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itkImageRegistrationMethodv4.hxx
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/*=========================================================================
*
* Copyright NumFOCUS
*
* 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
*
* https://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 "itkSmoothingRecursiveGaussianImageFilter.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"
#include "itkPrintHelper.h"
namespace itk
{
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>;
auto mutualInformationMetric = DefaultMetricType::New();
mutualInformationMetric->SetNumberOfHistogramBins(20);
mutualInformationMetric->SetUseMovingImageGradientFilter(false);
mutualInformationMetric->SetUseFixedImageGradientFilter(false);
mutualInformationMetric->SetUseSampledPointSet(false);
this->m_Metric = mutualInformationMetric;
using DefaultScalesEstimatorType = RegistrationParameterScalesFromPhysicalShift<DefaultMetricType>;
auto scalesEstimator = DefaultScalesEstimatorType::New();
scalesEstimator->SetMetric(mutualInformationMetric);
scalesEstimator->SetTransformForward(true);
using DefaultOptimizerType = GradientDescentOptimizerv4Template<RealType>;
auto 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;
const DecoratedOutputTransformPointer transformDecorator =
itkDynamicCastInDebugMode<DecoratedOutputTransformType *>(this->MakeOutput(0).GetPointer());
this->ProcessObject::SetNthOutput(0, transformDecorator);
this->m_OutputTransform = transformDecorator->GetModifiable();
// Default to a 3-level image registration: force the initialization of the ivars that depend on the number of levels.
this->SetNumberOfLevels(3);
this->m_ShrinkFactorsPerLevel.resize(this->m_NumberOfLevels);
this->m_ShrinkFactorsPerLevel[0].Fill(2);
this->m_ShrinkFactorsPerLevel[1].Fill(1);
this->m_ShrinkFactorsPerLevel[2].Fill(1);
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 = MetricSamplingStrategyEnum::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>
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));
}
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)
{
const OptimizerWeightsValueType difference =
itk::Math::abs(NumericTraits<OptimizerWeightsValueType>::OneValue() - this->m_OptimizerWeights[i]);
if (difference > tolerance)
{
this->m_OptimizerWeightsAreIdentity = false;
break;
}
}
}
this->Modified();
}
}
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
const typename MultiMetricType::Pointer multiMetric = dynamic_cast<MultiMetricType *>(this->m_Metric.GetPointer());
// Sanity checks and find the virtual domain image
if (level == 0)
{
const SizeValueType numberOfObjectPairs = static_cast<unsigned int>(0.5 * this->GetNumberOfIndexedInputs());
if (numberOfObjectPairs == 0)
{
itkExceptionMacro("There are no input objects.");
}
if (this->m_Metric->GetMetricCategory() == ObjectToObjectMetricBaseTemplateEnums::MetricCategory::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() == ObjectToObjectMetricBaseTemplateEnums::MetricCategory::IMAGE_METRIC)
{
this->m_FirstImageMetricIndex = 0;
}
else if (this->m_Metric->GetMetricCategory() == ObjectToObjectMetricBaseTemplateEnums::MetricCategory::MULTI_METRIC)
{
for (SizeValueType n = 0; n < this->m_NumberOfMetrics; ++n)
{
if (multiMetric->GetMetricQueue()[n]->GetMetricCategory() ==
ObjectToObjectMetricBaseTemplateEnums::MetricCategory::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() == ObjectToObjectMetricBaseTemplateEnums::MetricCategory::IMAGE_METRIC ||
(this->m_Metric->GetMetricCategory() == ObjectToObjectMetricBaseTemplateEnums::MetricCategory::MULTI_METRIC &&
multiMetric->GetMetricQueue()[n]->GetMetricCategory() ==
ObjectToObjectMetricBaseTemplateEnums::MetricCategory::IMAGE_METRIC))
{
if (this->m_Metric->GetMetricCategory() == ObjectToObjectMetricBaseTemplateEnums::MetricCategory::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() ==
ObjectToObjectMetricBaseTemplateEnums::MetricCategory::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())
{
auto 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() == ObjectToObjectMetricBaseTemplateEnums::MetricCategory::MULTI_METRIC)
{
if (fixedInitialTransform)
{
multiMetric->SetFixedTransform(fixedInitialTransform);
}
else
{
using IdentityTransformType = IdentityTransform<RealType, ImageDimension>;
auto 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() ==
ObjectToObjectMetricBaseTemplateEnums::MetricCategory::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() ==
ObjectToObjectMetricBaseTemplateEnums::MetricCategory::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>;
auto caster = CasterType::New();
caster->SetInput(currentLevelVirtualDomainImage);
caster->Update();
dynamic_cast<PointSetMetricType *>(multiMetric->GetMetricQueue()[n].GetPointer())
->SetVirtualDomainFromImage(caster->GetOutput());
}
}
}
}
else if (this->m_Metric->GetMetricCategory() == ObjectToObjectMetricBaseTemplateEnums::MetricCategory::IMAGE_METRIC)
{
const typename ImageMetricType::Pointer imageMetric = dynamic_cast<ImageMetricType *>(this->m_Metric.GetPointer());
if (fixedInitialTransform)
{
imageMetric->SetFixedTransform(fixedInitialTransform);
}
else
{
using IdentityTransformType = IdentityTransform<RealType, ImageDimension>;
auto defaultFixedInitialTransform = IdentityTransformType::New();
imageMetric->SetFixedTransform(defaultFixedInitialTransform);
}
imageMetric->SetMovingTransform(this->m_CompositeTransform);
if (currentLevelVirtualDomainImage.IsNotNull())
{
imageMetric->SetVirtualDomainFromImage(currentLevelVirtualDomainImage);
}
}
else if (this->m_Metric->GetMetricCategory() ==
ObjectToObjectMetricBaseTemplateEnums::MetricCategory::POINT_SET_METRIC)
{
const typename PointSetMetricType::Pointer pointSetMetric =
dynamic_cast<PointSetMetricType *>(this->m_Metric.GetPointer());
if (fixedInitialTransform)
{
pointSetMetric->SetFixedTransform(fixedInitialTransform);
}
else
{
using IdentityTransformType = IdentityTransform<RealType, ImageDimension>;
auto 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>;
auto 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() == ObjectToObjectMetricBaseTemplateEnums::MetricCategory::IMAGE_METRIC ||
(this->m_Metric->GetMetricCategory() == ObjectToObjectMetricBaseTemplateEnums::MetricCategory::MULTI_METRIC &&
multiMetric->GetMetricQueue()[n]->GetMetricCategory() ==
ObjectToObjectMetricBaseTemplateEnums::MetricCategory::IMAGE_METRIC))
{
if (this->m_SmoothingSigmasPerLevel[level] > 0)
{
using FixedImageSmoothingFilterType = SmoothingRecursiveGaussianImageFilter<FixedImageType, FixedImageType>;
auto fixedImageSmoothingFilter = FixedImageSmoothingFilterType::New();
typename FixedImageSmoothingFilterType::SigmaArrayType fixedImageSigmaArray(
this->m_SmoothingSigmasPerLevel[level]);
if (!this->m_SmoothingSigmasAreSpecifiedInPhysicalUnits)
{
auto & fixedSpacing = this->GetFixedImage(n)->GetSpacing();
for (unsigned int i = 0; i < fixedImageSigmaArray.Size(); ++i)
{
fixedImageSigmaArray[i] *= fixedSpacing[i];
}
}
fixedImageSmoothingFilter->SetSigmaArray(fixedImageSigmaArray);
fixedImageSmoothingFilter->SetInput(this->GetFixedImage(n));
this->m_FixedSmoothImages[n] = fixedImageSmoothingFilter->GetOutput();
fixedImageSmoothingFilter->Update();
fixedImageSmoothingFilter->GetOutput()->DisconnectPipeline();
using MovingImageSmoothingFilterType = SmoothingRecursiveGaussianImageFilter<MovingImageType, MovingImageType>;
auto movingImageSmoothingFilter = MovingImageSmoothingFilterType::New();
typename MovingImageSmoothingFilterType::SigmaArrayType movingImageSigmaArray(
this->m_SmoothingSigmasPerLevel[level]);
if (!this->m_SmoothingSigmasAreSpecifiedInPhysicalUnits)
{
auto & movingSpacing = this->GetMovingImage(n)->GetSpacing();
for (unsigned int i = 0; i < movingImageSigmaArray.Size(); ++i)
{
movingImageSigmaArray[i] *= movingSpacing[i];
}
}
movingImageSmoothingFilter->SetSigmaArray(movingImageSigmaArray);
movingImageSmoothingFilter->SetInput(this->GetMovingImage(n));
this->m_MovingSmoothImages[n] = movingImageSmoothingFilter->GetOutput();
movingImageSmoothingFilter->Update();
movingImageSmoothingFilter->GetOutput()->DisconnectPipeline();
}
else
{
this->m_MovingSmoothImages[n] = this->GetMovingImage(n);
this->m_FixedSmoothImages[n] = this->GetFixedImage(n);
}
// Update the image metric
if (this->m_Metric->GetMetricCategory() == ObjectToObjectMetricBaseTemplateEnums::MetricCategory::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() ==
ObjectToObjectMetricBaseTemplateEnums::MetricCategory::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() ==
ObjectToObjectMetricBaseTemplateEnums::MetricCategory::POINT_SET_METRIC ||
(this->m_Metric->GetMetricCategory() ==
ObjectToObjectMetricBaseTemplateEnums::MetricCategory::MULTI_METRIC &&
multiMetric->GetMetricQueue()[n]->GetMetricCategory() ==
ObjectToObjectMetricBaseTemplateEnums::MetricCategory::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() == ObjectToObjectMetricBaseTemplateEnums::MetricCategory::MULTI_METRIC)
{
multiMetric->GetMetricQueue()[n]->SetFixedObject(this->GetFixedPointSet(n));
multiMetric->GetMetricQueue()[n]->SetMovingObject(this->GetMovingPointSet(n));
}
else if (this->m_Metric->GetMetricCategory() ==
ObjectToObjectMetricBaseTemplateEnums::MetricCategory::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 != MetricSamplingStrategyEnum::NONE)
{
this->SetMetricSamplePoints();
}
// Update the optimizer
this->m_Optimizer->SetMetric(this->m_Metric);
if (this->m_Optimizer->CanUseScales() &&
((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;
}
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();
}
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();
}
}
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();
}
}
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;
}
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)
{
constexpr auto shrinkFactors = MakeFilled<ShrinkFactorsPerDimensionContainerType>(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();
}
}
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;
const 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
{
const 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
{
const 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)
{
auto samplePointSet = MetricSamplePointSetType::New();
using SamplePointType = typename MetricSamplePointSetType::PointType;
using RandomizerType = Statistics::MersenneTwisterRandomVariateGenerator;
auto randomizer = RandomizerType::New();
if (m_ReseedIterator)
{
randomizer->SetSeed();
}
else
{
randomizer->SetSeed(m_CurrentRandomSeed++);
}
unsigned long index = 0;
switch (this->m_MetricSamplingStrategy)
{
case MetricSamplingStrategyEnum::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->IsInsideInWorldSpace(point))
{
samplePointSet->SetPoint(index, point);
++index;
}
}
++count;
}
break;
}
case MetricSamplingStrategyEnum::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
{