Examples/RegistrationITKv4/DeformableRegistration8.cxx

/*=========================================================================
 *
 *  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.
 *
 *=========================================================================*/
 
// Software Guide : BeginLatex
//
// This example illustrates the use of the \doxygen{BSplineTransform}
// class for performing registration of two $3D$ images and for the case of
// multi-modality images. The image metric of choice in this case is the
// \doxygen{MattesMutualInformationImageToImageMetricv4}.
//
// \index{itk::BSplineTransform}
// \index{itk::BSplineTransform!DeformableRegistration}
// \index{itk::LBFGSBOptimizerv4}
//
// Software Guide : EndLatex
 
#include "itkImageRegistrationMethodv4.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::LBFGSBOptimizerv4!header}
//
//  Software Guide : EndLatex
 
// Software Guide : BeginCodeSnippet
#include "itkBSplineTransform.h"
#include "itkLBFGSBOptimizerv4.h"
#include "itkMattesMutualInformationImageToImageMetricv4.h"
// Software Guide : EndCodeSnippet
 
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
 
#include "itkResampleImageFilter.h"
#include "itkCastImageFilter.h"
#include "itkSquaredDifferenceImageFilter.h"
 
#include "itkTransformFileReader.h"
 
#include "itkBSplineTransformInitializer.h"
#include "itkTransformToDisplacementFieldFilter.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::LBFGSBOptimizerv4;
  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->GetCurrentMetricValue() << "   ";
    std::cout << optimizer->GetInfinityNormOfProjectedGradient() << 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 << " [filenameForFinalTransformParameters] "
                 "[numberOfGridNodesInOneDimension]";
    std::cerr << std::endl;
    return EXIT_FAILURE;
  }
 
  constexpr unsigned int ImageDimension = 3;
  using PixelType = float;
 
  using FixedImageType = itk::Image<PixelType, ImageDimension>;
  using MovingImageType = itk::Image<PixelType, ImageDimension>;
 
 
  //  Software Guide : BeginLatex
  //
  //  We instantiate now the type of the \code{BSplineTransform} using
  //  as template parameters the type for coordinates representation, the
  //  dimension of the space, and the order of the BSpline.
  //
  //  \index{BSplineTransform!New}
  //  \index{BSplineTransform!Instantiation}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  const unsigned int     SpaceDimension = ImageDimension;
  constexpr unsigned int SplineOrder = 3;
  using CoordinateRepType = double;
 
  using TransformType =
    itk::BSplineTransform<CoordinateRepType, SpaceDimension, SplineOrder>;
  // Software Guide : EndCodeSnippet
 
 
  using OptimizerType = itk::LBFGSBOptimizerv4;
 
 
  using MetricType =
    itk::MattesMutualInformationImageToImageMetricv4<FixedImageType,
                                                     MovingImageType>;
 
  using RegistrationType =
    itk::ImageRegistrationMethodv4<FixedImageType, MovingImageType>;
 
  auto metric = MetricType::New();
  auto optimizer = OptimizerType::New();
  auto registration = RegistrationType::New();
 
 
  registration->SetMetric(metric);
  registration->SetOptimizer(optimizer);
 
  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]);
 
  FixedImageType::ConstPointer fixedImage = fixedImageReader->GetOutput();
 
  registration->SetFixedImage(fixedImage);
  registration->SetMovingImage(movingImageReader->GetOutput());
 
  fixedImageReader->Update();
 
  //  Software Guide : BeginLatex
  //
  //  The transform object is constructed, initialized like previous example
  //  and passed to the registration method.
  //
  //  \index{itk::ImageRegistrationMethodv4!SetInitialTransform()}
  //  \index{itk::ImageRegistrationMethodv4!InPlaceOn()}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  auto transform = TransformType::New();
  // Software Guide : EndCodeSnippet
 
  // Initialize the transform
  unsigned int numberOfGridNodesInOneDimension = 5;
 
  if (argc > 8)
  {
    numberOfGridNodesInOneDimension = std::stoi(argv[8]);
  }
 
  using InitializerType =
    itk::BSplineTransformInitializer<TransformType, FixedImageType>;
 
  auto transformInitializer = InitializerType::New();
 
  TransformType::MeshSizeType meshSize;
  meshSize.Fill(numberOfGridNodesInOneDimension - SplineOrder);
 
  transformInitializer->SetTransform(transform);
  transformInitializer->SetImage(fixedImage);
  transformInitializer->SetTransformDomainMeshSize(meshSize);
  transformInitializer->InitializeTransform();
 
  // Set transform to identity
  transform->SetIdentity();
 
  // Software Guide : BeginCodeSnippet
  registration->SetInitialTransform(transform);
  registration->InPlaceOn();
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  Next we set the parameters of the LBFGSB Optimizer.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  const unsigned int numParameters = transform->GetNumberOfParameters();
  OptimizerType::BoundSelectionType boundSelect(numParameters);
  OptimizerType::BoundValueType     upperBound(numParameters);
  OptimizerType::BoundValueType     lowerBound(numParameters);
 
  boundSelect.Fill(OptimizerType::UNBOUNDED);
  upperBound.Fill(0.0);
  lowerBound.Fill(0.0);
 
  optimizer->SetBoundSelection(boundSelect);
  optimizer->SetUpperBound(upperBound);
  optimizer->SetLowerBound(lowerBound);
 
  optimizer->SetCostFunctionConvergenceFactor(1.e7);
  optimizer->SetGradientConvergenceTolerance(1e-6);
  optimizer->SetNumberOfIterations(200);
  optimizer->SetMaximumNumberOfFunctionEvaluations(30);
  optimizer->SetMaximumNumberOfCorrections(5);
  // Software Guide : EndCodeSnippet
 
  // Create the Command observer and register it with the optimizer.
  //
  auto observer = CommandIterationUpdate::New();
  optimizer->AddObserver(itk::IterationEvent(), observer);
 
  //  A single level registration process is run using
  //  the shrink factor 1 and smoothing sigma 0.
  //
  constexpr unsigned int numberOfLevels = 1;
 
  RegistrationType::ShrinkFactorsArrayType shrinkFactorsPerLevel;
  shrinkFactorsPerLevel.SetSize(numberOfLevels);
  shrinkFactorsPerLevel[0] = 1;
 
  RegistrationType::SmoothingSigmasArrayType smoothingSigmasPerLevel;
  smoothingSigmasPerLevel.SetSize(numberOfLevels);
  smoothingSigmasPerLevel[0] = 0;
 
  registration->SetNumberOfLevels(numberOfLevels);
  registration->SetSmoothingSigmasPerLevel(smoothingSigmasPerLevel);
  registration->SetShrinkFactorsPerLevel(shrinkFactorsPerLevel);
 
  //  Software Guide : BeginLatex
  //
  //  Next we set the parameters of the Mattes Mutual Information Metric.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  metric->SetNumberOfHistogramBins(50);
  // Software Guide : EndCodeSnippet
 
  // 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;
  }
 
  // Report the time and memory taken by the registration
  chronometer.Report(std::cout);
  memorymeter.Report(std::cout);
 
  OptimizerType::ParametersType finalParameters = transform->GetParameters();
 
  std::cout << "Last Transform Parameters" << std::endl;
  std::cout << finalParameters << std::endl;
 
  // Finally we use the last transform in order to resample the image.
  //
  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 VectorPixelType = itk::Vector<float, ImageDimension>;
    using DisplacementFieldImageType =
      itk::Image<VectorPixelType, ImageDimension>;
 
    using DisplacementFieldGeneratorType =
      itk::TransformToDisplacementFieldFilter<DisplacementFieldImageType,
                                              CoordinateRepType>;
 
    auto dispfieldGenerator = DisplacementFieldGeneratorType::New();
    dispfieldGenerator->UseReferenceImageOn();
    dispfieldGenerator->SetReferenceImage(fixedImage);
    dispfieldGenerator->SetTransform(transform);
    try
    {
      dispfieldGenerator->Update();
    }
    catch (const itk::ExceptionObject & err)
    {
      std::cerr << "Exception detected while generating deformation field";
      std::cerr << " : " << err << std::endl;
      return EXIT_FAILURE;
    }
    using FieldWriterType = itk::ImageFileWriter<DisplacementFieldImageType>;
    auto fieldWriter = FieldWriterType::New();
 
    fieldWriter->SetInput(dispfieldGenerator->GetOutput());
 
    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 > 7)
  {
    std::ofstream parametersFile;
    parametersFile.open(argv[7]);
    parametersFile << finalParameters << std::endl;
    parametersFile.close();
  }
 
  return EXIT_SUCCESS;
}