text: /*=========================================================================
   *
   *  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.
   *  You may obtain a copy of the License at
   *
   *         http://www.apache.org/licenses/LICENSE-2.0.txt
   *
   *  Unless required by applicable law or agreed to in writing, software
   *  distributed under the License is distributed on an "AS IS" BASIS,
   *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   *  See the License for the specific language governing permissions and
   *  limitations under the License.
   *
   *=========================================================================*/
  
  //  Software Guide : BeginCommandLineArgs
  //    INPUTS:  {BrainProtonDensitySliceBorder20.png}
  //    INPUTS:  {BrainProtonDensitySliceRotated10.png}
  //    OUTPUTS: {ImageRegistration5Output.png}
  //    OUTPUTS: {ImageRegistration5DifferenceAfter.png}
  //    OUTPUTS: {ImageRegistration5DifferenceBefore.png}
  //    ARGUMENTS:    0.1
  //  Software Guide : EndCommandLineArgs
  
  //  Software Guide : BeginCommandLineArgs
  //    INPUTS:  {BrainProtonDensitySliceBorder20.png}
  //    INPUTS:  {BrainProtonDensitySliceR10X13Y17.png}
  //    OUTPUTS: {ImageRegistration5Output2.png}
  //    OUTPUTS: {ImageRegistration5DifferenceAfter2.png}
  //    OUTPUTS: {ImageRegistration5DifferenceBefore2.png}
  //    ARGUMENTS:    1.0
  //  Software Guide : EndCommandLineArgs
  
  
  // Software Guide : BeginLatex
  //
  // This example illustrates the use of the \doxygen{CenteredRigid2DTransform}
  // for performing rigid registration in $2D$. The example code is for the
  // most part identical to that presented in Section
  // \ref{sec:IntroductionImageRegistration}.  The main difference is the use
  // of the CenteredRigid2DTransform here instead of the
  // \doxygen{TranslationTransform}.
  //
  // \index{itk::CenteredRigid2DTransform}
  //
  // Software Guide : EndLatex
  
  #include "itkImageRegistrationMethod.h"
  #include "itkMeanSquaresImageToImageMetric.h"
  #include "itkRegularStepGradientDescentOptimizer.h"
  
  
  //  Software Guide : BeginLatex
  //
  //  In addition to the headers included in previous examples, the
  //  following header must also be included.
  //
  //  \index{itk::CenteredRigid2DTransform!header}
  //
  //  Software Guide : EndLatex
  
  // Software Guide : BeginCodeSnippet
  #include "itkCenteredRigid2DTransform.h"
  // Software Guide : EndCodeSnippet
  
  
  #include "itkImageFileReader.h"
  #include "itkImageFileWriter.h"
  
  #include "itkResampleImageFilter.h"
  #include "itkSubtractImageFilter.h"
  #include "itkRescaleIntensityImageFilter.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:
    typedef  CommandIterationUpdate   Self;
    typedef  itk::Command             Superclass;
    typedef itk::SmartPointer<Self>   Pointer;
    itkNewMacro( Self );
  
  protected:
    CommandIterationUpdate() {};
  
  public:
    typedef itk::RegularStepGradientDescentOptimizer  OptimizerType;
    typedef   const OptimizerType *                   OptimizerPointer;
  
    void Execute(itk::Object *caller, const itk::EventObject & event)
      {
      Execute( (const itk::Object *)caller, event);
      }
  
    void Execute(const itk::Object * object, const itk::EventObject & event)
      {
      OptimizerPointer optimizer =
        dynamic_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  [differenceAfterRegistration] ";
      std::cerr << " [differenceBeforeRegistration] ";
      std::cerr << " [initialStepLength] "<< std::endl;
      return EXIT_FAILURE;
      }
  
    const    unsigned int    Dimension = 2;
    typedef  unsigned char   PixelType;
  
    typedef itk::Image< PixelType, Dimension >  FixedImageType;
    typedef itk::Image< PixelType, Dimension >  MovingImageType;
  
  
    //  Software Guide : BeginLatex
    //
    //  The transform type is instantiated using the code below. The only
    //  template parameter for this class is the representation type of the
    //  space coordinates.
    //
    //  \index{itk::CenteredRigid2DTransform!Instantiation}
    //
    //  Software Guide : EndLatex
  
    // Software Guide : BeginCodeSnippet
    typedef itk::CenteredRigid2DTransform< double > TransformType;
    // Software Guide : EndCodeSnippet
  
  
    typedef itk::RegularStepGradientDescentOptimizer       OptimizerType;
    typedef itk::MeanSquaresImageToImageMetric<
                                      FixedImageType,
                                      MovingImageType >    MetricType;
    typedef itk:: LinearInterpolateImageFunction<
                                      MovingImageType,
                                      double          >    InterpolatorType;
    typedef itk::ImageRegistrationMethod<
                                      FixedImageType,
                                      MovingImageType >    RegistrationType;
  
    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::CenteredRigid2DTransform!New()}
    //  \index{itk::CenteredRigid2DTransform!Pointer}
    //  \index{itk::RegistrationMethod!SetTransform()}
    //
    //  Software Guide : EndLatex
  
    // Software Guide : BeginCodeSnippet
    TransformType::Pointer  transform = TransformType::New();
    registration->SetTransform( transform );
    // Software Guide : EndCodeSnippet
  
  
    typedef itk::ImageFileReader< FixedImageType  > FixedImageReaderType;
    typedef itk::ImageFileReader< MovingImageType > MovingImageReaderType;
  
    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, the input images are taken from readers. The code
    //  below updates the readers in order to ensure that the image parameters
    //  (size, origin and spacing) are valid when used to initialize the
    //  transform.  We intend to use the center of the fixed image as the
    //  rotation center and then use the vector between the fixed image center
    //  and the moving image center as the initial translation to be applied
    //  after the rotation.
    //
    //  Software Guide : EndLatex
  
    // Software Guide : BeginCodeSnippet
    fixedImageReader->Update();
    movingImageReader->Update();
    // Software Guide : EndCodeSnippet
  
    typedef FixedImageType::SpacingType    SpacingType;
    typedef FixedImageType::PointType      OriginType;
    typedef FixedImageType::RegionType     RegionType;
    typedef FixedImageType::SizeType       SizeType;
  
    //  Software Guide : BeginLatex
    //
    //  The center of rotation is computed using the origin, size and spacing of
    //  the fixed image.
    //
    //  Software Guide : EndLatex
  
    // Software Guide : BeginCodeSnippet
    FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
  
    const SpacingType fixedSpacing = fixedImage->GetSpacing();
    const OriginType  fixedOrigin  = fixedImage->GetOrigin();
    const RegionType  fixedRegion  = fixedImage->GetLargestPossibleRegion();
    const SizeType    fixedSize    = fixedRegion.GetSize();
  
    TransformType::InputPointType centerFixed;
  
    centerFixed[0] = fixedOrigin[0] + fixedSpacing[0] * fixedSize[0] / 2.0;
    centerFixed[1] = fixedOrigin[1] + fixedSpacing[1] * fixedSize[1] / 2.0;
    // Software Guide : EndCodeSnippet
  
  
    //  Software Guide : BeginLatex
    //
    //  The center of the moving image is computed in a similar way.
    //
    //  Software Guide : EndLatex
  
    // Software Guide : BeginCodeSnippet
    MovingImageType::Pointer movingImage = movingImageReader->GetOutput();
  
    const SpacingType movingSpacing = movingImage->GetSpacing();
    const OriginType  movingOrigin  = movingImage->GetOrigin();
    const RegionType  movingRegion  = movingImage->GetLargestPossibleRegion();
    const SizeType    movingSize    = movingRegion.GetSize();
  
    TransformType::InputPointType centerMoving;
  
    centerMoving[0] = movingOrigin[0] + movingSpacing[0] * movingSize[0] / 2.0;
    centerMoving[1] = movingOrigin[1] + movingSpacing[1] * movingSize[1] / 2.0;
    // Software Guide : EndCodeSnippet
  
  
    //  Software Guide : BeginLatex
    //
    //   The most straightforward method of initializing the transform parameters
    //   is to configure the transform and then get its parameters with the
    //   method \code{GetParameters()}. Here we initialize the transform by
    //   passing the center of the fixed image as the rotation center with the
    //   \code{SetCenter()} method. Then the translation is set as the vector
    //   relating the center of the moving image to the center of the fixed
    //   image.  This last vector is passed with the method
    //   \code{SetTranslation()}.
    //
    //  Software Guide : EndLatex
  
    // Software Guide : BeginCodeSnippet
    transform->SetCenter( centerFixed );
    transform->SetTranslation( centerMoving - centerFixed );
    // Software Guide : EndCodeSnippet
  
  
    //  Software Guide : BeginLatex
    //
    //  Let's finally initialize the rotation with a zero angle.
    //
    //  Software Guide : EndLatex
  
    // Software Guide : BeginCodeSnippet
    transform->SetAngle( 0.0 );
    // Software Guide : EndCodeSnippet
  
  
    //  Software Guide : BeginLatex
    //
    //  Now we pass the current transform's parameters 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 rotation and translation is
    //  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 angle that is measured in radians, while the other
    //  parameters correspond to translations that are measured in millimeters.
    //  For this reason we use small factors in the scales associated with
    //  translations and the coordinates of the rotation center .
    //
    //  Software Guide : EndLatex
  
    // Software Guide : BeginCodeSnippet
    typedef OptimizerType::ScalesType       OptimizerScalesType;
    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 );
    // Software Guide : EndCodeSnippet
  
  
    //  Software Guide : BeginLatex
    //
    //  Next we set the normal parameters of the optimization method. In this
    //  case we are using an \doxygen{RegularStepGradientDescentOptimizer}.
    //  Below, we define the optimization parameters like the relaxation factor,
    //  initial step length, minimal step length and number of iterations. These
    //  last two act as stopping criteria for the optimization.
    //
    //  \index{Regular\-Step\-Gradient\-Descent\-Optimizer!SetRelaxationFactor()}
    //  \index{Regular\-Step\-Gradient\-Descent\-Optimizer!SetMaximumStepLength()}
    //  \index{Regular\-Step\-Gradient\-Descent\-Optimizer!SetMinimumStepLength()}
    //  \index{Regular\-Step\-Gradient\-Descent\-Optimizer!SetNumberOfIterations()}
    //
    //  Software Guide : EndLatex
  
    // Software Guide : BeginCodeSnippet
    double initialStepLength = 0.1;
    // Software Guide : EndCodeSnippet
  
    if( argc > 6 )
      {
      initialStepLength = atof( argv[6] );
      }
  
    // Software Guide : BeginCodeSnippet
    optimizer->SetRelaxationFactor( 0.6 );
    optimizer->SetMaximumStepLength( initialStepLength );
    optimizer->SetMinimumStepLength( 0.001 );
    optimizer->SetNumberOfIterations( 200 );
    // 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 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 * 180.0 / vnl_math::pi;
  
    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;
  
  
    //  Software Guide : BeginLatex
    //
    //  Let's execute this example over two of the images provided in
    //  \code{Examples/Data}:
    //
    //  \begin{itemize}
    //  \item \code{BrainProtonDensitySliceBorder20.png}
    //  \item \code{BrainProtonDensitySliceRotated10.png}
    //  \end{itemize}
    //
    //  The second image is the result of intentionally rotating the first image
    //  by $10$ degrees around the geometrical center of the image. Both images
    //  have unit-spacing and are shown in Figure
    //  \ref{fig:FixedMovingImageRegistration5}. The registration takes $20$
    //  iterations and produces the results:
    //
    //  \begin{center}
    //  \begin{verbatim}
    //  [0.177458, 110.489, 128.488, 0.0106296, 0.00194103]
    //  \end{verbatim}
    //  \end{center}
    //
    //  These results are interpreted as
    //
    //  \begin{itemize}
    //  \item Angle         =                  $0.177458$     radians
    //  \item Center        = $( 110.489    , 128.488      )$ millimeters
    //  \item Translation   = $(   0.0106296,   0.00194103 )$ millimeters
    //  \end{itemize}
    //
    //  As expected, these values match the misalignment intentionally introduced
    //  into the moving image quite well, since $10$ degrees is about $0.174532$
    //  radians.
    //
    // \begin{figure}
    // \center
    // \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceBorder20.eps}
    // \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceRotated10.eps}
    // \itkcaption[Rigid2D Registration input images]{Fixed and moving images
    // are provided as input to the registration method using the CenteredRigid2D
    // transform.}
    // \label{fig:FixedMovingImageRegistration5}
    // \end{figure}
    //
    //
    // \begin{figure}
    // \center
    // \includegraphics[width=0.32\textwidth]{ImageRegistration5Output.eps}
    // \includegraphics[width=0.32\textwidth]{ImageRegistration5DifferenceBefore.eps}
    // \includegraphics[width=0.32\textwidth]{ImageRegistration5DifferenceAfter.eps}
    // \itkcaption[Rigid2D Registration output images]{Resampled moving image
    // (left). Differences between the fixed and moving images, before (center)
    // and after (right) registration using the CenteredRigid2D transform.}
    // \label{fig:ImageRegistration5Outputs}
    // \end{figure}
    //
    // Figure \ref{fig:ImageRegistration5Outputs} shows from left to right the
    // resampled moving image after registration, the difference between fixed
    // and moving images before registration, and the difference between fixed
    // and resampled moving image after registration. It can be seen from the
    // last difference image that the rotational component has been solved but
    // that a small centering misalignment persists.
    //
    // \begin{figure}
    // \center
    // \includegraphics[height=0.32\textwidth]{ImageRegistration5TraceMetric1.eps}
    // \includegraphics[height=0.32\textwidth]{ImageRegistration5TraceAngle1.eps}
    // \includegraphics[height=0.32\textwidth]{ImageRegistration5TraceTranslations1.eps}
    // \itkcaption[Rigid2D Registration output plots]{Metric values, rotation
    // angle and translations during registration with the CenteredRigid2D
    // transform.}
    // \label{fig:ImageRegistration5Plots}
    // \end{figure}
    //
    //  Figure \ref{fig:ImageRegistration5Plots} shows plots of the main output
    //  parameters produced from the registration process. This includes, the
    //  metric values at every iteration, the angle values at every iteration,
    //  and the translation components of the transform as the registration
    //  progress.
    //
    //  Software Guide : EndLatex
  
  
    typedef itk::ResampleImageFilter<
                              MovingImageType,
                              FixedImageType >    ResampleFilterType;
  
    TransformType::Pointer finalTransform = TransformType::New();
  
    finalTransform->SetParameters( finalParameters );
    finalTransform->SetFixedParameters( transform->GetFixedParameters() );
  
    ResampleFilterType::Pointer resample = ResampleFilterType::New();
  
    resample->SetTransform( finalTransform );
    resample->SetInput( movingImageReader->GetOutput() );
  
    resample->SetSize(    fixedImage->GetLargestPossibleRegion().GetSize() );
    resample->SetOutputOrigin(  fixedImage->GetOrigin() );
    resample->SetOutputSpacing( fixedImage->GetSpacing() );
    resample->SetOutputDirection( fixedImage->GetDirection() );
    resample->SetDefaultPixelValue( 100 );
  
    typedef itk::ImageFileWriter< FixedImageType >  WriterFixedType;
  
    WriterFixedType::Pointer      writer =  WriterFixedType::New();
  
    writer->SetFileName( argv[3] );
  
    writer->SetInput( resample->GetOutput()   );
  
    try
      {
      writer->Update();
      }
    catch( itk::ExceptionObject & excp )
      {
      std::cerr << "ExceptionObject while writing the resampled image !" << std::endl;
      std::cerr << excp << std::endl;
      return EXIT_FAILURE;
      }
  
    typedef itk::Image< float, Dimension > DifferenceImageType;
  
    typedef itk::SubtractImageFilter<
                             FixedImageType,
                             FixedImageType,
                             DifferenceImageType > DifferenceFilterType;
  
    DifferenceFilterType::Pointer difference = DifferenceFilterType::New();
  
    typedef  unsigned char  OutputPixelType;
  
    typedef itk::Image< OutputPixelType, Dimension > OutputImageType;
  
    typedef itk::RescaleIntensityImageFilter<
                                    DifferenceImageType,
                                    OutputImageType >   RescalerType;
  
    RescalerType::Pointer intensityRescaler = RescalerType::New();
  
    intensityRescaler->SetOutputMinimum(   0 );
    intensityRescaler->SetOutputMaximum( 255 );
  
    difference->SetInput1( fixedImageReader->GetOutput() );
    difference->SetInput2( resample->GetOutput() );
  
    resample->SetDefaultPixelValue( 1 );
  
    intensityRescaler->SetInput( difference->GetOutput() );
  
    typedef itk::ImageFileWriter< OutputImageType >  WriterType;
  
    WriterType::Pointer      writer2 =  WriterType::New();
  
    writer2->SetInput( intensityRescaler->GetOutput() );
  
  
    try
      {
      // Compute the difference image between the
      // fixed and moving image after registration.
      if( argc > 4 )
        {
        writer2->SetFileName( argv[4] );
        writer2->Update();
        }
  
      // Compute the difference image between the
      // fixed and resampled moving image after registration.
      TransformType::Pointer identityTransform = TransformType::New();
      identityTransform->SetIdentity();
      resample->SetTransform( identityTransform );
      if( argc > 5 )
        {
        writer2->SetFileName( argv[5] );
        writer2->Update();
        }
      }
    catch( itk::ExceptionObject & excp )
      {
      std::cerr << "Error while writing difference images" << std::endl;
      std::cerr << excp << std::endl;
      return EXIT_FAILURE;
      }
  
    //  Software Guide : BeginLatex
    //
    //  Let's now consider the case in which rotations and translations are
    //  present in the initial registration, as in the following pair
    //  of images:
    //
    //  \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 then translating it $13mm$ in $X$ and $17mm$ in $Y$.
    //  Both images have unit-spacing and are shown in Figure
    //  \ref{fig:FixedMovingImageRegistration5b}. In order to accelerate
    //  convergence it is convenient to use a larger step length as shown here.
    //
    //  \code{optimizer->SetMaximumStepLength( 1.0 );}
    //
    //  The registration now takes $46$ iterations and produces the following
    //  results:
    //
    //  \begin{center}
    //  \begin{verbatim}
    //  [0.174454, 110.361, 128.647, 12.977, 15.9761]
    //  \end{verbatim}
    //  \end{center}
    //
    //  These parameters are interpreted as
    //
    //  \begin{itemize}
    //  \item Angle         =                     $0.174454$   radians
    //  \item Center        = $( 110.361     , 128.647      )$ millimeters
    //  \item Translation   = $(  12.977     ,  15.9761     )$ millimeters
    //  \end{itemize}
    //
    //  These values approximately match the initial misalignment intentionally
    //  introduced into the moving image, since $10$ degrees is about $0.174532$
    //  radians. The horizontal translation is well resolved while the vertical
    //  translation ends up being off by about one millimeter.
    //
    // \begin{figure}
    // \center
    // \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceBorder20.eps}
    // \includegraphics[width=0.44\textwidth]{BrainProtonDensitySliceR10X13Y17.eps}
    // \itkcaption[Rigid2D Registration input images]{Fixed and moving images
    // provided as input to the registration method using the CenteredRigid2D
    // transform.}
    // \label{fig:FixedMovingImageRegistration5b}
    // \end{figure}
    //
    //
    // \begin{figure}
    // \center
    // \includegraphics[width=0.32\textwidth]{ImageRegistration5Output2.eps}
    // \includegraphics[width=0.32\textwidth]{ImageRegistration5DifferenceBefore2.eps}
    // \includegraphics[width=0.32\textwidth]{ImageRegistration5DifferenceAfter2.eps}
    // \itkcaption[Rigid2D Registration output images]{Resampled moving image
    // (left). Differences between the fixed and moving images, before (center)
    // and after (right) registration with the CenteredRigid2D transform.}
    // \label{fig:ImageRegistration5Outputs2}
    // \end{figure}
    //
    // Figure \ref{fig:ImageRegistration5Outputs2} shows the output of the
    // registration. The rightmost image of this figure shows the difference
    // between the fixed image and the resampled moving image after registration.
    //
    // \begin{figure}
    // \center
    // \includegraphics[height=0.32\textwidth]{ImageRegistration5TraceMetric2.eps}
    // \includegraphics[height=0.32\textwidth]{ImageRegistration5TraceAngle2.eps}
    // \includegraphics[height=0.32\textwidth]{ImageRegistration5TraceTranslations2.eps}
    // \itkcaption[Rigid2D Registration output plots]{Metric values, rotation
    // angle and translations during the registration using the CenteredRigid2D
    // transform on an image with rotation and translation mis-registration.}
    // \label{fig:ImageRegistration5Plots2}
    // \end{figure}
    //
    //  Figure \ref{fig:ImageRegistration5Plots2} shows plots of the main output
    //  registration parameters when the rotation and translations are combined.
    //  These results include, the metric values at every iteration, the angle
    //  values at every iteration, and the translation components of the
    //  registration as the registration converges. It can be seen from the
    //  smoothness of these plots that a larger step length could have been
    //  supported easily by the optimizer. You may want to modify this value in
    //  order to get a better idea of how to tune the parameters.
    //
    //  Software Guide : EndLatex
  
  
    return EXIT_SUCCESS;
  }
  