test/RegistrationITKv3/ImageRegistration12.cxx

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 *  Copyright Insight Software Consortium
 *
 *  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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 *         http://www.apache.org/licenses/LICENSE-2.0.txt
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//
// This example illustrates the use of \code{SpatialObject}s as masks for selecting the
// pixels that should contribute to the computation of Image Metrics. This
// example is almost identical to ImageRegistration6 with the exception that
// the \code{SpatialObject} masks are created and passed to the image metric.
//
//

#include "itkImageRegistrationMethod.h"
#include "itkMeanSquaresImageToImageMetric.h"
#include "itkRegularStepGradientDescentOptimizer.h"

#include "itkCenteredRigid2DTransform.h"
#include "itkCenteredTransformInitializer.h"

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

#include "itkResampleImageFilter.h"
#include "itkCastImageFilter.h"
#include "itkSquaredDifferenceImageFilter.h"

//
//  The most important header in this example is the one corresponding to the
//  \doxygen{ImageMaskSpatialObject} class.
//
//  \index{itk::ImageMaskSpatialObject!header}
//

#include "itkImageMaskSpatialObject.h"

//
//  The following section of code implements a command observer
//  that will 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 << optimizer->GetCurrentPosition() << std::endl;
    }
};

#include "itkTestDriverIncludeRequiredIOFactories.h"
int main( int argc, char *argv[] )
{
  RegisterRequiredFactories();
  if( argc < 5 )
    {
    std::cerr << "Missing Parameters " << std::endl;
    std::cerr << "Usage: " << argv[0];
    std::cerr << " fixedImageFile  movingImageFile fixedImageMaskFile";
    std::cerr << " outputImagefile  [differenceOutputfile] ";
    std::cerr << " [differenceBeforeRegistration] "<< std::endl;
    return EXIT_FAILURE;
    }

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

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

  using TransformType = itk::CenteredRigid2DTransform< double >;

  using OptimizerType = itk::RegularStepGradientDescentOptimizer;
  using MetricType = itk::MeanSquaresImageToImageMetric<
                                    FixedImageType,
                                    MovingImageType >;
  using InterpolatorType = itk:: LinearInterpolateImageFunction<
                                    MovingImageType,
                                    double          >;
  using RegistrationType = itk::ImageRegistrationMethod<
                                    FixedImageType,
                                    MovingImageType >;

  MetricType::Pointer         metric        = MetricType::New();
  OptimizerType::Pointer      optimizer     = OptimizerType::New();
  InterpolatorType::Pointer   interpolator  = InterpolatorType::New();
  RegistrationType::Pointer   registration  = RegistrationType::New();

  registration->SetMetric(        metric        );
  registration->SetOptimizer(     optimizer     );
  registration->SetInterpolator(  interpolator  );

  TransformType::Pointer  transform = TransformType::New();
  registration->SetTransform( transform );

  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();

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

  using TransformInitializerType = itk::CenteredTransformInitializer<
                                    TransformType,
                                    FixedImageType,
                                    MovingImageType >;
  TransformInitializerType::Pointer initializer = TransformInitializerType::New();

  initializer->SetTransform(   transform );
  initializer->SetFixedImage(  fixedImageReader->GetOutput() );
  initializer->SetMovingImage( movingImageReader->GetOutput() );

  initializer->MomentsOn();

  initializer->InitializeTransform();

  transform->SetAngle( 0.0 );

  registration->SetInitialTransformParameters( transform->GetParameters() );

  using OptimizerScalesType = OptimizerType::ScalesType;
  OptimizerScalesType optimizerScales( transform->GetNumberOfParameters() );
  const double translationScale = 1.0 / 1000.0;

  optimizerScales[0] = 1.0;
  optimizerScales[1] = translationScale;
  optimizerScales[2] = translationScale;
  optimizerScales[3] = translationScale;
  optimizerScales[4] = translationScale;

  optimizer->SetScales( optimizerScales );

  optimizer->SetMaximumStepLength( 0.1    );
  optimizer->SetMinimumStepLength( 0.001 );
  optimizer->SetNumberOfIterations( 200 );

  // Create the Command observer and register it with the optimizer.
  //
  CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
  optimizer->AddObserver( itk::IterationEvent(), observer );

  //
  //  Here we instantiate the type of the \doxygen{ImageMaskSpatialObject}
  //  using the same dimension of the images to be registered.
  //
  //  \index{itk::ImageMaskSpatialObject!Instantiation}
  //

  using MaskType = itk::ImageMaskSpatialObject< Dimension >;

  //
  //  Then we use the type for creating the spatial object mask that will
  //  restrict the registration to a reduced region of the image.
  //
  //  \index{itk::ImageMaskSpatialObject!New}
  //  \index{itk::ImageMaskSpatialObject!Pointer}
  //

  MaskType::Pointer  spatialObjectMask = MaskType::New();

  //
  //  The mask in this case is read from a binary file using the
  //  \code{ImageFileReader} instantiated for an \code{unsigned char} pixel
  //  type.
  //

  using ImageMaskType = itk::Image< unsigned char, Dimension >;

  using MaskReaderType = itk::ImageFileReader< ImageMaskType >;

  //
  //  The reader is constructed and a filename is passed to it.
  //

  MaskReaderType::Pointer  maskReader = MaskReaderType::New();

  maskReader->SetFileName( argv[3] );

  //
  //  As usual, the reader is triggered by invoking its \code{Update()} method.
  //  Since this may eventually throw an exception, the call must be placed in
  //  a \code{try/catch} block. Note that a full fledged application will place
  //  this \code{try/catch} block at a much higher level, probably under the
  //  control of the GUI.
  //

  try
    {
    maskReader->Update();
    }
  catch( itk::ExceptionObject & err )
    {
    std::cerr << "ExceptionObject caught !" << std::endl;
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
    }

  //
  //  The output of the mask reader is connected as input to the
  //  \code{ImageMaskSpatialObject}.
  //
  //  \index{itk::ImageMaskSpatialObject!SetImage()}
  //

  spatialObjectMask->SetImage( maskReader->GetOutput() );

  //
  //  Finally, the spatial object mask is passed to the image metric.
  //
  //  \index{itk::ImageToImageMetric!SetFixedImage()}
  //

  metric->SetFixedImageMask( spatialObjectMask );

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

  OptimizerType::ParametersType finalParameters =
                    registration->GetLastTransformParameters();

  const double finalAngle           = finalParameters[0];
  const double finalRotationCenterX = finalParameters[1];
  const double finalRotationCenterY = finalParameters[2];
  const double finalTranslationX    = finalParameters[3];
  const double finalTranslationY    = finalParameters[4];

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

  // Print out results
  //
  const double finalAngleInDegrees = finalAngle * 45.0 / std::atan(1.0);

  std::cout << "Result = " << std::endl;
  std::cout << " Angle (radians) " << finalAngle  << std::endl;
  std::cout << " Angle (degrees) " << finalAngleInDegrees  << std::endl;
  std::cout << " Center X      = " << finalRotationCenterX  << std::endl;
  std::cout << " Center Y      = " << finalRotationCenterY  << std::endl;
  std::cout << " Translation X = " << finalTranslationX  << std::endl;
  std::cout << " Translation Y = " << finalTranslationY  << std::endl;
  std::cout << " Iterations    = " << numberOfIterations << std::endl;
  std::cout << " Metric value  = " << bestValue          << std::endl;

  //
  //  Let's execute this example over some of the images provided in
  //  \code{Examples/Data}, for example:
  //
  //  \begin{itemize}
  //  \item \code{BrainProtonDensitySliceBorder20.png}
  //  \item \code{BrainProtonDensitySliceR10X13Y17.png}
  //  \end{itemize}
  //
  //  The second image is the result of intentionally rotating the first
  //  image by $10$ degrees and shifting it $13mm$ in $X$ and $17mm$ in
  //  $Y$. Both images have unit-spacing and are shown in Figure
  //  \ref{fig:FixedMovingImageRegistration5}.
  //

  transform->SetParameters( finalParameters );

  TransformType::MatrixType matrix = transform->GetMatrix();
  TransformType::OffsetType offset = transform->GetOffset();

  std::cout << "Matrix = " << std::endl << matrix << std::endl;
  std::cout << "Offset = " << std::endl << offset << std::endl;

  //
  //  Now we resample the moving image using the transform resulting from the
  //  registration process.
  //

  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 );

  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[4] );

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

  using DifferenceFilterType = itk::SquaredDifferenceImageFilter<
                                  FixedImageType,
                                  FixedImageType,
                                  OutputImageType >;

  DifferenceFilterType::Pointer difference = DifferenceFilterType::New();

  WriterType::Pointer writer2 = WriterType::New();
  writer2->SetInput( difference->GetOutput() );

  // Compute the difference image between the
  // fixed and resampled moving image.
  if( argc >= 6 )
    {
    difference->SetInput1( fixedImageReader->GetOutput() );
    difference->SetInput2( resample->GetOutput() );
    writer2->SetFileName( argv[5] );
    writer2->Update();
    }

  // Compute the difference image between the
  // fixed and moving image before registration.
  if( argc >= 7 )
    {
    writer2->SetFileName( argv[6] );
    difference->SetInput1( fixedImageReader->GetOutput() );
    difference->SetInput2( movingImageReader->GetOutput() );
    writer2->Update();
    }

  return EXIT_SUCCESS;
}