Examples/RegistrationITKv4/MultiResImageRegistration2.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.
 *  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:  {BrainT1SliceBorder20.png}
//    INPUTS:  {BrainProtonDensitySliceShifted13x17y.png}
//    OUTPUTS: {MultiResImageRegistration2Output.png}
//    ARGUMENTS:    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 "itkCenteredTransformInitializer.h"
#include "itkMultiResolutionImageRegistrationMethod.h"
#include "itkMattesMutualInformationImageToImageMetric.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:
  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
    {
    auto optimizer = static_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{0};
};

//  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:
  using Self = RegistrationInterfaceCommand;
  using Superclass = itk::Command;
  using Pointer = itk::SmartPointer<Self>;
  itkNewMacro( Self );

protected:
  RegistrationInterfaceCommand() = default;

public:
  using RegistrationType = TRegistration;
  using RegistrationPointer = RegistrationType *;
  using OptimizerType = itk::RegularStepGradientDescentOptimizer;
  using OptimizerPointer = OptimizerType *;
  void Execute(itk::Object * object, const itk::EventObject & event) override
  {
    if( !(itk::IterationEvent().CheckEvent( &event )) )
      {
      return;
      }
    auto registration = static_cast<RegistrationPointer>( object );
    auto optimizer =
      static_cast< OptimizerPointer >( registration->GetModifiableOptimizer() );

    std::cout << "-------------------------------------" << std::endl;
    std::cout << "MultiResolution Level : "
              << registration->GetCurrentLevel()  << std::endl;
    std::cout << std::endl;

    if ( registration->GetCurrentLevel() == 0 )
      {
      optimizer->SetMaximumStepLength( 16.00 );
      optimizer->SetMinimumStepLength(  0.01 );
      }
    else
      {
      optimizer->SetMaximumStepLength( optimizer->GetMaximumStepLength() / 4.0 );
      optimizer->SetMinimumStepLength( optimizer->GetMinimumStepLength() / 10.0 );
      }
  }
  void Execute(const itk::Object * , const itk::EventObject & ) override
    { 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::cerr << " [useExplicitPDFderivatives ] " << std::endl;
    std::cerr << " [numberOfBins] [numberOfSamples ] " << std::endl;
    return EXIT_FAILURE;
    }

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

  using FixedImageType = itk::Image< PixelType, Dimension >;
  using MovingImageType = itk::Image< PixelType, Dimension >;

  using InternalPixelType = float;
  using InternalImageType = itk::Image< InternalPixelType, Dimension >;

  //  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
  using TransformType = itk::AffineTransform< double, Dimension >;
  // Software Guide : EndCodeSnippet

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

  using OptimizerScalesType = OptimizerType::ScalesType;

  using RegistrationType = itk::MultiResolutionImageRegistrationMethod<
                                    InternalImageType,
                                    InternalImageType    >;

  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

  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] );

  using FixedCastFilterType = itk::CastImageFilter<
                        FixedImageType, InternalImageType >;
  using MovingCastFilterType = itk::CastImageFilter<
                        MovingImageType, InternalImageType >;
  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 \doxygen{CenteredTransformInitializer}. 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
  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();
  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 / 1e7; // scale for translation on X
  optimizerScales[5] = 1.0 / 1e7; // 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( 128 );
  metric->SetNumberOfSpatialSamples( 50000 );

  if( argc > 8 )
    {
    // optionally, override the values with numbers taken from the command line arguments.
    metric->SetNumberOfHistogramBins( std::stoi( argv[8] ) );
    }

  if( argc > 9 )
    {
    // optionally, override the values with numbers taken from the command line arguments.
    metric->SetNumberOfSpatialSamples( std::stoi( argv[9] ) );
    }

 //  Software Guide : BeginLatex
  //
  //  Given that the Mattes Mutual Information metric uses a random iterator in
  //  order to collect the samples from the images, it is usually convenient to
  //  initialize the seed of the random number generator.
  //
  //  \index{itk::Mattes\-Mutual\-Information\-Image\-To\-Image\-Metric!ReinitializeSeed()}
  //
  //  Software Guide : EndLatex

  // Software Guide : BeginCodeSnippet
  metric->ReinitializeSeed( 76926294 );
  // Software Guide : EndCodeSnippet

  if( argc > 7 )
    {
    // Define whether to calculate the metric derivative by explicitly
    // computing the derivatives of the joint PDF with respect to the Transform
    // parameters, or doing it by progressively accumulating contributions from
    // each bin in the joint PDF.
    metric->SetUseExplicitPDFDerivatives( std::stoi( argv[7] ) );
    }

  //  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(  200  );
  optimizer->SetRelaxationFactor( 0.8 );

  // 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.
  //
  using CommandType = RegistrationInterfaceCommand<RegistrationType>;
  CommandType::Pointer command = CommandType::New();
  registration->AddObserver( itk::IterationEvent(), command );
  registration->SetNumberOfLevels( 3 );

  try
    {
    registration->Update();
    std::cout << "Optimizer stop condition: "
              << registration->GetOptimizer()->GetStopConditionDescription()
              << std::endl;
    }
  catch( itk::ExceptionObject & err )
    {
    std::cout << "ExceptionObject caught !" << std::endl;
    std::cout << err << std::endl;
    return EXIT_FAILURE;
    }

  std::cout << "Optimizer Stopping Condition = "
            << optimizer->GetStopCondition() << std::endl;

  using ParametersType = RegistrationType::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
  //  misalignment 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

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

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

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

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

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

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

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

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

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

  //  Software Guide : BeginLatex
  //
  // \begin{figure}
  // \center
  // \includegraphics[width=0.32\textwidth]{MultiResImageRegistration2Output}
  // \includegraphics[width=0.32\textwidth]{MultiResImageRegistration2CheckerboardBefore}
  // \includegraphics[width=0.32\textwidth]{MultiResImageRegistration2CheckerboardAfter}
  // \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}
  // \includegraphics[height=0.44\textwidth]{MultiResImageRegistration2TraceMetric}
  // \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
  //
  using CheckerBoardFilterType = itk::CheckerBoardImageFilter< FixedImageType >;

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

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

  caster->SetInput( checker->GetOutput() );
  writer->SetInput( caster->GetOutput()   );

  resample->SetDefaultPixelValue( 0 );

  // 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 EXIT_SUCCESS;
}