MultiResImageRegistration2.cxx

/*=========================================================================

  Program:   Insight Segmentation & Registration Toolkit
  Module:    MultiResImageRegistration2.cxx
  Language:  C++
  Date:      $Date$
  Version:   $Revision$

  Copyright (c) Insight Software Consortium. All rights reserved.
  See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.

     This software is distributed WITHOUT ANY WARRANTY; without even 
     the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR 
     PURPOSE.  See the above copyright notices for more information.

=========================================================================*/
#if defined(_MSC_VER)
#pragma warning ( disable : 4786 )
#endif

//  Software Guide : BeginCommandLineArgs
//    INPUTS:  {BrainT1SliceBorder20.png}
//    INPUTS:  {BrainProtonDensitySliceShifted13x17y.png}
//    OUTPUTS: {MultiResImageRegistration2Output.png}
//    100
//    OUTPUTS: {MultiResImageRegistration2CheckerboardBefore.png}
//    OUTPUTS: {MultiResImageRegistration2CheckerboardAfter.png}
//  Software Guide : EndCommandLineArgs

// Software Guide : BeginLatex
//
//  This example illustrates the use of more complex components of the
//  registration framework. In particular, it introduces the use of the
//  \doxygen{AffineTransform} and the importance of fine-tuning the scale
//  parameters of the optimizer.
//
// \index{itk::ImageRegistrationMethod!AffineTransform}
// \index{itk::ImageRegistrationMethod!Scaling parameter space}
// \index{itk::AffineTransform!Image Registration}
//
// The AffineTransform is a linear transformation that maps lines into
// lines. It can be used to represent translations, rotations, anisotropic
// scaling, shearing or any combination of them. Details about the affine
// transform can be seen in Section~\ref{sec:AffineTransform}.
//
// In order to use the AffineTransform class, the following header
// must be included.
//
// \index{itk::AffineTransform!Header}
//
// Software Guide : EndLatex 


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


#include "itkMultiResolutionImageRegistrationMethod.h"
#include "itkMattesMutualInformationImageToImageMetric.h"
#include "itkLinearInterpolateImageFunction.h"
#include "itkRegularStepGradientDescentOptimizer.h"
#include "itkRecursiveMultiResolutionPyramidImageFilter.h"
#include "itkImage.h"


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

#include "itkResampleImageFilter.h"
#include "itkCastImageFilter.h"
#include "itkCheckerBoardImageFilter.h"

//  The following section of code implements an 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(): m_CumulativeIterationIndex(0) {};
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() << "  " <<
        m_CumulativeIterationIndex++ << std::endl;
    }
private:
  unsigned int m_CumulativeIterationIndex;
};


//  The following section of code implements a Command observer
//  that will control the modification of optimizer parameters
//  at every change of resolution level.
//
template <typename TRegistration>
class RegistrationInterfaceCommand : public itk::Command 
{
public:
  typedef  RegistrationInterfaceCommand   Self;
  typedef  itk::Command                   Superclass;
  typedef  itk::SmartPointer<Self>        Pointer;
  itkNewMacro( Self );
protected:
  RegistrationInterfaceCommand() {};
public:
  typedef   TRegistration                              RegistrationType;
  typedef   RegistrationType *                         RegistrationPointer;
  typedef   itk::RegularStepGradientDescentOptimizer   OptimizerType;
  typedef   OptimizerType *                            OptimizerPointer;
  void Execute(itk::Object * object, const itk::EventObject & event)
  {
    if( !(itk::IterationEvent().CheckEvent( &event )) )
      {
      return;
      }
    RegistrationPointer registration =
                        dynamic_cast<RegistrationPointer>( object );
    OptimizerPointer optimizer = dynamic_cast< OptimizerPointer >( 
                       registration->GetOptimizer() );

    if ( registration->GetCurrentLevel() == 0 )
      {
      optimizer->SetMaximumStepLength( 16.00 );  
      optimizer->SetMinimumStepLength(  2.50 );
      }
    else
      {
      optimizer->SetMaximumStepLength( 
                optimizer->GetCurrentStepLength() );
      optimizer->SetMinimumStepLength(
                optimizer->GetMinimumStepLength() / 10.0 );
      }
  }
  void Execute(const itk::Object * , const itk::EventObject & )
    { return; }
};



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 [backgroundGrayLevel]";
    std::cerr << " [checkerboardbefore] [CheckerBoardAfter]" << std::endl;
    return 1;
    }
  
  const    unsigned int    Dimension = 2;
  typedef  unsigned short  PixelType;
  
  typedef itk::Image< PixelType, Dimension >  FixedImageType;
  typedef itk::Image< PixelType, Dimension >  MovingImageType;

  typedef   float     InternalPixelType;
  typedef itk::Image< InternalPixelType, Dimension > InternalImageType;


  //  Software Guide : BeginLatex
  //  
  //  The configuration of the registration method in this example closely
  //  follows the procedure in the previous section. The main changes involve the
  //  construction and initialization of the transform. The instantiation of
  //  the transform type requires only the dimension of the space and the
  //  type used for representing space coordinates.
  //  
  //  \index{itk::AffineTransform!Instantiation}
  //
  //  Software Guide : EndLatex 
  
  // Software Guide : BeginCodeSnippet
  typedef itk::AffineTransform< double, Dimension > TransformType;
  // Software Guide : EndCodeSnippet


  typedef itk::RegularStepGradientDescentOptimizer       OptimizerType;
  typedef itk::LinearInterpolateImageFunction< 
                                    InternalImageType,
                                    double             > InterpolatorType;
  typedef itk::MattesMutualInformationImageToImageMetric< 
                                          InternalImageType, 
                                          InternalImageType >    MetricType;

  typedef OptimizerType::ScalesType       OptimizerScalesType;

  typedef itk::MultiResolutionImageRegistrationMethod< 
                                    InternalImageType, 
                                    InternalImageType    > RegistrationType;
  typedef itk::RecursiveMultiResolutionPyramidImageFilter<
                                    InternalImageType,
                                    InternalImageType  >    FixedImagePyramidType;
  typedef itk::RecursiveMultiResolutionPyramidImageFilter<
                                    InternalImageType,
                                    InternalImageType  >   MovingImagePyramidType;

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

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


  //  Software Guide : BeginLatex
  //
  //  The transform is constructed using the standard \code{New()} method and
  //  assigning it to a SmartPointer.
  //
  //  \index{itk::AffineTransform!New()}
  //  \index{itk::AffineTransform!Pointer}
  //  \index{itk::Multi\-Resolution\-Image\-Registration\-Method!SetTransform()}
  //
  //  Software Guide : EndLatex 

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


  FixedImagePyramidType::Pointer fixedImagePyramid = 
      FixedImagePyramidType::New();
  MovingImagePyramidType::Pointer movingImagePyramid =
      MovingImagePyramidType::New();


  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] );

  typedef itk::CastImageFilter< 
                        FixedImageType, InternalImageType > FixedCastFilterType;
  typedef itk::CastImageFilter< 
                        MovingImageType, InternalImageType > MovingCastFilterType;
  FixedCastFilterType::Pointer fixedCaster   = FixedCastFilterType::New();
  MovingCastFilterType::Pointer movingCaster = MovingCastFilterType::New();

  fixedCaster->SetInput(  fixedImageReader->GetOutput() );
  movingCaster->SetInput( movingImageReader->GetOutput() );

  registration->SetFixedImage(    fixedCaster->GetOutput()    );
  registration->SetMovingImage(   movingCaster->GetOutput()   );

  fixedCaster->Update();

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


  //  Software Guide : BeginLatex
  //  
  //  One of the easiest ways of preparing a consistent set of parameters for
  //  the transform is to use the transform itself.  We can simplify the task
  //  of initialization by taking advantage of the additional convenience
  //  methods that most transforms have. In this case, we simply force the
  //  transform to be initialized as an identity transform. The method
  //  \code{SetIdentity()} is used to that end. Once the transform is
  //  initialized, we can invoke its \code{GetParameters()} method to extract
  //  the array of parameters. Finally the array is passed to the registration
  //  method using its \code{SetInitialTransformParameters()} method.
  //
  //  Software Guide : EndLatex 

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


  //  Software Guide : BeginLatex
  //  
  //  The set of parameters in the AffineTransform have different
  //  dynamic ranges. Typically the parameters associated with the matrix
  //  have values around $[-1:1]$, although they are not restricted to this
  //  interval.  Parameters associated with translations, on the other hand,
  //  tend to have much higher values, typically in the order of $10.0$ to
  //  $100.0$. This difference in dynamic range negatively affects the
  //  performance of gradient descent optimizers. ITK provides a mechanism to
  //  compensate for such differences in values among the parameters when
  //  they are passed to the optimizer. The mechanism consists of providing an
  //  array of scale factors to the optimizer. These factors re-normalize the
  //  gradient components before they are used to compute the step of the
  //  optimizer at the current iteration. In our particular case, a common
  //  choice for the scale parameters is to set to $1.0$ all those associated
  //  with the matrix coefficients, that is, the first $N \times N$
  //  factors. Then, we set the remaining scale factors to a small value. The
  //  following code sets up the scale coefficients.
  //
  //  Software Guide : EndLatex 

  // Software Guide : BeginCodeSnippet
  OptimizerScalesType optimizerScales( transform->GetNumberOfParameters() );

  optimizerScales[0] = 1.0; // scale for M11
  optimizerScales[1] = 1.0; // scale for M12
  optimizerScales[2] = 1.0; // scale for M21
  optimizerScales[3] = 1.0; // scale for M22

  optimizerScales[4] = 1.0 / 1000000.0; // scale for translation on X
  optimizerScales[5] = 1.0 / 1000000.0; // scale for translation on Y
  // Software Guide : EndCodeSnippet


  //  Software Guide : BeginLatex
  //
  //  Here the affine transform is represented by the matrix $\bf{M}$ and the
  //  vector $\bf{T}$. The transformation of a point $\bf{P}$ into $\bf{P'}$
  //  is expressed as
  //
  //  \begin{equation}
  //  \left[ 
  //  \begin{array}{c}
  //  {P'}_x  \\  {P'}_y  \\  \end{array} 
  //  \right]
  //  =
  //  \left[ 
  //  \begin{array}{cc}
  //  M_{11} & M_{12} \\ M_{21} & M_{22} \\  \end{array}
  //  \right]
  //  \cdot 
  //  \left[ 
  //  \begin{array}{c}
  //  P_x  \\ P_y  \\  \end{array} 
  //  \right]
  //  +
  //  \left[ 
  //  \begin{array}{c}
  //  T_x  \\ T_y  \\  \end{array} 
  //  \right] 
  //  \end{equation}
  //
  //
  //  Software Guide : EndLatex 


  //  Software Guide : BeginLatex
  //  
  //  The array of scales is then passed to the optimizer using the
  //  \code{SetScales()} method.
  //
  //  \index{itk::Optimizer!SetScales()}
  //
  //  Software Guide : EndLatex 

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


  metric->SetNumberOfHistogramBins( 20 );
  metric->SetNumberOfSpatialSamples( 10000 );


  //  Software Guide : BeginLatex
  //  
  //  The step length has to be proportional to the expected values of the
  //  parameters in the search space. Since the expected values of the matrix
  //  coefficients are around $1.0$, the initial step of the optimization
  //  should be a small number compared to $1.0$. As a guideline, it is
  //  useful to think of the matrix coefficients as combinations of
  //  $cos(\theta)$ and $sin(\theta)$.  This leads to use values close to the
  //  expected rotation measured in radians. For example, a rotation of $1.0$
  //  degree is about $0.017$ radians. As in the previous example, the
  //  maximum and minimum step length of the optimizer are set by the
  //  \code{RegistrationInterfaceCommand} when it is called at the beginning
  //  of registration at each multi-resolution level.
  //
  //  Software Guide : EndLatex 

  optimizer->SetNumberOfIterations(    50   );


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


  // Create the Command interface observer and register it with the optimizer.
  //
  typedef RegistrationInterfaceCommand<RegistrationType> CommandType;
  CommandType::Pointer command = CommandType::New();
  registration->AddObserver( itk::IterationEvent(), command );
  registration->SetNumberOfLevels( 3 );

  try 
    { 
    registration->StartRegistration(); 
    } 
  catch( itk::ExceptionObject & err ) 
    { 
    std::cout << "ExceptionObject caught !" << std::endl; 
    std::cout << err << std::endl; 
    return -1;
    } 

  typedef RegistrationType::ParametersType ParametersType;
  ParametersType finalParameters = registration->GetLastTransformParameters();
  
  double TranslationAlongX = finalParameters[4];
  double TranslationAlongY = finalParameters[5];
  
  unsigned int numberOfIterations = optimizer->GetCurrentIteration();
  
  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;


  //  Software Guide : BeginLatex
  //  
  //  Let's execute this example using the same multi-modality images as
  //  before.  The registration converges after $5$ iterations in the first
  //  level, $7$ in the second level and $4$ in the third level. The final
  //  results when printed as an array of parameters are
  //
  //  \begin{verbatim}
  // [1.00164, 0.00147688, 0.00168372, 1.0027, 12.6296, 16.4768]
  //  \end{verbatim}
  //
  //  By reordering them as coefficient of matrix $\bf{M}$ and vector $\bf{T}$
  //  they can now be seen as
  //
  //  \begin{equation}
  //  M = 
  //  \left[ 
  //  \begin{array}{cc}
  //  1.00164 & 0.0014 \\ 0.00168 & 1.0027 \\  \end{array}
  //  \right]
  //  \mbox{ and }
  //  T =
  //  \left[ 
  //  \begin{array}{c}
  //  12.6296  \\  16.4768  \\  \end{array} 
  //  \right] 
  //  \end{equation}
  //
  //  In this form, it is easier to interpret the effect of the
  //  transform. The matrix $\bf{M}$ is responsible for scaling, rotation and
  //  shearing while $\bf{T}$ is responsible for translations.  It can be seen
  //  that the translation values in this case closely match the true
  //  misaligment introduced in the moving image.
  // 
  //  It is important to note that once the images are registered at a
  //  sub-pixel level, any further improvement of the registration relies
  //  heavily on the quality of the interpolator. It may then be reasonable to
  //  use a coarse and fast interpolator in the lower resolution levels and
  //  switch to a high-quality but slow interpolator in the final resolution
  //  level.
  //
  //  Software Guide : EndLatex 


  typedef itk::ResampleImageFilter< 
                            MovingImageType, 
                            FixedImageType >    ResampleFilterType;

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

  finalTransform->SetParameters( finalParameters );

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

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

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

  PixelType backgroundGrayLevel = 100;
  if( argc > 4 )
    {
    backgroundGrayLevel = atoi( argv[4] );
    }

  resample->SetSize(    fixedImage->GetLargestPossibleRegion().GetSize() );
  resample->SetOutputOrigin(  fixedImage->GetOrigin() );
  resample->SetOutputSpacing( fixedImage->GetSpacing() );
  resample->SetDefaultPixelValue( backgroundGrayLevel );


  typedef  unsigned char  OutputPixelType;
  typedef itk::Image< OutputPixelType, Dimension > OutputImageType;
  typedef itk::CastImageFilter< 
                        FixedImageType,
                        OutputImageType > CastFilterType;
  typedef itk::ImageFileWriter< OutputImageType >  WriterType;

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

  writer->SetFileName( argv[3] );

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


  //  Software Guide : BeginLatex
  // 
  // \begin{figure}
  // \center
  // \includegraphics[width=0.32\textwidth]{MultiResImageRegistration2Output.eps}
  // \includegraphics[width=0.32\textwidth]{MultiResImageRegistration2CheckerboardBefore.eps}
  // \includegraphics[width=0.32\textwidth]{MultiResImageRegistration2CheckerboardAfter.eps}
  // \itkcaption[Multi-Resolution Registration Input Images]{Mapped moving image
  // (left) and composition of fixed and moving images before (center) and
  // after (right) multi-resolution registration with the AffineTransform class.}
  // \label{fig:MultiResImageRegistration2Output}
  // \end{figure}
  //
  //  The result of resampling the moving image is shown in the left image
  //  of Figure \ref{fig:MultiResImageRegistration2Output}. The center and
  //  right images of the figure present a checkerboard composite of the fixed
  //  and moving images before and after registration.
  //
  //  Software Guide : EndLatex 

  //  Software Guide : BeginLatex
  //  
  // \begin{figure}
  // \center
  // \includegraphics[height=0.44\textwidth]{MultiResImageRegistration2TraceTranslations.eps}
  // \includegraphics[height=0.44\textwidth]{MultiResImageRegistration2TraceMetric.eps}
  // \itkcaption[Multi-Resolution Registration output plots]{Sequence of
  // translations and metric values at each iteration of the optimizer for
  // multi-resolution with the AffineTransform class.}
  // \label{fig:MultiResImageRegistration2Trace}
  // \end{figure}
  //
  //  Figure \ref{fig:MultiResImageRegistration2Trace} (left) presents the
  //  sequence of translations followed by the optimizer as it searched the
  //  parameter space. The right side of the same figure shows the sequence of
  //  metric values computed as the optimizer explored the parameter space.
  //
  //  Software Guide : EndLatex 

  //
  // Generate checkerboards before and after registration
  //
  typedef itk::CheckerBoardImageFilter< FixedImageType > CheckerBoardFilterType;

  CheckerBoardFilterType::Pointer checker = CheckerBoardFilterType::New();

  checker->SetInput1( fixedImage );
  checker->SetInput2( resample->GetOutput() );

  caster->SetInput( checker->GetOutput() );
  writer->SetInput( caster->GetOutput()   );
  
  // Write out checkerboard outputs
  // Before registration
  TransformType::Pointer identityTransform = TransformType::New();
  identityTransform->SetIdentity();
  resample->SetTransform( identityTransform );

  if( argc > 5 )
    {
    writer->SetFileName( argv[5] );
    writer->Update();
    }

 
  // After registration
  resample->SetTransform( finalTransform );
  if( argc > 6 )
    {
    writer->SetFileName( argv[6] );
    writer->Update();
    }


  return 0;
}