WikiExamples/Registration/ImageRegistrationMethod.cxx

#include "itkCastImageFilter.h"
#include "itkEllipseSpatialObject.h"
#include "itkImage.h"
#include "itkImageRegistrationMethod.h"
#include "itkLinearInterpolateImageFunction.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkMeanSquaresImageToImageMetric.h"
#include "itkRegularStepGradientDescentOptimizer.h"
#include "itkResampleImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkSpatialObjectToImageFilter.h"
#include "itkTranslationTransform.h"

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

using ImageType = itk::Image< PixelType, Dimension >;

static void CreateEllipseImage(ImageType::Pointer image);
static void CreateSphereImage(ImageType::Pointer image);

int main(int, char *[] )
{
  //  The transform that will map the fixed image into the moving image.
  using TransformType = itk::TranslationTransform< double, Dimension >;
  
  //  An optimizer is required to explore the parameter space of the transform
  //  in search of optimal values of the metric.
  using OptimizerType = itk::RegularStepGradientDescentOptimizer;
  
  //  The metric will compare how well the two images match each other. Metric
  //  types are usually parameterized by the image types as it can be seen in
  //  the following type declaration.
  using MetricType = itk::MeanSquaresImageToImageMetric< 
      ImageType,
      ImageType >;
  
  //  Finally, the type of the interpolator is declared. The interpolator will
  //  evaluate the intensities of the moving image at non-grid positions.
  using InterpolatorType = itk:: LinearInterpolateImageFunction<
      ImageType,
      double          >;
  
  //  The registration method type is instantiated using the types of the
  //  fixed and moving images. This class is responsible for interconnecting
  //  all the components that we have described so far.
  using RegistrationType = itk::ImageRegistrationMethod<
      ImageType,
      ImageType >;

  // Create components
  MetricType::Pointer         metric        = MetricType::New();
  TransformType::Pointer      transform     = TransformType::New();
  OptimizerType::Pointer      optimizer     = OptimizerType::New();
  InterpolatorType::Pointer   interpolator  = InterpolatorType::New();
  RegistrationType::Pointer   registration  = RegistrationType::New();

  // Each component is now connected to the instance of the registration method.
  registration->SetMetric(        metric        );
  registration->SetOptimizer(     optimizer     );
  registration->SetTransform(     transform     );
  registration->SetInterpolator(  interpolator  );

  // Get the two images
  ImageType::Pointer  fixedImage  = ImageType::New();
  ImageType::Pointer movingImage = ImageType::New();

  CreateSphereImage(fixedImage);
  CreateEllipseImage(movingImage);

  // Write the two synthetic inputs
  using WriterType = itk::ImageFileWriter< ImageType >;
  
  WriterType::Pointer      fixedWriter =  WriterType::New();
  fixedWriter->SetFileName("fixed.png");
  fixedWriter->SetInput( fixedImage);
  fixedWriter->Update();

  WriterType::Pointer      movingWriter =  WriterType::New();
  movingWriter->SetFileName("moving.png");
  movingWriter->SetInput( movingImage);
  movingWriter->Update();
  
  // Set the registration inputs
  registration->SetFixedImage(fixedImage);
  registration->SetMovingImage(movingImage);
  
  registration->SetFixedImageRegion(
    fixedImage->GetLargestPossibleRegion() );

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

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

  registration->SetInitialTransformParameters( initialParameters );

  optimizer->SetMaximumStepLength( 4.00 );
  optimizer->SetMinimumStepLength( 0.01 );

  // Set a stopping criterion
  optimizer->SetNumberOfIterations( 200 );

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

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

  //  The result of the registration process is an array of parameters that
  //  defines the spatial transformation in an unique way. This final result is
  //  obtained using the \code{GetLastTransformParameters()} method.
              
  ParametersType finalParameters = registration->GetLastTransformParameters();

  //  In the case of the \doxygen{TranslationTransform}, there is a
  //  straightforward interpretation of the parameters.  Each element of the
  //  array corresponds to a translation along one spatial dimension.
              
  const double TranslationAlongX = finalParameters[0];
  const double TranslationAlongY = finalParameters[1];

  //  The optimizer can be queried for the actual number of iterations
  //  performed to reach convergence.  The \code{GetCurrentIteration()}
  //  method returns this value. A large number of iterations may be an
  //  indication that the maximum step length has been set too small, which
  //  is undesirable since it results in long computational times.
              
  const unsigned int numberOfIterations = optimizer->GetCurrentIteration();

  //  The value of the image metric corresponding to the last set of parameters
  //  can be obtained with the \code{GetValue()} method of the optimizer.
              
  const double bestValue = optimizer->GetValue();

  // Print out results
  //
  std::cout << "Result = " << std::endl;
  std::cout << " Translation X = " << TranslationAlongX  << std::endl;
  std::cout << " Translation Y = " << TranslationAlongY  << std::endl;
  std::cout << " Iterations    = " << numberOfIterations << std::endl;
  std::cout << " Metric value  = " << bestValue          << std::endl;

  //  It is common, as the last step of a registration task, to use the
  //  resulting transform to map the moving image into the fixed image space.
  //  This is easily done with the \doxygen{ResampleImageFilter}. Please
  //  refer to Section~\ref{sec:ResampleImageFilter} for details on the use
  //  of this filter.  First, a ResampleImageFilter type is instantiated
  //  using the image types. It is convenient to use the fixed image type as
  //  the output type since it is likely that the transformed moving image
  //  will be compared with the fixed image.
              
  using ResampleFilterType = itk::ResampleImageFilter<
      ImageType,
      ImageType >;

  //  A resampling filter is created and the moving image is connected as  its input.

  ResampleFilterType::Pointer resampler = ResampleFilterType::New();
  resampler->SetInput( movingImage);
  
  //  The Transform that is produced as output of the Registration method is
  //  also passed as input to the resampling filter. Note the use of the
  //  methods \code{GetOutput()} and \code{Get()}. This combination is needed
  //  here because the registration method acts as a filter whose output is a
  //  transform decorated in the form of a \doxygen{DataObject}. For details in
  //  this construction you may want to read the documentation of the
  //  \doxygen{DataObjectDecorator}.

  resampler->SetTransform( registration->GetOutput()->Get() );

  //  As described in Section \ref{sec:ResampleImageFilter}, the
  //  ResampleImageFilter requires additional parameters to be specified, in
  //  particular, the spacing, origin and size of the output image. The default
  //  pixel value is also set to a distinct gray level in order to highlight
  //  the regions that are mapped outside of the moving image.  

  resampler->SetSize( fixedImage->GetLargestPossibleRegion().GetSize() );
  resampler->SetOutputOrigin(  fixedImage->GetOrigin() );
  resampler->SetOutputSpacing( fixedImage->GetSpacing() );
  resampler->SetOutputDirection( fixedImage->GetDirection() );
  resampler->SetDefaultPixelValue( 100 );
  
  //  The output of the filter is passed to a writer that will store the
  //  image in a file. An \doxygen{CastImageFilter} is used to convert the
  //  pixel type of the resampled image to the final type used by the
  //  writer. The cast and writer filters are instantiated below.
                  
  using CastFilterType = itk::CastImageFilter<
      ImageType,
      ImageType >;
  
  WriterType::Pointer      writer =  WriterType::New();
  CastFilterType::Pointer  caster =  CastFilterType::New();
  writer->SetFileName("output.png");

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

  /*
  //  The fixed image and the transformed moving image can easily be compared
  //  using the \doxygen{SubtractImageFilter}. This pixel-wise filter computes
  //  the difference between homologous pixels of its two input images.
                      
                      
  using DifferenceFilterType = itk::SubtractImageFilter<
      FixedImageType,
      FixedImageType,
      FixedImageType >;

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

  difference->SetInput1( fixedImageReader->GetOutput() );
  difference->SetInput2( resampler->GetOutput() );
  */
  

  return EXIT_SUCCESS;
}

void CreateEllipseImage(ImageType::Pointer image)
{
  using EllipseType = itk::EllipseSpatialObject< Dimension >;

  using SpatialObjectToImageFilterType = itk::SpatialObjectToImageFilter<
    EllipseType, ImageType >;

  SpatialObjectToImageFilterType::Pointer imageFilter =
    SpatialObjectToImageFilterType::New();

  ImageType::SizeType size;
  size[ 0 ] =  100;
  size[ 1 ] =  100;

  imageFilter->SetSize( size );

  ImageType::SpacingType spacing;
  spacing.Fill(1);
  imageFilter->SetSpacing(spacing);

  EllipseType::Pointer ellipse    = EllipseType::New();
  EllipseType::ArrayType radiusArray;
  radiusArray[0] = 10;
  radiusArray[1] = 20;
  ellipse->SetRadius(radiusArray);

  using TransformType = EllipseType::TransformType;
  TransformType::Pointer transform = TransformType::New();
  transform->SetIdentity();

  TransformType::OutputVectorType  translation;
  translation[ 0 ] =  65;
  translation[ 1 ] =  45;
  transform->Translate( translation, false );

  ellipse->SetObjectToParentTransform( transform );

  imageFilter->SetInput(ellipse);

  ellipse->SetDefaultInsideValue(255);
  ellipse->SetDefaultOutsideValue(0);
  imageFilter->SetUseObjectValue( true );
  imageFilter->SetOutsideValue( 0 );

  imageFilter->Update();

  image->Graft(imageFilter->GetOutput());

}

void CreateSphereImage(ImageType::Pointer image)
{
 using EllipseType = itk::EllipseSpatialObject< Dimension >;

  using SpatialObjectToImageFilterType = itk::SpatialObjectToImageFilter<
    EllipseType, ImageType >;

  SpatialObjectToImageFilterType::Pointer imageFilter =
    SpatialObjectToImageFilterType::New();

  ImageType::SizeType size;
  size[ 0 ] =  100;
  size[ 1 ] =  100;

  imageFilter->SetSize( size );

  ImageType::SpacingType spacing;
  spacing.Fill(1);
  imageFilter->SetSpacing(spacing);

  EllipseType::Pointer ellipse    = EllipseType::New();
  EllipseType::ArrayType radiusArray;
  radiusArray[0] = 10;
  radiusArray[1] = 10;
  ellipse->SetRadius(radiusArray);

  using TransformType = EllipseType::TransformType;
  TransformType::Pointer transform = TransformType::New();
  transform->SetIdentity();

  TransformType::OutputVectorType  translation;
  translation[ 0 ] =  50;
  translation[ 1 ] =  50;
  transform->Translate( translation, false );

  ellipse->SetObjectToParentTransform( transform );

  imageFilter->SetInput(ellipse);

  ellipse->SetDefaultInsideValue(255);
  ellipse->SetDefaultOutsideValue(0);
  imageFilter->SetUseObjectValue( true );
  imageFilter->SetOutsideValue( 0 );

  imageFilter->Update();

  image->Graft(imageFilter->GetOutput());
}