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itkSimpleImageRegistrationTest.cxx
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itkSimpleImageRegistrationTest.cxx
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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.
*
*=========================================================================*/
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkImageRegistrationMethodv4.h"
#include "itkAffineTransform.h"
#include "itkANTSNeighborhoodCorrelationImageToImageMetricv4.h"
#include "itkGaussianSmoothingOnUpdateDisplacementFieldTransform.h"
#include "itkGaussianSmoothingOnUpdateDisplacementFieldTransformParametersAdaptor.h"
#include "itkJointHistogramMutualInformationImageToImageMetricv4.h"
#include "itkMersenneTwisterRandomVariateGenerator.h"
#include "itkTestingMacros.h"
template <typename TFilter>
class CommandIterationUpdate : public itk::Command
{
public:
using Self = CommandIterationUpdate;
using Superclass = itk::Command;
using Pointer = itk::SmartPointer<Self>;
itkNewMacro(Self);
protected:
CommandIterationUpdate() = default;
public:
void
Execute(itk::Object * caller, const itk::EventObject & event) override
{
Execute((const itk::Object *)caller, event);
}
void
Execute(const itk::Object * object, const itk::EventObject & event) override
{
if (object == nullptr)
{
itkExceptionMacro("Command update on null object");
}
std::cout << "Observing from class " << object->GetNameOfClass();
if (!object->GetObjectName().empty())
{
std::cout << " \"" << object->GetObjectName() << '"';
}
std::cout << std::endl;
const auto * filter = static_cast<const TFilter *>(object);
if (typeid(event) != typeid(itk::MultiResolutionIterationEvent) || object == nullptr)
{
return;
}
unsigned int currentLevel = filter->GetCurrentLevel();
typename TFilter::ShrinkFactorsPerDimensionContainerType shrinkFactors =
filter->GetShrinkFactorsPerDimension(currentLevel);
typename TFilter::SmoothingSigmasArrayType smoothingSigmas = filter->GetSmoothingSigmasPerLevel();
typename TFilter::TransformParametersAdaptorsContainerType adaptors =
filter->GetTransformParametersAdaptorsPerLevel();
const itk::ObjectToObjectOptimizerBase * optimizerBase = filter->GetOptimizer();
using GradientDescentOptimizerv4Type = itk::GradientDescentOptimizerv4;
typename GradientDescentOptimizerv4Type::ConstPointer optimizer =
dynamic_cast<const GradientDescentOptimizerv4Type *>(optimizerBase);
if (!optimizer)
{
itkGenericExceptionMacro("Error dynamic_cast failed");
}
typename GradientDescentOptimizerv4Type::DerivativeType gradient = optimizer->GetGradient();
std::cout << " CL Current level: " << currentLevel << std::endl;
std::cout << " SF Shrink factor: " << shrinkFactors << std::endl;
std::cout << " SS Smoothing sigma: " << smoothingSigmas[currentLevel] << std::endl;
if (adaptors[currentLevel])
{
std::cout << " RFP Required fixed params: " << adaptors[currentLevel]->GetRequiredFixedParameters()
<< std::endl;
}
std::cout << " LR Final learning rate: " << optimizer->GetLearningRate() << std::endl;
std::cout << " FM Final metric value: " << optimizer->GetCurrentMetricValue() << std::endl;
std::cout << " SC Optimizer scales: " << optimizer->GetScales() << std::endl;
std::cout << " FG Final metric gradient (sample of values): ";
if (gradient.GetSize() < 10)
{
std::cout << gradient;
}
else
{
for (itk::SizeValueType i = 0; i < gradient.GetSize(); i += (gradient.GetSize() / 16))
{
std::cout << gradient[i] << ' ';
}
}
std::cout << std::endl;
}
};
template <unsigned int VImageDimension, typename TPixel>
int
PerformSimpleImageRegistration(int argc, char * argv[])
{
if (argc < 7)
{
std::cerr << "Missing parameters." << std::endl;
std::cerr << "Usage: " << itkNameOfTestExecutableMacro(argv);
std::cerr << " pixelType imageDimension fixedImage movingImage outputImage numberOfAffineIterations "
"numberOfDeformableIterations"
<< std::endl;
return EXIT_FAILURE;
}
using PixelType = TPixel;
using FixedImageType = itk::Image<PixelType, VImageDimension>;
using MovingImageType = itk::Image<PixelType, VImageDimension>;
using ImageReaderType = itk::ImageFileReader<FixedImageType>;
auto fixedImageReader = ImageReaderType::New();
fixedImageReader->SetFileName(argv[3]);
fixedImageReader->Update();
typename FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
fixedImage->Update();
fixedImage->DisconnectPipeline();
auto movingImageReader = ImageReaderType::New();
movingImageReader->SetFileName(argv[4]);
movingImageReader->Update();
typename MovingImageType::Pointer movingImage = movingImageReader->GetOutput();
movingImage->Update();
movingImage->DisconnectPipeline();
using AffineTransformType = itk::AffineTransform<double, VImageDimension>;
auto affineTransform = AffineTransformType::New();
using AffineRegistrationType = itk::ImageRegistrationMethodv4<FixedImageType, MovingImageType>;
auto affineSimple = AffineRegistrationType::New();
ITK_EXERCISE_BASIC_OBJECT_METHODS(affineSimple, ImageRegistrationMethodv4, ProcessObject);
affineSimple->SetObjectName("affineSimple");
// Test exceptions
itk::SizeValueType numberOfLevels = affineSimple->GetNumberOfLevels();
ITK_TRY_EXPECT_EXCEPTION(affineSimple->GetShrinkFactorsPerDimension(numberOfLevels + 1));
affineSimple->SetFixedImage(fixedImage);
ITK_TEST_SET_GET_VALUE(fixedImage, affineSimple->GetFixedImage());
affineSimple->SetMovingImage(movingImage);
ITK_TEST_SET_GET_VALUE(movingImage, affineSimple->GetMovingImage());
affineSimple->SetInitialTransform(affineTransform);
auto smoothingSigmasAreSpecifiedInPhysicalUnits = true;
ITK_TEST_SET_GET_BOOLEAN(
affineSimple, SmoothingSigmasAreSpecifiedInPhysicalUnits, smoothingSigmasAreSpecifiedInPhysicalUnits);
auto inPlace = true;
ITK_TEST_SET_GET_BOOLEAN(affineSimple, InPlace, inPlace);
auto initializeCenterOfLinearOutputTransform = true;
ITK_TEST_SET_GET_BOOLEAN(
affineSimple, InitializeCenterOfLinearOutputTransform, initializeCenterOfLinearOutputTransform);
using MIMetricType = itk::JointHistogramMutualInformationImageToImageMetricv4<FixedImageType, MovingImageType>;
auto mutualInformationMetric = MIMetricType::New();
mutualInformationMetric->SetNumberOfHistogramBins(20);
mutualInformationMetric->SetUseMovingImageGradientFilter(false);
mutualInformationMetric->SetUseFixedImageGradientFilter(false);
mutualInformationMetric->SetUseSampledPointSet(false);
mutualInformationMetric->SetVirtualDomainFromImage(fixedImage);
affineSimple->SetMetric(mutualInformationMetric);
using AffineScalesEstimatorType = itk::RegistrationParameterScalesFromPhysicalShift<MIMetricType>;
auto scalesEstimator1 = AffineScalesEstimatorType::New();
scalesEstimator1->SetMetric(mutualInformationMetric);
scalesEstimator1->SetTransformForward(true);
// Smooth by specified gaussian sigmas for each level. These values are specified in
// physical units. Sigmas of zero cause inconsistency between some platforms.
{
typename AffineRegistrationType::SmoothingSigmasArrayType smoothingSigmasPerLevel;
smoothingSigmasPerLevel.SetSize(3);
smoothingSigmasPerLevel[0] = 2;
smoothingSigmasPerLevel[1] = 1;
smoothingSigmasPerLevel[2] = 1; // 0;
affineSimple->SetSmoothingSigmasPerLevel(smoothingSigmasPerLevel);
ITK_TEST_SET_GET_VALUE(smoothingSigmasPerLevel, affineSimple->GetSmoothingSigmasPerLevel());
}
typename AffineRegistrationType::RealType metricSamplingPercentage = 1.0;
affineSimple->SetMetricSamplingPercentage(metricSamplingPercentage);
typename AffineRegistrationType::MetricSamplingPercentageArrayType metricSamplingPercentagePerLevel;
metricSamplingPercentagePerLevel.SetSize(numberOfLevels);
metricSamplingPercentagePerLevel.Fill(metricSamplingPercentage);
ITK_TEST_SET_GET_VALUE(metricSamplingPercentagePerLevel, affineSimple->GetMetricSamplingPercentagePerLevel());
affineSimple->SetMetricSamplingPercentagePerLevel(metricSamplingPercentagePerLevel);
ITK_TEST_SET_GET_VALUE(metricSamplingPercentagePerLevel, affineSimple->GetMetricSamplingPercentagePerLevel());
using GradientDescentOptimizerv4Type = itk::GradientDescentOptimizerv4;
typename GradientDescentOptimizerv4Type::Pointer affineOptimizer =
dynamic_cast<GradientDescentOptimizerv4Type *>(affineSimple->GetModifiableOptimizer());
if (!affineOptimizer)
{
itkGenericExceptionMacro("Error dynamic_cast failed");
}
#ifdef NDEBUG
affineOptimizer->SetNumberOfIterations(std::stoi(argv[6]));
#else
affineOptimizer->SetNumberOfIterations(1);
#endif
affineOptimizer->SetDoEstimateLearningRateOnce(false); // true by default
affineOptimizer->SetDoEstimateLearningRateAtEachIteration(true);
affineOptimizer->SetScalesEstimator(scalesEstimator1);
using AffineCommandType = CommandIterationUpdate<AffineRegistrationType>;
auto affineObserver = AffineCommandType::New();
affineSimple->AddObserver(itk::MultiResolutionIterationEvent(), affineObserver);
{
using ImageMetricType = itk::ImageToImageMetricv4<FixedImageType, MovingImageType>;
typename ImageMetricType::Pointer imageMetric =
dynamic_cast<ImageMetricType *>(affineSimple->GetModifiableMetric());
// imageMetric->SetUseFloatingPointCorrection(true);
imageMetric->SetFloatingPointCorrectionResolution(1e4);
}
//
// Now do the displacement field transform with gaussian smoothing using
// the composite transform.
//
using RealType = typename AffineRegistrationType::RealType;
using VectorType = itk::Vector<RealType, VImageDimension>;
constexpr VectorType zeroVector{};
using DisplacementFieldType = itk::Image<VectorType, VImageDimension>;
auto displacementField = DisplacementFieldType::New();
displacementField->CopyInformation(fixedImage);
displacementField->SetRegions(fixedImage->GetBufferedRegion());
displacementField->Allocate();
displacementField->FillBuffer(zeroVector);
using DisplacementFieldTransformType =
itk::GaussianSmoothingOnUpdateDisplacementFieldTransform<RealType, VImageDimension>;
auto fieldTransform = DisplacementFieldTransformType::New();
fieldTransform->SetGaussianSmoothingVarianceForTheUpdateField(0);
fieldTransform->SetGaussianSmoothingVarianceForTheTotalField(1.5);
fieldTransform->SetDisplacementField(displacementField);
using DisplacementFieldRegistrationType = itk::ImageRegistrationMethodv4<FixedImageType, MovingImageType>;
typename DisplacementFieldRegistrationType::Pointer displacementFieldSimple =
DisplacementFieldRegistrationType::New();
displacementFieldSimple->SetObjectName("displacementFieldSimple");
using CorrelationMetricType = itk::ANTSNeighborhoodCorrelationImageToImageMetricv4<FixedImageType, MovingImageType>;
auto correlationMetric = CorrelationMetricType::New();
typename CorrelationMetricType::RadiusType radius;
radius.Fill(4);
correlationMetric->SetRadius(radius);
correlationMetric->SetUseMovingImageGradientFilter(false);
correlationMetric->SetUseFixedImageGradientFilter(false);
// correlationMetric->SetUseFloatingPointCorrection(true);
// correlationMetric->SetFloatingPointCorrectionResolution(1e4);
using ScalesEstimatorType = itk::RegistrationParameterScalesFromPhysicalShift<CorrelationMetricType>;
auto scalesEstimator = ScalesEstimatorType::New();
scalesEstimator->SetMetric(correlationMetric);
scalesEstimator->SetTransformForward(true);
auto optimizer = GradientDescentOptimizerv4Type::New();
optimizer->SetLearningRate(1.0);
#ifdef NDEBUG
optimizer->SetNumberOfIterations(std::stoi(argv[7]));
#else
optimizer->SetNumberOfIterations(1);
#endif
optimizer->SetScalesEstimator(scalesEstimator);
optimizer->SetDoEstimateLearningRateOnce(false); // true by default
optimizer->SetDoEstimateLearningRateAtEachIteration(true);
displacementFieldSimple->SetFixedImage(fixedImage);
displacementFieldSimple->SetMovingImage(movingImage);
numberOfLevels = 3;
displacementFieldSimple->SetNumberOfLevels(numberOfLevels);
ITK_TEST_SET_GET_VALUE(numberOfLevels, displacementFieldSimple->GetNumberOfLevels());
typename AffineRegistrationType::DecoratedOutputTransformType * transformOutputNonConst =
affineSimple->GetTransformOutput();
const typename AffineRegistrationType::DecoratedOutputTransformType * transformOutputConst =
affineSimple->GetTransformOutput();
ITK_TEST_EXPECT_EQUAL(transformOutputNonConst, transformOutputConst);
displacementFieldSimple->SetMovingInitialTransformInput(transformOutputNonConst);
displacementFieldSimple->SetMetric(correlationMetric);
displacementFieldSimple->SetOptimizer(optimizer);
displacementFieldSimple->SetInitialTransform(fieldTransform);
displacementFieldSimple->InPlaceOn();
typename DisplacementFieldRegistrationType::OptimizerWeightsType optimizerWeights;
optimizerWeights.SetSize(VImageDimension);
optimizerWeights.Fill(0.995);
displacementFieldSimple->SetOptimizerWeights(optimizerWeights);
// Shrink the virtual domain by specified factors for each level. See documentation
// for the itkShrinkImageFilter for more detailed behavior.
typename DisplacementFieldRegistrationType::ShrinkFactorsArrayType shrinkFactorsPerLevel;
shrinkFactorsPerLevel.SetSize(3);
shrinkFactorsPerLevel[0] = 3;
shrinkFactorsPerLevel[1] = 2;
shrinkFactorsPerLevel[2] = 1;
displacementFieldSimple->SetShrinkFactorsPerLevel(shrinkFactorsPerLevel);
// Smooth by specified gaussian sigmas for each level. These values are specified in
// physical units.
typename DisplacementFieldRegistrationType::SmoothingSigmasArrayType smoothingSigmasPerLevel;
smoothingSigmasPerLevel.SetSize(3);
smoothingSigmasPerLevel[0] = 2;
smoothingSigmasPerLevel[1] = 1;
smoothingSigmasPerLevel[2] = 1;
displacementFieldSimple->SetSmoothingSigmasPerLevel(smoothingSigmasPerLevel);
using DisplacementFieldTransformAdaptorType =
itk::GaussianSmoothingOnUpdateDisplacementFieldTransformParametersAdaptor<DisplacementFieldTransformType>;
typename DisplacementFieldRegistrationType::TransformParametersAdaptorsContainerType adaptors;
for (unsigned int level = 0; level < shrinkFactorsPerLevel.Size(); ++level)
{
// We use the shrink image filter to calculate the fixed parameters of the virtual
// domain at each level. To speed up calculation and avoid unnecessary memory
// usage, we could calculate these fixed parameters directly.
using ShrinkFilterType = itk::ShrinkImageFilter<DisplacementFieldType, DisplacementFieldType>;
auto shrinkFilter = ShrinkFilterType::New();
shrinkFilter->SetShrinkFactors(shrinkFactorsPerLevel[level]);
shrinkFilter->SetInput(displacementField);
shrinkFilter->Update();
typename DisplacementFieldTransformAdaptorType::Pointer fieldTransformAdaptor =
DisplacementFieldTransformAdaptorType::New();
fieldTransformAdaptor->SetRequiredSpacing(shrinkFilter->GetOutput()->GetSpacing());
fieldTransformAdaptor->SetRequiredSize(shrinkFilter->GetOutput()->GetBufferedRegion().GetSize());
fieldTransformAdaptor->SetRequiredDirection(shrinkFilter->GetOutput()->GetDirection());
fieldTransformAdaptor->SetRequiredOrigin(shrinkFilter->GetOutput()->GetOrigin());
adaptors.push_back(fieldTransformAdaptor);
}
displacementFieldSimple->SetTransformParametersAdaptorsPerLevel(adaptors);
using DisplacementFieldRegistrationCommandType = CommandIterationUpdate<DisplacementFieldRegistrationType>;
typename DisplacementFieldRegistrationCommandType::Pointer displacementFieldObserver =
DisplacementFieldRegistrationCommandType::New();
displacementFieldSimple->AddObserver(itk::IterationEvent(), displacementFieldObserver);
ITK_TRY_EXPECT_NO_EXCEPTION(displacementFieldSimple->Update());
std::cout << "CurrentIteration: " << displacementFieldSimple->GetCurrentIteration() << std::endl;
std::cout << "CurrentMetricValue: " << displacementFieldSimple->GetCurrentMetricValue() << std::endl;
std::cout << "CurrentConvergenceValue: " << displacementFieldSimple->GetCurrentConvergenceValue() << std::endl;
std::cout << "IsConverged: " << displacementFieldSimple->GetIsConverged() << std::endl;
using ImageMetricType = itk::ImageToImageMetricv4<FixedImageType, MovingImageType>;
typename ImageMetricType::ConstPointer imageMetric = dynamic_cast<const ImageMetricType *>(affineSimple->GetMetric());
std::cout << " Affine parameters after registration: " << std::endl
<< affineOptimizer->GetCurrentPosition() << std::endl
<< " Last LearningRate: " << affineOptimizer->GetLearningRate() << std::endl
<< " Use FltPtCorrex: " << imageMetric->GetUseFloatingPointCorrection() << std::endl
<< " FltPtCorrexRes: " << imageMetric->GetFloatingPointCorrectionResolution() << std::endl
<< " Number of work units used:" << std::endl
<< " metric: " << imageMetric->GetNumberOfWorkUnitsUsed() << std::endl
<< " optimizer: " << affineOptimizer->GetNumberOfWorkUnits() << std::endl;
std::cout << "After displacement registration: " << std::endl
<< "Last LearningRate: " << optimizer->GetLearningRate() << std::endl
<< "Use FltPtCorrex: " << correlationMetric->GetUseFloatingPointCorrection() << std::endl
<< "FltPtCorrexRes: " << correlationMetric->GetFloatingPointCorrectionResolution() << std::endl
<< "Number of work units used:" << std::endl
<< " metric: " << correlationMetric->GetNumberOfWorkUnitsUsed()
<< " optimizer: " << displacementFieldSimple->GetOptimizer()->GetNumberOfWorkUnits() << std::endl;
using CompositeTransformType = itk::CompositeTransform<RealType, VImageDimension>;
auto compositeTransform = CompositeTransformType::New();
compositeTransform->AddTransform(affineSimple->GetModifiableTransform());
compositeTransform->AddTransform(displacementFieldSimple->GetModifiableTransform());
using ResampleFilterType = itk::ResampleImageFilter<MovingImageType, FixedImageType>;
auto resampler = ResampleFilterType::New();
resampler->SetTransform(compositeTransform);
resampler->SetInput(movingImage);
resampler->SetSize(fixedImage->GetLargestPossibleRegion().GetSize());
resampler->SetOutputOrigin(fixedImage->GetOrigin());
resampler->SetOutputSpacing(fixedImage->GetSpacing());
resampler->SetOutputDirection(fixedImage->GetDirection());
resampler->SetDefaultPixelValue(0);
resampler->Update();
using WriterType = itk::ImageFileWriter<FixedImageType>;
auto writer = WriterType::New();
writer->SetFileName(argv[5]);
writer->SetInput(resampler->GetOutput());
writer->Update();
return EXIT_SUCCESS;
}
int
itkSimpleImageRegistrationTest(int argc, char * argv[])
{
if (argc < 7)
{
std::cerr << "Missing parameters." << std::endl;
std::cerr << "Usage: " << itkNameOfTestExecutableMacro(argv);
std::cerr << " pixelType imageDimension fixedImage movingImage outputImage numberOfAffineIterations "
"numberOfDeformableIterations"
<< std::endl;
return EXIT_FAILURE;
}
itk::Statistics::MersenneTwisterRandomVariateGenerator::GetInstance()->SetSeed(121212);
switch (std::stoi(argv[2]))
{
case 2:
if (strcmp(argv[1], "float") == 0)
{
return PerformSimpleImageRegistration<2, float>(argc, argv);
}
else
{
return PerformSimpleImageRegistration<2, double>(argc, argv);
}
case 3:
if (strcmp(argv[1], "float") == 0)
{
return PerformSimpleImageRegistration<3, float>(argc, argv);
}
else
{
return PerformSimpleImageRegistration<3, double>(argc, argv);
}
default:
std::cerr << "Unsupported dimension" << std::endl;
exit(EXIT_FAILURE);
}
}