Examples/RegistrationITKv4/DeformableRegistration14.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.
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// Software Guide : BeginLatex
//
// This example illustrates the use of the
// \doxygen{RegularStepGradientDescentOptimizer} in the context of a
// deformable registration problem. The code of this example is almost
// identical to the one in Section~\ref{sec:DeformableRegistration8}.
//
// \index{itk::BSplineTransform}
// \index{itk::BSplineTransform!DeformableRegistration}
// \index{itk::RegularStepGradientDescentOptimizer}
//
//
// Software Guide : EndLatex
 
#include "itkImageRegistrationMethod.h"
#include "itkMattesMutualInformationImageToImageMetric.h"
 
#include "itkTimeProbesCollectorBase.h"
#include "itkMemoryProbesCollectorBase.h"
 
//  Software Guide : BeginLatex
//
//  The following are the most relevant headers to this example.
//
//  \index{itk::BSplineTransform!header}
//  \index{itk::RegularStepGradientDescentOptimizer!header}
//
//  Software Guide : EndLatex
 
// Software Guide : BeginCodeSnippet
#include "itkBSplineTransform.h"
#include "itkRegularStepGradientDescentOptimizer.h"
// Software Guide : EndCodeSnippet
 
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
 
#include "itkResampleImageFilter.h"
#include "itkCastImageFilter.h"
#include "itkSquaredDifferenceImageFilter.h"
 
#include "itkTransformFileReader.h"
 
//  The following section of code implements a Command observer
//  used to monitor the evolution of the registration process.
//
#include "itkCommand.h"
class CommandIterationUpdate : public itk::Command
{
public:
  using Self = CommandIterationUpdate;
  using Superclass = itk::Command;
  using Pointer = itk::SmartPointer<Self>;
  itkNewMacro(Self);
 
protected:
  CommandIterationUpdate() = default;
 
public:
  using OptimizerType = itk::RegularStepGradientDescentOptimizer;
  using OptimizerPointer = const OptimizerType *;
 
  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
  {
    auto optimizer = static_cast<OptimizerPointer>(object);
    if (!(itk::IterationEvent().CheckEvent(&event)))
    {
      return;
    }
    std::cout << optimizer->GetCurrentIteration() << "   ";
    std::cout << optimizer->GetValue() << "   ";
    std::cout << std::endl;
  }
};
 
int
main(int argc, char * argv[])
{
  if (argc < 4)
  {
    std::cerr << "Missing Parameters " << std::endl;
    std::cerr << "Usage: " << argv[0];
    std::cerr << " fixedImageFile  movingImageFile outputImagefile  ";
    std::cerr << " [differenceOutputfile] [differenceBeforeRegistration] ";
    std::cerr << " [deformationField] ";
    std::cerr << " [useExplicitPDFderivatives ] [useCachingBSplineWeights ] ";
    std::cerr << " [filenameForFinalTransformParameters] ";
    std::cerr << " [maximumStepLength] [maximumNumberOfIterations]";
    std::cerr << std::endl;
    return EXIT_FAILURE;
  }
 
  constexpr unsigned int ImageDimension = 3;
  using PixelType = short;
 
  using FixedImageType = itk::Image<PixelType, ImageDimension>;
  using MovingImageType = itk::Image<PixelType, ImageDimension>;
 
  constexpr unsigned int SpaceDimension = ImageDimension;
  constexpr unsigned int SplineOrder = 3;
  using CoordinateRepType = double;
 
  using TransformType =
    itk::BSplineTransform<CoordinateRepType, SpaceDimension, SplineOrder>;
 
  using OptimizerType = itk::RegularStepGradientDescentOptimizer;
 
  using MetricType =
    itk::MattesMutualInformationImageToImageMetric<FixedImageType,
                                                   MovingImageType>;
 
  using InterpolatorType =
    itk::LinearInterpolateImageFunction<MovingImageType, double>;
 
  using RegistrationType =
    itk::ImageRegistrationMethod<FixedImageType, MovingImageType>;
 
  auto metric = MetricType::New();
  auto optimizer = OptimizerType::New();
  auto interpolator = InterpolatorType::New();
  auto registration = RegistrationType::New();
 
  registration->SetMetric(metric);
  registration->SetOptimizer(optimizer);
  registration->SetInterpolator(interpolator);
 
  auto transform = TransformType::New();
  registration->SetTransform(transform);
 
  using FixedImageReaderType = itk::ImageFileReader<FixedImageType>;
  using MovingImageReaderType = itk::ImageFileReader<MovingImageType>;
 
  auto fixedImageReader = FixedImageReaderType::New();
  auto movingImageReader = MovingImageReaderType::New();
 
  fixedImageReader->SetFileName(argv[1]);
  movingImageReader->SetFileName(argv[2]);
 
  const FixedImageType::ConstPointer fixedImage =
    fixedImageReader->GetOutput();
 
  registration->SetFixedImage(fixedImage);
  registration->SetMovingImage(movingImageReader->GetOutput());
 
  fixedImageReader->Update();
 
  const FixedImageType::RegionType fixedRegion =
    fixedImage->GetBufferedRegion();
 
  registration->SetFixedImageRegion(fixedRegion);
 
  constexpr unsigned int numberOfGridNodesInOneDimension = 5;
 
  // Software Guide : BeginCodeSnippet
 
  TransformType::PhysicalDimensionsType fixedPhysicalDimensions;
  TransformType::MeshSizeType           meshSize;
  TransformType::OriginType             fixedOrigin;
 
  for (unsigned int i = 0; i < SpaceDimension; ++i)
  {
    fixedOrigin[i] = fixedImage->GetOrigin()[i];
    fixedPhysicalDimensions[i] =
      fixedImage->GetSpacing()[i] *
      static_cast<double>(
        fixedImage->GetLargestPossibleRegion().GetSize()[i] - 1);
  }
  meshSize.Fill(numberOfGridNodesInOneDimension - SplineOrder);
 
  transform->SetTransformDomainOrigin(fixedOrigin);
  transform->SetTransformDomainPhysicalDimensions(fixedPhysicalDimensions);
  transform->SetTransformDomainMeshSize(meshSize);
  transform->SetTransformDomainDirection(fixedImage->GetDirection());
 
  using ParametersType = TransformType::ParametersType;
 
  const unsigned int numberOfParameters = transform->GetNumberOfParameters();
 
  ParametersType parameters(numberOfParameters);
 
  parameters.Fill(0.0);
 
  transform->SetParameters(parameters);
 
  registration->SetInitialTransformParameters(transform->GetParameters());
  // Software Guide : EndCodeSnippet
 
  parameters.Fill(0.0);
 
  transform->SetParameters(parameters);
 
  registration->SetInitialTransformParameters(transform->GetParameters());
  // Software Guide : EndCodeSnippet
 
  //  Software Guide : BeginLatex
  //
  //  Next we set the parameters of the RegularStepGradientDescentOptimizer
  //  object.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  optimizer->SetMaximumStepLength(10.0);
  optimizer->SetMinimumStepLength(0.01);
 
  optimizer->SetRelaxationFactor(0.7);
  optimizer->SetNumberOfIterations(50);
  // Software Guide : EndCodeSnippet
 
  // Optionally, get the step length from the command line arguments
  if (argc > 12)
  {
    optimizer->SetMaximumStepLength(std::stod(argv[12]));
  }
 
  // Optionally, get the number of iterations from the command line arguments
  if (argc > 13)
  {
    optimizer->SetNumberOfIterations(std::stoi(argv[13]));
  }
 
  // Create the Command observer and register it with the optimizer.
  //
  auto observer = CommandIterationUpdate::New();
  optimizer->AddObserver(itk::IterationEvent(), observer);
 
  metric->SetNumberOfHistogramBins(50);
 
  const auto numberOfSamples =
    static_cast<unsigned int>(fixedRegion.GetNumberOfPixels() * 20.0 / 100.0);
 
  metric->SetNumberOfSpatialSamples(numberOfSamples);
  metric->ReinitializeSeed(76926294);
 
  if (argc > 7)
  {
    // Define whether to calculate the metric derivative by explicitly
    // computing the derivatives of the joint PDF with respect to the
    // Transform parameters, or doing it by progressively accumulating
    // contributions from each bin in the joint PDF.
    metric->SetUseExplicitPDFDerivatives(std::stoi(argv[7]));
  }
 
  if (argc > 8)
  {
    // Define whether to cache the BSpline weights and indexes corresponding
    // to each one of the samples used to compute the metric. Enabling caching
    // will make the algorithm run faster but it will have a cost on the
    // amount of memory that needs to be allocated. This option is only
    // relevant when using the BSplineTransform.
    metric->SetUseCachingOfBSplineWeights(std::stoi(argv[8]));
  }
 
  // Add time and memory probes
  itk::TimeProbesCollectorBase   chronometer;
  itk::MemoryProbesCollectorBase memorymeter;
 
  std::cout << std::endl << "Starting Registration" << std::endl;
 
  try
  {
    memorymeter.Start("Registration");
    chronometer.Start("Registration");
 
    registration->Update();
 
    chronometer.Stop("Registration");
    memorymeter.Stop("Registration");
 
    std::cout << "Optimizer stop condition = "
              << registration->GetOptimizer()->GetStopConditionDescription()
              << std::endl;
  }
  catch (const itk::ExceptionObject & err)
  {
    std::cerr << "ExceptionObject caught !" << std::endl;
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
  }
 
  const OptimizerType::ParametersType finalParameters =
    registration->GetLastTransformParameters();
 
  // Report the time and memory taken by the registration
  chronometer.Report(std::cout);
  memorymeter.Report(std::cout);
 
  transform->SetParameters(finalParameters);
 
  using ResampleFilterType =
    itk::ResampleImageFilter<MovingImageType, FixedImageType>;
 
  auto resample = ResampleFilterType::New();
 
  resample->SetTransform(transform);
  resample->SetInput(movingImageReader->GetOutput());
 
  resample->SetSize(fixedImage->GetLargestPossibleRegion().GetSize());
  resample->SetOutputOrigin(fixedImage->GetOrigin());
  resample->SetOutputSpacing(fixedImage->GetSpacing());
  resample->SetOutputDirection(fixedImage->GetDirection());
 
  // This value is set to zero in order to make easier to perform
  // regression testing in this example. However, for didactic
  // exercise it will be better to set it to a medium gray value
  // such as 100 or 128.
  resample->SetDefaultPixelValue(0);
 
  using OutputPixelType = short;
 
  using OutputImageType = itk::Image<OutputPixelType, ImageDimension>;
 
  using CastFilterType =
    itk::CastImageFilter<FixedImageType, OutputImageType>;
 
  using WriterType = itk::ImageFileWriter<OutputImageType>;
 
  auto writer = WriterType::New();
  auto caster = CastFilterType::New();
 
  writer->SetFileName(argv[3]);
 
  caster->SetInput(resample->GetOutput());
  writer->SetInput(caster->GetOutput());
 
  try
  {
    writer->Update();
  }
  catch (const itk::ExceptionObject & err)
  {
    std::cerr << "ExceptionObject caught !" << std::endl;
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
  }
 
  using DifferenceFilterType =
    itk::SquaredDifferenceImageFilter<FixedImageType,
                                      FixedImageType,
                                      OutputImageType>;
 
  auto difference = DifferenceFilterType::New();
 
  auto writer2 = WriterType::New();
  writer2->SetInput(difference->GetOutput());
 
  // Compute the difference image between the
  // fixed and resampled moving image.
  if (argc > 4)
  {
    difference->SetInput1(fixedImageReader->GetOutput());
    difference->SetInput2(resample->GetOutput());
    writer2->SetFileName(argv[4]);
    try
    {
      writer2->Update();
    }
    catch (const itk::ExceptionObject & err)
    {
      std::cerr << "ExceptionObject caught !" << std::endl;
      std::cerr << err << std::endl;
      return EXIT_FAILURE;
    }
  }
 
  // Compute the difference image between the
  // fixed and moving image before registration.
  if (argc > 5)
  {
    writer2->SetFileName(argv[5]);
    difference->SetInput1(fixedImageReader->GetOutput());
    difference->SetInput2(movingImageReader->GetOutput());
    try
    {
      writer2->Update();
    }
    catch (const itk::ExceptionObject & err)
    {
      std::cerr << "ExceptionObject caught !" << std::endl;
      std::cerr << err << std::endl;
      return EXIT_FAILURE;
    }
  }
 
  // Generate the explicit deformation field resulting from
  // the registration.
  if (argc > 6)
  {
 
    using VectorType = itk::Vector<float, ImageDimension>;
    using DisplacementFieldType = itk::Image<VectorType, ImageDimension>;
 
    auto field = DisplacementFieldType::New();
    field->SetRegions(fixedRegion);
    field->SetOrigin(fixedImage->GetOrigin());
    field->SetSpacing(fixedImage->GetSpacing());
    field->SetDirection(fixedImage->GetDirection());
    field->Allocate();
 
    using FieldIterator = itk::ImageRegionIterator<DisplacementFieldType>;
    FieldIterator fi(field, fixedRegion);
 
    fi.GoToBegin();
 
    TransformType::InputPointType    fixedPoint;
    TransformType::OutputPointType   movingPoint;
    DisplacementFieldType::IndexType index;
 
    VectorType displacement;
 
    while (!fi.IsAtEnd())
    {
      index = fi.GetIndex();
      field->TransformIndexToPhysicalPoint(index, fixedPoint);
      movingPoint = transform->TransformPoint(fixedPoint);
      displacement = movingPoint - fixedPoint;
      fi.Set(displacement);
      ++fi;
    }
 
    using FieldWriterType = itk::ImageFileWriter<DisplacementFieldType>;
    auto fieldWriter = FieldWriterType::New();
 
    fieldWriter->SetInput(field);
 
    fieldWriter->SetFileName(argv[6]);
    try
    {
      fieldWriter->Update();
    }
    catch (const itk::ExceptionObject & excp)
    {
      std::cerr << "Exception thrown " << std::endl;
      std::cerr << excp << std::endl;
      return EXIT_FAILURE;
    }
  }
 
  // Optionally, save the transform parameters in a file
  if (argc > 9)
  {
    std::ofstream parametersFile;
    parametersFile.open(argv[9]);
    parametersFile << finalParameters << std::endl;
    parametersFile.close();
  }
 
  return EXIT_SUCCESS;
}