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itkWeightedVotingFusionImageFilter.hxx
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itkWeightedVotingFusionImageFilter.hxx
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/*=========================================================================
*
* Copyright Insight Software Consortium
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0.txt
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
*=========================================================================*/
#ifndef itkWeightedVotingFusionImageFilter_hxx
#define itkWeightedVotingFusionImageFilter_hxx
#include "itkWeightedVotingFusionImageFilter.h"
#include "itkImageRegionIteratorWithIndex.h"
#include "itkProgressReporter.h"
#include <algorithm>
#include <numeric>
#include <vnl/algo/vnl_cholesky.h>
#include <vnl/algo/vnl_svd.h>
#include <vnl/vnl_inverse.h>
namespace itk {
template<typename TInputImage, typename TOutputImage>
WeightedVotingFusionImageFilter<TInputImage, TOutputImage>
::WeightedVotingFusionImageFilter() :
m_IsWeightedAveragingComplete( false ),
m_NumberOfAtlases( 0 ),
m_NumberOfAtlasSegmentations( 0 ),
m_NumberOfAtlasModalities( 0 ),
m_Alpha( 0.1 ),
m_Beta( 2.0 ),
m_RetainLabelPosteriorProbabilityImages( false ),
m_RetainAtlasVotingWeightImages( false ),
m_ConstrainSolutionToNonnegativeWeights( false )
{
this->m_MaskImage = nullptr;
this->m_CountImage = nullptr;
this->m_NeighborhoodSearchRadiusImage = nullptr;
this->SetSimilarityMetric( Superclass::PEARSON_CORRELATION );
}
template <typename TInputImage, typename TOutputImage>
void
WeightedVotingFusionImageFilter<TInputImage, TOutputImage>
::UpdateInputs()
{
// Set all the inputs
this->SetNumberOfIndexedInputs( this->m_NumberOfAtlases * this->m_NumberOfAtlasModalities +
this->m_NumberOfAtlasSegmentations + this->m_TargetImage.size() + this->m_LabelExclusionImages.size() );
SizeValueType nthInput = 0;
for( SizeValueType i = 0; i < this->m_TargetImage.size(); i++ )
{
this->SetNthInput( nthInput++, this->m_TargetImage[i] );
}
for( SizeValueType i = 0; i < this->m_NumberOfAtlases; i++ )
{
for( SizeValueType j = 0; j < this->m_NumberOfAtlasModalities; j++ )
{
this->SetNthInput( nthInput++, this->m_AtlasImages[i][j] );
}
}
for( SizeValueType i = 0; i < this->m_NumberOfAtlasSegmentations; i++ )
{
this->SetNthInput( nthInput++, this->m_AtlasSegmentations[i] );
}
typename LabelExclusionMap::const_iterator it;
for( it = m_LabelExclusionImages.begin(); it != m_LabelExclusionImages.end(); ++it )
{
this->SetNthInput( nthInput++, it->second );
}
if( this->m_MaskImage.IsNotNull() )
{
this->SetNthInput( nthInput++, this->m_MaskImage );
}
this->Modified();
}
template <typename TInputImage, typename TOutputImage>
void
WeightedVotingFusionImageFilter<TInputImage, TOutputImage>
::GenerateInputRequestedRegion()
{
Superclass::GenerateInputRequestedRegion();
// Get the output requested region
RegionType outRegion = this->GetOutput()->GetRequestedRegion();
// Pad this region by the search window and patch size
if( this->m_NeighborhoodSearchRadiusImage.IsNull() )
{
outRegion.PadByRadius( this->GetNeighborhoodSearchRadius() );
}
else
{
NeighborhoodRadiusType maxNeighborhoodSearchRadius;
maxNeighborhoodSearchRadius.Fill( 0 );
ImageRegionConstIterator<RadiusImageType> ItR( this->m_NeighborhoodSearchRadiusImage,
this->m_NeighborhoodSearchRadiusImage->GetRequestedRegion() );
for( ItR.GoToBegin(); !ItR.IsAtEnd(); ++ItR )
{
RadiusValueType localSearchRadius = ItR.Get();
if( localSearchRadius > maxNeighborhoodSearchRadius[0] )
{
maxNeighborhoodSearchRadius.Fill( localSearchRadius );
}
}
outRegion.PadByRadius( maxNeighborhoodSearchRadius );
}
outRegion.PadByRadius( this->GetNeighborhoodPatchRadius() );
// Iterate over all the inputs to this filter
for( SizeValueType i = 0; i < this->m_TargetImage.size(); i++ )
{
InputImageType *input = this->m_TargetImage[i];
if( i == 0 )
{
this->SetTargetImageRegion( input->GetRequestedRegion() );
}
RegionType region = outRegion;
region.Crop( input->GetLargestPossibleRegion() );
input->SetRequestedRegion( region );
}
for( SizeValueType i = 0; i < this->m_NumberOfAtlases; i++ )
{
for( SizeValueType j = 0; j < this->m_NumberOfAtlasModalities; j++ )
{
InputImageType *input = this->m_AtlasImages[i][j];
RegionType region = outRegion;
region.Crop( input->GetLargestPossibleRegion() );
input->SetRequestedRegion( region );
}
}
for( SizeValueType i = 0; i < this->m_NumberOfAtlasSegmentations; i++ )
{
LabelImageType *input = this->m_AtlasSegmentations[i];
RegionType region = outRegion;
region.Crop( input->GetLargestPossibleRegion() );
input->SetRequestedRegion( region );
}
typename LabelExclusionMap::const_iterator it;
for( it = m_LabelExclusionImages.begin(); it != m_LabelExclusionImages.end(); ++it )
{
LabelImageType *input = it->second;
RegionType region = outRegion;
region.Crop( input->GetLargestPossibleRegion() );
input->SetRequestedRegion( region );
}
if( this->m_MaskImage.IsNotNull() )
{
MaskImageType *input = this->m_MaskImage;
RegionType region = outRegion;
region.Crop( input->GetLargestPossibleRegion() );
input->SetRequestedRegion( region );
}
}
template <typename TInputImage, typename TOutputImage>
void
WeightedVotingFusionImageFilter<TInputImage, TOutputImage>
::GenerateData()
{
this->BeforeThreadedGenerateData();
/**
* Multithread processing for the weighted averaging
*/
typename ImageSource<TOutputImage>::ThreadStruct str1;
str1.Filter = this;
// this->GetMultiThreader()->SetGlobalDefaultNumberOfThreads( this->GetNumberOfThreads() );
this->GetMultiThreader()->SetSingleMethod( this->ThreaderCallback, &str1 );
this->GetMultiThreader()->SingleMethodExecute();
this->m_IsWeightedAveragingComplete = true;
/**
* Multithread processing for the image(s) reconstruction
*/
typename ImageSource<TOutputImage>::ThreadStruct str2;
str2.Filter = this;
// this->GetMultiThreader()->SetGlobalDefaultNumberOfThreads( this->GetNumberOfThreads() );
this->GetMultiThreader()->SetSingleMethod( this->ThreaderCallback, &str2 );
this->GetMultiThreader()->SingleMethodExecute();
this->AfterThreadedGenerateData();
}
template <typename TInputImage, typename TOutputImage>
void
WeightedVotingFusionImageFilter<TInputImage, TOutputImage>
::BeforeThreadedGenerateData()
{
Superclass::BeforeThreadedGenerateData();
if( this->m_NumberOfAtlasSegmentations != this->m_NumberOfAtlases )
{
// Set the number of atlas segmentations to 0 since we're just going to
// doing joint intensity fusion
this->m_NumberOfAtlasSegmentations = 0;
}
// Check to see if the number of target images is equal to 1 or equal to the number
// of atlas modalities
if( this->m_TargetImage.size() != 1 && this->m_TargetImage.size() != this->m_NumberOfAtlasModalities )
{
itkExceptionMacro( "The number of target images must be 1 or must be the number of atlas modalities." );
}
// Find all the unique labels in the atlas segmentations
this->m_LabelSet.clear();
for( unsigned int i = 0; i < this->m_NumberOfAtlasSegmentations; i++ )
{
ImageRegionConstIteratorWithIndex<LabelImageType> It(
this->m_AtlasSegmentations[i], this->m_AtlasSegmentations[i]->GetRequestedRegion() );
for( It.GoToBegin(); !It.IsAtEnd(); ++It )
{
if( !this->m_MaskImage ||
this->m_MaskImage->GetPixel( It.GetIndex() ) != NumericTraits<LabelType>::ZeroValue() )
{
this->m_LabelSet.insert( It.Get() );
}
}
}
// Initialize the posterior maps
this->m_LabelPosteriorProbabilityImages.clear();
typename LabelSetType::const_iterator labelIt;
for( labelIt = this->m_LabelSet.begin(); labelIt != this->m_LabelSet.end(); ++labelIt )
{
typename ProbabilityImageType::Pointer labelProbabilityImage = ProbabilityImageType::New();
labelProbabilityImage->CopyInformation( this->m_TargetImage[0] );
labelProbabilityImage->SetRegions( this->m_TargetImage[0]->GetRequestedRegion() );
labelProbabilityImage->SetLargestPossibleRegion( this->m_TargetImage[0]->GetLargestPossibleRegion() );
labelProbabilityImage->Allocate( true );
this->m_LabelPosteriorProbabilityImages.insert(
std::pair<LabelType, ProbabilityImagePointer>( *labelIt, labelProbabilityImage ) );
}
// Initialize the atlas voting weight images
if( this->m_RetainAtlasVotingWeightImages )
{
this->m_AtlasVotingWeightImages.clear();
this->m_AtlasVotingWeightImages.resize( this->m_NumberOfAtlases );
for( SizeValueType i = 0; i < this->m_NumberOfAtlases; i++ )
{
this->m_AtlasVotingWeightImages[i] = ProbabilityImageType::New();
this->m_AtlasVotingWeightImages[i]->CopyInformation( this->m_TargetImage[0] );
this->m_AtlasVotingWeightImages[i]->SetRegions( this->m_TargetImage[0]->GetRequestedRegion() );
this->m_AtlasVotingWeightImages[i]->SetLargestPossibleRegion( this->m_TargetImage[0]->GetLargestPossibleRegion() );
this->m_AtlasVotingWeightImages[i]->Allocate( true );
}
}
// Do the joint intensity fusion
this->m_JointIntensityFusionImage.clear();
this->m_JointIntensityFusionImage.resize( this->m_NumberOfAtlasModalities );
for( SizeValueType i = 0; i < this->m_NumberOfAtlasModalities; i++ )
{
this->m_JointIntensityFusionImage[i] = InputImageType::New();
this->m_JointIntensityFusionImage[i]->CopyInformation( this->m_TargetImage[0] );
this->m_JointIntensityFusionImage[i]->SetRegions( this->m_TargetImage[0]->GetRequestedRegion() );
this->m_JointIntensityFusionImage[i]->SetLargestPossibleRegion( this->m_TargetImage[0]->GetLargestPossibleRegion() );
this->m_JointIntensityFusionImage[i]->Allocate( true );
}
// Initialize the weight sum image
this->m_WeightSumImage = ProbabilityImageType::New();
this->m_WeightSumImage->CopyInformation( this->m_TargetImage[0] );
this->m_WeightSumImage->SetRegions( this->m_TargetImage[0]->GetRequestedRegion() );
this->m_WeightSumImage->SetLargestPossibleRegion( this->m_TargetImage[0]->GetLargestPossibleRegion() );
this->m_WeightSumImage->Allocate( true );
// Initialize the count image
this->m_CountImage = CountImageType::New();
this->m_CountImage->CopyInformation( this->m_TargetImage[0] );
this->m_CountImage->SetRegions( this->m_TargetImage[0]->GetRequestedRegion() );
this->m_CountImage->SetLargestPossibleRegion( this->m_TargetImage[0]->GetLargestPossibleRegion() );
this->m_CountImage->Allocate( true );
// Determine the ordered search offset list (or map if an search radius image is specified)
typename InputImageType::SpacingType spacing = this->m_TargetImage[0]->GetSpacing();
NeighborhoodOffsetListType orderedNeighborhoodSearchOffsetList;
orderedNeighborhoodSearchOffsetList.clear();
this->m_NeighborhoodSearchOffsetSetsMap.clear();
if( this->m_NeighborhoodSearchRadiusImage.IsNull() )
{
ConstNeighborhoodIterator<InputImageType> It( this->GetNeighborhoodSearchRadius(),
this->GetInput(), this->GetInput()->GetRequestedRegion() );
DistanceIndexVectorType squaredDistances;
squaredDistances.resize( this->GetNeighborhoodSearchSize() );
for( unsigned int n = 0; n < this->GetNeighborhoodSearchSize(); n++ )
{
NeighborhoodOffsetType offset = ( It.GetNeighborhood() ).GetOffset( n );
squaredDistances[n].first = n;
squaredDistances[n].second = 0.0;
for( unsigned int d = 0; d < ImageDimension; d++ )
{
squaredDistances[n].second += itk::Math::sqr ( offset[d] * spacing[d] );
}
}
std::sort( squaredDistances.begin(), squaredDistances.end(), DistanceIndexComparator() );
for( unsigned int n = 0; n < this->GetNeighborhoodSearchSize(); n++ )
{
orderedNeighborhoodSearchOffsetList.push_back( ( It.GetNeighborhood() ).GetOffset( squaredDistances[n].first ) );
}
this->SetNeighborhoodSearchOffsetList( orderedNeighborhoodSearchOffsetList );
}
else
{
ImageRegionConstIterator<RadiusImageType> ItR( this->m_NeighborhoodSearchRadiusImage,
this->m_NeighborhoodSearchRadiusImage->GetRequestedRegion() );
for( ItR.GoToBegin(); !ItR.IsAtEnd(); ++ItR )
{
RadiusValueType localSearchRadius = ItR.Get();
if( localSearchRadius > 0 &&
this->m_NeighborhoodSearchOffsetSetsMap.find( localSearchRadius ) ==
this->m_NeighborhoodSearchOffsetSetsMap.end() )
{
NeighborhoodRadiusType localNeighborhoodSearchRadius;
localNeighborhoodSearchRadius.Fill( localSearchRadius );
std::vector<NeighborhoodOffsetType> localNeighborhoodSearchOffsetList;
ConstNeighborhoodIterator<InputImageType> It( localNeighborhoodSearchRadius,
this->GetInput(), this->GetInput()->GetRequestedRegion() );
RadiusValueType localNeighborhoodSearchSize = ( It.GetNeighborhood() ).Size();
DistanceIndexVectorType squaredDistances;
squaredDistances.resize( localNeighborhoodSearchSize );
for( unsigned int n = 0; n < localNeighborhoodSearchSize; n++ )
{
NeighborhoodOffsetType offset = ( It.GetNeighborhood() ).GetOffset( n );
squaredDistances[n].first = n;
squaredDistances[n].second = 0.0;
for( unsigned int d = 0; d < ImageDimension; d++ )
{
squaredDistances[n].second += itk::Math::sqr ( offset[d] * spacing[d] );
}
}
std::sort( squaredDistances.begin(), squaredDistances.end(), DistanceIndexComparator() );
for( unsigned int n = 0; n < localNeighborhoodSearchSize; n++ )
{
localNeighborhoodSearchOffsetList.push_back( ( It.GetNeighborhood() ).GetOffset( squaredDistances[n].first ) );
}
this->m_NeighborhoodSearchOffsetSetsMap[localSearchRadius] = localNeighborhoodSearchOffsetList;
}
}
}
this->AllocateOutputs();
}
template <typename TInputImage, typename TOutputImage>
void
WeightedVotingFusionImageFilter<TInputImage, TOutputImage>
::ThreadedGenerateData( const RegionType ®ion, ThreadIdType threadId )
{
if( !this->m_IsWeightedAveragingComplete )
{
this->ThreadedGenerateDataForWeightedAveraging( region, threadId );
}
else
{
this->ThreadedGenerateDataForReconstruction( region, threadId );
}
}
template <typename TInputImage, typename TOutputImage>
void
WeightedVotingFusionImageFilter<TInputImage, TOutputImage>
::ThreadedGenerateDataForWeightedAveraging( const RegionType & region, ThreadIdType threadId )
{
ProgressReporter progress( this, threadId, region.GetNumberOfPixels(), 100 );
typename OutputImageType::Pointer output = this->GetOutput();
SizeValueType numberOfTargetModalities = this->m_TargetImage.size();
MatrixType absoluteAtlasPatchDifferences( this->m_NumberOfAtlases,
this->GetNeighborhoodPatchSize() * numberOfTargetModalities );
MatrixType originalAtlasPatchIntensities( this->m_NumberOfAtlases,
this->GetNeighborhoodPatchSize() * this->m_NumberOfAtlasModalities );
std::vector<SizeValueType> minimumAtlasOffsetIndices( this->m_NumberOfAtlases );
bool useOnlyFirstAtlasImage = true;
if( numberOfTargetModalities == this->m_NumberOfAtlasModalities )
{
useOnlyFirstAtlasImage = false;
}
std::vector<NeighborhoodOffsetType> searchNeighborhoodOffsetList = this->GetNeighborhoodSearchOffsetList();
// Iterate over the input region
ConstNeighborhoodIteratorType ItN( this->GetNeighborhoodPatchRadius(), this->m_TargetImage[0], region );
for( ItN.GoToBegin(); !ItN.IsAtEnd(); ++ItN )
{
progress.CompletedPixel();
IndexType currentCenterIndex = ItN.GetIndex();
if( this->m_MaskImage &&
this->m_MaskImage->GetPixel( currentCenterIndex ) == NumericTraits<LabelType>::ZeroValue() )
{
continue;
}
// Do not do the following check from Paul's original code. Since we're incorporating
// joint intensity fusion, we want to calculate at every voxel (except outside of a
// possible mask) even if there are no segmentation labels at that voxel.
// // Check to see if there are any non-zero labels
// if( this->m_NumberOfAtlasSegmentations > 0 )
// {
// bool nonBackgroundLabelExistAtThisVoxel = false;
// for( SizeValueType i = 0; i < this->m_NumberOfAtlasSegmentations; i++ )
// {
// if( this->m_AtlasSegmentations[i]->GetPixel( currentCenterIndex ) > 0 )
// {
// nonBackgroundLabelExistAtThisVoxel = true;
// break;
// }
// }
// if( ! nonBackgroundLabelExistAtThisVoxel )
// {
// continue;
// }
// }
// Determine the search neighborhood offset list for the current center voxel
if( this->m_NeighborhoodSearchRadiusImage.IsNotNull() )
{
RadiusValueType localSearchRadius =
this->m_NeighborhoodSearchRadiusImage->GetPixel( currentCenterIndex );
if( localSearchRadius <= 0 )
{
continue;
}
searchNeighborhoodOffsetList = this->m_NeighborhoodSearchOffsetSetsMap[localSearchRadius];
}
SizeValueType searchNeighborhoodSize = searchNeighborhoodOffsetList.size();
InputImagePixelVectorType normalizedTargetPatch =
this->VectorizeImageListPatch( this->m_TargetImage, currentCenterIndex, true );
absoluteAtlasPatchDifferences.fill( 0.0 );
originalAtlasPatchIntensities.fill( 0.0 );
// In each atlas, search for a patch that matches the target patch
for( SizeValueType i = 0; i < this->m_NumberOfAtlases; i++ )
{
RealType minimumPatchSimilarity = NumericTraits<RealType>::max();
SizeValueType minimumPatchOffsetIndex = 0;
for( SizeValueType j = 0; j < searchNeighborhoodSize; j++ )
{
IndexType searchIndex = currentCenterIndex + searchNeighborhoodOffsetList[j];
if( !output->GetRequestedRegion().IsInside( searchIndex ) )
{
continue;
}
RealType patchSimilarity = this->ComputeNeighborhoodPatchSimilarity(
this->m_AtlasImages[i], searchIndex, normalizedTargetPatch, useOnlyFirstAtlasImage );
if( patchSimilarity < minimumPatchSimilarity )
{
minimumPatchSimilarity = patchSimilarity;
minimumPatchOffsetIndex = j;
}
}
// Once the patch has been found, normalize it and then compute the absolute
// difference with target patch
IndexType minimumIndex = currentCenterIndex +
searchNeighborhoodOffsetList[minimumPatchOffsetIndex];
InputImagePixelVectorType normalizedMinimumAtlasPatch;
if( numberOfTargetModalities == this->m_NumberOfAtlasModalities )
{
normalizedMinimumAtlasPatch =
this->VectorizeImageListPatch( this->m_AtlasImages[i], minimumIndex, true );
}
else
{
normalizedMinimumAtlasPatch =
this->VectorizeImagePatch( this->m_AtlasImages[i][0], minimumIndex, true );
}
typename InputImagePixelVectorType::const_iterator itA = normalizedMinimumAtlasPatch.begin();
typename InputImagePixelVectorType::const_iterator itT = normalizedTargetPatch.begin();
while( itA != normalizedMinimumAtlasPatch.end() )
{
RealType value = std::fabs( *itA - *itT );
absoluteAtlasPatchDifferences(i, itA - normalizedMinimumAtlasPatch.begin()) = value;
++itA;
++itT;
}
InputImagePixelVectorType originalMinimumAtlasPatch =
this->VectorizeImageListPatch( this->m_AtlasImages[i], minimumIndex, false );
typename InputImagePixelVectorType::const_iterator itO = originalMinimumAtlasPatch.begin();
while( itO != originalMinimumAtlasPatch.end() )
{
originalAtlasPatchIntensities(i, itO - originalMinimumAtlasPatch.begin()) = *itO;
++itO;
}
minimumAtlasOffsetIndices[i] = minimumPatchOffsetIndex;
}
// Allocate Mx
MatrixType Mx( this->m_NumberOfAtlases, this->m_NumberOfAtlases );
// Compute Mx values
for( SizeValueType i = 0; i < this->m_NumberOfAtlases; i++ )
{
for( SizeValueType j = 0; j <= i; j++ )
{
RealType mxValue = 0.0;
for( unsigned int k = 0; k < this->GetNeighborhoodPatchSize() * numberOfTargetModalities; k++ )
{
mxValue += absoluteAtlasPatchDifferences[i][k] * absoluteAtlasPatchDifferences[j][k];
}
mxValue /= static_cast<RealType>( this->GetNeighborhoodPatchSize() - 1 );
if( this->m_Beta == 2.0 )
{
mxValue *= mxValue;
}
else
{
mxValue = std::pow( mxValue, this->m_Beta );
}
if( !std::isfinite( mxValue ) )
{
mxValue = 0.0;
}
Mx(i, j) = Mx(j, i) = mxValue;
}
}
// Compute the weights by solving for the inverse of Mx
MatrixType MxBar( this->m_NumberOfAtlases, this->m_NumberOfAtlases, 0.0 );
MxBar.fill_diagonal( this->m_Alpha );
MxBar += Mx;
// Define a vector of all ones
VectorType ones( this->m_NumberOfAtlases, 1.0 );
VectorType W( this->m_NumberOfAtlases, 1.0 );
if( this->m_ConstrainSolutionToNonnegativeWeights )
{
W = this->NonNegativeLeastSquares( MxBar, ones, 1e-6 );
}
else
{
vnl_cholesky cholesky( MxBar, vnl_cholesky::estimate_condition );
if( cholesky.rcond() > itk::Math::sqrteps )
{
// well-conditioned matrix
W = cholesky.solve( ones );
}
else
{
// ill-conditioned matrix
W = vnl_svd<RealType>( MxBar ).solve( ones );
}
for(double & i : W)
{
if( i < 0.0 )
{
i = 0.0;
}
}
}
// Normalize the weights
W *= 1.0 / dot_product( W, ones );
// Do joint intensity fusion
VectorType estimatedNeighborhoodIntensities = W;
estimatedNeighborhoodIntensities.post_multiply( originalAtlasPatchIntensities );
for( SizeValueType i = 0; i < this->m_NumberOfAtlasModalities; i++ )
{
for( SizeValueType j = 0; j < this->GetNeighborhoodPatchSize(); j++ )
{
IndexType neighborhoodIndex = ItN.GetIndex( j );
if( !output->GetRequestedRegion().IsInside( neighborhoodIndex ) )
{
continue;
}
if( this->m_MaskImage &&
this->m_MaskImage->GetPixel( neighborhoodIndex ) == NumericTraits<LabelType>::ZeroValue() )
{
continue;
}
RealType estimatedValue = (
estimatedNeighborhoodIntensities[i * this->GetNeighborhoodPatchSize() + j] +
this->m_JointIntensityFusionImage[i]->GetPixel( neighborhoodIndex ) );
if( !std::isfinite( estimatedValue ) )
{
estimatedValue = 0.0;
}
this->m_JointIntensityFusionImage[i]->SetPixel( neighborhoodIndex,
static_cast<InputImagePixelType>( estimatedValue ) );
if( i == 0 )
{
this->m_CountImage->SetPixel( neighborhoodIndex,
this->m_CountImage->GetPixel( neighborhoodIndex ) + 1 );
}
}
}
if( this->m_NumberOfAtlasSegmentations > 0 )
{
// Perform voting using Hongzhi's averaging scheme. Iterate over all segmentation patches
for( SizeValueType n = 0; n < this->GetNeighborhoodPatchSize(); n++ )
{
IndexType neighborhoodIndex = ItN.GetIndex( n );
if( !output->GetRequestedRegion().IsInside( neighborhoodIndex ) )
{
continue;
}
for( SizeValueType i = 0; i < this->m_NumberOfAtlasSegmentations; i++ )
{
// The segmentation at the corresponding patch location in atlas i
IndexType minimumIndex = neighborhoodIndex +
searchNeighborhoodOffsetList[minimumAtlasOffsetIndices[i]];
if( !output->GetRequestedRegion().IsInside( minimumIndex ) )
{
continue;
}
LabelType label = this->m_AtlasSegmentations[i]->GetPixel( minimumIndex );
if( this->m_LabelSet.find( label ) == this->m_LabelSet.end() )
{
continue;
}
// Add that weight the posterior map for voxel at idx
this->m_LabelPosteriorProbabilityImages[label]->SetPixel( neighborhoodIndex,
this->m_LabelPosteriorProbabilityImages[label]->GetPixel( neighborhoodIndex ) + W[i] );
this->m_WeightSumImage->SetPixel( neighborhoodIndex,
this->m_WeightSumImage->GetPixel( neighborhoodIndex ) + W[i] );
if( this->m_RetainAtlasVotingWeightImages )
{
this->m_AtlasVotingWeightImages[i]->SetPixel( neighborhoodIndex,
this->m_AtlasVotingWeightImages[i]->GetPixel( neighborhoodIndex ) + W[i] );
}
}
}
}
}
}
template <typename TInputImage, typename TOutputImage>
void
WeightedVotingFusionImageFilter<TInputImage, TOutputImage>
::ThreadedGenerateDataForReconstruction( const RegionType ®ion, ThreadIdType threadId )
{
ProgressReporter progress( this, threadId, 2 * region.GetNumberOfPixels(), 100 );
typename OutputImageType::Pointer output = this->GetOutput();
// Perform voting at each voxel
ImageRegionIteratorWithIndex<OutputImageType> It( output, region );
for( It.GoToBegin(); !It.IsAtEnd(); ++It )
{
progress.CompletedPixel();
IndexType index = It.GetIndex();
if( this->m_MaskImage &&
this->m_MaskImage->GetPixel( It.GetIndex() ) == NumericTraits<LabelType>::ZeroValue() )
{
continue;
}
RealType maxPosteriorProbability = 0.0;
LabelType winningLabel = NumericTraits<LabelType>::ZeroValue();
typename LabelSetType::const_iterator labelIt;
for( labelIt = this->m_LabelSet.begin(); labelIt != this->m_LabelSet.end(); ++labelIt )
{
// check if the label is excluded
typename LabelExclusionMap::const_iterator xIt = this->m_LabelExclusionImages.find( *labelIt );
bool isLabelExcluded = ( xIt != m_LabelExclusionImages.end() && xIt->second->GetPixel( index ) != 0 );
if( !isLabelExcluded )
{
typename ProbabilityImageType::PixelType posteriorProbability =
this->m_LabelPosteriorProbabilityImages[*labelIt]->GetPixel( index );
// Vote!
if( maxPosteriorProbability < posteriorProbability )
{
maxPosteriorProbability = posteriorProbability;
winningLabel = *labelIt;
}
}
}
It.Set( winningLabel );
}
ImageRegionIteratorWithIndex<ProbabilityImageType> ItW( this->m_WeightSumImage,
region );
for( ItW.GoToBegin(); !ItW.IsAtEnd(); ++ItW )
{
progress.CompletedPixel();
typename ProbabilityImageType::PixelType weightSum = ItW.Get();
IndexType index = ItW.GetIndex();
if( weightSum < 0.1 )
{
continue;
}
if( this->m_RetainLabelPosteriorProbabilityImages )
{
typename LabelSetType::const_iterator labelIt;
for( labelIt = this->m_LabelSet.begin(); labelIt != this->m_LabelSet.end(); ++labelIt )
{
typename ProbabilityImageType::PixelType labelProbability =
this->m_LabelPosteriorProbabilityImages[*labelIt]->GetPixel( index );
this->m_LabelPosteriorProbabilityImages[*labelIt]->SetPixel( index, labelProbability / weightSum );
}
}
if( this->m_RetainAtlasVotingWeightImages )
{
for( SizeValueType i = 0; i < this->m_NumberOfAtlases; i++ )
{
typename ProbabilityImageType::PixelType votingWeight =
this->m_AtlasVotingWeightImages[i]->GetPixel( index );
this->m_AtlasVotingWeightImages[i]->SetPixel( index, votingWeight / weightSum );
}
}
}
}
template <typename TInputImage, typename TOutputImage>
void
WeightedVotingFusionImageFilter<TInputImage, TOutputImage>
::AfterThreadedGenerateData()
{
// Clear posterior maps if not kept
if( !this->m_RetainLabelPosteriorProbabilityImages )
{
this->m_LabelPosteriorProbabilityImages.clear();
}
// Normalize the joint intensity fusion images.
for( SizeValueType i = 0; i < this->m_NumberOfAtlasModalities; i++ )
{
ImageRegionIterator<InputImageType> ItJ( this->m_JointIntensityFusionImage[i],
this->m_JointIntensityFusionImage[i]->GetRequestedRegion() );
ImageRegionIterator<CountImageType> ItC( this->m_CountImage,
this->m_CountImage->GetRequestedRegion() );
for( ItJ.GoToBegin(), ItC.GoToBegin(); !ItJ.IsAtEnd(); ++ItJ, ++ItC )
{
typename CountImageType::PixelType count = ItC.Get();
if( count > 0 )
{
ItJ.Set( ItJ.Get() / static_cast<RealType>( count ) );
}
}
}
}
template <typename TInputImage, typename TOutputImage>
typename WeightedVotingFusionImageFilter<TInputImage, TOutputImage>::VectorType
WeightedVotingFusionImageFilter<TInputImage, TOutputImage>
::NonNegativeLeastSquares( const MatrixType A, const VectorType y, const RealType tolerance )
{
// Algorithm based on
// Lawson, Charles L.; Hanson, Richard J. (1995). Solving Least Squares Problems. SIAM.
// cf https://en.wikipedia.org/wiki/Non-negative_least_squares
SizeValueType m = A.rows();
SizeValueType n = A.cols();
// This fortran implementation sets a maximum iteration number of 3 times the
// number of columns:
// http://www.netlib.org/lawson-hanson/all
const SizeValueType maximumNumberOfIterations = 3 * n;
// Initialization
VectorType P( n, 0 );
VectorType R( n, 1 );
VectorType x( n, 0 );
VectorType s( n, 0 );
VectorType w = A.transpose() * ( y - A * x );
RealType wMaxValue = w.max_value();
SizeValueType maxIndex = NumericTraits<SizeValueType>::max();
wMaxValue = NumericTraits<RealType>::NonpositiveMin();
for( SizeValueType i = 0; i < n; i++ )
{
if( R[i] == 1 && wMaxValue < w[i] )
{
maxIndex = i;
wMaxValue = w[i];
}
}
// Outer loop
SizeValueType numberOfIterations = 0;
while( R.sum() > 0 && wMaxValue > tolerance &&
numberOfIterations++ < maximumNumberOfIterations )
{
P[maxIndex] = 1;
R[maxIndex] = 0;
SizeValueType sizeP = P.sum();
MatrixType AP( m, sizeP, 0 );
SizeValueType jIndex = 0;
for( SizeValueType j = 0; j < n; j++ )
{
if( P[j] == 1 )
{
AP.set_column( jIndex++, A.get_column( j ) );
}
}
VectorType sP = vnl_svd<RealType>( AP ).pinverse() * y;
SizeValueType iIndex = 0;
for( SizeValueType i = 0; i < n; i++ )
{
if( R[i] != 0 )
{
s[i] = 0;
}
else
{
s[i] = sP[iIndex++];
}
}
// Inner loop
while( sP.min_value() <= tolerance && sizeP > 0 )
{
RealType alpha = NumericTraits<RealType>::max();
for( SizeValueType i = 0; i < n; i++ )
{
if( P[i] == 1 && s[i] <= tolerance )
{
RealType value = x[i] / ( x[i] - s[i] );
if( value < alpha )
{
alpha = value;
}
}
}
x += alpha * ( s - x );
for( SizeValueType i = 0; i < n; i++ )
{
if( P[i] == 1 && std::fabs( x[i] ) < tolerance )
{
P[i] = 0;
R[i] = 1;
}
}
sizeP = P.sum();
if( sizeP == 0 )
{
break;
}
AP.set_size( m, sizeP );
jIndex = 0;
for( SizeValueType j = 0; j < n; j++ )
{
if( P[j] == 1 )
{
AP.set_column( jIndex++, A.get_column( j ) );
}
}
sP = vnl_svd<RealType>( AP ).pinverse() * y;
iIndex = 0;
for( SizeValueType i = 0; i < n; i++ )
{
if( R[i] != 0 )
{
s[i] = 0;
}
else
{
s[i] = sP[iIndex++];
}
}
}
x = s;
w = A.transpose() * ( y - A * x );
maxIndex = NumericTraits<SizeValueType>::max();
wMaxValue = NumericTraits<RealType>::NonpositiveMin();
for( SizeValueType i = 0; i < n; i++ )
{
if( R[i] == 1 && wMaxValue < w[i] )
{
maxIndex = i;
wMaxValue = w[i];
}
}
}
return x;
}
template <typename TInputImage, typename TOutputImage>