Examples/RegistrationITKv4/ImageRegistration16.cxx

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 *  Copyright NumFOCUS
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 *  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 how to do registration with a 2D Translation
//  Transform, the Normalized Mutual Information metric and the Amoeba
//  optimizer.
//
// Software Guide : EndLatex
 
 
// Software Guide : BeginCodeSnippet
#include "itkImageRegistrationMethod.h"
 
#include "itkTranslationTransform.h"
#include "itkMattesMutualInformationImageToImageMetric.h"
#include "itkAmoebaOptimizer.h"
#include "itkMersenneTwisterRandomVariateGenerator.h"
 
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
 
#include "itkResampleImageFilter.h"
#include "itkCastImageFilter.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() { m_IterationNumber = 0; }
 
public:
  using OptimizerType = itk::AmoebaOptimizer;
  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 << m_IterationNumber++ << "   ";
    std::cout << optimizer->GetCachedValue() << "   ";
    std::cout << optimizer->GetCachedCurrentPosition() << std::endl;
  }
 
private:
  unsigned long m_IterationNumber;
};
 
int
main(int argc, char * argv[])
{
  if (argc < 4)
  {
    std::cerr << "Missing Parameters " << std::endl;
    std::cerr << "Usage: " << argv[0];
    std::cerr << " fixedImageFile  movingImageFile ";
    std::cerr << " outputImagefile ";
    std::cerr << " [initialTx] [initialTy]";
    std::cerr << "[useExplicitPDFderivatives ] " << std::endl;
    return EXIT_FAILURE;
  }
 
  constexpr unsigned int Dimension = 2;
  using PixelType = unsigned char;
 
  using FixedImageType = itk::Image<PixelType, Dimension>;
  using MovingImageType = itk::Image<PixelType, Dimension>;
 
  using TransformType = itk::TranslationTransform<double, Dimension>;
 
  using OptimizerType = itk::AmoebaOptimizer;
  using InterpolatorType =
    itk::LinearInterpolateImageFunction<MovingImageType, double>;
  using RegistrationType =
    itk::ImageRegistrationMethod<FixedImageType, MovingImageType>;
 
 
  using MetricType =
    itk::MattesMutualInformationImageToImageMetric<FixedImageType,
                                                   MovingImageType>;
 
 
  auto transform = TransformType::New();
  auto optimizer = OptimizerType::New();
  auto interpolator = InterpolatorType::New();
  auto registration = RegistrationType::New();
 
  registration->SetOptimizer(optimizer);
  registration->SetTransform(transform);
  registration->SetInterpolator(interpolator);
 
 
  auto metric = MetricType::New();
  registration->SetMetric(metric);
 
 
  //  Software Guide : BeginLatex
  //
  //  The metric requires two parameters to be selected: the number
  //  of bins used to compute the entropy and the number of spatial samples
  //  used to compute the density estimates. In typical application, 50
  //  histogram bins are sufficient and the metric is relatively insensitive
  //  to changes in the number of bins. The number of spatial samples
  //  to be used depends on the content of the image. If the images are
  //  smooth and do not contain much detail, then using approximately
  //  $1$ percent of the pixels will do. On the other hand, if the images
  //  are detailed, it may be necessary to use a much higher proportion,
  //  such as $20$ percent.
  //
  //  \index{itk::Mattes\-Mutual\-Information\-Image\-To\-Image\-Metric!SetNumberOfHistogramBins()}
  //  \index{itk::Mattes\-Mutual\-Information\-Image\-To\-Image\-Metric!SetNumberOfSpatialSamples()}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  metric->SetNumberOfHistogramBins(20);
  metric->SetNumberOfSpatialSamples(10000);
  // Software Guide : EndCodeSnippet
 
  // For consistent results when regression testing.
  metric->ReinitializeSeed(121212);
 
  if (argc > 6)
  {
    // 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[6]));
  }
 
 
  const unsigned int numberOfParameters = transform->GetNumberOfParameters();
 
 
  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]);
 
  registration->SetFixedImage(fixedImageReader->GetOutput());
  registration->SetMovingImage(movingImageReader->GetOutput());
 
  fixedImageReader->Update();
  movingImageReader->Update();
 
  const FixedImageType::ConstPointer fixedImage =
    fixedImageReader->GetOutput();
 
  registration->SetFixedImageRegion(fixedImage->GetBufferedRegion());
 
 
  transform->SetIdentity();
 
  using ParametersType = RegistrationType::ParametersType;
 
  ParametersType initialParameters = transform->GetParameters();
 
  initialParameters[0] = 0.0;
  initialParameters[1] = 0.0;
 
  if (argc > 5)
  {
    initialParameters[0] = std::stod(argv[4]);
    initialParameters[1] = std::stod(argv[5]);
  }
 
  registration->SetInitialTransformParameters(initialParameters);
 
  std::cout << "Initial transform parameters = ";
  std::cout << initialParameters << std::endl;
 
 
  //  Software Guide : BeginLatex
  //
  //  The AmoebaOptimizer moves a simplex around the cost surface.  Here we
  //  set the initial size of the simplex (5 units in each of the parameters)
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  OptimizerType::ParametersType simplexDelta(numberOfParameters);
  simplexDelta.Fill(5.0);
 
  optimizer->AutomaticInitialSimplexOff();
  optimizer->SetInitialSimplexDelta(simplexDelta);
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  We also adjust the tolerances on the optimizer to define convergence.
  //  Here, we used a tolerance on the parameters of 0.1 (which will be one
  //  tenth of image unit, in this case pixels). We also set the tolerance on
  //  the cost function value to define convergence.  The metric we are using
  //  returns the value of Mutual Information.  So we set the function
  //  convergence to be 0.001 bits (bits are the appropriate units for
  //  measuring Information).
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  optimizer->SetParametersConvergenceTolerance(0.1); // 1/10th pixel
  optimizer->SetFunctionConvergenceTolerance(0.001); // 0.001 bits
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  In the case where the optimizer never succeeds in reaching the desired
  //  precision tolerance, it is prudent to establish a limit on the number of
  //  iterations to be performed. This maximum number is defined with the
  //  method \code{SetMaximumNumberOfIterations()}.
  //
  //  \index{itk::Amoeba\-Optimizer!SetMaximumNumberOfIterations()}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  optimizer->SetMaximumNumberOfIterations(200);
  // Software Guide : EndCodeSnippet
 
 
  // Create the Command observer and register it with the optimizer.
  //
  auto observer = CommandIterationUpdate::New();
  optimizer->AddObserver(itk::IterationEvent(), observer);
 
 
  try
  {
    registration->Update();
    std::cout << "Optimizer stop condition: "
              << registration->GetOptimizer()->GetStopConditionDescription()
              << std::endl;
  }
  catch (const itk::ExceptionObject & err)
  {
    std::cout << "ExceptionObject caught !" << std::endl;
    std::cout << err << std::endl;
    return EXIT_FAILURE;
  }
 
 
  ParametersType finalParameters = registration->GetLastTransformParameters();
 
  const double finalTranslationX = finalParameters[0];
  const double finalTranslationY = finalParameters[1];
 
  const double bestValue = optimizer->GetValue();
 
 
  // Print out results
  //
  std::cout << "Result = " << std::endl;
  std::cout << " Translation X = " << finalTranslationX << std::endl;
  std::cout << " Translation Y = " << finalTranslationY << std::endl;
  std::cout << " Metric value  = " << bestValue << std::endl;
 
 
  using ResampleFilterType =
    itk::ResampleImageFilter<MovingImageType, FixedImageType>;
 
  auto finalTransform = TransformType::New();
 
  finalTransform->SetParameters(finalParameters);
  finalTransform->SetFixedParameters(transform->GetFixedParameters());
 
  auto resample = ResampleFilterType::New();
 
  resample->SetTransform(finalTransform);
  resample->SetInput(movingImageReader->GetOutput());
 
 
  resample->SetSize(fixedImage->GetLargestPossibleRegion().GetSize());
  resample->SetOutputOrigin(fixedImage->GetOrigin());
  resample->SetOutputSpacing(fixedImage->GetSpacing());
  resample->SetOutputDirection(fixedImage->GetDirection());
  resample->SetDefaultPixelValue(100);
 
 
  using OutputImageType = itk::Image<PixelType, Dimension>;
 
  using WriterType = itk::ImageFileWriter<OutputImageType>;
 
  auto writer = WriterType::New();
 
  writer->SetFileName(argv[3]);
 
  writer->SetInput(resample->GetOutput());
  writer->Update();
  // Software Guide : EndCodeSnippet
 
  return EXIT_SUCCESS;
}