Examples/RegistrationITKv3/ImageRegistration7.cxx

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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 : BeginCommandLineArgs
//    INPUTS:  {BrainProtonDensitySliceBorder20.png}
//    INPUTS:  {BrainProtonDensitySliceR10X13Y17S12.png}
//    OUTPUTS: {ImageRegistration7Output.png}
//    OUTPUTS: {ImageRegistration7DifferenceBefore.png}
//    OUTPUTS: {ImageRegistration7DifferenceAfter.png}
//    ARGUMENTS:    1.0   1.0   0.0
//  Software Guide : EndCommandLineArgs

// Software Guide : BeginLatex
//
// This example illustrates the use of the \doxygen{CenteredSimilarity2DTransform}
// class for performing registration in $2D$. The of example code is for
// the most part identical to the code presented in Section
// \ref{sec:InitializingRegistrationWithMoments}.  The main difference is the
// use of \doxygen{CenteredSimilarity2DTransform} here rather than the
// \doxygen{CenteredRigid2DTransform} class.
//
// A similarity transform can be seen as a composition of rotations,
// translations and uniform scaling. It preserves angles and map lines into
// lines. This transform is implemented in the toolkit as deriving from a rigid
// $2D$ transform and with a scale parameter added.
//
// When using this transform, attention should be paid to the fact that scaling
// and translations are not independent.  In the same way that rotations can
// locally be seen as translations, scaling also result in local displacements.
// Scaling is performed in general with respect to the origin of coordinates.
// However, we already saw how ambiguous that could be in the case of
// rotations. For this reason, this transform also allows users to setup a
// specific center. This center is use both for rotation and scaling.
//
//
// \index{itk::CenteredSimilarity2DTransform}
//
// Software Guide : EndLatex

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


#include "itkCenteredTransformInitializer.h"


//  Software Guide : BeginLatex
//
//  In addition to the headers included in previous examples, here the
//  following header must be included.
//
//  \index{itk::CenteredSimilarity2DTransform!header}
//
//  Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
#include "itkCenteredSimilarity2DTransform.h"
// Software Guide : EndCodeSnippet


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

#include "itkResampleImageFilter.h"
#include "itkCastImageFilter.h"
#include "itkSubtractImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkIdentityTransform.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() {};

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
    {
    OptimizerPointer 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 < 4 )
    {
    std::cerr << "Missing Parameters " << std::endl;
    std::cerr << "Usage: " << argv[0];
    std::cerr << " fixedImageFile  movingImageFile ";
    std::cerr << " outputImagefile  [differenceBeforeRegistration] ";
    std::cerr << " [differenceAfterRegistration] ";
    std::cerr << " [steplength] ";
    std::cerr << " [initialScaling] [initialAngle] ";
    std::cerr << 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 >;


  //  Software Guide : BeginLatex
  //
  //  The Transform class is instantiated using the code below. The only
  //  template parameter of this class is the representation type of the
  //  space coordinates.
  //
  //  \index{itk::CenteredSimilarity2DTransform!Instantiation}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using TransformType = itk::CenteredSimilarity2DTransform< double >;
  // Software Guide : EndCodeSnippet


  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  );


  //  Software Guide : BeginLatex
  //
  //  The transform object is constructed below and passed to the registration
  //  method.
  //
  //  \index{itk::CenteredSimilarity2DTransform!New()}
  //  \index{itk::CenteredSimilarity2DTransform!Pointer}
  //  \index{itk::RegistrationMethod!SetTransform()}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  TransformType::Pointer  transform = TransformType::New();
  registration->SetTransform( transform );
  // Software Guide : EndCodeSnippet


  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() );


  //  Software Guide : BeginLatex
  //
  //  In this example, we again use the helper class
  //  \doxygen{CenteredTransformInitializer} to compute a reasonable
  //  value for the initial center of rotation and the translation.
  //
  //  Software Guide : EndLatex


// Software Guide : BeginCodeSnippet
  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();
// Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  The remaining parameters of the transform are initialized below.
  //
  //  \index{itk::CenteredSimilarity2DTransform!SetScale()}
  //  \index{itk::CenteredSimilarity2DTransform!SetAngle()}
  //
  //  Software Guide : EndLatex

  double initialScale = 1.0;

  if( argc > 7 )
    {
    initialScale =  std::stod( argv[7] );
    }

  double initialAngle = 0.0;

  if( argc > 8 )
    {
    initialAngle =  std::stod( argv[8] );
    }

  // Software Guide : BeginCodeSnippet
  transform->SetScale( initialScale );
  transform->SetAngle( initialAngle );
  // Software Guide : EndCodeSnippet

  //  Software Guide : BeginLatex
  //
  //  We now pass the parameter of the current transform as the initial
  //  parameters to be used when the registration process starts.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  registration->SetInitialTransformParameters( transform->GetParameters() );
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  Keeping in mind that the scale of units in scaling, rotation and
  //  translation are quite different, we take advantage of the scaling
  //  functionality provided by the optimizers. We know that the first element
  //  of the parameters array corresponds to the scale factor, the second
  //  corresponds to the angle, third and forth are the center of rotation and
  //  fifth and sixth are the remaining translation. We use henceforth small
  //  factors in the scales associated with translations and the rotation
  //  center.
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  using OptimizerScalesType = OptimizerType::ScalesType;
  OptimizerScalesType optimizerScales( transform->GetNumberOfParameters() );
  const double translationScale = 1.0 / 100.0;

  optimizerScales[0] = 10.0;
  optimizerScales[1] =  1.0;
  optimizerScales[2] =  translationScale;
  optimizerScales[3] =  translationScale;
  optimizerScales[4] =  translationScale;
  optimizerScales[5] =  translationScale;

  optimizer->SetScales( optimizerScales );
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  We set also the normal parameters of the optimization method. In this
  //  case we are using A
  //  \doxygen{RegularStepGradientDescentOptimizer}. Below, we define the
  //  optimization parameters like initial step length, minimal step length
  //  and number of iterations. These last two act as stopping criteria for
  //  the optimization.
  //
  //  Software Guide : EndLatex

  double steplength = 1.0;

  if( argc > 6 )
    {
    steplength = std::stod( argv[6] );
    }

  // Software Guide : BeginCodeSnippet
  optimizer->SetMaximumStepLength( steplength );
  optimizer->SetMinimumStepLength( 0.0001 );
  optimizer->SetNumberOfIterations( 500 );
  // Software Guide : EndCodeSnippet


  // Create the Command observer and register it with the optimizer.
  //
  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::cerr << "ExceptionObject caught !" << std::endl;
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
    }

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


  const double finalScale           = finalParameters[0];
  const double finalAngle           = finalParameters[1];
  const double finalRotationCenterX = finalParameters[2];
  const double finalRotationCenterY = finalParameters[3];
  const double finalTranslationX    = finalParameters[4];
  const double finalTranslationY    = finalParameters[5];

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

  const double bestValue = optimizer->GetValue();


  // Print out results
  //
  const double finalAngleInDegrees = finalAngle * 180.0 / itk::Math::pi;

  std::cout << std::endl;
  std::cout << "Result = " << std::endl;
  std::cout << " Scale         = " << finalScale  << 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;


  //  Software Guide : BeginLatex
  //
  //  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{BrainProtonDensitySliceR10X13Y17S12.png}
  //  \end{itemize}
  //
  //  The second image is the result of intentionally rotating the first image
  //  by $10$ degrees, scaling by $1/1.2$ and then translating by $(-13,-17)$.
  //  Both images have unit-spacing and are shown in Figure
  //  \ref{fig:FixedMovingImageRegistration7}. The registration takes $16$
  //  iterations and produces:
  //
  //  \begin{center}
  //  \begin{verbatim}
  //  [0.833222, -0.174521, 111.437, 131.741, -12.8272, -12.7862]
  //  \end{verbatim}
  //  \end{center}
  //
  //  That are interpreted as
  //
  //  \begin{itemize}
  //  \item Scale factor  =                     $0.833222$
  //  \item Angle         =                     $0.174521$   radians
  //  \item Center        = $( 111.437     , 131.741     )$ millimeters
  //  \item Translation   = $( -12.8272    , -12.7862    )$ millimeters
  //  \end{itemize}
  //
  //
  //  These values approximate the misalignment intentionally introduced into
  //  the moving image. Since $10$ degrees is about $0.174532$ radians.
  //
  // \begin{figure}
  // \center
  // \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceBorder20}
  // \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceR10X13Y17S12}
  // \itkcaption[Fixed and Moving image registered with
  // CenteredSimilarity2DTransform]{Fixed and Moving image provided as input to the
  // registration method using the Similarity2D transform.}
  // \label{fig:FixedMovingImageRegistration7}
  // \end{figure}
  //
  //
  // \begin{figure}
  // \center
  // \includegraphics[width=0.32\textwidth]{ImageRegistration7Output}
  // \includegraphics[width=0.32\textwidth]{ImageRegistration7DifferenceBefore}
  // \includegraphics[width=0.32\textwidth]{ImageRegistration7DifferenceAfter}
  // \itkcaption[Output of the CenteredSimilarity2DTransform registration]{Resampled
  // moving image (left). Differences between fixed and
  // moving images, before (center) and after (right) registration with the
  // Similarity2D transform.}
  // \label{fig:ImageRegistration7Outputs}
  // \end{figure}
  //
  // Figure \ref{fig:ImageRegistration7Outputs} shows the output of the
  // registration. The right image shows the squared magnitude of pixel
  // differences between the fixed image and the resampled moving image.
  //
  // \begin{figure}
  // \center
  // \includegraphics[height=0.32\textwidth]{ImageRegistration7TraceMetric}
  // \includegraphics[height=0.32\textwidth]{ImageRegistration7TraceAngle}
  // \includegraphics[height=0.32\textwidth]{ImageRegistration7TraceScale}
  // \includegraphics[height=0.32\textwidth]{ImageRegistration7TraceTranslations}
  // \itkcaption[CenteredSimilarity2DTransform registration plots]{Plots of the Metric,
  // rotation angle and translations during
  // the registration using
  // Similarity2D transform.}
  // \label{fig:ImageRegistration7Plots}
  // \end{figure}
  //
  //  Figure \ref{fig:ImageRegistration7Plots} shows the plots of the main
  //  output parameters of the registration process. The metric values at every
  //  iteration are shown on the top. The angle values are shown in the plot at
  //  left while the translation components of the registration are presented
  //  in the plot at right.
  //
  //  Software Guide : EndLatex


  using ResampleFilterType = itk::ResampleImageFilter< MovingImageType,
                                    FixedImageType >;

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

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

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

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

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

  resampler->SetSize(    fixedImage->GetLargestPossibleRegion().GetSize() );
  resampler->SetOutputOrigin(  fixedImage->GetOrigin() );
  resampler->SetOutputSpacing( fixedImage->GetSpacing() );
  resampler->SetOutputDirection( fixedImage->GetDirection() );
  resampler->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[3] );


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


  using DifferenceFilterType = itk::SubtractImageFilter<
                                  FixedImageType,
                                  FixedImageType,
                                  FixedImageType >;

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


  using RescalerType = itk::RescaleIntensityImageFilter<
                                  FixedImageType,
                                  OutputImageType >;

  RescalerType::Pointer intensityRescaler = RescalerType::New();

  intensityRescaler->SetInput( difference->GetOutput() );
  intensityRescaler->SetOutputMinimum(   0 );
  intensityRescaler->SetOutputMaximum( 255 );

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

  resampler->SetDefaultPixelValue( 1 );

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


  // Compute the difference image between the
  // fixed and resampled moving image.
  if( argc > 5 )
    {
    writer2->SetFileName( argv[5] );
    writer2->Update();
    }


  using IdentityTransformType = itk::IdentityTransform< double, Dimension >;
  IdentityTransformType::Pointer identity = IdentityTransformType::New();

  // Compute the difference image between the
  // fixed and moving image before registration.
  if( argc > 4 )
    {
    resampler->SetTransform( identity );
    writer2->SetFileName( argv[4] );
    writer2->Update();
    }


  return EXIT_SUCCESS;
}