilter ::GenerateInputRequestedRegion() { // call the superclass's implementation Superclass::GenerateInputRequestedRegion(); // request the largest possible region for the input image InputImagePointer inputPtr = const_cast< InputImageType * >( this->GetInput() ); if( inputPtr ) { inputPtr->SetRequestedRegionToLargestPossibleRegion(); } return; } template void WarpTensorImageMultiTransformFilter ::GenerateOutputInformation() { // call the superclass's implementation of this method Superclass::GenerateOutputInformation(); OutputImagePointer outputPtr = this->GetOutput(); if ( !outputPtr ) { return; } typename TOutputImage::RegionType outputLargestPossibleRegion; outputLargestPossibleRegion.SetSize( this->m_OutputSize ); // outputLargestPossibleRegion.SetIndex( 0 ); outputPtr->SetLargestPossibleRegion( outputLargestPossibleRegion ); outputPtr->SetSpacing( this->m_OutputSpacing ); outputPtr->SetOrigin( this->m_OutputOrigin ); outputPtr->SetDirection( this->m_OutputDirection ); //this->m_FullWarp->SetLargestPossibleRegion( outputLargestPossibleRegion ); //this->m_FullWarp->SetSpacing( this->m_OutputSpacing ); //this->m_FullWarp->SetOrigin( this->m_OutputOrigin ); //this->m_FullWarp->SetDirection( this->m_OutputDirection ); //this->m_FullWarp->Allocate(); // determine if the deformation field is the same dimension as the image // so that it does not need interpolation in the first step DetermineFirstDeformNoInterp(); } template void WarpTensorImageMultiTransformFilter ::SetSmoothScale(double scale) { /* if (m_SmoothScale != scale){ // compute the new cached // std::cout << "change smooth scale: " << m_SmoothScale << " ---> " << scale << std::endl; m_SmoothScale = scale; typename InputImageType::SpacingType inputSpacing = this->GetInput()->GetSpacing(); typename InputImageType::RegionType::SizeType inputSize = this->GetInput()->GetLargestPossibleRegion().GetSize(); typename InputImageType::SpacingType outputSpacing; typename InputImageType::RegionType::SizeType outputSize; double minimumSpacing = inputSpacing.GetVnlVector().min_value(); double maximumSpacing = inputSpacing.GetVnlVector().max_value(); InputImagePointer image = const_cast (this->GetInput()); for ( unsigned int d = 0; d < ImageDimension; d++ ) { double scaling = vnl_math_min( 1.0 / scale * minimumSpacing / inputSpacing[d], static_cast( inputSize[d] ) / 32.0 ); outputSpacing[d] = inputSpacing[d] * scaling; outputSize[d] = static_cast( inputSpacing[d] * static_cast( inputSize[d] ) / outputSpacing[d] + 0.5 ); double sigma = 0.25 * ( outputSpacing[d] / inputSpacing[d] ); if (sigma < 0) sigma=0; typedef RecursiveGaussianImageFilter GaussianFilterType; typename GaussianFilterType::Pointer smoother = GaussianFilterType::New(); smoother->SetInputImage( image ); smoother->SetDirection( d ); smoother->SetNormalizeAcrossScale( false ); // std::cout << "scale = " << scale << " => " << "sigma of dim " << d << ": " << sigma << " out size " << outputSize << " spc1 " << outputSpacing << " in " << inputSpacing << std::endl; smoother->SetSigma( sigma ); if ( smoother->GetSigma() > 0.0 ) { smoother->Update(); image = smoother->GetOutput(); } } SetOutputSpacing( outputSpacing ); SetOutputOrigin( this->GetInput()->GetOrigin() ); SetOutputSize(outputSize); } */ InputImagePointer image = const_cast (this->GetInput()); m_CachedSmoothImage = image; } /** * Compute the output for the region specified by outputRegionForThread. */ template void WarpTensorImageMultiTransformFilter ::ThreadedGenerateData( const OutputImageRegionType& outputRegionForThread, int threadId ) { InputImageConstPointer inputPtr = this->GetInput(); OutputImagePointer outputPtr = this->GetOutput(); //std::cout << "inputPtr->GetOrigin():" << inputPtr->GetOrigin() << std::endl; //std::cout << "outputPtr->GetOrigin():" << outputPtr->GetOrigin() << std::endl; // exit(-1); // Need to get full warp for reorientation of tensors IndexType index; index.Fill(0); this->m_EdgePaddingValue=inputPtr->GetPixel(index); // support progress methods/callbacks ProgressReporter progress(this, threadId, outputRegionForThread.GetNumberOfPixels()); // iterator for the output image ImageRegionIteratorWithIndex outputIt(outputPtr, outputRegionForThread); while( !outputIt.IsAtEnd() ) { PointType point1, point2; // get the output image index IndexType index = outputIt.GetIndex(); outputPtr->TransformIndexToPhysicalPoint( index, point1 ); bool isinside = MultiTransformPoint(point1, point2, m_bFirstDeformNoInterp, index); // std::cout << "point1:" << point1 << " point2:" << point2 << " index:" << index << std::endl; // exit(-1); //DeformationFieldType::PixelType diff; //diff[0] = point2[0]-point1[0]; //diff[1] = point2[1]-point1[1]; //diff[2] = point2[2]-point1[2]; // warp the image // get the interpolated value if( isinside && (m_Interpolator->IsInsideBuffer( point2 )) ) { PixelType value = static_cast(m_Interpolator->Evaluate(point2)); outputIt.Set( value ); } else { // std::cout << "OUTSIDE" << " isinside:" << isinside << " m_Interpolator->IsInsideBuffer( point2 ):" << m_Interpolator->IsInsideBuffer( point2 ) << std::endl; outputIt.Set( m_EdgePaddingValue ); } ++outputIt; } progress.CompletedPixel(); } //template //void //WarpTensorImageMultiTransformFilter //::UpdateSizeByScale() //{ // // // DeformationFieldPointer field = this->GetDeformationField(); // // // // SetOutputSpacing( field->GetSpacing() / m_SmoothScale ); // // SetOutputOrigin( field->GetOrigin() ); // // // // typename InputImageType::SizeType imgsz = field->GetLargestPossibleRegion().GetSize(); // // for(int ii = 0; ii < InputImageType::ImageDimension; ii++) imgsz[ii] = (typename InputImageType::SizeType::SizeValueType) (imgsz[ii] * m_SmoothScale + 0.5); // // // // SetOutputSize(imgsz); // //} template void WarpTensorImageMultiTransformFilter ::PushBackAffineTransform(const TransformType* t) { if (t){ VarTransformType t1; t1.aex.aff = const_cast (t); m_TransformList.push_back(SingleTransformItemType(EnumAffineType, t1)); } } template void WarpTensorImageMultiTransformFilter ::PushBackDeformationFieldTransform(const DeformationFieldType* t) { if (t){ VarTransformType t1; t1.dex.field = const_cast (t); t1.dex.vinterp = DefaultVectorInterpolatorType::New(); t1.dex.vinterp->SetInputImage(t1.dex.field); m_TransformList.push_back(SingleTransformItemType(EnumDeformationFieldType, t1)); } } template bool WarpTensorImageMultiTransformFilter ::MultiTransformSinglePoint(const PointType &point1, PointType &point2) { IndexType null_index; bool isinside = MultiTransformPoint(point1, point2, false, null_index); return isinside; } template bool WarpTensorImageMultiTransformFilter ::IsOutOfNumericBoundary(const PointType &p) { const DisplacementScalarValueType kMaxDisp = itk::NumericTraits::max(); bool b = false; for (int i = 0; i < ImageDimension; i++) { if (p[i]>=kMaxDisp ){ b = true; break; } } return b; } template bool WarpTensorImageMultiTransformFilter ::MultiInverseAffineOnlySinglePoint(const PointType &p1, PointType &p2) { bool isinside = true; PointType point1 = p1, point2; typename TransformListType::iterator it = m_TransformList.begin(); for(;it!=m_TransformList.end(); it++){ SingleTransformType ttype = it->first; switch(ttype){ case EnumAffineType: { TransformTypePointer aff = it->second.aex.aff; TransformTypePointer aff_inv = TransformTypePointer::ObjectType::New(); // std::cout << "aff before:" << aff << std::endl; aff->GetInverse(aff_inv); // aff->GetInverse(aff); // std::cout << "aff after:" << aff << std::endl; // std::cout << "aff_inv after:" << aff_inv << std::endl; point2 = aff_inv->TransformPoint(point1); point1 = point2; isinside = true; break; } case EnumDeformationFieldType: break; default: itkExceptionMacro(<< "Single Transform Not Supported!"); } if (IsOutOfNumericBoundary(point2)) {isinside = false; break;} point1 = point2; } p2 = point2; return isinside; } template bool WarpTensorImageMultiTransformFilter ::MultiTransformPoint(const PointType &p1, PointType &p2, bool bFisrtDeformNoInterp, const IndexType &index) { bool isinside = false; PointType point1 = p1, point2; typename TransformListType::iterator it = m_TransformList.begin(); for(;it!=m_TransformList.end(); it++){ SingleTransformType ttype = it->first; switch(ttype){ case EnumAffineType: { TransformTypePointer aff = it->second.aex.aff; point2 = aff->TransformPoint(point1); point1 = point2; isinside = true; } break; case EnumDeformationFieldType: { DeformationFieldPointer fieldPtr = it->second.dex.field; if (bFisrtDeformNoInterp && it==m_TransformList.begin() ){ // use discrete coordinates DisplacementType displacement = fieldPtr->GetPixel(index); for(int j = 0; jTransformPhysicalPointToContinuousIndex(point1, contind); isinside = fieldPtr->GetLargestPossibleRegion().IsInside( contind ); VectorInterpolatorPointer vinterp = it->second.dex.vinterp; typename DefaultVectorInterpolatorType::OutputType disp2; if (isinside) disp2 = vinterp->EvaluateAtContinuousIndex( contind ); else disp2.Fill(0); for (int jj=0; jj void WarpTensorImageMultiTransformFilter ::DetermineFirstDeformNoInterp() { m_bFirstDeformNoInterp = false; if (m_TransformList.size() >0 ){ if ((m_TransformList.front().first == EnumDeformationFieldType)){ DeformationFieldPointer field = m_TransformList.front().second.dex.field; m_bFirstDeformNoInterp = (this->GetOutputSize() == field->GetLargestPossibleRegion().GetSize()) & (this->GetOutputSpacing() ==field->GetSpacing()) & (this->GetOutputOrigin() == field->GetOrigin()); // std::cout << "in set: field size: " << field->GetLargestPossibleRegion().GetSize() // << "output spacing: " << this->GetOutputSize() << std::endl; // std::cout << field->GetSpacing() << " | " << this->GetOutputSpacing() << std::endl; // std::cout << field->GetOrigin() << " | " << this->GetOutputOrigin() << std::endl; } } } } // end namespace itk #endif //__itkWarpTensorImageMultiTransformFilter_txx ants-1.9.2+svn680.dfsg/Utilities/000077500000000000000000000000001147325206600164335ustar00rootroot00000000000000ants-1.9.2+svn680.dfsg/Utilities/BinaryImageToMeshFilter.h000066400000000000000000000364531147325206600232740ustar00rootroot00000000000000/******************************************************************************** * Create a VTK mesh from the white matter image *******************************************************************************/ #ifndef __BinaryImageToMeshFilter_h_ #define __BinaryImageToMeshFilter_h_ #include "itkImage.h" #include "itkResampleImageFilter.h" #include "itkWindowedSincInterpolateImageFunction.h" #include "itkNearestNeighborInterpolateImageFunction.h" #include "itkAntiAliasBinaryImageFilter.h" #include "itkUnaryFunctorImageFilter.h" //#include "itkBinaryWellComposed3DImageFilter.h" #include "itkMinimumMaximumImageCalculator.h" #include "itkCommand.h" #include #include #include #include #include #include #include #include #include #include #include #include #include using namespace std; template void ConnectITKToVTK(itk::VTKImageExport *fltExport,vtkImageImport *fltImport) { fltImport->SetUpdateInformationCallback( fltExport->GetUpdateInformationCallback()); fltImport->SetPipelineModifiedCallback( fltExport->GetPipelineModifiedCallback()); fltImport->SetWholeExtentCallback( fltExport->GetWholeExtentCallback()); fltImport->SetSpacingCallback( fltExport->GetSpacingCallback()); fltImport->SetOriginCallback( fltExport->GetOriginCallback()); fltImport->SetScalarTypeCallback( fltExport->GetScalarTypeCallback()); fltImport->SetNumberOfComponentsCallback( fltExport->GetNumberOfComponentsCallback()); fltImport->SetPropagateUpdateExtentCallback( fltExport->GetPropagateUpdateExtentCallback()); fltImport->SetUpdateDataCallback( fltExport->GetUpdateDataCallback()); fltImport->SetDataExtentCallback( fltExport->GetDataExtentCallback()); fltImport->SetBufferPointerCallback( fltExport->GetBufferPointerCallback()); fltImport->SetCallbackUserData( fltExport->GetCallbackUserData()); } class UnaryFunctorBinaryToFloat { public: UnaryFunctorBinaryToFloat() { m_InsideValue = 1.0f; } void SetInvertImage(bool invert) { m_InsideValue = invert ? -1.0f : 1.0f; } inline float operator()(short in) { return in == 0 ? -m_InsideValue : m_InsideValue; } inline bool operator != (const UnaryFunctorBinaryToFloat &otro) const { return this->m_InsideValue != otro.m_InsideValue; } private: float m_InsideValue; }; template class BinaryImageToMeshFilter : public itk::ProcessObject { public: typedef BinaryImageToMeshFilter Self; typedef itk::ProcessObject Superclass; typedef itk::SmartPointer Pointer; typedef itk::SmartPointer ConstPointer; typedef itk::Image FloatImageType; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Image dimension. */ itkStaticConstMacro(ImageDimension, unsigned int, TImage::ImageDimension); /** Get the result mesh */ vtkPolyData *GetMesh() { return fltTriangle->GetOutput(); } /** Get the intermediate antialiased image */ FloatImageType *GetAntiAliasImage() { return fltAlias->GetOutput(); } /** Whether to invert the binary image */ itkSetMacro(InvertInput,bool); itkGetMacro(InvertInput,bool); /** Whether to invert the binary image */ itkSetMacro(ResampleScaleFactor,float); itkGetMacro(ResampleScaleFactor,float); /** Whether to decimate the mesh and how much, 0 for none*/ itkSetMacro(DecimateFactor, double); itkGetMacro(DecimateFactor, double); /** Smoothing iterations, or 0 for none */ itkSetMacro(SmoothingIterations, int); itkGetMacro(SmoothingIterations, int); /** Set the input */ void SetInput(TImage *image) { this->SetNthInput(0,image); } /** Update method (why?) */ void Update() { this->GenerateData(); } /** Set the anti-aliasing quality parameter */ void SetAntiAliasMaxRMSError(double value) { fltAlias->SetMaximumRMSError(value); } /** Get the 'distance image' based on anti-aliasing the binary image */ FloatImageType *GetDistanceImage() { return fltAlias->GetOutput(); } /** Get the floating point 'resampled' image, if resampling was enabled */ FloatImageType *GetResampledImage() { if(m_ResampleScaleFactor != 1.0f) return fltResample->GetOutput(); else return fltToFloat->GetOutput(); } void PrintMeshStatistics(vtkPolyData *mesh) { vector cellHist(100,0); for(vtkIdType i = 0;i < mesh->GetNumberOfCells();i++) { cellHist[mesh->GetCellType(i)]++; } cout << " mesh has " << mesh->GetNumberOfPoints() << " points." << endl; cout << " mesh has " << mesh->GetNumberOfCells() << " cells. " << endl; cout << " mesh has " << cellHist[VTK_VERTEX] << " vtk_vertex" << endl; cout << " mesh has " << cellHist[VTK_LINE] << " vtk_line" << endl; cout << " mesh has " << cellHist[VTK_POLY_LINE] << " vtk_poly_line" << endl; cout << " mesh has " << cellHist[VTK_TRIANGLE] << " vtk_triangle" << endl; cout << " mesh has " << cellHist[VTK_TRIANGLE_STRIP] << " vtk_triangle_strip" << endl; } protected: BinaryImageToMeshFilter() { // Set the cardinality of the filter this->SetNumberOfInputs(1); this->SetNumberOfOutputs(1); // Begin with the well-connectedness filter //fltTopology = TopologyFilter::New(); // Create the converter to float fltToFloat = ToFloatFilter::New(); // fltToFloat->SetInput(this->GetInput());//fltTopology->GetOutput()); typename FloatImageType::Pointer imgPipeEnd = fltToFloat->GetOutput(); // Initialize the interpolation function fnInterpolate = ResampleFunction::New(); // Create a resampling image filter fltResample = ResampleFilter::New(); fltResample->SetInput(imgPipeEnd); fltResample->SetTransform(itk::IdentityTransform::New()); fltResample->SetInterpolator(fnInterpolate); imgPipeEnd = fltResample->GetOutput(); // Create an anti-aliasing image filter fltAlias = AAFilter::New(); fltAlias->SetMaximumRMSError(0.024); fltAlias->SetInput(imgPipeEnd); imgPipeEnd = fltAlias->GetOutput(); // Cast the image to VTK fltExport = ExportFilter::New(); fltImport = vtkImageImport::New(); fltExport->SetInput(imgPipeEnd); ConnectITKToVTK(fltExport.GetPointer(),fltImport); // Compute marching cubes vtkImageData *importPipeEnd = fltImport->GetOutput(); fltMarching = vtkImageMarchingCubes::New(); fltMarching->SetInput(importPipeEnd); fltMarching->ComputeScalarsOff(); fltMarching->ComputeGradientsOff(); fltMarching->SetNumberOfContours(1); fltMarching->SetValue(0,0.0f); vtkPolyData *meshPipeEnd = fltMarching->GetOutput(); // Keep the largest connected component fltConnect = vtkPolyDataConnectivityFilter::New(); fltConnect->SetInput(meshPipeEnd); fltConnect->SetExtractionModeToLargestRegion(); meshPipeEnd = fltMarching->GetOutput(); // Clean up the data // fltClean = vtkCleanPolyData::New(); //fltClean->SetInput(meshPipeEnd); // meshPipeEnd = fltClean->GetOutput(); // Decimate the data fltDecimate = vtkDecimatePro::New(); fltDecimate->SetInput(meshPipeEnd); fltDecimate->PreserveTopologyOn(); meshPipeEnd = fltDecimate->GetOutput(); // Smooth the data, keeping it on the contour fltSmoothMesh = vtkSmoothPolyDataFilter::New(); fltSmoothMesh->SetInput(meshPipeEnd); fltSmoothMesh->SetSource(meshPipeEnd); meshPipeEnd = fltSmoothMesh->GetOutput(); // Compute triangle strips for faster display fltTriangle = vtkTriangleFilter::New(); fltTriangle->SetInput(meshPipeEnd); meshPipeEnd = fltTriangle->GetOutput(); // Set up progress typedef itk::MemberCommand CommandType; typename CommandType::Pointer cmd = CommandType::New(); cmd->SetCallbackFunction(this, &Self::ProgressCommand); // Add progress to the two slow filters fltResample->AddObserver(itk::ProgressEvent(),cmd); fltAlias->AddObserver(itk::ProgressEvent(),cmd); // Invert - NO m_InvertInput = false; // Resample - NO m_ResampleScaleFactor = 1.0f; // Decimate - NO m_DecimateFactor = 0.0f; } ~BinaryImageToMeshFilter() { // CLean up fltMarching->Delete(); fltConnect->Delete(); fltImport->Delete(); fltTriangle->Delete(); //fltClean->Delete(); fltDecimate->Delete(); fltSmoothMesh->Delete(); } /** Generate Data */ virtual void GenerateData( void ) { // Run the computation cout << "Computing mesh from binary image" << endl; // Get the input and output pointers typename TImage::ConstPointer inputImage = reinterpret_cast(this->GetInput(0)); // Pass the input to the topology filter //fltTopology->SetInput(inputImage); // Compute the max/min of the image to set fore/back typedef itk::MinimumMaximumImageCalculator CalcType; typename CalcType::Pointer calc = CalcType::New(); calc->SetImage(inputImage); calc->Compute(); // Set the background and foreground in the topology filter //fltTopology->SetBackgroundValue(calc->GetMinimum()); //fltTopology->SetForegroundValue(calc->GetMaximum()); //fltTopology->Update(); // Pass the input to the remapper fltToFloat->SetInput(inputImage); // Set the inversion if necessary m_ToFloatFunctor.SetInvertImage(m_InvertInput); fltToFloat->SetFunctor(m_ToFloatFunctor); // Convert the image to floats fltToFloat->Update(); // Peform resampling only if necessary if(m_ResampleScaleFactor != 1.0) { // Include the filter in the pipeline fltAlias->SetInput(fltResample->GetOutput()); // Set the size parameter FloatImageType::SizeType szOutput = inputImage->GetBufferedRegion().GetSize(); szOutput[0] = (unsigned long) (szOutput[0] * m_ResampleScaleFactor); szOutput[1] = (unsigned long) (szOutput[1] * m_ResampleScaleFactor); szOutput[2] = (unsigned long) (szOutput[2] * m_ResampleScaleFactor); fltResample->SetSize(szOutput); // Set the scale and origin FloatImageType::SpacingType xSpacing = inputImage->GetSpacing(); xSpacing[0] /= m_ResampleScaleFactor; xSpacing[1] /= m_ResampleScaleFactor; xSpacing[2] /= m_ResampleScaleFactor; fltResample->SetOutputSpacing(xSpacing); fltResample->SetOutputOrigin(inputImage->GetOrigin()); // Compute the resampling cout << " resampling the image " << endl; fltResample->Update(); cout << endl; } else { // Exclude the filter from the pipeline fltAlias->SetInput(fltToFloat->GetOutput()); } // Run the filters if(fltAlias->GetMaximumRMSError() > 0.0) { cout << " anti-aliasing the image " << endl; fltAlias->Update(); fltExport->SetInput(fltAlias->GetOutput()); } else { fltExport->SetInput(fltAlias->GetInput()); } bool verbose=true; if (verbose) cout << endl << " converting image to VTK" << endl; fltImport->Update(); if (verbose) cout << " running marching cubes algorithm" << endl; fltMarching->Update(); if (verbose) cout << " mesh has " << fltMarching->GetOutput()->GetNumberOfCells() << " cells and " << fltMarching->GetOutput()->GetNumberOfPoints() << " points. " << endl; if (verbose) cout << " extracting the largest component" << endl; fltConnect->Update(); if (verbose) cout << " mesh has " << fltConnect->GetOutput()->GetNumberOfCells() << " cells and " << fltConnect->GetOutput()->GetNumberOfPoints() << " points. " << endl; //if (verbose) cout << " cleaning the mesh " << endl; //fltClean->Update(); // if (verbose) cout << " mesh has " // << fltClean->GetOutput()->GetNumberOfCells() << " cells and " // << fltClean->GetOutput()->GetNumberOfPoints() << " points. " << endl; // If decimation is on, run it if(m_DecimateFactor > 0.0 ) { if (verbose) cout << " decimating the mesh by factor of " << m_DecimateFactor << endl; fltDecimate->SetTargetReduction(m_DecimateFactor); fltDecimate->Update(); fltTriangle->SetInput(fltDecimate->GetOutput()); // if (verbose) cout << " mesh has " // << fltClean->GetOutput()->GetNumberOfCells() << " cells and " // << fltClean->GetOutput()->GetNumberOfPoints() << " points. " << endl; } //else // { // fltTriangle->SetInput(fltClean->GetOutput()); // } if (verbose) cout << " converting mesh to triangles" << endl; fltTriangle->Update(); m_Result = fltTriangle->GetOutput(); if (verbose) cout << " mesh has " << fltTriangle->GetOutput()->GetNumberOfCells() << " cells and " << fltTriangle->GetOutput()->GetNumberOfPoints() << " points. " << endl; // If smoothing is on, run it if(m_SmoothingIterations > 0 ) { if (verbose) cout << " smoothing the mesh " << m_SmoothingIterations << endl; fltSmoothMesh->SetNumberOfIterations(m_SmoothingIterations); fltSmoothMesh->SetInput(m_Result); fltSmoothMesh->SetSource(m_Result); fltSmoothMesh->Update(); //m_Result = fltSmoothMesh->GetOutput(); std::cout << " Done " << std::endl; } } private: typedef itk::ImageRegionIterator IteratorType; typedef itk::ImageRegionConstIterator ConstIteratorType; // Topology correction filter // typedef itk::BinaryWellComposed3DImageFilter TopologyFilter; // Functor for remapping to float UnaryFunctorBinaryToFloat m_ToFloatFunctor; // Filter to remap image to floating point typedef itk::UnaryFunctorImageFilter< TImage, FloatImageType, UnaryFunctorBinaryToFloat> ToFloatFilter; // Windowed sinc for resampling typedef itk::Function::WelchWindowFunction<4> WindowFunction; typedef itk::NearestNeighborInterpolateImageFunction< FloatImageType, double> ResampleFunction; // Filter to resample image typedef itk::ResampleImageFilter ResampleFilter; // Antialiasing filter typedef itk::AntiAliasBinaryImageFilter AAFilter; // Export to VTK filter typedef itk::VTKImageExport ExportFilter; // typename TopologyFilter::Pointer fltTopology; typename AAFilter::Pointer fltAlias; typename ExportFilter::Pointer fltExport; typename ToFloatFilter::Pointer fltToFloat; typename ResampleFilter::Pointer fltResample; typename ResampleFunction::Pointer fnInterpolate; vtkImageImport *fltImport; vtkImageMarchingCubes *fltMarching; vtkPolyDataConnectivityFilter *fltConnect; vtkCleanPolyData *fltClean; vtkTriangleFilter *fltTriangle; vtkDecimatePro *fltDecimate; vtkSmoothPolyDataFilter *fltSmoothMesh; bool m_InvertInput; float m_ResampleScaleFactor; double m_DecimateFactor; int m_SmoothingIterations; vtkPolyData *m_Result; void ProgressCommand(itk::Object *source, const itk::EventObject &evt) { // Get the elapsed progress itk::ProcessObject *po = reinterpret_cast(source); float progress = po->GetProgress(); cout << setprecision(4) << (100 * progress) << " " << flush; } }; #endif ants-1.9.2+svn680.dfsg/Utilities/ReadWriteImage.h000066400000000000000000000405151147325206600214420ustar00rootroot00000000000000#ifndef __ReadWriteImage_h_ #define __ReadWriteImage_h_ #include #include #include #include "itkVector.h" #include "itkImage.h" #include "itkImageFileWriter.h" #include "itkImageFileReader.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkWarpImageFilter.h" //#include "itkInverseWarpImageFilter.h" #include "itkAffineTransform.h" #include "itkImageRegionIterator.h" #include "itkResampleImageFilter.h" #include "itkVectorIndexSelectionCastImageFilter.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkNearestNeighborInterpolateImageFunction.h" #include "itkVectorIndexSelectionCastImageFilter.h" #include "itkLogTensorImageFilter.h" #include "itkExpTensorImageFilter.h" #include bool ANTSFileExists(std::string strFilename) { struct stat stFileInfo; bool blnReturn; int intStat; // Attempt to get the file attributes intStat = stat(strFilename.c_str(),&stFileInfo); if(intStat == 0) { // We were able to get the file attributes // so the file obviously exists. blnReturn = true; } else { // We were not able to get the file attributes. // This may mean that we don't have permission to // access the folder which contains this file. If you // need to do that level of checking, lookup the // return values of stat which will give you // more details on why stat failed. blnReturn = false; } return(blnReturn); } // Nifti stores DTI values in lower tri format but itk uses upper tri // currently, nifti io does nothing to deal with this. if this changes // the function below should be modified/eliminated. template void NiftiDTICheck(itk::SmartPointer &target, const char *file, bool makeLower) { typedef typename TImageType::PixelType PixType; return; //typedef itk::ImageFileWriter Writer; //typename Writer::Pointer writer = Writer::New(); //writer->SetInput( target ); //writer->SetFileName( "testdt.nii" ); //writer->Update(); // Check for nifti file std::string filename = file; std::string::size_type pos1 = filename.find( ".nii" ); std::string::size_type pos2 = filename.find( ".nia" ); if ((pos1 == std::string::npos) && (pos2 == std::string::npos)) return; if (PixType::Length != 6) return; std::cout << "Performing lower/upper triangular format check for Nifti DTI" << std::endl; // swap elements 2 and 3 for lower<->upper conversion itk::ImageRegionIteratorWithIndex iter(target,target->GetLargestPossibleRegion()); unsigned int looksLikeLower = 0; unsigned int looksLikeUpper = 0; unsigned int nBadVoxels = 0; unsigned int count = 0; unsigned int el2Neg = 0; unsigned int el3Neg = 0; while (!iter.IsAtEnd()) { bool isValid = true; for (unsigned int i=0; i<6; i++) { if ( iter.Get()[i] != iter.Get()[i] ) { ++nBadVoxels; isValid = false; } } double el2 = iter.Get()[2]; double el3 = iter.Get()[3]; if (el2 < 0) ++el2Neg; if (el3 < 0) ++el3Neg; if (isValid) { if (el2 > el3) { ++looksLikeLower; } else { ++looksLikeUpper; } } ++count; ++iter; } //std::cout << "Invalid: " << nBadVoxels << "/" << count << std::endl; //std::cout << "Lower: " << looksLikeLower << ", Upper: " << looksLikeUpper << std::endl; //std::cout << "el2Neg: " << el2Neg << ", el3Neg: " << el3Neg << std::endl; if ( ((looksLikeUpper > looksLikeLower) && makeLower) || ( (looksLikeLower > looksLikeUpper) && !makeLower) ) { std::cout << "Performing lower/upper triangular format swap for Nifti DTI" << std::endl; iter.GoToBegin(); while (!iter.IsAtEnd()) { PixType pix = iter.Get(); typename PixType::ValueType temp; temp = pix[2]; pix[2] = pix[3]; pix[3] = temp; iter.Set(pix); ++iter; } } } template void ReadTensorImage(itk::SmartPointer &target, const char *file, bool takelog=true) { if ( ! ANTSFileExists(std::string(file)) ) { std::cout << " file " << std::string(file) << " does not exist . " << std::endl; return ; } // Read the image files begin typedef TImageType ImageType; typedef itk::ImageFileReader< ImageType > FileSourceType; typedef typename ImageType::PixelType PixType; typedef itk::LogTensorImageFilter LogFilterType; typename FileSourceType::Pointer reffilter = FileSourceType::New(); reffilter->SetFileName( file ); try { reffilter->Update(); } catch( itk::ExceptionObject & e ) { std::cout << "Exception caught during reference file reading " << std::endl; std::cout << e << " file " << file << std::endl; target=NULL; return; } target=reffilter->GetOutput(); NiftiDTICheck(target, file, false); if (takelog) { typename LogFilterType::Pointer logFilter = LogFilterType::New(); logFilter->SetInput( reffilter->GetOutput() ); logFilter->Update(); target = logFilter->GetOutput(); std::cout << "Returning Log(D) for log-euclidean math ops" << std::endl; } } template //void ReadImage(typename TImageType::Pointer target, const char *file) void ReadImage(itk::SmartPointer &target, const char *file) { if ( std::string(file).length() < 3 ) { std::cout << " bad file name " << std::string(file) << std::endl ; target=NULL; return; } if ( ! ANTSFileExists(std::string(file)) ) { std::cout << " file " << std::string(file) << " does not exist . " << std::endl; target=NULL; return ; } /*// std::cout << " reading b " << std::string(file) << std::endl; typedef itk::ImageFileReader readertype; typename readertype::Pointer reader = readertype::New(); reader->SetFileName(file); reader->Update(); target=(reader->GetOutput()); */ // std::cout << " Entering Read " << file << std::endl; // Read the image files begin typedef TImageType ImageType; typedef itk::ImageFileReader< ImageType > FileSourceType; typedef typename ImageType::PixelType PixType; // const unsigned int ImageDimension=ImageType::ImageDimension; typename FileSourceType::Pointer reffilter = FileSourceType::New(); reffilter->SetFileName( file ); try { reffilter->Update(); } catch( itk::ExceptionObject & e ) { std::cout << "Exception caught during reference file reading " << std::endl; std::cout << e << " file " << file << std::endl; target=NULL; exit(1); return; } //typename ImageType::DirectionType dir; //dir.SetIdentity(); // reffilter->GetOutput()->SetDirection(dir); //std::cout << " setting pointer " << std::endl; target=reffilter->GetOutput(); // reffilter->GetOutput()->Print( std::cout ); // std::cout << " read direction " << << std::endl; //if (reffilter->GetImageIO()->GetNumberOfComponents() == 6) //NiftiDTICheck(target,file); } template typename ImageType::Pointer ReadImage(char* fn ) { // Read the image files begin typedef itk::ImageFileReader< ImageType > FileSourceType; typedef typename ImageType::PixelType PixType; // const unsigned int ImageDimension=ImageType::ImageDimension; typename FileSourceType::Pointer reffilter = FileSourceType::New(); reffilter->SetFileName( fn ); try { reffilter->Update(); } catch( itk::ExceptionObject & e ) { std::cerr << "Exception caught during reference file reading " << std::endl; std::cerr << e << std::endl; return NULL; } typename ImageType::DirectionType dir; dir.SetIdentity(); // reffilter->GetOutput()->SetDirection(dir); typename ImageType::Pointer target = reffilter->GetOutput(); //if (reffilter->GetImageIO->GetNumberOfComponents() == 6) //NiftiDTICheck(target,fn); return target; } template typename ImageType::Pointer ReadTensorImage(char* fn, bool takelog=true ) { // Read the image files begin typedef itk::ImageFileReader< ImageType > FileSourceType; typedef typename ImageType::PixelType PixType; typedef itk::LogTensorImageFilter LogFilterType; // const unsigned int ImageDimension=ImageType::ImageDimension; typename FileSourceType::Pointer reffilter = FileSourceType::New(); reffilter->SetFileName( fn ); try { reffilter->Update(); } catch( itk::ExceptionObject & e ) { std::cerr << "Exception caught during reference file reading " << std::endl; std::cerr << e << std::endl; return NULL; } typename ImageType::Pointer target = reffilter->GetOutput(); NiftiDTICheck(target, fn, false); if (takelog) { typename LogFilterType::Pointer logFilter = LogFilterType::New(); logFilter->SetInput( target->GetOutput() ); logFilter->Update(); target = logFilter->GetOutput(); } return target; } template void WriteImage(itk::SmartPointer image, const char *file) { if ( std::string(file).length() < 3 ) return; typename itk::ImageFileWriter::Pointer writer = itk::ImageFileWriter::New(); writer->SetFileName(file); if (!image) { std::cout <<" file " << file << " does not exist " << std::endl; exit(1); } // typename TImageType::DirectionType dir; //dir.SetIdentity(); //image->SetDirection(dir); // std::cout << " now Write direction " << image->GetOrigin() << std::endl; //if (writer->GetImageIO->GetNumberOfComponents() == 6) //NiftiDTICheck(image,file); writer->SetInput(image); writer->Update(); } template void WriteTensorImage(itk::SmartPointer image, const char *file, bool takeexp=true) { typedef typename TImageType::PixelType PixType; typedef itk::ExpTensorImageFilter ExpFilterType; typename itk::ImageFileWriter::Pointer writer = itk::ImageFileWriter::New(); writer->SetFileName(file); typename TImageType::Pointer writeImage = image; if (takeexp) { typename ExpFilterType::Pointer expFilter = ExpFilterType::New(); expFilter->SetInput( image ); expFilter->Update(); writeImage = expFilter->GetOutput(); std::cout << "Taking Exp(D) before writing" << std::endl; } // convert from upper tri to lower tri NiftiDTICheck(writeImage, file, true);// BA May 30 2009 -- remove b/c ITK fixed NIFTI reader writer->SetInput(writeImage); writer->Update(); } template typename TField::Pointer ReadWarpFromFile( std::string warpfn, std::string ext) { typedef TField FieldType; typedef typename FieldType::PixelType VectorType; enum { ImageDimension = FieldType::ImageDimension }; typedef itk::Image RealImageType; typedef RealImageType ImageType; // typedef itk::Vector VectorType; // typedef itk::Image FieldType; // std::cout << " warp file name " << warpfn + ext << std::endl; std::string fn=""; // First - read the vector fields // NOTE : THE FIELD SHOULD WARP INPUT1 TO INPUT2, THUS SHOULD POINT // FROM INPUT2 TO INPUT1 fn=warpfn+"x"+ext; typename RealImageType::Pointer xvec=ReadImage((char*)fn.c_str()); // std::cout << " done reading " << fn << std::endl; fn=warpfn+"y"+ext; typename RealImageType::Pointer yvec=ReadImage((char*)fn.c_str()); //std::cout << " done reading " << fn << std::endl; fn=warpfn+"z"+ext; typename RealImageType::Pointer zvec=NULL; //std::cout << " done reading " << fn << std::endl; if (ImageDimension == 3) zvec=ReadImage((char*)fn.c_str()); typename FieldType::Pointer field=FieldType::New(); field->SetLargestPossibleRegion( xvec->GetLargestPossibleRegion() ); field->SetBufferedRegion( xvec->GetLargestPossibleRegion() ); field->SetLargestPossibleRegion( xvec->GetLargestPossibleRegion() ); field->SetOrigin(xvec->GetOrigin()); field->SetDirection(xvec->GetDirection()); field->Allocate(); itk::ImageRegionIteratorWithIndex it( xvec, xvec->GetLargestPossibleRegion() ); // std::cout << " spacing xv " << xvec->GetSpacing()[0] //<< " field " << field->GetSpacing()[0] << std::endl; field->SetSpacing(xvec->GetSpacing()); field->SetOrigin(xvec->GetOrigin()); unsigned int ct = 0; for (it.GoToBegin(); !it.IsAtEnd(); ++it) { ct++; typename ImageType::IndexType index=it.GetIndex(); VectorType disp; disp[0]=xvec->GetPixel(index); disp[1]=yvec->GetPixel(index); if (ImageDimension == 3)disp[2]=zvec->GetPixel(index); field->SetPixel(index,disp); // if (ct == 10000) std::cout << " 10000th pix " << disp << std::endl; } return field; } template typename TImage::Pointer MakeNewImage(typename TImage::Pointer image1, typename TImage::PixelType initval ) { typedef itk::ImageRegionIteratorWithIndex Iterator; typename TImage::Pointer varimage=TImage::New(); varimage->SetLargestPossibleRegion( image1->GetLargestPossibleRegion() ); varimage->SetBufferedRegion( image1->GetLargestPossibleRegion() ); varimage->SetLargestPossibleRegion( image1->GetLargestPossibleRegion() ); varimage->Allocate(); varimage->SetSpacing(image1->GetSpacing()); varimage->SetOrigin(image1->GetOrigin()); varimage->SetDirection(image1->GetDirection()); Iterator vfIter2( varimage, varimage->GetLargestPossibleRegion() ); for( vfIter2.GoToBegin(); !vfIter2.IsAtEnd(); ++vfIter2 ) { if (initval >= 0) vfIter2.Set(initval); else vfIter2.Set(image1->GetPixel(vfIter2.GetIndex())); } return varimage; } template void WriteDisplacementField(TField* field,std::string filename) { typedef TField FieldType; typedef typename FieldType::PixelType VectorType; enum { ImageDimension = FieldType::ImageDimension }; typedef itk::Image RealImageType; // Initialize the caster to the displacement field typedef itk::VectorIndexSelectionCastImageFilter IndexSelectCasterType; for (unsigned int dim=0; dimSetInput( field ); fieldCaster->SetIndex( dim ); fieldCaster->Update(); // Set up the output filename std::string outfile=filename+static_cast('x'+dim)+std::string("vec.nii.gz"); std::cout << "Writing displacements to " << outfile << " spacing " << field->GetSpacing()[0] << std::endl; typename RealImageType::Pointer fieldcomponent=fieldCaster->GetOutput(); fieldcomponent->SetSpacing(field->GetSpacing()); fieldcomponent->SetOrigin(field->GetOrigin()); fieldcomponent->SetDirection(field->GetDirection()); WriteImage(fieldcomponent, outfile.c_str()); } std::cout << "...done" << std::endl; return; } template void WriteDisplacementField2(TField* field, std::string filename, std::string app) { typedef TField FieldType; typedef typename FieldType::PixelType VectorType; enum { ImageDimension = FieldType::ImageDimension }; typedef itk::Image RealImageType; // Initialize the caster to the displacement field typedef itk::VectorIndexSelectionCastImageFilter IndexSelectCasterType; for (unsigned int dim=0; dimSetInput( field ); fieldCaster->SetIndex( dim ); fieldCaster->Update(); // Set up the output filename std::string outfile=filename+static_cast('x'+dim)+std::string(app); std::cout << "Writing displacements to " << outfile << " spacing " << field->GetSpacing()[0] << std::endl; typename RealImageType::Pointer fieldcomponent=fieldCaster->GetOutput(); fieldcomponent->SetSpacing(field->GetSpacing()); fieldcomponent->SetOrigin(field->GetOrigin()); WriteImage(fieldcomponent, outfile.c_str()); } std::cout << "...done" << std::endl; return; } #endif ants-1.9.2+svn680.dfsg/Utilities/antsCommandLineOption.cxx000066400000000000000000000047361147325206600234360ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: antsCommandLineOption.cxx,v $ Language: C++ Date: $Date: 2009/01/22 22:48:30 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include "antsCommandLineOption.h" namespace itk { namespace ants { CommandLineOption ::CommandLineOption() : m_ShortName( '\0' ), m_LongName( "" ), m_Description( "" ) { this->m_Values.clear(); this->m_UsageOptions.clear(); } void CommandLineOption ::AddValue( std::string value, char leftDelimiter, char rightDelimiter ) { std::string::size_type leftDelimiterPos = value.find( leftDelimiter ); std::string::size_type rightDelimiterPos = value.find( rightDelimiter ); if( leftDelimiterPos == std::string::npos || rightDelimiterPos == std::string::npos ) { this->m_Values.push_front( value ); ValueStackType parameters; this->m_Parameters.push_front( parameters ); } else { ValueStackType parameters; this->m_Values.push_front( value.substr( 0, leftDelimiterPos ) ); std::string::size_type leftPos = leftDelimiterPos; std::string::size_type rightPos = value.find( ',', leftPos+1 ); while( rightPos != std::string::npos ) { parameters.push_back( value.substr( leftPos+1, rightPos - leftPos - 1 ) ); leftPos = rightPos; rightPos = value.find( ',', leftPos+1 ); } rightPos = rightDelimiterPos; parameters.push_back( value.substr( leftPos+1, rightPos - leftPos - 1 ) ); this->m_Parameters.push_front( parameters ); } this->Modified(); } void CommandLineOption ::SetValue( unsigned int i, std::string value ) { this->m_Values[i] = value; this->Modified(); } void CommandLineOption ::SetUsageOption( unsigned int i, std::string usage ) { if( i >= this->m_UsageOptions.size() ) { this->m_UsageOptions.resize( i + 1 ); } this->m_UsageOptions[i] = usage; this->Modified(); } } // end namespace ants } // end namespace itk ants-1.9.2+svn680.dfsg/Utilities/antsCommandLineOption.h000066400000000000000000000117651147325206600230630ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: antsCommandLineOption.h,v $ Language: C++ Date: $Date: 2009/01/22 22:48:30 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __antsCommandLineOption_h #define __antsCommandLineOption_h #include "itkDataObject.h" #include "itkObjectFactory.h" #include "itkMacro.h" #include "itkNumericTraits.h" #include #include #include namespace itk { namespace ants { /** \class CommandLineOption \brief Simple data structure for holding command line options. An option can have multiple values with each value holding 0 or more parameters. E.g. suppose we were creating an image registration program which has several transformation model options such as 'rigid', 'affine', and 'deformable'. A instance of the command line option could have a long name of "transformation", short name 't', and description "Transformation model---rigid, affine, or deformable". The values for this option would be "rigid", "affine", and "deformable". Each value would then hold parameters that relate to that value. For example, a possible subsection of the command line would be " --transformation rigid[parameter1,parameter2,etc.] -m mutualinformation[parameter1] --optimization gradientdescent" */ class ITK_EXPORT CommandLineOption : public DataObject { public: typedef CommandLineOption Self; typedef DataObject Superclass; typedef SmartPointer Pointer; itkNewMacro( Self ); itkTypeMacro( Option, DataObject ); typedef std::string ValueType; typedef std::deque ValueStackType; typedef std::deque ParameterStackType; ValueStackType GetValues() { return this->m_Values; } unsigned int GetNumberOfValues() { return this->m_Values.size(); } std::string GetValue( unsigned int i = 0 ) { if( i < this->m_Values.size() ) { return this->m_Values[i]; } else { return std::string( "" ); } } ValueStackType GetUsageOptions() { return this->m_UsageOptions; } unsigned int GetNumberOfUsageOptions() { return this->m_UsageOptions.size(); } std::string GetUsageOption( unsigned int i = 0 ) { if( i < this->m_UsageOptions.size() ) { return this->m_UsageOptions[i]; } else { return std::string( "" ); } } ValueStackType GetParameters( unsigned int i = 0 ) { if( i < this->m_Parameters.size() ) { return this->m_Parameters[i]; } else { ValueStackType empty; return empty; } } std::string GetParameter( unsigned int i, unsigned int j ) { if( i < this->m_Parameters.size() && j < this->m_Parameters[i].size() ) { return this->m_Parameters[i][j]; } else { return std::string( "" ); } } std::string GetParameter( unsigned int j ) { return this->GetParameter( 0, j ); } unsigned int GetNumberOfParameters( unsigned int i = 0 ) { if( i < this->m_Parameters.size() ) { return this->m_Parameters[i].size(); } else { return 0; } } itkSetMacro( ShortName, char ); itkGetMacro( ShortName, char ); itkSetMacro( LongName, std::string ); itkGetMacro( LongName, std::string ); itkSetMacro( Description, std::string ); itkGetMacro( Description, std::string ); void AddValue( std::string, char, char ); void AddValue( std::string s ) { this->AddValue( s, '[', ']' ); } void SetValue( unsigned int, std::string ); void SetUsageOption( unsigned int, std::string ); protected: CommandLineOption(); virtual ~CommandLineOption() {}; private: char m_ShortName; std::string m_LongName; std::string m_Description; ValueStackType m_UsageOptions; ValueStackType m_Values; ParameterStackType m_Parameters; }; } // end namespace ants } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Utilities/antsCommandLineParser.cxx000066400000000000000000000351121147325206600234120ustar00rootroot00000000000000/*========================================================================= Program: Insight Segmentation & Registration Toolkit Module: $RCSfile: antsCommandLineParser.cxx,v $ Language: C++ Date: $Date: 2009/01/22 22:43:11 $ Version: $Revision: 1.1 $ Copyright (c) Insight Software Consortium. All rights reserved. See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #include "antsCommandLineParser.h" #include namespace itk { namespace ants { CommandLineParser ::CommandLineParser() { this->m_Options.clear(); this->m_Command.clear(); this->m_CommandDescription.clear(); this->m_UnknownOptions.clear(); this->m_LeftDelimiter = '['; this->m_RightDelimiter = ']'; } void CommandLineParser ::AddOption( OptionType::Pointer option ) { if( ( option->GetShortName() != '\0' || !this->GetOption( option->GetShortName() ) ) || ( !option->GetLongName().empty() || !this->GetOption( option->GetLongName() ) ) ) { this->m_Options.push_back( option ); } else { if( option->GetShortName() != '\0' && this->GetOption( option->GetShortName() ) ) { itkWarningMacro( "Duplicate short option '-" << option->GetShortName() << "'" ); } if( !( option->GetLongName().empty() ) && this->GetOption( option->GetLongName() ) ) { itkWarningMacro( "Duplicate long option '--" << option->GetLongName() << "'" ); } } } void CommandLineParser ::Parse( unsigned int argc, char **argv ) { std::vector arguments = this->RegroupCommandLineArguments( argc, argv ); unsigned int n = 0; this->m_Command = arguments[n++]; while( n < arguments.size() ) { std::string argument = arguments[n++]; std::string name; name.clear(); if( argument.find( "--" ) == 0 ) { name = argument.substr( 2, argument.length()-1 ); } else if( argument.find( "-" ) == 0 && argument.find( "--" ) > 0 ) { name = argument.substr( 1, 2 ); } if( !( name.empty() ) ) { OptionType::Pointer option = this->GetOption( name ); if( !option ) { OptionType::Pointer unknownOption = OptionType::New(); if( name.length() > 1 ) { unknownOption->SetLongName( name ); } else { unknownOption->SetShortName( name.at( 0 ) ); } if( n == arguments.size() ) { unknownOption->AddValue( "1", this->m_LeftDelimiter, this->m_RightDelimiter ); } else { for( unsigned int m = n; m < arguments.size(); m++ ) { std::string value = arguments[m]; if( value.find( "-" ) != 0 ) { unknownOption->AddValue( value, this->m_LeftDelimiter, this->m_RightDelimiter ); } else { if( m == n ) { unknownOption->AddValue( "1", this->m_LeftDelimiter, this->m_RightDelimiter ); } n = m; break; } } } this->m_UnknownOptions.push_back( unknownOption ); } else // the option exists { if( n == arguments.size() ) { option->AddValue( "1", this->m_LeftDelimiter, this->m_RightDelimiter ); } else { for( unsigned int m = n; m < arguments.size(); m++ ) { std::string value = arguments[m]; if( value.find( "-" ) != 0 ) { option->AddValue( value, this->m_LeftDelimiter, this->m_RightDelimiter ); } else { if( m == n ) { option->AddValue( "1", this->m_LeftDelimiter, this->m_RightDelimiter ); } n = m; break; } } } } } } } std::vector CommandLineParser ::RegroupCommandLineArguments( unsigned int argc, char **argv ) { /** * Inclusion of this function allows the user to use spaces inside * the left and right delimiters. Also replace other left and right * delimiters. */ std::vector arguments; std::string currentArg( "" ); bool isArgOpen = false; for( unsigned int n = 0; n < argc; n++ ) { std::string a( argv[n] ); // replace left delimiters std::replace( a.begin(), a.end(), '{', '[' ); std::replace( a.begin(), a.end(), '(', '[' ); std::replace( a.begin(), a.end(), '<', '[' ); // replace right delimiters std::replace( a.begin(), a.end(), '}', ']' ); std::replace( a.begin(), a.end(), ')', ']' ); std::replace( a.begin(), a.end(), '>', ']' ); if( isArgOpen ) { std::size_t leftDelimiterPosition = a.find( this->m_LeftDelimiter ); if( leftDelimiterPosition != std::string::npos ) { itkExceptionMacro( "Incorrect command line specification." ); } std::size_t rightDelimiterPosition = a.find( this->m_RightDelimiter ); if( rightDelimiterPosition != std::string::npos ) { if( rightDelimiterPosition < a.length()-1 ) { itkExceptionMacro( "Incorrect command line specification." ); } else { currentArg += a; arguments.push_back( currentArg ); currentArg.clear(); isArgOpen = false; } } else { currentArg += a; } } else { std::size_t leftDelimiterPosition = a.find( this->m_LeftDelimiter ); std::size_t rightDelimiterPosition = a.find( this->m_RightDelimiter ); if( leftDelimiterPosition == std::string::npos ) { if( rightDelimiterPosition == std::string::npos ) { currentArg += a; arguments.push_back( currentArg ); currentArg.clear(); } else { itkExceptionMacro( "Incorrect command line specification." ); } } else if( leftDelimiterPosition != std::string::npos && rightDelimiterPosition != std::string::npos && leftDelimiterPosition < rightDelimiterPosition ) { if( rightDelimiterPosition < a.length()-1 ) { itkExceptionMacro( "Incorrect command line specification." ); } currentArg += a; arguments.push_back( currentArg ); currentArg.clear(); isArgOpen = false; } else if( rightDelimiterPosition == std::string::npos && leftDelimiterPosition != std::string::npos ) { currentArg += a; isArgOpen = true; } } } return arguments; } CommandLineParser::OptionType::Pointer CommandLineParser ::GetOption( std::string name ) { if( name.length() == 1 ) { return this->GetOption( name.at( 0 ) ); } OptionListType::iterator it; for( it = this->m_Options.begin(); it != this->m_Options.end(); it++ ) { if( name.compare( (*it)->GetLongName() ) == 0 ) { return *it; } } return NULL; } CommandLineParser::OptionType::Pointer CommandLineParser ::GetOption( char name ) { OptionListType::iterator it; for( it = this->m_Options.begin(); it != this->m_Options.end(); it++ ) { if( name == (*it)->GetShortName() ) { return *it; } } return NULL; } void CommandLineParser ::PrintMenu( std::ostream& os, Indent indent, bool printShortVersion ) const { os << std::endl; os << "COMMAND: " << std::endl; os << indent << this->m_Command << std::endl; if( !this->m_CommandDescription.empty() && !printShortVersion ) { std::stringstream ss1; ss1 << indent << indent; std::stringstream ss2; ss2 << this->m_CommandDescription; std::string description = this->BreakUpStringIntoNewLines( ss2.str(), ss1.str(), 80 ); os << indent << indent << description << std::endl; } os << std::endl; os << "OPTIONS: " << std::endl; OptionListType::const_iterator it; for( it = this->m_Options.begin(); it != this->m_Options.end(); it++ ) { os << indent; std::stringstream ss; ss << indent; if( (*it)->GetShortName() != '\0' ) { os << "-" << (*it)->GetShortName(); ss << Indent( 2 ); if( !( (*it)->GetLongName() ).empty() ) { os << ", " << "--" << (*it)->GetLongName() << " " << std::flush; ss << Indent( 5 + ( (*it)->GetLongName() ).length() ); } else { os << " " << std::flush; ss << Indent( 1 ); } } else { os << "--" << (*it)->GetLongName() << " " << std::flush; ss << Indent( 3 + ( (*it)->GetLongName() ).length() ); } if( (*it)->GetNumberOfUsageOptions() > 0 ) { os << (*it)->GetUsageOption( 0 ) << std::endl; for( unsigned int i = 1; i < (*it)->GetNumberOfUsageOptions(); i++ ) { os << ss.str() << (*it)->GetUsageOption( i ) << std::endl; } } else { os << std::endl; } if( !( (*it)->GetDescription().empty() ) && !printShortVersion ) { std::stringstream ss1; ss1 << indent << indent; std::stringstream ss2; ss2 << (*it)->GetDescription(); std::string description = this->BreakUpStringIntoNewLines( ss2.str(), ss1.str(), 80 ); os << indent << indent << description << std::endl; } if( !printShortVersion ) { if( (*it)->GetValues().size() == 1 ) { os << indent << indent << ": " << (*it)->GetValue( 0 ); if( (*it)->GetParameters( 0 ).size() > 0 ) { os << "["; if( (*it)->GetParameters( 0 ).size() == 1 ) { os << (*it)->GetParameter( 0, 0 ); } else { for( unsigned int i = 0; i < (*it)->GetParameters( 0 ).size()-1; i++ ) { os << (*it)->GetParameter( 0, i ) << ","; } os << (*it)->GetParameter( 0, (*it)->GetParameters( 0 ).size()-1 ); } os << "]"; } os << std::endl; } else if( (*it)->GetValues().size() > 1 ) { os << indent << indent << ": "; for( unsigned int n = 0; n < (*it)->GetValues().size()-1; n++ ) { os << (*it)->GetValue( n ); if( (*it)->GetParameters( n ).size() > 0 ) { os << "["; if( (*it)->GetParameters( n ).size() == 1 ) { os << (*it)->GetParameter( n, 0 ) << "], "; } else { for( unsigned int i = 0; i < (*it)->GetParameters( n ).size()-1; i++ ) { os << (*it)->GetParameter( n, i ) << ","; } os << (*it)->GetParameter( n, (*it)->GetParameters( n ).size()-1 ) << "], "; } } else { os << ", "; } } unsigned int n = (*it)->GetValues().size()-1; os << (*it)->GetValue( n ); if( (*it)->GetParameters( n ).size() > 0 ) { os << "["; if( (*it)->GetParameters( n ).size() == 1 ) { os << (*it)->GetParameter( n, 0 ) << "]"; } else { for( unsigned int i = 0; i < (*it)->GetParameters( n ).size()-1; i++ ) { os << (*it)->GetParameter( n, i ) << ","; } os << (*it)->GetParameter( n, (*it)->GetParameters( n ).size()-1 ) << "]"; } } } os << std::endl; } } } std::string CommandLineParser ::BreakUpStringIntoNewLines( std::string longString, std::string indentString, unsigned int numberOfCharactersPerLine ) const { std::vector tokens; this->TokenizeString( longString, tokens, " " ); std::string newString( "" ); unsigned int currentTokenId = 0; unsigned int currentLineLength = 0; while( currentTokenId < tokens.size() ) { if( tokens[currentTokenId].length() >= numberOfCharactersPerLine ) { newString += ( std::string( "\n" ) + tokens[currentTokenId] + std::string( "\n" ) ); currentTokenId++; currentLineLength = 0; } else if( currentTokenId < tokens.size() && currentLineLength + tokens[currentTokenId].length() > numberOfCharactersPerLine ) { newString += ( std::string( "\n" ) + indentString ); currentLineLength = 0; } else { newString += ( tokens[currentTokenId] + std::string( " " ) ); currentLineLength += ( tokens[currentTokenId].length() + 1 ); currentTokenId++; } } return newString; } void CommandLineParser ::TokenizeString( std::string str, std::vector &tokens, std::string delimiters ) const { // Skip delimiters at beginning. std::string::size_type lastPos = str.find_first_not_of( delimiters, 0 ); // Find first "non-delimiter". std::string::size_type pos = str.find_first_of( delimiters, lastPos ); while( std::string::npos != pos || std::string::npos != lastPos) { // Found a token, add it to the vector. tokens.push_back( str.substr( lastPos, pos - lastPos ) ); // Skip delimiters. Note the "not_of" lastPos = str.find_first_not_of( delimiters, pos ); // Find next "non-delimiter" pos = str.find_first_of( delimiters, lastPos ); } } /** * Standard "PrintSelf" method */ void CommandLineParser ::PrintSelf( std::ostream& os, Indent indent) const { Superclass::PrintSelf( os, indent ); os << indent << "Command: " << this->m_Command << std::endl; os << indent << "Options: " << std::endl; OptionListType::const_iterator it; for( it = this->m_Options.begin(); it != this->m_Options.end(); it++ ) { (*it)->Print( os, indent ); } if( this->m_UnknownOptions.size() ) { os << indent << "Unknown Options: " << std::endl; OptionListType::const_iterator its; for( its = this->m_UnknownOptions.begin(); its != this->m_UnknownOptions.end(); its++ ) { (*its)->Print( os, indent ); } } } } // end namespace ants } // end namespace itk ants-1.9.2+svn680.dfsg/Utilities/antsCommandLineParser.h000066400000000000000000000117251147325206600230430ustar00rootroot00000000000000/*========================================================================= Program: Insight Segmentation & Registration Toolkit Module: $RCSfile: antsCommandLineParser.h,v $ Language: C++ Date: $Date: 2009/01/22 22:43:11 $ Version: $Revision: 1.1 $ Copyright (c) Insight Software Consortium. All rights reserved. See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __antsCommandLineParser_h #define __antsCommandLineParser_h #include "antsCommandLineOption.h" #include "itkDataObject.h" #include "itkObjectFactory.h" #include "itkMacro.h" #include "itkNumericTraits.h" #include #include #include #include namespace itk { namespace ants { /** \class CommandLineParser \brief Simple command line parser. \par Parses the standard ( argc, argv ) variables which are stored as options in the helper class antsCommandLineOption. Also contains routines for converting types including std::vectors using 'x' as a delimiter. For example, I can specify the 3-element std::vector {10, 20, 30} as "10x20x30". */ class ITK_EXPORT CommandLineParser : public DataObject { public: /** Standard class typedefs. */ typedef CommandLineParser Self; typedef DataObject Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); /** Run-time type information (and related methods). */ itkTypeMacro( CommandLineParser, DataObject ); typedef CommandLineOption OptionType; typedef std::list OptionListType; typedef std::list StringListType; /** * Interface routines */ OptionType::Pointer GetOption( char ); OptionType::Pointer GetOption( std::string ); void Parse( unsigned int, char ** ); void AddOption( OptionType::Pointer ); void PrintMenu( std::ostream& os, Indent indent, bool printShortVersion = false ) const; itkSetStringMacro( Command ); itkGetStringMacro( Command ); itkSetStringMacro( CommandDescription ); itkGetStringMacro( CommandDescription ); OptionListType GetOptions() const { return this->m_Options; } OptionListType GetUnknownOptions() const { return this->m_UnknownOptions; } template TValue Convert( std::string optionString ) { TValue value; std::istringstream iss( optionString ); iss >> value; return value; } template std::vector ConvertVector( std::string optionString ) { std::vector values; std::string::size_type crosspos = optionString.find( 'x', 0 ); if( crosspos == std::string::npos ) { values.push_back( this->Convert( optionString ) ); } else { std::string element = optionString.substr( 0, crosspos ); TValue value; std::istringstream iss( element ); iss >> value; values.push_back( value ); while( crosspos != std::string::npos ) { std::string::size_type crossposfrom = crosspos; crosspos = optionString.find( 'x', crossposfrom + 1 ); if( crosspos == std::string::npos ) { element = optionString.substr( crossposfrom + 1, optionString.length() ); } else { element = optionString.substr( crossposfrom + 1, crosspos ); } std::istringstream iss( element ); iss >> value; values.push_back( value ); } } return values; } protected: CommandLineParser(); virtual ~CommandLineParser() {} void PrintSelf( std::ostream& os, Indent indent ) const; private: CommandLineParser( const Self& ); //purposely not implemented void operator=( const Self& ); //purposely not implemented std::vector RegroupCommandLineArguments( unsigned int, char ** ); std::string BreakUpStringIntoNewLines( std::string, const std::string, unsigned int ) const; void TokenizeString( std::string, std::vector &, std::string ) const; OptionListType m_Options; std::string m_Command; std::string m_CommandDescription; OptionListType m_UnknownOptions; char m_LeftDelimiter; char m_RightDelimiter; }; } // end namespace ants } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Utilities/itkBSplineControlPointImageFilter.h000066400000000000000000000353131147325206600253410ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkBSplineControlPointImageFilter.h,v $ Language: C++ Date: $Date: 2009/05/04 14:10:34 $ Version: $Revision: 1.5 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkBSplineControlPointImageFilter_h #define __itkBSplineControlPointImageFilter_h #include "itkImageToImageFilter.h" #include "itkBSplineKernelFunction.h" #include "itkCoxDeBoorBSplineKernelFunction.h" #include "itkFixedArray.h" #include "itkPointSet.h" #include "itkSingleValuedCostFunction.h" #include "itkVariableSizeMatrix.h" #include "itkVector.h" #include "itkVectorContainer.h" #include "vnl/vnl_matrix.h" namespace itk { /** * \class BSplineControlPointImageFilter * * \brief Auxilary class for the output of the class * itkBSplineScatteredDataPointSetToImageFilter. * * \par The output of the class itkBSplineScatteredDataPointSetToImageFilter * is a control point grid defining a B-spline object. This class is used to * hold various routines meant to operate on that control point grid. In * addition to specifying the control point grid as the input, the user * must also supply the spline order and the parametric domain (i.e. size, * domain, origin, direction, spacing). The output of the filter is the sampled * B-spline object. * * Other operations include * 1. Evaluation of the B-spline object at any point in the domain. * 2. Evaluation of the gradient. * 3. Evaluation of the Jacobian. * 4. Evaluation of the Hessian. * 5. Refinement of the input control point lattice such that the * corresponding B-spline mesh resolution is twice as great as * the original while maintaining the same valued B-spline object. * 6. Inverse estimation. Given a user-specified data point, one can * find the parameters which minimize the "distance" between the evaluated * data point and the B-spline object evaluated at those parameters. * This is useful, for example, in determining the parametric values of * a point on the curve closest to a user-specified point. */ /** * Class definition for ParameterCostFunction */ template class ITK_EXPORT ParameterCostFunction : public SingleValuedCostFunction { public: typedef ParameterCostFunction Self; typedef SingleValuedCostFunction Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Extract dimension from input image. */ itkStaticConstMacro( ParametricDimension, unsigned int, TControlPointLattice::ImageDimension ); /** Run-time type information (and related methods). */ itkTypeMacro( ParameterCostFunction, SingleValuedCostFunction ); /** Method for creation through the object factory. */ itkNewMacro( Self ); typedef Superclass::MeasureType MeasureType; typedef Superclass::DerivativeType DerivativeType; typedef Superclass::ParametersType ParametersType; typedef FixedArray ArrayType; itkSetObjectMacro( ControlPointLattice, TControlPointLattice ); itkSetMacro( Origin, typename TControlPointLattice::PointType ); itkSetMacro( Spacing, typename TControlPointLattice::SpacingType ); itkSetMacro( Size, typename TControlPointLattice::SizeType ); itkSetMacro( Direction, typename TControlPointLattice::DirectionType ); itkSetMacro( SplineOrder, ArrayType ); itkSetMacro( CloseDimension, ArrayType ); itkSetMacro( DataPoint, typename TControlPointLattice::PixelType ); virtual MeasureType GetValue( const ParametersType & parameters ) const; virtual void GetDerivative( const ParametersType & parameters, DerivativeType & derivative ) const; virtual unsigned int GetNumberOfParameters() const; protected: ParameterCostFunction(); virtual ~ParameterCostFunction(); private: ParameterCostFunction(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented typename TControlPointLattice::Pointer m_ControlPointLattice; typename TControlPointLattice::PointType m_Origin; typename TControlPointLattice::SpacingType m_Spacing; typename TControlPointLattice::SizeType m_Size; typename TControlPointLattice::DirectionType m_Direction; ArrayType m_SplineOrder; ArrayType m_CloseDimension; typename TControlPointLattice::PixelType m_DataPoint; }; /** * Class definition for BSplineControlPointImageFilter */ template class BSplineControlPointImageFilter : public ImageToImageFilter { public: typedef BSplineControlPointImageFilter Self; typedef ImageToImageFilter Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Extract dimension from input image. */ itkStaticConstMacro( ImageDimension, unsigned int, TInputImage::ImageDimension ); typedef TInputImage ControlPointLatticeType; typedef TOutputImage ImageType; /** Image typedef support. */ typedef typename ImageType::PixelType PixelType; typedef typename ImageType::RegionType RegionType; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::PointType PointType; typedef typename ImageType::PointType ContinuousIndexType; typedef typename TOutputImage::SpacingType SpacingType; typedef typename TOutputImage::PointType OriginType; typedef typename TOutputImage::SizeType SizeType; typedef typename TOutputImage::DirectionType DirectionType; /** Other typedef */ typedef float RealType; typedef Image RealImageType; typedef FixedArray ArrayType; typedef VariableSizeMatrix GradientType; typedef RealImageType HessianType; /** PointSet typedef support. */ typedef PointSet PointSetType; typedef typename PointSetType::PixelType PointDataType; typedef typename PointSetType::PointDataContainer PointDataContainerType; /** Interpolation kernel type (default spline order = 3) */ typedef CoxDeBoorBSplineKernelFunction<3> KernelType; typedef BSplineKernelFunction<0> KernelOrder0Type; typedef BSplineKernelFunction<1> KernelOrder1Type; typedef BSplineKernelFunction<2> KernelOrder2Type; typedef BSplineKernelFunction<3> KernelOrder3Type; /** Helper functions */ void SetSplineOrder( unsigned int ); void SetSplineOrder( ArrayType ); itkGetConstReferenceMacro( SplineOrder, ArrayType ); itkSetMacro( CloseDimension, ArrayType ); itkGetConstReferenceMacro( CloseDimension, ArrayType ); /** Gets and sets for the output image. */ itkSetMacro( Spacing, SpacingType ); itkGetConstMacro( Spacing, SpacingType ); itkSetMacro( Origin, OriginType ); itkGetConstMacro( Origin, OriginType ); itkSetMacro( Size, SizeType ); itkGetConstMacro( Size, SizeType ); itkSetMacro( Direction, DirectionType ); itkGetConstMacro( Direction, DirectionType ); /** * Evaluate the resulting B-spline object at a specified * point or index within the image domain. */ void EvaluateAtPoint( PointType, PixelType & ); void EvaluateAtIndex( IndexType, PixelType & ); void EvaluateAtContinuousIndex( ContinuousIndexType, PixelType & ); /** * Evaluate the resulting B-spline object at a specified * parameteric point. Note that the parameterization over * each dimension of the B-spline object is [0, 1). */ void Evaluate( PointType, PixelType & ); /** * Evaluate the gradient of the resulting B-spline object at a specified * point or index within the image domain. */ void EvaluateGradientAtPoint( PointType, GradientType & ); void EvaluateGradientAtIndex( IndexType, GradientType & ); void EvaluateGradientAtContinuousIndex( ContinuousIndexType, GradientType & ); /** * Evaluate the gradient of the resulting B-spline object * at a specified parameteric point. Note that the * parameterization over each dimension of the B-spline * object is [0, 1). */ void EvaluateGradient( PointType, GradientType & ); /** * Evaluate the Jacobian of the resulting B-spline object at a specified * point or index within the image domain. */ void EvaluateJacobianAtPoint( PointType pt, GradientType &jac ) { this->EvaluateGradientAtPoint( pt, jac ); GradientType I( jac.Rows(), jac.Cols() ); I.SetIdentity(); jac += I; } void EvaluateJacobianAtIndex( IndexType idx, GradientType &jac ) { this->EvaluateGradientAtIndex( idx, jac ); GradientType I( jac.Rows(), jac.Cols() ); I.SetIdentity(); jac += I; } void EvaluateJacobianAtContinuousIndex( ContinuousIndexType cidx, GradientType &jac ) { this->EvaluateGradientAtContinuousIndex( cidx, jac ); GradientType I( jac.Rows(), jac.Cols() ); I.SetIdentity(); jac += I; } /** * Evaluate the Jacobian with respect to the image of the resulting B-spline * object at a specified point or index within the image domain. */ void EvaluateSpatialJacobianAtPoint( PointType pt, GradientType &jac ) { this->EvaluateGradientAtPoint( pt, jac ); for ( unsigned int i = 0; i < jac.Cols(); i++ ) { RealType factor = static_cast( this->GetOutput()->GetLargestPossibleRegion().GetSize()[i] ) * this->GetOutput()->GetSpacing()[i]; for ( unsigned int j = 0; j < jac.Rows(); j++ ) { jac(i, j) *= factor; if ( i == j ) { jac(i, j) += 1.0; } } } } void EvaluateSpatialJacobianAtIndex( IndexType idx, GradientType &jac ) { PointType pt; this->GetOutput()->TransformIndexToPhysicalPoint( idx, pt ); this->EvaluateSpatialJacobianAtPoint( pt, jac ); } void EvaluateSpatialJacobianAtContinuousIndex( ContinuousIndexType cidx, GradientType &jac ) { PointType pt; this->GetOutput()->TransformContinuousIndexToPhysicalPoint( cidx, pt ); this->EvaluateSpatialJacobianAtPoint( pt, jac ); } /** * Evaluate the Hessian of the resulting B-spline object at a specified * point or index within the image domain. Since the Hessian for a vector * function is a 3-tensor, one must specify the component. */ void EvaluateHessianAtPoint( PointType, HessianType &, unsigned int ); void EvaluateHessianAtIndex( IndexType, HessianType &, unsigned int ); void EvaluateHessianAtContinuousIndex( ContinuousIndexType, GradientType &, unsigned int ); void EvaluateHessian( PointType, GradientType &, unsigned int ); /** * Given a B-spline object value and an initial parametric guess, use * bounded optimization (LBFGSB) to find the parameters corresponding to the * B-spline object value. */ void CalculateParametersClosestToDataPoint( PointDataType, PointType & ); /** * Generate a refined control point lattice from the input control point * lattice such that the resolution is doubled for each level. */ typename ControlPointLatticeType::Pointer RefineControlPointLattice( ArrayType ); protected: BSplineControlPointImageFilter(); virtual ~BSplineControlPointImageFilter(); void PrintSelf( std::ostream& os, Indent indent ) const; void GenerateData(); private: BSplineControlPointImageFilter( const Self& ); //purposely not implemented void operator=( const Self& ); //purposely not implemented void GenerateOutputImageFast(); void CollapsePhiLattice( ControlPointLatticeType *, ControlPointLatticeType *, RealType, unsigned int ); void SetNumberOfLevels( ArrayType ); /** Parameters for the output image. */ SizeType m_Size; SpacingType m_Spacing; OriginType m_Origin; DirectionType m_Direction; ArrayType m_NumberOfLevels; bool m_DoMultilevel; unsigned int m_MaximumNumberOfLevels; vnl_matrix m_RefinedLatticeCoefficients[ImageDimension]; ArrayType m_CloseDimension; ArrayType m_SplineOrder; ArrayType m_NumberOfControlPoints; typename KernelType::Pointer m_Kernel[ImageDimension]; typename KernelOrder0Type::Pointer m_KernelOrder0; typename KernelOrder1Type::Pointer m_KernelOrder1; typename KernelOrder2Type::Pointer m_KernelOrder2; typename KernelOrder3Type::Pointer m_KernelOrder3; RealType m_BSplineEpsilon; inline typename RealImageType::IndexType NumberToIndex( unsigned int number, typename RealImageType::SizeType size ) { typename RealImageType::IndexType k; k[0] = 1; for ( unsigned int i = 1; i < ImageDimension; i++ ) { k[i] = size[ImageDimension-i-1]*k[i-1]; } typename RealImageType::IndexType index; for ( unsigned int i = 0; i < ImageDimension; i++ ) { index[ImageDimension-i-1] = static_cast( number/k[ImageDimension-i-1] ); number %= k[ImageDimension-i-1]; } return index; } }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkBSplineControlPointImageFilter.txx" #endif #endif ants-1.9.2+svn680.dfsg/Utilities/itkBSplineControlPointImageFilter.txx000066400000000000000000001000361147325206600257300ustar00rootroot00000000000000#ifndef __itkBSplineControlPointImageFilter_txx #define __itkBSplineControlPointImageFilter_txx #include "itkBSplineControlPointImageFilter.h" #include "itkImageDuplicator.h" #include "itkImageRegionConstIteratorWithIndex.h" #include "itkImageRegionIterator.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkLBFGSBOptimizer.h" namespace itk { /** * ParameterCostFunction class definitions */ template ParameterCostFunction ::ParameterCostFunction() { this->m_ControlPointLattice = NULL; } template ParameterCostFunction ::~ParameterCostFunction() { } template typename ParameterCostFunction::MeasureType ParameterCostFunction ::GetValue( const ParametersType & parameters ) const { typename TControlPointLattice::PointType point; for( unsigned int d = 0; d < ParametricDimension; d++ ) { point[d] = parameters[d]; if( this->m_CloseDimension[d] ) { MeasureType sign = -1.0; if( point[d] < 0.0 ) { sign = 1.0; } while( point[d] < 0 || point[d] >= 1.0 ) { point[d] += sign; } } } typedef BSplineControlPointImageFilter BSplineFilterType; typename BSplineFilterType::Pointer bspliner = BSplineFilterType::New(); bspliner->SetOrigin( this->m_Origin ); bspliner->SetSpacing( this->m_Spacing ); bspliner->SetSize( this->m_Size ); bspliner->SetDirection( this->m_Direction ); bspliner->SetSplineOrder( this->m_SplineOrder ); bspliner->SetCloseDimension( this->m_CloseDimension ); bspliner->SetInput( this->m_ControlPointLattice ); typename TControlPointLattice::PixelType value; bspliner->Evaluate( point, value ); MeasureType metric = 0.0; for( unsigned int d = 0; d < value.Size(); d++ ) { metric += vnl_math_sqr( value[d] - this->m_DataPoint[d] ); } return metric; } template void ParameterCostFunction ::GetDerivative( const ParametersType & parameters, DerivativeType & derivative ) const { typename TControlPointLattice::PointType point; for( unsigned int d = 0; d < ParametricDimension; d++ ) { point[d] = parameters[d]; if( this->m_CloseDimension[d] ) { MeasureType sign = -1.0; if( point[d] < 0.0 ) { sign = 1.0; } while( point[d] < 0 || point[d] >= 1.0 ) { point[d] += sign; } } } typedef BSplineControlPointImageFilter BSplineFilterType; typename BSplineFilterType::Pointer bspliner = BSplineFilterType::New(); bspliner->SetOrigin( this->m_Origin ); bspliner->SetSpacing( this->m_Spacing ); bspliner->SetSize( this->m_Size ); bspliner->SetDirection( this->m_Direction ); bspliner->SetSplineOrder( this->m_SplineOrder ); bspliner->SetCloseDimension( this->m_CloseDimension ); bspliner->SetInput( this->m_ControlPointLattice ); typename TControlPointLattice::PixelType value; bspliner->Evaluate( point, value ); typename BSplineFilterType::GradientType gradient; bspliner->EvaluateGradient( point, gradient ); derivative.SetSize( this->GetNumberOfParameters() ); derivative.Fill( 0.0 ); for( unsigned int i = 0; i < gradient.Rows(); i++ ) { for( unsigned int j = 0; j < gradient.Cols(); j++ ) { derivative[j] += ( 2.0 * ( value[i] - this->m_DataPoint[i] ) * gradient(i, j) ); } } } template unsigned int ParameterCostFunction ::GetNumberOfParameters() const { return ParametricDimension; } /** * BSplineControlPointImageFilter class definitions */ template BSplineControlPointImageFilter ::BSplineControlPointImageFilter() { this->m_SplineOrder.Fill( 3 ); for( unsigned int i = 0; i < ImageDimension; i++ ) { this->m_NumberOfControlPoints[i] = ( this->m_SplineOrder[i]+1 ); this->m_Kernel[i] = KernelType::New(); this->m_Kernel[i]->SetSplineOrder( this->m_SplineOrder[i] ); } this->m_KernelOrder0 = KernelOrder0Type::New(); this->m_KernelOrder1 = KernelOrder1Type::New(); this->m_KernelOrder2 = KernelOrder2Type::New(); this->m_KernelOrder3 = KernelOrder3Type::New(); this->m_NumberOfLevels.Fill( 1 ); this->m_CloseDimension.Fill( 0 ); this->m_BSplineEpsilon = vcl_numeric_limits::epsilon(); } template BSplineControlPointImageFilter ::~BSplineControlPointImageFilter() { } template void BSplineControlPointImageFilter ::SetNumberOfLevels( ArrayType levels ) { this->m_NumberOfLevels = levels; this->m_MaximumNumberOfLevels = 1; for( unsigned int i = 0; i < ImageDimension; i++ ) { if( this->m_NumberOfLevels[i] == 0 ) { itkExceptionMacro( "The number of levels in each dimension must be greater than 0" ); } if( this->m_NumberOfLevels[i] > this->m_MaximumNumberOfLevels ) { this->m_MaximumNumberOfLevels = this->m_NumberOfLevels[i]; } } itkDebugMacro( "Setting m_NumberOfLevels to " << this->m_NumberOfLevels ); itkDebugMacro( "Setting m_MaximumNumberOfLevels to " << this->m_MaximumNumberOfLevels ); if( this->m_MaximumNumberOfLevels > 1 ) { this->m_DoMultilevel = true; } else { this->m_DoMultilevel = false; } this->SetSplineOrder( this->m_SplineOrder ); this->Modified(); } template void BSplineControlPointImageFilter ::SetSplineOrder( unsigned int order ) { this->m_SplineOrder.Fill( order ); this->SetSplineOrder( this->m_SplineOrder ); } template void BSplineControlPointImageFilter ::SetSplineOrder( ArrayType order ) { itkDebugMacro( "Setting m_SplineOrder to " << order ); this->m_SplineOrder = order; for( unsigned int i = 0; i < ImageDimension; i++ ) { if( this->m_SplineOrder[i] == 0 ) { itkExceptionMacro( "The spline order in each dimension must be greater than 0" ); } this->m_Kernel[i] = KernelType::New(); this->m_Kernel[i]->SetSplineOrder( this->m_SplineOrder[i] ); if( this->m_DoMultilevel ) { typename KernelType::MatrixType C; C = this->m_Kernel[i]->GetShapeFunctionsInZeroToOneInterval(); vnl_matrix R; vnl_matrix S; R.set_size( C.rows(), C.cols() ); S.set_size( C.rows(), C.cols() ); for( unsigned int j = 0; j < C.rows(); j++ ) { for( unsigned int k = 0; k < C.cols(); k++ ) { R(j, k) = S(j, k) = static_cast( C(j, k) ); } } for( unsigned int j = 0; j < C.cols(); j++ ) { RealType c = vcl_pow( static_cast( 2.0 ), static_cast( C.cols()-j-1 ) ); for( unsigned int k = 0; k < C.rows(); k++) { R(k, j) *= c; } } R = R.transpose(); R.flipud(); S = S.transpose(); S.flipud(); this->m_RefinedLatticeCoefficients[i] = ( vnl_svd( R ).solve( S ) ).extract( 2, S.cols() ); } } this->Modified(); } template void BSplineControlPointImageFilter ::GenerateData() { for( unsigned int i = 0; i < ImageDimension; i++) { if( this->m_Size[i] == 0 ) { itkExceptionMacro( "Size must be specified." ); } } this->GetOutput()->SetOrigin( this->m_Origin ); this->GetOutput()->SetSpacing( this->m_Spacing ); this->GetOutput()->SetRegions( this->m_Size ); this->GetOutput()->SetDirection( this->m_Direction ); this->GetOutput()->Allocate(); /** * Calculate the appropriate epsilon value. */ unsigned int maximumNumberOfSpans = 0; for( unsigned int d = 0; d < ImageDimension; d++ ) { unsigned int numberOfSpans = this->m_NumberOfControlPoints[d] - this->m_SplineOrder[d]; numberOfSpans <<= ( this->m_NumberOfLevels[d] - 1 ); if( numberOfSpans > maximumNumberOfSpans ) { maximumNumberOfSpans = numberOfSpans; } } this->m_BSplineEpsilon = 10.0 * vcl_numeric_limits::epsilon(); while( static_cast( maximumNumberOfSpans ) == static_cast( maximumNumberOfSpans ) - this->m_BSplineEpsilon ) { this->m_BSplineEpsilon *= 10.0; } this->m_BSplineEpsilon = 0.0001; for( unsigned int i = 0; i < ImageDimension; i++) { this->m_NumberOfControlPoints[i] = this->GetInput()->GetLargestPossibleRegion().GetSize()[i]; } this->GenerateOutputImageFast(); } template typename BSplineControlPointImageFilter ::ControlPointLatticeType::Pointer BSplineControlPointImageFilter ::RefineControlPointLattice( ArrayType numberOfLevels ) { this->SetNumberOfLevels( numberOfLevels ); typedef ImageDuplicator ImageDuplicatorType; typename ImageDuplicatorType::Pointer Duplicator = ImageDuplicatorType::New(); Duplicator->SetInputImage( this->GetInput() ); Duplicator->Update(); typename ControlPointLatticeType::Pointer psiLattice = ControlPointLatticeType::New(); psiLattice = Duplicator->GetOutput(); for( unsigned int m = 1; m < this->m_MaximumNumberOfLevels; m++ ) { ArrayType numberOfNewControlPoints; for( unsigned int i = 0; i < ImageDimension; i++ ) { numberOfNewControlPoints[i] = psiLattice->GetLargestPossibleRegion().GetSize()[i]; } for( unsigned int i = 0; i < ImageDimension; i++ ) { if( m < this->m_NumberOfLevels[i] ) { numberOfNewControlPoints[i] = 2 * numberOfNewControlPoints[i]-this->m_SplineOrder[i]; } } typename RealImageType::RegionType::SizeType size; for( unsigned int i = 0; i < ImageDimension; i++ ) { if( this->m_CloseDimension[i] ) { size[i] = numberOfNewControlPoints[i] - this->m_SplineOrder[i]; } else { size[i] = numberOfNewControlPoints[i]; } } typename ControlPointLatticeType::Pointer refinedLattice = ControlPointLatticeType::New(); refinedLattice->SetRegions( size ); refinedLattice->Allocate(); PixelType data; data.Fill( 0.0 ); refinedLattice->FillBuffer( data ); typename ControlPointLatticeType::IndexType idx; typename ControlPointLatticeType::IndexType idx_Psi; typename ControlPointLatticeType::IndexType tmp; typename ControlPointLatticeType::IndexType tmp_Psi; typename ControlPointLatticeType::IndexType off; typename ControlPointLatticeType::IndexType off_Psi; typename ControlPointLatticeType::RegionType::SizeType size_Psi; size.Fill( 2 ); unsigned int N = 1; for( unsigned int i = 0; i < ImageDimension; i++ ) { N *= ( this->m_SplineOrder[i] + 1 ); size_Psi[i] = this->m_SplineOrder[i] + 1; } ImageRegionIteratorWithIndex It( refinedLattice, refinedLattice->GetLargestPossibleRegion() ); It.GoToBegin(); while( !It.IsAtEnd() ) { idx = It.GetIndex(); for( unsigned int i = 0; i < ImageDimension; i++ ) { if( m < this->m_NumberOfLevels[i] ) { idx_Psi[i] = static_cast( 0.5*idx[i] ); } else { idx_Psi[i] = static_cast( idx[i] ); } } for( unsigned int i = 0; i < ( 2 << ImageDimension - 1 ); i++ ) { PixelType sum( 0.0 ); PixelType val; off = this->NumberToIndex( i, size ); bool outOfBoundary = false; for( unsigned int j = 0; j < ImageDimension; j++ ) { tmp[j] = idx[j] + off[j]; if( tmp[j] >= static_cast( numberOfNewControlPoints[j] ) && !this->m_CloseDimension[j] ) { outOfBoundary = true; break; } if( this->m_CloseDimension[j] ) { tmp[j] %= refinedLattice->GetLargestPossibleRegion().GetSize()[j]; } } if( outOfBoundary ) { continue; } for( unsigned int j = 0; j < N; j++ ) { off_Psi = this->NumberToIndex( j, size_Psi ); bool outOfBoundary = false; for( unsigned int k = 0; k < ImageDimension; k++ ) { tmp_Psi[k] = idx_Psi[k] + off_Psi[k]; if( tmp_Psi[k] >= static_cast( this->GetInput()->GetLargestPossibleRegion().GetSize()[k] ) && !this->m_CloseDimension[k] ) { outOfBoundary = true; break; } if( this->m_CloseDimension[k] ) { tmp_Psi[k] %= psiLattice->GetLargestPossibleRegion().GetSize()[k]; } } if( outOfBoundary ) { continue; } RealType coeff = 1.0; for( unsigned int k = 0; k < ImageDimension; k++ ) { coeff *= this->m_RefinedLatticeCoefficients[k]( off[k], off_Psi[k] ); } val = psiLattice->GetPixel( tmp_Psi ); val *= coeff; sum += val; } refinedLattice->SetPixel( tmp, sum ); } bool IsEvenIndex = false; while( !IsEvenIndex && !It.IsAtEnd() ) { ++It; idx = It.GetIndex(); IsEvenIndex = true; for( unsigned int i = 0; i < ImageDimension; i++ ) { if( idx[i] % 2 ) { IsEvenIndex = false; } } } } typedef ImageDuplicator ImageDuplicatorType; typename ImageDuplicatorType::Pointer Duplicator = ImageDuplicatorType::New(); Duplicator->SetInputImage( refinedLattice ); Duplicator->Update(); psiLattice = Duplicator->GetOutput(); } return psiLattice; } template void BSplineControlPointImageFilter ::GenerateOutputImageFast() { typename ControlPointLatticeType::Pointer collapsedPhiLattices[ImageDimension+1]; for( unsigned int i = 0; i < ImageDimension; i++ ) { collapsedPhiLattices[i] = ControlPointLatticeType::New(); collapsedPhiLattices[i]->SetOrigin( this->GetInput()->GetOrigin() ); collapsedPhiLattices[i]->SetSpacing( this->GetInput()->GetSpacing() ); typename ControlPointLatticeType::SizeType size; size.Fill( 1 ); for( unsigned int j = 0; j < i; j++ ) { size[j] = this->GetInput()->GetLargestPossibleRegion().GetSize()[j]; } collapsedPhiLattices[i]->SetRegions( size ); collapsedPhiLattices[i]->Allocate(); } typedef ImageDuplicator DuplicatorType; typename DuplicatorType::Pointer duplicator = DuplicatorType::New(); duplicator->SetInputImage( this->GetInput() ); duplicator->Update(); collapsedPhiLattices[ImageDimension] = duplicator->GetOutput(); ArrayType totalNumberOfSpans; for( unsigned int i = 0; i < ImageDimension; i++ ) { if( this->m_CloseDimension[i] ) { totalNumberOfSpans[i] = this->GetInput()->GetLargestPossibleRegion().GetSize()[i]; } else { totalNumberOfSpans[i] = this->GetInput()->GetLargestPossibleRegion().GetSize()[i] - this->m_SplineOrder[i]; } } FixedArray U; FixedArray currentU; currentU.Fill( -1 ); typename ImageType::IndexType startIndex = this->GetOutput()->GetRequestedRegion().GetIndex(); typename ControlPointLatticeType::IndexType startPhiIndex = this->GetInput()->GetLargestPossibleRegion().GetIndex(); ImageRegionIteratorWithIndex It( this->GetOutput(), this->GetOutput()->GetRequestedRegion() ); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { typename ImageType::IndexType idx = It.GetIndex(); for( unsigned int i = 0; i < ImageDimension; i++ ) { U[i] = static_cast( totalNumberOfSpans[i] ) * static_cast( idx[i] - startIndex[i] ) / static_cast( this->m_Size[i] - 1 ); if( vnl_math_abs( U[i] - static_cast( totalNumberOfSpans[i] ) ) <= this->m_BSplineEpsilon ) { U[i] = static_cast( totalNumberOfSpans[i] ) - this->m_BSplineEpsilon; } if( U[i] >= static_cast( totalNumberOfSpans[i] ) ) { itkExceptionMacro( "The collapse point component " << U[i] << " is outside the corresponding parametric domain of [0, " << totalNumberOfSpans[i] << "]." ); } } for( int i = ImageDimension-1; i >= 0; i-- ) { if( U[i] != currentU[i] ) { for( int j = i; j >= 0; j-- ) { this->CollapsePhiLattice( collapsedPhiLattices[j+1], collapsedPhiLattices[j], U[j], j ); currentU[j] = U[j]; } break; } } It.Set( collapsedPhiLattices[0]->GetPixel( startPhiIndex ) ); } } template void BSplineControlPointImageFilter ::CollapsePhiLattice( ControlPointLatticeType *lattice, ControlPointLatticeType *collapsedLattice, RealType u, unsigned int dimension ) { ImageRegionIteratorWithIndex It ( collapsedLattice, collapsedLattice->GetLargestPossibleRegion() ); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { PointDataType data; data.Fill( 0.0 ); typename ControlPointLatticeType::IndexType idx = It.GetIndex(); for( unsigned int i = 0; i < this->m_SplineOrder[dimension] + 1; i++ ) { idx[dimension] = static_cast( u ) + i; RealType v = u - idx[dimension] + 0.5*static_cast( this->m_SplineOrder[dimension] - 1 ); RealType B = 0.0; switch( this->m_SplineOrder[dimension] ) { case 0: { B = this->m_KernelOrder0->Evaluate( v ); break; } case 1: { B = this->m_KernelOrder1->Evaluate( v ); break; } case 2: { B = this->m_KernelOrder2->Evaluate( v ); break; } case 3: { B = this->m_KernelOrder3->Evaluate( v ); break; } default: { B = this->m_Kernel[dimension]->Evaluate( v ); break; } } if( this->m_CloseDimension[dimension] ) { idx[dimension] %= lattice->GetLargestPossibleRegion().GetSize()[dimension]; } data += ( lattice->GetPixel( idx ) * B ); } It.Set( data ); } } template void BSplineControlPointImageFilter ::EvaluateAtPoint( PointType point, PixelType &data ) { for( unsigned int i = 0; i < ImageDimension; i++) { point[i] -= this->m_Origin[i]; point[i] /= ( static_cast( this->m_Size[i]-1 ) * this->m_Spacing[i] ); } this->Evaluate( point, data ); } template void BSplineControlPointImageFilter ::EvaluateAtIndex( IndexType idx, PixelType &data ) { PointType point; this->GetOutput()->TransformIndexToPhysicalPoint( idx, point ); this->EvaluateAtPoint( point, data ); } template void BSplineControlPointImageFilter ::EvaluateAtContinuousIndex( ContinuousIndexType idx, PixelType &data ) { PointType point; this->GetOutput()->TransformContinuousIndexToPhysicalPoint( idx, point ); this->EvaluateAtPoint( point, data ); } template void BSplineControlPointImageFilter ::Evaluate( PointType params, PixelType &data ) { vnl_vector p( ImageDimension ); for( unsigned int i = 0; i < ImageDimension; i++ ) { if( params[i] == NumericTraits::One ) { params[i] = NumericTraits::One - this->m_BSplineEpsilon; } if( params[i] < 0.0 || params[i] >= 1.0 ) { itkExceptionMacro( "The specified point " << params << " is outside the reparameterized domain [0, 1)." ); } p[i] = static_cast( params[i] ) * static_cast( this->GetInput()->GetLargestPossibleRegion().GetSize()[i] - this->m_SplineOrder[i] ); } typename RealImageType::RegionType::SizeType size; for( unsigned int i = 0; i < ImageDimension; i++ ) { size[i] = this->m_SplineOrder[i] + 1; } typename RealImageType::Pointer w; w = RealImageType::New(); w->SetRegions( size ); w->Allocate(); PointDataType val; data.Fill( 0.0 ); ImageRegionIteratorWithIndex Itw( w, w->GetLargestPossibleRegion() ); for( Itw.GoToBegin(); !Itw.IsAtEnd(); ++Itw ) { RealType B = 1.0; typename RealImageType::IndexType idx = Itw.GetIndex(); for( unsigned int i = 0; i < ImageDimension; i++ ) { RealType u = p[i] - static_cast( static_cast( p[i] ) + idx[i] ) + 0.5*static_cast( this->m_SplineOrder[i] - 1 ); switch( this->m_SplineOrder[i] ) { case 0: { B *= this->m_KernelOrder0->Evaluate( u ); break; } case 1: { B *= this->m_KernelOrder1->Evaluate( u ); break; } case 2: { B *= this->m_KernelOrder2->Evaluate( u ); break; } case 3: { B *= this->m_KernelOrder3->Evaluate( u ); break; } default: { B *= this->m_Kernel[i]->Evaluate( u ); break; } } } for( unsigned int i = 0; i < ImageDimension; i++ ) { idx[i] += static_cast( p[i] ); if( this->m_CloseDimension[i] ) { idx[i] %= this->GetInput()->GetLargestPossibleRegion().GetSize()[i]; } } if( this->GetInput()->GetLargestPossibleRegion().IsInside( idx ) ) { val = this->GetInput()->GetPixel( idx ); val *= B; data += val; } } } template void BSplineControlPointImageFilter ::EvaluateGradientAtPoint( PointType point, GradientType &gradient ) { for( unsigned int i = 0; i < ImageDimension; i++) { point[i] -= this->m_Origin[i]; point[i] /= ( static_cast( this->m_Size[i] - 1 ) * this->m_Spacing[i] ); } this->EvaluateGradient( point, gradient ); } template void BSplineControlPointImageFilter ::EvaluateGradientAtIndex( IndexType idx, GradientType &gradient ) { PointType point; this->GetOutput()->TransformIndexToPhysicalPoint( idx, point ); this->EvaluateGradientAtPoint( point, gradient ); } template void BSplineControlPointImageFilter ::EvaluateGradientAtContinuousIndex( ContinuousIndexType idx, GradientType &gradient ) { PointType point; this->GetOutput()->TransformContinuousIndexToPhysicalPoint( idx, gradient ); this->EvaluateGradientAtPoint( point, gradient ); } template void BSplineControlPointImageFilter ::EvaluateGradient( PointType params, GradientType &gradient ) { vnl_vector p( ImageDimension ); for( unsigned int i = 0; i < ImageDimension; i++ ) { if( params[i] < 0.0 || params[i] > 1.0 ) { itkExceptionMacro( "The specified point " << params << " is outside the reparameterized image domain [0, 1)." ); } if( params[i] == 1.0 ) { params[i] -= this->m_BSplineEpsilon; } p[i] = static_cast( params[i] ) * static_cast( this->GetInput()->GetLargestPossibleRegion().GetSize()[i] - this->m_SplineOrder[i] ); } typename RealImageType::RegionType::SizeType size; for( unsigned int i = 0; i < ImageDimension; i++ ) { size[i] = this->m_SplineOrder[i] + 1; } typename RealImageType::Pointer w; w = RealImageType::New(); w->SetRegions( size ); w->Allocate(); PixelType val; gradient.SetSize( val.Size(), ImageDimension ); gradient.Fill( 0.0 ); ImageRegionIteratorWithIndex Itw( w, w->GetLargestPossibleRegion() ); for( unsigned int j = 0; j < gradient.Cols(); j++ ) { for( Itw.GoToBegin(); !Itw.IsAtEnd(); ++Itw ) { RealType B = 1.0; typename RealImageType::IndexType idx = Itw.GetIndex(); for( unsigned int i = 0; i < ImageDimension; i++ ) { RealType u = p[i] - static_cast( static_cast( p[i] ) + idx[i] ) + 0.5*static_cast( this->m_SplineOrder[i] - 1 ); if( j == i ) { B *= this->m_Kernel[i]->EvaluateDerivative( u ); } else { B *= this->m_Kernel[i]->Evaluate( u ); } } for( unsigned int i = 0; i < ImageDimension; i++ ) { idx[i] += static_cast( p[i] ); if( this->m_CloseDimension[i] ) { idx[i] %= this->GetInput()->GetLargestPossibleRegion().GetSize()[i]; } } if( this->GetInput()->GetLargestPossibleRegion().IsInside( idx ) ) { val = this->GetInput()->GetPixel( idx ); val *= B; for( unsigned int i = 0; i < val.Size(); i++ ) { gradient( i, j ) += val[i]; } } } } } template void BSplineControlPointImageFilter ::EvaluateHessian( PointType params, GradientType &hessian, unsigned int component = 0 ) { vnl_vector p( ImageDimension ); for( unsigned int i = 0; i < ImageDimension; i++ ) { if( params[i] < 0.0 || params[i] > 1.0 ) { itkExceptionMacro( "The specified point " << params << " is outside the reparameterized image domain [0, 1)." ); } if( params[i] == 1.0 ) { params[i] = 1.0 - this->m_BSplineEpsilon; } p[i] = static_cast( params[i] ) * static_cast( this->GetInput()->GetLargestPossibleRegion().GetSize()[i] - this->m_SplineOrder[i] ); } typename RealImageType::RegionType::SizeType size; for( unsigned int i = 0; i < ImageDimension; i++ ) { size[i] = this->m_SplineOrder[i] + 1; } typename RealImageType::Pointer w; w = RealImageType::New(); w->SetRegions( size ); w->Allocate(); RealType val; hessian.SetSize( ImageDimension, ImageDimension ); hessian.Fill( 0.0 ); ImageRegionIteratorWithIndex Itw( w, w->GetLargestPossibleRegion() ); for( unsigned int j = 0; j < hessian.Rows(); j++ ) { for( unsigned int k = j; k < hessian.Cols(); k++ ) { for( Itw.GoToBegin(); !Itw.IsAtEnd(); ++Itw ) { RealType B = 1.0; typename RealImageType::IndexType idx = Itw.GetIndex(); for( unsigned int i = 0; i < ImageDimension; i++ ) { RealType u = p[i] - static_cast( static_cast( p[i] ) + idx[i] ) + 0.5 * static_cast( this->m_SplineOrder[i] - 1 ); if( i == j && j == k ) { B *= this->m_Kernel[i]->EvaluateNthDerivative( u, 2 ); } else if( ( i == j || i == k ) && j != k ) { B *= this->m_Kernel[i]->EvaluateDerivative( u ); } else { B *= this->m_Kernel[i]->Evaluate( u ); } } for( unsigned int i = 0; i < ImageDimension; i++ ) { idx[i] += static_cast( p[i] ); if( this->m_CloseDimension[i] ) { idx[i] %= this->GetInput()->GetLargestPossibleRegion().GetSize()[i]; } } if( this->GetInput()->GetLargestPossibleRegion().IsInside( idx ) ) { val = this->GetInput()->GetPixel( idx )[component]; val *= B; hessian( j, k ) += val; hessian( k, j ) += val; } } } } } template void BSplineControlPointImageFilter ::CalculateParametersClosestToDataPoint( PointDataType point, PointType ¶ms ) { typedef ParameterCostFunction CostFunctionType; typename CostFunctionType::Pointer costFunction = CostFunctionType::New(); costFunction->SetControlPointLattice( const_cast( this->GetInput() ) ); costFunction->SetOrigin( this->m_Origin ); costFunction->SetSpacing( this->m_Spacing ); costFunction->SetSize( this->m_Size ); costFunction->SetDirection( this->m_Direction ); costFunction->SetSplineOrder( this->m_SplineOrder ); costFunction->SetCloseDimension( this->m_CloseDimension ); costFunction->SetDataPoint( point ); /** * Rescale parameters between [0, 1) */ typename LBFGSBOptimizer::ParametersType initialParameters; initialParameters.SetSize( ImageDimension ); for( unsigned int d = 0; d < ImageDimension; d++ ) { initialParameters[d] = ( params[d] - this->m_Origin[d] ) / ( static_cast( this->m_Size[d] - 1 ) * this->m_Spacing[d] ); } typename LBFGSBOptimizer::BoundSelectionType boundSelection; boundSelection.SetSize( ImageDimension ); for( unsigned int d = 0; d < ImageDimension; d++ ) { if( this->m_CloseDimension[d] ) { boundSelection[d] = 0; } else { boundSelection[d] = 2; } } typename LBFGSBOptimizer::BoundValueType lowerBounds; typename LBFGSBOptimizer::BoundValueType upperBounds; lowerBounds.SetSize( ImageDimension ); upperBounds.SetSize( ImageDimension ); for( unsigned int d = 0; d < ImageDimension; d++ ) { lowerBounds[d] = 0.0; upperBounds[d] = 1.0; } typename LBFGSBOptimizer::Pointer optimizer = LBFGSBOptimizer::New(); optimizer->SetMinimize( true ); optimizer->SetCostFunction( costFunction ); optimizer->SetInitialPosition( initialParameters ); optimizer->SetCostFunctionConvergenceFactor( 1e1 ); optimizer->SetLowerBound( lowerBounds ); optimizer->SetUpperBound( upperBounds ); optimizer->SetBoundSelection( boundSelection ); optimizer->SetProjectedGradientTolerance( 1e-5 ); optimizer->StartOptimization(); typename LBFGSBOptimizer::ParametersType finalParameters = optimizer->GetCurrentPosition(); for( unsigned int i = 0; i < ImageDimension; i++) { point[i] -= this->m_Origin[i]; point[i] /= ( static_cast( this->m_Size[i] - 1 ) * this->m_Spacing[i] ); } for( unsigned int d = 0; d < ImageDimension; d++ ) { params[d] = finalParameters[d] * this->m_Spacing[d] * static_cast( this->m_Size[d] - 1 ) + this->m_Origin[d]; } } /** * Standard "PrintSelf" method */ template void BSplineControlPointImageFilter ::PrintSelf( std::ostream& os, Indent indent) const { Superclass::PrintSelf( os, indent ); for( unsigned int i = 0; i < ImageDimension; i++ ) { this->m_Kernel[i]->Print( os, indent ); } os << indent << "Spline order: " << this->m_SplineOrder << std::endl; os << indent << "Close dimension: " << this->m_CloseDimension << std::endl; os << indent << "Parametric domain" << std::endl; os << indent << " Origin: " << this->m_Origin << std::endl; os << indent << " Spacing: " << this->m_Spacing << std::endl; os << indent << " Size: " << this->m_Size << std::endl; os << indent << " Direction: " << this->m_Direction << std::endl; } } //end namespace itk #endif ants-1.9.2+svn680.dfsg/Utilities/itkBSplineScatteredDataPointSetToImageFilter.h000066400000000000000000000252501147325206600274070ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkBSplineScatteredDataPointSetToImageFilter.h,v $ Language: C++ Date: $Date: 2009-10-11 19:24:30 $ Version: $Revision: 1.10 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkBSplineScatteredDataPointSetToImageFilter_h #define __itkBSplineScatteredDataPointSetToImageFilter_h #include "itkPointSetToImageFilter.h" #include "itkBSplineKernelFunction.h" #include "itkCoxDeBoorBSplineKernelFunction.h" #include "itkFixedArray.h" #include "itkVariableSizeMatrix.h" #include "itkVector.h" #include "itkVectorContainer.h" #include "vnl/vnl_matrix.h" namespace itk { /** \class BSplineScatteredDataPointSetToImageFilter.h * \brief Image filter which provides a B-spline output approximation. * * Given an n-D image with scattered data, this filter finds * a fast approximation to that irregularly spaced data using uniform * B-splines. The traditional method of inverting the observation * matrix to find a least-squares fit is made obsolete. Therefore, * memory issues are not a concern and inverting large matrices is * not applicable. In addition, this allows fitting to be multi-threaded. * This class generalizes from Lee's original paper to encompass * n-D data in m-D parametric space and any *feasible* B-spline order as well * as the option of specifying a confidence value for each point. * * In addition to specifying the input point set, one must specify the number * of control points. The specified number of control points must be * > m_SplineOrder. If one wishes to use the multilevel component of * this algorithm, one must also specify the number of levels in the * hierarchy. If this is desired, the number of control points becomes * the number of control points for the coarsest level. The algorithm * then increases the number of control points at each level so that * the B-spline n-D grid is refined to twice the previous level. * * \author Nicholas J. Tustison * * Contributed by Nicholas J. Tustison, James C. Gee * in the Insight Journal paper: * http://hdl.handle.net/1926/140 * * \par REFERENCE * S. Lee, G. Wolberg, and S. Y. Shin, "Scattered Data Interpolation * with Multilevel B-Splines", IEEE Transactions on Visualization and * Computer Graphics, 3(3):228-244, 1997. * * \par REFERENCE * N.J. Tustison and J.C. Gee, "Generalized n-D C^k Scattered Data Approximation * with COnfidence Values", Proceedings of the MIAR conference, August 2006. */ template class BSplineScatteredDataPointSetToImageFilter : public PointSetToImageFilter { public: typedef BSplineScatteredDataPointSetToImageFilter Self; typedef PointSetToImageFilter Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Extract dimension from input and output image. */ itkStaticConstMacro( ImageDimension, unsigned int, TOutputImage::ImageDimension ); typedef TOutputImage ImageType; typedef TInputPointSet PointSetType; /** Image typedef support. */ typedef typename ImageType::PixelType PixelType; typedef typename ImageType::RegionType RegionType; typedef typename ImageType::SizeType SizeType; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::PointType ContinuousIndexType; /** PointSet typedef support. */ typedef typename PointSetType::PointType PointType; typedef typename PointSetType::PixelType PointDataType; typedef typename PointSetType::PointDataContainer PointDataContainerType; /** Other typedef */ typedef float RealType; typedef VectorContainer WeightsContainerType; typedef Image PointDataImageType; typedef typename PointDataImageType::Pointer PointDataImagePointer; typedef Image RealImageType; typedef typename RealImageType::Pointer RealImagePointer; typedef FixedArray ArrayType; typedef VariableSizeMatrix GradientType; /** * Interpolation kernel type (default spline order = 3) */ typedef CoxDeBoorBSplineKernelFunction<3> KernelType; typedef BSplineKernelFunction<0> KernelOrder0Type; typedef BSplineKernelFunction<1> KernelOrder1Type; typedef BSplineKernelFunction<2> KernelOrder2Type; typedef BSplineKernelFunction<3> KernelOrder3Type; /** Helper functions */ void SetNumberOfLevels( unsigned int ); void SetNumberOfLevels( ArrayType ); itkGetConstReferenceMacro( NumberOfLevels, ArrayType ); void SetSplineOrder( unsigned int ); void SetSplineOrder( ArrayType ); itkGetConstReferenceMacro( SplineOrder, ArrayType ); itkSetMacro( NumberOfControlPoints, ArrayType ); itkGetConstReferenceMacro( NumberOfControlPoints, ArrayType ); itkGetConstReferenceMacro( CurrentNumberOfControlPoints, ArrayType ); itkSetMacro( CloseDimension, ArrayType ); itkGetConstReferenceMacro( CloseDimension, ArrayType ); itkSetMacro( GenerateOutputImage, bool ); itkGetConstReferenceMacro( GenerateOutputImage, bool ); itkBooleanMacro( GenerateOutputImage ); void SetPointWeights( WeightsContainerType * weights ); /** * Get the control point lattice. */ itkSetMacro( PhiLattice, PointDataImagePointer ); itkGetConstMacro( PhiLattice, PointDataImagePointer ); /** * Evaluate the resulting B-spline object at a specified * point or index within the image domain. */ void EvaluateAtPoint( PointType, PointDataType & ); void EvaluateAtIndex( IndexType, PointDataType & ); void EvaluateAtContinuousIndex( ContinuousIndexType, PointDataType & ); /** * Evaluate the resulting B-spline object at a specified * parametric point. Note that the parameterization over * each dimension of the B-spline object is [0, 1]. */ void Evaluate( PointType, PointDataType & ); /** * Evaluate the gradient of the resulting B-spline object at a specified * point or index within the image domain. */ void EvaluateGradientAtPoint( PointType, GradientType & ); void EvaluateGradientAtIndex( IndexType, GradientType & ); void EvaluateGradientAtContinuousIndex( ContinuousIndexType, GradientType & ); /** * Evaluate the gradient of the resulting B-spline object * at a specified parametric point. Note that the * parameterization over each dimension of the B-spline * object is [0, 1]. */ void EvaluateGradient( PointType, GradientType & ); protected: BSplineScatteredDataPointSetToImageFilter(); virtual ~BSplineScatteredDataPointSetToImageFilter(); void PrintSelf( std::ostream& os, Indent indent ) const; /** Multithreaded function which generates the control point lattice. */ void ThreadedGenerateData( const RegionType&, int ); void BeforeThreadedGenerateData(); void AfterThreadedGenerateData(); /** Only the points are divided among the threads, so always return * a valid number */ int SplitRequestedRegion(int, int, RegionType&) { return this->GetNumberOfThreads(); } void GenerateData(); private: //purposely not implemented BSplineScatteredDataPointSetToImageFilter(const Self&); void operator=(const Self&); void RefineControlPointLattice(); void UpdatePointSet(); void GenerateOutputImage(); void GenerateOutputImageFast(); void CollapsePhiLattice( PointDataImageType *, PointDataImageType *, RealType, unsigned int ); bool m_DoMultilevel; bool m_GenerateOutputImage; bool m_UsePointWeights; unsigned int m_MaximumNumberOfLevels; unsigned int m_CurrentLevel; ArrayType m_NumberOfControlPoints; ArrayType m_CurrentNumberOfControlPoints; ArrayType m_CloseDimension; ArrayType m_SplineOrder; ArrayType m_NumberOfLevels; typename WeightsContainerType::Pointer m_PointWeights; typename PointDataImageType::Pointer m_PhiLattice; typename PointDataImageType::Pointer m_PsiLattice; typename PointDataContainerType::Pointer m_InputPointData; typename PointDataContainerType::Pointer m_OutputPointData; typename KernelType::Pointer m_Kernel[ImageDimension]; typename KernelOrder0Type::Pointer m_KernelOrder0; typename KernelOrder1Type::Pointer m_KernelOrder1; typename KernelOrder2Type::Pointer m_KernelOrder2; typename KernelOrder3Type::Pointer m_KernelOrder3; std::vector m_OmegaLatticePerThread; std::vector m_DeltaLatticePerThread; RealType m_BSplineEpsilon; vnl_matrix m_RefinedLatticeCoefficients[ImageDimension]; inline typename RealImageType::IndexType NumberToIndex( unsigned int number, typename RealImageType::SizeType size ) { typename RealImageType::IndexType k; k[0] = 1; for ( unsigned int i = 1; i < ImageDimension; i++ ) { k[i] = size[ImageDimension-i-1]*k[i-1]; } typename RealImageType::IndexType index; for ( unsigned int i = 0; i < ImageDimension; i++ ) { index[ImageDimension-i-1] = static_cast( number/k[ImageDimension-i-1] ); number %= k[ImageDimension-i-1]; } return index; } }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkBSplineScatteredDataPointSetToImageFilter.txx" #endif #endif ants-1.9.2+svn680.dfsg/Utilities/itkBSplineScatteredDataPointSetToImageFilter.txx000066400000000000000000001222221147325206600300000ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkBSplineScatteredDataPointSetToImageFilter.txx,v $ Language: C++ Date: $Date: 2009-10-02 12:36:37 $ Version: $Revision: 1.20 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkBSplineScatteredDataPointSetToImageFilter_txx #define __itkBSplineScatteredDataPointSetToImageFilter_txx #include "itkBSplineScatteredDataPointSetToImageFilter.h" #include "itkImageRegionConstIteratorWithIndex.h" #include "itkImageRegionIterator.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkImageDuplicator.h" #include "itkCastImageFilter.h" #include "itkNumericTraits.h" #include "vnl/vnl_math.h" #include "vnl/algo/vnl_matrix_inverse.h" #include "vnl/vnl_vector.h" #include "vcl_limits.h" namespace itk { /** * \author Nicholas J. Tustison * * Contributed by Nicholas J. Tustison, James C. Gee * in the Insight Journal paper: * http://hdl.handle.net/1926/140 */ template BSplineScatteredDataPointSetToImageFilter ::BSplineScatteredDataPointSetToImageFilter() { this->m_SplineOrder.Fill( 3 ); for( unsigned int i = 0; i < ImageDimension; i++ ) { this->m_NumberOfControlPoints[i] = ( this->m_SplineOrder[i] + 1 ); this->m_Kernel[i] = KernelType::New(); this->m_Kernel[i]->SetSplineOrder( this->m_SplineOrder[i] ); } this->m_KernelOrder0 = KernelOrder0Type::New(); this->m_KernelOrder1 = KernelOrder1Type::New(); this->m_KernelOrder2 = KernelOrder2Type::New(); this->m_KernelOrder3 = KernelOrder3Type::New(); this->m_CloseDimension.Fill( 0 ); this->m_DoMultilevel = false; this->m_GenerateOutputImage = true; this->m_NumberOfLevels.Fill( 1 ); this->m_MaximumNumberOfLevels = 1; this->m_PhiLattice = NULL; this->m_PsiLattice = PointDataImageType::New(); this->m_InputPointData = PointDataContainerType::New(); this->m_OutputPointData = PointDataContainerType::New(); this->m_PointWeights = WeightsContainerType::New(); this->m_UsePointWeights = false; this->m_BSplineEpsilon = vcl_numeric_limits::epsilon(); } template BSplineScatteredDataPointSetToImageFilter ::~BSplineScatteredDataPointSetToImageFilter() { } template void BSplineScatteredDataPointSetToImageFilter ::SetSplineOrder( unsigned int order ) { this->m_SplineOrder.Fill( order ); this->SetSplineOrder( this->m_SplineOrder ); } template void BSplineScatteredDataPointSetToImageFilter ::SetSplineOrder( ArrayType order ) { itkDebugMacro( "Setting m_SplineOrder to " << order ); this->m_SplineOrder = order; for( unsigned int i = 0; i < ImageDimension; i++ ) { if( this->m_SplineOrder[i] == 0 ) { itkExceptionMacro( "The spline order in each dimension must be greater than 0" ); } this->m_Kernel[i] = KernelType::New(); this->m_Kernel[i]->SetSplineOrder( this->m_SplineOrder[i] ); if( this->m_DoMultilevel ) { typename KernelType::MatrixType C; C = this->m_Kernel[i]->GetShapeFunctionsInZeroToOneInterval(); vnl_matrix R; vnl_matrix S; R.set_size( C.rows(), C.cols() ); S.set_size( C.rows(), C.cols() ); for( unsigned int j = 0; j < C.rows(); j++ ) { for( unsigned int k = 0; k < C.cols(); k++ ) { R(j, k) = S(j, k) = static_cast( C(j, k) ); } } for( unsigned int j = 0; j < C.cols(); j++ ) { RealType c = vcl_pow( static_cast( 2.0 ), static_cast( C.cols() )-j-1 ); for( unsigned int k = 0; k < C.rows(); k++) { R(k, j) *= c; } } R = R.transpose(); R.flipud(); S = S.transpose(); S.flipud(); this->m_RefinedLatticeCoefficients[i] = ( vnl_svd( R ).solve( S ) ).extract( 2, S.cols() ); } } this->Modified(); } template void BSplineScatteredDataPointSetToImageFilter ::SetNumberOfLevels( unsigned int levels ) { this->m_NumberOfLevels.Fill( levels ); this->SetNumberOfLevels( this->m_NumberOfLevels ); } template void BSplineScatteredDataPointSetToImageFilter ::SetNumberOfLevels( ArrayType levels ) { this->m_NumberOfLevels = levels; this->m_MaximumNumberOfLevels = 1; for( unsigned int i = 0; i < ImageDimension; i++ ) { if( this->m_NumberOfLevels[i] == 0 ) { itkExceptionMacro( "The number of levels in each dimension must be greater than 0" ); } if( this->m_NumberOfLevels[i] > this->m_MaximumNumberOfLevels ) { this->m_MaximumNumberOfLevels = this->m_NumberOfLevels[i]; } } itkDebugMacro( "Setting m_NumberOfLevels to " << this->m_NumberOfLevels ); itkDebugMacro( "Setting m_MaximumNumberOfLevels to " << this->m_MaximumNumberOfLevels ); if( this->m_MaximumNumberOfLevels > 1 ) { this->m_DoMultilevel = true; } else { this->m_DoMultilevel = false; } this->SetSplineOrder( this->m_SplineOrder ); this->Modified(); } template void BSplineScatteredDataPointSetToImageFilter ::SetPointWeights( WeightsContainerType * weights ) { this->m_UsePointWeights = true; this->m_PointWeights = weights; this->Modified(); } template void BSplineScatteredDataPointSetToImageFilter ::GenerateData() { /** * Create the output image */ itkDebugMacro( "Size: " << this->m_Size ); itkDebugMacro( "Origin: " << this->m_Origin ); itkDebugMacro( "Spacing: " << this->m_Spacing ); itkDebugMacro( "Direction: " << this->m_Direction ); for( unsigned int i = 0; i < ImageDimension; i++) { if( this->m_Size[i] == 0 ) { itkExceptionMacro( "Size must be specified." ); } } this->GetOutput()->SetOrigin( this->m_Origin ); this->GetOutput()->SetSpacing( this->m_Spacing ); this->GetOutput()->SetDirection( this->m_Direction ); this->GetOutput()->SetRegions( this->m_Size ); this->GetOutput()->Allocate(); /** * Perform some error checking on the input */ if( this->m_UsePointWeights && ( this->m_PointWeights->Size() != this->GetInput()->GetNumberOfPoints() ) ) { itkExceptionMacro( "The number of weight points and input points must be equal." ); } for( unsigned int i = 0; i < ImageDimension; i++ ) { if( this->m_NumberOfControlPoints[i] < this->m_SplineOrder[i] + 1 ) { itkExceptionMacro( "The number of control points must be greater than the spline order." ); } } /** * Calculate the appropriate epsilon value. */ unsigned int maximumNumberOfSpans = 0; for( unsigned int d = 0; d < ImageDimension; d++ ) { unsigned int numberOfSpans = this->m_NumberOfControlPoints[d] - this->m_SplineOrder[d]; numberOfSpans <<= ( this->m_NumberOfLevels[d] - 1 ); if( numberOfSpans > maximumNumberOfSpans ) { maximumNumberOfSpans = numberOfSpans; } } this->m_BSplineEpsilon = 10.0 * vcl_numeric_limits::epsilon(); while( static_cast( maximumNumberOfSpans ) == static_cast( maximumNumberOfSpans ) - this->m_BSplineEpsilon ) { this->m_BSplineEpsilon *= 10.0; } this->m_InputPointData->Initialize(); this->m_OutputPointData->Initialize(); if( this->GetInput()->GetNumberOfPoints() > 0 ) { typename PointSetType::PointDataContainer::ConstIterator It; It = this->GetInput()->GetPointData()->Begin(); while( It != this->GetInput()->GetPointData()->End() ) { if( !this->m_UsePointWeights ) { this->m_PointWeights->InsertElement( It.Index(), 1.0 ); } this->m_InputPointData->InsertElement( It.Index(), It.Value() ); this->m_OutputPointData->InsertElement( It.Index(), It.Value() ); ++It; } } this->m_CurrentLevel = 0; this->m_CurrentNumberOfControlPoints = this->m_NumberOfControlPoints; /** * Set up multithread processing to handle generating the * control point lattice. */ typename ImageSource::ThreadStruct str1; str1.Filter = this; this->GetMultiThreader()->SetNumberOfThreads( this->GetNumberOfThreads() ); this->GetMultiThreader()->SetSingleMethod( this->ThreaderCallback, &str1 ); /** * Multithread the generation of the control point lattice. */ this->BeforeThreadedGenerateData(); this->GetMultiThreader()->SingleMethodExecute(); this->AfterThreadedGenerateData(); this->UpdatePointSet(); if( this->m_DoMultilevel ) { this->m_PsiLattice->SetRegions( this->m_PhiLattice->GetLargestPossibleRegion() ); this->m_PsiLattice->Allocate(); PointDataType P( 0.0 ); this->m_PsiLattice->FillBuffer( P ); } for( this->m_CurrentLevel = 1; this->m_CurrentLevel < this->m_MaximumNumberOfLevels; this->m_CurrentLevel++ ) { ImageRegionIterator ItPsi( this->m_PsiLattice, this->m_PsiLattice->GetLargestPossibleRegion() ); ImageRegionIterator ItPhi( this->m_PhiLattice, this->m_PhiLattice->GetLargestPossibleRegion() ); for( ItPsi.GoToBegin(), ItPhi.GoToBegin(); !ItPsi.IsAtEnd(); ++ItPsi, ++ItPhi ) { ItPsi.Set( ItPhi.Get() + ItPsi.Get() ); } this->RefineControlPointLattice(); for( unsigned int i = 0; i < ImageDimension; i++ ) { if( this->m_CurrentLevel < this->m_NumberOfLevels[i] ) { this->m_CurrentNumberOfControlPoints[i] = 2*this->m_CurrentNumberOfControlPoints[i]-this->m_SplineOrder[i]; } } itkDebugMacro( "Current Level = " << this->m_CurrentLevel ); itkDebugMacro( " Current number of control points = " << this->m_CurrentNumberOfControlPoints ); RealType avg_p = 0.0; RealType totalWeight = 0.0; typename PointDataContainerType::Iterator ItIn = this->m_InputPointData->Begin(); typename PointDataContainerType::Iterator ItOut = this->m_OutputPointData->Begin(); while( ItIn != this->m_InputPointData->End() ) { this->m_InputPointData->InsertElement( ItIn.Index(), ItIn.Value() - ItOut.Value() ); if( this->GetDebug() ) { RealType weight = this->m_PointWeights->GetElement( ItIn.Index() ); avg_p += ( ItIn.Value() - ItOut.Value() ).GetNorm() * weight; totalWeight += weight; } ++ItIn; ++ItOut; } if( totalWeight > 0 ) { itkDebugMacro( "The average weighted difference norm of the point set is " << avg_p / totalWeight ); } /** * Set up multithread processing to handle generating the * control point lattice. */ typename ImageSource::ThreadStruct str2; str2.Filter = this; this->GetMultiThreader()->SetNumberOfThreads( this->GetNumberOfThreads() ); this->GetMultiThreader()->SetSingleMethod( this->ThreaderCallback, &str2 ); /** * Multithread the generation of the control point lattice. */ this->BeforeThreadedGenerateData(); this->GetMultiThreader()->SingleMethodExecute(); this->AfterThreadedGenerateData(); this->UpdatePointSet(); } if( this->m_DoMultilevel ) { ImageRegionIterator ItPsi( this->m_PsiLattice, this->m_PsiLattice->GetLargestPossibleRegion() ); ImageRegionIterator ItPhi( this->m_PhiLattice, this->m_PhiLattice->GetLargestPossibleRegion() ); for( ItPsi.GoToBegin(), ItPhi.GoToBegin(); !ItPsi.IsAtEnd(); ++ItPsi, ++ItPhi ) { ItPsi.Set( ItPhi.Get() + ItPsi.Get() ); } typedef ImageDuplicator ImageDuplicatorType; typename ImageDuplicatorType::Pointer Duplicator = ImageDuplicatorType::New(); Duplicator->SetInputImage( this->m_PsiLattice ); Duplicator->Update(); this->m_PhiLattice = Duplicator->GetOutput(); this->UpdatePointSet(); } if( this->m_GenerateOutputImage ) { //this->GenerateOutputImage(); this->GenerateOutputImageFast(); } } template void BSplineScatteredDataPointSetToImageFilter ::BeforeThreadedGenerateData() { this->m_DeltaLatticePerThread.resize( this->GetNumberOfThreads() ); this->m_OmegaLatticePerThread.resize( this->GetNumberOfThreads() ); } template void BSplineScatteredDataPointSetToImageFilter ::ThreadedGenerateData( const RegionType &itkNotUsed(region), int threadId ) { /** * Ignore the output region as we're only interested in dividing the * points among the threads. */ typename RealImageType::RegionType::SizeType size; for( unsigned int i = 0; i < ImageDimension; i++ ) { if( this->m_CloseDimension[i] ) { size[i] = this->m_CurrentNumberOfControlPoints[i] - this->m_SplineOrder[i]; } else { size[i] = this->m_CurrentNumberOfControlPoints[i]; } } this->m_OmegaLatticePerThread[threadId] = RealImageType::New(); this->m_OmegaLatticePerThread[threadId]->SetRegions( size ); this->m_OmegaLatticePerThread[threadId]->Allocate(); this->m_OmegaLatticePerThread[threadId]->FillBuffer( 0.0 ); this->m_DeltaLatticePerThread[threadId] = PointDataImageType::New(); this->m_DeltaLatticePerThread[threadId]->SetRegions( size ); this->m_DeltaLatticePerThread[threadId]->Allocate(); this->m_DeltaLatticePerThread[threadId]->FillBuffer( 0.0 ); for( unsigned int i = 0; i < ImageDimension; i++ ) { size[i] = this->m_SplineOrder[i] + 1; } typename RealImageType::Pointer w = RealImageType::New(); w->SetRegions( size ); w->Allocate(); typename PointDataImageType::Pointer phi = PointDataImageType::New(); phi->SetRegions( size ); phi->Allocate(); ImageRegionIteratorWithIndex Itw( w, w->GetLargestPossibleRegion() ); ImageRegionIteratorWithIndex Itp( phi, phi->GetLargestPossibleRegion() ); vnl_vector p( ImageDimension ); vnl_vector r( ImageDimension ); for( unsigned int i = 0; i < ImageDimension; i++ ) { r[i] = static_cast( this->m_CurrentNumberOfControlPoints[i] - this->m_SplineOrder[i] ) / ( static_cast( this->m_Size[i] - 1 ) * this->m_Spacing[i] ); } /** * Determine which points should be handled by this particular thread. */ int numberOfThreads = this->GetNumberOfThreads(); unsigned long numberOfPointsPerThread = static_cast( this->GetInput()->GetNumberOfPoints() / numberOfThreads ); unsigned int start = threadId * numberOfPointsPerThread; unsigned int end = start + numberOfPointsPerThread; if( threadId == this->GetNumberOfThreads() - 1 ) { end = this->GetInput()->GetNumberOfPoints(); } for( unsigned int n = start; n < end; n++ ) { PointType point; point.Fill( 0.0 ); this->GetInput()->GetPoint( n, &point ); for( unsigned int i = 0; i < ImageDimension; i++ ) { unsigned int totalNumberOfSpans = this->m_CurrentNumberOfControlPoints[i] - this->m_SplineOrder[i]; p[i] = ( point[i] - this->m_Origin[i] ) * r[i]; if( vnl_math_abs( p[i] - static_cast( totalNumberOfSpans ) ) <= this->m_BSplineEpsilon ) { p[i] = static_cast( totalNumberOfSpans ) - this->m_BSplineEpsilon; } if( p[i] >= static_cast( totalNumberOfSpans ) ) { itkExceptionMacro( "The reparameterized point component " << p[i] << " is outside the corresponding parametric domain of [0, " << totalNumberOfSpans << "]." ); } } RealType w2_sum = 0.0; for( Itw.GoToBegin(); !Itw.IsAtEnd(); ++Itw ) { RealType B = 1.0; typename RealImageType::IndexType idx = Itw.GetIndex(); for( unsigned int i = 0; i < ImageDimension; i++ ) { RealType u = static_cast( p[i] - static_cast( p[i] ) - idx[i] ) + 0.5*static_cast( this->m_SplineOrder[i] - 1 ); switch( this->m_SplineOrder[i] ) { case 0: { B *= this->m_KernelOrder0->Evaluate( u ); break; } case 1: { B *= this->m_KernelOrder1->Evaluate( u ); break; } case 2: { B *= this->m_KernelOrder2->Evaluate( u ); break; } case 3: { B *= this->m_KernelOrder3->Evaluate( u ); break; } default: { B *= this->m_Kernel[i]->Evaluate( u ); break; } } } Itw.Set( B ); w2_sum += B*B; } for( Itp.GoToBegin(), Itw.GoToBegin(); !Itp.IsAtEnd(); ++Itp, ++Itw ) { typename RealImageType::IndexType idx = Itw.GetIndex(); for( unsigned int i = 0; i < ImageDimension; i++ ) { idx[i] += static_cast( p[i] ); if( this->m_CloseDimension[i] ) { idx[i] %= this->m_DeltaLatticePerThread[threadId] ->GetLargestPossibleRegion().GetSize()[i]; } } RealType wc = this->m_PointWeights->GetElement( n ); RealType t = Itw.Get(); this->m_OmegaLatticePerThread[threadId]->SetPixel( idx, this->m_OmegaLatticePerThread[threadId]->GetPixel( idx ) + wc*t*t ); PointDataType data = this->m_InputPointData->GetElement( n ); data *= ( t / w2_sum ); Itp.Set( data ); data *= ( t * t * wc ); this->m_DeltaLatticePerThread[threadId]->SetPixel( idx, this->m_DeltaLatticePerThread[threadId]->GetPixel( idx ) + data ); } } } template void BSplineScatteredDataPointSetToImageFilter ::AfterThreadedGenerateData() { /** * Accumulate all the delta lattice and omega lattice values to * calculate the final phi lattice. */ ImageRegionIterator ItD( this->m_DeltaLatticePerThread[0], this->m_DeltaLatticePerThread[0]->GetLargestPossibleRegion() ); ImageRegionIterator ItO( this->m_OmegaLatticePerThread[0], this->m_OmegaLatticePerThread[0]->GetLargestPossibleRegion() ); for( int n = 1; n < this->GetNumberOfThreads(); n++ ) { ImageRegionIterator Itd( this->m_DeltaLatticePerThread[n], this->m_DeltaLatticePerThread[n]->GetLargestPossibleRegion() ); ImageRegionIterator Ito( this->m_OmegaLatticePerThread[n], this->m_OmegaLatticePerThread[n]->GetLargestPossibleRegion() ); ItD.GoToBegin(); ItO.GoToBegin(); Itd.GoToBegin(); Ito.GoToBegin(); while( !ItD.IsAtEnd() ) { ItD.Set( ItD.Get() + Itd.Get() ); ItO.Set( ItO.Get() + Ito.Get() ); ++ItD; ++ItO; ++Itd; ++Ito; } } /** * Generate the control point lattice */ typename RealImageType::RegionType::SizeType size; for( unsigned int i = 0; i < ImageDimension; i++ ) { if( this->m_CloseDimension[i] ) { size[i] = this->m_CurrentNumberOfControlPoints[i]-this->m_SplineOrder[i]; } else { size[i] = this->m_CurrentNumberOfControlPoints[i]; } } this->m_PhiLattice = PointDataImageType::New(); this->m_PhiLattice->SetRegions( size ); this->m_PhiLattice->Allocate(); this->m_PhiLattice->FillBuffer( 0.0 ); ImageRegionIterator ItP( this->m_PhiLattice, this->m_PhiLattice->GetLargestPossibleRegion() ); for( ItP.GoToBegin(), ItO.GoToBegin(), ItD.GoToBegin(); !ItP.IsAtEnd(); ++ItP, ++ItO, ++ItD ) { PointDataType P; P.Fill( 0 ); if( ItO.Get() != 0 ) { P = ItD.Get() / ItO.Get(); for( unsigned int i = 0; i < P.Size(); i++ ) { if( vnl_math_isnan( P[i] ) || vnl_math_isinf( P[i] ) ) { P[i] = 0; } } ItP.Set( P ); } } } template void BSplineScatteredDataPointSetToImageFilter ::RefineControlPointLattice() { ArrayType NumberOfNewControlPoints = this->m_CurrentNumberOfControlPoints; for( unsigned int i = 0; i < ImageDimension; i++ ) { if( this->m_CurrentLevel < this->m_NumberOfLevels[i] ) { NumberOfNewControlPoints[i] = 2*NumberOfNewControlPoints[i]-this->m_SplineOrder[i]; } } typename RealImageType::RegionType::SizeType size; for( unsigned int i = 0; i < ImageDimension; i++ ) { if( this->m_CloseDimension[i] ) { size[i] = NumberOfNewControlPoints[i] - this->m_SplineOrder[i]; } else { size[i] = NumberOfNewControlPoints[i]; } } typename PointDataImageType::Pointer RefinedLattice = PointDataImageType::New(); RefinedLattice->SetRegions( size ); RefinedLattice->Allocate(); PointDataType data; data.Fill( 0.0 ); RefinedLattice->FillBuffer( data ); typename PointDataImageType::IndexType idx; typename PointDataImageType::IndexType idx_Psi; typename PointDataImageType::IndexType tmp; typename PointDataImageType::IndexType tmp_Psi; typename PointDataImageType::IndexType off; typename PointDataImageType::IndexType off_Psi; typename PointDataImageType::RegionType::SizeType size_Psi; size.Fill( 2 ); unsigned int N = 1; for( unsigned int i = 0; i < ImageDimension; i++ ) { N *= ( this->m_SplineOrder[i] + 1 ); size_Psi[i] = this->m_SplineOrder[i] + 1; } ImageRegionIteratorWithIndex It( RefinedLattice, RefinedLattice->GetLargestPossibleRegion() ); It.GoToBegin(); while( !It.IsAtEnd() ) { idx = It.GetIndex(); for( unsigned int i = 0; i < ImageDimension; i++ ) { if( this->m_CurrentLevel < this->m_NumberOfLevels[i] ) { idx_Psi[i] = static_cast( 0.5*idx[i] ); } else { idx_Psi[i] = static_cast( idx[i] ); } } for( unsigned int i = 0; i < ( 2 << (ImageDimension-1) ); i++ ) { PointDataType sum( 0.0 ); PointDataType val( 0.0 ); off = this->NumberToIndex( i, size ); bool OutOfBoundary = false; for( unsigned int j = 0; j < ImageDimension; j++ ) { tmp[j] = idx[j] + off[j]; if( tmp[j] >= static_cast( NumberOfNewControlPoints[j] ) && !this->m_CloseDimension[j] ) { OutOfBoundary = true; break; } if( this->m_CloseDimension[j] ) { tmp[j] %= RefinedLattice->GetLargestPossibleRegion().GetSize()[j]; } } if( OutOfBoundary ) { continue; } for( unsigned int j = 0; j < N; j++ ) { off_Psi = this->NumberToIndex( j, size_Psi ); bool IsOutOfBoundary = false; for( unsigned int k = 0; k < ImageDimension; k++ ) { tmp_Psi[k] = idx_Psi[k] + off_Psi[k]; if( tmp_Psi[k] >= static_cast( this->m_CurrentNumberOfControlPoints[k] ) && !this->m_CloseDimension[k] ) { IsOutOfBoundary = true; break; } if( this->m_CloseDimension[k] ) { tmp_Psi[k] %= this->m_PsiLattice->GetLargestPossibleRegion().GetSize()[k]; } } if( IsOutOfBoundary ) { continue; } RealType coeff = 1.0; for( unsigned int k = 0; k < ImageDimension; k++ ) { coeff *= this->m_RefinedLatticeCoefficients[k]( off[k], off_Psi[k] ); } val = this->m_PsiLattice->GetPixel( tmp_Psi ); val *= coeff; sum += val; } RefinedLattice->SetPixel( tmp, sum ); } bool IsEvenIndex = false; while( !IsEvenIndex && !It.IsAtEnd() ) { ++It; idx = It.GetIndex(); IsEvenIndex = true; for( unsigned int i = 0; i < ImageDimension; i++ ) { if( idx[i] % 2 ) { IsEvenIndex = false; } } } } typedef ImageDuplicator ImageDuplicatorType; typename ImageDuplicatorType::Pointer Duplicator = ImageDuplicatorType::New(); Duplicator->SetInputImage( RefinedLattice ); Duplicator->Update(); this->m_PsiLattice = Duplicator->GetOutput(); } template void BSplineScatteredDataPointSetToImageFilter ::UpdatePointSet() { typename PointDataImageType::Pointer collapsedPhiLattices[ImageDimension+1]; for( unsigned int i = 0; i < ImageDimension; i++ ) { collapsedPhiLattices[i] = PointDataImageType::New(); collapsedPhiLattices[i]->SetOrigin( this->m_PhiLattice->GetOrigin() ); collapsedPhiLattices[i]->SetSpacing( this->m_PhiLattice->GetSpacing() ); collapsedPhiLattices[i]->SetDirection( this->m_PhiLattice->GetDirection() ); typename PointDataImageType::SizeType size; size.Fill( 1 ); for( unsigned int j = 0; j < i; j++ ) { size[j] = this->m_PhiLattice->GetLargestPossibleRegion().GetSize()[j]; } collapsedPhiLattices[i]->SetRegions( size ); collapsedPhiLattices[i]->Allocate(); } collapsedPhiLattices[ImageDimension] = this->m_PhiLattice; ArrayType totalNumberOfSpans; for( unsigned int i = 0; i < ImageDimension; i++ ) { if( this->m_CloseDimension[i] ) { totalNumberOfSpans[i] = this->m_PhiLattice->GetLargestPossibleRegion().GetSize()[i]; } else { totalNumberOfSpans[i] = this->m_PhiLattice->GetLargestPossibleRegion().GetSize()[i] - this->m_SplineOrder[i]; } } FixedArray U; FixedArray currentU; currentU.Fill( -1 ); typename PointDataImageType::IndexType startPhiIndex = this->m_PhiLattice->GetLargestPossibleRegion().GetIndex(); typename PointDataContainerType::ConstIterator ItIn; ItIn = this->m_InputPointData->Begin(); while( ItIn != this->m_InputPointData->End() ) { PointType point; point.Fill( 0.0 ); this->GetInput()->GetPoint( ItIn.Index(), &point ); for( unsigned int i = 0; i < ImageDimension; i++ ) { U[i] = static_cast( totalNumberOfSpans[i] ) * static_cast( point[i] - this->m_Origin[i] ) / ( static_cast( this->m_Size[i] - 1 ) * this->m_Spacing[i] ); if( vnl_math_abs( U[i] - static_cast( totalNumberOfSpans[i] ) ) <= this->m_BSplineEpsilon ) { U[i] = static_cast( totalNumberOfSpans[i] ) - this->m_BSplineEpsilon; } if( U[i] >= static_cast( totalNumberOfSpans[i] ) ) { itkExceptionMacro( "The collapse point component " << U[i] << " is outside the corresponding parametric domain of [0, " << totalNumberOfSpans[i] << "]." ); } } for( int i = ImageDimension-1; i >= 0; i-- ) { if( U[i] != currentU[i] ) { for( int j = i; j >= 0; j-- ) { this->CollapsePhiLattice( collapsedPhiLattices[j+1], collapsedPhiLattices[j], U[j], j ); currentU[j] = U[j]; } break; } } this->m_OutputPointData->InsertElement( ItIn.Index(), collapsedPhiLattices[0]->GetPixel( startPhiIndex ) ); ++ItIn; } } template void BSplineScatteredDataPointSetToImageFilter ::GenerateOutputImage() { ImageRegionIteratorWithIndex It( this->GetOutput(), this->GetOutput()->GetBufferedRegion() ); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { PointDataType data; this->EvaluateAtIndex( It.GetIndex(), data ); It.Set( data ); } } template void BSplineScatteredDataPointSetToImageFilter ::GenerateOutputImageFast() { typename PointDataImageType::Pointer collapsedPhiLattices[ImageDimension+1]; for( unsigned int i = 0; i < ImageDimension; i++ ) { collapsedPhiLattices[i] = PointDataImageType::New(); collapsedPhiLattices[i]->SetOrigin( this->m_PhiLattice->GetOrigin() ); collapsedPhiLattices[i]->SetSpacing( this->m_PhiLattice->GetSpacing() ); collapsedPhiLattices[i]->SetDirection( this->m_PhiLattice->GetDirection() ); typename PointDataImageType::SizeType size; size.Fill( 1 ); for( unsigned int j = 0; j < i; j++ ) { size[j] = this->m_PhiLattice->GetLargestPossibleRegion().GetSize()[j]; } collapsedPhiLattices[i]->SetRegions( size ); collapsedPhiLattices[i]->Allocate(); } collapsedPhiLattices[ImageDimension] = this->m_PhiLattice; ArrayType totalNumberOfSpans; for( unsigned int i = 0; i < ImageDimension; i++ ) { if( this->m_CloseDimension[i] ) { totalNumberOfSpans[i] = this->m_PhiLattice->GetLargestPossibleRegion().GetSize()[i]; } else { totalNumberOfSpans[i] = this->m_PhiLattice->GetLargestPossibleRegion().GetSize()[i] - this->m_SplineOrder[i]; } } FixedArray U; FixedArray currentU; currentU.Fill( -1 ); typename ImageType::IndexType startIndex = this->GetOutput()->GetRequestedRegion().GetIndex(); typename PointDataImageType::IndexType startPhiIndex = this->m_PhiLattice->GetLargestPossibleRegion().GetIndex(); ImageRegionIteratorWithIndex It( this->GetOutput(), this->GetOutput()->GetRequestedRegion() ); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { typename ImageType::IndexType idx = It.GetIndex(); for( unsigned int i = 0; i < ImageDimension; i++ ) { U[i] = static_cast( totalNumberOfSpans[i] ) * static_cast( idx[i] - startIndex[i] ) / static_cast( this->m_Size[i] - 1 ); if( vnl_math_abs( U[i] - static_cast( totalNumberOfSpans[i] ) ) <= this->m_BSplineEpsilon ) { U[i] = static_cast( totalNumberOfSpans[i] ) - this->m_BSplineEpsilon; } if( U[i] >= static_cast( totalNumberOfSpans[i] ) ) { itkExceptionMacro( "The collapse point component " << U[i] << " is outside the corresponding parametric domain of [0, " << totalNumberOfSpans[i] << "]." ); } } for( int i = ImageDimension-1; i >= 0; i-- ) { if( U[i] != currentU[i] ) { for( int j = i; j >= 0; j-- ) { this->CollapsePhiLattice( collapsedPhiLattices[j+1], collapsedPhiLattices[j], U[j], j ); currentU[j] = U[j]; } break; } } It.Set( collapsedPhiLattices[0]->GetPixel( startPhiIndex ) ); } } template void BSplineScatteredDataPointSetToImageFilter ::CollapsePhiLattice( PointDataImageType *lattice, PointDataImageType *collapsedLattice, RealType u, unsigned int dimension ) { ImageRegionIteratorWithIndex It ( collapsedLattice, collapsedLattice->GetLargestPossibleRegion() ); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { PointDataType data; data.Fill( 0.0 ); typename PointDataImageType::IndexType idx = It.GetIndex(); for( unsigned int i = 0; i < this->m_SplineOrder[dimension] + 1; i++ ) { idx[dimension] = static_cast( u ) + i; RealType v = u - idx[dimension] + 0.5*static_cast( this->m_SplineOrder[dimension] - 1 ); RealType B; switch( this->m_SplineOrder[dimension] ) { case 0: { B = this->m_KernelOrder0->Evaluate( v ); break; } case 1: { B = this->m_KernelOrder1->Evaluate( v ); break; } case 2: { B = this->m_KernelOrder2->Evaluate( v ); break; } case 3: { B = this->m_KernelOrder3->Evaluate( v ); break; } default: { B = this->m_Kernel[dimension]->Evaluate( v ); break; } } if( this->m_CloseDimension[dimension] ) { idx[dimension] %= lattice->GetLargestPossibleRegion().GetSize()[dimension]; } data += ( lattice->GetPixel( idx ) * B ); } It.Set( data ); } } template void BSplineScatteredDataPointSetToImageFilter ::EvaluateAtPoint( PointType point, PointDataType &data ) { for( unsigned int i = 0; i < ImageDimension; i++) { point[i] -= this->m_Origin[i]; point[i] /= ( static_cast( this->m_Size[i] - 1 ) * this->m_Spacing[i] ); } this->Evaluate( point, data ); } template void BSplineScatteredDataPointSetToImageFilter ::EvaluateAtIndex( IndexType idx, PointDataType &data ) { PointType point; this->GetOutput()->TransformIndexToPhysicalPoint( idx, point ); this->EvaluateAtPoint( point, data ); } template void BSplineScatteredDataPointSetToImageFilter ::EvaluateAtContinuousIndex( ContinuousIndexType idx, PointDataType &data ) { PointType point; this->GetOutput()->TransformContinuousIndexToPhysicalPoint( idx, point ); this->EvaluateAtPoint( point, data ); } template void BSplineScatteredDataPointSetToImageFilter ::Evaluate( PointType params, PointDataType &data ) { vnl_vector p( ImageDimension ); for( unsigned int i = 0; i < ImageDimension; i++ ) { if( params[i] == NumericTraits::One ) { params[i] = NumericTraits::One - this->m_BSplineEpsilon; } if( params[i] < 0.0 || params[i] >= 1.0 ) { itkExceptionMacro( "The specified point " << params << " is outside the reparameterized domain [0, 1]." ); } p[i] = static_cast( params[i] ) * static_cast( this->m_CurrentNumberOfControlPoints[i] - this->m_SplineOrder[i] ); } typename RealImageType::RegionType::SizeType size; for( unsigned int i = 0; i < ImageDimension; i++ ) { size[i] = this->m_SplineOrder[i] + 1; } typename RealImageType::Pointer w; w = RealImageType::New(); w->SetRegions( size ); w->Allocate(); PointDataType val; data.Fill( 0.0 ); ImageRegionIteratorWithIndex Itw( w, w->GetLargestPossibleRegion() ); for( Itw.GoToBegin(); !Itw.IsAtEnd(); ++Itw ) { RealType B = 1.0; typename RealImageType::IndexType idx = Itw.GetIndex(); for( unsigned int i = 0; i < ImageDimension; i++ ) { RealType u = p[i] - static_cast( static_cast( p[i] ) + idx[i] ) + 0.5*static_cast( this->m_SplineOrder[i] - 1 ); switch( this->m_SplineOrder[i] ) { case 0: { B *= this->m_KernelOrder0->Evaluate( u ); break; } case 1: { B *= this->m_KernelOrder1->Evaluate( u ); break; } case 2: { B *= this->m_KernelOrder2->Evaluate( u ); break; } case 3: { B *= this->m_KernelOrder3->Evaluate( u ); break; } default: { B *= this->m_Kernel[i]->Evaluate( u ); break; } } } for( unsigned int i = 0; i < ImageDimension; i++ ) { idx[i] += static_cast( p[i] ); if( this->m_CloseDimension[i] ) { idx[i] %= this->m_PhiLattice->GetLargestPossibleRegion().GetSize()[i]; } } if( this->m_PhiLattice->GetLargestPossibleRegion().IsInside( idx ) ) { val = this->m_PhiLattice->GetPixel( idx ); val *= B; data += val; } } } template void BSplineScatteredDataPointSetToImageFilter ::EvaluateGradientAtPoint( PointType point, GradientType &gradient ) { for( unsigned int i = 0; i < ImageDimension; i++) { point[i] -= this->m_Origin[i]; point[i] /= ( static_cast( this->m_Size[i] - 1 ) * this->m_Spacing[i] ); } this->EvaluateGradient( point, gradient ); } template void BSplineScatteredDataPointSetToImageFilter ::EvaluateGradientAtIndex( IndexType idx, GradientType &gradient ) { PointType point; this->GetOutput()->TransformIndexToPhysicalPoint( idx, point ); this->EvaluateGradientAtPoint( point, gradient ); } template void BSplineScatteredDataPointSetToImageFilter ::EvaluateGradientAtContinuousIndex( ContinuousIndexType idx, GradientType &gradient ) { PointType point; this->GetOutput()->TransformContinuousIndexToPhysicalPoint( idx, gradient ); this->EvaluateGradientAtPoint( point, gradient ); } template void BSplineScatteredDataPointSetToImageFilter ::EvaluateGradient( PointType params, GradientType &gradient ) { vnl_vector p( ImageDimension ); for( unsigned int i = 0; i < ImageDimension; i++ ) { if( params[i] == NumericTraits::One ) { params[i] = NumericTraits::One - this->m_BSplineEpsilon; } if( params[i] < 0.0 || params[i] >= 1.0 ) { itkExceptionMacro( "The specified point " << params << " is outside the reparameterized domain [0, 1]." ); } p[i] = static_cast( params[i] ) * static_cast( this->m_CurrentNumberOfControlPoints[i] - this->m_SplineOrder[i] ); } typename RealImageType::RegionType::SizeType size; for( unsigned int i = 0; i < ImageDimension; i++ ) { size[i] = this->m_SplineOrder[i] + 1; } typename RealImageType::Pointer w; w = RealImageType::New(); w->SetRegions( size ); w->Allocate(); PointDataType val; gradient.SetSize( val.Size(), ImageDimension ); gradient.Fill( 0.0 ); ImageRegionIteratorWithIndex Itw( w, w->GetLargestPossibleRegion() ); for( unsigned int j = 0; j < gradient.Cols(); j++ ) { for( Itw.GoToBegin(); !Itw.IsAtEnd(); ++Itw ) { RealType B = 1.0; typename RealImageType::IndexType idx = Itw.GetIndex(); for( unsigned int i = 0; i < ImageDimension; i++ ) { RealType u = p[i] - static_cast( static_cast( p[i] ) + idx[i] ) + 0.5*static_cast( this->m_SplineOrder[i] - 1 ); if( j == i ) { B *= this->m_Kernel[i]->EvaluateDerivative( u ); } else { B *= this->m_Kernel[i]->Evaluate( u ); } } for( unsigned int i = 0; i < ImageDimension; i++ ) { idx[i] += static_cast( p[i] ); if( this->m_CloseDimension[i] ) { idx[i] %= this->m_PhiLattice->GetLargestPossibleRegion().GetSize()[i]; } } if( this->m_PhiLattice->GetLargestPossibleRegion().IsInside( idx ) ) { val = this->m_PhiLattice->GetPixel( idx ); val *= B; for( unsigned int i = 0; i < val.Size(); i++ ) { gradient( i, j ) += val[i]; } } } } } /** * Standard "PrintSelf" method */ template void BSplineScatteredDataPointSetToImageFilter ::PrintSelf( std::ostream& os, Indent indent) const { Superclass::PrintSelf( os, indent ); for( unsigned int i = 0; i < ImageDimension; i++ ) { this->m_Kernel[i]->Print( os, indent ); } os << indent << "B-spline order: " << this->m_SplineOrder << std::endl; os << indent << "Number Of control points: " << this->m_NumberOfControlPoints << std::endl; os << indent << "Close dimension: " << this->m_CloseDimension << std::endl; os << indent << "Number of levels " << this->m_NumberOfLevels << std::endl; } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Utilities/itkCoxDeBoorBSplineKernelFunction.h000066400000000000000000000135751147325206600252770ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkCoxDeBoorBSplineKernelFunction.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.16 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkCoxDeBoorBSplineKernelFunction_h #define __itkCoxDeBoorBSplineKernelFunction_h #include "itkKernelFunction.h" #include "itkNumericTraits.h" #include "vnl/vnl_math.h" #include "vnl/vnl_real_polynomial.h" #include "vnl/vnl_matrix.h" namespace itk { /** \class CoxDeBoorBSplineKernelFunction * \brief BSpline kernel used for density estimation and nonparameteric * regression. * * This class enscapsulates BSpline kernel for * density estimation or nonparameteric regression. * See documentation for KernelFunction for more details. * * This class is templated over the spline order to cohere with * the previous incarnation of this class. One can change the * order during an instantiation's existence. Note that * other authors have defined the B-spline order as being the * degree of spline + 1. In the ITK context (e.g. in this * class), the spline order is equivalent to the degree of * the spline. * * \author Nicholas J. Tustison * * Contributed by Nicholas J. Tustison, James C. Gee * in the Insight Journal paper: * http://hdl.handle.net/1926/140 * * * \sa KernelFunction * * \ingroup Functions */ template class ITK_EXPORT CoxDeBoorBSplineKernelFunction : public KernelFunction { public: /** Standard class typedefs. */ typedef CoxDeBoorBSplineKernelFunction Self; typedef KernelFunction Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Run-time type information (and related methods). */ itkTypeMacro( CoxDeBoorBSplineKernelFunction, KernelFunction ); typedef double RealType; typedef vnl_vector VectorType; typedef vnl_real_polynomial PolynomialType; typedef vnl_matrix MatrixType; /** Get/Sets the Spline Order */ void SetSplineOrder( unsigned int ); itkGetMacro( SplineOrder, unsigned int ); /** Evaluate the function. */ inline RealType Evaluate( const RealType & u ) const { RealType absValue = vnl_math_abs( u ); unsigned int which; if( this->m_SplineOrder % 2 == 0 ) { which = static_cast( absValue+0.5 ); } else { which = static_cast( absValue ); } if( which < this->m_BSplineShapeFunctions.rows() ) { return PolynomialType( this->m_BSplineShapeFunctions.get_row( which ) ).evaluate( absValue ); } else { return NumericTraits::Zero; } } /** Evaluate the first derivative. */ inline RealType EvaluateDerivative( const double & u ) const { return this->EvaluateNthDerivative( u, 1 ); } /** Evaluate the Nth derivative. */ inline RealType EvaluateNthDerivative( const double & u, unsigned int n ) const { RealType absValue = vnl_math_abs( u ); unsigned int which; if( this->m_SplineOrder % 2 == 0 ) { which = static_cast( absValue+0.5 ); } else { which = static_cast( absValue ); } if( which < this->m_BSplineShapeFunctions.rows() ) { PolynomialType polynomial( this->m_BSplineShapeFunctions.get_row( which ) ); for( unsigned int i = 0; i < n; i++ ) { polynomial = polynomial.derivative(); } RealType der = polynomial.evaluate( absValue ); if( u < NumericTraits::Zero && n % 2 != 0 ) { return -der; } else { return der; } } else { return NumericTraits::Zero; } } /** * For a specific order, return the ceil( 0.5*(m_SplineOrder+1) ) * pieces of the single basis function centered at zero for positive * parametric values. */ MatrixType GetShapeFunctions() { return this->m_BSplineShapeFunctions; } /** * For a specific order, generate and return the (this->m_SplineOrder+1) * pieces of the different basis functions in the [0, 1] interval. */ MatrixType GetShapeFunctionsInZeroToOneInterval(); protected: CoxDeBoorBSplineKernelFunction(); ~CoxDeBoorBSplineKernelFunction(); void PrintSelf( std::ostream& os, Indent indent ) const; private: CoxDeBoorBSplineKernelFunction( const Self& ); //purposely not implemented void operator=( const Self& ); //purposely not implemented /** * For a specific order, generate the (this->m_SplineOrder+1) pieces of * the single basis function centered at zero. */ void GenerateBSplineShapeFunctions( unsigned int ); /** * Use the CoxDeBoor recursion relation to generate the piecewise * polynomials which compose the basis function. * See, for example, L. Piegl, L. Tiller, "The NURBS Book," * Springer 1997, p. 50. */ PolynomialType CoxDeBoor( unsigned short, VectorType, unsigned int, unsigned int ); MatrixType m_BSplineShapeFunctions; unsigned int m_SplineOrder; }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkCoxDeBoorBSplineKernelFunction.txx" #endif #endif ants-1.9.2+svn680.dfsg/Utilities/itkCoxDeBoorBSplineKernelFunction.txx000066400000000000000000000122501147325206600256600ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkCoxDeBoorBSplineKernelFunction.txx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.16 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkCoxDeBoorBSplineKernelFunction_txx #define __itkCoxDeBoorBSplineKernelFunction_txx #include "itkCoxDeBoorBSplineKernelFunction.h" namespace itk { /** * \author Nicholas J. Tustison * * Contributed by Nicholas J. Tustison, James C. Gee * in the Insight Journal paper: * http://hdl.handle.net/1926/140 * */ template CoxDeBoorBSplineKernelFunction ::CoxDeBoorBSplineKernelFunction() { this->m_SplineOrder = VSplineOrder; this->GenerateBSplineShapeFunctions( this->m_SplineOrder+1 ); } template CoxDeBoorBSplineKernelFunction ::~CoxDeBoorBSplineKernelFunction() { } template void CoxDeBoorBSplineKernelFunction ::SetSplineOrder( unsigned int order ) { if ( order != this->m_SplineOrder ) { this->m_SplineOrder = order; this->GenerateBSplineShapeFunctions( this->m_SplineOrder+1 ); this->Modified(); } } template void CoxDeBoorBSplineKernelFunction ::GenerateBSplineShapeFunctions( unsigned int order ) { unsigned int NumberOfPieces = static_cast( 0.5*( order+1 ) ); this->m_BSplineShapeFunctions.set_size( NumberOfPieces, order ); VectorType knots( order+1 ); for( unsigned int i = 0; i < knots.size(); i++) { knots[i] = -0.5*static_cast( order ) + static_cast( i ); } for ( unsigned int i = 0; i < NumberOfPieces; i++ ) { PolynomialType poly = this->CoxDeBoor(order, knots, 0, static_cast( 0.5*( order ) ) + i ); this->m_BSplineShapeFunctions.set_row( i, poly.coefficients() ); } } template typename CoxDeBoorBSplineKernelFunction::PolynomialType CoxDeBoorBSplineKernelFunction ::CoxDeBoor( unsigned short order, VectorType knots, unsigned int whichBasisFunction, unsigned int whichPiece ) { VectorType tmp(2); PolynomialType poly1(0.0), poly2(0.0); RealType den; unsigned short p = order-1; unsigned short i = whichBasisFunction; if( p == 0 && whichBasisFunction == whichPiece ) { PolynomialType poly(1.0); return poly; } // Term 1 den = knots(i+p)-knots(i); if ( den == NumericTraits::Zero ) { PolynomialType poly(0.0); poly1 = poly; } else { tmp(0) = 1.0; tmp(1) = -knots(i); tmp /= den; poly1 = PolynomialType(tmp) * this->CoxDeBoor( order-1, knots, i, whichPiece ); } // Term 2 den = knots(i+p+1)-knots(i+1); if ( den == NumericTraits::Zero ) { PolynomialType poly(0.0); poly2 = poly; } else { tmp(0) = -1.0; tmp(1) = knots(i+p+1); tmp /= den; poly2 = PolynomialType(tmp) * this->CoxDeBoor( order-1, knots, i+1, whichPiece ); } return ( poly1 + poly2 ); } template typename CoxDeBoorBSplineKernelFunction::MatrixType CoxDeBoorBSplineKernelFunction ::GetShapeFunctionsInZeroToOneInterval() { int order = this->m_SplineOrder+1; unsigned int NumberOfPieces = static_cast( order ); MatrixType ShapeFunctions( NumberOfPieces, order ); VectorType knots( 2*order ); for( unsigned int i = 0; i < knots.size(); i++ ) { knots[i] = -static_cast( this->m_SplineOrder ) + static_cast( i ); } for( unsigned int i = 0; i < NumberOfPieces; i++ ) { PolynomialType poly = this->CoxDeBoor( order, knots, i, order-1 ); ShapeFunctions.set_row( i, poly.coefficients() ); } return ShapeFunctions; } template void CoxDeBoorBSplineKernelFunction ::PrintSelf( std::ostream& os, Indent indent ) const { Superclass::PrintSelf( os, indent ); os << indent << "Spline Order: " << this->m_SplineOrder << std::endl; os << indent << "Piecewise Polynomial Pieces: " << std::endl; for ( unsigned int i = 0; i < this->m_BSplineShapeFunctions.rows(); i++ ) { RealType a = 0.0; RealType b = 0.0; os << indent << indent; PolynomialType( this->m_BSplineShapeFunctions.get_row( i ) ).print( os ); if( i == 0 ) { if( this->m_SplineOrder % 2 == 0 ) { b = 0.5; } else { b = 1.0; } } else { a = b; b += 1.0; } os << ", X \\in [" << a << ", " << b << "]" << std::endl; } } } // namespace itk #endif ants-1.9.2+svn680.dfsg/Utilities/itkDecomposeTensorFunction.h000066400000000000000000000065501147325206600241410ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkDecomposeTensorFunction.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.16 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkDecomposeTensorFunction_h #define __itkDecomposeTensorFunction_h #if defined(_MSC_VER) #pragma warning ( disable : 4786 ) #endif #include "itkProcessObject.h" #include "itkVariableSizeMatrix.h" namespace itk { /** \class DecomposeTensorFunction * */ template > class ITK_EXPORT DecomposeTensorFunction : public ProcessObject { public: /** Standard class typedefs. */ typedef DecomposeTensorFunction Self; typedef ProcessObject Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Run-time type information (and related methods). */ itkTypeMacro( DecomposeTensorFunction, ProcessObject ); /** Extract some information from the image types. Dimensionality * of the two images is assumed to be the same. */ typedef TInput InputMatrixType; typedef TOutput OutputMatrixType; /** Define the data type and the vector of data type used in calculations. */ typedef TRealType RealType; // Wrappers for vnl routines void EvaluateEigenDecomposition( InputMatrixType&, OutputMatrixType&, OutputMatrixType& ); void EvaluateSymmetricEigenDecomposition( InputMatrixType&, OutputMatrixType&, OutputMatrixType& ); void EvaluateQRDecomposition( InputMatrixType&, OutputMatrixType&, OutputMatrixType& ); void EvaluateSVDDecomposition( InputMatrixType&, OutputMatrixType&, OutputMatrixType&, OutputMatrixType& ); void EvaluateSVDEconomyDecomposition( InputMatrixType&, OutputMatrixType&, OutputMatrixType& ); void EvaluateLeftPolarDecomposition( InputMatrixType&, OutputMatrixType&, OutputMatrixType& ); void EvaluateRightPolarDecomposition( InputMatrixType&, OutputMatrixType&, OutputMatrixType& ); void EvaluateCholeskyDecomposition( InputMatrixType&, OutputMatrixType& ); RealType EvaluateDeterminant( InputMatrixType& ); DecomposeTensorFunction(); virtual ~DecomposeTensorFunction() {} protected: void PrintSelf ( std::ostream& os, Indent indent ) const; private: DecomposeTensorFunction(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkDecomposeTensorFunction.txx" #endif #endif ants-1.9.2+svn680.dfsg/Utilities/itkDecomposeTensorFunction.txx000066400000000000000000000217071147325206600245360ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkDecomposeTensorFunction.txx,v $ Language: C++ Date: $Date: 2009/05/11 13:55:21 $ Version: $Revision: 1.17 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkDecomposeTensorFunction_txx #define __itkDecomposeTensorFunction_txx #include "itkDecomposeTensorFunction.h" #include "vnl/algo/vnl_cholesky.h" #include "vnl/algo/vnl_qr.h" #include "vnl/algo/vnl_real_eigensystem.h" #include "vnl/algo/vnl_svd.h" #include "vnl/algo/vnl_svd_economy.h" #include "vnl/algo/vnl_symmetric_eigensystem.h" #include "vnl/vnl_matrix.h" #include "vnl/vnl_matrix_fixed.h" #include "vnl/vnl_det.h" #include "vxl/vcl/vcl_complex.h" namespace itk { template DecomposeTensorFunction ::DecomposeTensorFunction() {} template void DecomposeTensorFunction ::EvaluateEigenDecomposition( InputMatrixType &M, OutputMatrixType &D, OutputMatrixType &V ) { unsigned int RowDimensions = M.Rows(); unsigned int ColumnDimensions = M.Cols(); D.SetSize( RowDimensions, RowDimensions ); V.SetSize( RowDimensions, RowDimensions ); D.Fill( 0.0 ); vnl_matrix v( RowDimensions, ColumnDimensions ); for( unsigned int j = 0; j < ColumnDimensions; j++ ) { for( unsigned int i = 0; i < RowDimensions; i++ ) { v.put( i, j, M[i][j] ); } } vnl_real_eigensystem eig( v ); for( unsigned int j = 0; j < ColumnDimensions; j++ ) { for( unsigned int i = 0; i < RowDimensions; i++ ) { V[i][j] = static_cast( (eig.Vreal).get( i, j ) ); if( i == j ) { D[i][j] = static_cast( vcl_real( eig.D(j) ) ); } } } } template void DecomposeTensorFunction ::EvaluateSymmetricEigenDecomposition( InputMatrixType &M, OutputMatrixType &D, OutputMatrixType &V ) { // The resulting eigenvectors and values are sorted in increasing order // so V.column(0) is the eigenvector corresponding to the // smallest eigenvalue. unsigned int RowDimensions = M.Rows(); unsigned int ColumnDimensions = M.Cols(); D.SetSize( RowDimensions, RowDimensions ); V.SetSize( RowDimensions, RowDimensions ); D.Fill( 0.0 ); vnl_symmetric_eigensystem eig( M.GetVnlMatrix() ); for( unsigned int j = 0; j < ColumnDimensions; j++ ) { for( unsigned int i = 0; i < RowDimensions; i++ ) { V[i][j] = (eig.V).get( i, j ); if( i == j ) { D[i][j] = eig.D(j); } } } } template void DecomposeTensorFunction ::EvaluateRightPolarDecomposition( InputMatrixType &M, OutputMatrixType &R, OutputMatrixType &S ) { OutputMatrixType U; OutputMatrixType W; OutputMatrixType V; this->EvaluateSVDDecomposition( M, U, W, V ); R = U * V.GetTranspose(); S = V * W * V.GetTranspose(); } template void DecomposeTensorFunction ::EvaluateLeftPolarDecomposition( InputMatrixType &M, OutputMatrixType &S, OutputMatrixType &R ) { OutputMatrixType U; OutputMatrixType W; OutputMatrixType V; this->EvaluateSVDDecomposition( M, U, W, V ); R = U * V.GetTranspose(); S = U * W * U.GetTranspose(); } template void DecomposeTensorFunction ::EvaluateQRDecomposition( InputMatrixType &M, OutputMatrixType &Q, OutputMatrixType &R ) { vnl_qr qr( M.GetVnlMatrix() ); Q.SetSize( qr.Q().rows(), qr.Q().cols() ); R.SetSize( qr.R().rows(), qr.R().cols() ); for( unsigned int i = 0; i < Q.Rows(); i++ ) { for( unsigned int j = 0; j < Q.Cols(); j++ ) { Q[i][j] = qr.Q()(i, j); } } for( unsigned int i = 0; i < R.Rows(); i++ ) { for( unsigned int j = 0; j < R.Cols(); j++ ) { R[i][j] = qr.R()(i, j); } } } template void DecomposeTensorFunction ::EvaluateSVDDecomposition( InputMatrixType &M, OutputMatrixType &U, OutputMatrixType &W, OutputMatrixType &V ) { unsigned int RowDimensions = M.Rows(); unsigned int ColumnDimensions = M.Cols(); U.SetSize( RowDimensions, RowDimensions ); V.SetSize( ColumnDimensions, ColumnDimensions ); W.SetSize( RowDimensions, ColumnDimensions ); vnl_svd svd( M.GetVnlMatrix() ); for( unsigned int i = 0; i < RowDimensions; i++ ) { for( unsigned int j = 0; j < RowDimensions; j++ ) { U[i][j] = svd.U(i, j); } } for( unsigned int i = 0; i < ColumnDimensions; i++ ) { for( unsigned int j = 0; j < ColumnDimensions; j++ ) { V[i][j] = svd.V(i, j); } } W.Fill( 0.0 ); unsigned int minDimensions = ColumnDimensions; if( static_cast( RowDimensions ) < static_cast( ColumnDimensions ) ) { minDimensions = RowDimensions; } for( unsigned int i = 0; i < minDimensions; i++ ) { W[i][i] = svd.W(i, i); } } template void DecomposeTensorFunction ::EvaluateSVDEconomyDecomposition( InputMatrixType &M, OutputMatrixType &W, OutputMatrixType &V ) { /** * Same as SVD except the routine does not return U --- * allows for faster computation. */ unsigned int RowDimensions = M.Rows(); unsigned int ColumnDimensions = M.Cols(); V.SetSize( ColumnDimensions, ColumnDimensions ); W.SetSize( RowDimensions, ColumnDimensions ); vnl_svd_economy svd( M.GetVnlMatrix() ); for( unsigned int i = 0; i < ColumnDimensions; i++ ) { for( unsigned int j = 0; j < ColumnDimensions; j++ ) { V[i][j] = svd.V()(i, j); } } W.Fill( 0.0 ); unsigned int minDimensions = ColumnDimensions; if( static_cast( RowDimensions ) < static_cast( ColumnDimensions ) ) { minDimensions = RowDimensions; } for( unsigned int i = 0; i < minDimensions; i++ ) { W[i][i] = svd.lambdas()[i]; } } template void DecomposeTensorFunction ::EvaluateCholeskyDecomposition( InputMatrixType &M, OutputMatrixType &L ) { // Assumes symmetric tensor of type double vnl_matrix m( M.Rows(), M.Cols() ); for( unsigned int j = 0; j < M.Cols(); j++ ) { for( unsigned int i = 0; i < M.Rows(); i++ ) { m.put( i, j, M[i][j] ); } } vnl_cholesky cholesky( m, vnl_cholesky::quiet ); L.SetSize( M.Rows(), M.Cols() ); for( unsigned int i = 0; i < L.Rows(); i++ ) { for( unsigned int j = 0; j < L.Cols(); j++ ) { L[i][j] = cholesky.L_badly_named_method().get(i, j); } } } template typename DecomposeTensorFunction::RealType DecomposeTensorFunction ::EvaluateDeterminant( InputMatrixType &M ) { RealType det = 0.0; if( M.Rows() == M.Cols() && M.Rows() >= 2 && M.Rows() <= 4 ) { vnl_matrix_fixed m2; vnl_matrix_fixed m3; vnl_matrix_fixed m4; for( unsigned int i = 0; i < M.Rows(); i++ ) { for( unsigned int j = 0; j < M.Cols(); j++ ) { switch( M.Rows() ) { case 2: m2.put( i, j, M[i][j] ); break; case 3: m3.put( i, j, M[i][j] ); break; case 4: m4.put( i, j, M[i][j] ); break; } } } switch( M.Rows() ) { case 2: det = static_cast( vnl_det( m2 ) ); break; case 3: det = static_cast( vnl_det( m3 ) ); break; case 4: det = static_cast( vnl_det( m4 ) ); break; } } else { vnl_qr qr( M.GetVnlMatrix() ); det = static_cast( qr.determinant() ); } return det; } template void DecomposeTensorFunction ::PrintSelf( std::ostream& os, Indent indent ) const { } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Utilities/itkDeformationFieldFromMultiTransformFilter.h000066400000000000000000000122441147325206600274330ustar00rootroot00000000000000#ifndef ITKDEFORMATIONFIELDFROMMULTITRANSFORMFILTER_H_ #define ITKDEFORMATIONFIELDFROMMULTITRANSFORMFILTER_H_ #include "itkWarpImageMultiTransformFilter.h" namespace itk { template < class TOutputImage, class TDeformationField, class TTransform > class ITK_EXPORT DeformationFieldFromMultiTransformFilter : public WarpImageMultiTransformFilter< TOutputImage, TOutputImage, TDeformationField, TTransform> { public: /** Standard class typedefs. */ typedef TOutputImage TInputImage; typedef DeformationFieldFromMultiTransformFilter Self; typedef WarpImageMultiTransformFilter Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Run-time type information (and related methods) */ itkTypeMacro( DeformationFieldFromMultiTransformFilter, WarpImageMultiTransformFilter ); /** Typedef to describe the output image region type. */ typedef typename TOutputImage::RegionType OutputImageRegionType; /** Inherit some types from the superclass. */ typedef typename Superclass::InputImageType InputImageType; typedef typename Superclass::InputImagePointer InputImagePointer; typedef typename Superclass::OutputImageType OutputImageType; typedef typename Superclass::OutputImagePointer OutputImagePointer; typedef typename Superclass::InputImageConstPointer InputImageConstPointer; typedef typename OutputImageType::IndexType IndexType; typedef typename OutputImageType::SizeType SizeType; typedef typename OutputImageType::PixelType PixelType; typedef typename OutputImageType::SpacingType SpacingType; /** Determine the image dimension. */ itkStaticConstMacro(ImageDimension, unsigned int, TOutputImage::ImageDimension ); itkStaticConstMacro(InputImageDimension, unsigned int, TInputImage::ImageDimension ); itkStaticConstMacro(DeformationFieldDimension, unsigned int, TDeformationField::ImageDimension ); /** Deformation field typedef support. */ typedef TDeformationField DeformationFieldType; typedef typename DeformationFieldType::Pointer DeformationFieldPointer; typedef typename DeformationFieldType::PixelType DisplacementType; typedef typename DisplacementType::ValueType DisplacementScalarValueType; typedef typename Superclass::PointType PointType; virtual void GenerateInputRequestedRegion() { Superclass::GenerateInputRequestedRegion(); } virtual void BeforeThreadedGenerateData() {}; virtual void AfterThreadedGenerateData() {}; virtual void GenerateOutputInformation(){ // call the superclass's implementation Superclass::GenerateOutputInformation(); }; protected: DeformationFieldFromMultiTransformFilter() : Superclass(){ this->SetNumberOfRequiredInputs( 0 ); const DisplacementScalarValueType kMaxDisp = itk::NumericTraits::max(); Superclass::m_EdgePaddingValue.Fill(kMaxDisp); } ~DeformationFieldFromMultiTransformFilter() {}; void PrintSelf(std::ostream& os, Indent indent) const{ Superclass::PrintSelf(os, indent); }; /** WarpImageMultiTransformFilter is implemented as a multi-threaded filter. * As such, it needs to provide and implementation for * ThreadedGenerateData(). */ void ThreadedGenerateData(const OutputImageRegionType& outputRegionForThread, int threadId ){ OutputImagePointer outputPtr = this->GetOutput(); // support progress methods/callbacks ProgressReporter progress(this, threadId, outputRegionForThread.GetNumberOfPixels()); // iterator for the output image ImageRegionIteratorWithIndex outputIt(outputPtr, outputRegionForThread); // int cnt = 0; while( !outputIt.IsAtEnd() ) { PointType point1, point2; // get the output image index IndexType index = outputIt.GetIndex(); outputPtr->TransformIndexToPhysicalPoint( index, point1 ); bool isinside = MultiTransformPoint(point1, point2, Superclass::m_bFirstDeformNoInterp, index); if (isinside){ PixelType value; for(int ii=0; ii::max(); for(int ii=0; ii class ITK_EXPORT GaussianInterpolateImageFunction : public InterpolateImageFunction { public: /** Standard class typedefs. */ typedef GaussianInterpolateImageFunction Self; typedef InterpolateImageFunction Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Run-time type information (and related methods). */ itkTypeMacro(GaussianInterpolateImageFunction, InterpolateImageFunction); /** Method for creation through the object factory. */ itkNewMacro(Self); /** OutputType typedef support. */ typedef typename Superclass::OutputType OutputType; /** InputImageType typedef support. */ typedef typename Superclass::InputImageType InputImageType; /** RealType typedef support. */ typedef typename Superclass::RealType RealType; /** Dimension underlying input image. */ itkStaticConstMacro(VDim, unsigned int,Superclass::ImageDimension); /** Index typedef support. */ typedef typename Superclass::IndexType IndexType; /** ContinuousIndex typedef support. */ typedef typename Superclass::ContinuousIndexType ContinuousIndexType; /** Compute internals */ virtual void ComputeBoundingBox() { const TInputImage *img = this->GetInputImage(); if(img == NULL) return; // Set the bounding box for(size_t d = 0; d < VDim; d++) { bb_start[d] = -0.5; bb_end[d] = img->GetBufferedRegion().GetSize()[d] - 0.5; nt[d] = (int)(bb_end[d] - bb_start[d] + 0.5); dx[d].set_size(nt[d]); gx[d].set_size(nt[d]); sf[d] = 1.0 / (sqrt(2.0) * sigma[d] / img->GetSpacing()[d]); cut[d] = sigma[d] * alpha / img->GetSpacing()[d]; } } /** Set input */ virtual void SetInputImage(const TInputImage *img) { // Call parent method Superclass::SetInputImage(img); this->ComputeBoundingBox(); } void SetParameters(double *sigma, double alpha) { // Set the parameters for(size_t d = 0; d < VDim; d++) this->sigma[d] = sigma[d]; this->alpha = alpha; // If the image already set, recompute this->ComputeBoundingBox(); } /** Evaluate the function at a ContinuousIndex position * * Returns the linearly interpolated image intensity at a * specified point position. No bounds checking is done. * The point is assume to lie within the image buffer. * * ImageFunction::IsInsideBuffer() can be used to check bounds before * calling the method. */ virtual OutputType EvaluateAtContinuousIndex( const ContinuousIndexType & index ) const { return EvaluateAtContinuousIndex(index, NULL); } virtual OutputType EvaluateAtContinuousIndex( const ContinuousIndexType &index, OutputType *grad) const { // The bound variables for x, y, z int i0[VDim], i1[VDim]; // Compute the ERF difference arrays for(size_t d = 0; d < VDim; d++) { double *pdx = const_cast(dx[d].data_block()); double *pgx = grad ? const_cast(gx[d].data_block()) : NULL; compute_erf_array(pdx, i0[d], i1[d], bb_start[d], nt[d], cut[d], index[d], sf[d], pgx); } // Get a pointer to the output value double sum_me = 0.0, sum_m = 0.0; vnl_vector_fixed dsum_me(0.0), dsum_m(0.0), dw; // Loop over the voxels in the region identified ImageRegion region; for(size_t d = 0; d < VDim; d++) { region.SetIndex(d, i0[d]); region.SetSize(d, i1[d] - i0[d]); } for( ImageRegionConstIteratorWithIndex it(this->GetInputImage(), region); !it.IsAtEnd(); ++it) { size_t j = it.GetIndex()[0]; double w = dx[0][j]; if(grad) { dw[0] = gx[0][j]; for(size_t d = 1; d < VDim; d++) dw[d] = dx[0][j]; } for(size_t d = 1; d < VDim; d++) { j = it.GetIndex()[d]; w *= dx[d][j]; if(grad) { for(size_t q = 0; q < VDim; q++) dw[q] *= (d == q) ? gx[d][j] : dx[d][j]; } } double V = it.Get(); sum_me += V * w; sum_m += w; if(grad) { for(size_t q = 0; q < VDim; q++) { dsum_me[q] += V * dw[q]; dsum_m[q] += dw[q]; } } } double rc = sum_me / sum_m; if(grad) { for(size_t q = 0; q < VDim; q++) { grad[q] = (dsum_me[q] - rc * dsum_m[q]) / sum_m; grad[q] /= -1.4142135623730951 * sigma[q]; } } // return sum_me / sum_m; return rc; } protected: GaussianInterpolateImageFunction() {} ~GaussianInterpolateImageFunction(){}; void PrintSelf(std::ostream& os, Indent indent) const { this->Superclass::PrintSelf(os,indent); } private: GaussianInterpolateImageFunction( const Self& ); //purposely not implemented void operator=( const Self& ); //purposely not implemented /** Number of neighbors used in the interpolation */ static const unsigned long m_Neighbors; vnl_vector dx[VDim], gx[VDim]; double bb_start[VDim], bb_end[VDim], sf[VDim], cut[VDim]; int nt[VDim], stride[VDim]; double sigma[VDim], alpha; void compute_erf_array ( double *dx_erf, // The output array of erf(p+i+1) - erf(p+i) int &k0, int &k1, // The range of integration 0 <= k0 < k1 <= n double b, // Lower bound of the bounding box int n, // Size of the bounding box in steps double cut, // The distance at which to cut off double p, // the value p double sfac, // scaling factor 1 / (Sqrt[2] sigma) double *gx_erf = NULL // Output derivative/erf array (optional) ) const { // Determine the range of voxels along the line where to evaluate erf k0 = (int) floor(p - b - cut); k1 = (int) ceil(p - b + cut); if(k0 < 0) k0 = 0; if(k1 > n) k1 = n; // Start at the first voxel double t = (b - p + k0) * sfac; double e_last = vnl_erf(t); double g_last = gx_erf ? 1.128379167095513 * exp(- t * t) : 0.0; for(int i = k0; i < k1; i++) { t += sfac; double e_now = vnl_erf(t); dx_erf[i] = e_now - e_last; if(gx_erf) { double g_now = 1.128379167095513 * exp(- t * t); gx_erf[i] = g_now - g_last; g_last = g_now; } e_last = e_now; } } }; } // end namespace itk // Define instantiation macro for this template. #define ITK_TEMPLATE_GaussianInterpolateImageFunction(_, EXPORT, x, y) namespace itk { \ _(2(class EXPORT GaussianInterpolateImageFunction< ITK_TEMPLATE_2 x >)) \ namespace Templates { typedef GaussianInterpolateImageFunction< ITK_TEMPLATE_2 x > \ GaussianInterpolateImageFunction##y; } \ } #endif ants-1.9.2+svn680.dfsg/Utilities/itkGaussianProbabilityDensityFunction.h000066400000000000000000000140571147325206600263440ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkGaussianProbabilityDensityFunction.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.16 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkGaussianProbabilityDensityFunction_h #define __itkGaussianProbabilityDensityFunction_h #include "itkArray.h" #include "itkMersenneTwisterRandomVariateGenerator.h" #include "itkVariableSizeMatrix.h" #include "vnl/algo/vnl_matrix_inverse.h" #include "vnl/algo/vnl_determinant.h" #include "vnl/vnl_math.h" #include "itkMatrix.h" #include "itkDensityFunction.h" namespace itk{ namespace Statistics{ /** \class GaussianProbabilityDensityFunction * \brief GaussianProbabilityDensityFunction class represents Gaussian * Density Function. * * This class keeps parameter to define Gaussian Density Function and has * method to return the probability density of an instance (pattern). * If the all element of the covariance matrix is zero the "usual" density * calculations ignored. if the measurement vector to be evaluated is equal to * the mean, then the Evaluate method will return maximum value of * RealType and return 0 for others * */ template< class TMeasurementVector > class ITK_EXPORT GaussianProbabilityDensityFunction : public DensityFunction< TMeasurementVector > { public: /** Standard class typedefs */ typedef GaussianProbabilityDensityFunction Self; typedef DensityFunction Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Strandard macros */ itkTypeMacro( GaussianProbabilityDensityFunction, DensityFunction ); itkNewMacro( Self ); /** Typedef alias for the measurement vectors */ typedef TMeasurementVector MeasurementVectorType; /** Length of each measurement vector */ typedef typename Superclass::MeasurementVectorSizeType MeasurementVectorSizeType; typedef double RealType; /** Type of the mean vector */ typedef Array MeanType; /** Type of the covariance matrix */ typedef VariableSizeMatrix MatrixType; typedef Array EigenValuesType; typedef VariableSizeMatrix EigenVectorsType; typedef typename Statistics ::MersenneTwisterRandomVariateGenerator GeneratorType; /** Sets the mean */ void SetMean( const MeanType mean ) { if( this->GetMeasurementVectorSize() ) { if( mean.Size() != this->GetMeasurementVectorSize() ) { itkExceptionMacro( "GaussianProbabilityDensityFunction::SetMean() " << "Size of measurement vectors in the sample must the same as " << "the size of the mean." ); } } else { this->SetMeasurementVectorSize( mean.Size() ); } if ( this->m_Mean != mean ) { this->m_Mean = mean; this->Modified(); } } /** Gets the mean */ const MeanType GetMean() const { return this->m_Mean; } /** Sets the covariance matrix. * Also, this function calculates inverse covariance and pre factor of * Gaussian Distribution to speed up GetProbability */ void SetCovariance( MatrixType cov ); /** Gets the covariance matrix */ MatrixType GetCovariance(); MatrixType GetInverseCovariance(); void SetSigma( RealType sigma ) { if ( this->m_Sigma != sigma ) { if ( sigma <= 0 ) { itkExceptionMacro( "Sigma must be greater than 0." ); } this->m_Sigma = sigma; this->m_UseAnisotropicCovariance = false; this->m_PreFactor = 1.0 / ( this->m_Sigma * vcl_pow( sqrt( 2.0 * vnl_math::pi ), RealType( this->GetMeasurementVectorSize() ) ) ); this->Modified(); } } const RealType GetSigma() const { return this->m_Sigma; } itkSetMacro( GenerateRandomSamples, bool ); itkGetConstMacro( GenerateRandomSamples, bool ); itkBooleanMacro( GenerateRandomSamples ); /** Gets the probability density of a measurement vector. */ RealType Evaluate( const MeasurementVectorType &measurement ) const; /** Gets the probability density of a measurement vector. */ MeasurementVectorType GenerateRandomSample(); protected: GaussianProbabilityDensityFunction( void ); virtual ~GaussianProbabilityDensityFunction( void ) {} void PrintSelf( std::ostream& os, Indent indent ) const; private: MeanType m_Mean; // mean MatrixType m_Covariance; // covariance matrix RealType m_Sigma; // sigma - use for isotropic gaussian bool m_GenerateRandomSamples; EigenValuesType m_EigenValues; EigenVectorsType m_EigenVectors; // if isotropic covariance is used, we don't want to make // unnecessary calculations. bool m_UseAnisotropicCovariance; // inverse covariance matrix which is automatically calculated // when covariace matrix is set. This speed up the GetProbability() MatrixType m_InverseCovariance; // pre_factor which is automatically calculated // when covariace matirx is set. This speeds up the GetProbability() RealType m_PreFactor; /** if the all element of the given covarinace is zero, then this * value set to true */ bool m_IsCovarianceZero; typename GeneratorType::Pointer m_Randomizer; }; } // end of namespace Statistics } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkGaussianProbabilityDensityFunction.txx" #endif #endif ants-1.9.2+svn680.dfsg/Utilities/itkGaussianProbabilityDensityFunction.txx000066400000000000000000000200531147325206600267310ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkGaussianProbabilityDensityFunction.txx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.17 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkGaussianProbabilityDensityFunction_txx #define __itkGaussianProbabilityDensityFunction_txx #include "itkGaussianProbabilityDensityFunction.h" #include "itkDecomposeTensorFunction.h" namespace itk { namespace Statistics { template GaussianProbabilityDensityFunction ::GaussianProbabilityDensityFunction() { this->m_Mean.SetSize( this->GetMeasurementVectorSize() ); this->m_Mean.Fill( 0.0 ); this->SetSigma( 1.0 ); this->m_UseAnisotropicCovariance = false; this->m_GenerateRandomSamples = false; this->m_Randomizer = GeneratorType::New(); this->m_Randomizer->SetSeed(); } template void GaussianProbabilityDensityFunction ::PrintSelf( std::ostream& os, Indent indent ) const { Superclass::PrintSelf(os,indent); os << indent << "Mean: "; os << this->m_Mean << std::endl; if( this->m_UseAnisotropicCovariance ) { os << indent << "Covariance: " << std::endl; os << this->m_Covariance.GetVnlMatrix(); os << indent << "InverseCovariance: " << std::endl; os << indent << this->m_InverseCovariance.GetVnlMatrix(); } else { os << indent << "Sigma: " << this->m_Sigma << std::endl; } os << indent << "Prefactor: " << this->m_PreFactor << std::endl; } template void GaussianProbabilityDensityFunction ::SetCovariance( MatrixType cov ) { if( this->m_Covariance != cov ) { // Sanity check if( cov.GetVnlMatrix().rows() != cov.GetVnlMatrix().cols() ) { itkExceptionMacro( << "Covariance matrix must be square" ); } if( this->GetMeasurementVectorSize() ) { if( cov.GetVnlMatrix().rows() != this->GetMeasurementVectorSize() ) { itkExceptionMacro( "Length of measurement vectors in the sample must be" << " the same as the size of the covariance." ); } } else { this->SetMeasurementVectorSize( cov.GetVnlMatrix().rows() ); } this->m_Covariance = cov; this->m_IsCovarianceZero = this->m_Covariance.GetVnlMatrix().is_zero(); if( !this->m_IsCovarianceZero ) { // allocate the memory for this->m_InverseCovariance matrix this->m_InverseCovariance.GetVnlMatrix() = vnl_matrix_inverse( this->m_Covariance.GetVnlMatrix() ); // the determinant of the covaraince matrix typedef DecomposeTensorFunction DecomposerType; typename DecomposerType::Pointer decomposer = DecomposerType::New(); RealType det = decomposer->EvaluateDeterminant( this->m_Covariance ); // calculate coefficient C of multivariate gaussian this->m_PreFactor = 1.0 / ( vcl_sqrt( det ) * vcl_pow( sqrt( 2.0 * vnl_math::pi ), RealType( this->GetMeasurementVectorSize() ) ) ); // calculate eigendecomposition if( this->m_GenerateRandomSamples ) { typename DecomposerType::OutputMatrixType eigValues; decomposer->EvaluateSymmetricEigenDecomposition( this->m_Covariance, eigValues, this->m_EigenVectors ); this->m_EigenValues.SetSize( this->GetMeasurementVectorSize() ); for( unsigned int i = 0; i < this->m_EigenValues.GetSize(); i++ ) { this->m_EigenValues[i] = eigValues[i][i]; } } } this->m_UseAnisotropicCovariance = true; this->Modified(); } } template typename GaussianProbabilityDensityFunction::MatrixType GaussianProbabilityDensityFunction ::GetCovariance() { if( this->m_UseAnisotropicCovariance ) { return this->m_Covariance; } else { MatrixType covariance( this->GetMeasurementVectorSize(), this->GetMeasurementVectorSize() ); covariance.SetIdentity(); covariance *= ( this->m_Sigma * this->m_Sigma ); return covariance; } } template typename GaussianProbabilityDensityFunction::MatrixType GaussianProbabilityDensityFunction ::GetInverseCovariance() { if( this->m_UseAnisotropicCovariance ) { return this->m_InverseCovariance; } else { MatrixType invCovariance( this->GetMeasurementVectorSize(), this->GetMeasurementVectorSize() ); invCovariance.SetIdentity(); invCovariance *= ( 1.0 / ( this->m_Sigma * this->m_Sigma ) ); return invCovariance; } } template inline typename GaussianProbabilityDensityFunction::RealType GaussianProbabilityDensityFunction ::Evaluate( const MeasurementVectorType &measurement ) const { RealType temp; const MeasurementVectorSizeType measurementVectorSize = this->GetMeasurementVectorSize(); MeanType tempVector; MeasurementVectorTraits::SetLength( tempVector, measurementVectorSize ); // Compute |y - mean | for( unsigned int i = 0; i < measurementVectorSize; i++ ) { tempVector[i] = measurement[i] - this->m_Mean[i]; } if( this->m_UseAnisotropicCovariance ) { MeanType tempVector2; MeasurementVectorTraits::SetLength( tempVector2, measurementVectorSize ); // Compute |y - mean | * inverse(cov) for( unsigned int i = 0; i < measurementVectorSize; i++ ) { temp = 0; for( unsigned int j = 0; j < measurementVectorSize; j++ ) { temp += tempVector[j] * this->m_InverseCovariance.GetVnlMatrix().get(j, i); } tempVector2[i] = temp; } // Compute |y - mean | * inverse(cov) * |y - mean|^T temp = 0; for( unsigned int i = 0; i < measurementVectorSize; i++ ) { temp += tempVector2[i] * tempVector[i]; } return this->m_PreFactor * vcl_exp( -0.5 * temp ); } else { temp = 0; for( unsigned int i = 0; i < measurementVectorSize; i++ ) { temp += ( tempVector[i] * tempVector[i] ); } return this->m_PreFactor * vcl_exp( -0.5 * temp / ( vnl_math_sqr( this->m_Sigma ) ) ); } } template typename GaussianProbabilityDensityFunction ::MeasurementVectorType GaussianProbabilityDensityFunction ::GenerateRandomSample() { MeasurementVectorType measurement; measurement.Fill( 0 ); if( this->m_GenerateRandomSamples ) { if( this->m_UseAnisotropicCovariance ) { MeanType sample( this->GetMeasurementVectorSize() ); for( unsigned int d = 0; d < this->GetMeasurementVectorSize(); d++ ) { sample[d] = this->m_Randomizer->GetNormalVariate( 0.0, this->m_EigenValues[d] ); } sample = this->m_EigenVectors * sample + this->m_Mean; for( unsigned int d = 0; d < this->GetMeasurementVectorSize(); d++ ) { measurement[d] = sample[d]; } } else { MeanType sample( this->GetMeasurementVectorSize() ); for( unsigned int d = 0; d < this->GetMeasurementVectorSize(); d++ ) { sample[d] = this->m_Randomizer->GetNormalVariate( 0.0, vnl_math_sqr( this->m_Sigma ) ); } sample += this->m_Mean; for( unsigned int d = 0; d < this->GetMeasurementVectorSize(); d++ ) { measurement[d] = sample[d]; } } } return measurement; } } // end namespace Statistics } // end of namespace itk #endif ants-1.9.2+svn680.dfsg/Utilities/itkGeneralToBSplineDeformationFieldFilter.h000066400000000000000000000072501147325206600267570ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkGeneralToBSplineDeformationFieldFilter.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.16 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkGeneralToBSplineDeformationFieldFilter_h #define __itkGeneralToBSplineDeformationFieldFilter_h #include "itkImageToImageFilter.h" #include "itkBSplineScatteredDataPointSetToImageFilter.h" #include "itkPointSet.h" namespace itk { template class GeneralToBSplineDeformationFieldFilter : public ImageToImageFilter { public: /** Standard class typedefs. */ typedef GeneralToBSplineDeformationFieldFilter Self; typedef ImageToImageFilter< TInputImage, TOutputImage> Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); /** Extract dimension from input and output image. */ itkStaticConstMacro( ImageDimension, unsigned int, TInputImage::ImageDimension ); /** Convenient typedefs for simplifying declarations. */ typedef TInputImage InputImageType; typedef TOutputImage OutputImageType; typedef typename InputImageType::PixelType InputPixelType; typedef typename OutputImageType::PixelType OutputPixelType; typedef typename InputPixelType::ValueType InputPixelComponentType; typedef InputPixelType VectorType; typedef InputPixelComponentType RealType; typedef Image RealImageType; typedef PointSet PointSetType; typedef BSplineScatteredDataPointSetToImageFilter BSplineFilterType; typedef typename BSplineFilterType::ArrayType ArrayType; // itkSetMacro( ConfidenceImage, RealImageType ); // itkGetConstMacro( ConfidenceImage, RealImageType ); itkSetMacro( NumberOfControlPoints, ArrayType ); itkGetConstMacro( NumberOfControlPoints, ArrayType ); itkSetMacro( NumberOfLevels, unsigned int ); itkGetConstMacro( NumberOfLevels, unsigned int ); itkSetMacro( SplineOrder, unsigned int ); itkGetConstMacro( SplineOrder, unsigned int ); itkSetMacro( IgnorePixelValue, InputPixelType ); itkGetConstMacro( IgnorePixelValue, InputPixelType ); protected: GeneralToBSplineDeformationFieldFilter(); virtual ~GeneralToBSplineDeformationFieldFilter(); void PrintSelf( std::ostream& os, Indent indent ) const; void GenerateData(); private: // typename RealImageType::Pointer m_ConfidenceImage; InputPixelType m_IgnorePixelValue; unsigned int m_NumberOfLevels; unsigned int m_SplineOrder; ArrayType m_NumberOfControlPoints; }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkGeneralToBSplineDeformationFieldFilter.txx" #endif #endif ants-1.9.2+svn680.dfsg/Utilities/itkGeneralToBSplineDeformationFieldFilter.txx000066400000000000000000000070741147325206600273570ustar00rootroot00000000000000#ifndef __itkGeneralToBSplineDeformationFieldFilter_txx #define __itkGeneralToBSplineDeformationFieldFilter_txx #include "itkGeneralToBSplineDeformationFieldFilter.h" #include "itkBSplineControlPointImageFilter.h" #include "itkImageRegionConstIteratorWithIndex.h" namespace itk { template GeneralToBSplineDeformationFieldFilter ::GeneralToBSplineDeformationFieldFilter() { this->m_IgnorePixelValue.Fill( NumericTraits::max() ); this->m_SplineOrder = 3; this->m_NumberOfControlPoints.Fill( this->m_SplineOrder + 1 ); // this->m_ConfidenceImage = NULL; } template GeneralToBSplineDeformationFieldFilter ::~GeneralToBSplineDeformationFieldFilter() { } template void GeneralToBSplineDeformationFieldFilter ::GenerateData() { typename PointSetType::Pointer fieldPoints = PointSetType::New(); fieldPoints->Initialize(); // typename BSplineFilterType::WeightsContainerType confidenceValues; // confidenceValues->Initialize(); ImageRegionConstIteratorWithIndex It( this->GetInput(), this->GetInput()->GetRequestedRegion() ); itkDebugMacro( << "Extracting points from input deformation field. " ) unsigned int N = 0; for ( It.GoToBegin(); !It.IsAtEnd(); ++It ) { InputPixelType data = It.Get(); if ( data != this->m_IgnorePixelValue ) { typename PointSetType::PointType point; this->GetInput()->TransformIndexToPhysicalPoint( It.GetIndex(), point ); fieldPoints->SetPointData( N, data ); fieldPoints->SetPoint( N, point ); // if ( this->m_ConfidenceImage ) // { // confidenceValues->InsertElement // ( N, this->m_ConfidenceImage->GetPixel( It.GetIndex() ) ); // } N++; } } itkDebugMacro( "Calculating the B-spline deformation field. " ); typename OutputImageType::PointType origin; typename OutputImageType::SpacingType spacing; typename OutputImageType::SizeType size; for ( unsigned int i = 0; i < ImageDimension; i++ ) { origin[i] = this->GetInput( 0 )->GetOrigin()[i]; spacing[i] = this->GetInput( 0 )->GetSpacing()[i]; size[i] = this->GetInput( 0 )->GetRequestedRegion().GetSize()[i]; } typename BSplineFilterType::ArrayType close; close.Fill( false ); typename BSplineFilterType::Pointer bspliner = BSplineFilterType::New(); bspliner->SetOrigin( origin ); bspliner->SetSpacing( spacing ); bspliner->SetSize( size ); bspliner->SetNumberOfLevels( this->m_NumberOfLevels ); bspliner->SetSplineOrder( this->m_SplineOrder ); bspliner->SetNumberOfControlPoints( this->m_NumberOfControlPoints ); bspliner->SetCloseDimension( close ); bspliner->SetInput( fieldPoints ); bspliner->SetGenerateOutputImage( true ); bspliner->Update(); this->SetNthOutput( 0, bspliner->GetOutput() ); } /** * Standard "PrintSelf" method */ template void GeneralToBSplineDeformationFieldFilter ::PrintSelf( std::ostream& os, Indent indent) const { Superclass::PrintSelf( os, indent ); os << indent << "Ignore pixel value: " << this->m_IgnorePixelValue << std::endl; os << indent << "Number of control points: " << this->m_NumberOfControlPoints << std::endl; os << indent << "Number of levels: " << this->m_NumberOfLevels << std::endl; os << indent << "Spline order: " << this->m_SplineOrder << std::endl; } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Utilities/itkGridImageSource.h000066400000000000000000000120231147325206600223230ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkGridImageSource.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.16 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkGridImageSource_h #define __itkGridImageSource_h #include "itkImageSource.h" #include "itkFixedArray.h" #include "itkKernelFunction.h" #include "itkVectorContainer.h" #include "vnl/vnl_vector.h" namespace itk { /** \class GridImageSource * \brief Generate an n-dimensional image of a grid. * * GridImageSource generates an image of a grid. * * The output image may be of any dimension. * * \ingroup DataSources */ template class ITK_EXPORT GridImageSource : public ImageSource { public: /** Standard class typedefs. */ typedef GridImageSource Self; typedef ImageSource Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Run-time type information (and related methods). */ itkTypeMacro( GridImageSource, ImageSource ); /** Method for creation through the object factory. */ itkNewMacro(Self); typedef double RealType; /** Dimensionality of the output image */ itkStaticConstMacro( ImageDimension, unsigned int, TOutputImage::ImageDimension ); /** Typedef for the output image types. */ typedef TOutputImage ImageType; typedef typename TOutputImage::RegionType ImageRegionType; typedef typename TOutputImage::PixelType PixelType; typedef typename TOutputImage::SpacingType SpacingType; typedef typename TOutputImage::PointType OriginType; typedef typename TOutputImage::SizeType SizeType; /** Other convenient types. */ typedef FixedArray ArrayType; typedef FixedArray BoolArrayType; typedef vnl_vector PixelArrayType; typedef VectorContainer PixelArrayContainerType; /** Gets and sets for the output image. */ itkSetMacro( Spacing, SpacingType ); itkGetConstMacro( Spacing, SpacingType ); itkSetMacro( Origin, OriginType ); itkGetConstMacro( Origin, OriginType ); itkSetMacro( Size, SizeType ); itkGetConstMacro( Size, SizeType ); /** Gets and sets for grid parameters */ itkSetObjectMacro( KernelFunction, KernelFunction ); itkGetObjectMacro( KernelFunction, KernelFunction ); itkSetMacro( Sigma, ArrayType ); itkGetConstMacro( Sigma, ArrayType ); itkSetMacro( GridSpacing, ArrayType ); itkGetConstMacro( GridSpacing, ArrayType ); itkSetMacro( GridOffset, ArrayType ); itkGetConstMacro( GridOffset, ArrayType ); itkSetMacro( WhichDimensions, BoolArrayType ); itkGetConstMacro( WhichDimensions, BoolArrayType ); itkSetMacro( Scale, RealType ); itkGetConstMacro( Scale, RealType ); protected: GridImageSource(); ~GridImageSource(); void PrintSelf(std::ostream& os, Indent indent) const; virtual void ThreadedGenerateData(const ImageRegionType& outputRegionForThread, int threadId ); virtual void BeforeThreadedGenerateData(); virtual void GenerateOutputInformation(); private: GridImageSource(const GridImageSource&); //purposely not implemented void operator=(const GridImageSource&); //purposely not implemented /** Parameters for the output image. */ SizeType m_Size; //size SpacingType m_Spacing; //spacing OriginType m_Origin; //origin /** Parameters for the grid. */ /** Internal variable to speed up the calculation of pixel values */ typename PixelArrayContainerType::Pointer m_PixelArrays; /** The kernel function used to create the grid */ typename KernelFunction::Pointer m_KernelFunction; /** The standard deviation of the gaussians or width of the box functions. */ ArrayType m_Sigma; /** The grid spacing of the peaks. */ ArrayType m_GridSpacing; /** The grid spacing of the peaks. */ ArrayType m_GridOffset; /** Which dimensions which are gridded. */ BoolArrayType m_WhichDimensions; /** A scale factor multiplied by the true value of the grid. */ RealType m_Scale; }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkGridImageSource.txx" #endif #endif ants-1.9.2+svn680.dfsg/Utilities/itkGridImageSource.txx000066400000000000000000000125541147325206600227300ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkGridImageSource.txx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.16 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkGridImageSource_txx #define _itkGridImageSource_txx #include "itkGaussianKernelFunction.h" #include "itkGridImageSource.h" #include "itkImageLinearIteratorWithIndex.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkProgressReporter.h" namespace itk { template GridImageSource ::GridImageSource() { this->m_Size.Fill( 64 ); this->m_Spacing.Fill( 1.0 ); this->m_Origin.Fill( 0.0 ); this->m_Sigma.Fill( 0.5 ); this->m_GridSpacing.Fill( 4.0 ); this->m_GridOffset.Fill( 0.0 ); this->m_WhichDimensions.Fill( true ); this->m_Scale = 255.0; this->m_KernelFunction = dynamic_cast( GaussianKernelFunction::New().GetPointer() ); } template GridImageSource ::~GridImageSource() { } template void GridImageSource ::BeforeThreadedGenerateData() { typename ImageType::Pointer output = this->GetOutput( 0 ); this->m_PixelArrays = PixelArrayContainerType::New(); m_PixelArrays->Initialize(); for ( unsigned int i = 0; i < ImageDimension; i++ ) { if ( this->m_GridOffset[i] > this->m_GridSpacing[i] ) { this->m_GridOffset[i] = this->m_GridSpacing[i]; } PixelArrayType pixels = m_PixelArrays->CreateElementAt( i ); pixels.set_size( this->m_Size[i] ); pixels.fill( 1 ); if ( this->m_WhichDimensions[i] ) { ImageLinearIteratorWithIndex It( output, output->GetRequestedRegion() ); It.SetDirection( i ); /** Add two extra functions in the front and one in the back to ensure coverage */ unsigned int numberOfGaussians = static_cast( ceil( this->m_Size[i]*this->m_Spacing[i]/this->m_GridSpacing[i] ) + 4 ); for ( It.GoToBegin(); !It.IsAtEndOfLine(); ++It ) { typename ImageType::IndexType index = It.GetIndex(); typename ImageType::PointType point; output->TransformIndexToPhysicalPoint( index, point ); RealType val = 0; for ( unsigned int j = 0; j < numberOfGaussians; j++ ) { RealType num = point[i] - static_cast( j-2 )*this->m_GridSpacing[i] - this->m_Origin[i] - this->m_GridOffset[i]; val += this->m_KernelFunction->Evaluate( num/this->m_Sigma[i] ); } pixels[index[i]] = val; } pixels = 1.0 - pixels/pixels.max_value(); } this->m_PixelArrays->SetElement( i, pixels ); } } template void GridImageSource ::ThreadedGenerateData( const ImageRegionType& outputRegionForThread, int threadId ) { // Support progress methods/callbacks ProgressReporter progress( this, threadId, outputRegionForThread.GetNumberOfPixels() ); typename ImageType::Pointer output = this->GetOutput( 0 ); ImageRegionIteratorWithIndex It( output, outputRegionForThread ); for ( It.GoToBegin(); !It.IsAtEnd(); ++It ) { RealType val = 1.0; typename ImageType::IndexType index = It.GetIndex(); for ( unsigned int i = 0; i < ImageDimension; i++ ) { val *= this->m_PixelArrays->GetElement( i )[index[i]]; } It.Set( this->m_Scale * val ); progress.CompletedPixel(); } } template void GridImageSource ::PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf(os,indent); os << indent << "Output image information: " << std::endl; os << indent << " Size = " << this->m_Size << std::endl; os << indent << " Spacing = " << this->m_Spacing << std::endl; os << indent << " Origin = " << this->m_Origin << std::endl; os << indent << " Scale = " << this->m_Scale << std::endl; os << indent << "Grid information: " << std::endl; os << indent << " Kernel = " << this->m_KernelFunction << std::endl; os << indent << " Sigma = " << this->m_Sigma << std::endl; os << indent << " Grid spacing = " << this->m_GridSpacing << std::endl; os << indent << " Grid offset = " << this->m_GridOffset << std::endl; } //---------------------------------------------------------------------------- template void GridImageSource ::GenerateOutputInformation() { ImageType *output; output = this->GetOutput( 0 ); typename ImageType::IndexType index; index.Fill( 0 ); typename ImageType::RegionType largestPossibleRegion; largestPossibleRegion.SetSize( this->m_Size ); largestPossibleRegion.SetIndex( index ); output->SetLargestPossibleRegion( largestPossibleRegion ); output->SetSpacing( this->m_Spacing ); output->SetOrigin( this->m_Origin ); } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Utilities/itkLabelOverlapMeasuresImageFilter.h000066400000000000000000000151161147325206600255060ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkLabelOverlapMeasuresImageFilter.h,v $ Language: C++ Date: $Date: $ Version: $Revision: $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkLabelOverlapMeasuresImageFilter_h #define __itkLabelOverlapMeasuresImageFilter_h #include "itkImageToImageFilter.h" #include "itkFastMutexLock.h" #include "itkNumericTraits.h" #include "itk_hash_map.h" namespace itk { /** \class LabelOverlapMeasuresImageFilter * \brief Computes overlap measures between the set same set of labels of * pixels of two images. Background is assumed to be 0. * * \sa LabelOverlapMeasuresImageFilter * * \ingroup MultiThreaded */ template class ITK_EXPORT LabelOverlapMeasuresImageFilter : public ImageToImageFilter { public: /** Standard Self typedef */ typedef LabelOverlapMeasuresImageFilter Self; typedef ImageToImageFilter Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); /** Runtime information support. */ itkTypeMacro( LabelOverlapMeasuresImageFilter, ImageToImageFilter ); /** Image related typedefs. */ typedef TLabelImage LabelImageType; typedef typename TLabelImage::Pointer LabelImagePointer; typedef typename TLabelImage::ConstPointer LabelImageConstPointer; typedef typename TLabelImage::RegionType RegionType; typedef typename TLabelImage::SizeType SizeType; typedef typename TLabelImage::IndexType IndexType; typedef typename TLabelImage::PixelType LabelType; /** Type to use form computations. */ typedef typename NumericTraits::RealType RealType; /** \class LabelLabelOverlapMeasuress * \brief Metrics stored per label */ class LabelSetMeasures { public: // default constructor LabelSetMeasures() { m_Source = 0; m_Target = 0; m_Union = 0; m_Intersection = 0; m_SourceComplement = 0; m_TargetComplement = 0; } // added for completeness LabelSetMeasures& operator=( const LabelSetMeasures& l ) { m_Source = l.m_Source; m_Target = l.m_Target; m_Union = l.m_Union; m_Intersection = l.m_Intersection; m_SourceComplement = l.m_SourceComplement; m_TargetComplement = l.m_TargetComplement; } unsigned long m_Source; unsigned long m_Target; unsigned long m_Union; unsigned long m_Intersection; unsigned long m_SourceComplement; unsigned long m_TargetComplement; }; /** Type of the map used to store data per label */ typedef hash_map MapType; typedef typename MapType::iterator MapIterator; typedef typename MapType::const_iterator MapConstIterator; /** Image related typedefs. */ itkStaticConstMacro( ImageDimension, unsigned int, TLabelImage::ImageDimension ); /** Set the source image. */ void SetSourceImage( const LabelImageType * image ) { this->SetNthInput( 0, const_cast( image ) ); } /** Set the target image. */ void SetTargetImage( const LabelImageType * image ) { this->SetNthInput( 1, const_cast( image ) ); } /** Get the source image. */ const LabelImageType * GetSourceImage( void ) { return this->GetInput( 0 ); } /** Get the target image. */ const LabelImageType * GetTargetImage( void ) { return this->GetInput( 1 ); } /** Get the label set measures */ MapType GetLabelSetMeasures() { return this->m_LabelSetMeasures; } /** * tric overlap measures */ /** measures over all labels */ RealType GetTotalOverlap(); RealType GetUnionOverlap(); RealType GetMeanOverlap(); RealType GetVolumeSimilarity(); RealType GetFalseNegativeError(); RealType GetFalsePositiveError(); /** measures over individual labels */ RealType GetTargetOverlap( LabelType ); RealType GetUnionOverlap( LabelType ); RealType GetMeanOverlap( LabelType ); RealType GetVolumeSimilarity( LabelType ); RealType GetFalseNegativeError( LabelType ); RealType GetFalsePositiveError( LabelType ); /** alternative names */ RealType GetJaccardCoefficient() { return this->GetUnionOverlap(); } RealType GetJaccardCoefficient( LabelType label ) { return this->GetUnionOverlap( label ); } RealType GetDiceCoefficient() { return this->GetMeanOverlap(); } RealType GetDiceCoefficient( LabelType label ) { return this->GetMeanOverlap( label ); } #ifdef ITK_USE_CONCEPT_CHECKING /** Begin concept checking */ itkConceptMacro( Input1HasNumericTraitsCheck, ( Concept::HasNumericTraits ) ); /** End concept checking */ #endif protected: LabelOverlapMeasuresImageFilter(); ~LabelOverlapMeasuresImageFilter(){}; void PrintSelf( std::ostream& os, Indent indent ) const; /** * Pass the input through unmodified. Do this by setting the output to the * source this by setting the output to the source image in the * AllocateOutputs() method. */ void AllocateOutputs(); void BeforeThreadedGenerateData(); void AfterThreadedGenerateData(); /** Multi-thread version GenerateData. */ void ThreadedGenerateData( const RegionType&, int ); // Override since the filter needs all the data for the algorithm void GenerateInputRequestedRegion(); // Override since the filter produces all of its output void EnlargeOutputRequestedRegion( DataObject *data ); private: LabelOverlapMeasuresImageFilter( const Self& ); //purposely not implemented void operator=( const Self& ); //purposely not implemented std::vector m_LabelSetMeasuresPerThread; MapType m_LabelSetMeasures; SimpleFastMutexLock m_Mutex; }; // end of class } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkLabelOverlapMeasuresImageFilter.txx" #endif #endif ants-1.9.2+svn680.dfsg/Utilities/itkLabelOverlapMeasuresImageFilter.txx000066400000000000000000000324211147325206600261000ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkLabelOverlapMeasuresImageFilter.txx,v $ Language: C++ Date: $Date: $ Version: $Revision: $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkLabelOverlapMeasuresImageFilter_txx #define _itkLabelOverlapMeasuresImageFilter_txx #include "itkLabelOverlapMeasuresImageFilter.h" #include "itkImageRegionConstIterator.h" #include "itkProgressReporter.h" namespace itk { #if defined(__GNUC__) && (__GNUC__ <= 2) //NOTE: This class needs a mutex for gnu 2.95 /** Used for mutex locking */ #define LOCK_HASHMAP this->m_Mutex.Lock() #define UNLOCK_HASHMAP this->m_Mutex.Unlock() #else #define LOCK_HASHMAP #define UNLOCK_HASHMAP #endif template LabelOverlapMeasuresImageFilter ::LabelOverlapMeasuresImageFilter() { // this filter requires two input images this->SetNumberOfRequiredInputs( 2 ); } template void LabelOverlapMeasuresImageFilter ::GenerateInputRequestedRegion() { Superclass::GenerateInputRequestedRegion(); if( this->GetSourceImage() ) { LabelImagePointer source = const_cast ( this->GetSourceImage() ); source->SetRequestedRegionToLargestPossibleRegion(); } if( this->GetTargetImage() ) { LabelImagePointer target = const_cast ( this->GetTargetImage() ); target->SetRequestedRegionToLargestPossibleRegion(); } } template void LabelOverlapMeasuresImageFilter ::EnlargeOutputRequestedRegion( DataObject *data ) { Superclass::EnlargeOutputRequestedRegion( data ); data->SetRequestedRegionToLargestPossibleRegion(); } template void LabelOverlapMeasuresImageFilter ::AllocateOutputs() { // Pass the source through as the output LabelImagePointer image = const_cast( this->GetSourceImage() ); this->SetNthOutput( 0, image ); // Nothing that needs to be allocated for the remaining outputs } template void LabelOverlapMeasuresImageFilter ::BeforeThreadedGenerateData() { int numberOfThreads = this->GetNumberOfThreads(); // Resize the thread temporaries this->m_LabelSetMeasuresPerThread.resize( numberOfThreads ); // Initialize the temporaries for( int n = 0; n < numberOfThreads; n++ ) { this->m_LabelSetMeasuresPerThread[n].clear(); } // Initialize the final map this->m_LabelSetMeasures.clear(); } template void LabelOverlapMeasuresImageFilter ::AfterThreadedGenerateData() { // Run through the map for each thread and accumulate the set measures. for( int n = 0; n < this->GetNumberOfThreads(); n++ ) { // iterate over the map for this thread for( MapConstIterator threadIt = this->m_LabelSetMeasuresPerThread[n].begin(); threadIt != this->m_LabelSetMeasuresPerThread[n].end(); ++threadIt ) { // does this label exist in the cumulative stucture yet? MapIterator mapIt = this->m_LabelSetMeasures.find( ( *threadIt ).first ); if( mapIt == this->m_LabelSetMeasures.end() ) { // create a new entry typedef typename MapType::value_type MapValueType; mapIt = this->m_LabelSetMeasures.insert( MapValueType( (*threadIt).first, LabelSetMeasures() ) ).first; } // accumulate the information from this thread (*mapIt).second.m_Source += (*threadIt).second.m_Source; (*mapIt).second.m_Target += (*threadIt).second.m_Target; (*mapIt).second.m_Union += (*threadIt).second.m_Union; (*mapIt).second.m_Intersection += (*threadIt).second.m_Intersection; (*mapIt).second.m_SourceComplement += (*threadIt).second.m_SourceComplement; (*mapIt).second.m_TargetComplement += (*threadIt).second.m_TargetComplement; } // end of thread map iterator loop } // end of thread loop } template void LabelOverlapMeasuresImageFilter ::ThreadedGenerateData( const RegionType& outputRegionForThread, int threadId ) { ImageRegionConstIterator ItS( this->GetSourceImage(), outputRegionForThread ); ImageRegionConstIterator ItT( this->GetTargetImage(), outputRegionForThread ); // support progress methods/callbacks ProgressReporter progress( this, threadId, 2*outputRegionForThread.GetNumberOfPixels() ); for( ItS.GoToBegin(), ItT.GoToBegin(); !ItS.IsAtEnd(); ++ItS, ++ItT ) { LabelType sourceLabel = ItS.Get(); LabelType targetLabel = ItT.Get(); // is the label already in this thread? MapIterator mapItS = this->m_LabelSetMeasuresPerThread[threadId].find( sourceLabel ); MapIterator mapItT = this->m_LabelSetMeasuresPerThread[threadId].find( targetLabel ); if( mapItS == this->m_LabelSetMeasuresPerThread[threadId].end() ) { // create a new label set measures object typedef typename MapType::value_type MapValueType; LOCK_HASHMAP; mapItS = this->m_LabelSetMeasuresPerThread[threadId].insert( MapValueType( sourceLabel, LabelSetMeasures() ) ).first; UNLOCK_HASHMAP; } if( mapItT == this->m_LabelSetMeasuresPerThread[threadId].end() ) { // create a new label set measures object typedef typename MapType::value_type MapValueType; LOCK_HASHMAP; mapItT = this->m_LabelSetMeasuresPerThread[threadId].insert( MapValueType( targetLabel, LabelSetMeasures() ) ).first; UNLOCK_HASHMAP; } (*mapItS).second.m_Source++; (*mapItT).second.m_Target++; if( sourceLabel == targetLabel ) { (*mapItS).second.m_Intersection++; (*mapItS).second.m_Union++; } else { (*mapItS).second.m_Union++; (*mapItT).second.m_Union++; (*mapItS).second.m_SourceComplement++; (*mapItT).second.m_TargetComplement++; } progress.CompletedPixel(); } } /** * measures */ template typename LabelOverlapMeasuresImageFilter::RealType LabelOverlapMeasuresImageFilter ::GetTotalOverlap() { RealType numerator = 0.0; RealType denominator = 0.0; for( MapIterator mapIt = this->m_LabelSetMeasures.begin(); mapIt != this->m_LabelSetMeasures.end(); ++mapIt ) { // Do not include the background in the final value. if( (*mapIt).first == NumericTraits::Zero ) { continue; } numerator += static_cast( (*mapIt).second.m_Intersection ); denominator += static_cast( (*mapIt).second.m_Target ); } return ( numerator / denominator ); } template typename LabelOverlapMeasuresImageFilter::RealType LabelOverlapMeasuresImageFilter ::GetTargetOverlap( LabelType label ) { MapIterator mapIt = this->m_LabelSetMeasures.find( label ); if( mapIt == this->m_LabelSetMeasures.end() ) { itkWarningMacro( "Label " << label << " not found." ); return 0.0; } RealType value = static_cast( (*mapIt).second.m_Intersection ) / static_cast( (*mapIt).second.m_Target ); return value; } template typename LabelOverlapMeasuresImageFilter::RealType LabelOverlapMeasuresImageFilter ::GetUnionOverlap() { RealType numerator = 0.0; RealType denominator = 0.0; for( MapIterator mapIt = this->m_LabelSetMeasures.begin(); mapIt != this->m_LabelSetMeasures.end(); ++mapIt ) { // Do not include the background in the final value. if( (*mapIt).first == NumericTraits::Zero ) { continue; } numerator += static_cast( (*mapIt).second.m_Intersection ); denominator += static_cast( (*mapIt).second.m_Union ); } return ( numerator / denominator ); } template typename LabelOverlapMeasuresImageFilter::RealType LabelOverlapMeasuresImageFilter ::GetUnionOverlap( LabelType label ) { MapIterator mapIt = this->m_LabelSetMeasures.find( label ); if( mapIt == this->m_LabelSetMeasures.end() ) { itkWarningMacro( "Label " << label << " not found." ); return 0.0; } RealType value = static_cast( (*mapIt).second.m_Intersection ) / static_cast( (*mapIt).second.m_Union ); return value; } template typename LabelOverlapMeasuresImageFilter::RealType LabelOverlapMeasuresImageFilter ::GetMeanOverlap() { RealType uo = this->GetUnionOverlap(); return ( 2.0 * uo / ( 1.0 + uo ) ); } template typename LabelOverlapMeasuresImageFilter::RealType LabelOverlapMeasuresImageFilter ::GetMeanOverlap( LabelType label ) { RealType uo = this->GetUnionOverlap( label ); return ( 2.0 * uo / ( 1.0 + uo ) ); } template typename LabelOverlapMeasuresImageFilter::RealType LabelOverlapMeasuresImageFilter ::GetVolumeSimilarity() { RealType numerator = 0.0; RealType denominator = 0.0; for( MapIterator mapIt = this->m_LabelSetMeasures.begin(); mapIt != this->m_LabelSetMeasures.end(); ++mapIt ) { // Do not include the background in the final value. if( (*mapIt).first == NumericTraits::Zero ) { continue; } numerator += ( ( static_cast( (*mapIt).second.m_Source ) - static_cast( (*mapIt).second.m_Target ) ) ); denominator += ( ( static_cast( (*mapIt).second.m_Source ) + static_cast( (*mapIt).second.m_Target ) ) ); } return ( 2.0 * numerator / denominator ); } template typename LabelOverlapMeasuresImageFilter::RealType LabelOverlapMeasuresImageFilter ::GetVolumeSimilarity( LabelType label ) { MapIterator mapIt = this->m_LabelSetMeasures.find( label ); if( mapIt == this->m_LabelSetMeasures.end() ) { itkWarningMacro( "Label " << label << " not found." ); return 0.0; } RealType value = 2.0 * ( static_cast( (*mapIt).second.m_Source ) - static_cast( (*mapIt).second.m_Target ) ) / ( static_cast( (*mapIt).second.m_Source ) + static_cast( (*mapIt).second.m_Target ) ); return value; } template typename LabelOverlapMeasuresImageFilter::RealType LabelOverlapMeasuresImageFilter ::GetFalseNegativeError() { RealType numerator = 0.0; RealType denominator = 0.0; for( MapIterator mapIt = this->m_LabelSetMeasures.begin(); mapIt != this->m_LabelSetMeasures.end(); ++mapIt ) { // Do not include the background in the final value. if( (*mapIt).first == NumericTraits::Zero ) { continue; } numerator += static_cast( (*mapIt).second.m_TargetComplement ); denominator += static_cast( (*mapIt).second.m_Target ); } return ( numerator / denominator ); } template typename LabelOverlapMeasuresImageFilter::RealType LabelOverlapMeasuresImageFilter ::GetFalseNegativeError( LabelType label ) { MapIterator mapIt = this->m_LabelSetMeasures.find( label ); if( mapIt == this->m_LabelSetMeasures.end() ) { itkWarningMacro( "Label " << label << " not found." ); return 0.0; } RealType value = static_cast( (*mapIt).second.m_TargetComplement ) / static_cast( (*mapIt).second.m_Target ); return value; } template typename LabelOverlapMeasuresImageFilter::RealType LabelOverlapMeasuresImageFilter ::GetFalsePositiveError() { RealType numerator = 0.0; RealType denominator = 0.0; for( MapIterator mapIt = this->m_LabelSetMeasures.begin(); mapIt != this->m_LabelSetMeasures.end(); ++mapIt ) { // Do not include the background in the final value. if( (*mapIt).first == NumericTraits::Zero ) { continue; } numerator += static_cast( (*mapIt).second.m_SourceComplement ); denominator += static_cast( (*mapIt).second.m_Source ); } return ( numerator / denominator ); } template typename LabelOverlapMeasuresImageFilter::RealType LabelOverlapMeasuresImageFilter ::GetFalsePositiveError( LabelType label ) { MapIterator mapIt = this->m_LabelSetMeasures.find( label ); if( mapIt == this->m_LabelSetMeasures.end() ) { itkWarningMacro( "Label " << label << " not found." ); return 0.0; } RealType value = static_cast( (*mapIt).second.m_SourceComplement ) / static_cast( (*mapIt).second.m_Source ); return value; } template void LabelOverlapMeasuresImageFilter ::PrintSelf( std::ostream& os, Indent indent ) const { Superclass::PrintSelf( os, indent ); } }// end namespace itk #endif ants-1.9.2+svn680.dfsg/Utilities/itkLabeledPointSetFileReader.h000066400000000000000000000106511147325206600242600ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkLabeledPointSetFileReader.h,v $ Language: C++ Date: $Date: 2009/03/04 23:10:58 $ Version: $Revision: 1.17 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkLabeledPointSetFileReader_h #define __itkLabeledPointSetFileReader_h #include "itkMesh.h" #include "itkMeshSource.h" #include "itkArray.h" #include "itkImage.h" #include "itkVectorContainer.h" #include namespace itk { /** \class LabeledPointSetFileReader * \brief * Reads a file and creates an itkMesh. * */ template class LabeledPointSetFileReader : public MeshSource { public: /** Standard "Self" typedef. */ typedef LabeledPointSetFileReader Self; typedef MeshSource Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); /** Extract dimension from the output mesh. */ itkStaticConstMacro( Dimension, unsigned int, TOutputMesh::PointType::Dimension ); /** Run-time type information (and related methods). */ itkTypeMacro( LabeledPointSetFileReader, MeshSource ); /** Hold on to the type information specified by the template parameters. */ typedef TOutputMesh OutputMeshType; typedef typename OutputMeshType::MeshTraits MeshTraits; typedef typename OutputMeshType::Superclass PointSetType; typedef typename OutputMeshType::PointType PointType; typedef typename MeshTraits::PixelType PixelType; typedef Array MultiComponentScalarType; typedef Array LineType; typedef VectorContainer MultiComponentScalarSetType; typedef VectorContainer LineSetType; typedef Image LabeledPointSetImageType; typedef std::vector LabelSetType; /** Set/Get the name of the file to be read. */ itkSetStringMacro( FileName ); itkGetStringMacro( FileName ); itkSetMacro( ExtractBoundaryPoints, bool ); itkGetMacro( ExtractBoundaryPoints, bool ); itkBooleanMacro( ExtractBoundaryPoints ); /** * Percentage of points selected randomnly */ itkSetClampMacro( RandomPercentage, double, 0.0, 1.0 ); itkGetConstMacro( RandomPercentage, double ); LabelSetType* GetLabelSet() { return &this->m_LabelSet; } unsigned int GetNumberOfLabels() { return this->m_LabelSet.size(); } MultiComponentScalarSetType* GetMultiComponentScalars() { return this->m_MultiComponentScalars.GetPointer(); } LineSetType* GetLines() { return this->m_Lines.GetPointer(); } protected: LabeledPointSetFileReader(); ~LabeledPointSetFileReader() {} void PrintSelf( std::ostream& os, Indent indent ) const; /** Reads the file */ void GenerateData(); bool m_ExtractBoundaryPoints; std::string m_FileName; double m_RandomPercentage; LabelSetType m_LabelSet; typename MultiComponentScalarSetType::Pointer m_MultiComponentScalars; typename LineSetType::Pointer m_Lines; private: LabeledPointSetFileReader( const Self& ); // purposely not implemented void operator=( const Self& ); // purposely not implemented void ReadPointsFromImageFile(); void ReadPointsFromAvantsFile(); void ReadVTKFile(); void ReadPointsFromVTKFile(); void ReadScalarsFromVTKFile(); void ReadLinesFromVTKFile(); }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkLabeledPointSetFileReader.txx" #endif #endif //_itkLabeledPointSetFileReader_h ants-1.9.2+svn680.dfsg/Utilities/itkLabeledPointSetFileReader.txx000066400000000000000000000370501147325206600246560ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkLabeledPointSetFileReader.txx,v $ Language: C++ Date: $Date: 2009/03/13 19:48:16 $ Version: $Revision: 1.23 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkLabeledPointSetFileReader_txx #define __itkLabeledPointSetFileReader_txx #include "itkLabeledPointSetFileReader.h" #include "itkBinaryThresholdImageFilter.h" #include "itkImageFileReader.h" #include "itkImageRegionIterator.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkLabelContourImageFilter.h" #include "itkMersenneTwisterRandomVariateGenerator.h" #include "itkByteSwapper.h" #include #include #include namespace itk { // // Constructor // template LabeledPointSetFileReader ::LabeledPointSetFileReader() { this->m_RandomPercentage = 1.0; this->m_ExtractBoundaryPoints = false; this->m_MultiComponentScalars = NULL; this->m_Lines = NULL; // // Create the output // typename TOutputMesh::Pointer output = TOutputMesh::New(); this->ProcessObject::SetNumberOfRequiredOutputs( 1 ); this->ProcessObject::SetNthOutput( 0, output.GetPointer() ); } template void LabeledPointSetFileReader ::GenerateData() { if( this->m_FileName == "" ) { itkExceptionMacro( "No input FileName" ); return; } // // Read input file // std::ifstream inputFile( m_FileName.c_str() ); if( !inputFile.is_open() ) { itkExceptionMacro("Unable to open file\n" "inputFilename= " << m_FileName ); return; } else { inputFile.close(); } /** * Get filename extension */ std::string::size_type pos = this->m_FileName.rfind( "." ); std::string extension( this->m_FileName, pos+1, this->m_FileName.length()-1 ); if( extension == "txt" ) { this->ReadPointsFromAvantsFile(); } else if( extension == "vtk" ) { this->ReadVTKFile(); } else // try reading the file as an image { this->ReadPointsFromImageFile(); } if( this->m_RandomPercentage < 1.0 ) { typedef Statistics::MersenneTwisterRandomVariateGenerator GeneratorType; typename GeneratorType::Pointer generator = GeneratorType::New(); generator->SetSeed(); typename OutputMeshType::Pointer output = OutputMeshType::New(); output->Initialize(); if( this->GetOutput()->GetNumberOfPoints() > 0 ) { typename OutputMeshType::PointsContainerIterator It = this->GetOutput()->GetPoints()->Begin(); unsigned long count = 0; while( It != this->GetOutput()->GetPoints()->End() ) { if( generator->GetVariateWithClosedRange() <= this->m_RandomPercentage ) { output->SetPoint( count, It.Value() ); PixelType label; bool elementExists = this->GetOutput()->GetPointData() ->GetElementIfIndexExists( It.Index(), &label ); if( elementExists ) { output->SetPointData( count, label ); } count++; } ++It; } } this->GraftOutput( output ); } this->m_LabelSet.clear(); /** * If the number of points does not match the number of * point data, fill the point data with zeros */ if( !this->GetOutput()->GetPointData() || this->GetOutput()->GetPointData()->Size() != this->GetOutput()->GetPoints()->Size() ) { itkWarningMacro( "Number of points does not match number of labels. " << "Filling point data with label zero." ); typename OutputMeshType::PointsContainerIterator It = this->GetOutput()->GetPoints()->Begin(); while( It != this->GetOutput()->GetPoints()->End() ) { this->GetOutput()->SetPointData( It.Index(), NumericTraits::Zero ); ++It; } } if( this->GetOutput()->GetNumberOfPoints() > 0 ) { typename OutputMeshType::PointDataContainerIterator ItD = this->GetOutput()->GetPointData()->Begin(); while( ItD != this->GetOutput()->GetPointData()->End() ) { if( find( this->m_LabelSet.begin(), this->m_LabelSet.end(), ItD.Value() ) == this->m_LabelSet.end() ) { this->m_LabelSet.push_back( ItD.Value() ); } ++ItD; } } } template void LabeledPointSetFileReader ::ReadPointsFromAvantsFile() { typename OutputMeshType::Pointer outputMesh = this->GetOutput(); std::ifstream inputFile( m_FileName.c_str() ); unsigned long count = 0; while( !inputFile.eof() ) { PointType point; PixelType label; if( Dimension == 2 ) { float trash; inputFile >> point >> trash >> label; } else // Dimension == 3 { inputFile >> point >> label; } if( ( point.GetVectorFromOrigin() ).GetSquaredNorm() > 0.0 || label != 0 ) { outputMesh->SetPointData( count, label ); outputMesh->SetPoint( count, point ); count++; } } inputFile.close(); } template void LabeledPointSetFileReader ::ReadVTKFile() { this->ReadPointsFromVTKFile(); this->ReadScalarsFromVTKFile(); this->ReadLinesFromVTKFile(); } template void LabeledPointSetFileReader ::ReadPointsFromVTKFile() { typename OutputMeshType::Pointer outputMesh = this->GetOutput(); std::ifstream inputFile( this->m_FileName.c_str() ); std::string line; bool isBinary = false; while( !inputFile.eof() ) { std::getline( inputFile, line ); if (line.find( "BINARY" ) != std::string::npos ) { isBinary = true; } if( line.find( "POINTS" ) != std::string::npos ) { break; } } itkDebugMacro( "POINTS line" << line ); std::string pointLine( line, strlen( "POINTS " ), line.length() ); itkDebugMacro( "pointLine " << pointLine ); int numberOfPoints = -1; if( sscanf( pointLine.c_str(),"%d",&numberOfPoints ) != 1 ) { itkExceptionMacro( "ERROR: Failed to read numberOfPoints\n" " pointLine = " << pointLine ); return; } itkDebugMacro( "numberOfPoints = " << numberOfPoints ); if( numberOfPoints < 1 ) { itkExceptionMacro( "numberOfPoints < 1" << " numberOfPoints = " << numberOfPoints ); return; } outputMesh->GetPoints()->Reserve( numberOfPoints ); // // Load the point coordinates into the itk::Mesh // PointType point; if (isBinary) { itkDebugMacro( "Data is binary" ); float p; float * ptData = new float [ numberOfPoints*3 ]; inputFile.read( reinterpret_cast< char * >( ptData ), 3 * numberOfPoints * sizeof(p) ); ByteSwapper::SwapRangeFromSystemToBigEndian(ptData,numberOfPoints*3); for (long i = 0; i < numberOfPoints; i++ ) { for (long j = 0; j < Dimension; j++ ) { point[j] = ptData[i*3+j]; } outputMesh->SetPoint( i, point ); } delete [] ptData; } else { for( long i = 0; i < numberOfPoints; i++ ) { if( Dimension == 2 ) { float trash; inputFile >> point >> trash; } else // Dimension = 3 { inputFile >> point; } outputMesh->SetPoint( i, point ); } } inputFile.close(); } template void LabeledPointSetFileReader ::ReadScalarsFromVTKFile() { typename OutputMeshType::Pointer outputMesh = this->GetOutput(); std::ifstream inputFile( this->m_FileName.c_str() ); std::string line; bool isBinary = false; // // Find the labels associated with each pixel // while( !inputFile.eof() ) { std::getline( inputFile, line ); if (line.find( "BINARY" ) != std::string::npos ) { isBinary = true; } if( line.find( "SCALARS" ) != std::string::npos ) { break; } } if( inputFile.eof() ) { inputFile.close(); return; } std::string::size_type pos = line.rfind( " " ); std::string temp = std::string( line, pos+1, line.length()-1 ); unsigned int numberOfComponents = std::atoi( temp.c_str() ); std::getline( inputFile, line ); if (isBinary) { int numberOfValues = outputMesh->GetNumberOfPoints()*numberOfComponents; float p; int * scalarData = new int [ numberOfValues ]; inputFile.read( reinterpret_cast< char * >( scalarData ), numberOfComponents * sizeof(p) ); ByteSwapper::SwapRangeFromSystemToBigEndian(scalarData,numberOfValues); if( numberOfComponents == 1 ) { //PixelType label; for( unsigned long i = 0; i < outputMesh->GetNumberOfPoints(); i++ ) { outputMesh->SetPointData( i, scalarData[i] ); } // itkExceptionMacro( "Only single label components are readable" ); } else { this->m_MultiComponentScalars = MultiComponentScalarSetType::New(); this->m_MultiComponentScalars->Initialize(); for( unsigned long i = 0; i < outputMesh->GetNumberOfPoints(); i++ ) { MultiComponentScalarType scalar; scalar.SetSize( numberOfComponents ); for( unsigned int d = 0; d < numberOfComponents; d++ ) { scalar[d] = scalarData[i*numberOfComponents + d]; } this->m_MultiComponentScalars->InsertElement( i, scalar ); } } delete [] scalarData; } else { if( numberOfComponents == 1 ) { PixelType label; for( unsigned long i = 0; i < outputMesh->GetNumberOfPoints(); i++ ) { inputFile >> label; outputMesh->SetPointData( i, label ); } // itkExceptionMacro( "Only single label components are readable" ); } else { this->m_MultiComponentScalars = MultiComponentScalarSetType::New(); this->m_MultiComponentScalars->Initialize(); for( unsigned long i = 0; i < outputMesh->GetNumberOfPoints(); i++ ) { MultiComponentScalarType scalar; scalar.SetSize( numberOfComponents ); for( unsigned int d = 0; d < numberOfComponents; d++ ) { inputFile >> scalar[d]; } this->m_MultiComponentScalars->InsertElement( i, scalar ); } } } inputFile.close(); } template void LabeledPointSetFileReader ::ReadLinesFromVTKFile() { typename OutputMeshType::Pointer outputMesh = this->GetOutput(); std::ifstream inputFile( this->m_FileName.c_str() ); std::string line; bool isBinary = false; // // Find the labels associated with each pixel // while( !inputFile.eof() ) { std::getline( inputFile, line ); if (line.find( "BINARY" ) != std::string::npos ) { isBinary = true; } if( line.find( "LINES" ) != std::string::npos ) { break; } } if( inputFile.eof() ) { inputFile.close(); return; } std::string::size_type pos = line.rfind( " " ); std::string temp = std::string( line, 6, pos-1 ); unsigned int numberOfLines = std::atoi( temp.c_str() ); temp = std::string(line, pos, line.length()-1 ); unsigned int numberOfValues = std::atoi( temp.c_str() ); this->m_Lines = LineSetType::New(); this->m_Lines->Initialize(); if (isBinary) { int p; int * lineData = new int [ numberOfValues ]; inputFile.read( reinterpret_cast< char * >( lineData ), numberOfValues * sizeof(p) ); ByteSwapper::SwapRangeFromSystemToBigEndian(lineData,numberOfValues); unsigned long valueId = 0; unsigned long lineId = 0; while (valueId < numberOfValues) { int lineLength = lineData[valueId]; ++valueId; LineType polyLine; polyLine.SetSize( lineLength ); for (long i = 0; i < lineLength; i++) { polyLine[i] = lineData[valueId]; ++valueId; } this->m_Lines->InsertElement( lineId, polyLine ); ++lineId; } delete [] lineData; } else { for( unsigned int i = 0; i < numberOfLines; i++ ) { LineType line; unsigned int numberOfPoints; inputFile >> numberOfPoints; line.SetSize( numberOfPoints ); for( unsigned int d = 0; d < numberOfPoints; d++ ) { inputFile >> line[d]; } this->m_Lines->InsertElement( i, line ); } } inputFile.close(); } template void LabeledPointSetFileReader ::ReadPointsFromImageFile() { typename OutputMeshType::Pointer outputMesh = this->GetOutput(); typedef ImageFileReader ImageReaderType; typename ImageReaderType::Pointer imageReader = ImageReaderType::New(); imageReader->SetFileName( this->m_FileName.c_str() ); imageReader->Update(); if( !this->m_ExtractBoundaryPoints ) { ImageRegionIteratorWithIndex It( imageReader->GetOutput(), imageReader->GetOutput()->GetLargestPossibleRegion() ); unsigned long count = 0; for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { PixelType label = It.Get(); if( label != NumericTraits::Zero ) { typename LabeledPointSetImageType::PointType imagePoint; imageReader->GetOutput()->TransformIndexToPhysicalPoint( It.GetIndex(), imagePoint ); PointType point; point.CastFrom( imagePoint ); outputMesh->SetPoint( count, point ); outputMesh->SetPointData( count, label ); count++; } } } else { typedef LabelContourImageFilter ContourFilterType; typename ContourFilterType::Pointer contourFilter = ContourFilterType::New(); contourFilter->SetInput( imageReader->GetOutput() ); contourFilter->SetFullyConnected( true ); contourFilter->SetBackgroundValue( 0 ); contourFilter->Update(); this->m_LabelSet.clear(); ImageRegionIteratorWithIndex It ( contourFilter->GetOutput(), contourFilter->GetOutput()->GetLargestPossibleRegion() ); unsigned long count = 0; for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { PixelType label = It.Get(); if( It.Get() > 0 ) { typename LabeledPointSetImageType::PointType imagePoint; contourFilter->GetOutput()->TransformIndexToPhysicalPoint( It.GetIndex(), imagePoint ); PointType point; point.CastFrom( imagePoint ); outputMesh->SetPoint( count, point ); outputMesh->SetPointData( count, It.Get() ); count++; if( find( this->m_LabelSet.begin(), this->m_LabelSet.end(), label ) == this->m_LabelSet.end() ) { this->m_LabelSet.push_back( label ); } } } } } template void LabeledPointSetFileReader ::PrintSelf( std::ostream& os, Indent indent ) const { Superclass::PrintSelf(os,indent); os << indent << "FileName: " << this->m_FileName << std::endl; os << indent << "RandomPercentage: " << this->m_RandomPercentage << std::endl; } } //end of namespace itk #endif ants-1.9.2+svn680.dfsg/Utilities/itkLabeledPointSetFileWriter.h000066400000000000000000000117311147325206600243320ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkLabeledPointSetFileWriter.h,v $ Language: C++ Date: $Date: 2009/03/04 23:10:58 $ Version: $Revision: 1.17 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkLabeledPointSetFileWriter_h #define __itkLabeledPointSetFileWriter_h #include "itkMesh.h" #include "itkArray.h" #include "itkImage.h" #include "itkVectorContainer.h" namespace itk { /** \class LabeledPointSetFileWriter * \brief * Writes an itkMesh to a file in various txt file formats. * */ template class LabeledPointSetFileWriter : public Object { public: /** Standard "Self" typedef. */ typedef LabeledPointSetFileWriter Self; typedef Object Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory */ itkNewMacro( Self ); /** Write the Input mesh to the Output file. * Use either Update() or Write(). */ void Update( void ); void Write( void ); /** Extract dimension from the output mesh. */ itkStaticConstMacro( Dimension, unsigned int, TInputMesh::PointType::Dimension ); /** Run-time type information (and related methods). */ itkTypeMacro( LabeledPointSetFileWriter, Object ); /** Hold on to the type information specified by the template parameters. */ typedef TInputMesh InputMeshType; typedef typename TInputMesh::Pointer InputMeshPointer; typedef typename InputMeshType::MeshTraits MeshTraits; typedef typename InputMeshType::Superclass PointSetType; typedef typename InputMeshType::PointType PointType; typedef typename MeshTraits::PixelType PixelType; typedef Array MultiComponentScalarType; typedef Array LineType; typedef VectorContainer MultiComponentScalarSetType; typedef VectorContainer LineSetType; typedef Image LabeledPointSetImageType; typedef typename LabeledPointSetImageType::SizeType ImageSizeType; typedef typename LabeledPointSetImageType::PointType ImageOriginType; typedef typename LabeledPointSetImageType::SpacingType ImageSpacingType; typedef typename LabeledPointSetImageType::DirectionType ImageDirectionType; /** Set the Input */ void SetInput( InputMeshType * input ); /** Set/Get the name of the file where data are written. */ itkSetStringMacro( FileName ); itkGetStringMacro( FileName ); /** Specify other attributes */ itkSetMacro( Lines, typename LineSetType::Pointer ); itkSetMacro( MultiComponentScalars, typename MultiComponentScalarSetType::Pointer ); /** Specify image attributes if output is an image. */ itkSetMacro( ImageSize, ImageSizeType ); itkGetConstMacro( ImageSize, ImageSizeType ); itkSetMacro( ImageOrigin, ImageOriginType ); itkGetConstMacro( ImageOrigin, ImageOriginType ); itkSetMacro( ImageSpacing, ImageSpacingType ); itkGetConstMacro( ImageSpacing, ImageSpacingType ); itkSetMacro( ImageDirection, ImageDirectionType ); itkGetConstMacro( ImageDirection, ImageDirectionType ); protected: LabeledPointSetFileWriter(); virtual ~LabeledPointSetFileWriter(); virtual void GenerateData(); std::string m_FileName; InputMeshPointer m_Input; typename MultiComponentScalarSetType::Pointer m_MultiComponentScalars; typename LineSetType::Pointer m_Lines; /** * If output is an image type, the attributes must be specified. */ ImageSizeType m_ImageSize; ImageSpacingType m_ImageSpacing; ImageOriginType m_ImageOrigin; ImageDirectionType m_ImageDirection; void PrintSelf(std::ostream& os, Indent indent) const; private: LabeledPointSetFileWriter(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented void WritePointsToAvantsFile(); void WritePointsToImageFile(); void WriteVTKFile(); void WritePointsToVTKFile(); void WriteScalarsToVTKFile(); void WriteLinesToVTKFile(); }; } // namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkLabeledPointSetFileWriter.txx" #endif #endif ants-1.9.2+svn680.dfsg/Utilities/itkLabeledPointSetFileWriter.txx000066400000000000000000000243671147325206600247370ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkLabeledPointSetFileWriter.txx,v $ Language: C++ Date: $Date: 2009/03/04 23:10:58 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkLabeledPointSetFileWriter_txx #define __itkLabeledPointSetFileWriter_txx #include "itkLabeledPointSetFileWriter.h" #include "itkImageFileWriter.h" #include namespace itk { // // Constructor // template LabeledPointSetFileWriter ::LabeledPointSetFileWriter() { this->m_Input = NULL; this->m_FileName = ""; this->m_MultiComponentScalars = NULL; this->m_Lines = NULL; this->m_ImageSize.Fill( 0 ); } // // Destructor // template LabeledPointSetFileWriter ::~LabeledPointSetFileWriter() { } // // Set the input mesh // template void LabeledPointSetFileWriter ::SetInput(InputMeshType * input) { this->m_Input = input; } // // Write the input mesh to the output file // template void LabeledPointSetFileWriter ::Update() { this->GenerateData(); } // // Write the input mesh to the output file // template void LabeledPointSetFileWriter ::Write() { this->GenerateData(); } template void LabeledPointSetFileWriter ::GenerateData() { if( this->m_FileName == "" ) { itkExceptionMacro( "No FileName" ); return; } if( this->m_ImageSize[0] == 0 ) { this->m_ImageSize.Fill( 100 ); this->m_ImageOrigin.CastFrom( this->m_Input->GetBoundingBox()->GetMinimum() ); for( unsigned int d = 0; d < Dimension; d++ ) { this->m_ImageSpacing[d] = ( this->m_Input->GetBoundingBox()->GetMaximum()[d] - this->m_Input->GetBoundingBox()->GetMinimum()[d] ) / static_cast( this->m_ImageSize[d] + 1 ); } this->m_ImageDirection.SetIdentity(); } // // Read output file // std::ofstream outputFile( m_FileName.c_str() ); if( !outputFile.is_open() ) { itkExceptionMacro( "Unable to open file\n" "outputFilename= " << m_FileName ); return; } else { outputFile.close(); } /** * Get filename extension */ std::string::size_type pos = this->m_FileName.rfind( "." ); std::string extension( this->m_FileName, pos+1, this->m_FileName.length()-1 ); if( extension == "txt" ) { this->WritePointsToAvantsFile(); } else if( extension == "vtk" ) { this->WriteVTKFile(); } else { try { this->WritePointsToImageFile(); } catch(...) { itkExceptionMacro( "Unknown extension: " << extension ); } } } template void LabeledPointSetFileWriter ::WriteVTKFile() { this->WritePointsToVTKFile(); this->WriteLinesToVTKFile(); this->WriteScalarsToVTKFile(); } template void LabeledPointSetFileWriter ::WritePointsToVTKFile() { // // Write to output file // std::ofstream outputFile( this->m_FileName.c_str() ); outputFile << "# vtk DataFile Version 2.0" << std::endl; outputFile << "File written by itkLabeledPointSetFileWriter" << std::endl; outputFile << "ASCII" << std::endl; outputFile << "DATASET POLYDATA" << std::endl; // POINTS go first unsigned int numberOfPoints = this->m_Input->GetNumberOfPoints(); outputFile << "POINTS " << numberOfPoints << " float" << std::endl; typename InputMeshType::PointsContainerIterator pointIterator = this->m_Input->GetPoints()->Begin(); typename InputMeshType::PointsContainerIterator pointEnd = this->m_Input->GetPoints()->End(); while( pointIterator != pointEnd ) { PointType point = pointIterator.Value(); outputFile << point[0] << " " << point[1]; if( Dimension == 2 ) { outputFile << " 0 " << std::endl; } else if( Dimension == 3 ) { outputFile << " " << point[2] << " " << std::endl; } pointIterator++; } outputFile.close(); } template void LabeledPointSetFileWriter ::WriteScalarsToVTKFile() { // // Write to output file // std::ofstream outputFile( this->m_FileName.c_str(), std::ios::app ); // No point data conditions if (!this->m_Input->GetPointData()) return; if (this->m_Input->GetPointData()->Size() == 0) return; unsigned int numberOfPoints = this->m_Input->GetNumberOfPoints(); outputFile << std::endl; outputFile << "POINT_DATA " << numberOfPoints << std::endl; std::string type = std::string( "float" ); if( !this->m_MultiComponentScalars ) { outputFile << "SCALARS pointLabels " << type << " 1" << std::endl; outputFile << "LOOKUP_TABLE default" << std::endl; typename InputMeshType::PointDataContainerIterator pointDataIterator = this->m_Input->GetPointData()->Begin(); typename InputMeshType::PointDataContainerIterator pointDataEnd = this->m_Input->GetPointData()->End(); while( pointDataIterator != pointDataEnd ) { outputFile << pointDataIterator.Value() << " "; pointDataIterator++; } outputFile << std::endl; } else { MultiComponentScalarType scalar = this->m_MultiComponentScalars->GetElement( 0 ); unsigned int numberOfComponents = scalar.GetSize(); outputFile << "SCALARS scalars " << type << numberOfComponents << std::endl; outputFile << "LOOKUP_TABLE default" << std::endl; typename MultiComponentScalarSetType::Iterator It = this->m_MultiComponentScalars->Begin(); typename MultiComponentScalarSetType::Iterator ItEnd = this->m_MultiComponentScalars->End(); while( It != ItEnd ) { outputFile << It.Value() << " "; It++; } outputFile << std::endl; } outputFile.close(); } template void LabeledPointSetFileWriter ::WriteLinesToVTKFile() { if( this->m_Lines ) { std::ofstream outputFile( this->m_FileName.c_str(), std::ios::app ); unsigned int numberOfLines = this->m_Lines->Size(); unsigned int totalSize = 0; typename LineSetType::Iterator It = this->m_Lines->Begin(); typename LineSetType::Iterator ItEnd = this->m_Lines->End(); while( It != ItEnd ) { totalSize += ( It.Value() ).Size(); totalSize++; It++; } outputFile << "LINES " << numberOfLines << " " << totalSize << std::endl; It = this->m_Lines->Begin(); while( It != ItEnd ) { unsigned int numberOfPoints = ( It.Value() ).Size(); outputFile << numberOfPoints << " "; for( unsigned int d = 0; d < numberOfPoints; d++ ) { outputFile << ( It.Value() )[d] << " "; } outputFile << std::endl; ++It; } outputFile << std::endl; outputFile.close(); } } template void LabeledPointSetFileWriter ::WritePointsToAvantsFile() { // // Write to output file // std::ofstream outputFile( this->m_FileName.c_str() ); outputFile << "0 0 0 0" << std::endl; if( this->m_Input->GetNumberOfPoints() > 0 ) { typename InputMeshType::PointsContainerIterator pointIterator = this->m_Input->GetPoints()->Begin(); typename InputMeshType::PointsContainerIterator pointEnd = this->m_Input->GetPoints()->End(); typename InputMeshType::PointDataContainerIterator pointDataIterator = this->m_Input->GetPointData()->Begin(); while( pointIterator != pointEnd ) { PointType point = pointIterator.Value(); outputFile << point[0] << " " << point[1]; if( Dimension == 2 ) { outputFile << " 0 "; } else if( Dimension == 3 ) { outputFile << " " << point[2] << " "; } outputFile << pointDataIterator.Value() << std::endl; pointIterator++; pointDataIterator++; } } outputFile << "0 0 0 0" << std::endl; outputFile.close(); } template void LabeledPointSetFileWriter ::WritePointsToImageFile() { typename LabeledPointSetImageType::Pointer outputImage = LabeledPointSetImageType::New(); outputImage->SetDirection( this->m_ImageDirection ); outputImage->SetRegions( this->m_ImageSize ); outputImage->SetOrigin( this->m_ImageOrigin ); outputImage->SetSpacing( this->m_ImageSpacing ); outputImage->Allocate(); outputImage->FillBuffer( NumericTraits::Zero ); if( this->m_Input->GetNumberOfPoints() > 0 ) { typename InputMeshType::PointsContainerIterator pointIterator = this->m_Input->GetPoints()->Begin(); typename InputMeshType::PointsContainerIterator pointEnd = this->m_Input->GetPoints()->End(); typename InputMeshType::PointDataContainerIterator pointDataIterator = this->m_Input->GetPointData()->Begin(); while( pointIterator != pointEnd ) { PointType point = pointIterator.Value(); PixelType label = pointDataIterator.Value(); typename LabeledPointSetImageType::IndexType index; typename LabeledPointSetImageType::PointType ipoint; ipoint.CastFrom( point ); if( outputImage->TransformPhysicalPointToIndex( ipoint, index ) ) { outputImage->SetPixel( index, label ); } pointIterator++; pointDataIterator++; } } typedef ImageFileWriter WriterType; typename WriterType::Pointer writer = WriterType::New(); writer->SetFileName( this->m_FileName.c_str() ); writer->SetInput( outputImage ); writer->Update(); } template void LabeledPointSetFileWriter ::PrintSelf( std::ostream& os, Indent indent ) const { Superclass::PrintSelf(os,indent); os << indent << "FileName: " << this->m_FileName << std::endl; } } //end of namespace itk #endif ants-1.9.2+svn680.dfsg/Utilities/itkManifoldParzenWindowsPointSetFunction.h000066400000000000000000000144251147325206600270020ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkManifoldParzenWindowsPointSetFunction.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.16 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkManifoldParzenWindowsPointSetFunction_h #define __itkManifoldParzenWindowsPointSetFunction_h #include "itkPointSetFunction.h" #include "itkGaussianProbabilityDensityFunction.h" #include "itkKdTreeGenerator.h" #include "itkListSample.h" #include "itkMatrix.h" #include "itkMersenneTwisterRandomVariateGenerator.h" #include "itkMeshSource.h" #include "itkPointSet.h" #include "itkVector.h" #include "itkWeightedCentroidKdTreeGenerator.h" #include namespace itk { /** \class ManifoldParzenWindowsPointSetFunction.h * \brief point set filter. */ template class ITK_EXPORT ManifoldParzenWindowsPointSetFunction : public PointSetFunction { public: typedef ManifoldParzenWindowsPointSetFunction Self; typedef PointSetFunction Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Extract dimension from output image. */ itkStaticConstMacro( Dimension, unsigned int, TPointSet::PointDimension ); typedef typename Superclass::InputPointSetType InputPointSetType; typedef typename Superclass::InputPointType InputPointType; /** Point set typedef support. */ typedef TPointSet PointSetType; typedef typename PointSetType::PointType PointType; typedef typename PointSetType ::PointsContainerConstIterator PointsContainerConstIterator; typedef Vector MeasurementVectorType; typedef typename Statistics::ListSample SampleType; typedef typename Statistics ::WeightedCentroidKdTreeGenerator TreeGeneratorType; typedef typename TreeGeneratorType::KdTreeType KdTreeType; typedef typename KdTreeType ::InstanceIdentifierVectorType NeighborhoodIdentifierType; /** Other typedef */ typedef TOutput RealType; typedef TOutput OutputType; typedef Vector VectorType; typedef typename Statistics ::MersenneTwisterRandomVariateGenerator RandomizerType; typedef typename Statistics ::GaussianProbabilityDensityFunction GaussianType; typedef std::vector GaussianContainerType; typedef typename GaussianType::MatrixType CovarianceMatrixType; /** Helper functions */ itkSetMacro( CovarianceKNeighborhood, unsigned int ); itkGetConstMacro( CovarianceKNeighborhood, unsigned int ); itkSetMacro( EvaluationKNeighborhood, unsigned int ); itkGetConstMacro( EvaluationKNeighborhood, unsigned int ); itkSetMacro( RegularizationSigma, RealType ); itkGetConstMacro( RegularizationSigma, RealType ); itkSetMacro( KernelSigma, RealType ); itkGetConstMacro( KernelSigma, RealType ); itkSetMacro( BucketSize, unsigned int ); itkGetConstMacro( BucketSize, unsigned int ); itkSetMacro( Normalize, bool ); itkGetConstMacro( Normalize, bool ); itkBooleanMacro( Normalize ); itkSetMacro( UseAnisotropicCovariances, bool ); itkGetConstMacro( UseAnisotropicCovariances, bool ); itkBooleanMacro( UseAnisotropicCovariances ); virtual void SetInputPointSet( const InputPointSetType * ptr ); virtual TOutput Evaluate( const InputPointType& point ) const; PointType GenerateRandomSample(); typename GaussianType::Pointer GetGaussian( unsigned int i ) { if ( i < this->m_Gaussians.size() ) { return this->m_Gaussians[i].GetPointer(); } else { return NULL; } this->Modified(); } void SetGaussian( unsigned int i, typename GaussianType::Pointer gaussian ) { if ( i >= this->m_Gaussians.size() ) { this->m_Gaussians.resize( i+1 ); } this->m_Gaussians[i] = gaussian; this->Modified(); } void GenerateKdTree(); NeighborhoodIdentifierType GetNeighborhoodIdentifiers( MeasurementVectorType, unsigned int ); NeighborhoodIdentifierType GetNeighborhoodIdentifiers( InputPointType, unsigned int ); protected: ManifoldParzenWindowsPointSetFunction(); virtual ~ManifoldParzenWindowsPointSetFunction(); void PrintSelf( std::ostream& os, Indent indent ) const; void GenerateData(); private: //purposely not implemented ManifoldParzenWindowsPointSetFunction( const Self& ); void operator=( const Self& ); unsigned int m_CovarianceKNeighborhood; unsigned int m_EvaluationKNeighborhood; unsigned int m_BucketSize; RealType m_RegularizationSigma; RealType m_KernelSigma; typename TreeGeneratorType::Pointer m_KdTreeGenerator; typename SampleType::Pointer m_SamplePoints; typename RandomizerType::Pointer m_Randomizer; GaussianContainerType m_Gaussians; bool m_Normalize; bool m_UseAnisotropicCovariances; }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkManifoldParzenWindowsPointSetFunction.txx" #endif #endif ants-1.9.2+svn680.dfsg/Utilities/itkManifoldParzenWindowsPointSetFunction.txx000066400000000000000000000261001147325206600273670ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkManifoldParzenWindowsPointSetFunction.txx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.17 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkManifoldParzenWindowsPointSetFunction_txx #define __itkManifoldParzenWindowsPointSetFunction_txx #include "itkManifoldParzenWindowsPointSetFunction.h" #include "vnl/vnl_vector.h" #include "vnl/vnl_math.h" namespace itk { template ManifoldParzenWindowsPointSetFunction ::ManifoldParzenWindowsPointSetFunction() { this->m_BucketSize = 4; this->m_CovarianceKNeighborhood = 5; this->m_EvaluationKNeighborhood = 50; this->m_SamplePoints = NULL; this->m_KdTreeGenerator = NULL; this->m_RegularizationSigma = 1.0; this->m_KernelSigma = 1.0; this->m_Normalize = true; this->m_UseAnisotropicCovariances = true; this->m_Randomizer = RandomizerType::New(); this->m_Randomizer->SetSeed(); } template ManifoldParzenWindowsPointSetFunction ::~ManifoldParzenWindowsPointSetFunction() { } template void ManifoldParzenWindowsPointSetFunction ::SetInputPointSet( const InputPointSetType * ptr ) { this->m_PointSet = ptr; /** * Generate KdTree and create set of gaussians from input point set */ this->m_SamplePoints = SampleType::New(); this->m_SamplePoints->SetMeasurementVectorSize( Dimension ); std::vector inputGaussians; inputGaussians.resize( this->GetInputPointSet()->GetNumberOfPoints() ); this->m_Gaussians.resize( this->GetInputPointSet()->GetNumberOfPoints() ); MeasurementVectorType mv; unsigned long count = 0; PointsContainerConstIterator It = this->GetInputPointSet()->GetPoints()->Begin(); while( It != this->GetInputPointSet()->GetPoints()->End() ) { PointType point = It.Value(); typename GaussianType::MeanType mean( Dimension ); for( unsigned int d = 0; d < Dimension; d++ ) { mv[d] = point[d]; mean[d] = point[d]; } this->m_SamplePoints->PushBack( mv ); inputGaussians[count] = GaussianType::New(); inputGaussians[count]->SetGenerateRandomSamples( false ); inputGaussians[count]->SetSigma( this->m_KernelSigma ); inputGaussians[count]->SetMean( mean ); count++; ++It; } this->m_KdTreeGenerator = TreeGeneratorType::New(); m_KdTreeGenerator->SetSample( this->m_SamplePoints ); m_KdTreeGenerator->SetBucketSize( this->m_BucketSize ); m_KdTreeGenerator->Update(); /** * Calculate covariance matrices */ It = this->GetInputPointSet()->GetPoints()->Begin(); while( It != this->GetInputPointSet()->GetPoints()->End() ) { PointType point = It.Value(); unsigned long index = It.Index(); this->m_Gaussians[index] = GaussianType::New(); this->m_Gaussians[index]->SetGenerateRandomSamples( true ); this->m_Gaussians[index]->SetMean( inputGaussians[index]->GetMean() ); if( this->m_CovarianceKNeighborhood > 0 && this->m_UseAnisotropicCovariances ) { CovarianceMatrixType Cout( Dimension, Dimension ); Cout.Fill( 0 ); MeasurementVectorType queryPoint; for( unsigned int d = 0; d < Dimension; d++ ) { queryPoint[d] = point[d]; } typename TreeGeneratorType::KdTreeType ::InstanceIdentifierVectorType neighbors; this->m_KdTreeGenerator->GetOutput()->Search( queryPoint, this->m_CovarianceKNeighborhood, neighbors ); RealType denominator = 0.0; for( unsigned int j = 0; j < this->m_CovarianceKNeighborhood; j++ ) { if( neighbors[j] != index && neighbors[j] < this->GetInputPointSet()->GetNumberOfPoints() ) { MeasurementVectorType neighbor = this->m_KdTreeGenerator->GetOutput()->GetMeasurementVector( neighbors[j] ); RealType kernelValue = inputGaussians[index]->Evaluate( neighbor ); denominator += kernelValue; if( kernelValue > 0.0 ) { for( unsigned int m = 0; m < Dimension; m++ ) { for( unsigned int n = m; n < Dimension; n++ ) { RealType covariance = kernelValue * ( neighbor[m] - queryPoint[m] ) * ( neighbor[n] - queryPoint[n] ); Cout( m, n ) += covariance; Cout( n, m ) += covariance; } } } } } if( this->m_Normalize && denominator > 0.0 ) { Cout /= denominator; } else { Cout /= static_cast( this->m_CovarianceKNeighborhood ); } for( unsigned int m = 0; m < Dimension; m++ ) { Cout( m, m ) += ( this->m_RegularizationSigma * this->m_RegularizationSigma ); } this->m_Gaussians[index]->SetCovariance( Cout ); } else { this->m_Gaussians[index]->SetSigma( this->m_RegularizationSigma ); } ++It; } } template void ManifoldParzenWindowsPointSetFunction ::GenerateKdTree() { /** * Generate KdTree and create set of gaussians from input point set */ this->m_SamplePoints = SampleType::New(); this->m_SamplePoints->SetMeasurementVectorSize( Dimension ); MeasurementVectorType mv; typename GaussianContainerType::const_iterator it; for( it = this->m_Gaussians.begin(); it != this->m_Gaussians.end(); ++it ) { typename GaussianType::MeanType mean = (*it)->GetMean(); for( unsigned int d = 0; d < Dimension; d++ ) { mv[d] = mean[d]; } this->m_SamplePoints->PushBack( mv ); } this->m_KdTreeGenerator = TreeGeneratorType::New(); this->m_KdTreeGenerator->SetSample( this->m_SamplePoints ); this->m_KdTreeGenerator->SetBucketSize( this->m_BucketSize ); this->m_KdTreeGenerator->Update(); } template TOutput ManifoldParzenWindowsPointSetFunction ::Evaluate( const InputPointType &point ) const { if( !this->m_KdTreeGenerator ) { MeasurementVectorType measurement; for( unsigned int d = 0; d < Dimension; d++ ) { measurement[d] = point[d]; } OutputType sum = 0.0; typename GaussianContainerType::const_iterator it; for( it = this->m_Gaussians.begin(); it != this->m_Gaussians.end(); ++it ) { sum += static_cast( (*it)->Evaluate( measurement ) ); } return static_cast( sum / static_cast( this->m_Gaussians.size() ) ); } else { MeasurementVectorType queryPoint; for( unsigned int d = 0; d < Dimension; d++ ) { queryPoint[d] = point[d]; } unsigned int numberOfNeighbors = vnl_math_min( this->m_EvaluationKNeighborhood, static_cast( this->m_Gaussians.size() ) ); OutputType sum = 0.0; if( numberOfNeighbors == this->m_Gaussians.size() ) { for( unsigned int j = 0; j < this->m_Gaussians.size(); j++ ) { sum += static_cast( this->m_Gaussians[j]->Evaluate( queryPoint ) ); } } else { typename TreeGeneratorType::KdTreeType ::InstanceIdentifierVectorType neighbors; this->m_KdTreeGenerator->GetOutput()->Search( queryPoint, static_cast( numberOfNeighbors ), neighbors ); for( unsigned int j = 0; j < numberOfNeighbors; j++ ) { sum += static_cast( this->m_Gaussians[neighbors[j]]->Evaluate( queryPoint ) ); } } return static_cast( sum / static_cast( this->m_Gaussians.size() ) ); } } template typename ManifoldParzenWindowsPointSetFunction ::NeighborhoodIdentifierType ManifoldParzenWindowsPointSetFunction ::GetNeighborhoodIdentifiers( MeasurementVectorType point, unsigned int numberOfNeighbors ) { if( numberOfNeighbors > this->m_KdTreeGenerator->GetOutput()->Size() ) { numberOfNeighbors = this->m_KdTreeGenerator->GetOutput()->Size(); } NeighborhoodIdentifierType neighbors; this->m_KdTreeGenerator->GetOutput()->Search( point, numberOfNeighbors, neighbors ); return neighbors; } template typename ManifoldParzenWindowsPointSetFunction ::NeighborhoodIdentifierType ManifoldParzenWindowsPointSetFunction ::GetNeighborhoodIdentifiers( InputPointType point, unsigned int numberOfNeighbors ) { MeasurementVectorType queryPoint( Dimension ); for( unsigned int d = 0; d < Dimension; d++ ) { queryPoint[d] = point[d]; } return this->GetNeighborhoodIdentifiers( queryPoint, numberOfNeighbors ); } template typename ManifoldParzenWindowsPointSetFunction ::PointType ManifoldParzenWindowsPointSetFunction ::GenerateRandomSample() { typename GaussianType::MeasurementVectorType gaussianSample; gaussianSample = this->m_Gaussians[this->m_Randomizer->GetIntegerVariate( this->GetInputPointSet()->GetNumberOfPoints()-1 ) ]->GenerateRandomSample(); PointType sample; for( unsigned int d = 0; d < Dimension; d++ ) { sample[d] = gaussianSample[d]; } return sample; } /** * Standard "PrintSelf" method */ template void ManifoldParzenWindowsPointSetFunction ::PrintSelf( std::ostream& os, Indent indent) const { os << indent << "Covariance: " << this->m_CovarianceKNeighborhood << std::endl; os << indent << "Evaluation: " << this->m_EvaluationKNeighborhood << std::endl; os << indent << "Regularization sigma: " << this->m_RegularizationSigma << std::endl; os << indent << "Kernel sigma: " << this->m_KernelSigma << std::endl; os << indent << "Bucket size: " << this->m_BucketSize << std::endl; os << indent << "Normalize: " << this->m_Normalize << std::endl; os << indent << "Use anisotropic covariances: " << this->m_UseAnisotropicCovariances << std::endl; } } //end namespace itk #endif ants-1.9.2+svn680.dfsg/Utilities/itkN3MRIBiasFieldCorrectionImageFilter.h000066400000000000000000000277621147325206600260660ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkN3MRIBiasFieldCorrectionImageFilter.h,v $ Language: C++ Date: $Date: 2009/06/09 16:22:05 $ Version: $Revision: 1.6 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkN3MRIBiasFieldCorrectionImageFilter_h #define __itkN3MRIBiasFieldCorrectionImageFilter_h #include "itkImageToImageFilter.h" #include "itkBSplineScatteredDataPointSetToImageFilter.h" #include "itkPointSet.h" #include "itkSingleValuedCostFunction.h" #include "itkVector.h" #include "vnl/vnl_vector.h" namespace itk { /** \class N3MRIBiasFieldCorrectionImageFilter.h * \brief Implementation of the N3 MRI bias field correction algorithm. * * The nonparametric nonuniform intensity normalization (N3) algorithm * is a method for correcting nonuniformity associated with MR images. * The algorithm assumes a simple parametric model (Gaussian) for the bias field * but does not require tissue class segmentation. In addition, there are * only a couple of parameters to tune with the default values performing * quite well. * * N3 has been publicly available as a set of perl scripts * (http://www.bic.mni.mcgill.ca/software/N3/) but, with this class, has been * reimplemented for the ITK library with only one minor variation involving * the b-spline fitting routine. We replaced the original fitting approach * with the itkBSplineScatteredDataPointSetToImageFilter which is not * susceptible to ill-conditioned matrix calculation as is the original proposed * fitting component. * * Notes for the user: * 0. Based on our experience with the filter, sometimes the scale of the bias * field is too small particularly for large mesh element sizes. Therefore, * we added an option (which is true by default) for finding the optimal * scaling of the bias field which is based on minimizing the coefficient * of variation of the corrected image. This cost function is given below * in N3BiasFieldScaleCostFunction. * 1. Since much of the image manipulation is done in the log space of the * intensities, input images with negative and small values (< 1) are * discouraged. * 2. The original authors recommend performing the bias field correction * on a downsampled version of the original image. * 3. A mask and/or confidence image can be supplied. * 4. The filter returns the corrected image. If the bias field is wanted, one * can reconstruct it using the class itkBSplineControlPointImageFilter. * See the IJ article and the test file for an example. * 5. The 'Z' parameter in Sled's 1998 paper is the square root * of the class variable 'm_WeinerFilterNoise'. * * \author Nicholas J. Tustison * * Contributed by Nicholas J. Tustison, James C. Gee * in the Insight Journal paper: * * \par REFERENCE * J.G. Sled, P. Zijdenbos and A.C. Evans, "A comparison of retrospective * intensity non-uniformity correction methods for MRI". Information * Processing Medical Imaging Proc, 15th Int. Conf. IMPI'97, vol.1230, * pp 459-464,1997. * * J.G. Sled, A.P. Zijdenbos and A.C. Evans. "A Nonparametric Method for * Automatic Correction of Intensity Nonuniformity in MRI Data" * IEEE Transactions on Medical Imaging, Vol 17, No 1. Feb 1998. * */ /** * Class definition for N3BiasFieldScaleCostFunction */ template class ITK_EXPORT N3BiasFieldScaleCostFunction : public SingleValuedCostFunction { public: typedef N3BiasFieldScaleCostFunction Self; typedef SingleValuedCostFunction Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Run-time type information (and related methods). */ itkTypeMacro( N3BiasFieldScaleCostFunction, SingleValuedCostFunction ); /** Method for creation through the object factory. */ itkNewMacro( Self ); typedef Superclass::MeasureType MeasureType; typedef Superclass::DerivativeType DerivativeType; typedef Superclass::ParametersType ParametersType; itkSetObjectMacro( InputImage, TInputImage ); itkSetObjectMacro( BiasFieldImage, TBiasFieldImage ); itkSetObjectMacro( MaskImage, TMaskImage ); itkSetObjectMacro( ConfidenceImage, TConfidenceImage ); itkSetMacro( MaskLabel, typename TMaskImage::PixelType ); itkGetConstMacro( MaskLabel, typename TMaskImage::PixelType ); virtual MeasureType GetValue( const ParametersType & parameters ) const; virtual void GetDerivative( const ParametersType & parameters, DerivativeType & derivative ) const; virtual unsigned int GetNumberOfParameters() const; protected: N3BiasFieldScaleCostFunction(); virtual ~N3BiasFieldScaleCostFunction(); private: N3BiasFieldScaleCostFunction(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented typename TInputImage::Pointer m_InputImage; typename TBiasFieldImage::Pointer m_BiasFieldImage; typename TMaskImage::Pointer m_MaskImage; typename TConfidenceImage::Pointer m_ConfidenceImage; typename TMaskImage::PixelType m_MaskLabel; }; /** * Class definition for N3MRIBiasFieldCorrectionImageFilter */ template::ImageDimension>, class TOutputImage = TInputImage> class ITK_EXPORT N3MRIBiasFieldCorrectionImageFilter : public ImageToImageFilter { public: /** Standard class typedefs. */ typedef N3MRIBiasFieldCorrectionImageFilter Self; typedef ImageToImageFilter Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Runtime information support. */ itkTypeMacro( N3MRIBiasFieldCorrectionImageFilter, ImageToImageFilter ); /** Standard New method. */ itkNewMacro( Self ); /** ImageDimension constants */ itkStaticConstMacro( ImageDimension, unsigned int, TInputImage::ImageDimension ); /** Some convenient typedefs. */ typedef TInputImage InputImageType; typedef TOutputImage OutputImageType; typedef TMaskImage MaskImageType; typedef typename MaskImageType::PixelType MaskPixelType; typedef float RealType; typedef Image RealImageType; /** B-spline smoothing filter typedefs */ typedef Vector ScalarType; typedef PointSet PointSetType; typedef Image ScalarImageType; typedef BSplineScatteredDataPointSetToImageFilter BSplineFilterType; typedef typename BSplineFilterType::PointDataImageType BiasFieldControlPointLatticeType; typedef typename BSplineFilterType::ArrayType ArrayType; void SetMaskImage( const MaskImageType *mask ) { this->SetNthInput( 1, const_cast( mask ) ); } const MaskImageType* GetMaskImage() const { return static_cast( const_cast ( this->ProcessObject::GetInput( 1 ) ) ); } void SetConfidenceImage( const RealImageType *image ) { this->SetNthInput( 2, const_cast( image ) ); } const RealImageType* GetConfidenceImage() const { return static_cast( const_cast ( this->ProcessObject::GetInput( 2 ) ) ); } void SetInput1( const TInputImage *input ) { this->SetInput( input ); } void SetInput2( const TMaskImage *mask ) { this->SetMaskImage( mask ); } void SetInput3( const RealImageType *image ) { this->SetConfidenceImage( image ); } itkSetMacro( MaskLabel, MaskPixelType ); itkGetConstMacro( MaskLabel, MaskPixelType ); itkSetMacro( NumberOfHistogramBins, unsigned int ); itkGetConstMacro( NumberOfHistogramBins, unsigned int ); itkSetMacro( WeinerFilterNoise, RealType ); itkGetConstMacro( WeinerFilterNoise, RealType ); itkSetMacro( BiasFieldFullWidthAtHalfMaximum, RealType ); itkGetConstMacro( BiasFieldFullWidthAtHalfMaximum, RealType ); itkSetMacro( MaximumNumberOfIterations, unsigned int ); itkGetConstMacro( MaximumNumberOfIterations, unsigned int ); itkSetMacro( ConvergenceThreshold, RealType ); itkGetConstMacro( ConvergenceThreshold, RealType ); itkSetMacro( SplineOrder, unsigned int ); itkGetConstMacro( SplineOrder, unsigned int ); itkSetMacro( NumberOfFittingLevels, ArrayType ); itkGetConstMacro( NumberOfFittingLevels, ArrayType ); void SetNumberOfFittingLevels( unsigned int n ) { ArrayType nlevels; nlevels.Fill( n ); this->SetNumberOfFittingLevels( nlevels ); } itkSetMacro( NumberOfControlPoints, ArrayType ); itkGetConstMacro( NumberOfControlPoints, ArrayType ); itkGetConstMacro( LogBiasFieldControlPointLattice, typename BiasFieldControlPointLatticeType::Pointer ); itkSetMacro( UseOptimalBiasFieldScaling, bool ); itkGetConstMacro( UseOptimalBiasFieldScaling, bool ); itkBooleanMacro( UseOptimalBiasFieldScaling ); itkGetConstMacro( BiasFieldScaling, RealType ); itkGetConstMacro( ElapsedIterations, unsigned int ); itkGetConstMacro( CurrentConvergenceMeasurement, RealType ); protected: N3MRIBiasFieldCorrectionImageFilter(); ~N3MRIBiasFieldCorrectionImageFilter() {}; void PrintSelf( std::ostream& os, Indent indent ) const; void GenerateData(); private: N3MRIBiasFieldCorrectionImageFilter( const Self& ); //purposely not implemented void operator=( const Self& ); //purposely not implemented typename RealImageType::Pointer SharpenImage( typename RealImageType::Pointer ); typename RealImageType::Pointer SmoothField( typename RealImageType::Pointer ); RealType CalculateConvergenceMeasurement( typename RealImageType::Pointer, typename RealImageType::Pointer ); RealType CalculateOptimalBiasFieldScaling( typename RealImageType::Pointer ); MaskPixelType m_MaskLabel; /** * Parameters for deconvolution with Weiner filter */ unsigned int m_NumberOfHistogramBins; RealType m_WeinerFilterNoise; RealType m_BiasFieldFullWidthAtHalfMaximum; /** * Convergence parameters */ unsigned int m_MaximumNumberOfIterations; unsigned int m_ElapsedIterations; RealType m_ConvergenceThreshold; RealType m_CurrentConvergenceMeasurement; /** * B-spline fitting parameters */ typename BiasFieldControlPointLatticeType::Pointer m_LogBiasFieldControlPointLattice; unsigned int m_SplineOrder; ArrayType m_NumberOfControlPoints; ArrayType m_NumberOfFittingLevels; /** * other parameters */ RealType m_BiasFieldScaling; bool m_UseOptimalBiasFieldScaling; }; // end of class } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkN3MRIBiasFieldCorrectionImageFilter.txx" #endif #endif ants-1.9.2+svn680.dfsg/Utilities/itkN3MRIBiasFieldCorrectionImageFilter.txx000066400000000000000000000647541147325206600264640ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkN3MRIBiasFieldCorrectionImageFilter.txx,v $ Language: C++ Date: $Date: 2009/06/09 16:22:05 $ Version: $Revision: 1.6 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkN3MRIBiasFieldCorrectionImageFilter_txx #define __itkN3MRIBiasFieldCorrectionImageFilter_txx #include "itkN3MRIBiasFieldCorrectionImageFilter.h" #include "itkDivideImageFilter.h" #include "itkExpImageFilter.h" #include "itkImageRegionIterator.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkIterationReporter.h" #include "itkLBFGSBOptimizer.h" #include "itkLogImageFilter.h" #include "itkSubtractImageFilter.h" #include "vnl/algo/vnl_fft_1d.h" #include "vnl/vnl_complex_traits.h" #include "vxl/vcl/vcl_complex.h" namespace itk { /** * N3BiasFieldScaleCostFunction class definitions */ template N3BiasFieldScaleCostFunction ::N3BiasFieldScaleCostFunction() { this->m_InputImage = NULL; this->m_BiasFieldImage = NULL; this->m_MaskImage = NULL; this->m_ConfidenceImage = NULL; this->m_MaskLabel = NumericTraits::One; } template N3BiasFieldScaleCostFunction ::~N3BiasFieldScaleCostFunction() { } template typename N3BiasFieldScaleCostFunction::MeasureType N3BiasFieldScaleCostFunction ::GetValue( const ParametersType & parameters ) const { ImageRegionConstIterator ItI( this->m_InputImage, this->m_InputImage->GetRequestedRegion() ); ImageRegionConstIterator ItB( this->m_BiasFieldImage, this->m_BiasFieldImage->GetRequestedRegion() ); MeasureType mu = 0.0; MeasureType N = 0.0; for( ItI.GoToBegin(), ItB.GoToBegin(); !ItI.IsAtEnd(); ++ItI, ++ItB ) { if( ( !this->m_MaskImage || this->m_MaskImage->GetPixel( ItI.GetIndex() ) == this->m_MaskLabel ) && ( !this->m_ConfidenceImage || this->m_ConfidenceImage->GetPixel( ItI.GetIndex() ) > 0.0 ) ) { mu += static_cast( ItI.Get() ) / ( parameters[0] * ( static_cast( ItB.Get() ) - 1.0 ) + 1.0 ); N += 1.0; } } mu /= N; MeasureType value = 0.0; for( ItI.GoToBegin(), ItB.GoToBegin(); !ItI.IsAtEnd(); ++ItI, ++ItB ) { if( ( !this->m_MaskImage || this->m_MaskImage->GetPixel( ItI.GetIndex() ) == this->m_MaskLabel ) && ( !this->m_ConfidenceImage || this->m_ConfidenceImage->GetPixel( ItI.GetIndex() ) > 0.0 ) ) { value += vnl_math_sqr( ( ItI.Get() / ( parameters[0] * ( static_cast( ItB.Get() ) - 1.0 ) + 1.0 ) ) / mu - 1.0 ); } } value /= ( N - 1.0 ); return value; } template void N3BiasFieldScaleCostFunction ::GetDerivative( const ParametersType & parameters, DerivativeType & derivative ) const { ImageRegionConstIterator ItI( this->m_InputImage, this->m_InputImage->GetRequestedRegion() ); ImageRegionConstIterator ItB( this->m_BiasFieldImage, this->m_BiasFieldImage->GetRequestedRegion() ); MeasureType mu = 0.0; MeasureType dmu = 0.0; MeasureType N = 0.0; for( ItI.GoToBegin(), ItB.GoToBegin(); !ItI.IsAtEnd(); ++ItI, ++ItB ) { if( ( !this->m_MaskImage || this->m_MaskImage->GetPixel( ItI.GetIndex() ) == this->m_MaskLabel ) && ( !this->m_ConfidenceImage || this->m_ConfidenceImage->GetPixel( ItI.GetIndex() ) > 0.0 ) ) { MeasureType d = parameters[0] * ( static_cast( ItB.Get() ) - 1.0 ) + 1.0; mu += ( static_cast( ItI.Get() ) / d ); dmu += -static_cast( ItI.Get() ) * ( static_cast( ItB.Get() ) - 1.0 ) / d; N += 1.0; } } mu /= N; dmu /= N; MeasureType value = 0.0; for( ItI.GoToBegin(), ItB.GoToBegin(); !ItI.IsAtEnd(); ++ItI, ++ItB ) { if( ( !this->m_MaskImage || this->m_MaskImage->GetPixel( ItI.GetIndex() ) == this->m_MaskLabel ) && ( !this->m_ConfidenceImage || this->m_ConfidenceImage->GetPixel( ItI.GetIndex() ) > 0.0 ) ) { MeasureType d = parameters[0] * ( static_cast( ItB.Get() ) - 1.0 ) + 1.0; MeasureType t = static_cast( ItI.Get() ) / d; MeasureType dt = -t * ( static_cast( ItB.Get() ) - 1.0 ); value += ( ( t / mu - 1.0 ) * ( dt / mu - dmu * t / ( vnl_math_sqr( mu ) ) ) ); } } derivative.SetSize( 1 ); derivative( 0 ) = 2.0 * value / ( N - 1 ); } template unsigned int N3BiasFieldScaleCostFunction ::GetNumberOfParameters() const { return NumericTraits::One; } /** * N3MRIBiasFieldCorrectionImageFilter class definitions */ template N3MRIBiasFieldCorrectionImageFilter ::N3MRIBiasFieldCorrectionImageFilter() { this->SetNumberOfRequiredInputs( 1 ); this->m_MaskLabel = NumericTraits::One; this->m_NumberOfHistogramBins = 200; this->m_WeinerFilterNoise = 0.01; this->m_BiasFieldFullWidthAtHalfMaximum = 0.15; this->m_MaximumNumberOfIterations = 50; this->m_ConvergenceThreshold = 0.001; this->m_SplineOrder = 3; this->m_NumberOfFittingLevels.Fill( 4 ); this->m_NumberOfControlPoints.Fill( 4 ); this->m_UseOptimalBiasFieldScaling = true; this->m_BiasFieldScaling = 1.0; } template void N3MRIBiasFieldCorrectionImageFilter ::GenerateData() { /** * Calculate the log of the input image. */ typename RealImageType::Pointer logInputImage = RealImageType::New(); typedef ExpImageFilter ExpImageFilterType; typedef LogImageFilter LogFilterType; typename LogFilterType::Pointer logFilter1 = LogFilterType::New(); logFilter1->SetInput( this->GetInput() ); logFilter1->Update(); logInputImage = logFilter1->GetOutput(); /** * Remove possible nans/infs from the log input image. */ ImageRegionIteratorWithIndex It( logInputImage, logInputImage->GetRequestedRegion() ); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { if( ( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( It.GetIndex() ) == this->m_MaskLabel ) && ( !this->GetConfidenceImage() || this->GetConfidenceImage()->GetPixel( It.GetIndex() ) > 0.0 ) ) { if( vnl_math_isnan( It.Get() ) || vnl_math_isinf( It.Get() ) || It.Get() < 0.0 ) { It.Set( 0.0 ); } } } /** * Provide an initial log bias field of zeros */ typename RealImageType::Pointer logBiasField = RealImageType::New(); logBiasField->SetOrigin( this->GetInput()->GetOrigin() ); logBiasField->SetRegions( this->GetInput()->GetRequestedRegion() ); logBiasField->SetSpacing( this->GetInput()->GetSpacing() ); logBiasField->SetDirection( this->GetInput()->GetDirection() ); logBiasField->Allocate(); logBiasField->FillBuffer( 0.0 ); /** * Iterate until convergence or iterative exhaustion. */ IterationReporter reporter( this, 0, 1 ); bool isConverged = false; this->m_ElapsedIterations = 0; while( !isConverged && this->m_ElapsedIterations++ < this->m_MaximumNumberOfIterations ) { typedef SubtractImageFilter SubtracterType; typename SubtracterType::Pointer subtracter1 = SubtracterType::New(); subtracter1->SetInput1( logInputImage ); subtracter1->SetInput2( logBiasField ); subtracter1->Update(); typename RealImageType::Pointer logSharpenedImage = this->SharpenImage( subtracter1->GetOutput() ); typename SubtracterType::Pointer subtracter2 = SubtracterType::New(); subtracter2->SetInput1( logInputImage ); subtracter2->SetInput2( logSharpenedImage ); subtracter2->Update(); typename RealImageType::Pointer newLogBiasField = this->SmoothField( subtracter2->GetOutput() ); this->m_CurrentConvergenceMeasurement = this->CalculateConvergenceMeasurement( logBiasField, newLogBiasField ); isConverged = ( this->m_CurrentConvergenceMeasurement < this->m_ConvergenceThreshold ); logBiasField = newLogBiasField; reporter.CompletedStep(); } typedef ExpImageFilter ExpImageFilterType; typename ExpImageFilterType::Pointer expFilter = ExpImageFilterType::New(); expFilter->SetInput( logBiasField ); expFilter->Update(); /** * Calculate the optimal scaling */ if( this->m_UseOptimalBiasFieldScaling ) { this->m_BiasFieldScaling = this->CalculateOptimalBiasFieldScaling( expFilter->GetOutput() ); ImageRegionIterator ItE( expFilter->GetOutput(), expFilter->GetOutput()->GetRequestedRegion() ); for( ItE.GoToBegin(); !ItE.IsAtEnd(); ++ItE ) { ItE.Set( this->m_BiasFieldScaling * ( ItE.Get() - 1.0 ) + 1.0 ); } } /** * Divide the input image by the bias field to get the final image. */ typedef DivideImageFilter DividerType; typename DividerType::Pointer divider = DividerType::New(); divider->SetInput1( this->GetInput() ); divider->SetInput2( expFilter->GetOutput() ); divider->Update(); this->SetNthOutput( 0, divider->GetOutput() ); } template typename N3MRIBiasFieldCorrectionImageFilter ::RealImageType::Pointer N3MRIBiasFieldCorrectionImageFilter ::SharpenImage( typename RealImageType::Pointer unsharpenedImage ) { /** * Build the histogram for the uncorrected image. Store copy * in a vnl_vector to utilize vnl FFT routines. Note that variables * in real space are denoted by a single uppercase letter whereas their * frequency counterparts are indicated by a trailing lowercase 'f'. */ RealType binMaximum = NumericTraits::NonpositiveMin(); RealType binMinimum = NumericTraits::max(); ImageRegionIterator ItU( unsharpenedImage, unsharpenedImage->GetLargestPossibleRegion() ); for( ItU.GoToBegin(); !ItU.IsAtEnd(); ++ItU ) { if( ( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItU.GetIndex() ) == this->m_MaskLabel ) && ( !this->GetConfidenceImage() || this->GetConfidenceImage()->GetPixel( ItU.GetIndex() ) > 0.0 ) ) { RealType pixel = ItU.Get(); if( pixel > binMaximum ) { binMaximum = pixel; } else if( pixel < binMinimum ) { binMinimum = pixel; } } } RealType histogramSlope = ( binMaximum - binMinimum ) / static_cast( this->m_NumberOfHistogramBins - 1 ); /** * Create the intensity profile (within the masked region, if applicable) * using a triangular parzen windowing scheme. */ vnl_vector H( this->m_NumberOfHistogramBins, 0.0 ); for( ItU.GoToBegin(); !ItU.IsAtEnd(); ++ItU ) { if( ( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItU.GetIndex() ) == this->m_MaskLabel ) && ( !this->GetConfidenceImage() || this->GetConfidenceImage()->GetPixel( ItU.GetIndex() ) > 0.0 ) ) { RealType pixel = ItU.Get(); float cidx = ( static_cast( pixel ) - binMinimum ) / histogramSlope; unsigned int idx = vnl_math_floor( cidx ); RealType offset = cidx - static_cast( idx ); if( offset == 0.0 ) { H[idx] += 1.0; } else if( idx < this->m_NumberOfHistogramBins - 1 ) { H[idx] += 1.0 - offset; H[idx+1] += offset; } } } /** * Determine information about the intensity histogram and zero-pad * histogram to a power of 2. */ RealType exponent = vcl_ceil( vcl_log( static_cast( this->m_NumberOfHistogramBins ) ) / vcl_log( 2.0 ) ) + 1; unsigned int paddedHistogramSize = static_cast( vcl_pow( static_cast( 2.0 ), exponent ) + 0.5 ); unsigned int histogramOffset = static_cast( 0.5 * ( paddedHistogramSize - this->m_NumberOfHistogramBins ) ); vnl_vector< vcl_complex > V( paddedHistogramSize, vcl_complex( 0.0, 0.0 ) ); for( unsigned int n = 0; n < this->m_NumberOfHistogramBins; n++ ) { V[n+histogramOffset] = H[n]; } /** * Instantiate the 1-d vnl fft routine */ vnl_fft_1d fft( paddedHistogramSize ); vnl_vector< vcl_complex > Vf( V ); fft.fwd_transform( Vf ); /** * Create the Gaussian filter. */ RealType scaledFWHM = this->m_BiasFieldFullWidthAtHalfMaximum / histogramSlope; RealType expFactor = 4.0 * vcl_log( 2.0 ) / vnl_math_sqr( scaledFWHM ); RealType scaleFactor = 2.0 * vcl_sqrt( vcl_log( 2.0 ) / vnl_math::pi ) / scaledFWHM; vnl_vector< vcl_complex > F( paddedHistogramSize, vcl_complex( 0.0, 0.0 ) ); F[0] = vcl_complex( scaleFactor, 0.0 ); unsigned int halfSize = static_cast( 0.5 * paddedHistogramSize ); for( unsigned int n = 1; n <= halfSize; n++ ) { F[n] = F[paddedHistogramSize - n] = vcl_complex( scaleFactor * vcl_exp( -vnl_math_sqr( static_cast( n ) ) * expFactor ), 0.0 ); } if( paddedHistogramSize % 2 == 0 ) { F[halfSize] = vcl_complex( scaleFactor * vcl_exp( 0.25 * -vnl_math_sqr( static_cast( paddedHistogramSize ) ) * expFactor ), 0.0 ); } vnl_vector< vcl_complex > Ff( F ); fft.fwd_transform( Ff ); /** * Create the Weiner deconvolution filter. */ vnl_vector< vcl_complex > Gf( paddedHistogramSize ); for( unsigned int n = 0; n < paddedHistogramSize; n++ ) { vcl_complex c = vnl_complex_traits< vcl_complex >::conjugate( Ff[n] ); Gf[n] = c / ( c * Ff[n] + this->m_WeinerFilterNoise ); } vnl_vector< vcl_complex > Uf( paddedHistogramSize ); for( unsigned int n = 0; n < paddedHistogramSize; n++ ) { Uf[n] = Vf[n] * Gf[n].real(); } vnl_vector< vcl_complex > U( Uf ); fft.bwd_transform( U ); for( unsigned int n = 0; n < paddedHistogramSize; n++ ) { U[n] = vcl_complex( vnl_math_max( U[n].real(), static_cast( 0.0 ) ), 0.0 ); } /** * Compute mapping E(u|v) */ vnl_vector< vcl_complex > numerator( paddedHistogramSize ); for( unsigned int n = 0; n < paddedHistogramSize; n++ ) { numerator[n] = vcl_complex( ( binMinimum + ( static_cast( n ) - histogramOffset ) * histogramSlope ) * U[n].real(), 0.0 ); } fft.fwd_transform( numerator ); for( unsigned int n = 0; n < paddedHistogramSize; n++ ) { numerator[n] *= Ff[n]; } fft.bwd_transform( numerator ); vnl_vector< vcl_complex > denominator( U ); fft.fwd_transform( denominator ); for( unsigned int n = 0; n < paddedHistogramSize; n++ ) { denominator[n] *= Ff[n]; } fft.bwd_transform( denominator ); vnl_vector E( paddedHistogramSize ); for( unsigned int n = 0; n < paddedHistogramSize; n++ ) { E[n] = numerator[n].real() / denominator[n].real(); if( vnl_math_isinf( E[n] ) || vnl_math_isnan( E[n] ) ) { E[n] = 0.0; } } /** * Remove the zero-padding from the mapping */ E = E.extract( this->m_NumberOfHistogramBins, histogramOffset ); /** * Sharpen the image with the new mapping, E(u|v) */ typename RealImageType::Pointer sharpenedImage = RealImageType::New(); sharpenedImage->SetOrigin( unsharpenedImage->GetOrigin() ); sharpenedImage->SetSpacing( unsharpenedImage->GetSpacing() ); sharpenedImage->SetRegions( unsharpenedImage->GetLargestPossibleRegion() ); sharpenedImage->SetDirection( unsharpenedImage->GetDirection() ); sharpenedImage->Allocate(); sharpenedImage->FillBuffer( 0.0 ); ImageRegionIterator ItC( sharpenedImage, sharpenedImage->GetLargestPossibleRegion() ); for( ItU.GoToBegin(), ItC.GoToBegin(); !ItU.IsAtEnd(); ++ItU, ++ItC ) { if( ( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItU.GetIndex() ) == this->m_MaskLabel ) && ( !this->GetConfidenceImage() || this->GetConfidenceImage()->GetPixel( ItU.GetIndex() ) > 0.0 ) ) { float cidx = ( ItU.Get() - binMinimum ) / histogramSlope; unsigned int idx = vnl_math_floor( cidx ); RealType correctedPixel = 0; if( idx < E.size() - 1 ) { correctedPixel = E[idx] + ( E[idx + 1] - E[idx] ) * ( cidx - static_cast( idx ) ); } else { correctedPixel = E[E.size() - 1]; } ItC.Set( correctedPixel ); } } return sharpenedImage; } template typename N3MRIBiasFieldCorrectionImageFilter ::RealImageType::Pointer N3MRIBiasFieldCorrectionImageFilter ::SmoothField( typename RealImageType::Pointer fieldEstimate ) { /** * Get original direction and change to identity temporarily for the * b-spline fitting. */ typename RealImageType::DirectionType direction = fieldEstimate->GetDirection(); typename RealImageType::DirectionType identity; identity.SetIdentity(); fieldEstimate->SetDirection( identity ); typename PointSetType::Pointer fieldPoints = PointSetType::New(); fieldPoints->Initialize(); typename BSplineFilterType::WeightsContainerType::Pointer weights = BSplineFilterType::WeightsContainerType::New(); weights->Initialize(); ImageRegionConstIteratorWithIndex It( fieldEstimate, fieldEstimate->GetRequestedRegion() ); unsigned int N = 0; for ( It.GoToBegin(); !It.IsAtEnd(); ++It ) { if( ( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( It.GetIndex() ) == this->m_MaskLabel ) && ( !this->GetConfidenceImage() || this->GetConfidenceImage()->GetPixel( It.GetIndex() ) > 0.0 ) ) { typename PointSetType::PointType point; fieldEstimate->TransformIndexToPhysicalPoint( It.GetIndex(), point ); ScalarType scalar; scalar[0] = It.Get(); fieldPoints->SetPointData( N, scalar ); fieldPoints->SetPoint( N, point ); if( this->GetConfidenceImage() ) { weights->InsertElement( N, this->GetConfidenceImage()->GetPixel( It.GetIndex() ) ); } else { weights->InsertElement( N, 1.0 ); } N++; } } fieldEstimate->SetDirection( direction ); typename BSplineFilterType::Pointer bspliner = BSplineFilterType::New(); bspliner->SetOrigin( fieldEstimate->GetOrigin() ); bspliner->SetSpacing( fieldEstimate->GetSpacing() ); bspliner->SetSize( fieldEstimate->GetLargestPossibleRegion().GetSize() ); bspliner->SetDirection( fieldEstimate->GetDirection() ); bspliner->SetGenerateOutputImage( true ); bspliner->SetNumberOfLevels( this->m_NumberOfFittingLevels ); bspliner->SetSplineOrder( this->m_SplineOrder ); bspliner->SetNumberOfControlPoints( this->m_NumberOfControlPoints ); bspliner->SetInput( fieldPoints ); bspliner->SetPointWeights( weights ); bspliner->Update(); /** * Save the bias field control points in case the user wants to * reconstruct the bias field. */ this->m_LogBiasFieldControlPointLattice = bspliner->GetPhiLattice(); typename RealImageType::Pointer smoothField = RealImageType::New(); smoothField->SetOrigin( fieldEstimate->GetOrigin() ); smoothField->SetSpacing( fieldEstimate->GetSpacing() ); smoothField->SetRegions( fieldEstimate->GetLargestPossibleRegion().GetSize() ); smoothField->SetDirection( direction ); smoothField->Allocate(); ImageRegionIterator ItB( bspliner->GetOutput(), bspliner->GetOutput()->GetLargestPossibleRegion() ); ImageRegionIterator ItF( smoothField, smoothField->GetLargestPossibleRegion() ); for( ItB.GoToBegin(), ItF.GoToBegin(); !ItB.IsAtEnd(); ++ItB, ++ItF ) { ItF.Set( ItB.Get()[0] ); } return smoothField; } template typename N3MRIBiasFieldCorrectionImageFilter ::RealType N3MRIBiasFieldCorrectionImageFilter ::CalculateConvergenceMeasurement( typename RealImageType::Pointer fieldEstimate1, typename RealImageType::Pointer fieldEstimate2 ) { typedef SubtractImageFilter SubtracterType; typename SubtracterType::Pointer subtracter = SubtracterType::New(); subtracter->SetInput1( fieldEstimate1 ); subtracter->SetInput2( fieldEstimate2 ); subtracter->Update(); /** * Calculate statistics over the mask region */ RealType mu = 0.0; RealType sigma = 0.0; RealType N = 0.0; ImageRegionConstIteratorWithIndex It( subtracter->GetOutput(), subtracter->GetOutput()->GetLargestPossibleRegion() ); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { if( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( It.GetIndex() ) == this->m_MaskLabel ) { RealType pixel = It.Get(); N += 1.0; if( N > 1.0 ) { sigma = sigma + vnl_math_sqr( pixel - mu ) * ( N - 1.0 ) / N; } mu = mu * ( 1.0 - 1.0 / N ) + pixel / N; } } sigma = vcl_sqrt( sigma / ( N - 1.0 ) ); /** * Although Sled's paper proposes convergence determination via * the coefficient of variation, the actual mnc implementation * utilizes the standard deviation as the convergence measurement. */ return sigma; } template typename N3MRIBiasFieldCorrectionImageFilter ::RealType N3MRIBiasFieldCorrectionImageFilter ::CalculateOptimalBiasFieldScaling( typename RealImageType::Pointer biasField ) { /** * This section is not described in Sled's paper but rather stems from * our own experience with N4ITK and the resulting innovation. For an initial * B-spline mesh of large resolution and a low number of fitting levels, * although the shape of of the bias field appears correect, the scale is too * small. This section finds an optimal scaling by minimizing the coefficient * of variation over masked region. */ typedef N3BiasFieldScaleCostFunction ScaleCostFunctionType; typename ScaleCostFunctionType::Pointer scaleCostFunction = ScaleCostFunctionType::New(); scaleCostFunction->SetInputImage( const_cast( this->GetInput() ) ); scaleCostFunction->SetBiasFieldImage( biasField ); scaleCostFunction->SetMaskImage( const_cast( this->GetMaskImage() ) ); scaleCostFunction->SetConfidenceImage( const_cast( this->GetConfidenceImage() ) ); typename LBFGSBOptimizer::BoundSelectionType boundSelection; boundSelection.SetSize( 1 ); boundSelection.Fill( 1 ); // only set a lower bound on the scale factor typename LBFGSBOptimizer::BoundValueType lowerBound; lowerBound.SetSize( 1 ); lowerBound.Fill( 1.0 ); typename LBFGSBOptimizer::BoundValueType upperBound; upperBound.SetSize( 1 ); upperBound.Fill( NumericTraits::max() ); typename LBFGSBOptimizer::ParametersType initialParameters; initialParameters.SetSize( 1 ); initialParameters.Fill( 1.0 ); typename LBFGSBOptimizer::Pointer optimizer = LBFGSBOptimizer::New(); optimizer->SetMinimize( true ); optimizer->SetCostFunction( scaleCostFunction ); optimizer->SetInitialPosition( initialParameters ); optimizer->SetCostFunctionConvergenceFactor( 1e1 ); optimizer->SetLowerBound( lowerBound ); optimizer->SetUpperBound( upperBound ); optimizer->SetBoundSelection( boundSelection ); optimizer->SetProjectedGradientTolerance( 1e-10 ); optimizer->StartOptimization(); typename LBFGSBOptimizer::ParametersType finalParameters = optimizer->GetCurrentPosition(); return finalParameters[0]; } template void N3MRIBiasFieldCorrectionImageFilter ::PrintSelf(std::ostream &os, Indent indent) const { Superclass::PrintSelf( os, indent ); os << indent << "Mask label: " << this->m_MaskLabel << std::endl; os << indent << "Number of histogram bins: " << this->m_NumberOfHistogramBins << std::endl; os << indent << "Weiner filter noise: " << this->m_WeinerFilterNoise << std::endl; os << indent << "Bias field FWHM: " << this->m_BiasFieldFullWidthAtHalfMaximum << std::endl; os << indent << "Maximum number of iterations: " << this->m_MaximumNumberOfIterations << std::endl; os << indent << "Convergence threshold: " << this->m_ConvergenceThreshold << std::endl; os << indent << "Spline order: " << this->m_SplineOrder << std::endl; os << indent << "Number of fitting levels: " << this->m_NumberOfFittingLevels << std::endl; os << indent << "Number of control points: " << this->m_NumberOfControlPoints << std::endl; if( this->m_UseOptimalBiasFieldScaling ) { os << indent << "Optimal bias field scaling: " << this->m_BiasFieldScaling << std::endl; } } }// end namespace itk #endif ants-1.9.2+svn680.dfsg/Utilities/itkN4MRIBiasFieldCorrectionImageFilter.h000066400000000000000000000233561147325206600260620ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkN4MRIBiasFieldCorrectionImageFilter.h,v $ Language: C++ Date: $Date: 2009/06/09 16:22:05 $ Version: $Revision: 1.6 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkN4MRIBiasFieldCorrectionImageFilter_h #define __itkN4MRIBiasFieldCorrectionImageFilter_h #include "itkImageToImageFilter.h" #include "itkArray.h" #include "itkBSplineScatteredDataPointSetToImageFilter.h" #include "itkPointSet.h" #include "itkSingleValuedCostFunction.h" #include "itkVector.h" #include "vnl/vnl_vector.h" namespace itk { /** \class N4MRIBiasFieldCorrectionImageFilter.h * \brief Implementation of the N4 MRI bias field correction algorithm. * * The nonparametric nonuniform intensity normalization (N4) algorithm * is a method for correcting nonuniformity associated with MR images. * The algorithm assumes a simple parametric model (Gaussian) for the bias field * but does not require tissue class segmentation. In addition, there are * only a couple of parameters to tune with the default values performing * quite well. * * N4 has been publicly available as a set of perl scripts * (http://www.bic.mni.mcgill.ca/software/N4/) but, with this class, has been * reimplemented for the ITK library with only one minor variation involving * the b-spline fitting routine. We replaced the original fitting approach * with the itkBSplineScatteredDataPointSetToImageFilter which is not * susceptible to ill-conditioned matrix calculation as is the original proposed * fitting component. * * Notes for the user: * 1. Since much of the image manipulation is done in the log space of the * intensities, input images with negative and small values (< 1) are * discouraged. * 2. The original authors recommend performing the bias field correction * on a downsampled version of the original image. * 3. A mask and/or confidence image can be supplied. * 4. The filter returns the corrected image. If the bias field is wanted, one * can reconstruct it using the class itkBSplineControlPointImageFilter. * See the IJ article and the test file for an example. * 5. The 'Z' parameter in Sled's 1998 paper is the square root * of the class variable 'm_WeinerFilterNoise'. * * \author Nicholas J. Tustison * * Contributed by Nicholas J. Tustison, James C. Gee * in the Insight Journal paper: * * \par REFERENCE * J.G. Sled, P. Zijdenbos and A.C. Evans, "A comparison of retrospective * intensity non-uniformity correction methods for MRI". Information * Processing Medical Imaging Proc, 15th Int. Conf. IMPI'97, vol.1230, * pp 459-464,1997. * * J.G. Sled, A.P. Zijdenbos and A.C. Evans. "A Nonparametric Method for * Automatic Correction of Intensity Nonuniformity in MRI Data" * IEEE Transactions on Medical Imaging, Vol 17, No 1. Feb 1998. * */ template::ImageDimension>, class TOutputImage = TInputImage> class ITK_EXPORT N4MRIBiasFieldCorrectionImageFilter : public ImageToImageFilter { public: /** Standard class typedefs. */ typedef N4MRIBiasFieldCorrectionImageFilter Self; typedef ImageToImageFilter Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Runtime information support. */ itkTypeMacro( N4MRIBiasFieldCorrectionImageFilter, ImageToImageFilter ); /** Standard New method. */ itkNewMacro( Self ); /** ImageDimension constants */ itkStaticConstMacro( ImageDimension, unsigned int, TInputImage::ImageDimension ); /** Some convenient typedefs. */ typedef TInputImage InputImageType; typedef TOutputImage OutputImageType; typedef TMaskImage MaskImageType; typedef typename MaskImageType::PixelType MaskPixelType; typedef float RealType; typedef Image RealImageType; /** B-spline smoothing filter typedefs */ typedef Vector ScalarType; typedef PointSet PointSetType; typedef Image ScalarImageType; typedef BSplineScatteredDataPointSetToImageFilter BSplineFilterType; typedef typename BSplineFilterType::PointDataImageType BiasFieldControlPointLatticeType; typedef typename BSplineFilterType::ArrayType ArrayType; typedef Array VariableSizeArrayType; void SetMaskImage( const MaskImageType *mask ) { this->SetNthInput( 1, const_cast( mask ) ); } const MaskImageType* GetMaskImage() const { return static_cast( const_cast ( this->ProcessObject::GetInput( 1 ) ) ); } void SetConfidenceImage( const RealImageType *image ) { this->SetNthInput( 2, const_cast( image ) ); } const RealImageType* GetConfidenceImage() const { return static_cast( const_cast ( this->ProcessObject::GetInput( 2 ) ) ); } void SetInput1( const TInputImage *input ) { this->SetInput( input ); } void SetInput2( const TMaskImage *mask ) { this->SetMaskImage( mask ); } void SetInput3( const RealImageType *image ) { this->SetConfidenceImage( image ); } itkSetMacro( MaskLabel, MaskPixelType ); itkGetConstMacro( MaskLabel, MaskPixelType ); itkSetMacro( NumberOfHistogramBins, unsigned int ); itkGetConstMacro( NumberOfHistogramBins, unsigned int ); itkSetMacro( WeinerFilterNoise, RealType ); itkGetConstMacro( WeinerFilterNoise, RealType ); itkSetMacro( BiasFieldFullWidthAtHalfMaximum, RealType ); itkGetConstMacro( BiasFieldFullWidthAtHalfMaximum, RealType ); itkSetMacro( MaximumNumberOfIterations, VariableSizeArrayType ); itkGetConstMacro( MaximumNumberOfIterations, VariableSizeArrayType ); itkSetMacro( ConvergenceThreshold, RealType ); itkGetConstMacro( ConvergenceThreshold, RealType ); itkSetMacro( SplineOrder, unsigned int ); itkGetConstMacro( SplineOrder, unsigned int ); itkSetMacro( NumberOfFittingLevels, ArrayType ); itkGetConstMacro( NumberOfFittingLevels, ArrayType ); void SetNumberOfFittingLevels( unsigned int n ) { ArrayType nlevels; nlevels.Fill( n ); this->SetNumberOfFittingLevels( nlevels ); } itkSetMacro( NumberOfControlPoints, ArrayType ); itkGetConstMacro( NumberOfControlPoints, ArrayType ); itkGetConstMacro( LogBiasFieldControlPointLattice, typename BiasFieldControlPointLatticeType::Pointer ); itkSetClampMacro( SigmoidNormalizedAlpha, RealType, 0.0, NumericTraits::max() ); itkGetConstMacro( SigmoidNormalizedAlpha, RealType ); itkSetClampMacro( SigmoidNormalizedBeta, RealType, 0.0, 1.0 ); itkGetConstMacro( SigmoidNormalizedBeta, RealType ); itkGetConstMacro( ElapsedIterations, unsigned int ); itkGetConstMacro( CurrentConvergenceMeasurement, RealType ); itkGetConstMacro( CurrentLevel, unsigned int ); protected: N4MRIBiasFieldCorrectionImageFilter(); ~N4MRIBiasFieldCorrectionImageFilter() {}; void PrintSelf( std::ostream& os, Indent indent ) const; void GenerateData(); private: N4MRIBiasFieldCorrectionImageFilter( const Self& ); //purposely not implemented void operator=( const Self& ); //purposely not implemented typename RealImageType::Pointer SharpenImage( typename RealImageType::Pointer ); typename RealImageType::Pointer UpdateBiasFieldEstimate( typename RealImageType::Pointer ); RealType CalculateConvergenceMeasurement( typename RealImageType::Pointer, typename RealImageType::Pointer ); MaskPixelType m_MaskLabel; /** * Parameters for deconvolution with Weiner filter */ unsigned int m_NumberOfHistogramBins; RealType m_WeinerFilterNoise; RealType m_BiasFieldFullWidthAtHalfMaximum; /** * Convergence parameters */ VariableSizeArrayType m_MaximumNumberOfIterations; unsigned int m_ElapsedIterations; RealType m_ConvergenceThreshold; RealType m_CurrentConvergenceMeasurement; unsigned int m_CurrentLevel; /** * B-spline fitting parameters */ typename BiasFieldControlPointLatticeType::Pointer m_LogBiasFieldControlPointLattice; unsigned int m_SplineOrder; ArrayType m_NumberOfControlPoints; ArrayType m_NumberOfFittingLevels; RealType m_SigmoidNormalizedAlpha; RealType m_SigmoidNormalizedBeta; }; // end of class } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkN4MRIBiasFieldCorrectionImageFilter.txx" #endif #endif ants-1.9.2+svn680.dfsg/Utilities/itkN4MRIBiasFieldCorrectionImageFilter.txx000066400000000000000000000605111147325206600264500ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkN4MRIBiasFieldCorrectionImageFilter.txx,v $ Language: C++ Date: $Date: 2009/06/09 16:22:05 $ Version: $Revision: 1.6 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkN4MRIBiasFieldCorrectionImageFilter_txx #define __itkN4MRIBiasFieldCorrectionImageFilter_txx #include "itkN4MRIBiasFieldCorrectionImageFilter.h" #include "itkAddImageFilter.h" #include "itkBSplineControlPointImageFilter.h" #include "itkDivideImageFilter.h" #include "itkExpImageFilter.h" #include "itkImageRegionIterator.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkIterationReporter.h" #include "itkLogImageFilter.h" #include "itkSubtractImageFilter.h" #include "vnl/algo/vnl_fft_1d.h" #include "vnl/vnl_complex_traits.h" #include "vxl/vcl/vcl_complex.h" namespace itk { template N4MRIBiasFieldCorrectionImageFilter ::N4MRIBiasFieldCorrectionImageFilter() { this->SetNumberOfRequiredInputs( 1 ); this->m_MaskLabel = NumericTraits::One; this->m_LogBiasFieldControlPointLattice = NULL; this->m_NumberOfHistogramBins = 200; this->m_WeinerFilterNoise = 0.01; this->m_BiasFieldFullWidthAtHalfMaximum = 0.15; this->m_SplineOrder = 3; this->m_NumberOfFittingLevels.Fill( 1 ); this->m_NumberOfControlPoints.Fill( 4 ); this->m_SigmoidNormalizedAlpha = 0.0; this->m_SigmoidNormalizedBeta = 0.5; this->m_MaximumNumberOfIterations.SetSize( 1 ); this->m_MaximumNumberOfIterations.Fill( 50 ); this->m_ConvergenceThreshold = 0.001; } template void N4MRIBiasFieldCorrectionImageFilter ::GenerateData() { /** * Calculate the log of the input image. */ typename RealImageType::Pointer logUncorrectedImage = RealImageType::New(); typedef ExpImageFilter ExpImageFilterType; typedef LogImageFilter LogFilterType; typename LogFilterType::Pointer logFilter = LogFilterType::New(); logFilter->SetInput( this->GetInput() ); logFilter->Update(); logUncorrectedImage = logFilter->GetOutput(); /** * Remove possible nans/infs from the log input image. */ ImageRegionIteratorWithIndex It( logUncorrectedImage, logUncorrectedImage->GetRequestedRegion() ); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { if( ( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( It.GetIndex() ) == this->m_MaskLabel ) && ( !this->GetConfidenceImage() || this->GetConfidenceImage()->GetPixel( It.GetIndex() ) > 0.0 ) ) { if( vnl_math_isnan( It.Get() ) || vnl_math_isinf( It.Get() ) || It.Get() < 0.0 ) { It.Set( 0.0 ); } } } /** * Provide an initial log bias field of zeros */ typename RealImageType::Pointer logBiasField = RealImageType::New(); logBiasField->SetOrigin( this->GetInput()->GetOrigin() ); logBiasField->SetRegions( this->GetInput()->GetRequestedRegion() ); logBiasField->SetSpacing( this->GetInput()->GetSpacing() ); logBiasField->SetDirection( this->GetInput()->GetDirection() ); logBiasField->Allocate(); logBiasField->FillBuffer( 0.0 ); /** * Iterate until convergence or iterative exhaustion. */ unsigned int maximumNumberOfLevels = 1; for( unsigned int d = 0; d < this->m_NumberOfFittingLevels.Size(); d++ ) { if( this->m_NumberOfFittingLevels[d] > maximumNumberOfLevels ) { maximumNumberOfLevels = this->m_NumberOfFittingLevels[d]; } } if( this->m_MaximumNumberOfIterations.Size() != maximumNumberOfLevels ) { itkExceptionMacro( "Number of iteration levels is not equal to the max number of levels." ); } for( this->m_CurrentLevel = 0; this->m_CurrentLevel < maximumNumberOfLevels; this->m_CurrentLevel++ ) { IterationReporter reporter( this, 0, 1 ); this->m_ElapsedIterations = 0; this->m_CurrentConvergenceMeasurement = NumericTraits::max(); while( this->m_ElapsedIterations++ < this->m_MaximumNumberOfIterations[this->m_CurrentLevel] && this->m_CurrentConvergenceMeasurement > this->m_ConvergenceThreshold ) { /** * Sharpen the current estimate of the uncorrected image. */ typename RealImageType::Pointer logSharpenedImage = this->SharpenImage( logUncorrectedImage ); typedef SubtractImageFilter SubtracterType; typename SubtracterType::Pointer subtracter1 = SubtracterType::New(); subtracter1->SetInput1( logUncorrectedImage ); subtracter1->SetInput2( logSharpenedImage ); subtracter1->Update(); /** * Smooth the residual bias field estimate and add the resulting * control point grid to get the new total bias field estimate. */ typename RealImageType::Pointer newLogBiasField = this->UpdateBiasFieldEstimate( subtracter1->GetOutput() ); this->m_CurrentConvergenceMeasurement = this->CalculateConvergenceMeasurement( logBiasField, newLogBiasField ); logBiasField = newLogBiasField; typename SubtracterType::Pointer subtracter2 = SubtracterType::New(); subtracter2->SetInput1( logFilter->GetOutput() ); subtracter2->SetInput2( logBiasField ); subtracter2->Update(); logUncorrectedImage = subtracter2->GetOutput(); reporter.CompletedStep(); } typedef BSplineControlPointImageFilter BSplineReconstructerType; typename BSplineReconstructerType::Pointer reconstructer = BSplineReconstructerType::New(); reconstructer->SetInput( this->m_LogBiasFieldControlPointLattice ); reconstructer->SetOrigin( logBiasField->GetOrigin() ); reconstructer->SetSpacing( logBiasField->GetSpacing() ); reconstructer->SetDirection( logBiasField->GetDirection() ); reconstructer->SetSize( logBiasField->GetLargestPossibleRegion().GetSize() ); reconstructer->Update(); typename BSplineReconstructerType::ArrayType numberOfLevels; numberOfLevels.Fill( 1 ); for( unsigned int d = 0; d < ImageDimension; d++ ) { if( this->m_NumberOfFittingLevels[d] + 1 >= this->m_CurrentLevel && this->m_CurrentLevel != maximumNumberOfLevels-1 ) { numberOfLevels[d] = 2; } } this->m_LogBiasFieldControlPointLattice = reconstructer-> RefineControlPointLattice( numberOfLevels ); } typedef ExpImageFilter ExpImageFilterType; typename ExpImageFilterType::Pointer expFilter = ExpImageFilterType::New(); expFilter->SetInput( logBiasField ); expFilter->Update(); /** * Divide the input image by the bias field to get the final image. */ typedef DivideImageFilter DividerType; typename DividerType::Pointer divider = DividerType::New(); divider->SetInput1( this->GetInput() ); divider->SetInput2( expFilter->GetOutput() ); divider->Update(); this->SetNthOutput( 0, divider->GetOutput() ); } template typename N4MRIBiasFieldCorrectionImageFilter ::RealImageType::Pointer N4MRIBiasFieldCorrectionImageFilter ::SharpenImage( typename RealImageType::Pointer unsharpenedImage ) { /** * Build the histogram for the uncorrected image. Store copy * in a vnl_vector to utilize vnl FFT routines. Note that variables * in real space are denoted by a single uppercase letter whereas their * frequency counterparts are indicated by a trailing lowercase 'f'. */ RealType binMaximum = NumericTraits::NonpositiveMin(); RealType binMinimum = NumericTraits::max(); ImageRegionIterator ItU( unsharpenedImage, unsharpenedImage->GetLargestPossibleRegion() ); for( ItU.GoToBegin(); !ItU.IsAtEnd(); ++ItU ) { if( ( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItU.GetIndex() ) == this->m_MaskLabel ) && ( !this->GetConfidenceImage() || this->GetConfidenceImage()->GetPixel( ItU.GetIndex() ) > 0.0 ) ) { RealType pixel = ItU.Get(); if( pixel > binMaximum ) { binMaximum = pixel; } else if( pixel < binMinimum ) { binMinimum = pixel; } } } RealType histogramSlope = ( binMaximum - binMinimum ) / static_cast( this->m_NumberOfHistogramBins - 1 ); /** * Create the intensity profile (within the masked region, if applicable) * using a triangular parzen windowing scheme. */ vnl_vector H( this->m_NumberOfHistogramBins, 0.0 ); for( ItU.GoToBegin(); !ItU.IsAtEnd(); ++ItU ) { if( ( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItU.GetIndex() ) == this->m_MaskLabel ) && ( !this->GetConfidenceImage() || this->GetConfidenceImage()->GetPixel( ItU.GetIndex() ) > 0.0 ) ) { RealType pixel = ItU.Get(); RealType cidx = ( static_cast( pixel ) - binMinimum ) / histogramSlope; unsigned int idx = vnl_math_floor( cidx ); RealType offset = cidx - static_cast( idx ); if( offset == 0.0 ) { H[idx] += 1.0; } else if( idx < this->m_NumberOfHistogramBins - 1 ) { H[idx] += 1.0 - offset; H[idx+1] += offset; } } } /** * Determine information about the intensity histogram and zero-pad * histogram to a power of 2. */ RealType exponent = vcl_ceil( vcl_log( static_cast( this->m_NumberOfHistogramBins ) ) / vcl_log( 2.0 ) ) + 1; unsigned int paddedHistogramSize = static_cast( vcl_pow( static_cast( 2.0 ), exponent ) + 0.5 ); unsigned int histogramOffset = static_cast( 0.5 * ( paddedHistogramSize - this->m_NumberOfHistogramBins ) ); vnl_vector< vcl_complex > V( paddedHistogramSize, vcl_complex( 0.0, 0.0 ) ); for( unsigned int n = 0; n < this->m_NumberOfHistogramBins; n++ ) { V[n+histogramOffset] = H[n]; } /** * Instantiate the 1-d vnl fft routine */ vnl_fft_1d fft( paddedHistogramSize ); vnl_vector< vcl_complex > Vf( V ); fft.fwd_transform( Vf ); /** * Create the Gaussian filter. */ RealType scaledFWHM = this->m_BiasFieldFullWidthAtHalfMaximum / histogramSlope; RealType expFactor = 4.0 * vcl_log( 2.0 ) / vnl_math_sqr( scaledFWHM ); RealType scaleFactor = 2.0 * vcl_sqrt( vcl_log( 2.0 ) / vnl_math::pi ) / scaledFWHM; vnl_vector< vcl_complex > F( paddedHistogramSize, vcl_complex( 0.0, 0.0 ) ); F[0] = vcl_complex( scaleFactor, 0.0 ); unsigned int halfSize = static_cast( 0.5 * paddedHistogramSize ); for( unsigned int n = 1; n <= halfSize; n++ ) { F[n] = F[paddedHistogramSize - n] = vcl_complex( scaleFactor * vcl_exp( -vnl_math_sqr( static_cast( n ) ) * expFactor ), 0.0 ); } if( paddedHistogramSize % 2 == 0 ) { F[halfSize] = vcl_complex( scaleFactor * vcl_exp( 0.25 * -vnl_math_sqr( static_cast( paddedHistogramSize ) ) * expFactor ), 0.0 ); } vnl_vector< vcl_complex > Ff( F ); fft.fwd_transform( Ff ); /** * Create the Weiner deconvolution filter. */ vnl_vector< vcl_complex > Gf( paddedHistogramSize ); for( unsigned int n = 0; n < paddedHistogramSize; n++ ) { vcl_complex c = vnl_complex_traits< vcl_complex >::conjugate( Ff[n] ); Gf[n] = c / ( c * Ff[n] + this->m_WeinerFilterNoise ); } vnl_vector< vcl_complex > Uf( paddedHistogramSize ); for( unsigned int n = 0; n < paddedHistogramSize; n++ ) { Uf[n] = Vf[n] * Gf[n].real(); } vnl_vector< vcl_complex > U( Uf ); fft.bwd_transform( U ); for( unsigned int n = 0; n < paddedHistogramSize; n++ ) { U[n] = vcl_complex( vnl_math_max( U[n].real(), static_cast( 0.0 ) ), 0.0 ); } /** * Compute mapping E(u|v) */ vnl_vector< vcl_complex > numerator( paddedHistogramSize ); for( unsigned int n = 0; n < paddedHistogramSize; n++ ) { numerator[n] = vcl_complex( ( binMinimum + ( static_cast( n ) - histogramOffset ) * histogramSlope ) * U[n].real(), 0.0 ); } fft.fwd_transform( numerator ); for( unsigned int n = 0; n < paddedHistogramSize; n++ ) { numerator[n] *= Ff[n]; } fft.bwd_transform( numerator ); vnl_vector< vcl_complex > denominator( U ); fft.fwd_transform( denominator ); for( unsigned int n = 0; n < paddedHistogramSize; n++ ) { denominator[n] *= Ff[n]; } fft.bwd_transform( denominator ); vnl_vector E( paddedHistogramSize ); for( unsigned int n = 0; n < paddedHistogramSize; n++ ) { E[n] = numerator[n].real() / denominator[n].real(); if( vnl_math_isinf( E[n] ) || vnl_math_isnan( E[n] ) ) { E[n] = 0.0; } } /** * Remove the zero-padding from the mapping */ E = E.extract( this->m_NumberOfHistogramBins, histogramOffset ); /** * Sharpen the image with the new mapping, E(u|v) */ typename RealImageType::Pointer sharpenedImage = RealImageType::New(); sharpenedImage->SetOrigin( unsharpenedImage->GetOrigin() ); sharpenedImage->SetSpacing( unsharpenedImage->GetSpacing() ); sharpenedImage->SetRegions( unsharpenedImage->GetLargestPossibleRegion() ); sharpenedImage->SetDirection( unsharpenedImage->GetDirection() ); sharpenedImage->Allocate(); sharpenedImage->FillBuffer( 0.0 ); ImageRegionIterator ItC( sharpenedImage, sharpenedImage->GetLargestPossibleRegion() ); for( ItU.GoToBegin(), ItC.GoToBegin(); !ItU.IsAtEnd(); ++ItU, ++ItC ) { if( ( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( ItU.GetIndex() ) == this->m_MaskLabel ) && ( !this->GetConfidenceImage() || this->GetConfidenceImage()->GetPixel( ItU.GetIndex() ) > 0.0 ) ) { RealType cidx = ( ItU.Get() - binMinimum ) / histogramSlope; unsigned int idx = vnl_math_floor( cidx ); RealType correctedPixel = 0; if( idx < E.size() - 1 ) { correctedPixel = E[idx] + ( E[idx + 1] - E[idx] ) * ( cidx - static_cast( idx ) ); } else { correctedPixel = E[E.size() - 1]; } ItC.Set( correctedPixel ); } } return sharpenedImage; } template typename N4MRIBiasFieldCorrectionImageFilter ::RealImageType::Pointer N4MRIBiasFieldCorrectionImageFilter ::UpdateBiasFieldEstimate( typename RealImageType::Pointer fieldEstimate ) { /** * Calculate min/max for sigmoid weighting. Calculate mean for offseting * bias field calculations since B-spline algorithm biases the result * to zero. */ RealType maxAbsValue = NumericTraits::NonpositiveMin(); RealType minAbsValue = NumericTraits::max(); if( this->m_SigmoidNormalizedAlpha > 0.0 ) { ImageRegionConstIteratorWithIndex It( fieldEstimate, fieldEstimate->GetRequestedRegion() ); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { if( ( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( It.GetIndex() ) == this->m_MaskLabel ) && ( !this->GetConfidenceImage() || this->GetConfidenceImage()->GetPixel( It.GetIndex() ) > 0.0 ) ) { RealType pixel = vnl_math_abs( It.Get() ); if( pixel > maxAbsValue ) { maxAbsValue = pixel; } else if( pixel < minAbsValue ) { minAbsValue = pixel; } } } } /** * Get original direction and change to identity temporarily for the * b-spline fitting. */ typename RealImageType::DirectionType direction = fieldEstimate->GetDirection(); typename RealImageType::DirectionType identity; identity.SetIdentity(); fieldEstimate->SetDirection( identity ); typename PointSetType::Pointer fieldPoints = PointSetType::New(); fieldPoints->Initialize(); typename BSplineFilterType::WeightsContainerType::Pointer weights = BSplineFilterType::WeightsContainerType::New(); weights->Initialize(); ImageRegionConstIteratorWithIndex It( fieldEstimate, fieldEstimate->GetRequestedRegion() ); unsigned int index = 0; for ( It.GoToBegin(); !It.IsAtEnd(); ++It ) { if( ( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( It.GetIndex() ) == this->m_MaskLabel ) && ( !this->GetConfidenceImage() || this->GetConfidenceImage()->GetPixel( It.GetIndex() ) > 0.0 ) ) { typename PointSetType::PointType point; fieldEstimate->TransformIndexToPhysicalPoint( It.GetIndex(), point ); ScalarType scalar; scalar[0] = It.Get(); fieldPoints->SetPointData( index, scalar ); fieldPoints->SetPoint( index, point ); RealType confidenceWeight = 1.0; if( this->GetConfidenceImage() ) { confidenceWeight = this->GetConfidenceImage()->GetPixel( It.GetIndex() ); } RealType sigmoidWeight = 1.0; if( this->m_SigmoidNormalizedAlpha > 0.0 ) { RealType alpha = ( maxAbsValue - minAbsValue ) / ( 12.0 * this->m_SigmoidNormalizedAlpha ); RealType beta = minAbsValue + ( maxAbsValue - minAbsValue ) * this->m_SigmoidNormalizedBeta; sigmoidWeight = 1.0 / ( 1.0 + vcl_exp( -( scalar[0] - beta ) / alpha ) ); } weights->InsertElement( index, sigmoidWeight * confidenceWeight ); index++; } } fieldEstimate->SetDirection( direction ); typename BSplineFilterType::Pointer bspliner = BSplineFilterType::New(); typename BSplineFilterType::ArrayType numberOfControlPoints; typename BSplineFilterType::ArrayType numberOfFittingLevels; numberOfFittingLevels.Fill( 1 ); for( unsigned int d = 0; d < ImageDimension; d++ ) { if( !this->m_LogBiasFieldControlPointLattice ) { numberOfControlPoints[d] = this->m_NumberOfControlPoints[d]; } else { numberOfControlPoints[d] = this->m_LogBiasFieldControlPointLattice-> GetLargestPossibleRegion().GetSize()[d]; } } typename ScalarImageType::PointType parametricOrigin = fieldEstimate->GetOrigin(); for( unsigned int d = 0; d < ImageDimension; d++ ) { parametricOrigin[d] += ( fieldEstimate->GetSpacing()[d] * fieldEstimate->GetLargestPossibleRegion().GetIndex()[d] ); } bspliner->SetOrigin( parametricOrigin ); bspliner->SetSpacing( fieldEstimate->GetSpacing() ); bspliner->SetSize( fieldEstimate->GetLargestPossibleRegion().GetSize() ); bspliner->SetDirection( fieldEstimate->GetDirection() ); bspliner->SetGenerateOutputImage( false ); bspliner->SetNumberOfLevels( numberOfFittingLevels ); bspliner->SetSplineOrder( this->m_SplineOrder ); bspliner->SetNumberOfControlPoints( numberOfControlPoints ); bspliner->SetInput( fieldPoints ); bspliner->SetPointWeights( weights ); bspliner->Update(); /** * Add the bias field control points to the current estimate. */ if( !this->m_LogBiasFieldControlPointLattice ) { this->m_LogBiasFieldControlPointLattice = bspliner->GetPhiLattice(); } else { typedef AddImageFilter AdderType; typename AdderType::Pointer adder = AdderType::New(); adder->SetInput1( this->m_LogBiasFieldControlPointLattice ); adder->SetInput2( bspliner->GetPhiLattice() ); adder->Update(); this->m_LogBiasFieldControlPointLattice = adder->GetOutput(); } typedef BSplineControlPointImageFilter BSplineReconstructerType; typename BSplineReconstructerType::Pointer reconstructer = BSplineReconstructerType::New(); reconstructer->SetInput( this->m_LogBiasFieldControlPointLattice ); reconstructer->SetOrigin( fieldEstimate->GetOrigin() ); reconstructer->SetSpacing( fieldEstimate->GetSpacing() ); reconstructer->SetDirection( direction ); reconstructer->SetSize( fieldEstimate->GetLargestPossibleRegion().GetSize() ); reconstructer->Update(); typename RealImageType::Pointer smoothField = RealImageType::New(); smoothField->SetOrigin( fieldEstimate->GetOrigin() ); smoothField->SetSpacing( fieldEstimate->GetSpacing() ); smoothField->SetRegions( fieldEstimate->GetLargestPossibleRegion() ); smoothField->SetDirection( direction ); smoothField->Allocate(); ImageRegionIterator ItR( reconstructer->GetOutput(), reconstructer->GetOutput()->GetLargestPossibleRegion() ); ImageRegionIterator ItF( smoothField, smoothField->GetLargestPossibleRegion() ); for( ItR.GoToBegin(), ItF.GoToBegin(); !ItR.IsAtEnd(); ++ItR, ++ItF ) { ItF.Set( ItR.Get()[0] ); } return smoothField; } template typename N4MRIBiasFieldCorrectionImageFilter ::RealType N4MRIBiasFieldCorrectionImageFilter ::CalculateConvergenceMeasurement( typename RealImageType::Pointer fieldEstimate1, typename RealImageType::Pointer fieldEstimate2 ) { typedef SubtractImageFilter SubtracterType; typename SubtracterType::Pointer subtracter = SubtracterType::New(); subtracter->SetInput1( fieldEstimate1 ); subtracter->SetInput2( fieldEstimate2 ); subtracter->Update(); /** * Calculate statistics over the mask region */ RealType mu = 0.0; RealType sigma = 0.0; RealType N = 0.0; ImageRegionConstIteratorWithIndex It( subtracter->GetOutput(), subtracter->GetOutput()->GetLargestPossibleRegion() ); for( It.GoToBegin(); !It.IsAtEnd(); ++It ) { if( ( !this->GetMaskImage() || this->GetMaskImage()->GetPixel( It.GetIndex() ) == this->m_MaskLabel ) && ( !this->GetConfidenceImage() || this->GetConfidenceImage()->GetPixel( It.GetIndex() ) > 0.0 ) ) { RealType pixel = vcl_exp( It.Get() ); N += 1.0; if( N > 1.0 ) { sigma = sigma + vnl_math_sqr( pixel - mu ) * ( N - 1.0 ) / N; } mu = mu * ( 1.0 - 1.0 / N ) + pixel / N; } } sigma = vcl_sqrt( sigma / ( N - 1.0 ) ); return ( sigma / mu ); } template void N4MRIBiasFieldCorrectionImageFilter ::PrintSelf(std::ostream &os, Indent indent) const { Superclass::PrintSelf( os, indent ); os << indent << "Mask label: " << this->m_MaskLabel << std::endl; os << indent << "Number of histogram bins: " << this->m_NumberOfHistogramBins << std::endl; os << indent << "Weiner filter noise: " << this->m_WeinerFilterNoise << std::endl; os << indent << "Bias field FWHM: " << this->m_BiasFieldFullWidthAtHalfMaximum << std::endl; os << indent << "Maximum number of iterations: " << this->m_MaximumNumberOfIterations << std::endl; os << indent << "Convergence threshold: " << this->m_ConvergenceThreshold << std::endl; os << indent << "Spline order: " << this->m_SplineOrder << std::endl; os << indent << "Number of fitting levels: " << this->m_NumberOfFittingLevels << std::endl; os << indent << "Number of control points: " << this->m_NumberOfControlPoints << std::endl; os << indent << "Sigmoid normalized alpha: " << this->m_SigmoidNormalizedAlpha << std::endl; os << indent << "Sigmoid normalized beta: " << this->m_SigmoidNormalizedBeta << std::endl; } }// end namespace itk #endif ants-1.9.2+svn680.dfsg/Utilities/itkOptimalSharpeningImageFilter.h000066400000000000000000000120611147325206600250510ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkOptimalSharpeningImageFilter.h,v $ Language: C++ Date: $Date: 2008/12/13 21:51:03 $ Version: $Revision: 1.2 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkOptimalSharpeningImageFilter_h #define __itkOptimalSharpeningImageFilter_h #include "itkNumericTraits.h" #include "itkImageToImageFilter.h" #include "itkImage.h" namespace itk { /** * \class OptimalSharpeningImageFilter * * This filter sharpens an image using a Laplacian. OptimalSharpening * highlights regions of rapid intensity change and therefore * highlights or enhances the edges. The result is an image that * appears more in focus. * * \par The OptimalSharpening at each pixel location is computed by * convolution with the itk::LaplacianOperator. * * \par Inputs and Outputs * The input to this filter is a scalar-valued itk::Image of arbitrary * dimension. The output is a scalar-valued itk::Image. * \sa Image * \sa Neighborhood * \sa NeighborhoodOperator * \sa NeighborhoodIterator * \sa LaplacianOperator * * \ingroup ImageFeatureExtraction */ template class ITK_EXPORT OptimalSharpeningImageFilter : public ImageToImageFilter< TInputImage, TOutputImage > { public: /** Standard "Self" & Superclass typedef. */ typedef OptimalSharpeningImageFilter Self; typedef ImageToImageFilter< TInputImage, TOutputImage > Superclass; /** Extract some information from the image types. Dimensionality * of the two images is assumed to be the same. */ typedef typename TOutputImage::PixelType OutputPixelType; typedef typename TOutputImage::InternalPixelType OutputInternalPixelType; typedef typename NumericTraits::RealType RealType; typedef typename TInputImage::PixelType InputPixelType; typedef typename TInputImage::InternalPixelType InputInternalPixelType; itkStaticConstMacro(ImageDimension, unsigned int, TOutputImage::ImageDimension); /** Image typedef support. */ typedef TInputImage InputImageType; typedef TOutputImage OutputImageType; typedef typename InputImageType::Pointer InputImagePointer; /** Smart pointer typedef support. */ typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Run-time type information (and related methods) */ itkTypeMacro(OptimalSharpeningImageFilter, ImageToImageFilter); /** Method for creation through the object factory. */ itkNewMacro(Self); /** OptimalSharpeningImageFilter needs a larger input requested * region than the output requested region (larger in the direction * of the derivative). As such, OptimalSharpeningImageFilter * needs to provide an implementation for * GenerateInputRequestedRegion() in order to inform the pipeline * execution model. * * \sa ImageToImageFilter::GenerateInputRequestedRegion() */ virtual void GenerateInputRequestedRegion() throw(InvalidRequestedRegionError); void SetSValue(float s) { this->m_SValue=s; } /** Use the image spacing information in calculations. Use this option if you * want derivatives in physical space. Default is UseImageSpacingOn. */ void SetUseImageSpacingOn() { this->SetUseImageSpacing(true); } /** Ignore the image spacing. Use this option if you want derivatives in isotropic pixel space. Default is UseImageSpacingOn. */ void SetUseImageSpacingOff() { this->SetUseImageSpacing(false); } /** Set/Get whether or not the filter will use the spacing of the input image in its calculations */ itkSetMacro(UseImageSpacing, bool); itkGetMacro(UseImageSpacing, bool); protected: OptimalSharpeningImageFilter() { m_UseImageSpacing = true; m_SValue=0.5; } virtual ~OptimalSharpeningImageFilter() {} /** Standard pipeline method. While this class does not implement a * ThreadedGenerateData(), its GenerateData() delegates all * calculations to an NeighborhoodOperatorImageFilter. Since the * NeighborhoodOperatorImageFilter is multithreaded, this filter is * multithreaded by default. */ void GenerateData(); void PrintSelf(std::ostream&, Indent) const; private: OptimalSharpeningImageFilter(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented bool m_UseImageSpacing; float m_SValue; }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkOptimalSharpeningImageFilter.txx" #endif #endif ants-1.9.2+svn680.dfsg/Utilities/itkOptimalSharpeningImageFilter.txx000066400000000000000000000202251147325206600254460ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkOptimalSharpeningImageFilter.txx,v $ Language: C++ Date: $Date: 2008/12/13 21:51:03 $ Version: $Revision: 1.2 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkOptimalSharpeningImageFilter_txx #define __itkOptimalSharpeningImageFilter_txx #include "itkOptimalSharpeningImageFilter.h" #include "itkNeighborhoodOperatorImageFilter.h" #include "itkLaplacianOperator.h" #include "itkZeroFluxNeumannBoundaryCondition.h" #include "itkProgressAccumulator.h" #include "itkMinimumMaximumImageCalculator.h" #include "itkImageRegionIterator.h" namespace itk { template< class TInputImage, class TOutputImage > void OptimalSharpeningImageFilter< TInputImage, TOutputImage > ::PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf(os,indent); os << indent << "UseImageSpacing = " << m_UseImageSpacing << std::endl; } template void OptimalSharpeningImageFilter ::GenerateInputRequestedRegion() throw (InvalidRequestedRegionError) { // call the superclass' implementation of this method. this should // copy the output requested region to the input requested region Superclass::GenerateInputRequestedRegion(); // get pointers to the input and output InputImagePointer inputPtr = const_cast< TInputImage * > ( this->GetInput() ); if ( !inputPtr ) { return; } // Build an operator so that we can determine the kernel size LaplacianOperator oper; oper.CreateOperator(); // get a copy of the input requested region (should equal the output // requested region) typename TInputImage::RegionType inputRequestedRegion; inputRequestedRegion = inputPtr->GetRequestedRegion(); // pad the input requested region by the operator radius inputRequestedRegion.PadByRadius( oper.GetRadius() ); // crop the input requested region at the input's largest possible region if ( inputRequestedRegion.Crop(inputPtr->GetLargestPossibleRegion()) ) { inputPtr->SetRequestedRegion( inputRequestedRegion ); return; } else { // Couldn't crop the region (requested region is outside the largest // possible region). Throw an exception. // store what we tried to request (prior to trying to crop) inputPtr->SetRequestedRegion( inputRequestedRegion ); // build an exception InvalidRequestedRegionError e(__FILE__, __LINE__); e.SetLocation(ITK_LOCATION); e.SetDescription("Requested region is (at least partially) outside the largest possible region."); e.SetDataObject(inputPtr); throw e; } } template< class TInputImage, class TOutputImage > void OptimalSharpeningImageFilter< TInputImage, TOutputImage > ::GenerateData() { // Create the Laplacian operator LaplacianOperator oper; double s[ImageDimension]; for (unsigned i = 0; i < ImageDimension; i++) { if (this->GetInput()->GetSpacing()[i] == 0.0 ) { itkExceptionMacro( << "Image spacing cannot be zero" ); } else { s[i] = 1.0 / this->GetInput()->GetSpacing()[i]; } } oper.SetDerivativeScalings( s ); oper.CreateOperator(); // do calculations in floating point typedef Image RealImageType; typedef NeighborhoodOperatorImageFilter NOIF; ZeroFluxNeumannBoundaryCondition nbc; typename NOIF::Pointer filter = NOIF::New(); filter->OverrideBoundaryCondition(static_cast(&nbc)); // Create a process accumulator for tracking the progress of this minipipeline ProgressAccumulator::Pointer progress = ProgressAccumulator::New(); progress->SetMiniPipelineFilter(this); // Register the filter with the with progress accumulator using // equal weight proportion progress->RegisterInternalFilter(filter,0.8f); // // set up the mini-pipline // filter->SetOperator(oper); filter->SetInput(this->GetInput()); filter->GetOutput() ->SetRequestedRegion( this->GetOutput()->GetRequestedRegion() ); // execute the mini-pipeline filter->Update(); // determine how the data will need to scaled to be properly combined typename MinimumMaximumImageCalculator::Pointer inputCalculator = MinimumMaximumImageCalculator::New(); typename MinimumMaximumImageCalculator::Pointer filteredCalculator = MinimumMaximumImageCalculator::New(); inputCalculator->SetImage(this->GetInput()); inputCalculator->SetRegion(this->GetOutput()->GetRequestedRegion()); inputCalculator->Compute(); filteredCalculator->SetImage(filter->GetOutput()); filteredCalculator->SetRegion(this->GetOutput()->GetRequestedRegion()); filteredCalculator->Compute(); RealType inputShift, inputScale, filteredShift, filteredScale; inputShift = static_cast(inputCalculator->GetMinimum()); inputScale = static_cast(inputCalculator->GetMaximum()) - static_cast(inputCalculator->GetMinimum()); filteredShift = filteredCalculator->GetMinimum(); // no need to cast filteredScale = filteredCalculator->GetMaximum() - filteredCalculator->GetMinimum(); ImageRegionIterator it(filter->GetOutput(), filter->GetOutput()->GetRequestedRegion()); ImageRegionConstIterator inIt(this->GetInput(), this->GetOutput()->GetRequestedRegion()); // combine the input and laplacian images RealType value, invalue; RealType inputSum = 0.0; RealType enhancedSum = 0.0; while ( !it.IsAtEnd() ) { value = it.Get(); // laplacian value // rescale to [0,1] value = (value - filteredShift) / filteredScale; // rescale to the input dynamic range value = value * inputScale + inputShift; // combine the input and laplacian image (note that we subtract // the laplacian due to the signs in our laplacian kernel). invalue = static_cast(inIt.Get()); value = invalue - value*this->m_SValue; it.Set( value ); inputSum += invalue; enhancedSum += value; ++it; ++inIt; } RealType inputMean = inputSum / static_cast(this->GetOutput()->GetRequestedRegion() .GetNumberOfPixels()); RealType enhancedMean = enhancedSum / static_cast(this->GetOutput()->GetRequestedRegion() .GetNumberOfPixels()); // update progress this->UpdateProgress( 0.9 ); // copy and cast the output typename TOutputImage::Pointer output = this->GetOutput(); output->SetBufferedRegion(output->GetRequestedRegion()); output->Allocate(); RealType inputMinimum = inputCalculator->GetMinimum(); RealType inputMaximum = inputCalculator->GetMaximum(); OutputPixelType castInputMinimum = static_cast(inputMinimum); OutputPixelType castInputMaximum = static_cast(inputMaximum); ImageRegionIterator outIt = ImageRegionIterator(output, output->GetRequestedRegion()); outIt.GoToBegin(); it.GoToBegin(); while (!outIt.IsAtEnd()) { value = it.Get(); // adjust value to make the mean intensities before and after match value = value - enhancedMean + inputMean; if (value < inputMinimum) { outIt.Set( castInputMinimum ); } else if (value > inputMaximum) { outIt.Set( castInputMaximum ); } else { outIt.Set( static_cast(value) ); } ++outIt; ++it; } // update progress this->UpdateProgress( 1.0 ); } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Utilities/itkPointSetFunction.h000066400000000000000000000107511147325206600225730ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkPointSetFunction.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.16 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkPointSetFunction_h #define __itkPointSetFunction_h #include "itkFunctionBase.h" #include "itkPoint.h" #include "itkIndex.h" #include "itkContinuousIndex.h" #include "itkImageBase.h" namespace itk { /** \class PointSetFunction * \brief Evaluates a function of an image at specified position. * * PointSetFunction is a baseclass for all objects that evaluates * a function of an image at index, continuous index or point. * This class is templated over the input image type, the type * of the function output and the coordinate representation type * (e.g. float or double). * * The input image is set via method SetInputPointSet(). * Methods Evaluate, EvaluateAtIndex and EvaluateAtContinuousIndex * respectively evaluates the function at an geometric point, * image index and continuous image index. * * \warning Image BufferedRegion information is cached during * in SetInputPointSet( image ). If the image BufferedRegion has changed * one must call SetInputPointSet( image ) again to update the cache * to the current values. * * \sa Point * \sa Index * \sa ContinuousIndex * * \ingroup PointSetFunctions */ template < class TInputPointSet, class TOutput, class TCoordRep = float > class ITK_EXPORT PointSetFunction : public FunctionBase { public: /** Dimension underlying input point set. */ itkStaticConstMacro(Dimension, unsigned int, TInputPointSet::PointDimension); /** Standard class typedefs. */ typedef PointSetFunction Self; typedef FunctionBase Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Run-time type information (and related methods). */ itkTypeMacro( PointSetFunction, FunctionBase ); /** InputPointSetType typedef support. */ typedef TInputPointSet InputPointSetType; /** InputPixel typedef support */ typedef typename InputPointSetType::PointType InputPointType; typedef typename InputPointSetType::PixelType InputPixelType; /** InputPointSetPointer typedef support */ typedef typename InputPointSetType::ConstPointer InputPointSetConstPointer; /** OutputType typedef support. */ typedef TOutput OutputType; /** CoordRepType typedef support. */ typedef TCoordRep CoordRepType; /** Set the input point set. * \warning this method caches BufferedRegion information. * If the BufferedRegion has changed, user must call * SetInputPointSet again to update cached values. */ virtual void SetInputPointSet( const InputPointSetType * ptr ); /** Get the input image. */ const InputPointSetType * GetInputPointSet() const { return m_PointSet.GetPointer(); } /** Evaluate the function at specified Point position. * Subclasses must provide this method. */ virtual TOutput Evaluate( const InputPointType& point ) const = 0; protected: PointSetFunction(); ~PointSetFunction() {} void PrintSelf(std::ostream& os, Indent indent) const; /** Const pointer to the input image. */ InputPointSetConstPointer m_PointSet; private: PointSetFunction(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace itk // Define instantiation macro for this template. #define ITK_TEMPLATE_PointSetFunction(_, EXPORT, x, y) namespace itk { \ _(3(class EXPORT PointSetFunction< ITK_TEMPLATE_3 x >)) \ namespace Templates { typedef PointSetFunction< ITK_TEMPLATE_3 x > PointSetFunction##y; } \ } #ifndef ITK_MANUAL_INSTANTIATION # include "itkPointSetFunction.txx" #endif #endif ants-1.9.2+svn680.dfsg/Utilities/itkPointSetFunction.txx000066400000000000000000000032261147325206600231660ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkPointSetFunction.txx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.16 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkPointSetFunction_txx #define __itkPointSetFunction_txx #include "itkPointSetFunction.h" namespace itk { /** * Constructor */ template PointSetFunction ::PointSetFunction() { m_PointSet = NULL; } /** * Standard "PrintSelf" method */ template void PointSetFunction ::PrintSelf( std::ostream& os, Indent indent) const { Superclass::PrintSelf( os, indent ); os << indent << "InputPointSet: " << m_PointSet.GetPointer() << std::endl; } /** * Initialize by setting the input point set */ template void PointSetFunction ::SetInputPointSet( const InputPointSetType * ptr ) { // set the input image m_PointSet = ptr; } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Utilities/itkSurfaceCurvatureBase.h000066400000000000000000000220451147325206600234030ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkSurfaceCurvatureBase.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.12 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _SurfaceCurvatureBase_h #define _SurfaceCurvatureBase_h #include #include #include #include // #include #include "itkObject.h" #include "itkProcessObject.h" #include "itkVectorContainer.h" #include "itkCastImageFilter.h" namespace itk { /** \class SurfaceCurvatureBase * * This class takes a surface as input and creates a local * geometric frame for each surface point. * * \note The origin of a neighborhood is always taken to be * the first point entered into and the * last point stored in the list. */ template < typename TSurface, unsigned int TDimension=3 > class SurfaceCurvatureBase : public ProcessObject { public: /** Standard class typedefs. */ typedef SurfaceCurvatureBase Self; typedef ProcessObject Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Run-time type information (and related methods). */ itkTypeMacro(SurfaceCurvatureBase,ProcessObject); /** Method for creation through the object factory. */ itkNewMacro(Self); /** Image types. */ typedef TSurface SurfaceType; /** Image dimension. */ // itkStaticConstMacro(ImageDimension, unsigned int, TImage::ImageDimension); itkStaticConstMacro(ImageDimension, unsigned int, TDimension); itkStaticConstMacro(SurfaceDimension, unsigned int, TDimension); typedef float RealType; typedef vnl_vector VectorType; typedef vnl_vector_fixed FixedVectorType; typedef vnl_vector_fixed PointType; typedef vnl_matrix MatrixType; typedef std::vector PointContainerType; typedef std::vector FunctionContainerType; /** Set input parameter file */ itkSetStringMacro( ParameterFileName ); /** Set input parameter file */ itkGetStringMacro( ParameterFileName ); /** Fill the point list with the points neighboring the current origin */ virtual void FindNeighborhood(unsigned int temp=0); /** A Euclidean relative of L.D. Griffin's compactness.*/ RealType ComputeMeanEuclideanDistance(); /** */ void ComputeAveragePoint(); void ProjectToTangentPlane(PointType); void EigenDecomposition(MatrixType D); /** Estimate the plane tangent to a point using the neighborhood * of the point. This is, in general, an over-constrained least * squares fitting problem. */ void EstimateTangentPlane(PointType); void WeightedEstimateTangentPlane(PointType); void TestEstimateTangentPlane(PointType); /** This function sets the reference tangent arbitrarily. * It can be overridden in case there is a better practical approach. */ void ChooseReferenceTangent(); /** This function fills the weight and angle vectors for * a given neighborhood. */ virtual void ComputeWeightsAndDirectionalKappaAndAngles(PointType origin); /** */ void ComputeFrame(PointType origin); /** */ void ComputeFrameAndKappa(PointType origin); void ShimshoniFrame(PointType origin); /** */ void ComputeJoshiFrame(PointType origin); /** */ void EstimateCurvature(RealType w1=3./8.,RealType w2=1./8., RealType w3=1./8.,RealType w4=3./8.); /** Use the Besl and Jain analytical curvature computation. * We use a least square polynomial fit to the local neighborhood * to estimate the mean and gaussian curvature. */ void JainMeanAndGaussianCurvature(PointType); /** This function returns a weight given a distance * It may be the identity function, a normalization * or a gaussianization of the input distance. */ virtual RealType GetWeight(PointType, PointType); /** This function returns the angle between the reference tangent and the projection onto the tangent plane of the vector between the neighborhood focus and its neighbor. */ virtual RealType GetTheta(PointType Neighbor,PointType origin); /** Estimate the directional curvature using Shimshoni's method (eq 6).*/ virtual void EstimateDirectionalCurvature(PointType, PointType); void PrintFrame(); virtual void ComputeFrameOverDomain(unsigned int which=3) {}; RealType ErrorEstimate(PointType, RealType sign=1.0 ); unsigned int CharacterizeSurface(); itkSetMacro(Origin,PointType); itkGetMacro(Origin,PointType); itkSetMacro(AveragePoint,PointType); itkGetMacro(AveragePoint,PointType); itkGetMacro(Normal,FixedVectorType); itkGetMacro(Sigma,RealType); itkGetMacro(MeanKappa,RealType); itkSetMacro(Sigma,RealType); itkGetMacro(UseGeodesicNeighborhood,bool); itkSetMacro(UseGeodesicNeighborhood,bool); /** Set normal estimates a 3D frame from a given normal */ void SetFrameFromNormal(FixedVectorType); /** We use the cross-product of the tangents times the image spacing to get the local area. */ RealType ComputeLocalArea(const double* spacing); /** We estimate the integral as a sum, assuming the local area (from compute local area) scales the value of the function at the pixel. See http://mathworld.wolfram.com/SurfaceIntegral.html*/ virtual RealType IntegrateFunctionOverNeighborhood(bool norm=false) { return 0; } void SwitchNormalSign() { m_Normal*=(-1.0); } // for conjugate harmonic function float dstarUestimate(); // get this from the local frame - 1st order vs 2nd order shape operator void EstimateMetricTensor(); protected: SurfaceCurvatureBase(); virtual ~SurfaceCurvatureBase(){}; /** Holds the value of Pi. */ double m_Pi; bool m_Debug; /** Data structures to contain points. */ PointContainerType m_PointList; /** Data structures to contain function values associated with points. */ FunctionContainerType m_FunctionValueList; /** This list contains the projection of the vectors onto the tangent plane (T_i Shimshoni). */ PointContainerType m_TangentProjectionList; /** The point that is the origin of the current neighborhood. */ PointType m_Origin; PointType m_AveragePoint; PointType m_PlaneVector; /** Data that represents single vectors */ FixedVectorType m_ArbitraryTangent; FixedVectorType m_Normal; FixedVectorType m_Tangent1; FixedVectorType m_Tangent2; RealType m_dX; // size in local x dir RealType m_dY; // size in local y dir FixedVectorType m_MetricTensor; VectorType m_ThetaVector; VectorType m_WeightVector; VectorType m_DirectionalKappaVector; /** Data for representing neighborhoods and derived from the vector frames*/ /** Approximate directional curvature */ RealType m_DirectionalKappa; RealType m_MetricTensorDeterminant; /** Approximate principal curvature 1*/ RealType m_Kappa1; /** Approximate principal curvature 2*/ RealType m_Kappa2; RealType m_GaussianKappa; RealType m_MeanKappa; /** Solution weights eq. (7) (8) Shimshoni */ RealType m_A; RealType m_B; RealType m_C; /** True Eigenvector weights */ RealType m_W1; RealType m_W2; /** Eigenvalues */ RealType m_Eval0; RealType m_Eval1; RealType m_Eval2; unsigned int m_CurrentNeighborhoodPointIndex; std::string m_ParameterFileName; /** We use this to avoid computing the frame in degenerate cases. */ RealType m_TotalDKap; RealType m_TotalArea; RealType m_Sigma; bool m_UseGeodesicNeighborhood; private: }; } // namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkSurfaceCurvatureBase.txx" #endif #endif ants-1.9.2+svn680.dfsg/Utilities/itkSurfaceCurvatureBase.txx000066400000000000000000000756311147325206600240100ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkSurfaceCurvatureBase.txx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.12 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _SurfaceCurvatureBase_txx #define _SurfaceCurvatureBase_txx #include #include #include // #include #include #include #include #include "itkSurfaceCurvatureBase.h" namespace itk { template SurfaceCurvatureBase ::SurfaceCurvatureBase() { m_Origin.fill(0.0); m_ArbitraryTangent.fill(0.); m_Normal.fill(0.); m_Tangent1.fill(0.); m_Tangent2.fill(0.); m_DirectionalKappa=0.0; m_Kappa1=0.0; m_Kappa2=0.0; m_GaussianKappa=0.0; m_A=0.0; m_B=0.0; m_C=0.0; m_W1=3./8.; m_W2=1./8.; m_Eval0=0.0; m_Eval1=0.0; m_Eval2=0.0; m_CurrentNeighborhoodPointIndex=0; m_ParameterFileName = ""; m_Pi= 3.14159265358979323846; m_Debug=true; m_Debug=false; m_UseGeodesicNeighborhood=false; m_TotalArea=0.0; } template void SurfaceCurvatureBase ::ComputeAveragePoint() { m_AveragePoint.fill(0.0); unsigned int npts = m_PointList.size(); unsigned int dim = SurfaceDimension; for (unsigned int i =0; i void SurfaceCurvatureBase ::ProjectToTangentPlane(PointType dif) { MatrixType id(ImageDimension,ImageDimension); id.fill(0.0); unsigned int i=0,j=0; MatrixType NN(ImageDimension,ImageDimension); for (i=0; i void SurfaceCurvatureBase ::SetFrameFromNormal(FixedVectorType N) { // WORKS ONLY FOR 3D! RealType mag=N.magnitude(); if (mag != 0.0) m_Normal=N/mag; else { m_Tangent1.fill(0.0); m_Tangent2.fill(0.0); return; } // now estimate the tangent plane if (fabs(m_Normal[0]) > 1.e-1) { float norm=1./m_Normal[0]; m_Tangent1[1]=norm*N[0]; m_Tangent1[0]=-1.*norm*(N[1]+N[2]); m_Tangent1[2]=norm*N[0]; } else if (fabs(m_Normal[1]) > 1.e-1) { float norm=1./m_Normal[1]; m_Tangent1[0]=norm*N[1]; m_Tangent1[1]=-1.*norm*(N[0]+N[2]); m_Tangent1[2]=norm*N[1]; } else if (fabs(m_Normal[2]) > 1.e-1) { float norm=1./m_Normal[2]; m_Tangent1[0]=norm*N[2]; m_Tangent1[2]=-1.*norm*(N[0]+N[1]); m_Tangent1[1]=norm*N[2]; } m_Tangent1/=m_Tangent1.magnitude(); m_Tangent2=vnl_cross_3d(m_Normal,m_Tangent1); m_Tangent2/=m_Tangent2.magnitude(); this->ChooseReferenceTangent(); } template void SurfaceCurvatureBase ::EigenDecomposition(MatrixType D) { // Compute estimated frame using eigensystem of D'*D { vnl_symmetric_eigensystem eig(D.transpose() * D); for (unsigned int j=0; jSetFrameFromNormal(m_Normal); m_Eval0=eig.get_eigenvalue(0); m_Eval1=eig.get_eigenvalue(1); m_Eval2=eig.get_eigenvalue(2); if (m_Debug){ vnl_vector a = eig.get_eigenvector(0); vcl_cout << "Eig residual = " << (D * a).magnitude() << vcl_endl; a = eig.get_eigenvector(1); vcl_cout << "Eig residual = " << (D * a).magnitude() << vcl_endl; a = eig.get_eigenvector(2); vcl_cout << "Eig residual = " << (D * a).magnitude() << vcl_endl; } } } template void SurfaceCurvatureBase::EstimateTangentPlane(PointType origin) { // Build cov matrix D unsigned int npts = m_PointList.size()-1; unsigned int dim = SurfaceDimension; MatrixType D(npts, dim); for (unsigned int i =0; iEigenDecomposition(D); m_dX=m_Tangent1.magnitude(); m_dY=m_Tangent2.magnitude(); m_Tangent1/=m_dX; m_Tangent2/=m_dY; this->ChooseReferenceTangent(); } template void SurfaceCurvatureBase::WeightedEstimateTangentPlane(PointType origin) { // Build cov matrix D RealType twi=0.0; unsigned int npts = m_PointList.size()-1; unsigned int dim = SurfaceDimension; MatrixType D(npts, dim); for (unsigned int i =0; iEigenDecomposition(D/twi); this->ChooseReferenceTangent(); } template void SurfaceCurvatureBase::ComputeFrame(PointType origin) { // Build cov matrix D unsigned int npts = m_PointList.size()-1; unsigned int dim = SurfaceDimension; RealType twi=1.,wi=0.; MatrixType D; bool method1=true; if (method1){ twi=0.0; D.set_size(dim, dim); D.fill(0.0); for (unsigned int i =0; iEigenDecomposition(D/twi); this->ChooseReferenceTangent(); } template void SurfaceCurvatureBase::ComputeFrameAndKappa(PointType origin) { // Build cov matrix D unsigned int npts = m_PointList.size()-1; unsigned int dim = SurfaceDimension; /* if (m_TotalDKap/(float)m_PointList.size() < 0.05) { m_Kappa1=0.0; m_Kappa2=0.0; m_A=0.; m_B=0.; m_C=0.0; m_GaussianKappa=0.0; m_TotalDKap=0.0; return; } */ MatrixType D; // dim=2; D.set_size(dim, dim); D.fill(0.0); for (unsigned int i =0; i eig(D.transpose() * D); m_Eval0=eig.get_eigenvalue(0); m_Eval1=eig.get_eigenvalue(1); m_Eval2=eig.get_eigenvalue(2); } template void SurfaceCurvatureBase ::ShimshoniFrame(PointType origin) { this->ComputeWeightsAndDirectionalKappaAndAngles(origin); this->ComputeFrameAndKappa(origin); this->EstimateCurvature(m_A,m_B,m_B,m_C); // this->EstimateCurvature(); } template void SurfaceCurvatureBase ::JainMeanAndGaussianCurvature(PointType origin) { // Build cov matrix D unsigned int npts = m_PointList.size()-1; if (npts < 7) { m_MeanKappa = 0; m_GaussianKappa = 0; m_Kappa1 = 0; m_Kappa2 = 0; return; } vnl_vector dists(npts); vnl_vector wts(npts); MatrixType D; D.set_size(npts,6); // each row contains [u^2 , uv, v^2, u, v, 1] for point p D.fill(0.0); float totwt=0.0; float wt=0.0; bool robust=true; robust=false; unsigned int i=0; for (i =0; i 0) dists/=totwt; } vnl_svd svd(D); vnl_vector a = svd.solve(dists); // std::cout << " a vec " << a << std::endl; // for (int tt=0; tt<6; tt++) // if (a[tt] < 1.e-6) a[tt]=0.0; /* double fu = a(0); //a(3); double fv = a(2); //a(4); double fuu = a(3); //2.*a(0); double fuv = a(1); //a(1); double fvv = a(4); //2.*a(2); */ double fu = a(3); double fv = a(4); double fuu = 2.*a(0); double fuv = a(1); double fvv = 2.*a(2); m_MeanKappa = (1.0 + fv*fv)*fuu - 2.0*fu*fv*fuv + (1.0 + fu*fu)*fvv; m_MeanKappa /= (2.0 * pow ( 1.0 + fu*fu + fv*fv , 1.5)); m_GaussianKappa = (fuu * fvv - fuv*fuv) / (( 1.0 + fu*fu + fv*fv )*( 1.0 + fu*fu + fv*fv )); m_Kappa1 = m_MeanKappa + sqrt( m_MeanKappa*m_MeanKappa - m_GaussianKappa ); m_Kappa2 = m_MeanKappa - sqrt( m_MeanKappa*m_MeanKappa - m_GaussianKappa ); if (origin[0]==20 && origin[1]==65 && origin[2]==39) { std::cout << " surf params " << a << std::endl; std::cout << " k1 " << m_Kappa1 << " k2 " << m_Kappa2 << std::endl; this->PrintFrame(); for (unsigned int jj=0; jj < npts; jj++) { std::cout << " point " << m_PointList[jj]; std::cout << " dist " << dists[jj] << std::endl; } } // NOTE: THIS IS THE ERROR ESTIMATE // vcl_cout << "SVD residual = " << (D * a).magnitude() << vcl_endl; // vcl_cout << " a " << a << vcl_endl; // MatrixType C(2,2); // C(0,0)=a(0); C(1,0)=a(1); C(0,1)=a(1); C(1,1)=a(2); // vnl_symmetric_eigensystem eig(C); // m_Kappa1=eig.get_eigenvalue(0); // m_Kappa2=eig.get_eigenvalue(1); // // m_MeanKappa=0.5*(m_Kappa1+m_Kappa2); // m_GaussianKappa = m_Kappa1*m_Kappa2; // // std::cout << " eval 0 " << m_Kappa1 << " eval 1 " << m_Kappa2 << std::endl; return; } // end jain frame template void SurfaceCurvatureBase ::ComputeJoshiFrame(PointType origin) { // Build cov matrix D unsigned int npts = m_PointList.size()-1; if (npts < 5) { m_MeanKappa = 0; m_GaussianKappa = 0; m_Kappa1 = 0; m_Kappa2 = 0; return; } vnl_vector dists(npts); MatrixType D; D.set_size(npts,3); D.fill(0.0); float totw=0; for (unsigned int i =0; i svd(D); vnl_vector c = svd.solve(dists); // NOTE: THIS IS THE ERROR ESTIMATE // vcl_cout << "SVD residual = " << (D * c).magnitude() << vcl_endl; // vcl_cout << " C " << c << vcl_endl; MatrixType C(2,2); C(0,0)=c(0); C(1,0)=c(1); C(0,1)=c(1); C(1,1)=c(2); vnl_symmetric_eigensystem eig(C); m_Kappa1=eig.get_eigenvalue(0); m_Kappa2=eig.get_eigenvalue(1); m_MeanKappa=0.5*(m_Kappa1+m_Kappa2); m_GaussianKappa = m_Kappa1*m_Kappa2; // std::cout << " eval 0 " << m_Kappa1 << " eval 1 " << m_Kappa2 << std::endl; } template typename SurfaceCurvatureBase::RealType SurfaceCurvatureBase ::ComputeLocalArea(const double* spacing) { PointType t1; PointType t2; for (unsigned int i=0; i< SurfaceDimension; i++) { t1[i]=m_Tangent1[i]*spacing[i]; t2[i]=m_Tangent2[i]*spacing[i]; } PointType temp=vnl_cross_3d( t1,t2); return temp.magnitude(); } template unsigned int SurfaceCurvatureBase ::CharacterizeSurface() { float th=1.e-6; if (fabs(m_GaussianKappa) < th*th) m_GaussianKappa=0.0; if (fabs(m_MeanKappa) < th) m_MeanKappa=0.0; if (m_MeanKappa > 0 && m_GaussianKappa > 0 ) return 8; else if (m_MeanKappa > 0 && m_GaussianKappa == 0) return 7; else if (m_MeanKappa > 0 && m_GaussianKappa < 0) return 6; else if (m_MeanKappa ==0 && m_GaussianKappa == 0) return 5; else if (m_MeanKappa ==0 && m_GaussianKappa < 0) return 4; else if (m_MeanKappa < 0 && m_GaussianKappa > 0) return 3; else if (m_MeanKappa < 0 && m_GaussianKappa == 0) return 2; else if (m_MeanKappa < 0 && m_GaussianKappa < 0) return 1; else return 0; } template typename SurfaceCurvatureBase::RealType SurfaceCurvatureBase ::ComputeMeanEuclideanDistance() { double totdist=0.0; unsigned int ct=0; unsigned int pls=m_PointList.size(); for (unsigned int i=0; i< pls; i++) { for (unsigned int j=0; j< pls; j++) { PointType p = m_PointList[j]-m_PointList[i]; totdist+=p.magnitude(); ct++; } } RealType meandist=totdist/(float)ct; // 4 pi r^2 = a, r^2 = a/(4pi) RealType spheremeandist=1.0;//sqrt((float)ct/(4.0*3.1418)); RealType compactness=meandist/spheremeandist; return 10.-compactness; } template void SurfaceCurvatureBase::TestEstimateTangentPlane(PointType origin) { // Read points from stdin MatrixType pts; std::cout << " input points " << std::endl; vcl_cin >> pts; // Build cov matrix D int npts = pts.rows(); int dim = pts.columns(); MatrixType D(npts, dim); // get average of points RealType count=0.0; PointType avgpt; avgpt.fill(0); for(unsigned int i = 0; i < npts; i++) { for(unsigned int j = 0; j < dim; j++) { avgpt(j)+=pts(i,j); count+=1.0; } } avgpt/=count; if (m_Debug) std::cout << " avg " << avgpt << std::endl; origin=avgpt; for(unsigned int i = 0; i < npts; i++) { for(unsigned int j = 0; j < dim; j++) D(i,j) = (pts(i,j)-origin(j))*(pts(i,j)-origin(j)); //D(i,dim) = 1; } // 1. Compute using SVD { vnl_svd svd(D); vnl_vector a = svd.nullvector(); vcl_cout << "SVD residual = " << (D * a).magnitude() << vcl_endl; vcl_cout << "SVD normal " << a << vcl_endl; } // 2. Compute using eigensystem of D'*D { vnl_symmetric_eigensystem eig(D.transpose() * D); vnl_vector a = eig.get_eigenvector(0); vcl_cout << "Eig residual = " << (D * a).magnitude() << vcl_endl; vcl_cout << " normal " << eig.get_eigenvector(0) << vcl_endl; vcl_cout << "Eigvec 1 " << eig.get_eigenvector(1) << vcl_endl; vcl_cout << "Eigvec 2 " << eig.get_eigenvector(2) << vcl_endl; vcl_cout << "Eigval normal " << eig.get_eigenvalue(0) << vcl_endl; vcl_cout << "Eigval 1 " << eig.get_eigenvalue(1) << vcl_endl; vcl_cout << "Eigval 2 " << eig.get_eigenvalue(2) << vcl_endl; } } template void SurfaceCurvatureBase::FindNeighborhood(unsigned int temp) { // Read points from stdin MatrixType pts; std::cout << " input points " << std::endl; vcl_cin >> pts; // Build cov matrix D unsigned int npts = pts.rows(); unsigned int dim = pts.columns(); m_AveragePoint.fill(0.0); for(unsigned int i = 0; i < npts; i++) { PointType pt; for(unsigned int j = 0; j < dim; j++) { pt(j)=pts(i,j); m_AveragePoint[j]+=pts(i,j); } if (i==0) m_Origin=pt; m_PointList.insert(m_PointList.begin(),pt); } m_AveragePoint/=(RealType)npts; if (m_Debug) { std:: cout << " point list size " << m_PointList.size() << std::endl; for(unsigned int i = 0; i < m_PointList.size(); i++) { std:: cout << " point " << m_PointList[i]; } std::cout << std::endl; } } /** This function sets the reference tangent arbitrarily. * It can be overridden in case there is a better practical approach. */ template void SurfaceCurvatureBase::ChooseReferenceTangent() { float w=1.; float w2=(1.0-w); //m_ArbitraryTangent[0]=w; m_ArbitraryTangent[1]=1.-w; m_ArbitraryTangent[2]=0.; m_ArbitraryTangent=w*m_Tangent2+(w2*m_Tangent1); m_ArbitraryTangent/=m_ArbitraryTangent.magnitude(); if (m_Debug) std::cout << " arb tan " << m_ArbitraryTangent << std::endl; } template void SurfaceCurvatureBase ::ComputeWeightsAndDirectionalKappaAndAngles(PointType origin) { unsigned int sz = m_PointList.size(); if (sz > 1 ) sz=sz-1; m_TangentProjectionList.clear(); m_DirectionalKappaVector.set_size(sz); m_DirectionalKappaVector.fill(0.0); m_WeightVector.set_size(sz); m_WeightVector.fill(0.0); m_ThetaVector.set_size(sz); m_ThetaVector.fill(0.0); RealType theta=0, w1=0,w2=0,totalWeight=0.0; RealType difMag=0,sqdifMag=0; m_TotalDKap=0.0; m_A=0.0; m_B=0.0; m_C=0.0; for (unsigned int ii=0; ii typename SurfaceCurvatureBase::RealType SurfaceCurvatureBase::GetWeight(PointType p1, PointType p2) { PointType d = p1 - p2; return d.magnitude(); } /** This function returns the angle between the reference tangent and the projection onto the tangent plane of the vector between the neighborhood focus and its neighbor. */ template typename SurfaceCurvatureBase::RealType SurfaceCurvatureBase ::GetTheta(PointType Q, PointType origin) { RealType theta=0.0; PointType Dif=Q-origin; // float dm=Dif.magnitude(); // if (dm==0) dm=1.0; // Dif/=dm; float u1=0.0; float u2=0.0; for (unsigned int i=0; i void SurfaceCurvatureBase ::EstimateDirectionalCurvature(PointType Q, PointType P) { PointType Dif=Q-P; m_DirectionalKappa=0.0; for (unsigned int i=0; i void SurfaceCurvatureBase ::EstimateCurvature(RealType w1, RealType w2,RealType w3, RealType w4) { /* if (m_TotalDKap/(float)m_PointList.size() < 0.05) { m_Kappa1=0.0; m_Kappa2=0.0; m_A=0.; m_B=0.; m_C=0.0; m_GaussianKappa=0.0; m_TotalDKap=0.0; return; }*/ RealType denom=(w4-w2*w3/w1); if (fabs(denom) > 1.e-12) m_Kappa2=(m_Eval2-m_Eval1*w3/w1)/denom; else { w1=m_W1; w2=m_W2; w3=m_W2; w4=m_W1; denom=(w4-w2*w3/w1); m_Kappa2=(m_Eval2-m_Eval1*w3/w1)/denom; } m_Kappa1=(m_Eval1-w2*m_Kappa1)/w1; m_MeanKappa=0.5*(m_Kappa1+m_Kappa2); m_GaussianKappa = m_Kappa1*m_Kappa2; // std::cout << " eval test " << w1*m_Kappa1 + w2*m_Kappa2 << " e1 " << m_Eval1 << std::endl; // std::cout << " eval test " << w3*m_Kappa1 + w4*m_Kappa2 << " e2 " << m_Eval2 << std::endl; } /** */ template void SurfaceCurvatureBase ::PrintFrame() { std::cout << " normal " << m_Normal << std::endl; std::cout << " t1 " << m_Tangent1 << std::endl; std::cout << " t2 " << m_Tangent2 << std::endl; std::cout << " k1 " << m_Kappa1 << " k2 " << m_Kappa2 << std::endl; std::cout << " e0 " << m_Eval0 << " e1 " << m_Eval1 << " e2 " << m_Eval2 < typename SurfaceCurvatureBase::RealType SurfaceCurvatureBase ::ErrorEstimate(PointType origin, RealType sign) { unsigned int npts = m_PointList.size()-1; unsigned int dim = SurfaceDimension; // float error=0.0; float toterror=0.0; double qpt=0.0; double qpnkpt=0.0; double costheta; double sintheta; double kp; RealType theta; PointType Q,est; for (unsigned int pp =0; ppGetTheta(Q,origin); costheta=cos(theta); sintheta=sin(theta); kp=m_Kappa1*costheta*costheta+m_Kappa2*sintheta*sintheta; if (kp != 0.0) { PointType qp=Q-origin; FixedVectorType normal=m_Normal*sign; float w1=0,w2=0; for(unsigned int j = 0; j < dim; j++) { qpt+=qp[j]*normal[j]*kp; w1+=qp[j]*m_Tangent1[j]; w2+=qp[j]*m_Tangent2[j]; qpnkpt+=qp[j]*m_PlaneVector[j]; } vnl_vector f2(4); f2(0) = 0.5*kp*kp; f2(1) = 0.0; f2(2) = 1. - qpnkpt; f2(3) = -1.* qpt; /** commenting out until we can get vnl_rpoly_roots working vnl_rpoly_roots roots(f2); // Evaluate results //vnl_real_polynomial p(f2); //for(unsigned int i = 0; i < p.degree(); i++) // vnl_test_assert("Root residual", vcl_abs(p.evaluate(roots[i])) < 1e-12); float minrel=9.e9; float mins=0.0; float s=0.0; vnl_vector so=roots.real(); // for (s=0.1; s<=2.0; s=s+.01) for (unsigned int ind=0; ind void SurfaceCurvatureBase ::EstimateMetricTensor() { float a=0; for (unsigned int i=0; i<3;i++) a+=m_Tangent1[i]*m_Tangent1[i]; m_MetricTensor[0]=a; float b=0; for (unsigned int i=0; i<3;i++) b+=m_Tangent2[i]*m_Tangent1[i]; m_MetricTensor[1]=b; float c=0; for (unsigned int i=0; i<3;i++) c+=m_Tangent2[i]*m_Tangent2[i]; m_MetricTensor[2]=c; m_MetricTensorDeterminant = sqrt(a*c-b*b); if (m_MetricTensorDeterminant < 0.0) std::cout << "bad metric tensor "; } template float SurfaceCurvatureBase ::dstarUestimate() { //this->FindNeighborhood(); make sure this also fills the function values functionvaluelist this->EstimateTangentPlane(m_Origin); // below, basically replace the heights with the function values // Build cov matrix D int npts = m_PointList.size(); int dim = SurfaceDimension; if (npts < 4) { return 0.0; } vnl_vector dists(npts); vnl_vector surfx(npts); vnl_vector surfy(npts); vnl_vector surfz(npts); vnl_vector funcvals(npts); vnl_vector wts(npts); MatrixType D; D.set_size(npts,3); // each row contains [ u, v, 1] for point p D.fill(0.0); float totwt=0.0; float totfunc=0.0; float wt=0.0; float meanu1=0; float meanu2=0; unsigned int i=0; for (i =0; imeanu2) mx=meanu1; else mx=meanu2; meanu1/=mx;meanu2/=mx; vnl_svd svd(D); float aa,bb,cc; vnl_vector a = svd.solve(surfx); vnl_vector b = svd.solve(surfy); vnl_vector c = svd.solve(surfz); double fu = a(0); double fv = a(1); aa=a(0)*a(0)+b(0)*b(0)+c(0)*c(0); bb=a(1)*a(0)+b(1)*b(0)+c(1)*c(0); cc=a(1)*a(1)+b(1)*b(1)+c(1)*c(1); vnl_vector func = svd.solve(funcvals); double Ux=func(0); double Uy=func(1); float dx=meanu1;//m_dX;//m_Eval1;//m_dX; float dy=meanu2;//m_dY;//m_Eval2;//m_dY; m_MetricTensor[0]=aa; m_MetricTensor[1]=bb; m_MetricTensor[2]=cc; float denom=sqrt(aa*cc-bb*bb); // denom=1.0; // std::cout << " denom " << denom << " dx " << dx << " dy " << dy << std::endl; //std::cout << " a " << aa << " b " << bb << " c " << cc << std::endl; float dstarU = 1.0/denom*( (bb*Ux - aa*Uy)*dx + (cc*Ux - bb*Uy)*dy ); //std::cout << " dstarU " << dstarU << std::endl; return dstarU; } } // namespace itk #endif ants-1.9.2+svn680.dfsg/Utilities/itkSurfaceImageCurvature.h000066400000000000000000000164051147325206600235560ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkSurfaceImageCurvature.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.12 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _SurfaceImageCurvature_h #define _SurfaceImageCurvature_h #include "itkNeighborhoodIterator.h" #include "itkSurfaceCurvatureBase.h" #include "itkGradientRecursiveGaussianImageFilter.h" #include "itkGradientImageFilter.h" namespace itk { /** \class SurfaceImageCurvature * * This class takes a surface as input and creates a local * geometric frame for each surface point. * * */ template < typename TSurface > class SurfaceImageCurvature : public SurfaceCurvatureBase < TSurface , 3 > { public: /** Standard class typedefs. */ typedef SurfaceImageCurvature Self; typedef SurfaceCurvatureBase Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Run-time type information (and related methods). */ itkTypeMacro(SurfaceImageCurvature,SurfaceCurvatureBase); /** Method for creation through the object factory. */ itkNewMacro(Self); /** Image related types. */ typedef typename TSurface::PixelType PixelType; enum { ImageDimension = TSurface::ImageDimension }; typedef Image ImageType; typedef typename ImageType::IndexType IndexType; typedef typename ImageType::SizeType SizeType; typedef ImageRegionIteratorWithIndex ImageIteratorType; /** Image dimension. */ itkStaticConstMacro(SurfaceDimension, unsigned int, TSurface::ImageDimension); typedef typename Superclass::RealType RealType; typedef typename Superclass::PointType VectorType; typedef typename Superclass::PointType FixedVectorType; typedef typename Superclass::PointType PointType; typedef typename Superclass::MatrixType MatrixType; typedef Image OutputImageType; typedef ImageRegionIteratorWithIndex OutputImageIteratorType; typedef typename OutputImageType::Pointer OutputImagePointer; typedef Image FrameImageType; /** Gradient filtering */ typedef CovariantVector GradientPixelType; typedef Image GradientImageType; typedef SmartPointer GradientImagePointer; typedef GradientRecursiveGaussianImageFilter< OutputImageType,GradientImageType > GradientImageFilterType; typedef GradientImageFilter< OutputImageType > GradientImageFilterType2; typedef typename GradientImageFilterType::Pointer GradientImageFilterPointer; typedef NeighborhoodIterator NeighborhoodIteratorType; /** Find all points within some distance of the origin. * The argument gives the number of times to apply the * mean shift algorithm to find the best neighborhood. */ void FindNeighborhood(unsigned int numMeanShifts=0); void FindEuclideanNeighborhood(PointType p); void FindGeodesicNeighborhood(); /** This applies one of the algorithms for finding the local curvature and frame. The default is joshi. */ void ComputeFrameOverDomain(unsigned int which=0); ImageType* GetInput(); void SetInput(typename ImageType::Pointer input); OutputImageType* GetOutput(); /** Apply the level set curvature equation over the whole image */ void LevelSetMeanCurvature(); /** Use the gradient of the image to estimate the normals everywhere. * Also compute area, if boolean is set. */ void EstimateNormalsFromGradient(); /** Use the Weingarten map to estimate the curvature.*/ void WeingartenMap(); /** Computes a neighborhood surface area function everywhere*/ void ComputeSurfaceArea(); /** Use the gradient estimated normal to get the local frame. Requires call to SetNormal to find tangents. */ void EstimateFrameFromGradient(IndexType); /** Implemented version of virtual function from parent class. Here, we just sum the computed function, held in CurvatureImage, over a neighborhood */ RealType IntegrateFunctionOverNeighborhood(bool norm=false); /** Get the neighborhood integral for every surface point.*/ RealType IntegrateFunctionOverSurface(bool norm=false); /** Postprocess the curvature function by, e.g., gaussian smoothing of the curvature (and perhaps frame) in the local neighbhorhood. */ void PostProcessGeometry(); itkSetMacro(NeighborhoodRadius,RealType); itkGetMacro(NeighborhoodRadius,RealType); itkSetMacro(UseLabel,bool); itkGetMacro(UseLabel,bool); itkSetMacro(kSign,float); itkSetMacro(Sigma,float); itkSetMacro(Threshold,float); itkGetMacro(FunctionImage,OutputImagePointer); itkSetMacro(FunctionImage,OutputImagePointer); void ProcessLabelImage(); float CurvatureAtIndex(IndexType index) { PointType p; for (unsigned int k=0; kSetOrigin(p); this->EstimateFrameFromGradient(index); this->FindNeighborhood(); this->WeingartenMap(); float fval=this->m_GaussianKappa; float kpix; fval=this->m_MeanKappa; if (fabs(fval) > 1) fval/=fval; kpix=kpix+m_kSign*fval; return kpix; } inline bool IsValidSurface(PixelType pix, IndexType ind) { // std::cout << "m_UseLabel "<< m_UseLabel << " pix " << pix << " Thresh " << m_Threshold << " ind " << ind << std::endl; if (this->m_UseLabel) { if ( pix == this->m_SurfaceLabel ) return true; else return false; } else { if (pix > this->m_Threshold ) return true; else return false; } } protected: SurfaceImageCurvature(); ~SurfaceImageCurvature(){}; void CopyImageToFunctionImage( OutputImagePointer,OutputImagePointer); /** This function changes the values of the label image for use with the fast marching image filter. */ private: PixelType m_SurfaceLabel; OutputImagePointer m_FunctionImage; RealType m_NeighborhoodRadius; SizeType m_ImageSize; GradientImagePointer m_GradientImage; NeighborhoodIteratorType m_ti; NeighborhoodIteratorType m_ti2; bool m_UseLabel; float m_kSign; float m_Sigma; float m_Threshold; }; } // namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkSurfaceImageCurvature.txx" #endif #endif ants-1.9.2+svn680.dfsg/Utilities/itkSurfaceImageCurvature.txx000066400000000000000000001021441147325206600241460ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkSurfaceImageCurvature.txx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.12 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _SurfaceImageCurvature_txx #define _SurfaceImageCurvature_txx #include #include "itkSurfaceImageCurvature.h" //#include "itkLevelSetCurvatureFunction.h" #include "itkCastImageFilter.h" #include "itkDiscreteGaussianImageFilter.h" #include #include #include namespace itk { /** Quick node class to help get the geodesic neighborhood */ template < typename TSurface > class GeodesicNode { public: /** Image related types. */ typedef TSurface ImageType; typedef typename ImageType::IndexType IndexType; unsigned long neighborhoodindex; float distance; bool connected; IndexType imageindex; GeodesicNode() { distance=0.0; connected=false; neighborhoodindex=0; } GeodesicNode(unsigned long i, float d, bool t, IndexType ind) { distance=d; connected=t; neighborhoodindex=i; imageindex=ind; } ~GeodesicNode(){} }; template class GeodesicNodePriority /* defines the comparison operator for the prioritiy queue */ { public: bool operator() ( pclass N1, pclass N2) { return N1.distance > N2.distance; } }; template SurfaceImageCurvature ::SurfaceImageCurvature() { // outputs are curvature image and normal image this->ProcessObject::SetNumberOfRequiredOutputs( 2 ); m_SurfaceLabel=1; m_GradientImage=NULL; m_UseLabel=false; m_kSign=-1.0; m_FunctionImage=NULL; m_Sigma=1.0; } template void SurfaceImageCurvature::ProcessLabelImage() { ImageType* image=GetInput(); if (!image) return; IndexType index; typename ImageType::RegionType requestedRegion; m_ImageSize=image->GetLargestPossibleRegion().GetSize(); ImageIteratorType ti( image, image->GetLargestPossibleRegion() ); ti.GoToBegin(); while(!ti.IsAtEnd() ) { PixelType pix=ti.Get(); index=ti.GetIndex(); if (ti.Get() == 2 ) { ti.Set(m_SurfaceLabel); } else ti.Set(0); ti.Set(0); // if (index[0] == 20 && index[1] < 100 && index[2] < 100) ti.Set(m_SurfaceLabel); float rad=23,d=0; IndexType cind={{120,120,80}}; for (unsigned int j=0; j void SurfaceImageCurvature::FindEuclideanNeighborhood (typename SurfaceImageCurvature::PointType rootpoint) { //Superclass::FindNeighborhood(); this->m_AveragePoint=this->m_Origin; this->m_PointList.insert(this->m_PointList.begin(),this->m_Origin); typename ImageType::SizeType rad; IndexType oindex,index; long offset=0; for (unsigned int i=0; iIsValidSurface(pix,index)) { PointType p; float dist=0.0; bool isorigin=true; for (unsigned int k=0; km_Origin[k])*(p(k)-this->m_Origin[k]); } dist=sqrt(dist); if (!isorigin && dist <= (m_NeighborhoodRadius)) { this->m_AveragePoint=this->m_AveragePoint+p; this->m_PointList.insert(this->m_PointList.begin(),p); } } } unsigned int npts=this->m_PointList.size(); if (npts > 0) this->m_AveragePoint/=(RealType)npts; else this->m_AveragePoint=this->m_Origin; if (this->m_Debug) { std:: cout << " point list size " << this->m_PointList.size() << std::endl; // for(int i = 0; i < this->m_PointList.size(); i++) { // std:: cout << " point " << this->m_PointList[i]; // } std::cout << std::endl; } } template void SurfaceImageCurvature::FindGeodesicNeighborhood() { typedef std::priority_queue < GeodesicNode , std::vector< GeodesicNode >, GeodesicNodePriority< GeodesicNode > > QType; QType nodeq; std::map > nodes; this->m_AveragePoint=this->m_Origin; this->m_PointList.insert(this->m_PointList.begin(),this->m_Origin); typename ImageType::SizeType rad; IndexType oindex,index; float dist=0.0; unsigned int k=0; unsigned long longindex=0; long offset=0; for (unsigned int i=0; im_Origin[i]+0.5); } index=oindex; for (k=0; k gnode(longindex,0.0,true,oindex); nodes[longindex]=gnode; nodeq.push(gnode); float lastdist=0.0; //if ( this->m_Origin[1]==146 && this->m_Origin[0] > 167 ) //if ( this->m_Origin[1]==146 && this->m_Origin[0] == 168 && this->m_Origin[2]==215) //{ //std::cout << " origin " << this->m_Origin << std::endl; //} while( !nodeq.empty() && lastdist <= m_NeighborhoodRadius) { GeodesicNode g=nodeq.top(); lastdist=g.distance; PointType q; // if ( g.distance < 2.0) // { // this->m_PointList.insert(this->m_PointList.begin(),q); // nodes[g.neighborhoodindex].connected=true; // } // else if ( lastdist <= m_NeighborhoodRadius) { m_ti2.SetLocation(g.imageindex); for (unsigned int jj=0; jjIsValidSurface( m_ti2.GetPixel(jj) ,index) && index[0] < m_ImageSize[0]-m_NeighborhoodRadius && index[0] > m_NeighborhoodRadius && index[1] < m_ImageSize[1]-m_NeighborhoodRadius && index[1] > m_NeighborhoodRadius && index[2] < m_ImageSize[2]-m_NeighborhoodRadius && index[2] > m_NeighborhoodRadius ) { longindex=0; dist=0; for (k=0; km_Origin[1]==146 && this->m_Origin[0] == 168 && this->m_Origin[2]==215) //{ // std::cout << " testing point " << index << " longind " << longindex << " dist " << dist << // " bool " << nodes[longindex].connected << std::endl; //} // if (!nodes[longindex].connected ) //&& !nodes[g.neighborhoodindex].connected) if ( !nodes[longindex].connected && (dist+lastdist) <= m_NeighborhoodRadius) { GeodesicNode gnode(longindex,dist+lastdist,true,index); // GeodesicNode gnode(longindex,dist,true,index); nodes[longindex]=gnode; nodeq.push(gnode); //if ( this->m_Origin[1]==146 && this->m_Origin[0] == 168 && this->m_Origin[2]==215) ///{ // std::cout << " inserting point " << index << std::endl; //} this->m_PointList.insert(this->m_PointList.begin(),q); this->m_AveragePoint=this->m_AveragePoint+q; } // else if ( dist > 0 && dist+lastdist < nodes[longindex].distance ) // { // nodes[longindex].distance=dist+lastdist; // } } } } nodeq.pop(); } this->m_AveragePoint=this->m_AveragePoint/((float)this->m_PointList.size()); } template void SurfaceImageCurvature::FindNeighborhood(unsigned int numMeanShifts) { if (this->m_UseGeodesicNeighborhood) { this->FindGeodesicNeighborhood(); } else { this->FindEuclideanNeighborhood(this->GetOrigin()); for (unsigned int dd=0; dd< numMeanShifts; dd++) { this->m_PointList.clear(); this->FindEuclideanNeighborhood(this->GetAveragePoint()); } } /* if (this->m_Origin[0]==170 && this->m_Origin[1]==137 && this->m_Origin[2]==81) if ( this->m_Origin[1]==146 && this->m_Origin[0] > 167 ) { std::cout << " origin " << this->m_Origin << std::endl; for (unsigned int tt=0; ttm_PointList.size()-1; tt++) { PointType p=this->m_Origin-this->m_PointList[tt]; float dist = p.magnitude(); std::cout << " pt dist " << dist << " point " << this->m_PointList[tt] << std::endl; } } */ } template void SurfaceImageCurvature ::LevelSetMeanCurvature() { /* ImageType* image=GetInput(); if (!image) return; IndexType index; typename ImageType::RegionType requestedRegion; this->m_ImageSize=image->GetLargestPossibleRegion().GetSize(); ImageIteratorType ti( image, image->GetLargestPossibleRegion() ); // Define a level set curvature calculator typedef LevelSetCurvatureFunction CurvatureType; typename CurvatureType::Pointer inCurvature = CurvatureType::New(); inCurvature->SetInputImage( image ); ti.GoToBegin(); while(!ti.IsAtEnd() ) { index=ti.GetIndex(); //if (ti.Get() == this->m_SurfaceLabel) if(this->IsValidSurface(ti.Get(),index)) { double curvature = inCurvature->EvaluateAtIndex( index ); this->m_FunctionImage->SetPixel(index,fabs(curvature)); } ++ti; } */ } template void SurfaceImageCurvature ::EstimateNormalsFromGradient() { typename ImageType::Pointer image=GetInput(); std::cout << " compute normals " << this->m_Sigma << " hood " << (this->m_NeighborhoodRadius) << " spacing " << image->GetSpacing() << std::endl; if (!image) return; typename ImageType::SizeType rad; typename ImageType::SizeType rad2; for (unsigned int t=0; tm_NeighborhoodRadius); rad2[t]=1; } this->m_ti.Initialize( rad , image, image->GetLargestPossibleRegion()); this->m_ti2.Initialize( rad2 , image, image->GetLargestPossibleRegion()); typedef itk::ImageRegionIteratorWithIndex IteratorType; IteratorType Iterator( image,image->GetLargestPossibleRegion().GetSize() ); bool wmgmcurv=true; Iterator.GoToBegin(); while( !Iterator.IsAtEnd() ) { float pix=Iterator.Get(); if (pix !=0 && pix != 1 && pix != 2) wmgmcurv=false; ++Iterator; } wmgmcurv=false; std::cout << " Using Binary Segmentation curv? " << wmgmcurv << std::endl; if (wmgmcurv) { typename OutputImageType::Pointer laplacian=OutputImageType::New(); laplacian->SetLargestPossibleRegion( image->GetLargestPossibleRegion() ); laplacian->SetBufferedRegion( image->GetLargestPossibleRegion() ); laplacian->Allocate(); laplacian->SetSpacing(image->GetSpacing()); Iterator.GoToBegin(); while( !Iterator.IsAtEnd() ) { IndexType ind=Iterator.GetIndex(); if (image->GetPixel(ind) == 2) laplacian->SetPixel(ind,1); else if (image->GetPixel(ind) == 1 ) laplacian->SetPixel(ind,0.); else laplacian->SetPixel(ind,0.); ++Iterator; } //smooth and then reset the values unsigned int totit=50; for(unsigned int iterations=0; iterationsGetPixel(ind) == 2) laplacian->SetPixel(ind,1); else if (image->GetPixel(ind) == 0 ) laplacian->SetPixel(ind,0.); ++Iterator; } typedef itk::DiscreteGaussianImageFilter dgf; typename dgf::Pointer filter = dgf::New(); filter->SetVariance(0.5); filter->SetUseImageSpacingOn(); filter->SetMaximumError(.01f); filter->SetInput(laplacian); filter->Update(); laplacian=filter->GetOutput(); Iterator.GoToBegin(); } // WriteImage(laplacian,"lap.hdr"); // std::cout << "Laplacian Solved " << std::endl; GradientImageFilterPointer filter=GradientImageFilterType::New(); filter->SetInput( laplacian); RealType sigma=this->m_Sigma; filter->SetSigma( sigma ); // Execute the filter filter->Update(); this->m_GradientImage=filter->GetOutput(); GradientPixelType zero; zero.Fill(0); Iterator.GoToBegin(); while( !Iterator.IsAtEnd() ) { IndexType ind=Iterator.GetIndex(); if (image->GetPixel(ind) != 1) { this->m_GradientImage->SetPixel(ind,zero); } ++Iterator; } } else { GradientImageFilterPointer filter=GradientImageFilterType::New(); filter->SetInput( image ); RealType sigma=this->m_Sigma; filter->SetSigma( sigma ); // Execute the filter filter->Update(); this->m_GradientImage=filter->GetOutput(); } std::cout << " compute normals done "; } template void SurfaceImageCurvature ::WeingartenMap() { MatrixType D; float totwt=0,wt=0; unsigned int j=0; unsigned int i=0; unsigned int npts=this->m_PointList.size()-1; if (npts < 4) { this->m_MeanKappa = 0; this->m_GaussianKappa = 0; this->m_Kappa1 = 0; this->m_Kappa2 = 0; return; } unsigned int vars=3; D.set_size(npts,vars); // each row contains [u^2 , uv, v^2, u, v, 1] for point p D.fill(0.0); MatrixType W(2,2); W.fill(0.0); vnl_vector xdists(npts); xdists.fill(0.0); vnl_vector ydists(npts); ydists.fill(0.0); vnl_vector zdists(npts); zdists.fill(0.0); // go through all the points // compute weight // compute dist of unit dif and tangents // compute dif of normal with grad at point PointType Q =this->m_Origin; PointType QN=this->m_Normal; for (j=0; jm_PointList[j]; float difmag=Dif.magnitude(); PointType unitDif=Dif/difmag; float u1=0.0; float u2=0.0; wt=1.0;///difmag; totwt+=wt; IndexType index; for (i=0; im_Tangent1[i]; u2+=Dif[i]*this->m_Tangent2[i]; index[i] = (long) (this->m_PointList[j][i]+0.5); } // if (fabs(u1) < 1.e-6) u1=1.e-6; // if (fabs(u2) < 1.e-6) u2=1.e-6; // get the normal at the point GradientPixelType norm=this->m_GradientImage->GetPixel(index); PointType PN; float nmag=0.0; for (i=0;i 1.e-9) PN/=(nmag); else PN*=0.0; // PointType dN=(PN-QN)*wt; xdists[j]=(PN[0]); ydists[j]=(PN[1]); zdists[j]=(PN[2]); /* float a=0; float b=0; for (i=0; im_Tangent1[i]; b+=dN[i]*this->m_Tangent2[i]; } if ( u1*u1 > u2*u2 ) { W(0,0)=W(0,0)+a; W(1,0)=W(1,0)+b; } else { W(0,1)=W(0,1)+a; W(1,1)=W(1,1)+b; } D(j,0)=u1*u1; D(j,1)=2.*u1*u2; D(j,2)=u2*u2; D(j,3)=u1; D(j,4)=u2; D(j,5)=1.0; */ // each row contains [u^2 , uv, v^2, u, v, 1] for point p if (vars == 6) { D(j,5)=u2*u2; // (0 , 2*u2) D(j,4)=u1*u1; // (2*u1, 0) D(j,3)=u1*u2; // (u2 , u1) } D(j,2)=u2; // (1 , 0) D(j,1)=u1; // (0 , 1) D(j,0)=1.0; } // W=W/totwt; vnl_svd svd(D); vnl_vector ax = svd.solve(xdists); ///totwt); vnl_vector ay = svd.solve(ydists); ///totwt); vnl_vector az = svd.solve(zdists); ///totwt); // now get the first partials of each of these terms w.r.t. u and v // dN/du = (dN/du \dot T_1) T_1+ (dNdu dot T_2) T_2 PointType dNdu; dNdu[0]=ax[1]; dNdu[1]=ay[1]; dNdu[2]=az[1]; PointType dNdv; dNdv[0]=ax[2]; dNdv[1]=ay[2]; dNdv[2]=az[2]; float a=0; float b=0; float c=0; float d=0; for (i=0; im_Tangent1[i]; b+=dNdv[i]*this->m_Tangent1[i]; c+=dNdu[i]*this->m_Tangent2[i]; d+=dNdv[i]*this->m_Tangent2[i]; } /* dNdu=a/(c+a)*this->m_Tangent1+c/(c+a)*this->m_Tangent2; dNdv=b/(b+d)*this->m_Tangent1+d/(b+d)*this->m_Tangent2; a=0; b=0; c=0; d=0; for (i=0; im_Tangent1[i]; b+=dNdv[i]*this->m_Tangent1[i]; c+=dNdu[i]*this->m_Tangent2[i]; d+=dNdv[i]*this->m_Tangent2[i]; } */ W(0,0)=a; W(0,1)=b; W(1,0)=c; W(1,1)=d; // Compute estimated frame using eigensystem of D'*D { vnl_real_eigensystem eig(W); // vnl_diag_matrix > DD(eig.D.rows());// /* for(i = 0; i < eig.D.n(); ++i) { vnl_test_assert("All real", vcl_imag(eig.D(i,i)) < 1e-15); DD(i,i) = vcl_real(eig.D(i,i)); } vcl_cout << "D = " << eig.D << vcl_endl; vcl_cout << "V = " << eig.V << vcl_endl; */ this->m_Kappa1=vcl_real(eig.D(1,1)); this->m_Kappa2=vcl_real(eig.D(0,0)); // std::cout << " k1 " << this->m_Kappa1 << " k2 " << this->m_Kappa2 << " pt "<< this->m_Origin << std::endl; this->m_MeanKappa=(this->m_Kappa1+this->m_Kappa2)*0.5; this->m_GaussianKappa=(this->m_Kappa1*this->m_Kappa2); } } template void SurfaceImageCurvature ::ComputeSurfaceArea() { ImageType* image=GetInput(); if (!image) return; //BUG FIXME if (!this->m_GradientImage) this->EstimateNormalsFromGradient(); IndexType index; const double* spacing = image->GetSpacing(); typename ImageType::RegionType requestedRegion; this->m_ImageSize=image->GetLargestPossibleRegion().GetSize(); ImageIteratorType ti( image, image->GetLargestPossibleRegion() ); RealType area=0.0; this->m_TotalArea=0.0; ti.GoToBegin(); unsigned int ct=0; while(!ti.IsAtEnd() ) { index=ti.GetIndex(); if ( //ti.Get() == this->m_SurfaceLabel && (this->IsValidSurface(ti.Get(),index)) && index[0] < this->m_ImageSize[0]-this->m_NeighborhoodRadius && index[0] > this->m_NeighborhoodRadius && index[1] < this->m_ImageSize[1]-this->m_NeighborhoodRadius && index[1] > this->m_NeighborhoodRadius && index[2] < this->m_ImageSize[2]-this->m_NeighborhoodRadius && index[2] > this->m_NeighborhoodRadius ) { ct++; // this->EstimateFrameFromGradient(index); // BUG FIXME // area=this->ComputeLocalArea(spacing); area=1.0; this->m_FunctionImage->SetPixel(index,area); this->m_TotalArea+=area; this->m_PointList.clear(); if (ct % 1000 == 0) std::cout << " ind " << index << " area " << area << std::endl; } ++ti; } std::cout << " surface area " << this->m_TotalArea << std::endl; return; } template void SurfaceImageCurvature ::EstimateFrameFromGradient(IndexType index) { GradientPixelType g=this->m_GradientImage->GetPixel(index); RealType mag=0.0; for (int i=0; i< ImageDimension; i++) { this->m_Normal(i)=(RealType) g[i]; mag+=g[i]*g[i]; } mag=sqrt(mag); if (mag <= 1.e-9) { this->m_Normal.fill(0.); } else this->m_Normal/=sqrt(mag); this->SetFrameFromNormal(this->m_Normal); } template typename SurfaceImageCurvature::RealType SurfaceImageCurvature ::IntegrateFunctionOverSurface(bool norm) { typename OutputImageType::Pointer image=this->m_FunctionImage; if (!image) { std::cout << " no image " << std::endl; return 0; } std::cout << " allocating temp image "; typename OutputImageType::Pointer tempimage=OutputImageType::New(); tempimage->SetLargestPossibleRegion( image->GetLargestPossibleRegion() ); tempimage->SetBufferedRegion( image->GetLargestPossibleRegion() ); tempimage->Allocate(); std::cout << " done allocating "; typename ImageType::SizeType rad; typename ImageType::SizeType rad2; for (unsigned int t=0; tm_NeighborhoodRadius); rad2[t]=1; } this->m_ti.Initialize( rad , this->GetInput(), image->GetLargestPossibleRegion()); this->m_ti2.Initialize( rad2 , this->GetInput(), image->GetLargestPossibleRegion()); IndexType index; typename ImageType::RegionType requestedRegion; ImageIteratorType ti( this->GetInput(), this->GetInput()->GetLargestPossibleRegion() ); std::cout << " begin integrate "; ti.GoToBegin(); unsigned int ct =0; std::cout << " begin while " << std::endl; while(!ti.IsAtEnd() ) { index=ti.GetIndex(); tempimage->SetPixel(index,0); if ( //ti.Get() == this->m_SurfaceLabel && (this->IsValidSurface(ti.Get(),index)) && index[0] < this->m_ImageSize[0]-this->m_NeighborhoodRadius && index[0] > this->m_NeighborhoodRadius && index[1] < this->m_ImageSize[1]-this->m_NeighborhoodRadius && index[1] > this->m_NeighborhoodRadius && index[2] < this->m_ImageSize[2]-this->m_NeighborhoodRadius && index[2] > this->m_NeighborhoodRadius ) { PointType p; ct++; for (unsigned int k=0; kSetOrigin(p); // std::cout << " find nhood "; this->FindNeighborhood(); // std::cout << " get area "; RealType area=this->IntegrateFunctionOverNeighborhood(norm); tempimage->SetPixel(index,area); if (ct % 10000 == 0) std::cout << " area is : " << area << " ct " << ct << " pix " << ti.Get() << std::endl; // if ( area > 1) std::cout << " ind " << index << " area " << area << std::endl; // SD why sometimes a pixel is NaN ? // if ( !(area > 0)) std::cout << " ind " << index << " area " << area << " pix " << ti.Get() << std::endl; } ++ti; } this->CopyImageToFunctionImage(tempimage,this->m_FunctionImage); return 0; } template void SurfaceImageCurvature ::CopyImageToFunctionImage(OutputImagePointer i1, OutputImagePointer i2) { if (!i1 || !i2) return; typename ImageType::RegionType requestedRegion; OutputImageIteratorType ti1( i1, i1->GetLargestPossibleRegion() ); OutputImageIteratorType ti2( i2, i2->GetLargestPossibleRegion() ); ti1.GoToBegin(); ti2.GoToBegin(); while(!ti1.IsAtEnd() ) { ti2.Set(ti1.Get()); ++ti1; ++ti2; } } template typename SurfaceImageCurvature::RealType SurfaceImageCurvature ::IntegrateFunctionOverNeighborhood(bool norm) { unsigned int npts = this->m_PointList.size(); double curvature=0.0,tw=0; std::cout << " npts " << npts; for (unsigned int pp =0; ppm_PointList[pp][k]; PointType dd=this->m_Origin-this->m_PointList[pp]; double wi=dd.magnitude(); if (wi!=0.0) wi=1./wi; tw+=wi; RealType func=this->m_FunctionImage->GetPixel( localindex ); std::cout << " pp " << pp << " func " << func << std::endl; if (norm) curvature += wi*func; else curvature += func; // curvature*=this->ComputeLocalArea(spacing); } //if (norm ) curvature/=tw; // SD sometimes tw is zero making curvature = NaN if (norm && tw!=0) curvature/=tw; this->m_PointList.clear(); return curvature; } template void SurfaceImageCurvature ::PostProcessGeometry() { typename ImageType::Pointer image=GetInput(); if (!image) return; IndexType index; typename ImageType::RegionType requestedRegion; this->m_ImageSize=image->GetLargestPossibleRegion().GetSize(); ImageIteratorType ti( image, image->GetLargestPossibleRegion() ); std::vector kvec; ti.GoToBegin(); while(!ti.IsAtEnd() ) { PixelType pix=ti.Get(); index=ti.GetIndex(); if ( //ti.Get() == this->m_SurfaceLabel && (this->IsValidSurface(ti.Get(),index)) && index[0] < this->m_ImageSize[0]-this->m_NeighborhoodRadius && index[0] > this->m_NeighborhoodRadius && index[1] < this->m_ImageSize[1]-this->m_NeighborhoodRadius && index[1] > this->m_NeighborhoodRadius && index[2] < this->m_ImageSize[2]-this->m_NeighborhoodRadius && index[2] > this->m_NeighborhoodRadius ) // { PointType p; for (unsigned int k=0; kSetOrigin(p); this->FindNeighborhood(); int npts = this->m_PointList.size()-1; int dim = SurfaceDimension; double curvature=0.0,tw=0.0; for (int pp =0; ppm_PointList[pp][k]; // PointType dd=this->m_Origin-this->m_PointList[pp]; // double wi=dd.magnitude(); // if (wi!=0.0) wi=1./wi; // tw+=wi;vector vec; curvature = this->m_FunctionImage->GetPixel( localindex ); kvec.push_back (curvature); } std::sort(kvec.begin(), kvec.end()); // Sort the vector this->m_PointList.clear(); // curvature/=tw; this->m_FunctionImage->SetPixel(index,kvec[kvec.size()/2]); kvec.clear(); } ++ti; } } template void SurfaceImageCurvature ::ComputeFrameOverDomain(unsigned int which) { ImageType* image=GetInput(); if (!image) return; IndexType index; typename ImageType::RegionType requestedRegion; this->m_ImageSize=image->GetLargestPossibleRegion().GetSize(); ImageIteratorType ti( image, image->GetLargestPossibleRegion() ); // exit(1); // Get Normals First! this->EstimateNormalsFromGradient(); unsigned int ct=1; unsigned long ct2=0; RealType kpix=0; double thresh=0.0; ti.GoToBegin(); while(!ti.IsAtEnd() ) { index=ti.GetIndex(); if (ct2 % 200000 == 0 && ct2 > 0) { std::cout << " ind " << index << " kp " << kpix << std::endl; } kpix=0.0; if ( //ti.Get() == this->m_SurfaceLabel && this->IsValidSurface(ti.Get(),index) && index[0] < this->m_ImageSize[0]-2*this->m_NeighborhoodRadius && index[0] > 2*this->m_NeighborhoodRadius && index[1] < this->m_ImageSize[1]-2*this->m_NeighborhoodRadius && index[1] > 2*this->m_NeighborhoodRadius && index[2] < this->m_ImageSize[2]-2*this->m_NeighborhoodRadius && index[2] > 2*this->m_NeighborhoodRadius ) // { // std::cout << " val " << (RealType) ti.Get() << std::endl; PointType p; for (unsigned int k=0; kSetOrigin(p); this->EstimateFrameFromGradient(index); this->FindNeighborhood(); switch (which) { case( 0 ) : this->ComputeJoshiFrame( this->m_Origin); break; case( 1 ) : this->JainMeanAndGaussianCurvature( this->m_Origin); break; case( 2 ) : this->ShimshoniFrame(this->m_Origin); break; case( 3 ) : this->WeingartenMap(); break; case( 4 ) : kpix=this->ComputeMeanEuclideanDistance(); break; default: this->WeingartenMap(); } // this->PrintFrame(); bool geterror=false; if (geterror){ float error=0.0; float temp1=this->ErrorEstimate(this->GetOrigin()); float temp2=this->ErrorEstimate(this->GetOrigin(),-1); if (temp1 < temp2) { error=temp1; }else{ error=temp2; this->SwitchNormalSign(); // this->ComputeWeightsAndDirectionalKappaAndAngles(this->GetOrigin()); // this->EstimateCurvature(this->m_A,this->m_B,this->m_B,this->m_C); // this->EstimateCurvature(); } std::cout << " best error " << error << std::endl; } // kpix=fabs(2.0/(3.1416)*atan((this->m_Kappa1+this->m_Kappa2)/(this->m_Kappa2-this->m_Kappa1))); // if (kpix >= 0) // kpix = (sqrt(this->m_Kappa1*this->m_Kappa1+this->m_Kappa2*this->m_Kappa2)); // kpix=1.0; // if (this->m_MeanKappa > 0.) kpix +=fabs(this->m_MeanKappa); // kpix =fabs(this->m_MeanKappa); // kpix = kpix+10.0; // if (this->m_Kappa1 > 0) kpix+=fabs(this->m_Kappa1); // if (this->m_Kappa2 > 0) kpix+=fabs(this->m_Kappa2); // else kpix = -1.0*(sqrt(this->m_Kappa1*this->m_Kappa1+this->m_Kappa2*this->m_Kappa2)); // std::cout << " kpix " << kpix << " thresh " << thresh << std::endl; // // if ( fabs(kpix) > 100 ) kpix=0.0; // else if (kpix < -1.0*thresh/ct ) kpix=2; // else kpix=0; kpix=1.1; float fval=this->m_GaussianKappa; fval=this->m_MeanKappa; // fval=fval*(1.0+fabs(this->m_Kappa2-this->m_Kappa1)); if (fabs(fval) > 1) fval/=fval; // if (fval > 0.0) kpix+=(fval); // gyri // if (fval < 0.0) kpix-=(fval); // sulci bright kpix=this->m_kSign*fval; //sulci if( vnl_math_isnan(kpix) || vnl_math_isinf(kpix) ) { this->m_Kappa1=0.0; this->m_Kappa2=0.0; this->m_MeanKappa=0.0; this->m_GaussianKappa=0.0; kpix=0.0; } // kpix=1.0+this->m_Kappa1*this->m_Kappa1+this->m_Kappa2*this->m_Kappa2; // kpix=fabs(this->m_Normal[0]); if (which == 5) kpix=this->CharacterizeSurface(); ct++; this->m_PointList.clear(); } thresh+=kpix; float offset=0; if ( fabs( image->GetPixel(index) - 0 ) > 1.e-6 ) offset=128.0; this->m_FunctionImage->SetPixel(index,offset+kpix); ct2++; ++ti; } std::cout << " average curvature " << thresh/(float)ct << " kSign " << this->m_kSign << std::endl; /* now get s.d. float sd=0.0; float avgc=thresh/(float)ct; ti.GoToBegin(); while(!ti.IsAtEnd() ) { PixelType pix=ti.Get(); if ( //ti.Get() == this->m_SurfaceLabel this->IsValidSurface(ti.Get(),index) ) sd+=(this->m_FunctionImage->GetPixel(ti.GetIndex())-avgc)* (this->m_FunctionImage->GetPixel(ti.GetIndex())-avgc); ++ti; } sd=sqrt(sd/(float)ct); float nsd=1.0; ti.GoToBegin(); while(!ti.IsAtEnd() ) { PixelType pix=ti.Get(); if ( //ti.Get() == this->m_SurfaceLabel this->IsValidSurface(ti.Get(),index) && (this->m_FunctionImage->GetPixel(ti.GetIndex()) - avgc) > nsd*sd ) this->m_FunctionImage->SetPixel(ti.GetIndex(),avgc+nsd*sd); ++ti; } */ } template typename SurfaceImageCurvature::ImageType* SurfaceImageCurvature ::GetInput(void) { if (this->GetNumberOfInputs() < 1) { return NULL; } return static_cast< ImageType * > (this->ProcessObject::GetInput(0) ); } /** * */ template typename SurfaceImageCurvature::OutputImageType * SurfaceImageCurvature ::GetOutput() { return static_cast (this->ProcessObject::GetOutput(0)); } template void SurfaceImageCurvature ::SetInput(typename ImageType::Pointer input) { // Process object is not const-correct so the const_cast is required here this->ProcessObject::SetNthInput(0, input); // const_cast< ImageType * >( input ) ); this->m_ImageSize=input->GetLargestPossibleRegion().GetSize(); typename OutputImageType::RegionType region; region.SetSize( this->m_ImageSize ); if (!this->m_FunctionImage) { this->m_FunctionImage=OutputImageType::New(); this->m_FunctionImage->SetLargestPossibleRegion( region ); this->m_FunctionImage->SetBufferedRegion( region ); this->m_FunctionImage->Allocate(); this->m_FunctionImage->SetSpacing( input->GetSpacing() ); this->m_FunctionImage->SetDirection( input->GetDirection() ); this->m_FunctionImage->SetOrigin( input->GetOrigin() ); } //this->ProcessLabelImage(); //this->ProcessObject::SetNthOutput( 0, this->m_FunctionImage ); } } // namespace itk #endif ants-1.9.2+svn680.dfsg/Utilities/itkSurfaceMeshCurvature.h000066400000000000000000000101121147325206600234150ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkSurfaceMeshCurvature.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.12 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _SurfaceMeshCurvature_h #define _SurfaceMeshCurvature_h #include "itkSurfaceCurvatureBase.h" namespace itk { /** \class SurfaceMeshCurvature * * This class takes a surface as input and creates a local * geometric frame for each surface point. * * */ template < typename TSurface , typename TSurfacePatch > class SurfaceMeshCurvature : public SurfaceCurvatureBase < TSurface , 3 > { public: /** Standard class typedefs. */ typedef SurfaceMeshCurvature Self; typedef SurfaceCurvatureBase Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Run-time type information (and related methods). */ itkTypeMacro(SurfaceMeshCurvature,SurfaceCurvatureBase); /** Method for creation through the object factory. */ itkNewMacro(Self); typedef typename Superclass::RealType RealType; typedef typename Superclass::PointType VectorType; typedef typename Superclass::PointType FixedVectorType; typedef typename Superclass::PointType PointType; typedef typename Superclass::MatrixType MatrixType; typedef TSurfacePatch SurfacePatch; typedef typename TSurfacePatch::Pointer SurfacePatchPointer; typedef typename TSurfacePatch::NodeLocationType VertexType; typedef typename TSurfacePatch::PixelType FunctionValueType; typedef TSurface* SurfacePointer; //probably vtkmesh /** Find all points within some distance of the origin. * The argument gives the number of times to apply the * mean shift algorithm to find the best neighborhood. */ virtual void FindNeighborhood(unsigned int numMeanShifts=0) { this->m_PointList.clear(); this->m_FunctionValueList.clear(); VertexType origin=m_SurfacePatch->GetLocation(); PointType pt; for(int j = 0; j < 3; j++) pt(j)=origin[j]; this->m_Origin=pt; this->m_PointList.insert(this->m_PointList.begin(),pt); this->m_FunctionValueList.insert(this->m_FunctionValueList.begin(),m_SurfacePatch->GetValue()); for (unsigned int i=0; im_Neighbors.size(); i++) { VertexType neigh=m_SurfacePatch->m_Neighbors[i]->GetLocation(); PointType pt; for(int j = 0; j < 3; j++) pt(j)=neigh[j]; this->m_PointList.insert(this->m_PointList.begin(),pt); this->m_FunctionValueList.insert(this->m_FunctionValueList.begin(),this->m_SurfacePatch->m_Neighbors[i]->GetValue(0)); } } void SetSurface(SurfacePointer s) { m_Surface=s;} void SetSurfacePatch( SurfacePatchPointer s) { m_SurfacePatch=s; } SurfaceMeshCurvature() { this->m_Origin.fill(0.0); this->m_ArbitraryTangent.fill(0.); this->m_Normal.fill(0.); this->m_Tangent1.fill(0.); this->m_Tangent2.fill(0.); this->m_DirectionalKappa=0.0; this->m_Kappa1=0.0; this->m_Kappa2=0.0; this->m_GaussianKappa=0.0; this->m_A=0.0; this->m_B=0.0; this->m_C=0.0; this->m_W1=3./8.; this->m_W2=1./8.; this->m_Eval0=0.0; this->m_Eval1=0.0; this->m_Eval2=0.0; this->m_CurrentNeighborhoodPointIndex=0; this->m_ParameterFileName = ""; this->m_Pi= 3.14159265358979323846; this->m_Debug=true; this->m_Debug=false; this->m_UseGeodesicNeighborhood=false; this->m_TotalArea=0.0; } ~SurfaceMeshCurvature(){}; protected: private: SurfacePointer m_Surface; SurfacePatchPointer m_SurfacePatch; }; } // namespace itk #ifndef ITK_MANUAL_INSTANTIATION //#include "itkSurfaceMeshCurvature.txx" #endif #endif ants-1.9.2+svn680.dfsg/Utilities/itkVectorFieldGradientImageFunction.h000066400000000000000000000215421147325206600256550ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkVectorFieldGradientImageFunction.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.16 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkVectorFieldGradientImageFunction_h #define __itkVectorFieldGradientImageFunction_h #include "itkImageFunction.h" #include "itkVariableSizeMatrix.h" namespace itk { /** \class VectorFieldGradientImageFunction * */ template > class ITK_EXPORT VectorFieldGradientImageFunction : public ImageFunction { public: /** Standard class typedefs. */ typedef VectorFieldGradientImageFunction Self; typedef ImageFunction Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro( Self ); /** Run-time type information (and related methods) */ itkTypeMacro( VectorFieldGradientImageFunction, ImageFunction ); /** Extract some information from the image types. Dimensionality * of the two images is assumed to be the same. */ typedef TInputImage InputImageType; typedef typename InputImageType::PixelType VectorType; typedef TOutput MatrixType; /** Declare typedefs of superclass */ typedef typename Superclass::PointType PointType; typedef typename Superclass::IndexType IndexType; typedef typename Superclass::ContinuousIndexType ContinuousIndexType; /** The dimensionality of the input and output images. */ itkStaticConstMacro( ImageDimension, unsigned int, TInputImage::ImageDimension ); /** Length of the vector pixel type of the input image. */ itkStaticConstMacro( VectorDimension, unsigned int, VectorType::Dimension ); /** Define the data type and the vector of data type used in calculations. */ typedef TRealType RealType; /** * Evaluate deformation gradient tensor */ MatrixType EvaluateDeformationGradientTensor( const PointType& ) const; MatrixType EvaluateDeformationGradientTensorAtIndex( const IndexType &idx ) const; MatrixType EvaluateDeformationGradientTensorAtContinuousIndex ( const ContinuousIndexType &idx ) const { PointType point; this->GetInputImage()->TransformContinuousIndexToPhysicalPoint( idx, point ); return this->EvaluateDeformationGradientTensor( point ); } /** * Evaluate Jacobian */ virtual MatrixType Evaluate( const PointType &point ) const { return this->EvaluateJacobian( point ); } virtual MatrixType EvaluateAtIndex( const IndexType &idx ) const { return this->EvaluateJacobianAtIndex( idx ); } virtual MatrixType EvaluateAtContinuousIndex( const ContinuousIndexType &idx ) const { return this->EvaluateJacobianAtContinuousIndex( idx ); } MatrixType EvaluateJacobian( const PointType & ) const; MatrixType EvaluateJacobianAtIndex( const IndexType &idx ) const; MatrixType EvaluateJacobianAtContinuousIndex( const ContinuousIndexType &idx ) const { PointType point; this->GetInputImage()->TransformContinuousIndexToPhysicalPoint( idx, point ); return this->EvaluateJacobian( point ); } /** * Evaluate Jacobian determinant */ RealType EvaluateJacobianDeterminant( const PointType& ) const; RealType EvaluateJacobianDeterminantAtIndex( const IndexType &idx ) const; RealType EvaluateJacobianDeterminantAtContinuousIndex( const ContinuousIndexType &idx ) const { PointType point; this->GetInputImage()->TransformContinuousIndexToPhysicalPoint( idx, point ); return this->EvaluateJacobianDeterminant( point ); } /** * Evaluate Lagrangian strain tensor */ MatrixType EvaluateLagrangianStrainTensor( const PointType& ) const; MatrixType EvaluateLagrangianStrainTensorAtIndex( const IndexType &idx ) const; MatrixType EvaluateLagrangianStrainTensorAtContinuousIndex( const ContinuousIndexType &idx ) const { PointType point; this->GetInputImage()->TransformContinuousIndexToPhysicalPoint( idx, point ); return this->EvaluateLagrangianStrainTensor( point ); } /** * Evaluate Lagrangian directional strain */ RealType EvaluateLagrangianDirectionalStrain( const PointType&, const VectorType& ) const; RealType EvaluateLagrangianDirectionalStrainAtIndex( const IndexType &idx, const VectorType &V ) const; RealType EvaluateLagrangianDirectionalStrainAtContinuousIndex( const ContinuousIndexType &idx, const VectorType &V ) const { PointType point; this->GetInputImage()->TransformContinuousIndexToPhysicalPoint( idx, point ); return this->EvaluateLagrangianDirectionalStrain( point, V ); } /** * Evaluate Eulerian strain tensor */ MatrixType EvaluateEulerianStrainTensor( const PointType& ) const; MatrixType EvaluateEulerianStrainTensorAtIndex( const IndexType &idx ) const; MatrixType EvaluateEulerianStrainTensorAtContinuousIndex( const ContinuousIndexType &idx ) const { PointType point; this->GetInputImage()->TransformContinuousIndexToPhysicalPoint( idx, point ); return this->EvaluateEulerianStrainTensor( point ); } /** * Evaluate Eulerian directional strain */ RealType EvaluateEulerianDirectionalStrain( const PointType&, const VectorType& ) const; RealType EvaluateEulerianDirectionalStrainAtIndex( const IndexType &idx, const VectorType &V ) const; RealType EvaluateEulerianDirectionalStrainAtContinuousIndex( const ContinuousIndexType &idx, const VectorType &V ) const { PointType point; this->GetInputImage()->TransformContinuousIndexToPhysicalPoint( idx, point ); return this->EvaluateEulerianDirectionalStrain( point, V ); } /** * Evaluate Right Cauchy-Green strain tensor */ MatrixType EvaluateRightCauchyGreenDeformationTensor( const PointType& ) const; MatrixType EvaluateRightCauchyGreenDeformationTensorAtIndex( const IndexType &idx ) const; MatrixType EvaluateRightCauchyGreenDeformationTensorAtContinuousIndex( const ContinuousIndexType &idx ) const { PointType point; this->GetInputImage()->TransformContinuousIndexToPhysicalPoint( idx, point ); return this->EvaluateRightCauchyGreenDeformationTensor( point ); } /** * Evaluate Left Cauchy-Green strain tensor */ MatrixType EvaluateLeftCauchyGreenDeformationTensor( const PointType& ) const; MatrixType EvaluateLeftCauchyGreenDeformationTensorAtIndex( const IndexType &idx ) const; MatrixType EvaluateLeftCauchyGreenDeformationTensorAtContinuousIndex( const ContinuousIndexType &idx ) const { PointType point; this->GetInputImage()->TransformContinuousIndexToPhysicalPoint( idx, point ); return this->EvaluateLeftCauchyGreenDeformationTensor( point ); } /** * Evaluate left stretch tensor */ MatrixType EvaluateLeftStretchTensor( const PointType& ) const; MatrixType EvaluateLeftStretchTensorAtIndex( const IndexType &idx ) const; MatrixType EvaluateLeftStretchTensorAtContinuousIndex( const ContinuousIndexType &idx ) const { PointType point; this->GetInputImage()->TransformContinuousIndexToPhysicalPoint( idx, point ); return this->EvaluateLeftStretchTensor( point ); } /** * Evaluate right stretch tensor */ MatrixType EvaluateRightStretchTensor( const PointType& ) const; MatrixType EvaluateRightStretchTensorAtIndex( const IndexType &idx ) const; MatrixType EvaluateRightStretchTensorAtContinuousIndex( const ContinuousIndexType &idx ) const { PointType point; this->GetInputImage()->TransformContinuousIndexToPhysicalPoint( idx, point ); return this->EvaluateRightStretchTensor( point ); } protected: VectorFieldGradientImageFunction(); virtual ~VectorFieldGradientImageFunction() {} void PrintSelf ( std::ostream& os, Indent indent ) const; private: VectorFieldGradientImageFunction(const Self&); //purposely not implemented MatrixType operator=(const Self&); //purposely not implemented }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkVectorFieldGradientImageFunction.txx" #endif #endif ants-1.9.2+svn680.dfsg/Utilities/itkVectorFieldGradientImageFunction.txx000066400000000000000000000517261147325206600262600ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkVectorFieldGradientImageFunction.txx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.16 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkVectorFieldGradientImageFunction_txx #define _itkVectorFieldGradientImageFunction_txx #include "itkVectorFieldGradientImageFunction.h" #include "itkDecomposeTensorFunction.h" #include "itkVectorLinearInterpolateImageFunction.h" #include "vnl/algo/vnl_matrix_inverse.h" #include "vnl/vnl_vector.h" namespace itk { template VectorFieldGradientImageFunction ::VectorFieldGradientImageFunction() { } template typename VectorFieldGradientImageFunction ::MatrixType VectorFieldGradientImageFunction ::EvaluateDeformationGradientTensor( const PointType &point ) const { if ( !this->IsInsideBuffer( point ) ) { unsigned int minDimension = ImageDimension; MatrixType F; F.SetSize( ImageDimension, VectorDimension ); F.Fill( 0.0 ); for ( unsigned int i = 0; i < minDimension; i++ ) { F[i][i] = 1.0; } itkWarningMacro( "The specified point, " << point << ", is outside the image boundaries." ); return F; } typename InputImageType::SpacingType spacing = this->GetInputImage()->GetSpacing(); typedef VectorLinearInterpolateImageFunction InterpolatorType; typename InterpolatorType::Pointer interpolator = InterpolatorType::New(); interpolator->SetInputImage( this->GetInputImage() ); MatrixType F; F.SetSize( ImageDimension, VectorDimension ); typename InterpolatorType::OutputType x; typename InterpolatorType::PointType ipoint; ipoint.CastFrom( point ); x = interpolator->Evaluate( ipoint ); for ( unsigned int i = 0; i < ImageDimension; i++ ) { typename InterpolatorType::PointType::VectorType delta; delta.Fill( 0.0 ); delta[i] = spacing[i]; typename InterpolatorType::OutputType xp1; typename InterpolatorType::OutputType xp2; typename InterpolatorType::OutputType xm1; typename InterpolatorType::OutputType xm2; if ( !this->IsInsideBuffer( point + delta ) ) { xp2 = xp1 = x; } else { xp1 = interpolator->Evaluate( ipoint + delta ); if ( this->IsInsideBuffer( point + delta*2.0 ) ) { xp2 = interpolator->Evaluate( ipoint + delta*2.0 ); } else { xp2 = xp1; } } if ( !this->IsInsideBuffer( point - delta ) ) { xm2 = xm1 = x; } else { xm1 = interpolator->Evaluate( ipoint - delta ); if ( this->IsInsideBuffer( point - delta*2.0 ) ) { xm2 = interpolator->Evaluate( ipoint - delta*2.0 ); } else { xm2 = xm1; } } RealType h = 0.5; xp1 = xp1*h + x*(1.0-h); xm1 = xm1*h + x*(1.0-h); xp2 = xp2*h + xp1*(1.0-h); xp2 = xm2*h + xm1*(1.0-h); RealType weight = 1.0 / ( 12.0*delta[i] ); for ( unsigned int j = 0; j < VectorDimension; j++ ) { F[i][j] = weight*( -xp2[j] + 8.0*xp1[j] - 8.0*xm1[j] + xm2[j] ); } } unsigned int minDimension = ImageDimension; if ( static_cast( VectorDimension ) < static_cast( ImageDimension ) ) { minDimension = VectorDimension; } for ( unsigned int i = 0; i < minDimension; i++ ) { F[i][i] += 1.0; } return F; } template typename VectorFieldGradientImageFunction ::MatrixType VectorFieldGradientImageFunction ::EvaluateDeformationGradientTensorAtIndex( const IndexType &index ) const { if ( !this->IsInsideBuffer( index ) ) { unsigned int minDimension = ImageDimension; MatrixType F; F.SetSize( ImageDimension, VectorDimension ); F.Fill( 0.0 ); for ( unsigned int i = 0; i < minDimension; i++ ) { F[i][i] = 1.0; } itkWarningMacro( "The specified index, " << index << ", is outside the image boundaries." ); return F; } MatrixType F; F.SetSize( ImageDimension, VectorDimension ); typename InputImageType::PixelType x; x = this->GetInputImage()->GetPixel( index ); typename InputImageType::SpacingType spacing = this->GetInputImage()->GetSpacing(); for ( unsigned int i = 0; i < ImageDimension; i++ ) { typename InputImageType::OffsetType offset1; offset1.Fill( 0 ); offset1[i] = 1; typename InputImageType::OffsetType offset2; offset2.Fill( 0 ); offset2[i] = 2; typename InputImageType::PixelType xp1; typename InputImageType::PixelType xp2; typename InputImageType::PixelType xm1; typename InputImageType::PixelType xm2; if ( !this->IsInsideBuffer( index + offset1 ) ) { xp2 = xp1 = x; } else { xp1 = this->GetInputImage()->GetPixel( index + offset1 ); if ( this->IsInsideBuffer( index + offset2 ) ) { xp2 = this->GetInputImage()->GetPixel( index + offset2 ); } else { xp2 = xp1; } } if ( !this->IsInsideBuffer( index - offset1 ) ) { xm2 = xm1 = x; } else { xm1 = this->GetInputImage()->GetPixel( index - offset1 ); if ( this->IsInsideBuffer( index - offset2 ) ) { xm2 = this->GetInputImage()->GetPixel( index - offset2 ); } else { xm2 = xm1; } } RealType h = 0.5; xp1 = xp1*h + x*(1.0-h); xm1 = xm1*h + x*(1.0-h); xp2 = xp2*h + xp1*(1.0-h); xp2 = xm2*h + xm1*(1.0-h); RealType weight = 1.0 / ( 12.0*static_cast( offset1[i] ) * spacing[i] ); for ( unsigned int j = 0; j < VectorDimension; j++ ) { F[i][j] = weight*( -xp2[j] + 8.0*xp1[j] - 8.0*xm1[j] + xm2[j] ); } } unsigned int minDimension = ImageDimension; if ( static_cast( VectorDimension ) < static_cast( ImageDimension ) ) { minDimension = VectorDimension; } for ( unsigned int i = 0; i < minDimension; i++ ) { F[i][i] += 1.0; } return F; } template typename VectorFieldGradientImageFunction ::MatrixType VectorFieldGradientImageFunction ::EvaluateJacobian( const PointType &point ) const { return this->EvaluateDeformationGradientTensor( point ); } template typename VectorFieldGradientImageFunction ::MatrixType VectorFieldGradientImageFunction ::EvaluateJacobianAtIndex( const IndexType &index ) const { return this->EvaluateDeformationGradientTensorAtIndex( index ); } template typename VectorFieldGradientImageFunction ::RealType VectorFieldGradientImageFunction ::EvaluateJacobianDeterminant( const PointType &point ) const { MatrixType J = this->EvaluateJacobian( point ); typedef DecomposeTensorFunction DecomposerType; typename DecomposerType::Pointer decomposer = DecomposerType::New(); return decomposer->EvaluateDeterminant( J ); } template typename VectorFieldGradientImageFunction ::RealType VectorFieldGradientImageFunction ::EvaluateJacobianDeterminantAtIndex( const IndexType &index ) const { MatrixType J = this->EvaluateJacobianAtIndex( index ); typedef DecomposeTensorFunction DecomposerType; typename DecomposerType::Pointer decomposer = DecomposerType::New(); return decomposer->EvaluateDeterminant( J ); } template typename VectorFieldGradientImageFunction ::MatrixType VectorFieldGradientImageFunction ::EvaluateLagrangianStrainTensor( const PointType &point ) const { MatrixType F = this->EvaluateDeformationGradientTensor( point ); MatrixType E(ImageDimension, ImageDimension); typename MatrixType::InternalMatrixType ff = F.GetTranspose() * F.GetVnlMatrix(); for ( unsigned int i = 0; i < ff.rows(); i++ ) { for ( unsigned int j = 0; j < ff.columns(); j++ ) { E[i][j] = ff.get( i, j ); if ( i == j ) { E[i][j] -= 1.0; } E[i][j] *= 0.5; } } return E; } template typename VectorFieldGradientImageFunction ::MatrixType VectorFieldGradientImageFunction ::EvaluateLagrangianStrainTensorAtIndex( const IndexType &index ) const { MatrixType F = this->EvaluateDeformationGradientTensorAtIndex( index ); MatrixType E(ImageDimension, ImageDimension); typename MatrixType::InternalMatrixType ff = F.GetTranspose() * F.GetVnlMatrix(); for ( unsigned int i = 0; i < ff.rows(); i++ ) { for ( unsigned int j = 0; j < ff.columns(); j++ ) { E[i][j] = ff.get( i, j ); if ( i == j ) { E[i][j] -= 1.0; } E[i][j] *= 0.5; } } return E; } template typename VectorFieldGradientImageFunction ::RealType VectorFieldGradientImageFunction ::EvaluateLagrangianDirectionalStrain( const PointType &point, const VectorType &V ) const { MatrixType E = this->EvaluateLagrangianStrainTensor( point ); vnl_vector v = E * V.GetVnlVector(); RealType s = 0.0; for ( unsigned int i = 0; i < v.size(); i++ ) { s += v[i]*V[i]; } return s; } template typename VectorFieldGradientImageFunction ::RealType VectorFieldGradientImageFunction ::EvaluateLagrangianDirectionalStrainAtIndex ( const IndexType &index, const VectorType &V ) const { MatrixType E = this->EvaluateLagrangianStrainTensorAtIndex( index ); vnl_vector v = E * V.GetVnlVector(); RealType s = 0.0; for ( unsigned int i = 0; i < v.size(); i++ ) { s += v[i]*V[i]; } return s; } template typename VectorFieldGradientImageFunction ::MatrixType VectorFieldGradientImageFunction ::EvaluateEulerianStrainTensor( const PointType &point ) const { MatrixType F = this->EvaluateDeformationGradientTensor( point ); MatrixType E(ImageDimension, ImageDimension); typename MatrixType::InternalMatrixType ff = vnl_matrix_inverse( F.GetVnlMatrix() * F.GetTranspose() ); for ( unsigned int i = 0; i < ff.rows(); i++ ) { for ( unsigned int j = 0; j < ff.columns(); j++ ) { E[i][j] = -ff.get( i, j ); if ( i == j ) { E[i][j] += 1.0; } E[i][j] *= 0.5; } } return E; } template typename VectorFieldGradientImageFunction ::MatrixType VectorFieldGradientImageFunction ::EvaluateEulerianStrainTensorAtIndex( const IndexType &index ) const { MatrixType F = this->EvaluateDeformationGradientTensorAtIndex( index ); MatrixType E(ImageDimension, ImageDimension); typename MatrixType::InternalMatrixType ff = vnl_matrix_inverse( F.GetVnlMatrix() * F.GetTranspose() ); for ( unsigned int i = 0; i < ff.rows(); i++ ) { for ( unsigned int j = 0; j < ff.columns(); j++ ) { E[i][j] = -ff.get( i, j ); if ( i == j ) { E[i][j] += 1.0; } E[i][j] *= 0.5; } } return E; } template typename VectorFieldGradientImageFunction ::RealType VectorFieldGradientImageFunction ::EvaluateEulerianDirectionalStrain( const PointType &point, const VectorType &V ) const { MatrixType E = this->EvaluateEulerianStrainTensor( point ); vnl_vector v = E * V.GetVnlVector(); RealType s = 0.0; for ( unsigned int i = 0; i < v.size(); i++ ) { s += v[i]*V[i]; } return s; } template typename VectorFieldGradientImageFunction ::RealType VectorFieldGradientImageFunction ::EvaluateEulerianDirectionalStrainAtIndex( const IndexType &index, const VectorType &V ) const { MatrixType E = this->EvaluateEulerianStrainTensorAtIndex( index ); vnl_vector v = E * V.GetVnlVector(); RealType s = 0.0; for ( unsigned int i = 0; i < v.size(); i++ ) { s += v[i]*V[i]; } return s; } template typename VectorFieldGradientImageFunction ::MatrixType VectorFieldGradientImageFunction ::EvaluateLeftCauchyGreenDeformationTensor( const PointType &point ) const { MatrixType F = this->EvaluateDeformationGradientTensor( point ); typename MatrixType::InternalMatrixType b = F.GetVnlMatrix() * F.GetTranspose(); MatrixType B; B.SetSize( F.Rows(), F.Cols() ); for ( unsigned int i = 0; i < F.Rows(); i++ ) { for ( unsigned int j = 0; j < F.Cols(); j++ ) { B[i][j] = b[i][j]; } } return B; } template typename VectorFieldGradientImageFunction ::MatrixType VectorFieldGradientImageFunction ::EvaluateLeftCauchyGreenDeformationTensorAtIndex( const IndexType &index ) const { MatrixType F = this->EvaluateDeformationGradientTensorAtIndex( index ); typename MatrixType::InternalMatrixType b = F.GetVnlMatrix() * F.GetTranspose(); MatrixType B; B.SetSize( F.Rows(), F.Cols() ); for ( unsigned int i = 0; i < F.Rows(); i++ ) { for ( unsigned int j = 0; j < F.Cols(); j++ ) { B[i][j] = b[i][j]; } } return B; } template typename VectorFieldGradientImageFunction ::MatrixType VectorFieldGradientImageFunction ::EvaluateRightCauchyGreenDeformationTensor( const PointType &point ) const { MatrixType F = this->EvaluateDeformationGradientTensor( point ); typename MatrixType::InternalMatrixType c = F.GetVnlMatrix() * F.GetTranspose(); MatrixType C; C.SetSize( F.Rows(), F.Cols() ); for ( unsigned int i = 0; i < F.Rows(); i++ ) { for ( unsigned int j = 0; j < F.Cols(); j++ ) { C[i][j] = c[i][j]; } } return C; } template typename VectorFieldGradientImageFunction ::MatrixType VectorFieldGradientImageFunction ::EvaluateRightCauchyGreenDeformationTensorAtIndex( const IndexType &index ) const { MatrixType F = this->EvaluateDeformationGradientTensorAtIndex( index ); typename MatrixType::InternalMatrixType c = F.GetVnlMatrix() * F.GetTranspose(); MatrixType C; C.SetSize( F.Rows(), F.Cols() ); for ( unsigned int i = 0; i < F.Rows(); i++ ) { for ( unsigned int j = 0; j < F.Cols(); j++ ) { C[i][j] = c[i][j]; } } return C; } template typename VectorFieldGradientImageFunction ::MatrixType VectorFieldGradientImageFunction ::EvaluateRightStretchTensor( const PointType &point ) const { MatrixType C = this->EvaluateRightCauchyGreenDeformationTensor( point ); MatrixType D; MatrixType V; typedef DecomposeTensorFunction DecomposerType; typename DecomposerType::Pointer decomposer = DecomposerType::New(); decomposer->EvaluateSymmetricEigenDecomposition( C, D, V ); MatrixType U; U.SetSize( C.Rows(), C.Cols() ); U.Fill( 0 ); for ( unsigned int d = 0; d < C.Rows(); d++ ) { RealType lambda = sqrt( D[d][d] ); for ( unsigned int i = 0; i < C.Rows(); i++ ) { for ( unsigned int j = 0; j < C.Cols(); j++ ) { U[i][j] += lambda*V[i][d]*V[j][d]; } } } return U; } template typename VectorFieldGradientImageFunction ::MatrixType VectorFieldGradientImageFunction ::EvaluateRightStretchTensorAtIndex( const IndexType &index ) const { MatrixType C = this->EvaluateRightCauchyGreenDeformationTensorAtIndex( index ); MatrixType D; MatrixType V; typedef DecomposeTensorFunction DecomposerType; typename DecomposerType::Pointer decomposer = DecomposerType::New(); decomposer->EvaluateSymmetricEigenDecomposition( C, D, V ); MatrixType U; U.SetSize( C.Rows(), C.Cols() ); U.Fill( 0 ); for ( unsigned int d = 0; d < C.Rows(); d++ ) { RealType lambda = sqrt( D[d][d] ); for ( unsigned int i = 0; i < C.Rows(); i++ ) { for ( unsigned int j = 0; j < C.Cols(); j++ ) { U[i][j] += lambda*V[i][d]*V[j][d]; } } } return U; } template typename VectorFieldGradientImageFunction ::MatrixType VectorFieldGradientImageFunction ::EvaluateLeftStretchTensor( const PointType &point ) const { MatrixType B = this->EvaluateLeftCauchyGreenDeformationTensor( point ); MatrixType D; MatrixType V; typedef DecomposeTensorFunction DecomposerType; typename DecomposerType::Pointer decomposer = DecomposerType::New(); decomposer->EvaluateSymmetricEigenDecomposition( B, D, V ); MatrixType U; U.SetSize( B.Rows(), B.Cols() ); U.Fill( 0 ); for ( unsigned int d = 0; d < B.Rows(); d++ ) { RealType lambda = sqrt( D[d][d] ); for ( unsigned int i = 0; i < B.Rows(); i++ ) { for ( unsigned int j = 0; j < B.Cols(); j++ ) { U[i][j] += lambda*V[i][d]*V[j][d]; } } } return U; } template typename VectorFieldGradientImageFunction ::MatrixType VectorFieldGradientImageFunction ::EvaluateLeftStretchTensorAtIndex( const IndexType &index ) const { MatrixType B = this->EvaluateLeftCauchyGreenDeformationTensorAtIndex( index ); MatrixType D; MatrixType V; typedef DecomposeTensorFunction DecomposerType; typename DecomposerType::Pointer decomposer = DecomposerType::New(); decomposer->EvaluateSymmetricEigenDecomposition( B, D, V ); MatrixType U; U.SetSize( B.Rows(), B.Cols() ); U.Fill( 0 ); for ( unsigned int d = 0; d < B.Rows(); d++ ) { RealType lambda = sqrt( D[d][d] ); for ( unsigned int i = 0; i < B.Rows(); i++ ) { for ( unsigned int j = 0; j < B.Cols(); j++ ) { U[i][j] += lambda*V[i][d]*V[j][d]; } } } return U; } template void VectorFieldGradientImageFunction ::PrintSelf( std::ostream& os, Indent indent ) const { Superclass::PrintSelf(os,indent); } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Utilities/itkVectorGaussianInterpolateImageFunction.h000066400000000000000000000227261147325206600271420ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkVectorGaussianInterpolateImageFunction.h,v $ Language: C++ Date: $Date: 2009/07/01 12:59:34 $ Version: $Revision: 1.5 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkVectorGaussianInterpolateImageFunction_h #define __itkVectorGaussianInterpolateImageFunction_h #include "itkInterpolateImageFunction.h" #include "vnl/vnl_erf.h" #include "itkImageRegionConstIteratorWithIndex.h" namespace itk { /** \class VectorGaussianInterpolateImageFunction * \brief Gaussianly interpolate an image at specified positions. * * VectorGaussianInterpolateImageFunction linearly interpolates image intensity at * a non-integer pixel position. This class is templated * over the input image type and the coordinate representation type * (e.g. float or double). * * This function works for N-dimensional images. * * \ingroup ImageFunctions ImageInterpolators */ template class ITK_EXPORT VectorGaussianInterpolateImageFunction : public InterpolateImageFunction { public: /** Standard class typedefs. */ typedef VectorGaussianInterpolateImageFunction Self; typedef InterpolateImageFunction Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Run-time type information (and related methods). */ itkTypeMacro(VectorGaussianInterpolateImageFunction, InterpolateImageFunction); /** Method for creation through the object factory. */ itkNewMacro(Self); /** OutputType typedef support. */ typedef typename Superclass::OutputType OutputType; /** InputImageType typedef support. */ typedef typename Superclass::InputImageType InputImageType; typedef typename Superclass::InputImageType::PixelType PixelType; /** RealType typedef support. */ typedef typename Superclass::RealType RealType; /** Dimension underlying input image. */ itkStaticConstMacro(VDim, unsigned int,Superclass::ImageDimension); /** Index typedef support. */ typedef typename Superclass::IndexType IndexType; /** ContinuousIndex typedef support. */ typedef typename Superclass::ContinuousIndexType ContinuousIndexType; /** Compute internals */ virtual void ComputeBoundingBox() { const TInputImage *img = this->GetInputImage(); if(img == NULL) return; // Set the bounding box for(size_t d = 0; d < VDim; d++) { bb_start[d] = -0.5; bb_end[d] = img->GetBufferedRegion().GetSize()[d] - 0.5; nt[d] = (int)(bb_end[d] - bb_start[d] + 0.5); dx[d].set_size(nt[d]); gx[d].set_size(nt[d]); this->sigma[d]=1; sf[d] = 1.0 / (sqrt(2.0) * this->sigma[d] / img->GetSpacing()[d]); // std::cout << " sigma " << this->sigma[d] << " spc " << img->GetSpacing()[d] << " sf " << sf[d] << std::endl; cut[d] = this->sigma[d] * alpha / img->GetSpacing()[d]; } this->m_ImageSize=this->GetInputImage()->GetLargestPossibleRegion().GetSize(); } /** Set input */ virtual void SetInputImage(const TInputImage *img) { // Call parent method Superclass::SetInputImage(img); this->ComputeBoundingBox(); } void SetParameters(double *sigma, double alpha) { // Set the parameters for(size_t d = 0; d < VDim; d++) this->sigma[d] = 1.0; //sigma[d]; this->alpha = alpha; // If the image already set, recompute this->ComputeBoundingBox(); } /** Evaluate the function at a ContinuousIndex position * * Returns the linearly interpolated image intensity at a * specified point position. No bounds checking is done. * The point is assume to lie within the image buffer. * * ImageFunction::IsInsideBuffer() can be used to check bounds before * calling the method. */ virtual OutputType EvaluateAtContinuousIndex( const ContinuousIndexType & index ) const { return EvaluateAtContinuousIndex(index, NULL); } virtual OutputType EvaluateAtContinuousIndex( const ContinuousIndexType &index, OutputType *grad) const { OutputType Vout; Vout.Fill(0); // The bound variables for x, y, z int i0[VDim], i1[VDim]; // Compute the ERF difference arrays // std::cout << " index " << index << " VD " << VDim << std::endl; for(size_t d = 0; d < VDim; d++) { if ( index[d] <= 0 || index[d] >= this->m_ImageSize[d]-1 || vnl_math_isnan(index[d]) || vnl_math_isinf(index[d]) ) return Vout; double *pdx = const_cast(dx[d].data_block()); double *pgx = grad ? const_cast(gx[d].data_block()) : NULL; compute_erf_array(pdx, i0[d], i1[d], bb_start[d], nt[d], cut[d], index[d], sf[d], pgx); } // Get a pointer to the output value // loop over vector length for ( unsigned int qq=0; qq < Vout.Size(); qq++ ) { double sum_me = 0.0, sum_m = 0.0; vnl_vector_fixed dsum_me(0.0), dsum_m(0.0), dw; // Loop over the voxels in the region identified ImageRegion region; for(size_t d = 0; d < VDim; d++) { region.SetIndex(d, i0[d]); region.SetSize(d, i1[d] - i0[d]); } for( ImageRegionConstIteratorWithIndex it(this->GetInputImage(), region); !it.IsAtEnd(); ++it) { size_t j = it.GetIndex()[0]; double w = dx[0][j]; if(grad) { dw[0] = gx[0][j]; for(size_t d = 1; d < VDim; d++) dw[d] = dx[0][j]; } for(size_t d = 1; d < VDim; d++) { j = it.GetIndex()[d]; w *= dx[d][j]; if(grad) { for(size_t q = 0; q < VDim; q++) dw[q] *= (d == q) ? gx[d][j] : dx[d][j]; } } double V = it.Get()[qq]; sum_me += V * w; sum_m += w; if(grad) { for(size_t q = 0; q < VDim; q++) { dsum_me[q] += V * dw[q]; dsum_m[q] += dw[q]; } } } double rc = sum_me / sum_m; if(grad) { for(size_t q = 0; q < VDim; q++) { grad[q] = (dsum_me[q] - rc * dsum_m[q]) / sum_m; grad[q] /= -1.4142135623730951 * this->sigma[q]; } } if (vnl_math_isnan(rc)) rc=0; Vout[qq]=rc; } // std::cout << " gaussian " << std::endl; // return sum_me / sum_m; return Vout; } protected: VectorGaussianInterpolateImageFunction() {} ~VectorGaussianInterpolateImageFunction(){}; void PrintSelf(std::ostream& os, Indent indent) const { this->Superclass::PrintSelf(os,indent); } private: VectorGaussianInterpolateImageFunction( const Self& ); //purposely not implemented void operator=( const Self& ); //purposely not implemented /** Number of neighbors used in the interpolation */ static const unsigned long m_Neighbors; typename InputImageType::SizeType m_ImageSize; vnl_vector dx[VDim], gx[VDim]; double bb_start[VDim], bb_end[VDim], sf[VDim], cut[VDim]; int nt[VDim], stride[VDim]; double sigma[VDim], alpha; void compute_erf_array ( double *dx_erf, // The output array of erf(p+i+1) - erf(p+i) int &k0, int &k1, // The range of integration 0 <= k0 < k1 <= n double b, // Lower bound of the bounding box int n, // Size of the bounding box in steps double cut, // The distance at which to cut off double p, // the value p double sfac, // scaling factor 1 / (Sqrt[2] sigma) double *gx_erf = NULL // Output derivative/erf array (optional) ) const { // Determine the range of voxels along the line where to evaluate erf k0 = (int) floor(p - b - cut); k1 = (int) ceil(p - b + cut); if(k0 < 0) k0 = 0; if(k1 > n) k1 = n; // Start at the first voxel double t = (b - p + k0) * sfac; // std::cout << " t " << t << " b " << b << " p " << p << " k0 " << k0 << " sfat " << sfac << std::endl; double e_last = vnl_erf(t); double g_last = gx_erf ? 1.128379167095513 * exp(- t * t) : 0.0; for(int i = k0; i < k1; i++) { t += sfac; // std::cout << " t2 " << t << std::endl; double e_now = vnl_erf(t); dx_erf[i] = e_now - e_last; if(gx_erf) { double g_now = 1.128379167095513 * exp(- t * t); gx_erf[i] = g_now - g_last; g_last = g_now; } e_last = e_now; } } }; } // end namespace itk // Define instantiation macro for this template. #define ITK_TEMPLATE_VectorGaussianInterpolateImageFunction(_, EXPORT, x, y) namespace itk { \ _(2(class EXPORT VectorGaussianInterpolateImageFunction< ITK_TEMPLATE_2 x >)) \ namespace Templates { typedef VectorGaussianInterpolateImageFunction< ITK_TEMPLATE_2 x > \ VectorGaussianInterpolateImageFunction##y; } \ } #endif ants-1.9.2+svn680.dfsg/Utilities/itkVectorImageFileReader.h000066400000000000000000000151251147325206600234500ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkVectorImageFileReader.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.16 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkVectorImageFileReader_h #define __itkVectorImageFileReader_h #include "itkImageIOBase.h" #include "itkImageSource.h" #include "itkExceptionObject.h" #include "itkSize.h" #include "itkImageRegion.h" #include "itkDefaultConvertPixelTraits.h" namespace itk { /** \brief Base exception class for IO conflicts. */ class VectorImageFileReaderException : public ExceptionObject { public: /** Run-time information. */ itkTypeMacro( VectorImageFileReaderException, ExceptionObject ); /** Constructor. */ VectorImageFileReaderException(const char *file, unsigned int line, const char* message = "Error in IO", const char* loc = "Unknown") : ExceptionObject(file, line, message, loc) { } /** Constructor. */ VectorImageFileReaderException(const std::string &file, unsigned int line, const char* message = "Error in IO", const char* loc = "Unknown") : ExceptionObject(file, line, message, loc) { } }; /** \brief Data source that reads image data from a single file. * * This source object is a general filter to read data from * a variety of file formats. It works with a ImageIOBase subclass * to actually do the reading of the data. Object factory machinery * can be used to automatically create the ImageIOBase, or the * ImageIOBase can be manually created and set. Note that this * class reads data from a single file; if you wish to read data * from a series of files use ImageSeriesReader. * * TImage is the type expected by the external users of the * filter. If data stored in the file is stored in a different format * then specified by TImage, than this filter converts data * between the file type and the external expected type. The * ConvertTraits template argument is used to do the conversion. * * A Pluggable factory pattern is used this allows different kinds of readers * to be registered (even at run time) without having to modify the * code in this class. Normally just setting the FileName with the * appropriate suffix is enough to get the reader to instantiate the * correct ImageIO and read the file properly. However, some files (like * raw binary format) have no accepted suffix, so you will have to * manually create the ImageIO instance of the write type. * * \sa ImageSeriesReader * \sa ImageIOBase * * \ingroup IOFilters * */ template > class ITK_EXPORT VectorImageFileReader : public ImageSource { public: /** Standard class typedefs. */ typedef VectorImageFileReader Self; typedef ImageSource Superclass; typedef SmartPointer Pointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Run-time type information (and related methods). */ itkTypeMacro(VectorImageFileReader, ImageSource); /** Image types */ typedef typename TImage::RegionType ImageRegionType; typedef typename TImage::InternalPixelType ImagePixelType; /** Deformation field types */ typedef typename TVectorImage::RegionType VectorImageRegionType; typedef typename TVectorImage::InternalPixelType VectorImagePixelType; /** Specify the file to read. This is forwarded to the IO instance. */ itkSetStringMacro(FileName); itkGetStringMacro(FileName); /** Set the Avants' naming convention On or Off */ itkSetMacro(UseAvantsNamingConvention,bool); itkGetConstReferenceMacro(UseAvantsNamingConvention,bool); itkBooleanMacro(UseAvantsNamingConvention); /** Set/Get the ImageIO helper class. Often this is created via the object * factory mechanism that determines whether a particular ImageIO can * read a certain file. This method provides a way to get the ImageIO * instance that is created. Or you can directly specify the ImageIO * to use to read a particular file in case the factory mechanism will * not work properly (e.g., unknown or unusual extension). */ void SetImageIO( ImageIOBase * imageIO ); itkGetObjectMacro(ImageIO,ImageIOBase); /** Prepare the allocation of the output image during the first back * propagation of the pipeline. */ virtual void GenerateOutputInformation(void); /** Give the reader a chance to indicate that it will produce more * output than it was requested to produce. VectorImageFileReader cannot * currently read a portion of an image (since the ImageIO objects * cannot read a portion of an image), so the VectorImageFileReader must * enlarge the RequestedRegion to the size of the image on disk. */ virtual void EnlargeOutputRequestedRegion(DataObject *output); protected: VectorImageFileReader(); ~VectorImageFileReader(); void PrintSelf(std::ostream& os, Indent indent) const; /** Convert a block of pixels from one type to another. */ void DoConvertBuffer(void* buffer, unsigned long numberOfPixels); /** Test whether the given filename exist and it is readable, this is intended to be called before attempting to use ImageIO classes for actually reading the file. If the file doesn't exist or it is not readable, and exception with an approriate message will be thrown. */ void TestFileExistanceAndReadability(); /** Does the real work. */ virtual void GenerateData(); ImageIOBase::Pointer m_ImageIO; bool m_UserSpecifiedImageIO; //keep track whether the ImageIO is user specified /** The file to be read. */ std::string m_FileName; private: VectorImageFileReader(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented std::string m_ExceptionMessage; typename TImage::Pointer m_Image; bool m_UseAvantsNamingConvention; }; } //namespace ITK #ifndef ITK_MANUAL_INSTANTIATION #include "itkVectorImageFileReader.txx" #endif #endif // __itkVectorImageFileReader_h ants-1.9.2+svn680.dfsg/Utilities/itkVectorImageFileReader.txx000066400000000000000000000551011147325206600240420ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkVectorImageFileReader.txx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.16 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkVectorImageFileReader_txx #define _itkVectorImageFileReader_txx #include "itkVectorImageFileReader.h" #include "itkObjectFactory.h" #include "itkImageIOFactory.h" #include "itkConvertPixelBuffer.h" #include "itkImageRegion.h" #include "itkPixelTraits.h" #include "itkVectorImage.h" #include "itkImageRegionIterator.h" #include #include namespace itk { template VectorImageFileReader ::VectorImageFileReader() { m_ImageIO = 0; m_FileName = ""; m_UserSpecifiedImageIO = false; m_UseAvantsNamingConvention = true; this->m_Image = TImage::New(); } template VectorImageFileReader ::~VectorImageFileReader() { } template void VectorImageFileReader ::PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf(os, indent); if (m_ImageIO) { os << indent << "ImageIO: \n"; m_ImageIO->Print(os, indent.GetNextIndent()); } else { os << indent << "ImageIO: (null)" << "\n"; } os << indent << "UserSpecifiedImageIO flag: " << m_UserSpecifiedImageIO << "\n"; os << indent << "m_FileName: " << m_FileName << "\n"; } template void VectorImageFileReader ::SetImageIO( ImageIOBase * imageIO) { itkDebugMacro("setting ImageIO to " << imageIO ); if (this->m_ImageIO != imageIO ) { this->m_ImageIO = imageIO; this->Modified(); } m_UserSpecifiedImageIO = true; } template void VectorImageFileReader ::GenerateOutputInformation(void) { typename TVectorImage::Pointer output = this->GetOutput(); itkDebugMacro(<<"Reading file for GenerateOutputInformation()" << m_FileName); // Check to see if we can read the file given the name or prefix // if ( m_FileName == "" ) { throw VectorImageFileReaderException(__FILE__, __LINE__, "FileName must be specified", ITK_LOCATION); } // Test if the files exist and if it can be open. // and exception will be thrown otherwise. // std::string tmpFileName = this->m_FileName; // Test if the file exists and if it can be opened. // An exception will be thrown otherwise. // We catch the exception because some ImageIO's may not actually // open a file. Still reports file error if no ImageIO is loaded. try { m_ExceptionMessage = ""; this->TestFileExistanceAndReadability(); } catch(itk::ExceptionObject &err) { m_ExceptionMessage = err.GetDescription(); } std::string::size_type pos = this->m_FileName.rfind( "." ); std::string extension( this->m_FileName, pos, this->m_FileName.length()-1 ); std::string filename = std::string( this->m_FileName, 0, pos ); std::string gzExtension( "" ); if ( extension == std::string( ".gz" ) ) { gzExtension = extension; std::string::size_type pos2 = filename.rfind( "." ); extension = std::string( filename, pos2, filename.length()-1 ); filename = std::string( this->m_FileName, 0, pos2 ); } // unsigned int dimension = itk::GetVectorDimension // ::VectorDimension; // Assume that the first image read contains all the information to generate // the output image. for ( unsigned int i = 0; i <= 1; i++ ) { this->m_FileName = filename; if ( this->m_UseAvantsNamingConvention ) { switch ( i ) { case 0: this->m_FileName += std::string( "xvec" ); break; case 1: this->m_FileName += std::string( "yvec" ); break; case 2: this->m_FileName += std::string( "zvec" ); break; default: this->m_FileName += std::string( "you_are_screwed_vec" ); break; } } else { itk::OStringStream buf; buf << i; this->m_FileName += ( std::string( "." ) + std::string( buf.str().c_str() ) ); } this->m_FileName += extension; if ( !gzExtension.empty() ) { this->m_FileName += std::string( ".gz" ); } if ( i == 0 ) { itkDebugMacro( << "Generating output information from the file " << this->m_FileName ); if ( m_UserSpecifiedImageIO == false ) //try creating via factory { m_ImageIO = ImageIOFactory::CreateImageIO( m_FileName.c_str(), ImageIOFactory::ReadMode ); } if ( m_ImageIO.IsNull() ) { OStringStream msg; msg << " Could not create IO object for file " << m_FileName.c_str() << std::endl; if (m_ExceptionMessage.size()) { msg << m_ExceptionMessage; } else { msg << " Tried to create one of the following:" << std::endl; std::list allobjects = ObjectFactoryBase::CreateAllInstance("itkImageIOBase"); for(std::list::iterator i = allobjects.begin(); i != allobjects.end(); ++i) { ImageIOBase* io = dynamic_cast(i->GetPointer()); msg << " " << io->GetNameOfClass() << std::endl; } msg << " You probably failed to set a file suffix, or" << std::endl; msg << " set the suffix to an unsupported type." << std::endl; } VectorImageFileReaderException e(__FILE__, __LINE__, msg.str().c_str(), ITK_LOCATION); throw e; return; } // Got to allocate space for the image. Determine the characteristics of // the image. // m_ImageIO->SetFileName(m_FileName.c_str()); m_ImageIO->ReadImageInformation(); typename TVectorImage::SizeType dimSize; double spacing[ TVectorImage::ImageDimension ]; double origin[ TVectorImage::ImageDimension ]; typename TVectorImage::DirectionType direction; std::vector axis; for(unsigned int k=0; kGetNumberOfDimensions() ) { dimSize[k] = m_ImageIO->GetDimensions(k); spacing[k] = m_ImageIO->GetSpacing(k); origin[k] = m_ImageIO->GetOrigin(k); // Please note: direction cosines are stored as columns of the // direction matrix axis = m_ImageIO->GetDirection(k); for (unsigned j=0; jGetNumberOfDimensions()) { direction[j][k] = axis[j]; } else { direction[j][k] = 0.0; } } } else { // Number of dimensions in the output is more than number of dimensions // in the ImageIO object (the file). Use default values for the size, // spacing, origin and direction for the final (degenerate) dimensions. dimSize[i] = 1; spacing[i] = 1.0; origin[i] = 0.0; for (unsigned j = 0; j < TImage::ImageDimension; j++) { if (i == j) { direction[j][k] = 1.0; } else { direction[j][k] = 0.0; } } } } output->SetSpacing( spacing ); // Set the image spacing output->SetOrigin( origin ); // Set the image origin output->SetDirection( direction ); // Set the image direction cosines //Copy MetaDataDictionary from instantiated reader to output image. output->SetMetaDataDictionary(m_ImageIO->GetMetaDataDictionary()); this->SetMetaDataDictionary(m_ImageIO->GetMetaDataDictionary()); this->m_Image->SetSpacing( spacing ); this->m_Image->SetOrigin( origin ); this->m_Image->SetDirection( direction ); this->m_Image->SetMetaDataDictionary(m_ImageIO->GetMetaDataDictionary()); typedef typename TVectorImage::IndexType IndexType; IndexType start; start.Fill(0); VectorImageRegionType region; region.SetSize(dimSize); region.SetIndex(start); ImageRegionType imageregion; imageregion.SetSize(dimSize); imageregion.SetIndex(start); // If a VectorImage, this requires us to set the // VectorLength before allocate //if( strcmp( output->GetNameOfClass(), "VectorImage" ) == 0 ) // { // typedef typename TImage::AccessorFunctorType AccessorFunctorType; // AccessorFunctorType::SetVectorLength( output, m_ImageIO->GetNumberOfComponents() ); // } output->SetLargestPossibleRegion( region ); this->m_Image->SetLargestPossibleRegion( imageregion ); } } this->m_FileName = tmpFileName; } template void VectorImageFileReader ::TestFileExistanceAndReadability() { std::string tmpFileName = this->m_FileName; std::string::size_type pos = this->m_FileName.rfind( "." ); std::string extension( this->m_FileName, pos, this->m_FileName.length()-1 ); std::string filename = std::string( this->m_FileName, 0, pos ); std::string gzExtension( "" ); if ( extension == std::string( ".gz" ) ) { gzExtension = extension; std::string::size_type pos2 = filename.rfind( "." ); extension = std::string( filename, pos2, filename.length()-1 ); filename = std::string( this->m_FileName, 0, pos2 ); } unsigned int dimension = itk::GetVectorDimension ::VectorDimension; for ( unsigned int i = 0; i < dimension; i++ ) { this->m_FileName = filename; if ( this->m_UseAvantsNamingConvention ) { switch ( i ) { case 0: this->m_FileName += std::string( "xvec" ); break; case 1: this->m_FileName += std::string( "yvec" ); break; case 2: this->m_FileName += std::string( "zvec" ); break; default: this->m_FileName += std::string( "you_are_screwed_vec" ); break; } } else { itk::OStringStream buf; buf << i; this->m_FileName += ( std::string( "." ) + std::string( buf.str().c_str() ) ); } this->m_FileName += extension; if ( !gzExtension.empty() ) { this->m_FileName += std::string( ".gz" ); } itkDebugMacro( << "Checking for the file " << this->m_FileName ); // Test if the file exists. if( ! itksys::SystemTools::FileExists( m_FileName.c_str() ) ) { VectorImageFileReaderException e(__FILE__, __LINE__); OStringStream msg; msg <<"The file doesn't exists. " << std::endl << "Filename = " << m_FileName << std::endl; e.SetDescription(msg.str().c_str()); throw e; return; } // Test if the file can be open for reading access. std::ifstream readTester; readTester.open( m_FileName.c_str() ); if( readTester.fail() ) { readTester.close(); OStringStream msg; msg <<"The file couldn't be opened for reading. " << std::endl << "Filename: " << m_FileName << std::endl; VectorImageFileReaderException e(__FILE__, __LINE__,msg.str().c_str(),ITK_LOCATION); throw e; return; } readTester.close(); } this->m_FileName = tmpFileName; } template void VectorImageFileReader ::EnlargeOutputRequestedRegion(DataObject *output) { typename TVectorImage::Pointer out = dynamic_cast(output); // the ImageIO object cannot stream, then set the RequestedRegion to the // LargestPossibleRegion if (!m_ImageIO->CanStreamRead()) { if (out) { out->SetRequestedRegion( out->GetLargestPossibleRegion() ); } else { throw VectorImageFileReaderException(__FILE__, __LINE__, "Invalid output object type"); } } } template void VectorImageFileReader ::GenerateData() { typename TVectorImage::Pointer output = this->GetOutput(); // allocate the output buffer output->SetBufferedRegion( output->GetRequestedRegion() ); output->Allocate(); this->m_Image->SetBufferedRegion( output->GetRequestedRegion() ); this->m_Image->Allocate(); // Test if the file exist and if it can be open. // and exception will be thrown otherwise. try { m_ExceptionMessage = ""; this->TestFileExistanceAndReadability(); } catch(itk::ExceptionObject &err) { m_ExceptionMessage = err.GetDescription(); } std::string tmpFileName = this->m_FileName; std::string::size_type pos = this->m_FileName.rfind( "." ); std::string extension( this->m_FileName, pos, this->m_FileName.length()-1 ); std::string filename = std::string( this->m_FileName, 0, pos ); std::string gzExtension( "" ); if ( extension == std::string( ".gz" ) ) { gzExtension = extension; std::string::size_type pos2 = filename.rfind( "." ); extension = std::string( filename, pos2, filename.length()-1 ); filename = std::string( this->m_FileName, 0, pos2 ); } unsigned int dimension = itk::GetVectorDimension ::VectorDimension; for ( unsigned int i = 0; i < dimension; i++ ) { this->m_FileName = filename; if ( this->m_UseAvantsNamingConvention ) { switch ( i ) { case 0: this->m_FileName += std::string( "xvec" ); break; case 1: this->m_FileName += std::string( "yvec" ); break; case 2: this->m_FileName += std::string( "zvec" ); break; default: this->m_FileName += std::string( "you_are_screwed_vec" ); break; } } else { itk::OStringStream buf; buf << i; this->m_FileName += ( std::string( "." ) + std::string( buf.str().c_str() ) ); } this->m_FileName += extension; if ( !gzExtension.empty() ) { this->m_FileName += std::string( ".gz" ); } itkDebugMacro( << "Reading image buffer from the file " << this->m_FileName ); // Tell the ImageIO to read the file m_ImageIO->SetFileName(m_FileName.c_str()); this->m_FileName = tmpFileName; ImageIORegion ioRegion(TImage::ImageDimension); ImageIORegion::SizeType ioSize = ioRegion.GetSize(); ImageIORegion::IndexType ioStart = ioRegion.GetIndex(); typename TImage::SizeType dimSize; for(unsigned int j=0; jGetNumberOfDimensions()) { dimSize[j] = m_ImageIO->GetDimensions(j); } else { // Number of dimensions in the output is more than number of dimensions // in the ImageIO object (the file). Use default values for the size, // spacing, and origin for the final (degenerate) dimensions. dimSize[j] = 1; } } for(unsigned int j = 0; j < dimSize.GetSizeDimension(); ++j) { ioSize[j] = dimSize[j]; } typedef typename TImage::IndexType IndexType; IndexType start; start.Fill(0); for(unsigned int j = 0; j < start.GetIndexDimension(); ++j) { ioStart[j] = start[j]; } ioRegion.SetSize(ioSize); ioRegion.SetIndex(ioStart); itkDebugMacro (<< "ioRegion: " << ioRegion); m_ImageIO->SetIORegion(ioRegion); char *loadBuffer = 0; // the size of the buffer is computed based on the actual number of // pixels to be read and the actual size of the pixels to be read // (as opposed to the sizes of the output) try { if ( m_ImageIO->GetComponentTypeInfo() != typeid(ITK_TYPENAME ConvertPixelTraits::ComponentType) || (m_ImageIO->GetNumberOfComponents() != ConvertPixelTraits::GetNumberOfComponents())) { // the pixel types don't match so a type conversion needs to be // performed itkDebugMacro(<< "Buffer conversion required from: " << m_ImageIO->GetComponentTypeInfo().name() << " to: " << typeid(ITK_TYPENAME ConvertPixelTraits::ComponentType).name()); loadBuffer = new char[m_ImageIO->GetImageSizeInBytes()]; m_ImageIO->Read( static_cast< void *>(loadBuffer) ); // See note below as to why the buffered region is needed and // not actualIOregion this->DoConvertBuffer(static_cast< void *>(loadBuffer), output->GetBufferedRegion().GetNumberOfPixels() ); } else // a type conversion is not necessary { itkDebugMacro(<< "No buffer conversion required."); ImagePixelType *buffer = this->m_Image->GetPixelContainer()->GetBufferPointer(); m_ImageIO->Read( buffer ); } } catch (...) { // if an exception is thrown catch it if (loadBuffer) { // clean up delete [] loadBuffer; loadBuffer = 0; } // then rethrow throw; } // clean up if (loadBuffer) { delete [] loadBuffer; loadBuffer = 0; } ImageRegionIterator Id( this->GetOutput(), this->GetOutput()->GetLargestPossibleRegion() ); ImageRegionIterator It( this->m_Image, this->m_Image->GetLargestPossibleRegion() ); Id.GoToBegin(); It.GoToBegin(); while ( !Id.IsAtEnd() || !It.IsAtEnd() ) { VectorImagePixelType V = Id.Get(); V[i] = static_cast< typename VectorImagePixelType::ValueType>( It.Get() ); Id.Set( V ); ++Id; ++It; } } } template void VectorImageFileReader ::DoConvertBuffer(void* inputData, unsigned long numberOfPixels) { // get the pointer to the destination buffer ImagePixelType *imageData = this->m_Image->GetPixelContainer()->GetBufferPointer(); // TODO: // Pass down the PixelType (RGB, VECTOR, etc.) so that any vector to // scalar conversion be type specific. i.e. RGB to scalar would use // a formula to convert to luminance, VECTOR to scalar would use // vector magnitude. // Create a macro as this code is a bit lengthy and repetitive // if the ImageIO pixel type is typeid(type) then use the ConvertPixelBuffer // class to convert the data block to TImage's pixel type // see DefaultConvertPixelTraits and ConvertPixelBuffer // The first else if block applies only to images of type itk::VectorImage // VectorImage needs to copy out the buffer differently.. The buffer is of // type InternalPixelType, but each pixel is really 'k' consecutive pixels. #define ITK_CONVERT_BUFFER_IF_BLOCK(type) \ else if( m_ImageIO->GetComponentTypeInfo() == typeid(type) ) \ { \ if( strcmp( this->GetOutput()->GetNameOfClass(), "VectorImage" ) == 0 ) \ { \ ConvertPixelBuffer< \ type, \ ImagePixelType, \ ConvertPixelTraits \ > \ ::ConvertVectorImage( \ static_cast(inputData), \ m_ImageIO->GetNumberOfComponents(), \ imageData, \ numberOfPixels); \ } \ else \ { \ ConvertPixelBuffer< \ type, \ ImagePixelType, \ ConvertPixelTraits \ > \ ::Convert( \ static_cast(inputData), \ m_ImageIO->GetNumberOfComponents(), \ imageData, \ numberOfPixels); \ } \ } if(0) { } ITK_CONVERT_BUFFER_IF_BLOCK(unsigned char) ITK_CONVERT_BUFFER_IF_BLOCK(char) ITK_CONVERT_BUFFER_IF_BLOCK(unsigned short) ITK_CONVERT_BUFFER_IF_BLOCK( short) ITK_CONVERT_BUFFER_IF_BLOCK(unsigned int) ITK_CONVERT_BUFFER_IF_BLOCK( int) ITK_CONVERT_BUFFER_IF_BLOCK(unsigned long) ITK_CONVERT_BUFFER_IF_BLOCK( long) ITK_CONVERT_BUFFER_IF_BLOCK(float) ITK_CONVERT_BUFFER_IF_BLOCK( double) else { VectorImageFileReaderException e(__FILE__, __LINE__); OStringStream msg; msg <<"Couldn't convert component type: " << std::endl << " " << m_ImageIO->GetComponentTypeAsString(m_ImageIO->GetComponentType()) << std::endl << "to one of: " << std::endl << " " << typeid(unsigned char).name() << std::endl << " " << typeid(char).name() << std::endl << " " << typeid(unsigned short).name() << std::endl << " " << typeid(short).name() << std::endl << " " << typeid(unsigned int).name() << std::endl << " " << typeid(int).name() << std::endl << " " << typeid(unsigned long).name() << std::endl << " " << typeid(long).name() << std::endl << " " << typeid(float).name() << std::endl << " " << typeid(double).name() << std::endl; e.SetDescription(msg.str().c_str()); e.SetLocation(ITK_LOCATION); throw e; return; } #undef ITK_CONVERT_BUFFER_IF_BLOCK } } //namespace ITK #endif ants-1.9.2+svn680.dfsg/Utilities/itkVectorImageFileWriter.h000066400000000000000000000161431147325206600235230ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkVectorImageFileWriter.h,v $ Language: C++ Date: $$ Version: $ $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkVectorImageFileWriter_h #define __itkVectorImageFileWriter_h #include "itkProcessObject.h" #include "itkImageIOBase.h" #include "itkExceptionObject.h" #include "itkSize.h" #include "itkImageIORegion.h" namespace itk { /** \brief Base exception class for IO problems during writing. */ class VectorImageFileWriterException : public ExceptionObject { public: /** Run-time information. */ itkTypeMacro( VectorImageFileWriterException, ExceptionObject ); /** Constructor. */ VectorImageFileWriterException(const char *file, unsigned int line, const char* message = "Error in IO", const char* loc = "Unknown" ) : ExceptionObject(file, line, message, loc) { } /** Constructor. */ VectorImageFileWriterException(const std::string &file, unsigned int line, const char* message = "Error in IO", const char* loc = "Unknown" ) : ExceptionObject(file, line, message, loc) { } }; /** \class VectorImageFileWriter * \brief Writes the deformation field as component images files. * * \sa VectorImageFileWriter * \sa ImageSeriesReader * \sa ImageIOBase * * \ingroup IOFilters */ template class ITK_EXPORT VectorImageFileWriter : public ProcessObject { public: /** Standard class typedefs. */ typedef VectorImageFileWriter Self; typedef ProcessObject Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Run-time type information (and related methods). */ itkTypeMacro(VectorImageFileWriter, VectorImageFileWriter); /** Some convenient typedefs. */ typedef TVectorImage VectorImageType; typedef typename VectorImageType::Pointer VectorImagePointer; typedef typename VectorImageType::RegionType VectorImageRegionType; typedef typename VectorImageType::PixelType VectorImagePixelType; typedef TImage ImageType; typedef typename ImageType::Pointer ImagePointer; typedef typename ImageType::RegionType ImageRegionType; typedef typename ImageType::PixelType ImagePixelType; /** Set/Get the image input of this writer. */ void SetInput(const VectorImageType *input); const VectorImageType * GetInput(void); const VectorImageType * GetInput(unsigned int idx); /** Specify the name of the output file to write. */ itkSetStringMacro(FileName); itkGetStringMacro(FileName); /** Set/Get the ImageIO helper class. Usually this is created via the object * factory mechanism that determines whether a particular ImageIO can * write a certain file. This method provides a way to get the ImageIO * instance that is created, or one can be manually set where the * IO factory mechanism may not work (for example, raw image files or * image files with non-standard filename suffix's. * If the user specifies the ImageIO, we assume she makes the * correct choice and will allow a file to be created regardless of * the file extension. If the factory has set the ImageIO, the * extension must be supported by the specified ImageIO. */ void SetImageIO (ImageIOBase* io) { if (this->m_ImageIO != io) { this->Modified(); this->m_ImageIO = io; } m_FactorySpecifiedImageIO = false; } itkGetObjectMacro(ImageIO,ImageIOBase); /** A special version of the Update() method for writers. It * invokes start and end events and handles releasing data. It * eventually calls GenerateData() which does the actual writing. * Note: the write method will write data specified by the * IORegion. If not set, then then the whole image is written. Note * that the region will be cropped to fit the input image's * LargestPossibleRegion. */ virtual void Write(void); /** Specify the region to write. If left NULL, then the whole image * is written. */ void SetIORegion(const ImageIORegion & region); itkGetConstReferenceMacro( IORegion, ImageIORegion ); /** Aliased to the Write() method to be consistent with the rest of the * pipeline. */ virtual void Update() {this->Write();} /** Set the compression On or Off */ itkSetMacro(UseCompression,bool); itkGetConstReferenceMacro(UseCompression,bool); itkBooleanMacro(UseCompression); /** Set the Avants' naming convention On or Off */ itkSetMacro(UseAvantsNamingConvention,bool); itkGetConstReferenceMacro(UseAvantsNamingConvention,bool); itkBooleanMacro(UseAvantsNamingConvention); /** Set the Avants' naming convention On or Off */ itkSetMacro(UseZhangNamingConvention,bool); itkGetConstReferenceMacro(UseZhangNamingConvention,bool); itkBooleanMacro(UseZhangNamingConvention); /** By default the MetaDataDictionary is taken from the input image and * passed to the ImageIO. In some cases, however, a user may prefer to * introduce her/his own MetaDataDictionary. This is often the case of * the ImageSeriesWriter. This flag defined whether the MetaDataDictionary * to use will be the one from the input image or the one already set in * the ImageIO object. */ itkSetMacro(UseInputMetaDataDictionary,bool); itkGetConstReferenceMacro(UseInputMetaDataDictionary,bool); itkBooleanMacro(UseInputMetaDataDictionary); protected: VectorImageFileWriter(); ~VectorImageFileWriter(); void PrintSelf(std::ostream& os, Indent indent) const; /** Does the real work. */ void GenerateData(void); private: VectorImageFileWriter(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented std::string m_FileName; std::string m_ComponentImageFileName; bool m_UseAvantsNamingConvention; bool m_UseZhangNamingConvention; ImagePointer m_Image; ImageIOBase::Pointer m_ImageIO; bool m_UserSpecifiedImageIO; //track whether the ImageIO is user specified ImageIORegion m_IORegion; bool m_UserSpecifiedIORegion; // //track whether the region is user specified bool m_FactorySpecifiedImageIO; //track whether the factory mechanism set the ImageIO bool m_UseCompression; bool m_UseInputMetaDataDictionary; // whether to use the MetaDataDictionary from the input or not. }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkVectorImageFileWriter.txx" #endif #endif // __itkVectorImageFileWriter_h ants-1.9.2+svn680.dfsg/Utilities/itkVectorImageFileWriter.txx000066400000000000000000000310151147325206600241120ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkVectorImageFileWriter.txx,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.16 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef _itkVectorImageFileWriter_txx #define _itkVectorImageFileWriter_txx #include "itkVectorImageFileWriter.h" #include "itkImageFileWriter.h" #include "itkDataObject.h" #include "itkObjectFactoryBase.h" #include "itkImageIOFactory.h" #include "itkCommand.h" #include "vnl/vnl_vector.h" #include "itkVectorImage.h" #include "itkVectorIndexSelectionCastImageFilter.h" namespace itk { //--------------------------------------------------------- template VectorImageFileWriter ::VectorImageFileWriter(): m_FileName(""), m_ImageIO(0), m_UserSpecifiedImageIO(false), m_UserSpecifiedIORegion(false) { m_UseCompression = false; m_UseInputMetaDataDictionary = true; m_FactorySpecifiedImageIO = false; m_UseAvantsNamingConvention = true; m_UseZhangNamingConvention = false; } //--------------------------------------------------------- template VectorImageFileWriter ::~VectorImageFileWriter() { } //--------------------------------------------------------- template void VectorImageFileWriter ::SetInput(const VectorImageType *input) { this->ProcessObject::SetNthInput(0, const_cast(input ) ); } //--------------------------------------------------------- template const typename VectorImageFileWriter::VectorImageType * VectorImageFileWriter ::GetInput(void) { if (this->GetNumberOfInputs() < 1) { return 0; } return static_cast (this->ProcessObject::GetInput(0)); } //--------------------------------------------------------- template const typename VectorImageFileWriter::VectorImageType * VectorImageFileWriter ::GetInput(unsigned int idx) { return static_cast (this->ProcessObject::GetInput(idx)); } //--------------------------------------------------------- template void VectorImageFileWriter ::SetIORegion (const ImageIORegion& region) { itkDebugMacro("setting IORegion to " << region ); if ( m_IORegion != region) { m_IORegion = region; this->Modified(); m_UserSpecifiedIORegion = true; } } //--------------------------------------------------------- template void VectorImageFileWriter ::GenerateData(void) { itkDebugMacro(<<"Writing file: " << m_ComponentImageFileName); // Make sure that the image is the right type and no more than // four components. typedef typename ImageType::PixelType ScalarType; if( strcmp( m_Image->GetNameOfClass(), "VectorImage" ) == 0 ) { typedef typename ImageType::InternalPixelType VectorImageScalarType; m_ImageIO->SetPixelTypeInfo( typeid(VectorImageScalarType) ); typedef typename ImageType::AccessorFunctorType AccessorFunctorType; m_ImageIO->SetNumberOfComponents( AccessorFunctorType::GetVectorLength( m_Image ) ); } else { // Set the pixel and component type; the number of components. m_ImageIO->SetPixelTypeInfo(typeid(ScalarType)); } // Setup the image IO for writing. // m_ImageIO->SetFileName(m_ComponentImageFileName.c_str()); //okay, now extract the data as a raw buffer pointer const void* dataPtr = (const void*) this->m_Image->GetBufferPointer(); m_ImageIO->Write(dataPtr); } //--------------------------------------------------------- template void VectorImageFileWriter ::Write() { typedef VectorIndexSelectionCastImageFilter SelectorType; typename SelectorType::Pointer selector = SelectorType::New(); selector->SetInput( this->GetInput() ); unsigned int dimension = itk::GetVectorDimension ::VectorDimension; std::string filename = this->m_FileName; std::string::size_type pos = this->m_FileName.rfind( "." ); std::string extension( this->m_FileName, pos, this->m_FileName.length()-1 ); std::string gzExtension( "" ); if ( extension == std::string( ".gz" ) ) { gzExtension = extension; filename = std::string( filename, 0, pos ); pos = filename.rfind( "." ); extension = std::string( filename, pos, this->m_FileName.length()-1 ); } for ( unsigned int i = 0; i < dimension; i++ ) { selector->SetIndex( i ); selector->Update(); std::string filename( this->m_FileName, 0, pos ); if ( this->m_UseAvantsNamingConvention && dimension <= 3 ) { switch ( i ) { case 0: filename += std::string( "xvec" ); break; case 1: filename += std::string( "yvec" ); break; case 2: filename += std::string( "zvec" ); break; default: filename += std::string( "you_are_screwed_vec" ); break; } } else if ( this->m_UseZhangNamingConvention && dimension == 6 ) { switch ( i ) { case 0: filename += std::string( "xx" ); break; case 1: filename += std::string( "yx" ); break; case 2: filename += std::string( "yy" ); break; case 3: filename += std::string( "zx" ); break; case 4: filename += std::string( "zy" ); break; case 5: filename += std::string( "zz" ); break; default: filename += std::string( "you_are_screwed" ); break; } } else { itk::OStringStream buf; buf << i; filename += ( std::string( "." ) + std::string( buf.str().c_str() ) ); } filename += extension; if ( !gzExtension.empty() ) { filename += std::string( ".gz" ); } m_ComponentImageFileName = filename; ImageType *input = selector->GetOutput(); this->m_Image = selector->GetOutput(); itkDebugMacro( <<"Writing an image file" ); // Make sure input is available if ( input == 0 ) { itkExceptionMacro(<< "No input to writer!"); } // Make sure that we can write the file given the name // if ( filename == "" ) { itkExceptionMacro(<<"No filename was specified"); } if ( m_ImageIO.IsNull() ) //try creating via factory { itkDebugMacro(<<"Attempting factory creation of ImageIO for file: " << filename); m_ImageIO = ImageIOFactory::CreateImageIO( filename.c_str(), ImageIOFactory::WriteMode ); m_FactorySpecifiedImageIO = true; } else { if( m_FactorySpecifiedImageIO && !m_ImageIO->CanWriteFile( filename.c_str() ) ) { itkDebugMacro(<<"ImageIO exists but doesn't know how to write file:" << m_FileName ); itkDebugMacro(<<"Attempting creation of ImageIO with a factory for file:" << m_FileName); m_ImageIO = ImageIOFactory::CreateImageIO( filename.c_str(), ImageIOFactory::WriteMode ); m_FactorySpecifiedImageIO = true; } } if ( m_ImageIO.IsNull() ) { ImageFileWriterException e(__FILE__, __LINE__); OStringStream msg; msg << " Could not create IO object for file " << filename.c_str() << std::endl; msg << " Tried to create one of the following:" << std::endl; std::list allobjects = ObjectFactoryBase::CreateAllInstance("itkImageIOBase"); for(std::list::iterator i = allobjects.begin(); i != allobjects.end(); ++i) { ImageIOBase* io = dynamic_cast(i->GetPointer()); msg << " " << io->GetNameOfClass() << std::endl; } msg << " You probably failed to set a file suffix, or" << std::endl; msg << " set the suffix to an unsupported type." << std::endl; e.SetDescription(msg.str().c_str()); e.SetLocation(ITK_LOCATION); throw e; } // NOTE: this const_cast<> is due to the lack of const-correctness // of the ProcessObject. ImageType *nonConstImage = input; typedef typename TImage::RegionType RegionType; if ( ! m_UserSpecifiedIORegion ) { // Make sure the data is up-to-date. if( nonConstImage->GetSource() ) { nonConstImage->GetSource()->UpdateLargestPossibleRegion(); } // Write the whole image ImageIORegion ioRegion(TImage::ImageDimension); RegionType region = this->m_Image->GetLargestPossibleRegion(); for(unsigned int i=0; iUpdate(); } // Setup the ImageIO // m_ImageIO->SetNumberOfDimensions(TImage::ImageDimension); RegionType region = this->m_Image->GetLargestPossibleRegion(); const typename TImage::SpacingType& spacing = this->m_Image->GetSpacing(); const typename TImage::PointType& origin = this->m_Image->GetOrigin(); const typename TImage::DirectionType& direction = this->m_Image->GetDirection(); for(unsigned int k=0; kSetDimensions(k,region.GetSize(k)); m_ImageIO->SetSpacing(k,spacing[k]); m_ImageIO->SetOrigin(k,origin[k]); vnl_vector< double > axisDirection(TVectorImage::ImageDimension); // Please note: direction cosines are stored as columns of the // direction matrix for(unsigned int j=0; jSetDirection( k, axisDirection ); } m_ImageIO->SetUseCompression(m_UseCompression); m_ImageIO->SetIORegion(m_IORegion); if( m_UseInputMetaDataDictionary ) { m_ImageIO->SetMetaDataDictionary(input->GetMetaDataDictionary()); } // Notify start event observers this->InvokeEvent( StartEvent() ); // Actually do something this->GenerateData(); // Notify end event observers this->InvokeEvent( EndEvent() ); // Release upstream data if requested if ( input->ShouldIReleaseData() ) { nonConstImage->ReleaseData(); } } } //--------------------------------------------------------- template void VectorImageFileWriter ::PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf(os,indent); os << indent << "File Name: " << (m_FileName.data() ? m_FileName.data() : "(none)") << std::endl; os << "Number of vector components (i.e. number of images created): " << itk::GetVectorDimension::VectorDimension << std::endl; os << indent << "Image IO: "; if ( m_ImageIO.IsNull() ) { os << "(none)\n"; } else { os << m_ImageIO << "\n"; } os << indent << "IO Region: " << m_IORegion << "\n"; if (m_UseCompression) { os << indent << "Compression: On\n"; } else { os << indent << "Compression: Off\n"; } if (m_UseInputMetaDataDictionary) { os << indent << "UseInputMetaDataDictionary: On\n"; } else { os << indent << "UseInputMetaDataDictionary: Off\n"; } if (m_FactorySpecifiedImageIO) { os << indent << "FactorySpecifiedmageIO: On\n"; } else { os << indent << "FactorySpecifiedmageIO: Off\n"; } } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Utilities/itkWarpImageMultiTransformFilter.h000066400000000000000000000303411147325206600252460ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkWarpImageMultiTransformFilter.h,v $ Language: C++ Date: $Date: 2009/01/08 21:36:48 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef ITKWARPIMAGEMULTITRANSFORMFILTER_H_ #define ITKWARPIMAGEMULTITRANSFORMFILTER_H_ #include "itkImageToImageFilter.h" #include "itkInterpolateImageFunction.h" #include "itkLinearInterpolateImageFunction.h" #include "itkVectorLinearInterpolateImageFunction.h" #include "itkVectorGaussianInterpolateImageFunction.h" #include "itkPoint.h" #include "itkFixedArray.h" #include "itkRecursiveGaussianImageFilter.h" #include namespace itk { /** \class WarpImageMultiTransformFilter * \brief Warps an image using an input deformation field. * * WarpImageMultiTransformFilter warps an existing image with respect to * a given deformation field. * * A deformation field is represented as a image whose pixel type is some * vector type with at least N elements, where N is the dimension of * the input image. The vector type must support element access via operator * []. * * The output image is produced by inverse mapping: the output pixels * are mapped back onto the input image. This scheme avoids the creation of * any holes and overlaps in the output image. * * Each vector in the deformation field represent the distance between * a geometric point in the input space and a point in the output space such * that: * * \f[ p_{in} = p_{out} + d \f] * * Typically the mapped position does not correspond to an integer pixel * position in the input image. Interpolation via an image function * is used to compute values at non-integer positions. The default * interpolation typed used is the LinearInterpolateImageFunction. * The user can specify a particular interpolation function via * SetInterpolator(). Note that the input interpolator must derive * from base class InterpolateImageFunction. * * Position mapped to outside of the input image buffer are assigned * a edge padding value. * * The LargetPossibleRegion for the output is inherited from the * input deformation field. The output image spacing and origin may be set * via SetOutputSpacing, SetOutputOrigin. The default are respectively a * vector of 1's and a vector of 0's. * * This class is templated over the type of the input image, the * type of the output image and the type of the deformation field. * * The input image is set via SetInput. The input deformation field * is set via SetDeformationField. * * This filter is implemented as a multithreaded filter. * * \warning This filter assumes that the input type, output type * and deformation field type all have the same number of dimensions. * * \ingroup GeometricTransforms MultiThreaded */ template < class TInputImage, class TOutputImage, class TDeformationField, class TTransform > class ITK_EXPORT WarpImageMultiTransformFilter : public ImageToImageFilter { public: /** transform order type **/ // typedef enum _TransformOrderType {AffineFirst=0, AffineLast} TransformOrderType; /** transform type **/ typedef enum _SingleTransformType {EnumAffineType=0, EnumDeformationFieldType} SingleTransformType; /** Standard class typedefs. */ typedef WarpImageMultiTransformFilter Self; typedef ImageToImageFilter Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Run-time type information (and related methods) */ itkTypeMacro( WarpImageMultiTransformFilter, ImageToImageFilter ); /** Typedef to describe the output image region type. */ typedef typename TOutputImage::RegionType OutputImageRegionType; /** Inherit some types from the superclass. */ typedef typename Superclass::InputImageType InputImageType; typedef typename Superclass::InputImagePointer InputImagePointer; typedef typename Superclass::OutputImageType OutputImageType; typedef typename Superclass::OutputImagePointer OutputImagePointer; typedef typename Superclass::InputImageConstPointer InputImageConstPointer; typedef typename OutputImageType::IndexType IndexType; typedef typename OutputImageType::SizeType SizeType; typedef typename OutputImageType::PixelType PixelType; typedef typename OutputImageType::SpacingType SpacingType; typedef typename OutputImageType::DirectionType DirectionType; /** Determine the image dimension. */ itkStaticConstMacro(ImageDimension, unsigned int, TOutputImage::ImageDimension ); itkStaticConstMacro(InputImageDimension, unsigned int, TInputImage::ImageDimension ); itkStaticConstMacro(DeformationFieldDimension, unsigned int, TDeformationField::ImageDimension ); /** Deformation field typedef support. */ typedef TDeformationField DeformationFieldType; typedef typename DeformationFieldType::Pointer DeformationFieldPointer; typedef typename DeformationFieldType::PixelType DisplacementType; typedef typename DisplacementType::ValueType DisplacementScalarValueType; /** songgang: Affine transform typedef support. */ typedef TTransform TransformType; typedef typename TransformType::Pointer TransformTypePointer; /** Interpolator typedef support. */ typedef double CoordRepType; typedef InterpolateImageFunction InterpolatorType; typedef typename InterpolatorType::Pointer InterpolatorPointer; typedef LinearInterpolateImageFunction DefaultInterpolatorType; typedef VectorLinearInterpolateImageFunction DefaultVectorInterpolatorType; typedef VectorGaussianInterpolateImageFunction DefaultVectorInterpolatorType2; typedef typename DefaultVectorInterpolatorType::Pointer VectorInterpolatorPointer; /** Point type */ typedef Point PointType; typedef struct _DeformationTypeEx{ DeformationFieldPointer field; VectorInterpolatorPointer vinterp; } DeformationTypeEx; typedef struct _AffineTypeEx{ TransformTypePointer aff; } AffineTypeEx; typedef struct _VarTransformType{ AffineTypeEx aex; DeformationTypeEx dex; } VarTransformType; typedef std::pair SingleTransformItemType; typedef std::list TransformListType; /** Set the interpolator function. */ itkSetObjectMacro( Interpolator, InterpolatorType ); /** Get a pointer to the interpolator function. */ itkGetObjectMacro( Interpolator, InterpolatorType ); /** Set the output image spacing. */ itkSetMacro(OutputSpacing, SpacingType); virtual void SetOutputSpacing( const double* values); /** Get the output image spacing. */ itkGetConstReferenceMacro(OutputSpacing, SpacingType); /** Set the output image spacing. */ itkSetMacro(OutputDirection, DirectionType); /** Get the output image spacing. */ itkGetConstReferenceMacro(OutputDirection, DirectionType); /** Set the output image origin. */ itkSetMacro(OutputOrigin, PointType); virtual void SetOutputOrigin( const double* values); /** Get the output image origin. */ itkGetConstReferenceMacro(OutputOrigin, PointType); /** Set the output image size. */ itkSetMacro(OutputSize, SizeType); // virtual void SetOutputSize( const double *values); itkGetConstReferenceMacro(OutputSize, SizeType); /** Set the edge padding value */ itkSetMacro( EdgePaddingValue, PixelType ); /** Get the edge padding value */ itkGetMacro( EdgePaddingValue, PixelType ); TransformListType & GetTransformList(){ return m_TransformList; } void PrintTransformList(); /** WarpImageMultiTransformFilter produces an image which is a different * size than its input image. As such, it needs to provide an * implemenation for GenerateOutputInformation() which set * the output information according the OutputSpacing, OutputOrigin * and the deformation field's LargestPossibleRegion. */ virtual void GenerateOutputInformation(); /** It is difficult to compute in advance the input image region * required to compute the requested output region. Thus the safest * thing to do is to request for the whole input image. * * For the deformation field, the input requested region * set to be the same as that of the output requested region. */ virtual void GenerateInputRequestedRegion(); /** This method is used to set the state of the filter before * multi-threading. */ virtual void BeforeThreadedGenerateData(); /** This method is used to set the state of the filter after * multi-threading. */ virtual void AfterThreadedGenerateData(); /** precompute the smoothed image if necessary **/ void SetSmoothScale(double scale); double GetSmoothScale() {return m_SmoothScale;}; // void UpdateSizeByScale(); void PushBackAffineTransform(const TransformType* t); void PushBackDeformationFieldTransform(const DeformationFieldType* t); void ComposeAffineOnlySequence(const PointType ¢er_output, TransformTypePointer &affine_output); bool MultiInverseAffineOnlySinglePoint(const PointType &point1, PointType &point2); bool MultiTransformSinglePoint(const PointType &point1, PointType &point2); bool MultiTransformPoint(const PointType &point1, PointType &point2, bool bFisrtDeformNoInterp, const IndexType &index); void DetermineFirstDeformNoInterp(); inline bool IsOutOfNumericBoundary(const PointType &p); // set interpolator from outside // virtual void SetInterpolator(InterpolatorPointer interp); #ifdef ITK_USE_CONCEPT_CHECKING /** Begin concept checking */ itkConceptMacro(SameDimensionCheck1, (Concept::SameDimension)); itkConceptMacro(SameDimensionCheck2, (Concept::SameDimension)); //removed to be compatible with vector form input image // itkConceptMacro(InputHasNumericTraitsCheck, // (Concept::HasNumericTraits)); itkConceptMacro(DeformationFieldHasNumericTraitsCheck, (Concept::HasNumericTraits)); /** End concept checking */ #endif bool m_bFirstDeformNoInterp; protected: WarpImageMultiTransformFilter(); ~WarpImageMultiTransformFilter() {}; void PrintSelf(std::ostream& os, Indent indent) const; /** WarpImageMultiTransformFilter is implemented as a multi-threaded filter. * As such, it needs to provide and implementation for * ThreadedGenerateData(). */ void ThreadedGenerateData(const OutputImageRegionType& outputRegionForThread, int threadId ); InterpolatorPointer m_Interpolator; PixelType m_EdgePaddingValue; SpacingType m_OutputSpacing; PointType m_OutputOrigin; SizeType m_OutputSize; DirectionType m_OutputDirection; TransformListType m_TransformList; double m_SmoothScale; InputImagePointer m_CachedSmoothImage; private: WarpImageMultiTransformFilter(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkWarpImageMultiTransformFilter.txx" #endif #endif /*ITKWARPIMAGEMULTITRANSFORMFILTER_H_*/ ants-1.9.2+svn680.dfsg/Utilities/itkWarpImageMultiTransformFilter.txx000066400000000000000000000500521147325206600256430ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkWarpImageMultiTransformFilter.txx,v $ Language: C++ Date: $Date: 2009/01/08 21:36:48 $ Version: $Revision: 1.18 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkWarpImageMultiTransformFilter_txx #define __itkWarpImageMultiTransformFilter_txx #include "itkWarpImageMultiTransformFilter.h" #include "itkImageRegionIterator.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkNumericTraits.h" #include "itkProgressReporter.h" #include "itkVectorLinearInterpolateImageFunction.h" #include namespace itk { /** * Default constructor. */ template WarpImageMultiTransformFilter ::WarpImageMultiTransformFilter() { // Setup the number of required inputs this->SetNumberOfRequiredInputs( 1 ); // Setup default values m_OutputSpacing.Fill( 1.0 ); m_OutputOrigin.Fill( 0.0 ); m_EdgePaddingValue = NumericTraits::Zero; // Setup default interpolator typename DefaultInterpolatorType::Pointer interp = DefaultInterpolatorType::New(); m_Interpolator = static_cast( interp.GetPointer() ); m_SmoothScale = -1; // m_bOutputDeformationField = false; // m_TransformOrder = AffineFirst; } //template //void //WarpImageMultiTransformFilter //::SetInterpolator1(InterpolatorPointer interp) //{ // m_Interpolator = static_cast (interp.GetPointer()); // std::cout << "set interpolator in WarpImage:" << interp << std::endl; //} template void WarpImageMultiTransformFilter ::PrintTransformList() { std::cout << "transform list: " << std::endl; typename TransformListType::iterator it = (m_TransformList.begin()); for(int ii=0; it != m_TransformList.end(); it++, ii++){ switch(it->first){ case EnumAffineType: std::cout << '[' << ii << "]: EnumAffineType" << std::endl; std::cout << it->second.aex.aff << std::endl; break; case EnumDeformationFieldType: std::cout << '[' << ii << "]: EnumDeformationFieldType: size" << it->second.dex.field->GetLargestPossibleRegion().GetSize() << std::endl; } } } /** * Standard PrintSelf method. */ template void WarpImageMultiTransformFilter ::PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf(os, indent); os << indent << "OutputSpacing: " << m_OutputSpacing << std::endl;; os << indent << "OutputOrigin: " << m_OutputOrigin << std::endl; os << indent << "EdgePaddingValue: " << static_cast::PrintType>(m_EdgePaddingValue) << std::endl; os << indent << "Interpolator: " << m_Interpolator.GetPointer() << std::endl; os << indent << "m_bFirstDeformNoInterp = " << m_bFirstDeformNoInterp << std::endl; } /** * Set the output image spacing. * */ template void WarpImageMultiTransformFilter ::SetOutputSpacing( const double* spacing) { SpacingType s(spacing); this->SetOutputSpacing( s ); } /** * Set the output image origin. * */ template void WarpImageMultiTransformFilter ::SetOutputOrigin( const double* origin) { PointType p(origin); this->SetOutputOrigin(p); } /** * Setup state of filter before multi-threading. * InterpolatorType::SetInputImage is not thread-safe and hence * has to be setup before ThreadedGenerateData */ template void WarpImageMultiTransformFilter ::BeforeThreadedGenerateData() { if( !m_Interpolator ) { itkExceptionMacro(<< "Interpolator not set"); } // Connect input image to interpolator // m_Interpolator->SetInputImage( this->GetInput() ); if (m_CachedSmoothImage.IsNull() && (this->GetInput() )){ m_CachedSmoothImage = const_cast (this->GetInput()); } m_Interpolator->SetInputImage( m_CachedSmoothImage ); } /** * Setup state of filter after multi-threading. */ template void WarpImageMultiTransformFilter ::AfterThreadedGenerateData() { // Disconnect input image from interpolator m_Interpolator->SetInputImage( NULL ); } template void WarpImageMultiTransformFilter ::GenerateInputRequestedRegion() { // call the superclass's implementation Superclass::GenerateInputRequestedRegion(); // request the largest possible region for the input image InputImagePointer inputPtr = const_cast< InputImageType * >( this->GetInput() ); if( inputPtr ) { inputPtr->SetRequestedRegionToLargestPossibleRegion(); } return; } template void WarpImageMultiTransformFilter ::GenerateOutputInformation() { // call the superclass's implementation of this method Superclass::GenerateOutputInformation(); OutputImagePointer outputPtr = this->GetOutput(); if ( !outputPtr ) { return; } typename TOutputImage::RegionType outputLargestPossibleRegion; outputLargestPossibleRegion.SetSize( this->m_OutputSize ); // outputLargestPossibleRegion.SetIndex( 0 ); outputPtr->SetLargestPossibleRegion( outputLargestPossibleRegion ); outputPtr->SetSpacing( this->m_OutputSpacing ); outputPtr->SetOrigin( this->m_OutputOrigin ); outputPtr->SetDirection( this->m_OutputDirection ); // determine if the deformation field is the same dimension as the image // so that it does not need interpolation in the first step DetermineFirstDeformNoInterp(); } template void WarpImageMultiTransformFilter ::SetSmoothScale(double scale) { /* if (m_SmoothScale != scale){ // compute the new cached // std::cout << "change smooth scale: " << m_SmoothScale << " ---> " << scale << std::endl; m_SmoothScale = scale; typename InputImageType::SpacingType inputSpacing = this->GetInput()->GetSpacing(); typename InputImageType::RegionType::SizeType inputSize = this->GetInput()->GetLargestPossibleRegion().GetSize(); typename InputImageType::SpacingType outputSpacing; typename InputImageType::RegionType::SizeType outputSize; double minimumSpacing = inputSpacing.GetVnlVector().min_value(); double maximumSpacing = inputSpacing.GetVnlVector().max_value(); InputImagePointer image = const_cast (this->GetInput()); for ( unsigned int d = 0; d < ImageDimension; d++ ) { double scaling = vnl_math_min( 1.0 / scale * minimumSpacing / inputSpacing[d], static_cast( inputSize[d] ) / 32.0 ); outputSpacing[d] = inputSpacing[d] * scaling; outputSize[d] = static_cast( inputSpacing[d] * static_cast( inputSize[d] ) / outputSpacing[d] + 0.5 ); double sigma = 0.25 * ( outputSpacing[d] / inputSpacing[d] ); if (sigma < 0) sigma=0; typedef RecursiveGaussianImageFilter GaussianFilterType; typename GaussianFilterType::Pointer smoother = GaussianFilterType::New(); smoother->SetInputImage( image ); smoother->SetDirection( d ); smoother->SetNormalizeAcrossScale( false ); // std::cout << "scale = " << scale << " => " << "sigma of dim " << d << ": " << sigma << " out size " << outputSize << " spc1 " << outputSpacing << " in " << inputSpacing << std::endl; smoother->SetSigma( sigma ); if ( smoother->GetSigma() > 0.0 ) { smoother->Update(); image = smoother->GetOutput(); } } SetOutputSpacing( outputSpacing ); SetOutputOrigin( this->GetInput()->GetOrigin() ); SetOutputSize(outputSize); } */ InputImagePointer image = const_cast (this->GetInput()); m_CachedSmoothImage = image; } /** * Compute the output for the region specified by outputRegionForThread. */ template void WarpImageMultiTransformFilter ::ThreadedGenerateData( const OutputImageRegionType& outputRegionForThread, int threadId ) { InputImageConstPointer inputPtr = this->GetInput(); OutputImagePointer outputPtr = this->GetOutput(); //std::cout << "inputPtr->GetOrigin():" << inputPtr->GetOrigin() << std::endl; //std::cout << "outputPtr->GetOrigin():" << outputPtr->GetOrigin() << std::endl; // exit(-1); IndexType index; index.Fill(0); this->m_EdgePaddingValue=inputPtr->GetPixel(index); // support progress methods/callbacks ProgressReporter progress(this, threadId, outputRegionForThread.GetNumberOfPixels()); // iterator for the output image ImageRegionIteratorWithIndex outputIt(outputPtr, outputRegionForThread); while( !outputIt.IsAtEnd() ) { PointType point1, point2; // get the output image index IndexType index = outputIt.GetIndex(); outputPtr->TransformIndexToPhysicalPoint( index, point1 ); bool isinside = MultiTransformPoint(point1, point2, m_bFirstDeformNoInterp, index); // std::cout << "point1:" << point1 << " point2:" << point2 << " index:" << index << std::endl; // exit(-1); // warp the image // get the interpolated value if( isinside && (m_Interpolator->IsInsideBuffer( point2 )) ) { PixelType value = static_cast(m_Interpolator->Evaluate(point2)); outputIt.Set( value ); } else { // std::cout << "OUTSIDE" << " isinside:" << isinside << " m_Interpolator->IsInsideBuffer( point2 ):" << m_Interpolator->IsInsideBuffer( point2 ) << std::endl; outputIt.Set( m_EdgePaddingValue ); } ++outputIt; } progress.CompletedPixel(); } //template //void //WarpImageMultiTransformFilter //::UpdateSizeByScale() //{ // // // DeformationFieldPointer field = this->GetDeformationField(); // // // // SetOutputSpacing( field->GetSpacing() / m_SmoothScale ); // // SetOutputOrigin( field->GetOrigin() ); // // // // typename InputImageType::SizeType imgsz = field->GetLargestPossibleRegion().GetSize(); // // for(int ii = 0; ii < InputImageType::ImageDimension; ii++) imgsz[ii] = (typename InputImageType::SizeType::SizeValueType) (imgsz[ii] * m_SmoothScale + 0.5); // // // // SetOutputSize(imgsz); // //} template void WarpImageMultiTransformFilter ::PushBackAffineTransform(const TransformType* t) { if (t){ VarTransformType t1; t1.aex.aff = const_cast (t); m_TransformList.push_back(SingleTransformItemType(EnumAffineType, t1)); } } template void WarpImageMultiTransformFilter ::PushBackDeformationFieldTransform(const DeformationFieldType* t) { if (t){ VarTransformType t1; t1.dex.field = const_cast (t); t1.dex.vinterp = DefaultVectorInterpolatorType::New(); t1.dex.vinterp->SetInputImage(t1.dex.field); // t1.dex.vinterp->SetParameters(NULL,1); m_TransformList.push_back(SingleTransformItemType(EnumDeformationFieldType, t1)); } } template bool WarpImageMultiTransformFilter ::MultiTransformSinglePoint(const PointType &point1, PointType &point2) { IndexType null_index; bool isinside = MultiTransformPoint(point1, point2, false, null_index); return isinside; } template bool WarpImageMultiTransformFilter ::IsOutOfNumericBoundary(const PointType &p) { const DisplacementScalarValueType kMaxDisp = itk::NumericTraits::max(); bool b = false; for (int i = 0; i < ImageDimension; i++) { if (p[i]>=kMaxDisp ){ b = true; break; } } return b; } template void WarpImageMultiTransformFilter ::ComposeAffineOnlySequence(const PointType ¢er_output, TransformTypePointer &affine_output) { // std::cout << "test " ; // TransformTypePointer affine_output = TransformType::New(); affine_output->SetIdentity(); affine_output->SetCenter(center_output); // std::cout << affine_output; typename TransformListType::iterator it = m_TransformList.begin(); for(;it!=m_TransformList.end(); it++){ SingleTransformType ttype = it->first; switch(ttype){ case EnumAffineType: { TransformTypePointer aff = it->second.aex.aff; affine_output->Compose(aff, 0); // std::cout << affine_output; break; } case EnumDeformationFieldType: std::cout << " Ignore deformation type ... " << std::endl; break; default: itkExceptionMacro(<< "Single Transform Not Supported!"); } } return; } template bool WarpImageMultiTransformFilter ::MultiInverseAffineOnlySinglePoint(const PointType &p1, PointType &p2) { bool isinside = true; PointType point1 = p1, point2; typename TransformListType::iterator it = m_TransformList.begin(); for(;it!=m_TransformList.end(); it++){ SingleTransformType ttype = it->first; switch(ttype){ case EnumAffineType: { TransformTypePointer aff = it->second.aex.aff; TransformTypePointer aff_inv = TransformTypePointer::ObjectType::New(); // std::cout << "aff before:" << aff << std::endl; aff->GetInverse(aff_inv); // aff->GetInverse(aff); // std::cout << "aff after:" << aff << std::endl; // std::cout << "aff_inv after:" << aff_inv << std::endl; point2 = aff_inv->TransformPoint(point1); point1 = point2; isinside = true; break; } case EnumDeformationFieldType: break; default: itkExceptionMacro(<< "Single Transform Not Supported!"); } if (IsOutOfNumericBoundary(point2)) {isinside = false; break;} point1 = point2; } p2 = point2; return isinside; } template bool WarpImageMultiTransformFilter ::MultiTransformPoint(const PointType &p1, PointType &p2, bool bFisrtDeformNoInterp, const IndexType &index) { bool isinside = false; PointType point1 = p1, point2; typename TransformListType::iterator it = m_TransformList.begin(); for(;it!=m_TransformList.end(); it++){ SingleTransformType ttype = it->first; switch(ttype){ case EnumAffineType: { TransformTypePointer aff = it->second.aex.aff; point2 = aff->TransformPoint(point1); point1 = point2; isinside = true; } break; case EnumDeformationFieldType: { DeformationFieldPointer fieldPtr = it->second.dex.field; if (bFisrtDeformNoInterp && it==m_TransformList.begin() ){ // use discrete coordinates DisplacementType displacement = fieldPtr->GetPixel(index); for(int j = 0; jTransformPhysicalPointToContinuousIndex(point1, contind); isinside = fieldPtr->GetLargestPossibleRegion().IsInside( contind ); VectorInterpolatorPointer vinterp = it->second.dex.vinterp; typename DefaultVectorInterpolatorType::OutputType disp2; if (isinside) disp2 = vinterp->EvaluateAtContinuousIndex( contind ); else disp2.Fill(0); for (int jj=0; jj void WarpImageMultiTransformFilter ::DetermineFirstDeformNoInterp() { m_bFirstDeformNoInterp = false; if (m_TransformList.size() >0 ){ if ((m_TransformList.front().first == EnumDeformationFieldType)){ DeformationFieldPointer field = m_TransformList.front().second.dex.field; m_bFirstDeformNoInterp = (this->GetOutputSize() == field->GetLargestPossibleRegion().GetSize()) & (this->GetOutputSpacing() ==field->GetSpacing()) & (this->GetOutputOrigin() == field->GetOrigin()); // std::cout << "in set: field size: " << field->GetLargestPossibleRegion().GetSize() // << "output spacing: " << this->GetOutputSize() << std::endl; // std::cout << field->GetSpacing() << " | " << this->GetOutputSpacing() << std::endl; // std::cout << field->GetOrigin() << " | " << this->GetOutputOrigin() << std::endl; } } } } // end namespace itk #endif //__itkWarpImageMultiTransformFilter_txx ants-1.9.2+svn680.dfsg/Utilities/itkWarpImageWAffineFilter.h000066400000000000000000000247071147325206600236100ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkWarpImageWAffineFilter.h,v $ Language: C++ Date: $Date: 2008/11/15 23:46:06 $ Version: $Revision: 1.15 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkWarpImageWAffineFilter_h #define __itkWarpImageWAffineFilter_h #include "itkImageToImageFilter.h" #include "itkInterpolateImageFunction.h" #include "itkLinearInterpolateImageFunction.h" #include "itkPoint.h" #include "itkFixedArray.h" #include "itkRecursiveGaussianImageFilter.h" namespace itk { /** \class WarpImageWAffineFilter * \brief Warps an image using an input deformation field. * * WarpImageWAffineFilter warps an existing image with respect to * a given deformation field. * * A deformation field is represented as a image whose pixel type is some * vector type with at least N elements, where N is the dimension of * the input image. The vector type must support element access via operator * []. * * The output image is produced by inverse mapping: the output pixels * are mapped back onto the input image. This scheme avoids the creation of * any holes and overlaps in the output image. * * Each vector in the deformation field represent the distance between * a geometric point in the input space and a point in the output space such * that: * * \f[ p_{in} = p_{out} + d \f] * * Typically the mapped position does not correspond to an integer pixel * position in the input image. Interpolation via an image function * is used to compute values at non-integer positions. The default * interpolation typed used is the LinearInterpolateImageFunction. * The user can specify a particular interpolation function via * SetInterpolator(). Note that the input interpolator must derive * from base class InterpolateImageFunction. * * Position mapped to outside of the input image buffer are assigned * a edge padding value. * * The LargetPossibleRegion for the output is inherited from the * input deformation field. The output image spacing and origin may be set * via SetOutputSpacing, SetOutputOrigin. The default are respectively a * vector of 1's and a vector of 0's. * * This class is templated over the type of the input image, the * type of the output image and the type of the deformation field. * * The input image is set via SetInput. The input deformation field * is set via SetDeformationField. * * This filter is implemented as a multithreaded filter. * * \warning This filter assumes that the input type, output type * and deformation field type all have the same number of dimensions. * * \ingroup GeometricTransforms MultiThreaded */ template < class TInputImage, class TOutputImage, class TDeformationField, class TTransform > class ITK_EXPORT WarpImageWAffineFilter : public ImageToImageFilter { public: /** transform order type **/ typedef enum _TransformOrderType {AffineFirst=0, AffineLast} TransformOrderType; /** Standard class typedefs. */ typedef WarpImageWAffineFilter Self; typedef ImageToImageFilter Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Run-time type information (and related methods) */ itkTypeMacro( WarpImageWAffineFilter, ImageToImageFilter ); /** Typedef to describe the output image region type. */ typedef typename TOutputImage::RegionType OutputImageRegionType; /** Inherit some types from the superclass. */ typedef typename Superclass::InputImageType InputImageType; typedef typename Superclass::InputImagePointer InputImagePointer; typedef typename Superclass::OutputImageType OutputImageType; typedef typename Superclass::OutputImagePointer OutputImagePointer; typedef typename Superclass::InputImageConstPointer InputImageConstPointer; typedef typename OutputImageType::IndexType IndexType; typedef typename OutputImageType::SizeType SizeType; typedef typename OutputImageType::PixelType PixelType; typedef typename OutputImageType::SpacingType SpacingType; /** Determine the image dimension. */ itkStaticConstMacro(ImageDimension, unsigned int, TOutputImage::ImageDimension ); itkStaticConstMacro(InputImageDimension, unsigned int, TInputImage::ImageDimension ); itkStaticConstMacro(DeformationFieldDimension, unsigned int, TDeformationField::ImageDimension ); /** Deformation field typedef support. */ typedef TDeformationField DeformationFieldType; typedef typename DeformationFieldType::Pointer DeformationFieldPointer; typedef typename DeformationFieldType::PixelType DisplacementType; /** songgang: Affine transform typedef support. */ typedef TTransform TransformType; typedef typename TransformType::Pointer TransformTypePointer; /** Interpolator typedef support. */ typedef double CoordRepType; typedef InterpolateImageFunction InterpolatorType; typedef typename InterpolatorType::Pointer InterpolatorPointer; typedef LinearInterpolateImageFunction DefaultInterpolatorType; /** Point type */ typedef Point PointType; /** Set the deformation field. */ void SetDeformationField( const DeformationFieldType * field ); /** Get a pointer the deformation field. */ DeformationFieldType * GetDeformationField(void); /** songgang: Set / Get the affine transform. */ // void SetAffineTransform( const TransformType * aff ); // TransformType * GetAffineTransform(void); itkSetObjectMacro(AffineTransform, TransformType); itkGetObjectMacro(AffineTransform, TransformType); itkSetEnumMacro(TransformOrder, TransformOrderType); itkGetEnumMacro(TransformOrder, TransformOrderType); /** Set the interpolator function. */ itkSetObjectMacro( Interpolator, InterpolatorType ); /** Get a pointer to the interpolator function. */ itkGetObjectMacro( Interpolator, InterpolatorType ); /** Set the output image spacing. */ itkSetMacro(OutputSpacing, SpacingType); virtual void SetOutputSpacing( const double* values); /** Get the output image spacing. */ itkGetConstReferenceMacro(OutputSpacing, SpacingType); /** Set the output image origin. */ itkSetMacro(OutputOrigin, PointType); virtual void SetOutputOrigin( const double* values); /** Set the output image size. */ itkSetMacro(OutputSize, SizeType); // virtual void SetOutputSize( const double *values); itkGetConstReferenceMacro(OutputSize, SizeType); /** Get the output image origin. */ itkGetConstReferenceMacro(OutputOrigin, PointType); /** Set the edge padding value */ itkSetMacro( EdgePaddingValue, PixelType ); /** Get the edge padding value */ itkGetMacro( EdgePaddingValue, PixelType ); /** WarpImageWAffineFilter produces an image which is a different * size than its input image. As such, it needs to provide an * implemenation for GenerateOutputInformation() which set * the output information according the OutputSpacing, OutputOrigin * and the deformation field's LargestPossibleRegion. */ virtual void GenerateOutputInformation(); /** It is difficult to compute in advance the input image region * required to compute the requested output region. Thus the safest * thing to do is to request for the whole input image. * * For the deformation field, the input requested region * set to be the same as that of the output requested region. */ virtual void GenerateInputRequestedRegion(); /** This method is used to set the state of the filter before * multi-threading. */ virtual void BeforeThreadedGenerateData(); /** This method is used to set the state of the filter after * multi-threading. */ virtual void AfterThreadedGenerateData(); /** precompute the smoothed image if necessary **/ void SetSmoothScale(double scale); double GetSmoothScale() {return m_SmoothScale;}; void UpdateSizeByScale(); #ifdef ITK_USE_CONCEPT_CHECKING /** Begin concept checking */ itkConceptMacro(SameDimensionCheck1, (Concept::SameDimension)); itkConceptMacro(SameDimensionCheck2, (Concept::SameDimension)); itkConceptMacro(InputHasNumericTraitsCheck, (Concept::HasNumericTraits)); itkConceptMacro(DeformationFieldHasNumericTraitsCheck, (Concept::HasNumericTraits)); /** End concept checking */ #endif protected: WarpImageWAffineFilter(); ~WarpImageWAffineFilter() {}; void PrintSelf(std::ostream& os, Indent indent) const; /** WarpImageWAffineFilter is implemented as a multi-threaded filter. * As such, it needs to provide and implementation for * ThreadedGenerateData(). */ void ThreadedGenerateData(const OutputImageRegionType& outputRegionForThread, int threadId ); private: WarpImageWAffineFilter(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented PixelType m_EdgePaddingValue; SpacingType m_OutputSpacing; PointType m_OutputOrigin; SizeType m_OutputSize; TransformOrderType m_TransformOrder; // songgang: affine transform TransformTypePointer m_AffineTransform; InterpolatorPointer m_Interpolator; /** cached smoothed image (for downsampling) **/ InputImagePointer m_CachedSmoothImage; double m_SmoothScale; }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkWarpImageWAffineFilter.txx" #endif #endif ants-1.9.2+svn680.dfsg/Utilities/itkWarpImageWAffineFilter.txx000066400000000000000000000375261147325206600242070ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkWarpImageWAffineFilter.txx,v $ Language: C++ Date: $Date: 2008/11/10 15:39:12 $ Version: $Revision: 1.12 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkWarpImageWAffineFilter_txx #define __itkWarpImageWAffineFilter_txx #include "itkWarpImageWAffineFilter.h" #include "itkImageRegionIterator.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkNumericTraits.h" #include "itkProgressReporter.h" #include "itkVectorLinearInterpolateImageFunction.h" namespace itk { /** * Default constructor. */ template WarpImageWAffineFilter ::WarpImageWAffineFilter() { // Setup the number of required inputs this->SetNumberOfRequiredInputs( 2 ); // Setup default values m_OutputSpacing.Fill( 1.0 ); m_OutputOrigin.Fill( 0.0 ); m_EdgePaddingValue = NumericTraits::Zero; // Setup default interpolator typename DefaultInterpolatorType::Pointer interp = DefaultInterpolatorType::New(); m_Interpolator = static_cast( interp.GetPointer() ); m_SmoothScale = -1; m_TransformOrder = AffineFirst; } /** * Standard PrintSelf method. */ template void WarpImageWAffineFilter ::PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf(os, indent); os << indent << "OutputSpacing: " << m_OutputSpacing << std::endl;; os << indent << "OutputOrigin: " << m_OutputOrigin << std::endl; os << indent << "EdgePaddingValue: " << static_cast::PrintType>(m_EdgePaddingValue) << std::endl; os << indent << "Interpolator: " << m_Interpolator.GetPointer() << std::endl; } /** * Set the output image spacing. * */ template void WarpImageWAffineFilter ::SetOutputSpacing( const double* spacing) { SpacingType s(spacing); this->SetOutputSpacing( s ); } /** * Set the output image origin. * */ template void WarpImageWAffineFilter ::SetOutputOrigin( const double* origin) { PointType p(origin); this->SetOutputOrigin(p); } /** * Set deformation field as Inputs[1] for this ProcessObject. * */ template void WarpImageWAffineFilter ::SetDeformationField( const DeformationFieldType * field ) { // const cast is needed because the pipeline is not const-correct. DeformationFieldType * input = const_cast< DeformationFieldType * >( field ); this->ProcessObject::SetNthInput( 1, input ); } /** * Return a pointer to the deformation field. */ template typename WarpImageWAffineFilter ::DeformationFieldType * WarpImageWAffineFilter ::GetDeformationField(void) { return static_cast ( this->ProcessObject::GetInput( 1 )); } /** * Setup state of filter before multi-threading. * InterpolatorType::SetInputImage is not thread-safe and hence * has to be setup before ThreadedGenerateData */ template void WarpImageWAffineFilter ::BeforeThreadedGenerateData() { if( !m_Interpolator ) { itkExceptionMacro(<< "Interpolator not set"); } // Connect input image to interpolator // m_Interpolator->SetInputImage( this->GetInput() ); if (m_CachedSmoothImage.IsNull()){ m_CachedSmoothImage = const_cast (this->GetInput()); } m_Interpolator->SetInputImage( m_CachedSmoothImage ); } /** * Setup state of filter after multi-threading. */ template void WarpImageWAffineFilter ::AfterThreadedGenerateData() { // Disconnect input image from interpolator m_Interpolator->SetInputImage( NULL ); } ///** // * Compute the output for the region specified by outputRegionForThread. // */ //template //void //WarpImageWAffineFilter //::ThreadedGenerateData( // const OutputImageRegionType& outputRegionForThread, // int threadId ) //{ // // InputImageConstPointer inputPtr = this->GetInput(); // OutputImagePointer outputPtr = this->GetOutput(); // DeformationFieldPointer fieldPtr = this->GetDeformationField(); // TransformTypePointer aff = this->GetAffineTransform(); // // // std::cout << aff << std::endl; // // // support progress methods/callbacks // ProgressReporter progress(this, threadId, outputRegionForThread.GetNumberOfPixels()); // // // iterator for the output image // ImageRegionIteratorWithIndex outputIt( // outputPtr, outputRegionForThread ); // // // iterator for the deformation field // ImageRegionIterator fieldIt( // fieldPtr, outputRegionForThread ); // // IndexType index; // PointType point1, point2, point3; // DisplacementType displacement; // // // int cnt = 0; // while( !outputIt.IsAtEnd() ) // { // // get the output image index // index = outputIt.GetIndex(); // outputPtr->TransformIndexToPhysicalPoint( index, point1 ); // // // get the required displacement // displacement = fieldIt.Get(); // // // compute the required input image point // for(unsigned int j = 0; j < ImageDimension; j++ ) // { // point2[j] = point1[j] + displacement[j]; // } // // // songgang: followed by affine transform // // use affine transform // point3 = aff->TransformPoint(point2); // // // get the interpolated value // if( m_Interpolator->IsInsideBuffer( point3 ) ) // { // PixelType value = static_cast( // m_Interpolator->Evaluate( point3 ) ); // outputIt.Set( value ); // } // else // { // outputIt.Set( m_EdgePaddingValue ); // } // ++outputIt; // ++fieldIt; // progress.CompletedPixel(); // } // //} template void WarpImageWAffineFilter ::GenerateInputRequestedRegion() { // call the superclass's implementation Superclass::GenerateInputRequestedRegion(); // request the largest possible region for the input image InputImagePointer inputPtr = const_cast< InputImageType * >( this->GetInput() ); if( inputPtr ) { inputPtr->SetRequestedRegionToLargestPossibleRegion(); } // just propagate up the output requested region for the // deformation field. DeformationFieldPointer fieldPtr = this->GetDeformationField(); // OutputImagePointer outputPtr = this->GetOutput(); if( fieldPtr ) { // fieldPtr->SetRequestedRegion( outputPtr->GetRequestedRegion() ); fieldPtr->SetRequestedRegionToLargestPossibleRegion(); } return; } template void WarpImageWAffineFilter ::GenerateOutputInformation() { // call the superclass's implementation of this method Superclass::GenerateOutputInformation(); OutputImagePointer outputPtr = this->GetOutput(); if ( !outputPtr ) { return; } typename TOutputImage::RegionType outputLargestPossibleRegion; outputLargestPossibleRegion.SetSize( m_OutputSize ); // outputLargestPossibleRegion.SetIndex( 0 ); outputPtr->SetLargestPossibleRegion( outputLargestPossibleRegion ); outputPtr->SetSpacing( m_OutputSpacing ); outputPtr->SetOrigin( m_OutputOrigin ); // DeformationFieldPointer fieldPtr = this->GetDeformationField(); // if( fieldPtr ) // { // outputPtr->SetLargestPossibleRegion( fieldPtr-> // GetLargestPossibleRegion() ); // } } template void WarpImageWAffineFilter ::SetSmoothScale(double scale) { if (m_SmoothScale != scale){ // compute the new cached m_SmoothScale = scale; typename InputImageType::SpacingType inputSpacing = this->GetInput()->GetSpacing(); typename InputImageType::RegionType::SizeType inputSize = this->GetInput()->GetLargestPossibleRegion().GetSize(); typename InputImageType::SpacingType outputSpacing; typename InputImageType::RegionType::SizeType outputSize; double minimumSpacing = inputSpacing.GetVnlVector().min_value(); double maximumSpacing = inputSpacing.GetVnlVector().max_value(); InputImagePointer image = const_cast (this->GetInput()); for ( unsigned int d = 0; d < ImageDimension; d++ ) { double scaling = vnl_math_min( 1.0 / scale * minimumSpacing / inputSpacing[d], static_cast( inputSize[d] ) / 32.0 ); outputSpacing[d] = inputSpacing[d] * scaling; outputSize[d] = static_cast( inputSpacing[d] * static_cast( inputSize[d] ) / outputSpacing[d] + 0.5 ); double sigma = 0.2 * ( outputSpacing[d] / inputSpacing[d] ); typedef RecursiveGaussianImageFilter GaussianFilterType; typename GaussianFilterType::Pointer smoother = GaussianFilterType::New(); smoother->SetInputImage( image ); smoother->SetDirection( d ); smoother->SetNormalizeAcrossScale( false ); // std::cout << "scale = " << scale << " => " << "sigma of dim " << d << ": " << sigma << " outsize " << outputSize << std::endl; smoother->SetSigma( sigma ); if ( smoother->GetSigma() > 0.0 ) { smoother->Update(); image = smoother->GetOutput(); } } m_CachedSmoothImage = image; SetOutputSpacing( outputSpacing ); SetOutputOrigin( this->GetInput()->GetOrigin() ); SetOutputSize(outputSize); } } /** * Compute the output for the region specified by outputRegionForThread. */ template void WarpImageWAffineFilter ::ThreadedGenerateData( const OutputImageRegionType& outputRegionForThread, int threadId ) { InputImageConstPointer inputPtr = this->GetInput(); OutputImagePointer outputPtr = this->GetOutput(); DeformationFieldPointer fieldPtr = this->GetDeformationField(); TransformTypePointer aff = this->GetAffineTransform(); // std::cout << aff << std::endl; // support progress methods/callbacks ProgressReporter progress(this, threadId, outputRegionForThread.GetNumberOfPixels()); // iterator for the output image ImageRegionIteratorWithIndex outputIt( outputPtr, outputRegionForThread ); // iterator for the deformation field ImageRegionIterator fieldIt( fieldPtr, outputRegionForThread ); IndexType index; PointType point1, point2, point3; DisplacementType displacement; std::cout << "m_TransformOrder: " << m_TransformOrder << std::endl; int cnt = 0; while( !outputIt.IsAtEnd() ) { // get the output image index index = outputIt.GetIndex(); outputPtr->TransformIndexToPhysicalPoint( index, point1 ); // get the required displacement displacement = fieldIt.Get(); // compute the required input image point bool isinside = false; switch (m_TransformOrder){ case AffineFirst: { for(unsigned int j = 0; j < ImageDimension; j++ ) { point2[j] = point1[j] + displacement[j]; } point3 = aff->TransformPoint(point2); isinside = true; // affine transform is always valid } break; case AffineLast: { point2 = aff->TransformPoint(point1); typedef itk::VectorLinearInterpolateImageFunction DefaultInterpolatorType; typename DefaultInterpolatorType::Pointer vinterp = DefaultInterpolatorType::New(); vinterp->SetInputImage(fieldPtr); typename DefaultInterpolatorType::ContinuousIndexType contind; // isinside = fieldPtr->TransformPhysicalPointToContinuousIndex(point2, contind); // explicitly written to avoid double / float type dismatching for (unsigned int i = 0; i < ImageDimension; i++) { contind[i] = ( (point2[i]- fieldPtr->GetOrigin()[i]) / fieldPtr->GetSpacing()[i] ); } isinside = fieldPtr->GetLargestPossibleRegion().IsInside( contind ); typename DefaultInterpolatorType::OutputType disp2; if (isinside) disp2 = vinterp->EvaluateAtContinuousIndex( contind ); else disp2.Fill(0); for (int jj=0; jjIsInsideBuffer( point3 )) ) { PixelType value = static_cast( m_Interpolator->Evaluate( point3 ) ); outputIt.Set( value ); } else { outputIt.Set( m_EdgePaddingValue ); } ++outputIt; ++fieldIt; progress.CompletedPixel(); } } template void WarpImageWAffineFilter ::UpdateSizeByScale() { DeformationFieldPointer field = this->GetDeformationField(); SetOutputSpacing( field->GetSpacing() / m_SmoothScale ); SetOutputOrigin( field->GetOrigin() ); typename InputImageType::SizeType imgsz = field->GetLargestPossibleRegion().GetSize(); for(int ii = 0; ii < InputImageType::ImageDimension; ii++) imgsz[ii] = (typename InputImageType::SizeType::SizeValueType) (imgsz[ii] * m_SmoothScale + 0.5); SetOutputSize(imgsz); } } // end namespace itk #endif ants-1.9.2+svn680.dfsg/Utilities/itkWarpTensorImageMultiTransformFilter.h000066400000000000000000000302371147325206600264450ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkWarpTensorImageMultiTransformFilter.h,v $ Language: C++ Date: $Date: 2009/01/28 22:37:55 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef ITKWarpTensorImageMultiTRANSFORMFILTER_H_ #define ITKWarpTensorImageMultiTRANSFORMFILTER_H_ #include "itkImageToImageFilter.h" #include "itkVectorInterpolateImageFunction.h" #include "itkLinearInterpolateImageFunction.h" #include "itkVectorLinearInterpolateImageFunction.h" #include "itkPoint.h" #include "itkFixedArray.h" #include "itkRecursiveGaussianImageFilter.h" #include namespace itk { /** \class WarpTensorImageMultiTransformFilter * \brief Warps an image using an input deformation field. * * WarpTensorImageMultiTransformFilter warps an existing image with respect to * a given deformation field. * * A deformation field is represented as a image whose pixel type is some * vector type with at least N elements, where N is the dimension of * the input image. The vector type must support element access via operator * []. * * The output image is produced by inverse mapping: the output pixels * are mapped back onto the input image. This scheme avoids the creation of * any holes and overlaps in the output image. * * Each vector in the deformation field represent the distance between * a geometric point in the input space and a point in the output space such * that: * * \f[ p_{in} = p_{out} + d \f] * * Typically the mapped position does not correspond to an integer pixel * position in the input image. Interpolation via an image function * is used to compute values at non-integer positions. The default * interpolation typed used is the LinearInterpolateImageFunction. * The user can specify a particular interpolation function via * SetInterpolator(). Note that the input interpolator must derive * from base class InterpolateImageFunction. * * Position mapped to outside of the input image buffer are assigned * a edge padding value. * * The LargetPossibleRegion for the output is inherited from the * input deformation field. The output image spacing and origin may be set * via SetOutputSpacing, SetOutputOrigin. The default are respectively a * vector of 1's and a vector of 0's. * * This class is templated over the type of the input image, the * type of the output image and the type of the deformation field. * * The input image is set via SetInput. The input deformation field * is set via SetDeformationField. * * This filter is implemented as a multithreaded filter. * * \warning This filter assumes that the input type, output type * and deformation field type all have the same number of dimensions. * * \ingroup GeometricTransforms MultiThreaded */ template < class TInputImage, class TOutputImage, class TDeformationField, class TTransform > class ITK_EXPORT WarpTensorImageMultiTransformFilter : public ImageToImageFilter { public: /** transform order type **/ // typedef enum _TransformOrderType {AffineFirst=0, AffineLast} TransformOrderType; /** transform type **/ typedef enum _SingleTransformType {EnumAffineType=0, EnumDeformationFieldType} SingleTransformType; /** Standard class typedefs. */ typedef WarpTensorImageMultiTransformFilter Self; typedef ImageToImageFilter Superclass; typedef SmartPointer Pointer; typedef SmartPointer ConstPointer; /** Method for creation through the object factory. */ itkNewMacro(Self); /** Run-time type information (and related methods) */ itkTypeMacro( WarpTensorImageMultiTransformFilter, ImageToImageFilter ); /** Typedef to describe the output image region type. */ typedef typename TOutputImage::RegionType OutputImageRegionType; /** Inherit some types from the superclass. */ typedef typename Superclass::InputImageType InputImageType; typedef typename Superclass::InputImagePointer InputImagePointer; typedef typename Superclass::OutputImageType OutputImageType; typedef typename Superclass::OutputImagePointer OutputImagePointer; typedef typename Superclass::InputImageConstPointer InputImageConstPointer; typedef typename OutputImageType::IndexType IndexType; typedef typename OutputImageType::SizeType SizeType; typedef typename OutputImageType::PixelType PixelType; typedef typename OutputImageType::SpacingType SpacingType; typedef typename OutputImageType::DirectionType DirectionType; /** Determine the image dimension. */ itkStaticConstMacro(ImageDimension, unsigned int, TOutputImage::ImageDimension ); itkStaticConstMacro(InputImageDimension, unsigned int, TInputImage::ImageDimension ); itkStaticConstMacro(DeformationFieldDimension, unsigned int, TDeformationField::ImageDimension ); /** Deformation field typedef support. */ typedef TDeformationField DeformationFieldType; typedef typename DeformationFieldType::Pointer DeformationFieldPointer; typedef typename DeformationFieldType::PixelType DisplacementType; typedef typename DisplacementType::ValueType DisplacementScalarValueType; /** songgang: Affine transform typedef support. */ typedef TTransform TransformType; typedef typename TransformType::Pointer TransformTypePointer; /** Interpolator typedef support. */ typedef double CoordRepType; typedef VectorInterpolateImageFunction InterpolatorType; typedef typename InterpolatorType::Pointer InterpolatorPointer; typedef VectorLinearInterpolateImageFunction DefaultInterpolatorType; typedef VectorLinearInterpolateImageFunction DefaultVectorInterpolatorType; typedef typename DefaultVectorInterpolatorType::Pointer VectorInterpolatorPointer; /** Point type */ typedef Point PointType; typedef struct _DeformationTypeEx{ DeformationFieldPointer field; VectorInterpolatorPointer vinterp; } DeformationTypeEx; typedef struct _AffineTypeEx{ TransformTypePointer aff; } AffineTypeEx; typedef struct _VarTransformType{ AffineTypeEx aex; DeformationTypeEx dex; } VarTransformType; typedef std::pair SingleTransformItemType; typedef std::list TransformListType; /** Set the interpolator function. */ itkSetObjectMacro( Interpolator, InterpolatorType ); /** Get a pointer to the interpolator function. */ itkGetObjectMacro( Interpolator, InterpolatorType ); /** Set the output image spacing. */ itkSetMacro(OutputSpacing, SpacingType); virtual void SetOutputSpacing( const double* values); /** Get the output image spacing. */ itkGetConstReferenceMacro(OutputSpacing, SpacingType); /** Set the output image spacing. */ itkSetMacro(OutputDirection, DirectionType); /** Get the output image spacing. */ itkGetConstReferenceMacro(OutputDirection, DirectionType); /** Set the output image origin. */ itkSetMacro(OutputOrigin, PointType); virtual void SetOutputOrigin( const double* values); /** Get the output image origin. */ itkGetConstReferenceMacro(OutputOrigin, PointType); /** Set the output image size. */ itkSetMacro(OutputSize, SizeType); // virtual void SetOutputSize( const double *values); itkGetConstReferenceMacro(OutputSize, SizeType); /** Set the edge padding value */ itkSetMacro( EdgePaddingValue, PixelType ); /** Get the edge padding value */ itkGetMacro( EdgePaddingValue, PixelType ); TransformListType & GetTransformList(){ return m_TransformList; } void PrintTransformList(); /** WarpTensorImageMultiTransformFilter produces an image which is a different * size than its input image. As such, it needs to provide an * implemenation for GenerateOutputInformation() which set * the output information according the OutputSpacing, OutputOrigin * and the deformation field's LargestPossibleRegion. */ virtual void GenerateOutputInformation(); /** It is difficult to compute in advance the input image region * required to compute the requested output region. Thus the safest * thing to do is to request for the whole input image. * * For the deformation field, the input requested region * set to be the same as that of the output requested region. */ virtual void GenerateInputRequestedRegion(); /** This method is used to set the state of the filter before * multi-threading. */ virtual void BeforeThreadedGenerateData(); /** This method is used to set the state of the filter after * multi-threading. */ virtual void AfterThreadedGenerateData(); /** precompute the smoothed image if necessary **/ void SetSmoothScale(double scale); double GetSmoothScale() {return m_SmoothScale;}; // void UpdateSizeByScale(); void PushBackAffineTransform(const TransformType* t); void PushBackDeformationFieldTransform(const DeformationFieldType* t); void ComposeAffineOnlySequence(const PointType ¢er_output, TransformTypePointer &affine_output); bool MultiInverseAffineOnlySinglePoint(const PointType &point1, PointType &point2); bool MultiTransformSinglePoint(const PointType &point1, PointType &point2); bool MultiTransformPoint(const PointType &point1, PointType &point2, bool bFisrtDeformNoInterp, const IndexType &index); void DetermineFirstDeformNoInterp(); inline bool IsOutOfNumericBoundary(const PointType &p); // set interpolator from outside // virtual void SetInterpolator(InterpolatorPointer interp); #ifdef ITK_USE_CONCEPT_CHECKING /** Begin concept checking */ itkConceptMacro(SameDimensionCheck1, (Concept::SameDimension)); itkConceptMacro(SameDimensionCheck2, (Concept::SameDimension)); //removed to be compatible with vector form input image // itkConceptMacro(InputHasNumericTraitsCheck, // (Concept::HasNumericTraits)); itkConceptMacro(DeformationFieldHasNumericTraitsCheck, (Concept::HasNumericTraits)); /** End concept checking */ #endif bool m_bFirstDeformNoInterp; protected: WarpTensorImageMultiTransformFilter(); ~WarpTensorImageMultiTransformFilter() {}; void PrintSelf(std::ostream& os, Indent indent) const; /** WarpTensorImageMultiTransformFilter is implemented as a multi-threaded filter. * As such, it needs to provide and implementation for * ThreadedGenerateData(). */ void ThreadedGenerateData(const OutputImageRegionType& outputRegionForThread, int threadId ); InterpolatorPointer m_Interpolator; PixelType m_EdgePaddingValue; SpacingType m_OutputSpacing; PointType m_OutputOrigin; SizeType m_OutputSize; DirectionType m_OutputDirection; TransformListType m_TransformList; double m_SmoothScale; InputImagePointer m_CachedSmoothImage; private: WarpTensorImageMultiTransformFilter(const Self&); //purposely not implemented void operator=(const Self&); //purposely not implemented }; } // end namespace itk #ifndef ITK_MANUAL_INSTANTIATION #include "itkWarpTensorImageMultiTransformFilter.txx" #endif #endif /*ITKWarpTensorImageMultiTRANSFORMFILTER_H_*/ ants-1.9.2+svn680.dfsg/Utilities/itkWarpTensorImageMultiTransformFilter.txx000066400000000000000000000503131147325206600270360ustar00rootroot00000000000000/*========================================================================= Program: Advanced Normalization Tools Module: $RCSfile: itkWarpTensorImageMultiTransformFilter.txx,v $ Language: C++ Date: $Date: 2009/01/28 22:38:04 $ Version: $Revision: 1.1 $ Copyright (c) ConsortiumOfANTS. All rights reserved. See accompanying COPYING.txt or http://sourceforge.net/projects/advants/files/ANTS/ANTSCopyright.txt for details. This software is distributed WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the above copyright notices for more information. =========================================================================*/ #ifndef __itkWarpTensorImageMultiTransformFilter_txx #define __itkWarpTensorImageMultiTransformFilter_txx #include "itkWarpTensorImageMultiTransformFilter.h" #include "itkImageRegionIterator.h" #include "itkImageRegionIteratorWithIndex.h" #include "itkNumericTraits.h" #include "itkProgressReporter.h" #include "itkVectorLinearInterpolateImageFunction.h" #include namespace itk { /** * Default constructor. */ template WarpTensorImageMultiTransformFilter ::WarpTensorImageMultiTransformFilter() { // Setup the number of required inputs this->SetNumberOfRequiredInputs( 1 ); // Setup default values m_OutputSpacing.Fill( 1.0 ); m_OutputOrigin.Fill( 0.0 ); PixelType Zero; Zero.SetIdentity(); Zero=Zero*1.e-6; m_EdgePaddingValue = Zero; // Setup default interpolator typename DefaultInterpolatorType::Pointer interp = DefaultInterpolatorType::New(); m_Interpolator = static_cast( interp.GetPointer() ); m_SmoothScale = -1; // m_bOutputDeformationField = false; // m_TransformOrder = AffineFirst; } //template //void //WarpTensorImageMultiTransformFilter //::SetInterpolator1(InterpolatorPointer interp) //{ // m_Interpolator = static_cast (interp.GetPointer()); // std::cout << "set interpolator in WarpImage:" << interp << std::endl; //} template void WarpTensorImageMultiTransformFilter ::PrintTransformList() { std::cout << "transform list: " << std::endl; typename TransformListType::iterator it = (m_TransformList.begin()); for(int ii=0; it != m_TransformList.end(); it++, ii++){ switch(it->first){ case EnumAffineType: std::cout << '[' << ii << "]: EnumAffineType" << std::endl; std::cout << it->second.aex.aff << std::endl; break; case EnumDeformationFieldType: std::cout << '[' << ii << "]: EnumDeformationFieldType: size" << it->second.dex.field->GetLargestPossibleRegion().GetSize() << std::endl; } } } /** * Standard PrintSelf method. */ template void WarpTensorImageMultiTransformFilter ::PrintSelf(std::ostream& os, Indent indent) const { Superclass::PrintSelf(os, indent); os << indent << "OutputSpacing: " << m_OutputSpacing << std::endl;; os << indent << "OutputOrigin: " << m_OutputOrigin << std::endl; os << indent << "EdgePaddingValue: " << static_cast::PrintType>(m_EdgePaddingValue) << std::endl; os << indent << "Interpolator: " << m_Interpolator.GetPointer() << std::endl; os << indent << "m_bFirstDeformNoInterp = " << m_bFirstDeformNoInterp << std::endl; } /** * Set the output image spacing. * */ template void WarpTensorImageMultiTransformFilter ::SetOutputSpacing( const double* spacing) { SpacingType s(spacing); this->SetOutputSpacing( s ); } /** * Set the output image origin. * */ template void WarpTensorImageMultiTransformFilter ::SetOutputOrigin( const double* origin) { PointType p(origin); this->SetOutputOrigin(p); } /** * Setup state of filter before multi-threading. * InterpolatorType::SetInputImage is not thread-safe and hence * has to be setup before ThreadedGenerateData */ template void WarpTensorImageMultiTransformFilter ::BeforeThreadedGenerateData() { if( !m_Interpolator ) { itkExceptionMacro(<< "Interpolator not set"); } // Connect input image to interpolator // m_Interpolator->SetInputImage( this->GetInput() ); if (m_CachedSmoothImage.IsNull() && (this->GetInput() )){ m_CachedSmoothImage = const_cast (this->GetInput()); } m_Interpolator->SetInputImage( m_CachedSmoothImage ); } /** * Setup state of filter after multi-threading. */ template void WarpTensorImageMultiTransformFilter ::AfterThreadedGenerateData() { // Disconnect input image from interpolator m_Interpolator->SetInputImage( NULL ); } template void WarpTensorImageMultiTransformFilter ::GenerateInputRequestedRegion() { // call the superclass's implementation Superclass::GenerateInputRequestedRegion(); // request the largest possible region for the input image InputImagePointer inputPtr = const_cast< InputImageType * >( this->GetInput() ); if( inputPtr ) { inputPtr->SetRequestedRegionToLargestPossibleRegion(); } return; } template void WarpTensorImageMultiTransformFilter ::GenerateOutputInformation() { // call the superclass's implementation of this method Superclass::GenerateOutputInformation(); OutputImagePointer outputPtr = this->GetOutput(); if ( !outputPtr ) { return; } typename TOutputImage::RegionType outputLargestPossibleRegion; outputLargestPossibleRegion.SetSize( this->m_OutputSize ); // outputLargestPossibleRegion.SetIndex( 0 ); outputPtr->SetLargestPossibleRegion( outputLargestPossibleRegion ); outputPtr->SetSpacing( this->m_OutputSpacing ); outputPtr->SetOrigin( this->m_OutputOrigin ); outputPtr->SetDirection( this->m_OutputDirection ); // determine if the deformation field is the same dimension as the image // so that it does not need interpolation in the first step DetermineFirstDeformNoInterp(); } template void WarpTensorImageMultiTransformFilter ::SetSmoothScale(double scale) { /* if (m_SmoothScale != scale){ // compute the new cached // std::cout << "change smooth scale: " << m_SmoothScale << " ---> " << scale << std::endl; m_SmoothScale = scale; typename InputImageType::SpacingType inputSpacing = this->GetInput()->GetSpacing(); typename InputImageType::RegionType::SizeType inputSize = this->GetInput()->GetLargestPossibleRegion().GetSize(); typename InputImageType::SpacingType outputSpacing; typename InputImageType::RegionType::SizeType outputSize; double minimumSpacing = inputSpacing.GetVnlVector().min_value(); double maximumSpacing = inputSpacing.GetVnlVector().max_value(); InputImagePointer image = const_cast (this->GetInput()); for ( unsigned int d = 0; d < ImageDimension; d++ ) { double scaling = vnl_math_min( 1.0 / scale * minimumSpacing / inputSpacing[d], static_cast( inputSize[d] ) / 32.0 ); outputSpacing[d] = inputSpacing[d] * scaling; outputSize[d] = static_cast( inputSpacing[d] * static_cast( inputSize[d] ) / outputSpacing[d] + 0.5 ); double sigma = 0.25 * ( outputSpacing[d] / inputSpacing[d] ); if (sigma < 0) sigma=0; typedef RecursiveGaussianImageFilter GaussianFilterType; typename GaussianFilterType::Pointer smoother = GaussianFilterType::New(); smoother->SetInputImage( image ); smoother->SetDirection( d ); smoother->SetNormalizeAcrossScale( false ); // std::cout << "scale = " << scale << " => " << "sigma of dim " << d << ": " << sigma << " out size " << outputSize << " spc1 " << outputSpacing << " in " << inputSpacing << std::endl; smoother->SetSigma( sigma ); if ( smoother->GetSigma() > 0.0 ) { smoother->Update(); image = smoother->GetOutput(); } } SetOutputSpacing( outputSpacing ); SetOutputOrigin( this->GetInput()->GetOrigin() ); SetOutputSize(outputSize); } */ InputImagePointer image = const_cast (this->GetInput()); m_CachedSmoothImage = image; } /** * Compute the output for the region specified by outputRegionForThread. */ template void WarpTensorImageMultiTransformFilter ::ThreadedGenerateData( const OutputImageRegionType& outputRegionForThread, int threadId ) { InputImageConstPointer inputPtr = this->GetInput(); OutputImagePointer outputPtr = this->GetOutput(); //std::cout << "inputPtr->GetOrigin():" << inputPtr->GetOrigin() << std::endl; //std::cout << "outputPtr->GetOrigin():" << outputPtr->GetOrigin() << std::endl; // exit(-1); IndexType index; index.Fill(0); this->m_EdgePaddingValue=inputPtr->GetPixel(index); // support progress methods/callbacks ProgressReporter progress(this, threadId, outputRegionForThread.GetNumberOfPixels()); // iterator for the output image ImageRegionIteratorWithIndex outputIt(outputPtr, outputRegionForThread); while( !outputIt.IsAtEnd() ) { PointType point1, point2; // get the output image index IndexType index = outputIt.GetIndex(); outputPtr->TransformIndexToPhysicalPoint( index, point1 ); bool isinside = MultiTransformPoint(point1, point2, m_bFirstDeformNoInterp, index); // std::cout << "point1:" << point1 << " point2:" << point2 << " index:" << index << std::endl; // exit(-1); // warp the image // get the interpolated value if( isinside && (m_Interpolator->IsInsideBuffer( point2 )) ) { PixelType value = static_cast(m_Interpolator->Evaluate(point2)); outputIt.Set( value ); } else { // std::cout << "OUTSIDE" << " isinside:" << isinside << " m_Interpolator->IsInsideBuffer( point2 ):" << m_Interpolator->IsInsideBuffer( point2 ) << std::endl; outputIt.Set( m_EdgePaddingValue ); } ++outputIt; } progress.CompletedPixel(); } //template //void //WarpTensorImageMultiTransformFilter //::UpdateSizeByScale() //{ // // // DeformationFieldPointer field = this->GetDeformationField(); // // // // SetOutputSpacing( field->GetSpacing() / m_SmoothScale ); // // SetOutputOrigin( field->GetOrigin() ); // // // // typename InputImageType::SizeType imgsz = field->GetLargestPossibleRegion().GetSize(); // // for(int ii = 0; ii < InputImageType::ImageDimension; ii++) imgsz[ii] = (typename InputImageType::SizeType::SizeValueType) (imgsz[ii] * m_SmoothScale + 0.5); // // // // SetOutputSize(imgsz); // //} template void WarpTensorImageMultiTransformFilter ::PushBackAffineTransform(const TransformType* t) { if (t){ VarTransformType t1; t1.aex.aff = const_cast (t); m_TransformList.push_back(SingleTransformItemType(EnumAffineType, t1)); } } template void WarpTensorImageMultiTransformFilter ::PushBackDeformationFieldTransform(const DeformationFieldType* t) { if (t){ VarTransformType t1; t1.dex.field = const_cast (t); t1.dex.vinterp = DefaultVectorInterpolatorType::New(); t1.dex.vinterp->SetInputImage(t1.dex.field); m_TransformList.push_back(SingleTransformItemType(EnumDeformationFieldType, t1)); } } template bool WarpTensorImageMultiTransformFilter ::MultiTransformSinglePoint(const PointType &point1, PointType &point2) { IndexType null_index; bool isinside = MultiTransformPoint(point1, point2, false, null_index); return isinside; } template bool WarpTensorImageMultiTransformFilter ::IsOutOfNumericBoundary(const PointType &p) { const DisplacementScalarValueType kMaxDisp = itk::NumericTraits::max(); bool b = false; for (int i = 0; i < ImageDimension; i++) { if (p[i]>=kMaxDisp ){ b = true; break; } } return b; } template void WarpTensorImageMultiTransformFilter ::ComposeAffineOnlySequence(const PointType ¢er_output, TransformTypePointer &affine_output) { // std::cout << "test " ; // TransformTypePointer affine_output = TransformType::New(); affine_output->SetIdentity(); affine_output->SetCenter(center_output); // std::cout << affine_output; typename TransformListType::iterator it = m_TransformList.begin(); for(;it!=m_TransformList.end(); it++){ SingleTransformType ttype = it->first; switch(ttype){ case EnumAffineType: { TransformTypePointer aff = it->second.aex.aff; affine_output->Compose(aff, 0); // std::cout << affine_output; break; } case EnumDeformationFieldType: std::cout << " Ignore deformation type ... " << std::endl; break; default: itkExceptionMacro(<< "Single Transform Not Supported!"); } } return; } template bool WarpTensorImageMultiTransformFilter ::MultiInverseAffineOnlySinglePoint(const PointType &p1, PointType &p2) { bool isinside = true; PointType point1 = p1, point2; typename TransformListType::iterator it = m_TransformList.begin(); for(;it!=m_TransformList.end(); it++){ SingleTransformType ttype = it->first; switch(ttype){ case EnumAffineType: { TransformTypePointer aff = it->second.aex.aff; TransformTypePointer aff_inv = TransformTypePointer::ObjectType::New(); // std::cout << "aff before:" << aff << std::endl; aff->GetInverse(aff_inv); // aff->GetInverse(aff); // std::cout << "aff after:" << aff << std::endl; // std::cout << "aff_inv after:" << aff_inv << std::endl; point2 = aff_inv->TransformPoint(point1); point1 = point2; isinside = true; break; } case EnumDeformationFieldType: break; default: itkExceptionMacro(<< "Single Transform Not Supported!"); } if (IsOutOfNumericBoundary(point2)) {isinside = false; break;} point1 = point2; } p2 = point2; return isinside; } template bool WarpTensorImageMultiTransformFilter ::MultiTransformPoint(const PointType &p1, PointType &p2, bool bFisrtDeformNoInterp, const IndexType &index) { bool isinside = false; PointType point1 = p1, point2; typename TransformListType::iterator it = m_TransformList.begin(); for(;it!=m_TransformList.end(); it++){ SingleTransformType ttype = it->first; switch(ttype){ case EnumAffineType: { TransformTypePointer aff = it->second.aex.aff; point2 = aff->TransformPoint(point1); point1 = point2; isinside = true; } break; case EnumDeformationFieldType: { DeformationFieldPointer fieldPtr = it->second.dex.field; if (bFisrtDeformNoInterp && it==m_TransformList.begin() ){ // use discrete coordinates DisplacementType displacement = fieldPtr->GetPixel(index); for(int j = 0; jTransformPhysicalPointToContinuousIndex(point1, contind); isinside = fieldPtr->GetLargestPossibleRegion().IsInside( contind ); VectorInterpolatorPointer vinterp = it->second.dex.vinterp; typename DefaultVectorInterpolatorType::OutputType disp2; if (isinside) disp2 = vinterp->EvaluateAtContinuousIndex( contind ); else disp2.Fill(0); for (int jj=0; jj void WarpTensorImageMultiTransformFilter ::DetermineFirstDeformNoInterp() { m_bFirstDeformNoInterp = false; if (m_TransformList.size() >0 ){ if ((m_TransformList.front().first == EnumDeformationFieldType)){ DeformationFieldPointer field = m_TransformList.front().second.dex.field; m_bFirstDeformNoInterp = (this->GetOutputSize() == field->GetLargestPossibleRegion().GetSize()) & (this->GetOutputSpacing() ==field->GetSpacing()) & (this->GetOutputOrigin() == field->GetOrigin()); // std::cout << "in set: field size: " << field->GetLargestPossibleRegion().GetSize() // << "output spacing: " << this->GetOutputSize() << std::endl; // std::cout << field->GetSpacing() << " | " << this->GetOutputSpacing() << std::endl; // std::cout << field->GetOrigin() << " | " << this->GetOutputOrigin() << std::endl; } } } } // end namespace itk #endif //__itkWarpTensorImageMultiTransformFilter_txx

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