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| ==SinglephaseChanAndVeseSparseFieldLevelSetSegmentation.cxx== | | {{warning|1=The media wiki content on this page is no longer maintained. The examples presented on the https://itk.org/Wiki/* pages likely require ITK version 4.13 or earlier releases. In many cases, the examples on this page no longer conform to the best practices for modern ITK versions.}} |
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| <source lang="cpp">
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| // The use of the ScalarChanAndVeseSparseLevelSetImageFilter is
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| // illustrated in the following example. The implementation of this filter in
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| // ITK is based on the paper by Chan And Vese. This
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| // implementation extends the functionality of the
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| // level-set filters in ITK by using region-based variational techniques. These methods
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| // do not rely on the presence of edges in the images.
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| //
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| // ScalarChanAndVeseSparseLevelSetImageFilter expects two inputs. The first is
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| // an initial level set in the form of an \doxygen{Image}. The second input
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| // is a feature image. For this algorithm, the feature image is the original
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| // raw or preprocessed image. Several parameters are required by the algorithm
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| // for regularization and weights of different energy terms. The user is encouraged to
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| // change different parameter settings to optimize the code example on their images.
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| //
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| // Let's start by including the headers of the main filters involved in the
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| // preprocessing.
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| //
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| #include "itkScalarChanAndVeseSparseLevelSetImageFilter.h"
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| #include "itkScalarChanAndVeseLevelSetFunctionData.h"
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| #include "itkConstrainedRegionBasedLevelSetFunctionSharedData.h"
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| #include "itkFastMarchingImageFilter.h"
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| #include "itkImageFileReader.h"
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| #include "itkImageFileWriter.h"
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| #include "itkImage.h"
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| #include "itkAtanRegularizedHeavisideStepFunction.h"
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| int main(int argc, char**argv)
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| {
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| if( argc < 6 )
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| {
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| std::cerr << "Missing arguments" << std::endl;
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| std::cerr << "Usage: " << std::endl;
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| std::cerr << argv[0] << " featureImage outputImage";
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| std::cerr << " startx starty seedValue" << std::endl;
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| return EXIT_FAILURE;
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| }
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| unsigned int nb_iteration = 500;
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| double rms = 0.;
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| double epsilon = 1.;
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| double curvature_weight = 0.;
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| double area_weight = 0.;
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| double reinitialization_weight = 0.;
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| double volume_weight = 0.;
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| double volume = 0.;
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| double l1 = 1.;
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| double l2 = 1.;
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| //
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| // We now define the image type using a particular pixel type and
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| // dimension. In this case the \code{float} type is used for the pixels
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| // due to the requirements of the smoothing filter.
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| //
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| const unsigned int Dimension = 2;
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| typedef float ScalarPixelType;
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| typedef itk::Image< ScalarPixelType, Dimension > InternalImageType;
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| typedef itk::ScalarChanAndVeseLevelSetFunctionData< InternalImageType,
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| InternalImageType > DataHelperType;
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| typedef itk::ConstrainedRegionBasedLevelSetFunctionSharedData<
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| InternalImageType, InternalImageType, DataHelperType > SharedDataHelperType;
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| typedef itk::ScalarChanAndVeseLevelSetFunction< InternalImageType,
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| InternalImageType, SharedDataHelperType > LevelSetFunctionType;
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| // We declare now the type of the numerically discretized Step and Delta functions that
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| // will be used in the level-set computations for foreground and background regions
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| //
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| typedef itk::AtanRegularizedHeavisideStepFunction< ScalarPixelType,
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| ScalarPixelType > DomainFunctionType;
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| DomainFunctionType::Pointer domainFunction = DomainFunctionType::New();
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| domainFunction->SetEpsilon( epsilon );
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| // We instantiate reader and writer types in the following lines.
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| //
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| typedef itk::ImageFileReader< InternalImageType > ReaderType;
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| typedef itk::ImageFileWriter< InternalImageType > WriterType;
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| ReaderType::Pointer reader = ReaderType::New();
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| WriterType::Pointer writer = WriterType::New();
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| reader->SetFileName( argv[1] );
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| reader->Update();
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| writer->SetFileName( argv[2] );
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| InternalImageType::Pointer featureImage = reader->GetOutput();
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| // We declare now the type of the FastMarchingImageFilter that
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| // will be used to generate the initial level set in the form of a distance
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| // map.
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| //
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| typedef itk::FastMarchingImageFilter< InternalImageType, InternalImageType >
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| FastMarchingFilterType;
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| FastMarchingFilterType::Pointer fastMarching = FastMarchingFilterType::New();
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| // The FastMarchingImageFilter requires the user to provide a seed
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| // point from which the level set will be generated. The user can actually
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| // pass not only one seed point but a set of them. Note the the
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| // FastMarchingImageFilter is used here only as a helper in the
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| // determination of an initial level set. We could have used the
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| // \doxygen{DanielssonDistanceMapImageFilter} in the same way.
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| //
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| // The seeds are passed stored in a container. The type of this
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| // container is defined as \code{NodeContainer} among the
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| // FastMarchingImageFilter traits.
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| //
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| typedef FastMarchingFilterType::NodeContainer NodeContainer;
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| typedef FastMarchingFilterType::NodeType NodeType;
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| NodeContainer::Pointer seeds = NodeContainer::New();
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| InternalImageType::IndexType seedPosition;
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| seedPosition[0] = atoi( argv[3] );
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| seedPosition[1] = atoi( argv[4] );
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| const double initialDistance = atof( argv[5] );
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| NodeType node;
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| const double seedValue = - initialDistance;
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| node.SetValue( seedValue );
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| node.SetIndex( seedPosition );
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| // The list of nodes is initialized and then every node is inserted using
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| // the \code{InsertElement()}.
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| //
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| seeds->Initialize();
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| seeds->InsertElement( 0, node );
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| // The set of seed nodes is passed now to the
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| // FastMarchingImageFilter with the method
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| // \code{SetTrialPoints()}.
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| //
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| fastMarching->SetTrialPoints( seeds );
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| // Since the FastMarchingImageFilter is used here just as a
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| // Distance Map generator. It does not require a speed image as input.
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| // Instead the constant value $1.0$ is passed using the
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| // \code{SetSpeedConstant()} method.
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| //
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| fastMarching->SetSpeedConstant( 1.0 );
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| // The FastMarchingImageFilter requires the user to specify the
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| // size of the image to be produced as output. This is done using the
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| // \code{SetOutputSize()}. Note that the size is obtained here from the
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| // output image of the smoothing filter. The size of this image is valid
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| // only after the \code{Update()} methods of this filter has been called
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| // directly or indirectly.
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| // | |
| fastMarching->SetOutputSize(
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| featureImage->GetBufferedRegion().GetSize() );
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| fastMarching->Update();
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| // We declare now the type of the ScalarChanAndVeseSparseLevelSetImageFilter that
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| // will be used to generate a segmentation.
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| //
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| typedef itk::ScalarChanAndVeseSparseLevelSetImageFilter< InternalImageType,
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| InternalImageType, InternalImageType, LevelSetFunctionType,
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| SharedDataHelperType > MultiLevelSetType;
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| MultiLevelSetType::Pointer levelSetFilter = MultiLevelSetType::New();
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| // We set the function count to 1 since a single level-set is being evolved.
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| //
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| levelSetFilter->SetFunctionCount( 1 );
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| // Set the feature image and initial level-set image as output of the
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| // fast marching image filter.
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| //
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| levelSetFilter->SetFeatureImage( featureImage );
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| levelSetFilter->SetLevelSet( 0, fastMarching->GetOutput() );
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| // Once activiated the level set evolution will stop if the convergence
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| // criteria or if the maximum number of iterations is reached. The
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| // convergence criteria is defined in terms of the root mean squared (RMS)
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| // change in the level set function. The evolution is said to have
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| // converged if the RMS change is below a user specified threshold. In a
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| // real application is desirable to couple the evolution of the zero set
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| // to a visualization module allowing the user to follow the evolution of
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| // the zero set. With this feedback, the user may decide when to stop the
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| // algorithm before the zero set leaks through the regions of low gradient
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| // in the contour of the anatomical structure to be segmented.
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| //
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| levelSetFilter->SetNumberOfIterations( nb_iteration );
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| levelSetFilter->SetMaximumRMSError( rms );
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| // Often, in real applications, images have different pixel resolutions. In such
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| // cases, it is best to use the native spacings to compute derivatives etc rather
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| // than sampling the images.
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| //
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| levelSetFilter->SetUseImageSpacing( 1 );
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| // For large images, we may want to compute the level-set over the initial supplied
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| // level-set image. This saves a lot of memory.
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| //
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| levelSetFilter->SetInPlace( false );
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| // For the level set with phase 0, set different parameters and weights. This may
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| // to be set in a loop for the case of multiple level-sets evolving simultaneously.
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| //
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| levelSetFilter->GetDifferenceFunction(0)->SetDomainFunction( domainFunction );
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| levelSetFilter->GetDifferenceFunction(0)->SetCurvatureWeight( curvature_weight );
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| levelSetFilter->GetDifferenceFunction(0)->SetAreaWeight( area_weight );
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| levelSetFilter->GetDifferenceFunction(0)->SetReinitializationSmoothingWeight( reinitialization_weight );
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| levelSetFilter->GetDifferenceFunction(0)->SetVolumeMatchingWeight( volume_weight );
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| levelSetFilter->GetDifferenceFunction(0)->SetVolume( volume );
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| levelSetFilter->GetDifferenceFunction(0)->SetLambda1( l1 );
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| levelSetFilter->GetDifferenceFunction(0)->SetLambda2( l2 );
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| levelSetFilter->Update();
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| writer->SetInput( levelSetFilter->GetOutput() );
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| try
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| {
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| writer->Update();
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| }
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| catch( itk::ExceptionObject & excep )
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| {
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| std::cerr << "Exception caught !" << std::endl;
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| std::cerr << excep << std::endl;
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| return -1;
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| }
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| return EXIT_SUCCESS;
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| }
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| </source>
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| {{ITKCMakeLists|SinglephaseChanAndVeseSparseFieldLevelSetSegmentation|}}
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