Examples/RegistrationITKv4/DeformableRegistration2.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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//  Software Guide : BeginCommandLineArgs
//    INPUTS:  RatLungSlice1.mha
//    INPUTS:  RatLungSlice2.mha
//    ARGUMENTS: DeformableRegistration2Output.mha
//    ARGUMENTS: DeformableRegistration2Field.mha
//  Software Guide : EndCommandLineArgs


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

#include "itkImageRegionIterator.h"

// Software Guide : BeginLatex
//
// This example demonstrates how to use the ``demons'' algorithm to deformably
// register two images. The first step is to include the header files.
//
// Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
#include "itkDemonsRegistrationFilter.h"
#include "itkHistogramMatchingImageFilter.h"
#include "itkCastImageFilter.h"
#include "itkResampleImageFilter.h"
#include "itkDisplacementFieldTransform.h"
// Software Guide : EndCodeSnippet


//  The following section of code implements a Command observer
//  that will monitor the evolution of the registration process.
//
  class CommandIterationUpdate : public itk::Command
  {
  public:
    using Self = CommandIterationUpdate;
    using Superclass = itk::Command;
    using Pointer = itk::SmartPointer<CommandIterationUpdate>;
    itkNewMacro( CommandIterationUpdate );
  protected:
    CommandIterationUpdate() = default;

    using InternalImageType = itk::Image< float, 2 >;
    using VectorPixelType = itk::Vector< float, 2 >;
    using DisplacementFieldType = itk::Image<  VectorPixelType, 2 >;

    using RegistrationFilterType = itk::DemonsRegistrationFilter<
                                InternalImageType,
                                InternalImageType,
                                DisplacementFieldType>;

  public:

    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
      {
         const auto * filter = static_cast< const RegistrationFilterType * >( object );
        if( !(itk::IterationEvent().CheckEvent( &event )) )
          {
          return;
          }
        std::cout << filter->GetMetric() << 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 ";
    std::cerr << " outputImageFile " << std::endl;
    std::cerr << " [outputDisplacementFieldFile] " << std::endl;
    return EXIT_FAILURE;
    }

  // Software Guide : BeginLatex
  //
  // Second, we declare the types of the images.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  constexpr unsigned int Dimension = 2;
  using PixelType = unsigned short;

  using FixedImageType = itk::Image< PixelType, Dimension >;
  using MovingImageType = itk::Image< PixelType, Dimension >;
  // Software Guide : EndCodeSnippet

  // Set up the file readers
  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] );


  // Software Guide : BeginLatex
  //
  // Image file readers are set up in a similar fashion to previous examples.
  // To support the re-mapping of the moving image intensity, we declare an
  // internal image type with a floating point pixel type and cast the input
  // images to the internal image type.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using InternalPixelType = float;
  using InternalImageType = itk::Image< InternalPixelType, Dimension >;
  using FixedImageCasterType = itk::CastImageFilter< FixedImageType,
                                InternalImageType >;
  using MovingImageCasterType = itk::CastImageFilter< MovingImageType,
                                InternalImageType >;

  FixedImageCasterType::Pointer fixedImageCaster = FixedImageCasterType::New();
  MovingImageCasterType::Pointer movingImageCaster
                                                = MovingImageCasterType::New();

  fixedImageCaster->SetInput( fixedImageReader->GetOutput() );
  movingImageCaster->SetInput( movingImageReader->GetOutput() );
  // Software Guide : EndCodeSnippet

  // Software Guide : BeginLatex
  //
  // The demons algorithm relies on the assumption that pixels representing the
  // same homologous point on an object have the same intensity on both the
  // fixed and moving images to be registered. In this example, we will
  // preprocess the moving image to match the intensity between the images
  // using the \doxygen{HistogramMatchingImageFilter}.
  //
  // \index{itk::HistogramMatchingImageFilter}
  //
  // The basic idea is to match the histograms of the two images at a
  // user-specified number of quantile values. For robustness, the histograms
  // are matched so that the background pixels are excluded from both
  // histograms.  For MR images, a simple procedure is to exclude all gray
  // values that are smaller than the mean gray value of the image.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using MatchingFilterType = itk::HistogramMatchingImageFilter<
                                    InternalImageType,
                                    InternalImageType >;
  MatchingFilterType::Pointer matcher = MatchingFilterType::New();
  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // For this example, we set the moving image as the source or input image and
  // the fixed image as the reference image.
  //
  // \index{itk::HistogramMatchingImageFilter!SetInput()}
  // \index{itk::HistogramMatchingImageFilter!SetSourceImage()}
  // \index{itk::HistogramMatchingImageFilter!SetReferenceImage()}
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  matcher->SetInput( movingImageCaster->GetOutput() );
  matcher->SetReferenceImage( fixedImageCaster->GetOutput() );
  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // We then select the number of bins to represent the histograms and the
  // number of points or quantile values where the histogram is to be
  // matched.
  //
  // \index{itk::HistogramMatchingImageFilter!SetNumberOfHistogramLevels()}
  // \index{itk::HistogramMatchingImageFilter!SetNumberOfMatchPoints()}
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  matcher->SetNumberOfHistogramLevels( 1024 );
  matcher->SetNumberOfMatchPoints( 7 );
  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // Simple background extraction is done by thresholding at the mean
  // intensity.
  //
  // \index{itk::HistogramMatchingImageFilter!ThresholdAtMeanIntensityOn()}
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  matcher->ThresholdAtMeanIntensityOn();
  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // In the \doxygen{DemonsRegistrationFilter}, the deformation field is
  // represented as an image whose pixels are floating point vectors.
  //
  // \index{itk::DemonsRegistrationFilter}
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using VectorPixelType = itk::Vector< float, Dimension >;
  using DisplacementFieldType = itk::Image<  VectorPixelType, Dimension >;
  using RegistrationFilterType = itk::DemonsRegistrationFilter<
                                InternalImageType,
                                InternalImageType,
                                DisplacementFieldType>;
  RegistrationFilterType::Pointer filter = RegistrationFilterType::New();
  // Software Guide : EndCodeSnippet


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


  // Software Guide : BeginLatex
  //
  // The input fixed image is simply the output of the fixed image casting
  // filter.  The input moving image is the output of the histogram matching
  // filter.
  //
  // \index{itk::DemonsRegistrationFilter!SetFixedImage()}
  // \index{itk::DemonsRegistrationFilter!SetMovingImage()}
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  filter->SetFixedImage( fixedImageCaster->GetOutput() );
  filter->SetMovingImage( matcher->GetOutput() );
  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // The demons registration filter has two parameters: the number of
  // iterations to be performed and the standard deviation of the Gaussian
  // smoothing kernel to be applied to the deformation field after each
  // iteration.
  // \index{itk::DemonsRegistrationFilter!SetNumberOfIterations()}
  // \index{itk::DemonsRegistrationFilter!SetStandardDeviations()}
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  filter->SetNumberOfIterations( 50 );
  filter->SetStandardDeviations( 1.0 );
  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // The registration algorithm is triggered by updating the filter. The
  // filter output is the computed deformation field.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  filter->Update();
  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // The \doxygen{ResampleImageFilter} can be used to warp the moving image with
  // the output deformation field. The default interpolator of the \doxygen{ResampleImageFilter},
  // is used but specification of the output image spacing and origin
  // are required.
  //
  // \index{itk::ResampleImageFilter}
  // \index{itk::ResampleImageFilter!SetInput()}
  // \index{itk::ResampleImageFilter!SetInterpolator()}
  // \index{itk::ResampleImageFilter!SetOutputSpacing()}
  // \index{itk::ResampleImageFilter!SetOutputOrigin()}
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using OutputPixelType = unsigned char;
  using OutputImageType = itk::Image< OutputPixelType, Dimension >;

  using InterpolatorPrecisionType = double;
  using WarperType =
    itk::ResampleImageFilter< MovingImageType, OutputImageType,
                              InterpolatorPrecisionType, float >;
  WarperType::Pointer warper = WarperType::New();

  warper->SetInput( movingImageReader->GetOutput() );
  warper->UseReferenceImageOn();
  warper->SetReferenceImage( fixedImageReader->GetOutput() );

  // Software Guide : EndCodeSnippet


  // Software Guide : BeginLatex
  //
  // The \code{ResampleImageFilter} requires a transform, so a
  // \doxygen{DisplacementFieldTransform} must be constructed then set
  // as the transform of the \code{ResampleImageFilter}. The resulting
  // warped or resampled image is written to file as per previous
  // examples.
  //
  // Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet

  using DisplacementFieldTransformType =
    itk::DisplacementFieldTransform<InternalPixelType, Dimension>;
  auto displacementTransform = DisplacementFieldTransformType::New();
  displacementTransform->SetDisplacementField( filter->GetOutput() );

  warper->SetTransform( displacementTransform );
  // Software Guide : EndCodeSnippet


  // Write warped image out to file
  using WriterType = itk::ImageFileWriter< OutputImageType >;

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

  writer->SetFileName( argv[3] );
  writer->SetInput( warper->GetOutput() );
  writer->Update();


  // Software Guide : BeginLatex
  //
  // Let's execute this example using the rat lung data from the previous example.
  // The associated data files can be found in \code{Examples/Data}:
  //
  // \begin{itemize}
  // \item \code{RatLungSlice1.mha}
  // \item \code{RatLungSlice2.mha}
  // \end{itemize}
  //
  // \begin{figure} \center
  // \includegraphics[width=0.44\textwidth]{DeformableRegistration2CheckerboardBefore}
  // \includegraphics[width=0.44\textwidth]{DeformableRegistration2CheckerboardAfter}
  // \itkcaption[Demon's deformable registration output]{Checkerboard comparisons
  // before and after demons-based deformable registration.}
  // \label{fig:DeformableRegistration2Output}
  // \end{figure}
  //
  // The result of the demons-based deformable registration is presented in
  // Figure \ref{fig:DeformableRegistration2Output}. The checkerboard
  // comparison shows that the algorithm was able to recover the misalignment
  // due to expiration.
  //
  // Software Guide : EndLatex


  // Software Guide : BeginLatex
  //
  // It may be also desirable to write the deformation field as an image of
  // vectors.  This can be done with the following code.
  //
  // Software Guide : EndLatex

  if( argc > 4 ) // if a fourth line argument has been provided...
    {

  // Software Guide : BeginCodeSnippet
  using FieldWriterType = itk::ImageFileWriter< DisplacementFieldType >;
  FieldWriterType::Pointer fieldWriter = FieldWriterType::New();
  fieldWriter->SetFileName( argv[4] );
  fieldWriter->SetInput( filter->GetOutput() );

  fieldWriter->Update();
  // Software Guide : EndCodeSnippet

  // Software Guide : BeginLatex
  //
  // Note that the file format used for writing the deformation field must be
  // capable of representing multiple components per pixel. This is the case
  // for the MetaImage and VTK file formats for example.
  //
  // Software Guide : EndLatex

    }


  if( argc > 5 ) // if a fifth line argument has been provided...
    {

  using VectorImage2DType = DisplacementFieldType;
  using Vector2DType = DisplacementFieldType::PixelType;

  VectorImage2DType::ConstPointer vectorImage2D = filter->GetOutput();

  VectorImage2DType::RegionType  region2D = vectorImage2D->GetBufferedRegion();
  VectorImage2DType::IndexType   index2D  = region2D.GetIndex();
  VectorImage2DType::SizeType    size2D   = region2D.GetSize();


  using Vector3DType = itk::Vector< float,       3 >;
  using VectorImage3DType = itk::Image< Vector3DType, 3 >;

  using VectorImage3DWriterType = itk::ImageFileWriter< VectorImage3DType >;

  VectorImage3DWriterType::Pointer writer3D = VectorImage3DWriterType::New();

  VectorImage3DType::Pointer vectorImage3D = VectorImage3DType::New();

  VectorImage3DType::RegionType  region3D;
  VectorImage3DType::IndexType   index3D;
  VectorImage3DType::SizeType    size3D;

  index3D[0] = index2D[0];
  index3D[1] = index2D[1];
  index3D[2] = 0;

  size3D[0]  = size2D[0];
  size3D[1]  = size2D[1];
  size3D[2]  = 1;

  region3D.SetSize( size3D );
  region3D.SetIndex( index3D );

  vectorImage3D->SetRegions( region3D );
  vectorImage3D->Allocate();

  using Iterator2DType = itk::ImageRegionConstIterator< VectorImage2DType >;

  using Iterator3DType = itk::ImageRegionIterator< VectorImage3DType >;

  Iterator2DType  it2( vectorImage2D, region2D );
  Iterator3DType  it3( vectorImage3D, region3D );

  it2.GoToBegin();
  it3.GoToBegin();

  Vector2DType vector2D;
  Vector3DType vector3D;

  vector3D[2] = 0; // set Z component to zero.

  while( !it2.IsAtEnd() )
    {
    vector2D = it2.Get();
    vector3D[0] = vector2D[0];
    vector3D[1] = vector2D[1];
    it3.Set( vector3D );
    ++it2;
    ++it3;
    }


  writer3D->SetInput( vectorImage3D );

  writer3D->SetFileName( argv[5] );

  try
    {
    writer3D->Update();
    }
  catch( itk::ExceptionObject & excp )
    {
    std::cerr << excp << std::endl;
    return EXIT_FAILURE;
    }


  }

  return EXIT_SUCCESS;
}