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Atropos.cxx
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Atropos.cxx
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#include <string>
#include <algorithm>
#include <vector>
#include <algorithm>
#include "antsUtilities.h"
#include "ReadWriteData.h"
#include "itkImage.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkImageRegionIterator.h"
#include "itkMaskImageFilter.h"
#include "itkNumericSeriesFileNames.h"
#include "itkSymmetricSecondRankTensor.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 "antsJointHistogramParzenShapeAndOrientationListSampleFunction.h"
#include "antsListSampleToListSampleFilter.h"
#include "antsManifoldParzenWindowsListSampleFunction.h"
#include "antsPassThroughListSampleFilter.h"
#include "antsPartialVolumeGaussianListSampleFunction.h"
#include "itkTimeProbe.h"
namespace ants
{
template <class TFilter>
class CommandIterationUpdate : public itk::Command
{
public:
typedef CommandIterationUpdate Self;
typedef itk::Command Superclass;
typedef itk::SmartPointer<Self> Pointer;
itkNewMacro( Self );
protected:
CommandIterationUpdate()
{
};
public:
void Execute(itk::Object *caller, const itk::EventObject & event) ITK_OVERRIDE
{
Execute( (const itk::Object *) caller, event);
}
void Execute(const itk::Object * object, const itk::EventObject & event) ITK_OVERRIDE
{
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 << "posterior probability = "
<< filter->GetCurrentPosteriorProbability();
typedef typename TFilter::RealType RealType;
RealType annealingTemperature = filter->GetInitialAnnealingTemperature()
* std::pow( filter->GetAnnealingRate(), static_cast<RealType>(
filter->GetElapsedIterations() ) );
annealingTemperature = vnl_math_max( annealingTemperature,
filter->GetMinimumAnnealingTemperature() );
std::cout << " (annealing temperature = "
<< annealingTemperature << ")" << std::endl;
}
};
template <unsigned int ImageDimension>
int AtroposSegmentation( itk::ants::CommandLineParser *parser )
{
typedef float PixelType;
typedef float RealType;
typedef itk::Image<PixelType, ImageDimension> InputImageType;
typedef unsigned int LabelType;
typedef itk::Image<LabelType, ImageDimension> LabelImageType;
bool verbose = false;
typename itk::ants::CommandLineParser::OptionType::Pointer verboseOption =
parser->GetOption( "verbose" );
if( verboseOption && verboseOption->GetNumberOfFunctions() )
{
verbose = parser->Convert<bool>( verboseOption->GetFunction( 0 )->GetName() );
}
if( verbose )
{
std::cout << std::endl << "Running Atropos for "
<< ImageDimension << "-dimensional images." << std::endl;
}
typedef itk::ants::AtroposSegmentationImageFilter
<InputImageType, LabelImageType> SegmentationFilterType;
typename SegmentationFilterType::Pointer segmenter
= SegmentationFilterType::New();
if( verbose )
{
typedef CommandIterationUpdate<SegmentationFilterType> CommandType;
typename CommandType::Pointer observer = CommandType::New();
segmenter->AddObserver( itk::IterationEvent(), observer );
}
/**
* 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->GetNumberOfFunctions() )
{
segmenter->SetMinimizeMemoryUsage( parser->Convert<bool>(
memoryOption->GetFunction( 0 )->GetName() ) );
}
/**
* Initialization
*/
typename itk::ants::CommandLineParser::OptionType::Pointer initializationOption =
parser->GetOption( "initialization" );
if( initializationOption && initializationOption->GetNumberOfFunctions() &&
initializationOption->GetFunction( 0 )->GetNumberOfParameters() < 1 )
{
if( verbose )
{
std::cerr << "Incorrect initialization option specification." << std::endl;
std::cerr << " " << initializationOption->GetDescription() << std::endl;
}
return EXIT_FAILURE;
}
else
{
segmenter->SetNumberOfTissueClasses( parser->Convert<unsigned int>(
initializationOption->GetFunction( 0 )->GetParameter( 0 ) ) );
std::string initializationStrategy = initializationOption->GetFunction( 0 )->GetName();
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->GetFunction( 0 )->GetNumberOfParameters() > 1 )
{
std::vector<float> clusterCenters = parser->ConvertVector<float>(
initializationOption->GetFunction( 0 )->GetParameter( 1 ) );
if( clusterCenters.size() != segmenter->GetNumberOfTissueClasses() )
{
if( verbose )
{
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->GetNumberOfTissueClasses() );
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->GetFunction( 0 )->GetNumberOfParameters() < 3 )
{
if( verbose )
{
std::cerr << "Incorrect initialization option specification."
<< std::endl;
std::cerr << " " << initializationOption->GetDescription()
<< std::endl;
}
return EXIT_FAILURE;
}
segmenter->SetPriorProbabilityWeight( parser->Convert<float>(
initializationOption->GetFunction( 0 )->GetParameter( 2 ) ) );
if( initializationOption->GetFunction( 0 )->GetNumberOfParameters() > 3 )
{
segmenter->SetProbabilityThreshold( parser->Convert<float>(
initializationOption->GetFunction( 0 )->GetParameter( 3 ) ) );
}
std::string filename = initializationOption->GetFunction( 0 )->GetParameter( 1 );
if( filename.find( std::string( "%" ) ) != std::string::npos )
{
itk::NumericSeriesFileNames::Pointer fileNamesCreator =
itk::NumericSeriesFileNames::New();
fileNamesCreator->SetStartIndex( 1 );
fileNamesCreator->SetEndIndex( segmenter->GetNumberOfTissueClasses() );
fileNamesCreator->SetSeriesFormat( filename.c_str() );
const std::vector<std::string> & imageNames
= fileNamesCreator->GetFileNames();
for( unsigned int k = 0; k < imageNames.size(); k++ )
{
typename InputImageType::Pointer image;
ReadImage<InputImageType>( image, imageNames[k].c_str() );
segmenter->SetPriorProbabilityImage( k + 1, image );
}
}
else
{
typedef itk::VectorImage<PixelType, ImageDimension> VectorImageType;
typename VectorImageType::Pointer image;
ReadImage<VectorImageType>( image, filename.c_str() );
if( image->GetNumberOfComponentsPerPixel()
!= segmenter->GetNumberOfTissueClasses() )
{
if( verbose )
{
std::cerr << "The number of components does not match the number of "
<< "classes." << std::endl;
}
return EXIT_FAILURE;
}
typedef itk::VectorIndexSelectionCastImageFilter<VectorImageType, InputImageType> CasterType;
typename CasterType::Pointer caster = CasterType::New();
caster->SetInput( image );
for( unsigned int k = 0; k < segmenter->GetNumberOfTissueClasses(); k++ )
{
caster->SetIndex( k );
caster->Update();
segmenter->SetPriorProbabilityImage( k + 1, caster->GetOutput() );
}
}
if( initializationOption->GetFunction( 0 )->GetNumberOfParameters() > 3 )
{
segmenter->SetProbabilityThreshold( parser->Convert<float>(
initializationOption->GetFunction( 0 )->GetParameter( 3 ) ) );
}
}
else if( !initializationStrategy.compare( std::string( "priorlabelimage" ) ) )
{
segmenter->SetInitializationStrategy(
SegmentationFilterType::PriorLabelImage );
if( initializationOption->GetFunction( 0 )->GetNumberOfParameters() < 3 )
{
if( verbose )
{
std::cerr << "Incorrect initialization option specification." << std::endl;
std::cerr << " " << initializationOption->GetDescription() << std::endl;
}
return EXIT_FAILURE;
}
segmenter->SetPriorProbabilityWeight( parser->Convert<float>(
initializationOption->GetFunction( 0 )->GetParameter( 2 ) ) );
std::string filename = initializationOption->GetFunction( 0 )->GetParameter( 1 );
typename LabelImageType::Pointer image;
ReadImage<LabelImageType>( image, filename.c_str() );
segmenter->SetPriorLabelImage( image );
}
else
{
if( verbose )
{
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 && posteriorOption->GetNumberOfFunctions() )
{
if( posteriorOption->GetFunction( 0 )->GetNumberOfParameters() > 0 )
{
segmenter->SetUseMixtureModelProportions( parser->Convert<bool>(
posteriorOption->GetFunction( 0 )->GetParameter( 0 ) ) );
RealType annealingTemperature = 1.0;
if( posteriorOption->GetFunction( 0 )->GetNumberOfParameters() > 1 )
{
annealingTemperature =
parser->Convert<RealType>( posteriorOption->GetFunction( 0 )->GetParameter( 1 ) );
if( annealingTemperature <= 0.0 )
{
if( verbose )
{
std::cerr << "Annealing temperature must be positive." << std::endl;
}
return EXIT_FAILURE;
}
}
segmenter->SetInitialAnnealingTemperature( annealingTemperature );
RealType annealingRate = 1.0;
if( posteriorOption->GetFunction( 0 )->GetNumberOfParameters() > 2 )
{
annealingRate =
parser->Convert<RealType>( posteriorOption->GetFunction( 0 )->GetParameter( 2 ) );
if( annealingRate < 0.0 || annealingRate > 1.0 )
{
if( verbose )
{
std::cerr << "Annealing rate must be in the range [0, 1]." << std::endl;
}
return EXIT_FAILURE;
}
}
segmenter->SetAnnealingRate( annealingRate );
if( posteriorOption->GetFunction( 0 )->GetNumberOfParameters() > 3 )
{
RealType minimumAnnealingTemperature =
parser->Convert<RealType>( posteriorOption->GetFunction( 0 )->GetParameter( 3 ) );
segmenter->SetMinimumAnnealingTemperature( minimumAnnealingTemperature );
}
}
std::string posteriorStrategy = posteriorOption->GetFunction( 0 )->GetName();
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 );
}
else if( !posteriorStrategy.compare( std::string( "sigmoid" ) ) )
{
segmenter->SetPosteriorProbabilityFormulation(
SegmentationFilterType::Sigmoid );
}
}
/**
* convergence options
*/
typename itk::ants::CommandLineParser::OptionType::Pointer convergenceOption =
parser->GetOption( "convergence" );
if( convergenceOption && convergenceOption->GetNumberOfFunctions() )
{
if( convergenceOption->GetFunction( 0 )->GetNumberOfParameters() == 0 )
{
segmenter->SetMaximumNumberOfIterations( parser->Convert<unsigned int>(
convergenceOption->GetFunction( 0 )->GetName() ) );
}
if( convergenceOption->GetFunction( 0 )->GetNumberOfParameters() > 0 )
{
segmenter->SetMaximumNumberOfIterations( parser->Convert<unsigned int>(
convergenceOption->GetFunction( 0 )->GetParameter( 0 ) ) );
}
if( convergenceOption->GetFunction( 0 )->GetNumberOfParameters() > 1 )
{
segmenter->SetConvergenceThreshold( parser->Convert<float>(
convergenceOption->GetFunction( 0 )->GetParameter( 1 ) ) );
}
}
/**
* mask image
*/
typename itk::ants::CommandLineParser::OptionType::Pointer maskOption =
parser->GetOption( "mask-image" );
if( maskOption && maskOption->GetNumberOfFunctions() )
{
try
{
typename LabelImageType::Pointer image;
ReadImage<LabelImageType>( image, maskOption->GetFunction( 0 )->GetName().c_str() );
segmenter->SetMaskImage( image );
// Check to see that the labels in the prior label image or the non-zero
// probability voxels in the prior probability images encompass the entire
// mask region.
if( segmenter->GetInitializationStrategy() ==
SegmentationFilterType::PriorLabelImage )
{
itk::ImageRegionConstIterator<LabelImageType> ItM( segmenter->GetMaskImage(),
segmenter->GetMaskImage()->GetLargestPossibleRegion() );
itk::ImageRegionConstIterator<LabelImageType> ItP( segmenter->GetPriorLabelImage(),
segmenter->GetPriorLabelImage()->GetLargestPossibleRegion() );
for( ItM.GoToBegin(), ItP.GoToBegin(); !ItM.IsAtEnd(); ++ItM, ++ItP )
{
if( ItM.Get() != itk::NumericTraits<LabelType>::ZeroValue() && ItP.Get() == 0 )
{
if( verbose )
{
std::cout << std::endl;
std::cout << "Warning: the labels in the the prior label image do "
<< "not encompass the entire mask region. As a result each unlabeled voxel will be "
<< "initially assigned a random label. The user might want to consider "
<< "various alternative strategies like assigning an additional "
<< "\"background\" label to the unlabeled voxels or propagating "
<< "the labels within the mask region."
<< std::endl;
std::cout << std::endl;
}
break;
}
}
}
else if( segmenter->GetInitializationStrategy() ==
SegmentationFilterType::PriorProbabilityImages )
{
itk::ImageRegionConstIteratorWithIndex<LabelImageType> ItM( segmenter->GetMaskImage(),
segmenter->GetMaskImage()->GetLargestPossibleRegion() );
for( ItM.GoToBegin(); !ItM.IsAtEnd(); ++ItM )
{
if( ItM.Get() != itk::NumericTraits<LabelType>::ZeroValue() )
{
RealType sumPriorProbability = 0.0;
for( unsigned int n = 0; n < segmenter->GetNumberOfTissueClasses(); n++ )
{
sumPriorProbability +=
segmenter->GetPriorProbabilityImage( n + 1 )->GetPixel( ItM.GetIndex() );
}
if( sumPriorProbability < segmenter->GetProbabilityThreshold() )
{
if( verbose )
{
std::cout << std::endl;
std::cout << "Warning: the sum of the priors from the the prior probability images are "
<< "less than the probability threshold within the mask region. As a result "
<< "each zero probability voxel will be "
<< "initially assigned a random label. The user might want to consider "
<< "various alternative strategies like assigning an additional "
<< "\"background\" label to the zero probability voxels or propagating "
<< "the probabilities within the mask region."
<< std::endl;
std::cout << std::endl;
}
break;
}
}
}
}
}
catch( ... )
{
}
}
else
{
if( verbose )
{
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->GetNumberOfFunctions() )
{
if( bsplineOption->GetFunction( 0 )->GetNumberOfParameters() > 0 )
{
std::vector<unsigned int> numLevels = parser->ConvertVector<unsigned int>(
bsplineOption->GetFunction( 0 )->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
{
if( verbose )
{
std::cerr << "Incorrect number of levels" << std::endl;
}
return EXIT_FAILURE;
}
segmenter->SetNumberOfLevels( numberOfFittingLevels );
}
if( bsplineOption->GetFunction( 0 )->GetNumberOfParameters() > 2 )
{
segmenter->SetSplineOrder( parser->Convert<unsigned int>(
bsplineOption->GetFunction( 0 )->GetParameter( 2 ) ) );
}
if( bsplineOption->GetFunction( 0 )->GetNumberOfParameters() > 1 )
{
std::vector<unsigned int> array = parser->ConvertVector<unsigned int>(
bsplineOption->GetFunction( 0 )->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
{
if( verbose )
{
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->GetNumberOfFunctions() )
{
if( labelOption->GetNumberOfFunctions() == 1 &&
( labelOption->GetFunction( 0 )->GetName() ).empty() )
{
typename SegmentationFilterType::LabelParameterMapType labelMap;
float labelLambda = parser->Convert<float>(
labelOption->GetFunction( 0 )->GetParameter( 0 ) );
float labelBoundaryProbability = 1.0;
if( labelOption->GetFunction( 0 )->GetNumberOfParameters() > 1 )
{
labelBoundaryProbability = parser->Convert<float>(
labelOption->GetFunction( 0 )->GetParameter( 1 ) );
if( labelBoundaryProbability < 0.0 )
{
labelBoundaryProbability = 0.0;
}
if( labelBoundaryProbability > 1.0 )
{
labelBoundaryProbability = 1.0;
}
}
for( unsigned int n = 1; n <= segmenter->GetNumberOfTissueClasses(); 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->GetNumberOfFunctions(); n++ )
{
typename SegmentationFilterType::LabelParametersType labelPair;
float labelLambda = parser->Convert<float>(
labelOption->GetFunction( n )->GetParameter( 0 ) );
float labelBoundaryProbability = 1.0;
if( labelOption->GetFunction( n )->GetNumberOfParameters() > 1 )
{
labelBoundaryProbability = parser->Convert<float>( labelOption->GetFunction( n )->GetParameter( 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<unsigned int>( labelOption->GetFunction( n )->GetName() );
labelMap[whichClass] = labelPair;
}
segmenter->SetPriorLabelParameterMap( labelMap );
}
}
/**
* intensity images
*/
typename itk::ants::CommandLineParser::OptionType::Pointer imageOption =
parser->GetOption( "intensity-image" );
if( imageOption && imageOption->GetNumberOfFunctions() )
{
unsigned int count = 0;
for( int n = imageOption->GetNumberOfFunctions() - 1; n >= 0; n-- )
{
std::string imagename;
if( imageOption->GetFunction( n )->GetNumberOfParameters() > 0 )
{
imagename = imageOption->GetFunction( n )->GetParameter( 0 );
}
else
{
imagename = imageOption->GetFunction( n )->GetName();
}
typename InputImageType::Pointer image;
ReadImage<InputImageType>( image, imagename.c_str() );
segmenter->SetIntensityImage( count, image );
if( imageOption->GetFunction( count )->GetNumberOfParameters() > 1 )
{
segmenter->SetAdaptiveSmoothingWeight( count,
parser->Convert<float>( imageOption->GetFunction( count )->GetParameter(
1 ) ) );
}
else
{
segmenter->SetAdaptiveSmoothingWeight( count, 0.0 );
}
count++;
}
}
else
{
if( verbose )
{
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->GetNumberOfFunctions() )
{
if( mrfOption->GetFunction( 0 )->GetNumberOfParameters() > 0 )
{
try
{
typedef typename SegmentationFilterType::RealImageType
MRFCoefficientImageType;
typedef itk::ImageFileReader<MRFCoefficientImageType>
MRFNeighborhoodImageReaderType;
typename MRFNeighborhoodImageReaderType::Pointer mrfNeighborhoodReader =
MRFNeighborhoodImageReaderType::New();
mrfNeighborhoodReader->SetFileName( mrfOption->GetFunction( 0 )->GetParameter( 0 ) );
typename MRFCoefficientImageType::Pointer mrfCoefficientImage =
mrfNeighborhoodReader->GetOutput();
mrfCoefficientImage->Update();
mrfCoefficientImage->DisconnectPipeline();
segmenter->SetMRFCoefficientImage( mrfCoefficientImage );
}
catch( ... )
{
segmenter->SetMRFSmoothingFactor( parser->Convert<float>( mrfOption->GetFunction( 0 )->GetParameter( 0 ) ) );
}
}
if( mrfOption->GetFunction( 0 )->GetNumberOfParameters() > 1 )
{
std::vector<unsigned int> array =
parser->ConvertVector<unsigned int>( mrfOption->GetFunction( 0 )->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
{
if( verbose )
{
std::cerr << "MRF radius size needs to be equal to the image dimension."
<< std::endl;
}
return EXIT_FAILURE;
}
segmenter->SetMRFRadius( radius );
}
}
/**
* ICM options
*/
typename itk::ants::CommandLineParser::OptionType::Pointer icmOption =
parser->GetOption( "icm" );
if( icmOption && icmOption->GetNumberOfFunctions() == 1 )
{
segmenter->SetUseAsynchronousUpdating( parser->Convert<bool>(
icmOption->GetFunction( 0 )->GetName() ) );
}
if( icmOption && icmOption->GetNumberOfFunctions() )
{
if( icmOption->GetFunction( 0 )->GetNumberOfParameters() > 0 )
{
segmenter->SetUseAsynchronousUpdating( parser->Convert<bool>( icmOption->GetFunction( 0 )->GetParameter( 0 ) ) );
}
if( icmOption->GetFunction( 0 )->GetNumberOfParameters() > 1 )
{
segmenter->SetMaximumNumberOfICMIterations( parser->Convert<unsigned int>( icmOption->GetFunction( 0 )->
GetParameter( 1 ) ) );
}
}
/**
* random seed
*/
typename itk::ants::CommandLineParser::OptionType::Pointer seedOption =
parser->GetOption( "use-random-seed" );
if( seedOption && seedOption->GetNumberOfFunctions() )
{
bool useRandomSeed = parser->Convert<bool>( seedOption->GetFunction( 0 )->GetName() );
if( !useRandomSeed )
{
// assign seed from itkMersenneTwisterRandomVariateGenerator.h (line 347)
segmenter->SetRandomizerInitializationSeed( 19650218UL );
}
}
/**
* euclidean distance
*/
typename itk::ants::CommandLineParser::OptionType::Pointer distanceOption =
parser->GetOption( "use-euclidean-distance" );
if( distanceOption && distanceOption->GetNumberOfFunctions() )
{
segmenter->SetUseEuclideanDistanceForPriorLabels(
parser->Convert<bool>( distanceOption->GetFunction( 0 )->GetName() ) );
}
/**
* likelihood
*/
typename itk::ants::CommandLineParser::OptionType::Pointer likelihoodOption =
parser->GetOption( "likelihood-model" );
if( likelihoodOption && likelihoodOption->GetNumberOfFunctions() )
{
std::string likelihoodModel = likelihoodOption->GetFunction( 0 )->GetName();
ConvertToLowerCase( likelihoodModel );
if( !likelihoodModel.compare( std::string( "gaussian" ) ) )
{
typedef typename SegmentationFilterType::SampleType SampleType;
typedef itk::ants::Statistics::GaussianListSampleFunction
<SampleType, float, float> LikelihoodType;
for( unsigned int n = 0; n < segmenter->GetNumberOfTissueClasses(); 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
<SampleType, float, float> LikelihoodType;
float regularizationSigma = 1.0;
if( likelihoodOption->GetFunction( 0 )->GetNumberOfParameters() > 0 )
{
regularizationSigma = parser->Convert<float>( likelihoodOption->GetFunction( 0 )->GetParameter( 0 ) );
}
unsigned int evalNeighborhood = 50;
if( likelihoodOption->GetFunction( 0 )->GetNumberOfParameters() > 1 )
{
evalNeighborhood = parser->Convert<unsigned int>( likelihoodOption->GetFunction( 0 )->GetParameter( 1 ) );
}
unsigned int covNeighborhood = 0;
if( likelihoodOption->GetFunction( 0 )->GetNumberOfParameters() > 2 )
{
covNeighborhood = parser->Convert<unsigned int>( likelihoodOption->GetFunction( 0 )->GetParameter( 2 ) );
}
float covSigma = 1.0;
if( likelihoodOption->GetFunction( 0 )->GetNumberOfParameters() > 3 )
{
covSigma = parser->Convert<float>( likelihoodOption->GetFunction( 0 )->GetParameter( 3 ) );
}
for( unsigned int n = 0; n < segmenter->GetNumberOfTissueClasses(); 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
<SampleType, float, float> LikelihoodType;
float sigma = 1.0;
if( likelihoodOption->GetFunction( 0 )->GetNumberOfParameters() > 0 )
{
sigma = parser->Convert<float>( likelihoodOption->GetFunction( 0 )->GetParameter( 0 ) );
}
unsigned int numberOfBins = 32;
if( likelihoodOption->GetFunction( 0 )->GetNumberOfParameters() > 1 )
{
numberOfBins = parser->Convert<unsigned int>( likelihoodOption->GetFunction( 0 )->GetParameter( 1 ) );
}
for( unsigned int n = 0; n < segmenter->GetNumberOfTissueClasses(); n++ )
{
typename LikelihoodType::Pointer hpwLikelihood =
LikelihoodType::New();
hpwLikelihood->SetSigma( sigma );
hpwLikelihood->SetNumberOfHistogramBins( numberOfBins );
segmenter->SetLikelihoodFunction( n, hpwLikelihood );
}
}
else if( !likelihoodModel.compare( std::string( "jointshapeandorientationprobability" ) ) )
{
if( segmenter->GetNumberOfIntensityImages() !=
static_cast<unsigned int>( ImageDimension * ( ImageDimension + 1 ) / 2 ) )
{
if( verbose )
{
std::cerr << " Expect images in upper triangular order " << std::endl;
std::cerr << " xx xy xz yy yz zz " << std::endl;
std::cerr << "Incorrect number of intensity images specified." << std::endl;
}
return EXIT_FAILURE;
}
typedef typename SegmentationFilterType::SampleType SampleType;
typedef itk::ants::Statistics::
JointHistogramParzenShapeAndOrientationListSampleFunction
<SampleType, float, float> LikelihoodType;
float shapeSigma = 2.0;
if( likelihoodOption->GetFunction( 0 )->GetNumberOfParameters() > 0 )
{
shapeSigma = parser->Convert<float>( likelihoodOption->GetFunction( 0 )->GetParameter( 0 ) );
}
unsigned int numberOfShapeBins = 64;
if( likelihoodOption->GetFunction( 0 )->GetNumberOfParameters() > 1 )
{
numberOfShapeBins = parser->Convert<unsigned int>( likelihoodOption->GetFunction( 0 )->GetParameter( 1 ) );
}
float orientationSigma = 1.0;
if( likelihoodOption->GetFunction( 0 )->GetNumberOfParameters() > 2 )
{
orientationSigma = parser->Convert<float>( likelihoodOption->GetFunction( 0 )->GetParameter( 2 ) );
}
unsigned int numberOfOrientationBins = 32;
if( likelihoodOption->GetFunction( 0 )->GetNumberOfParameters() > 3 )
{
numberOfOrientationBins =
parser->Convert<unsigned int>( likelihoodOption->GetFunction( 0 )->GetParameter( 3 ) );
}
for( unsigned int n = 0; n < segmenter->GetNumberOfTissueClasses(); n++ )
{
typename LikelihoodType::Pointer hpwLikelihood =
LikelihoodType::New();
hpwLikelihood->SetShapeSigma( shapeSigma );
hpwLikelihood->SetOrientationSigma( orientationSigma);
hpwLikelihood->SetNumberOfShapeJointHistogramBins( numberOfShapeBins );
hpwLikelihood->SetNumberOfOrientationJointHistogramBins( numberOfOrientationBins);
segmenter->SetLikelihoodFunction( n, hpwLikelihood );
}
}
else if( !likelihoodModel.compare( std::string( "logeuclideangaussian" ) ) )
{
if( segmenter->GetNumberOfIntensityImages() !=
static_cast<unsigned int>( ImageDimension * ( ImageDimension + 1 ) / 2 ) )
{
if( verbose )
{
std::cerr << " Expect images in upper triangular order " << std::endl;
std::cerr << " xx xy xz yy yz zz " << std::endl;
std::cerr << "Incorrect number of intensity images specified." << std::endl;
}
return EXIT_FAILURE;
}
typedef typename SegmentationFilterType::SampleType SampleType;
typedef itk::ants::Statistics::LogEuclideanGaussianListSampleFunction
<SampleType, float, float> LikelihoodType;
for( unsigned int n = 0; n < segmenter->GetNumberOfTissueClasses(); n++ )
{
typename LikelihoodType::Pointer gaussianLikelihood =
LikelihoodType::New();
segmenter->SetLikelihoodFunction( n, gaussianLikelihood );
}
}
else
{
if( verbose )
{
std::cerr << "Unrecognized likelihood model request." << std::endl;
}
return EXIT_FAILURE;
}
}
/**
* partial volume
*/
typename itk::ants::CommandLineParser::OptionType::Pointer pvOption =
parser->GetOption( "partial-volume-label-set" );
if( pvOption && pvOption->GetNumberOfFunctions() )
{
unsigned int labelSetCount = 0;
for( int n = pvOption->GetNumberOfFunctions() - 1; n >= 0; n-- )
{
typename SegmentationFilterType::PartialVolumeLabelSetType labelSet =
parser->ConvertVector<LabelType>( pvOption->GetFunction( n )->GetName() );
if( labelSet.size() != 2 )
{
if( verbose )
{
std::cerr << "Error: Currently Atropos only supports partial "
<< "volume label sets of size equal to 2." << std::endl;
}
return EXIT_FAILURE;
}
segmenter->AddPartialVolumeLabelSet( labelSet );
typedef typename SegmentationFilterType::SampleType SampleType;
typedef itk::ants::Statistics::PartialVolumeGaussianListSampleFunction
<SampleType, float, float> LikelihoodType;
typename LikelihoodType::Pointer partialVolumeLikelihood =
LikelihoodType::New();
segmenter->SetLikelihoodFunction( labelSetCount
+ segmenter->GetNumberOfTissueClasses(), partialVolumeLikelihood );
labelSetCount++;
}
typename itk::ants::CommandLineParser::OptionType::Pointer pvlOption =
parser->GetOption( "use-partial-volume-likelihoods" );
bool useLikelihoods = false;
if( pvlOption && pvlOption->GetNumberOfFunctions() )
{
std::string value = pvlOption->GetFunction( 0 )->GetName();
ConvertToLowerCase( value );
if( !value.compare( "true" ) || !value.compare( "1" ) )
{
useLikelihoods = true;
}
else
{
useLikelihoods = false;
}
}
segmenter->SetUsePartialVolumeLikelihoods( useLikelihoods );
}
/**
* outliers handling
*/