Examples/RegistrationITKv4/DeformableRegistration12.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 : BeginLatex
//
// This example illustrates the use of the
// \doxygen{BSplineTransform} class for performing
// registration of two $2D$ images. The example code is for the most
// part identical to the code presented in
// Section~\ref{sec:DeformableRegistration8}.  The major difference is
// that this example we set the image dimension to 2.
//
// \index{itk::BSplineTransform}
// \index{itk::BSplineTransform!DeformableRegistration}
// \index{itk::LBFGSBOptimizerv4}
//
//
// Software Guide : EndLatex

#include "itkImageRegistrationMethodv4.h"
#include "itkMattesMutualInformationImageToImageMetricv4.h"

#include "itkTimeProbesCollectorBase.h"
#include "itkMemoryProbesCollectorBase.h"


//  Software Guide : BeginLatex
//
//  The following are the most relevant headers to this example.
//
//  \index{itk::BSplineTransform!header}
//  \index{itk::LBFGSBOptimizerv4!header}
//
//  Software Guide : EndLatex

// Software Guide : BeginCodeSnippet
#include "itkBSplineTransform.h"
#include "itkLBFGSBOptimizerv4.h"
// Software Guide : EndCodeSnippet

//  Software Guide : BeginLatex
//
//  The parameter space of the \code{BSplineTransform} is composed by
//  the set of all the deformations associated with the nodes of the BSpline
//  grid.  This large number of parameters makes possible to represent a wide
//  variety of deformations, but it also has the price of requiring a
//  significant amount of computation time.
//
//  \index{itk::BSplineTransform!header}
//
//  Software Guide : EndLatex

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

#include "itkResampleImageFilter.h"
#include "itkCastImageFilter.h"
#include "itkSquaredDifferenceImageFilter.h"

//  The following section of code implements a Command observer
//  used to 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() = default;

public:
  using OptimizerType = itk::LBFGSBOptimizerv4;
  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->GetCurrentMetricValue() << "   ";
    std::cout << optimizer->GetInfinityNormOfProjectedGradient() << 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 outputImagefile  ";
    std::cerr << " [differenceOutputfile] [differenceBeforeRegistration] ";
    std::cerr << " [deformationField] ";
    std::cerr << " [filenameForFinalTransformParameters] ";
    std::cerr << " [numberOfGridNodesInOneDimension] ";
    std::cerr << std::endl;
    return EXIT_FAILURE;
    }

  constexpr unsigned int ImageDimension = 2;
  using PixelType = float;

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


  //  Software Guide : BeginLatex
  //
  //  We instantiate now the type of the \code{BSplineTransform} using
  //  as template parameters the type for coordinates representation, the
  //  dimension of the space, and the order of the BSpline.
  //
  //  \index{BSplineTransform!New}
  //  \index{BSplineTransform!Instantiation}
  //
  //  Software Guide : EndLatex

  using FixedImageReaderType = itk::ImageFileReader< FixedImageType  >;
  FixedImageReaderType::Pointer  fixedImageReader = FixedImageReaderType::New();
  fixedImageReader->SetFileName(  argv[1] );
  fixedImageReader->Update();
  FixedImageType::ConstPointer fixedImage = fixedImageReader->GetOutput();
  FixedImageType::RegionType fixedRegion = fixedImage->GetBufferedRegion();

  using MovingImageReaderType = itk::ImageFileReader< MovingImageType >;
  MovingImageReaderType::Pointer movingImageReader = MovingImageReaderType::New();
  movingImageReader->SetFileName( argv[2] );
  movingImageReader->Update();
  MovingImageType::ConstPointer movingImage = movingImageReader->GetOutput();

  // Software Guide : BeginCodeSnippet
  const unsigned int SpaceDimension = ImageDimension;
  constexpr unsigned int SplineOrder = 3;
  using CoordinateRepType = double;

  using TransformType = itk::BSplineTransform<
                            CoordinateRepType,
                            SpaceDimension,
                            SplineOrder >;
  // Software Guide : EndCodeSnippet

  using RegistrationType = itk::ImageRegistrationMethodv4<
                                        FixedImageType,
                                        MovingImageType >;
  RegistrationType::Pointer   registration  = RegistrationType::New();

  //  Software Guide : BeginLatex
  //
  //  Final BSpline transform will be the output of the registration method.
  //
  //  Software Guide : EndLatex
  //
  // Software Guide : BeginCodeSnippet
  TransformType::Pointer transform = TransformType::New();
  // Software Guide : EndCodeSnippet

  unsigned int numberOfGridNodesInOneDimension = 7;

  if( argc > 8 )
    {
    numberOfGridNodesInOneDimension = std::stoi( argv[8] );
    }

  // Software Guide : BeginCodeSnippet
  TransformType::PhysicalDimensionsType   fixedPhysicalDimensions;
  TransformType::MeshSizeType             meshSize;
  TransformType::OriginType               fixedOrigin;

  for( unsigned int i=0; i< SpaceDimension; i++ )
    {
    fixedOrigin[i] = fixedImage->GetOrigin()[i];
    fixedPhysicalDimensions[i] = fixedImage->GetSpacing()[i] *
    static_cast<double>(
      fixedImage->GetLargestPossibleRegion().GetSize()[i] - 1 );
    }
  meshSize.Fill( numberOfGridNodesInOneDimension - SplineOrder );

  transform->SetTransformDomainOrigin( fixedOrigin );
  transform->SetTransformDomainPhysicalDimensions( fixedPhysicalDimensions );
  transform->SetTransformDomainMeshSize( meshSize );
  transform->SetTransformDomainDirection( fixedImage->GetDirection() );

  registration->SetInitialTransform( transform );
  registration->InPlaceOn();

  using ParametersType = TransformType::ParametersType;

  const unsigned int numberOfParameters =  transform->GetNumberOfParameters();

  ParametersType parameters( numberOfParameters );

  parameters.Fill( 0.0 );

  transform->SetParameters( parameters );
  //  Software Guide : EndCodeSnippet

  using MetricType = itk::MattesMutualInformationImageToImageMetricv4<
                                                          FixedImageType,
                                                          MovingImageType >;
  MetricType::Pointer         metric        = MetricType::New();
  metric->SetNumberOfHistogramBins( 32 );
  metric->SetUseMovingImageGradientFilter( false );
  metric->SetUseFixedImageGradientFilter( false );
  metric->SetUseSampledPointSet( false );

  using OptimizerType = itk::LBFGSBOptimizerv4;
  OptimizerType::Pointer      optimizer     = OptimizerType::New();

  // Software Guide : BeginCodeSnippet
  const unsigned int numParameters = transform->GetNumberOfParameters();
  OptimizerType::BoundSelectionType boundSelect( numParameters );
  OptimizerType::BoundValueType upperBound( numParameters );
  OptimizerType::BoundValueType lowerBound( numParameters );

  boundSelect.Fill( 0 );
  upperBound.Fill( 0.0 );
  lowerBound.Fill( 0.0 );

  optimizer->SetBoundSelection( boundSelect );
  optimizer->SetUpperBound( upperBound );
  optimizer->SetLowerBound( lowerBound );

  optimizer->SetCostFunctionConvergenceFactor( 1.e7 );
  optimizer->SetGradientConvergenceTolerance( 1e-35 );
  optimizer->SetNumberOfIterations( 200 );
  optimizer->SetMaximumNumberOfFunctionEvaluations( 200 );
  optimizer->SetMaximumNumberOfCorrections( 7 );
  // Software Guide : EndCodeSnippet

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

  // One level registration is performed using the shrink factor 1 and smoothing sigma 1
  //
  constexpr unsigned int numberOfLevels = 1;

  RegistrationType::ShrinkFactorsArrayType shrinkFactorsPerLevel;
  shrinkFactorsPerLevel.SetSize( 1 );
  shrinkFactorsPerLevel[0] = 1;

  RegistrationType::SmoothingSigmasArrayType smoothingSigmasPerLevel;
  smoothingSigmasPerLevel.SetSize( 1 );
  smoothingSigmasPerLevel[0] = 0;

  registration->SetFixedImage( fixedImage );
  registration->SetMovingImage( movingImage );
  registration->SetMetric(        metric        );
  registration->SetOptimizer(     optimizer     );
  registration->SetNumberOfLevels( numberOfLevels );
  registration->SetSmoothingSigmasPerLevel( smoothingSigmasPerLevel );
  registration->SetShrinkFactorsPerLevel( shrinkFactorsPerLevel );


  // Add time and memory probes
  itk::TimeProbesCollectorBase chronometer;
  itk::MemoryProbesCollectorBase memorymeter;

  std::cout << std::endl << "Starting Registration" << std::endl;

  try
    {
    memorymeter.Start( "Registration" );
    chronometer.Start( "Registration" );

    registration->Update();

    chronometer.Stop( "Registration" );
    memorymeter.Stop( "Registration" );

    const OptimizerType::ConstPointer  outputOptimizer = dynamic_cast<const OptimizerType *>( registration->GetOptimizer() );
    if( outputOptimizer.IsNotNull() )
      {
      std::cout << "Optimizer stop condition = "
                << outputOptimizer->GetStopConditionDescription()
                << std::endl;
      }
    else
      {
      std::cerr << "Output optimizer is null." << std::endl;
      return EXIT_FAILURE;
      }
    }
  catch( itk::ExceptionObject & err )
    {
    std::cerr << "ExceptionObject caught !" << std::endl;
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
    }

  // While the registration filter is run, it updates the output transform parameters with the final registration parameters
  OptimizerType::ParametersType finalParameters = transform->GetParameters();

  // Report the time and memory taken by the registration
  chronometer.Report( std::cout );
  memorymeter.Report( std::cout );

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

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

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

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

  // This value is set to zero in order to make easier to perform
  // regression testing in this example. However, for didactic
  // exercise it will be better to set it to a medium gray value
  // such as 100 or 128.
  resample->SetDefaultPixelValue( 0 );

  using OutputPixelType = unsigned char;

  using OutputImageType = itk::Image< OutputPixelType, ImageDimension >;

  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()   );


  try
    {
    writer->Update();
    }
  catch( itk::ExceptionObject & err )
    {
    std::cerr << "ExceptionObject caught !" << std::endl;
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
    }

  using DifferenceFilterType = itk::SquaredDifferenceImageFilter<
                                  FixedImageType,
                                  FixedImageType,
                                  OutputImageType >;

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

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


  // Compute the difference image between the
  // fixed and resampled moving image.
  if( argc > 4 )
    {
    difference->SetInput1( fixedImageReader->GetOutput() );
    difference->SetInput2( resample->GetOutput() );
    writer2->SetFileName( argv[4] );
    try
      {
      writer2->Update();
      }
    catch( itk::ExceptionObject & err )
      {
      std::cerr << "ExceptionObject caught !" << std::endl;
      std::cerr << err << std::endl;
      return EXIT_FAILURE;
      }
    }


  // Compute the difference image between the
  // fixed and moving image before registration.
  if( argc > 5 )
    {
    writer2->SetFileName( argv[5] );
    difference->SetInput1( fixedImageReader->GetOutput() );
    difference->SetInput2( movingImageReader->GetOutput() );
    try
      {
      writer2->Update();
      }
    catch( itk::ExceptionObject & err )
      {
      std::cerr << "ExceptionObject caught !" << std::endl;
      std::cerr << err << std::endl;
      return EXIT_FAILURE;
      }
    }

  // Generate the explicit deformation field resulting from
  // the registration.
  if( argc > 6 )
    {

    using VectorType = itk::Vector< float, ImageDimension >;
    using DisplacementFieldType = itk::Image< VectorType, ImageDimension >;

    DisplacementFieldType::Pointer field = DisplacementFieldType::New();
    field->SetRegions( fixedRegion );
    field->SetOrigin( fixedImage->GetOrigin() );
    field->SetSpacing( fixedImage->GetSpacing() );
    field->SetDirection( fixedImage->GetDirection() );
    field->Allocate();

    using FieldIterator = itk::ImageRegionIterator< DisplacementFieldType >;
    FieldIterator fi( field, fixedRegion );

    fi.GoToBegin();

    TransformType::InputPointType  fixedPoint;
    TransformType::OutputPointType movingPoint;
    DisplacementFieldType::IndexType index;

    VectorType displacement;

    while( ! fi.IsAtEnd() )
      {
      index = fi.GetIndex();
      field->TransformIndexToPhysicalPoint( index, fixedPoint );
      movingPoint = transform->TransformPoint( fixedPoint );
      displacement = movingPoint - fixedPoint;
      fi.Set( displacement );
      ++fi;
      }

    using FieldWriterType = itk::ImageFileWriter< DisplacementFieldType >;
    FieldWriterType::Pointer fieldWriter = FieldWriterType::New();

    fieldWriter->SetInput( field );

    fieldWriter->SetFileName( argv[6] );
    try
      {
      fieldWriter->Update();
      }
    catch( itk::ExceptionObject & excp )
      {
      std::cerr << "Exception thrown " << std::endl;
      std::cerr << excp << std::endl;
      return EXIT_FAILURE;
      }
    }

  // Optionally, save the transform parameters in a file
  if( argc > 7 )
    {
    std::ofstream parametersFile;
    parametersFile.open( argv[7] );
    parametersFile << finalParameters << std::endl;
    parametersFile.close();
    }

  return EXIT_SUCCESS;
}