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itkImageRegistrationMethodv4.hxx
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itkImageRegistrationMethodv4.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 itkImageRegistrationMethodv4_hxx
#define itkImageRegistrationMethodv4_hxx
#include "itkImageRegistrationMethodv4.h"
#include "itkDiscreteGaussianImageFilter.h"
#include "itkGradientDescentOptimizerv4.h"
#include "itkImageRandomConstIteratorWithIndex.h"
#include "itkImageRegionConstIteratorWithIndex.h"
#include "itkImageToImageMetricv4.h"
#include "itkIterationReporter.h"
#include "itkMattesMutualInformationImageToImageMetricv4.h"
#include "itkMersenneTwisterRandomVariateGenerator.h"
#include "itkRegistrationParameterScalesFromPhysicalShift.h"
namespace itk
{
/**
* Constructor
*/
template<typename TFixedImage, typename TMovingImage, typename TTransform, typename TVirtualImage, typename TPointSet>
ImageRegistrationMethodv4<TFixedImage, TMovingImage, TTransform, TVirtualImage, TPointSet>
::ImageRegistrationMethodv4()
{
ProcessObject::SetNumberOfRequiredOutputs( 1 );
Self::SetPrimaryOutputName( "Transform" );
// indexed input are alternating fixed and moving images
Self::SetPrimaryInputName( "Fixed" );
Self::AddRequiredInputName( "Moving", 1 );
ProcessObject::SetNumberOfRequiredInputs( 2 );
// optional named inputs
Self::SetInput( "InitialTransform", nullptr );
Self::SetInput( "FixedInitialTransform", nullptr );
Self::SetInput( "MovingInitialTransform", nullptr );
this->m_VirtualDomainImage = nullptr;
Self::ReleaseDataBeforeUpdateFlagOff();
this->m_CurrentLevel = 0;
this->m_CurrentIteration = 0;
this->m_CurrentMetricValue = 0.0;
this->m_CurrentConvergenceValue = 0.0;
this->m_IsConverged = false;
this->m_NumberOfFixedObjects = 0;
this->m_NumberOfMovingObjects = 0;
Self::ReleaseDataBeforeUpdateFlagOff();
this->m_InPlace = true;
this->m_InitializeCenterOfLinearOutputTransform = true;
this->m_CompositeTransform = CompositeTransformType::New();
using DefaultMetricType = MattesMutualInformationImageToImageMetricv4<FixedImageType, MovingImageType, VirtualImageType, RealType>;
typename DefaultMetricType::Pointer mutualInformationMetric = DefaultMetricType::New();
mutualInformationMetric->SetNumberOfHistogramBins( 20 );
mutualInformationMetric->SetUseMovingImageGradientFilter( false );
mutualInformationMetric->SetUseFixedImageGradientFilter( false );
mutualInformationMetric->SetUseFixedSampledPointSet( false );
this->m_Metric = mutualInformationMetric;
using DefaultScalesEstimatorType = RegistrationParameterScalesFromPhysicalShift<DefaultMetricType>;
typename DefaultScalesEstimatorType::Pointer scalesEstimator = DefaultScalesEstimatorType::New();
scalesEstimator->SetMetric( mutualInformationMetric );
scalesEstimator->SetTransformForward( true );
using DefaultOptimizerType = GradientDescentOptimizerv4Template<RealType>;
typename DefaultOptimizerType::Pointer optimizer = DefaultOptimizerType::New();
optimizer->SetLearningRate( 1.0 );
optimizer->SetNumberOfIterations( 1000 );
optimizer->SetScalesEstimator( scalesEstimator );
this->m_Optimizer = optimizer;
this->m_OptimizerWeights.SetSize( 0 );
this->m_OptimizerWeightsAreIdentity = true;
DecoratedOutputTransformPointer transformDecorator =
itkDynamicCastInDebugMode< DecoratedOutputTransformType * >( this->MakeOutput(0).GetPointer() );
this->ProcessObject::SetNthOutput( 0, transformDecorator );
this->m_OutputTransform = transformDecorator->GetModifiable();
// By default we set up a 3-level image registration.
this->m_NumberOfLevels = 0;
this->SetNumberOfLevels( 3 );
this->m_ShrinkFactorsPerLevel.resize( this->m_NumberOfLevels );
ShrinkFactorsPerDimensionContainerType shrinkFactors;
shrinkFactors.Fill( 2 );
this->m_ShrinkFactorsPerLevel[0] = shrinkFactors;
shrinkFactors.Fill( 1 );
this->m_ShrinkFactorsPerLevel[1] = shrinkFactors;
shrinkFactors.Fill( 1 );
this->m_ShrinkFactorsPerLevel[2] = shrinkFactors;
this->m_SmoothingSigmasPerLevel.SetSize( this->m_NumberOfLevels );
this->m_SmoothingSigmasPerLevel[0] = 2;
this->m_SmoothingSigmasPerLevel[1] = 1;
this->m_SmoothingSigmasPerLevel[2] = 0;
this->m_SmoothingSigmasAreSpecifiedInPhysicalUnits = true;
this->m_ReseedIterator = false;
this->m_RandomSeed = Statistics::MersenneTwisterRandomVariateGenerator::GetNextSeed();
this->m_CurrentRandomSeed = this->m_RandomSeed;
this->m_MetricSamplingStrategy = NONE;
this->m_MetricSamplingPercentagePerLevel.SetSize( this->m_NumberOfLevels );
this->m_MetricSamplingPercentagePerLevel.Fill( 1.0 );
}
template<typename TFixedImage, typename TMovingImage, typename TTransform, typename TVirtualImage, typename TPointSet>
ImageRegistrationMethodv4<TFixedImage, TMovingImage, TTransform, TVirtualImage, TPointSet>
::~ImageRegistrationMethodv4()
{
}
template<typename TFixedImage, typename TMovingImage, typename TTransform, typename TVirtualImage, typename TPointSet>
void
ImageRegistrationMethodv4<TFixedImage, TMovingImage, TTransform, TVirtualImage, TPointSet>
::SetFixedImage( SizeValueType index, const FixedImageType *image )
{
itkDebugMacro( "setting fixed image input " << index << " to " << image );
if( image != static_cast<FixedImageType *>( this->ProcessObject::GetInput( 2 * index ) ) )
{
if( !this->ProcessObject::GetInput( 2 * index ) )
{
this->m_NumberOfFixedObjects++;
}
this->ProcessObject::SetNthInput( 2 * index, const_cast<FixedImageType *>( image ) );
this->Modified();
}
}
template<typename TFixedImage, typename TMovingImage, typename TTransform, typename TVirtualImage, typename TPointSet>
const typename ImageRegistrationMethodv4<TFixedImage, TMovingImage, TTransform, TVirtualImage, TPointSet>::FixedImageType *
ImageRegistrationMethodv4<TFixedImage, TMovingImage, TTransform, TVirtualImage, TPointSet>
::GetFixedImage( SizeValueType index ) const
{
itkDebugMacro( "returning fixed image input " << index << " of "
<< static_cast<const FixedImageType *>( this->ProcessObject::GetInput( 2 * index ) ) );
return static_cast<const FixedImageType *>( this->ProcessObject::GetInput( 2 * index ) );
}
template<typename TFixedImage, typename TMovingImage, typename TTransform, typename TVirtualImage, typename TPointSet>
void
ImageRegistrationMethodv4<TFixedImage, TMovingImage, TTransform, TVirtualImage, TPointSet>
::SetMovingImage( SizeValueType index, const MovingImageType *image )
{
itkDebugMacro( "setting moving image input " << index << " to " << image );
if( image != static_cast<MovingImageType *>( this->ProcessObject::GetInput( 2 * index + 1 ) ) )
{
if( !this->ProcessObject::GetInput( 2 * index + 1 ) )
{
this->m_NumberOfMovingObjects++;
}
this->ProcessObject::SetNthInput( 2 * index + 1, const_cast<MovingImageType *>( image ) );
this->Modified();
}
}
template<typename TFixedImage, typename TMovingImage, typename TTransform, typename TVirtualImage, typename TPointSet>
const typename ImageRegistrationMethodv4<TFixedImage, TMovingImage, TTransform, TVirtualImage, TPointSet>::MovingImageType *
ImageRegistrationMethodv4<TFixedImage, TMovingImage, TTransform, TVirtualImage, TPointSet>
::GetMovingImage( SizeValueType index ) const
{
itkDebugMacro( "returning moving image input " << index << " of "
<< static_cast<const MovingImageType *>( this->ProcessObject::GetInput( 2 * index + 1 ) ) );
return static_cast<const MovingImageType *>( this->ProcessObject::GetInput( 2 * index + 1 ) );
}
template<typename TFixedImage, typename TMovingImage, typename TTransform, typename TVirtualImage, typename TPointSet>
void
ImageRegistrationMethodv4<TFixedImage, TMovingImage, TTransform, TVirtualImage, TPointSet>
::SetFixedPointSet( SizeValueType index, const PointSetType *pointSet )
{
itkDebugMacro( "setting fixed point set input " << index << " to " << pointSet );
if( pointSet != static_cast<PointSetType *>( this->ProcessObject::GetInput( 2 * index ) ) )
{
if( !this->ProcessObject::GetInput( 2 * index ) )
{
this->m_NumberOfFixedObjects++;
}
this->ProcessObject::SetNthInput( 2 * index, const_cast<PointSetType *>( pointSet ) );
this->Modified();
}
}
template<typename TFixedImage, typename TMovingImage, typename TTransform, typename TVirtualImage, typename TPointSet>
const typename ImageRegistrationMethodv4<TFixedImage, TMovingImage, TTransform, TVirtualImage, TPointSet>::PointSetType *
ImageRegistrationMethodv4<TFixedImage, TMovingImage, TTransform, TVirtualImage, TPointSet>
::GetFixedPointSet( SizeValueType index ) const
{
itkDebugMacro( "returning fixed point set input " << index << " of "
<< static_cast<const PointSetType *>( this->ProcessObject::GetInput( 2 * index ) ) );
return static_cast<const PointSetType *>( this->ProcessObject::GetInput( 2 * index ) );
}
template<typename TFixedImage, typename TMovingImage, typename TTransform, typename TVirtualImage, typename TPointSet>
void
ImageRegistrationMethodv4<TFixedImage, TMovingImage, TTransform, TVirtualImage, TPointSet>
::SetMovingPointSet( SizeValueType index, const PointSetType *pointSet )
{
itkDebugMacro( "setting moving point set input " << index << " to " << pointSet );
if( pointSet != static_cast<PointSetType *>( this->ProcessObject::GetInput( 2 * index + 1 ) ) )
{
if( !this->ProcessObject::GetInput( 2 * index + 1 ) )
{
this->m_NumberOfMovingObjects++;
}
this->ProcessObject::SetNthInput( 2 * index + 1, const_cast<PointSetType *>( pointSet ) );
this->Modified();
}
}
template<typename TFixedImage, typename TMovingImage, typename TTransform, typename TVirtualImage, typename TPointSet>
const typename ImageRegistrationMethodv4<TFixedImage, TMovingImage, TTransform, TVirtualImage, TPointSet>::PointSetType *
ImageRegistrationMethodv4<TFixedImage, TMovingImage, TTransform, TVirtualImage, TPointSet>
::GetMovingPointSet( SizeValueType index ) const
{
itkDebugMacro( "returning moving point set input " << index << " of "
<< static_cast<const PointSetType *>( this->ProcessObject::GetInput( 2 * index + 1 ) ) );
return static_cast<const PointSetType *>( this->ProcessObject::GetInput( 2 * index + 1 ) );
}
/*
* Set optimizer weights and do checking for identity.
*/
template<typename TFixedImage, typename TMovingImage, typename TTransform, typename TVirtualImage, typename TPointSet>
void
ImageRegistrationMethodv4<TFixedImage, TMovingImage, TTransform, TVirtualImage, TPointSet>
::SetOptimizerWeights( OptimizerWeightsType & weights )
{
if( weights != this->m_OptimizerWeights )
{
itkDebugMacro( "setting optimizer weights to " << weights );
this->m_OptimizerWeights = weights;
// Check to see if optimizer weights are identity to avoid unnecessary
// computations.
this->m_OptimizerWeightsAreIdentity = true;
if( this->m_OptimizerWeights.Size() > 0 )
{
using OptimizerWeightsValueType = typename OptimizerWeightsType::ValueType;
auto tolerance = static_cast<OptimizerWeightsValueType>( 1e-4 );
for( SizeValueType i = 0; i < this->m_OptimizerWeights.Size(); i++ )
{
OptimizerWeightsValueType difference =
std::fabs( NumericTraits<OptimizerWeightsValueType>::OneValue() - this->m_OptimizerWeights[i] );
if( difference > tolerance )
{
this->m_OptimizerWeightsAreIdentity = false;
break;
}
}
}
this->Modified();
}
}
/*
* Initialize by setting the interconnects between components.
*/
template<typename TFixedImage, typename TMovingImage, typename TTransform, typename TVirtualImage, typename TPointSet>
void
ImageRegistrationMethodv4<TFixedImage, TMovingImage, TTransform, TVirtualImage, TPointSet>
::InitializeRegistrationAtEachLevel( const SizeValueType level )
{
// To avoid casting to a multimetric several times, we do it once and use it
// throughout this function if the current enumerated metric type is MULTI_METRIC
typename MultiMetricType::Pointer multiMetric = dynamic_cast<MultiMetricType *>( this->m_Metric.GetPointer() );
// Sanity checks and find the virtual domain image
if( level == 0 )
{
SizeValueType numberOfObjectPairs = static_cast<unsigned int>( 0.5 * this->GetNumberOfIndexedInputs() );
if( numberOfObjectPairs == 0 )
{
itkExceptionMacro( "There are no input objects." );
}
if( this->m_Metric->GetMetricCategory() == MetricType::MULTI_METRIC )
{
this->m_NumberOfMetrics = multiMetric->GetNumberOfMetrics();
if( this->m_NumberOfMetrics != numberOfObjectPairs )
{
itkExceptionMacro( "Mismatch between number of image pairs and the number of metrics." );
}
}
else
{
this->m_NumberOfMetrics = 1;
}
// The number of image pairs also includes nullptr image pairs for the point set
// metrics
if( this->m_NumberOfFixedObjects != this->m_NumberOfMovingObjects )
{
itkExceptionMacro( "The number of fixed and moving images is not equal." );
}
}
if( !this->m_Optimizer )
{
itkExceptionMacro( "The optimizer is not present." );
}
if( !this->m_Metric )
{
itkExceptionMacro( "The metric is not present." );
}
auto * movingInitialTransform = const_cast<InitialTransformType*>( this->GetMovingInitialTransform() );
auto * fixedInitialTransform = const_cast<InitialTransformType*>( this->GetFixedInitialTransform() );
this->m_CurrentIteration = 0;
this->m_CurrentMetricValue = 0.0;
this->m_CurrentConvergenceValue = 0.0;
this->m_IsConverged = false;
this->InvokeEvent( MultiResolutionIterationEvent() );
// For each level, we adapt the current transform. For many transforms, e.g.
// affine, the base transform adaptor does not do anything. However, in the
// case of other transforms, e.g. the b-spline and displacement field transforms
// the fixed parameters are changed to reflect an increase in transform resolution.
// This could involve increasing the mesh size of the B-spline transform or
// increase the resolution of the displacement field.
if( this->m_TransformParametersAdaptorsPerLevel[level] )
{
this->m_TransformParametersAdaptorsPerLevel[level]->SetTransform( this->m_OutputTransform );
this->m_TransformParametersAdaptorsPerLevel[level]->AdaptTransformParameters();
}
// Set-up the composite transform at initialization
// Also, find the virtual domain image
if( level == 0 )
{
this->m_CompositeTransform->ClearTransformQueue();
// Since we cannot instantiate a null object from an abstract class, we need to initialize the moving
// initial transform as an identity transform.
// Nevertheless, we do not need add this transform to the composite transform when it is only an
// identity transform. Simply by not setting that, we can save lots of time in jacobian computations
// of the composite transform since we can avoid some matrix multiplications.
// Skip adding an IdentityTransform to the m_CompositeTransform
if( movingInitialTransform != nullptr &&
std::string( movingInitialTransform->GetNameOfClass() ) != std::string( "IdentityTransform" ) )
{
this->m_CompositeTransform->AddTransform( movingInitialTransform );
}
// If the moving initial transform is a composite transform, unroll
// it into m_CompositeTransform.
this->m_CompositeTransform->FlattenTransformQueue();
if( this->m_InitializeCenterOfLinearOutputTransform )
{
this->InitializeCenterOfLinearOutputTransform();
}
this->m_CompositeTransform->AddTransform( this->m_OutputTransform );
if( this->m_OptimizerWeights.Size() > 0 )
{
this->m_Optimizer->SetWeights( this->m_OptimizerWeights );
}
// Get index of first image metric
this->m_FirstImageMetricIndex = -1;
if( this->m_NumberOfMetrics == 1 && this->m_Metric->GetMetricCategory() == MetricType::IMAGE_METRIC )
{
this->m_FirstImageMetricIndex = 0;
}
else if( this->m_Metric->GetMetricCategory() == MetricType::MULTI_METRIC )
{
for( SizeValueType n = 0; n < this->m_NumberOfMetrics; n++ )
{
if( multiMetric->GetMetricQueue()[n]->GetMetricCategory() == MetricType::IMAGE_METRIC )
{
this->m_FirstImageMetricIndex = n;
break;
}
}
}
{
VirtualImageBaseConstPointer virtualDomainBaseImage = this->GetCurrentLevelVirtualDomainImage();
if( virtualDomainBaseImage.IsNull() && this->m_FirstImageMetricIndex >= 0 )
{
virtualDomainBaseImage = this->GetFixedImage( this->m_FirstImageMetricIndex );
}
this->m_VirtualDomainImage = VirtualImageType::New();
this->m_VirtualDomainImage->CopyInformation( virtualDomainBaseImage );
this->m_VirtualDomainImage->SetRegions( virtualDomainBaseImage->GetLargestPossibleRegion() );
this->m_VirtualDomainImage->Allocate();
}
this->m_FixedImageMasks.clear();
this->m_FixedImageMasks.resize( this->m_NumberOfMetrics );
this->m_MovingImageMasks.clear();
this->m_MovingImageMasks.resize( this->m_NumberOfMetrics );
for( SizeValueType n = 0; n < this->m_NumberOfMetrics; n++ )
{
this->m_FixedImageMasks[n] = nullptr;
this->m_MovingImageMasks[n] = nullptr;
if( this->m_Metric->GetMetricCategory() == MetricType::IMAGE_METRIC ||
( this->m_Metric->GetMetricCategory() == MetricType::MULTI_METRIC &&
multiMetric->GetMetricQueue()[n]->GetMetricCategory() == MetricType::IMAGE_METRIC ) )
{
if( this->m_Metric->GetMetricCategory() == MetricType::MULTI_METRIC )
{
this->m_FixedImageMasks[n] = dynamic_cast<ImageMetricType *>( multiMetric->GetMetricQueue()[n].GetPointer() )->GetFixedImageMask();
this->m_MovingImageMasks[n] = dynamic_cast<ImageMetricType *>( multiMetric->GetMetricQueue()[n].GetPointer() )->GetMovingImageMask();
}
else if( this->m_Metric->GetMetricCategory() == MetricType::IMAGE_METRIC )
{
this->m_FixedImageMasks[n] = dynamic_cast<ImageMetricType *>( this->m_Metric.GetPointer() )->GetFixedImageMask();
this->m_MovingImageMasks[n] = dynamic_cast<ImageMetricType *>( this->m_Metric.GetPointer() )->GetMovingImageMask();
}
else
{
itkExceptionMacro( "Invalid metric type." )
}
}
}
}
this->m_CompositeTransform->SetOnlyMostRecentTransformToOptimizeOn();
// At each resolution and for each image pair (assuming an image metric), we
// 1. subsample the reference domain (typically the fixed image) and/or
// 2. smooth the fixed and moving images.
typename VirtualImageType::Pointer currentLevelVirtualDomainImage = nullptr;
if( this->m_VirtualDomainImage.IsNotNull() )
{
typename ShrinkFilterType::Pointer shrinkFilter = ShrinkFilterType::New();
shrinkFilter->SetShrinkFactors( this->m_ShrinkFactorsPerLevel[level] );
shrinkFilter->SetInput( this->m_VirtualDomainImage );
currentLevelVirtualDomainImage = shrinkFilter->GetOutput();
currentLevelVirtualDomainImage->Update();
}
else
{
itkExceptionMacro( "A virtual domain image is not found. It should be specified in one of the metrics." );
}
if( this->m_Metric->GetMetricCategory() == MetricType::MULTI_METRIC )
{
if( fixedInitialTransform )
{
multiMetric->SetFixedTransform( fixedInitialTransform );
}
else
{
using IdentityTransformType = IdentityTransform<RealType, ImageDimension>;
typename IdentityTransformType::Pointer defaultFixedInitialTransform = IdentityTransformType::New();
multiMetric->SetFixedTransform( defaultFixedInitialTransform );
}
multiMetric->SetMovingTransform( this->m_CompositeTransform );
if( currentLevelVirtualDomainImage.IsNotNull() )
{
multiMetric->SetVirtualDomainFromImage( currentLevelVirtualDomainImage );
}
for( SizeValueType n = 0; n < multiMetric->GetNumberOfMetrics(); n++ )
{
if( multiMetric->GetMetricQueue()[n]->GetMetricCategory() == MetricType::IMAGE_METRIC )
{
if( currentLevelVirtualDomainImage.IsNotNull() )
{
dynamic_cast<ImageMetricType *>( multiMetric->GetMetricQueue()[n].GetPointer() )->SetVirtualDomainFromImage( currentLevelVirtualDomainImage );
}
else
{
itkExceptionMacro( "Virtual domain image is not specified." );
}
}
else if( multiMetric->GetMetricQueue()[n]->GetMetricCategory() == MetricType::POINT_SET_METRIC )
{
if( currentLevelVirtualDomainImage.IsNotNull() )
{
// This casting is a hack as all the metrics should be coordinated such that
// they have identical virtual image types.
using CasterType = CastImageFilter<VirtualImageType, typename PointSetMetricType::VirtualImageType>;
typename CasterType::Pointer caster = CasterType::New();
caster->SetInput( currentLevelVirtualDomainImage );
caster->Update();
dynamic_cast<PointSetMetricType *>( multiMetric->GetMetricQueue()[n].GetPointer() )->SetVirtualDomainFromImage( caster->GetOutput() );
}
}
}
}
else if( this->m_Metric->GetMetricCategory() == MetricType::IMAGE_METRIC )
{
typename ImageMetricType::Pointer imageMetric = dynamic_cast<ImageMetricType *>( this->m_Metric.GetPointer() );
if( fixedInitialTransform )
{
imageMetric->SetFixedTransform( fixedInitialTransform );
}
else
{
using IdentityTransformType = IdentityTransform<RealType, ImageDimension>;
typename IdentityTransformType::Pointer defaultFixedInitialTransform = IdentityTransformType::New();
imageMetric->SetFixedTransform( defaultFixedInitialTransform );
}
imageMetric->SetMovingTransform( this->m_CompositeTransform );
if( currentLevelVirtualDomainImage.IsNotNull() )
{
imageMetric->SetVirtualDomainFromImage( currentLevelVirtualDomainImage );
}
}
else if( this->m_Metric->GetMetricCategory() == MetricType::POINT_SET_METRIC )
{
typename PointSetMetricType::Pointer pointSetMetric =
dynamic_cast<PointSetMetricType *>( this->m_Metric.GetPointer() );
if( fixedInitialTransform )
{
pointSetMetric->SetFixedTransform( fixedInitialTransform );
}
else
{
using IdentityTransformType = IdentityTransform<RealType, ImageDimension>;
typename IdentityTransformType::Pointer defaultFixedInitialTransform = IdentityTransformType::New();
pointSetMetric->SetFixedTransform( defaultFixedInitialTransform );
}
pointSetMetric->SetMovingTransform( this->m_CompositeTransform );
if( currentLevelVirtualDomainImage.IsNotNull() )
{
// This casting is a hack as all the metrics should be coordinated such that
// they have identical virtual image types.
using CasterType = CastImageFilter<VirtualImageType, typename PointSetMetricType::VirtualImageType>;
typename CasterType::Pointer caster = CasterType::New();
caster->SetInput( currentLevelVirtualDomainImage );
caster->Update();
pointSetMetric->SetVirtualDomainFromImage( caster->GetOutput() );
}
}
else
{
itkExceptionMacro( "Invalid metric conversion." );
}
// We update the fixed and moving images for the image metrics and
// the fixed and moving point sets for the point set metrics. Note
// that we set the point sets here just like we set the images.
// Although this isn't necessary, we want to leave the option for
// changing the point sets per level.
this->m_FixedSmoothImages.clear();
this->m_FixedSmoothImages.resize( this->m_NumberOfMetrics );
this->m_MovingSmoothImages.clear();
this->m_MovingSmoothImages.resize( this->m_NumberOfMetrics );
this->m_FixedPointSets.clear();
this->m_FixedPointSets.resize( this->m_NumberOfMetrics );
this->m_MovingPointSets.clear();
this->m_MovingPointSets.resize( this->m_NumberOfMetrics );
for( SizeValueType n = 0; n < this->m_NumberOfMetrics; n++ )
{
this->m_FixedSmoothImages[n] = nullptr;
this->m_MovingSmoothImages[n] = nullptr;
this->m_FixedPointSets[n] = nullptr;
this->m_MovingPointSets[n] = nullptr;
if( this->m_Metric->GetMetricCategory() == MetricType::IMAGE_METRIC ||
( this->m_Metric->GetMetricCategory() == MetricType::MULTI_METRIC &&
multiMetric->GetMetricQueue()[n]->GetMetricCategory() == MetricType::IMAGE_METRIC ) )
{
using FixedImageSmoothingFilterType = DiscreteGaussianImageFilter<FixedImageType, FixedImageType>;
typename FixedImageSmoothingFilterType::Pointer fixedImageSmoothingFilter = FixedImageSmoothingFilterType::New();
if( this->m_SmoothingSigmasAreSpecifiedInPhysicalUnits == true )
{
fixedImageSmoothingFilter->SetUseImageSpacingOn();
}
else
{
fixedImageSmoothingFilter->SetUseImageSpacingOff();
}
fixedImageSmoothingFilter->SetVariance( itk::Math::sqr( this->m_SmoothingSigmasPerLevel[level] ) );
fixedImageSmoothingFilter->SetMaximumError( 0.01 );
fixedImageSmoothingFilter->SetInput( this->GetFixedImage( n ) );
this->m_FixedSmoothImages[n] = fixedImageSmoothingFilter->GetOutput();
this->m_FixedSmoothImages[n]->Update();
this->m_FixedSmoothImages[n]->DisconnectPipeline();
using MovingImageSmoothingFilterType = DiscreteGaussianImageFilter<MovingImageType, MovingImageType>;
typename MovingImageSmoothingFilterType::Pointer movingImageSmoothingFilter = MovingImageSmoothingFilterType::New();
if( this->m_SmoothingSigmasAreSpecifiedInPhysicalUnits == true )
{
movingImageSmoothingFilter->SetUseImageSpacingOn();
}
else
{
movingImageSmoothingFilter->SetUseImageSpacingOff();
}
movingImageSmoothingFilter->SetVariance( itk::Math::sqr( this->m_SmoothingSigmasPerLevel[level] ) );
movingImageSmoothingFilter->SetMaximumError( 0.01 );
movingImageSmoothingFilter->SetInput( this->GetMovingImage( n ) );
this->m_MovingSmoothImages[n] = movingImageSmoothingFilter->GetOutput();
this->m_MovingSmoothImages[n]->Update();
this->m_MovingSmoothImages[n]->DisconnectPipeline();
// Update the image metric
if( this->m_Metric->GetMetricCategory() == MetricType::MULTI_METRIC )
{
multiMetric->GetMetricQueue()[n]->SetFixedObject( this->m_FixedSmoothImages[n] );
multiMetric->GetMetricQueue()[n]->SetMovingObject( this->m_MovingSmoothImages[n] );
dynamic_cast<ImageMetricType *>( multiMetric->GetMetricQueue()[n].GetPointer() )->SetFixedImageMask( this->m_FixedImageMasks[n] );
dynamic_cast<ImageMetricType *>( multiMetric->GetMetricQueue()[n].GetPointer() )->SetMovingImageMask( this->m_MovingImageMasks[n] );
}
else if( this->m_Metric->GetMetricCategory() == MetricType::IMAGE_METRIC )
{
this->m_Metric->SetFixedObject( this->m_FixedSmoothImages[n] );
this->m_Metric->SetMovingObject( this->m_MovingSmoothImages[n] );
dynamic_cast<ImageMetricType *>( this->m_Metric.GetPointer() )->SetFixedImageMask( this->m_FixedImageMasks[n] );
dynamic_cast<ImageMetricType *>( this->m_Metric.GetPointer() )->SetMovingImageMask( this->m_MovingImageMasks[n] );
}
else
{
itkExceptionMacro( "Invalid metric type." )
}
}
else if( this->m_Metric->GetMetricCategory() == MetricType::POINT_SET_METRIC ||
( this->m_Metric->GetMetricCategory() == MetricType::MULTI_METRIC &&
multiMetric->GetMetricQueue()[n]->GetMetricCategory() == MetricType::POINT_SET_METRIC ) )
{
this->m_FixedPointSets[n] = this->GetFixedPointSet( n );
this->m_MovingPointSets[n] = this->GetMovingPointSet( n );
// Update the point set metric
if( this->m_Metric->GetMetricCategory() == MetricType::MULTI_METRIC )
{
multiMetric->GetMetricQueue()[n]->SetFixedObject( this->GetFixedPointSet( n ) );
multiMetric->GetMetricQueue()[n]->SetMovingObject( this->GetMovingPointSet( n ) );
}
else if( this->m_Metric->GetMetricCategory() == MetricType::POINT_SET_METRIC )
{
this->m_Metric->SetFixedObject( this->GetFixedPointSet( n ) );
this->m_Metric->SetMovingObject( this->GetMovingPointSet( n ) );
}
else
{
itkExceptionMacro( "Invalid metric type." )
}
}
else
{
itkExceptionMacro( "Invalid metric type." )
}
}
if( this->m_MetricSamplingStrategy != NONE )
{
this->SetMetricSamplePoints();
}
// Update the optimizer
this->m_Optimizer->SetMetric( this->m_Metric );
if( ( this->m_Optimizer->GetScales() ).Size() != this->m_OutputTransform->GetNumberOfLocalParameters() )
{
using ScalesType = typename OptimizerType::ScalesType;
ScalesType scales;
scales.SetSize( this->m_OutputTransform->GetNumberOfLocalParameters() );
scales.Fill( NumericTraits<typename ScalesType::ValueType>::OneValue() );
this->m_Optimizer->SetScales( scales );
}
}
template<typename TFixedImage, typename TMovingImage, typename TTransform, typename TVirtualImage, typename TPointSet>
void
ImageRegistrationMethodv4<TFixedImage, TMovingImage, TTransform, TVirtualImage, TPointSet>
::AllocateOutputs()
{
const DecoratedInitialTransformType * decoratedInitialTransform = this->GetInitialTransformInput();
DecoratedOutputTransformType *decoratedOutputTransform = this->GetOutput();
if( decoratedInitialTransform )
{
const InitialTransformType * initialTransform = decoratedInitialTransform->Get();
if( initialTransform )
{
if( this->GetInPlace() )
{
// graft the input to the output which may fail if the types
// aren't compatible.
decoratedOutputTransform->Graft( decoratedInitialTransform );
if( decoratedOutputTransform->Get() )
{
this->m_OutputTransform = decoratedOutputTransform->GetModifiable();
// This is generally done in the ReleaseInputs methods,
// however we do not need it again
const_cast<DecoratedInitialTransformType *>( decoratedInitialTransform )->ReleaseData();
// successful in-place grafting
itkDebugMacro( "inplace allocation of output transform" );
return;
}
}
const auto * initialAsOutputTransform = dynamic_cast<const OutputTransformType*>( initialTransform );
if( initialAsOutputTransform )
{
// Clone performs a deep copy of the parameters and composition
this->m_OutputTransform = initialAsOutputTransform->Clone();
decoratedOutputTransform->Set( this->m_OutputTransform );
// successful deep copy from initial to output
itkDebugMacro( "clone copy allocation of output transform" );
return;
}
else
{
itkExceptionMacro( "Unable to convert InitialTransform input to the OutputTransform type" );
}
}
}
// fallback allocation and initialization
// initialize to identity? what happens if we re-run with optimized values?
itkDebugMacro( "fallback allocation of output transform" );
if( !decoratedOutputTransform->Get() )
{
// the output decorated component is null, allocate
OutputTransformPointer ptr;
Self::MakeOutputTransform( ptr );
decoratedOutputTransform->Set( ptr );
}
this->m_OutputTransform = this->GetModifiableTransform();
}
/*
* Start the registration
*/
template<typename TFixedImage, typename TMovingImage, typename TTransform, typename TVirtualImage, typename TPointSet>
void
ImageRegistrationMethodv4<TFixedImage, TMovingImage, TTransform, TVirtualImage, TPointSet>
::GenerateData()
{
this->AllocateOutputs();
// Ensure the same seed is used for each update
this->m_CurrentRandomSeed = this->m_RandomSeed;
for( this->m_CurrentLevel = 0; this->m_CurrentLevel < this->m_NumberOfLevels; this->m_CurrentLevel++ )
{
this->InitializeRegistrationAtEachLevel( this->m_CurrentLevel );
this->m_Metric->Initialize();
this->m_Optimizer->StartOptimization();
}
}
/**
* Set the moving transform adaptors per stage
*/
template<typename TFixedImage, typename TMovingImage, typename TTransform, typename TVirtualImage, typename TPointSet>
void
ImageRegistrationMethodv4<TFixedImage, TMovingImage, TTransform, TVirtualImage, TPointSet>
::SetTransformParametersAdaptorsPerLevel( TransformParametersAdaptorsContainerType & adaptors )
{
if( this->m_NumberOfLevels != adaptors.size() )
{
itkExceptionMacro( "The number of levels does not equal the number array size." );
}
else
{
itkDebugMacro( "Setting the transform parameter adaptors." );
this->m_TransformParametersAdaptorsPerLevel = adaptors;
this->Modified();
}
}
/**
* Get the moving transform adaptors per stage
*/
template<typename TFixedImage, typename TMovingImage, typename TTransform, typename TVirtualImage, typename TPointSet>
const typename ImageRegistrationMethodv4<TFixedImage, TMovingImage, TTransform, TVirtualImage, TPointSet>::TransformParametersAdaptorsContainerType &
ImageRegistrationMethodv4<TFixedImage, TMovingImage, TTransform, TVirtualImage, TPointSet>
::GetTransformParametersAdaptorsPerLevel() const
{
return this->m_TransformParametersAdaptorsPerLevel;
}
/**
* Set the number of levels
*/
template<typename TFixedImage, typename TMovingImage, typename TTransform, typename TVirtualImage, typename TPointSet>
void
ImageRegistrationMethodv4<TFixedImage, TMovingImage, TTransform, TVirtualImage, TPointSet>
::SetNumberOfLevels( const SizeValueType numberOfLevels )
{
if( this->m_NumberOfLevels != numberOfLevels )
{
this->m_NumberOfLevels = numberOfLevels;
// Set default transform adaptors which don't do anything to the input transform
// Similarly, fill in some default values for the shrink factors, smoothing sigmas,
// and learning rates.
this->m_TransformParametersAdaptorsPerLevel.clear();
for( SizeValueType level = 0; level < this->m_NumberOfLevels; level++ )
{
this->m_TransformParametersAdaptorsPerLevel.push_back( nullptr );
}
for( SizeValueType level = 0; level < this->m_NumberOfLevels; ++level )
{
ShrinkFactorsPerDimensionContainerType shrinkFactors;
shrinkFactors.Fill( 1 );
this->SetShrinkFactorsPerDimension( level, shrinkFactors );
}
this->m_SmoothingSigmasPerLevel.SetSize( this->m_NumberOfLevels );
this->m_SmoothingSigmasPerLevel.Fill( 1.0 );
this->m_MetricSamplingPercentagePerLevel.SetSize( this->m_NumberOfLevels );
this->m_MetricSamplingPercentagePerLevel.Fill( 1.0 );
this->Modified();
}
}
/**
* Get the metric samples
*/
template<typename TFixedImage, typename TMovingImage, typename TTransform, typename TVirtualImage, typename TPointSet>
void
ImageRegistrationMethodv4<TFixedImage, TMovingImage, TTransform, TVirtualImage, TPointSet>
::SetMetricSamplePoints()
{
using VirtualDomainImageType = typename ImageMetricType::VirtualImageType;
using VirtualDomainRegionType = typename VirtualDomainImageType::RegionType;
const VirtualDomainImageType * virtualImage = nullptr;
const FixedImageMaskType * fixedMaskImage = nullptr;
SizeValueType numberOfLocalMetrics = 1;
typename MultiMetricType::Pointer multiMetric = dynamic_cast<MultiMetricType *>( this->m_Metric.GetPointer() );
if( multiMetric )
{
numberOfLocalMetrics = multiMetric->GetNumberOfMetrics();
if( numberOfLocalMetrics < 1 )
{
itkExceptionMacro( "Input multi metric should have at least one metric component." );
}
else
{
typename ImageMetricType::Pointer firstMetric = dynamic_cast<ImageMetricType *>( multiMetric->GetMetricQueue()[0].GetPointer() );
if( firstMetric.IsNotNull() )
{
virtualImage = firstMetric->GetVirtualImage();
fixedMaskImage = firstMetric->GetFixedImageMask();
}
else
{
itkExceptionMacro( "Invalid metric conversion." );
}
}
}
else
{
typename ImageMetricType::Pointer singleMetric = dynamic_cast<ImageMetricType *>( this->m_Metric.GetPointer() );
if( singleMetric.IsNotNull() )
{
virtualImage = singleMetric->GetVirtualImage();
fixedMaskImage = singleMetric->GetFixedImageMask();
}
else
{
itkExceptionMacro( "Invalid metric conversion." );
}
}
const VirtualDomainRegionType & virtualDomainRegion = virtualImage->GetRequestedRegion();
const typename VirtualDomainImageType::SpacingType oneThirdVirtualSpacing = virtualImage->GetSpacing() / 3.0;
for( SizeValueType n = 0; n < numberOfLocalMetrics; n++ )
{
typename MetricSamplePointSetType::Pointer samplePointSet = MetricSamplePointSetType::New();
samplePointSet->Initialize();
using SamplePointType = typename MetricSamplePointSetType::PointType;
using RandomizerType = Statistics::MersenneTwisterRandomVariateGenerator;
typename RandomizerType::Pointer randomizer = RandomizerType::New();
if (m_ReseedIterator)
{
randomizer->SetSeed( );
}
else
{
randomizer->SetSeed( m_CurrentRandomSeed++ );
}
unsigned long index = 0;
switch( this->m_MetricSamplingStrategy )
{
case REGULAR:
{
const auto sampleCount = static_cast<unsigned long>(
std::ceil( 1.0 / this->m_MetricSamplingPercentagePerLevel[this->m_CurrentLevel] ) );
unsigned long count = sampleCount; //Start at sampleCount to keep behavior backwards identical, using first element.
ImageRegionConstIteratorWithIndex<VirtualDomainImageType> It( virtualImage, virtualDomainRegion );
for( It.GoToBegin(); !It.IsAtEnd(); ++It )
{
if( count == sampleCount )
{
count=0; //Reset counter
SamplePointType point;
virtualImage->TransformIndexToPhysicalPoint( It.GetIndex(), point );
// randomly perturb the point within a voxel (approximately)
for( SizeValueType d = 0; d < ImageDimension; d++ )
{
point[d] += randomizer->GetNormalVariate() * oneThirdVirtualSpacing[d];
}
if( !fixedMaskImage || fixedMaskImage->IsInside( point ) )
{
samplePointSet->SetPoint( index, point );
++index;
}
}
++count;
}
break;
}
case RANDOM:
{
const unsigned long totalVirtualDomainVoxels = virtualDomainRegion.GetNumberOfPixels();
const auto sampleCount = static_cast<unsigned long>(
static_cast<float>( totalVirtualDomainVoxels )
* this->m_MetricSamplingPercentagePerLevel[this->m_CurrentLevel] );
ImageRandomConstIteratorWithIndex<VirtualDomainImageType> ItR( virtualImage, virtualDomainRegion );
if (m_ReseedIterator)
{
ItR.ReinitializeSeed();
}
else
{
ItR.ReinitializeSeed( m_CurrentRandomSeed++ );
}
ItR.SetNumberOfSamples( sampleCount );