Examples/RegistrationITKv4/ImageRegistration18.cxx

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// Software Guide : BeginLatex
//
// This example illustrates how to use the
// \doxygen{GradientDifferenceImageToImageMetric}.
//
// This metric is particularly useful for registration scenarios where fitting
// the edges of both images is the most relevant criteria for registration
// success.
//
// \index{itk::ImageRegistrationMethod!Monitoring}
//
//
// Software Guide : EndLatex


#include "itkImageRegistrationMethod.h"
#include "itkTranslationTransform.h"
#include "itkGradientDifferenceImageToImageMetric.h"
#include "itkRegularStepGradientDescentOptimizer.h"

#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"

#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 << optimizer->GetCurrentPosition() << std::endl;
    }

};


int main( int argc, char *argv[] )
{
  if( argc < 3 )
    {
    std::cerr << "Missing Parameters " << std::endl;
    std::cerr << "Usage: " << argv[0];
    std::cerr << " fixedImageFile  movingImageFile ";
    std::cerr << "outputImagefile" << std::endl;
    std::cerr << "[initialTx] [initialTy]" << std::endl;
    return EXIT_FAILURE;
    }

  constexpr unsigned int Dimension = 2;
  using PixelType = unsigned short;

  using FixedImageType = itk::Image< PixelType, Dimension >;
  using MovingImageType = itk::Image< PixelType, Dimension >;

  using TransformType = itk::TranslationTransform< double, Dimension >;

  using OptimizerType = itk::RegularStepGradientDescentOptimizer;

  using InterpolatorType = itk::LinearInterpolateImageFunction<
                                    MovingImageType,
                                    double             >;

  using RegistrationType = itk::ImageRegistrationMethod<
                                    FixedImageType,
                                    MovingImageType   >;

  using MetricType = itk::GradientDifferenceImageToImageMetric<
                                      FixedImageType,
                                      MovingImageType >;

  TransformType::Pointer      transform     = TransformType::New();
  OptimizerType::Pointer      optimizer     = OptimizerType::New();
  InterpolatorType::Pointer   interpolator  = InterpolatorType::New();
  RegistrationType::Pointer   registration  = RegistrationType::New();

  registration->SetOptimizer(     optimizer     );
  registration->SetTransform(     transform     );
  registration->SetInterpolator(  interpolator  );

  MetricType::Pointer         metric        = MetricType::New();

  metric->SetDerivativeDelta( 0.5 );

  registration->SetMetric( metric  );

  using FixedImageReaderType = itk::ImageFileReader< FixedImageType  >;
  using MovingImageReaderType = itk::ImageFileReader< MovingImageType >;

  FixedImageReaderType::Pointer  fixedImageReader  = FixedImageReaderType::New();
  MovingImageReaderType::Pointer movingImageReader = MovingImageReaderType::New();

  fixedImageReader->SetFileName(  argv[1] );
  movingImageReader->SetFileName( argv[2] );

  registration->SetFixedImage(    fixedImageReader->GetOutput()    );
  registration->SetMovingImage(   movingImageReader->GetOutput()   );

  fixedImageReader->Update(); // This is needed to make the BufferedRegion below valid.

  registration->SetFixedImageRegion(
       fixedImageReader->GetOutput()->GetBufferedRegion() );

  using ParametersType = RegistrationType::ParametersType;
  ParametersType initialParameters( transform->GetNumberOfParameters() );

  initialParameters[0] = 0.0;  // Initial offset in mm along X
  initialParameters[1] = 0.0;  // Initial offset in mm along Y

  if( argc > 4 )
    {
    initialParameters[0] = std::stod( argv[4] );
    }

  if( argc > 5 )
    {
    initialParameters[1] = std::stod( argv[5] );
    }

  std::cout << "Initial parameters = " << initialParameters << std::endl;

  registration->SetInitialTransformParameters( initialParameters );

  optimizer->SetMaximumStepLength( 4.00 );
  optimizer->SetMinimumStepLength( 0.01 );
  optimizer->SetNumberOfIterations( 200 );
  optimizer->SetGradientMagnitudeTolerance( 1e-40 );

  optimizer->MaximizeOn();


  CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
  optimizer->AddObserver( itk::IterationEvent(), observer );

  try
    {
    registration->Update();
    std::cout << "Optimizer stop condition: "
              << registration->GetOptimizer()->GetStopConditionDescription()
              << std::endl;
    }
  catch( itk::ExceptionObject & err )
    {
    std::cout << "ExceptionObject caught !" << std::endl;
    std::cout << err << std::endl;
    return EXIT_FAILURE;
    }


  ParametersType finalParameters = registration->GetLastTransformParameters();

  const double TranslationAlongX = finalParameters[0];
  const double TranslationAlongY = finalParameters[1];

  const unsigned int numberOfIterations = optimizer->GetCurrentIteration();

  const double bestValue = optimizer->GetValue();

  std::cout << "Registration done !" << std::endl;
  std::cout << "Optimizer stop condition = "
              << registration->GetOptimizer()->GetStopConditionDescription()
              << std::endl;
  std::cout << "Number of iterations = " << numberOfIterations << std::endl;
  std::cout << "Translation along X  = " << TranslationAlongX << std::endl;
  std::cout << "Translation along Y  = " << TranslationAlongY << std::endl;
  std::cout << "Optimal metric value = " << bestValue << std::endl;


  // Prepare the resampling filter in order to map the moving image.
  //
  using ResampleFilterType = itk::ResampleImageFilter<
                            MovingImageType,
                            FixedImageType >;

  TransformType::Pointer finalTransform = TransformType::New();

  finalTransform->SetParameters( finalParameters );
  finalTransform->SetFixedParameters( transform->GetFixedParameters() );

  ResampleFilterType::Pointer resample = ResampleFilterType::New();

  resample->SetTransform( finalTransform );
  resample->SetInput( movingImageReader->GetOutput() );

  FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();

  resample->SetSize(    fixedImage->GetLargestPossibleRegion().GetSize() );
  resample->SetOutputOrigin(  fixedImage->GetOrigin() );
  resample->SetOutputSpacing( fixedImage->GetSpacing() );
  resample->SetOutputDirection( fixedImage->GetDirection() );
  resample->SetDefaultPixelValue( 100 );


  // Prepare a writer and caster filters to send the resampled moving image to
  // a file
  //
  using OutputPixelType = unsigned char;

  using OutputImageType = itk::Image< OutputPixelType, Dimension >;

  using CastFilterType = itk::CastImageFilter<
                        FixedImageType,
                        OutputImageType >;

  using WriterType = itk::ImageFileWriter< OutputImageType >;

  WriterType::Pointer      writer =  WriterType::New();
  CastFilterType::Pointer  caster =  CastFilterType::New();


  writer->SetFileName( argv[3] );

  caster->SetInput( resample->GetOutput() );
  writer->SetInput( caster->GetOutput()   );
  writer->Update();


  return EXIT_SUCCESS;
}