Examples/RegistrationITKv4/DeformableRegistration4.cxx

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 *  Copyright NumFOCUS
 *
 *  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 in an ITKv4
// registration framework. Due to the large number of parameters of
// the BSpline transform, we will use a \doxygen{LBFGSOptimizerv4}
// instead of a simple steepest descent or a conjugate gradient
// descent optimizer.
//
//
// \index{itk::BSplineTransform}
// \index{itk::BSplineTransform!DeformableRegistration}
// \index{itk::LBFGSOptimizerv4}
//
//
// Software Guide : EndLatex
 
#include "itkImageRegistrationMethodv4.h"
#include "itkCorrelationImageToImageMetricv4.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::LBFGSOptimizerv4!header}
//
//  Software Guide : EndLatex
 
// Software Guide : BeginCodeSnippet
#include "itkBSplineTransform.h"
#include "itkLBFGSOptimizerv4.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 it possible to represent a
//  wide variety of deformations, at the cost 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"
 
#include "itkBSplineTransformInitializer.h"
#include "itkTransformToDisplacementFieldFilter.h"
 
 
// NOTE: the LBFGSOptimizerv4 does not invoke events
 
 
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] ";
    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
 
  // Software Guide : BeginCodeSnippet
  constexpr unsigned int SpaceDimension = ImageDimension;
  constexpr unsigned int SplineOrder = 3;
  using CoordinateRepType = double;
 
  using TransformType =
    itk::BSplineTransform<CoordinateRepType, SpaceDimension, SplineOrder>;
  // Software Guide : EndCodeSnippet
 
  using OptimizerType = itk::LBFGSOptimizerv4;
 
 
  using MetricType =
    itk::CorrelationImageToImageMetricv4<FixedImageType, MovingImageType>;
 
  using RegistrationType =
    itk::ImageRegistrationMethodv4<FixedImageType, MovingImageType>;
 
  auto metric = MetricType::New();
  auto optimizer = OptimizerType::New();
  auto registration = RegistrationType::New();
 
 
  registration->SetMetric(metric);
  registration->SetOptimizer(optimizer);
 
  using FixedImageReaderType = itk::ImageFileReader<FixedImageType>;
  using MovingImageReaderType = itk::ImageFileReader<MovingImageType>;
 
  auto fixedImageReader = FixedImageReaderType::New();
  auto movingImageReader = MovingImageReaderType::New();
 
  fixedImageReader->SetFileName(argv[1]);
  movingImageReader->SetFileName(argv[2]);
 
  fixedImageReader->Update();
  const FixedImageType::ConstPointer fixedImage =
    fixedImageReader->GetOutput();
 
 
  //  Software Guide : BeginLatex
  //
  //  The transform object is constructed below.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  auto transform = TransformType::New();
  // Software Guide : EndCodeSnippet
 
  //  Software Guide : BeginLatex
  //
  //  Fixed parameters of the BSpline transform should be defined
  //  before the registration. These parameters define origin,
  //  dimension, direction and mesh size of the transform grid
  //  and are set based on specifications of the fixed image space
  //  lattice. We can use \doxygen{BSplineTransformInitializer} to
  //  initialize fixed parameters of a BSpline transform.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using InitializerType =
    itk::BSplineTransformInitializer<TransformType, FixedImageType>;
 
  auto transformInitializer = InitializerType::New();
 
  constexpr unsigned int numberOfGridNodesInOneDimension = 8;
 
  auto meshSize = itk::MakeFilled<TransformType::MeshSizeType>(
    numberOfGridNodesInOneDimension - SplineOrder);
 
  transformInitializer->SetTransform(transform);
  transformInitializer->SetImage(fixedImage);
  transformInitializer->SetTransformDomainMeshSize(meshSize);
  transformInitializer->InitializeTransform();
  // Software Guide : EndCodeSnippet
 
  //  Software Guide : BeginLatex
  //
  //  After setting the fixed parameters of the transform, we set the
  //  initial transform to be an identity transform. It is like setting
  //  all the transform parameters to zero in created parameter space.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  transform->SetIdentity();
  // Software Guide : EndCodeSnippet
 
  std::cout << "Initial Parameters = " << std::endl;
  std::cout << transform->GetParameters() << std::endl;
 
  //  Software Guide : BeginLatex
  //
  //  Then, the initialized transform is connected to the registration
  //  object and is set to be optimized directly during the registration
  //  process.
  //
  //  Calling \code{InPlaceOn()} means that the current initialized transform
  //  will optimized directly and is grafted to the output, so it can be
  //  considered as the output transform object. Otherwise, the initial
  //  transform will be copied or ``cloned'' to the output transform object,
  //  and the copied object will be optimized during the registration process.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  registration->SetInitialTransform(transform);
  registration->InPlaceOn();
  // Software Guide : EndCodeSnippet
 
 
  registration->SetFixedImage(fixedImage);
  registration->SetMovingImage(movingImageReader->GetOutput());
 
  //  Software Guide : BeginLatex
  //
  //  The \doxygen{RegistrationParameterScalesFromPhysicalShift} class
  //  is used to estimate the parameters scales before we set the optimizer.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using ScalesEstimatorType =
    itk::RegistrationParameterScalesFromPhysicalShift<MetricType>;
  auto scalesEstimator = ScalesEstimatorType::New();
  scalesEstimator->SetMetric(metric);
  scalesEstimator->SetTransformForward(true);
  scalesEstimator->SetSmallParameterVariation(1.0);
  // Software Guide : EndCodeSnippet
 
  //  Software Guide : BeginLatex
  //
  //  Now the scale estimator is passed to the \doxygen{LBFGSOptimizerv4},
  //  and we set other parameters of the optimizer as well.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  optimizer->SetGradientConvergenceTolerance(5e-2);
  optimizer->SetLineSearchAccuracy(1.2);
  optimizer->SetDefaultStepLength(1.5);
  optimizer->TraceOn();
  optimizer->SetMaximumNumberOfFunctionEvaluations(1000);
  optimizer->SetScalesEstimator(scalesEstimator);
  // Software Guide : EndCodeSnippet
 
  //  A single level registration process is run using
  //  the shrink factor 1 and smoothing sigma 0.
  //
  constexpr unsigned int numberOfLevels = 1;
 
  RegistrationType::ShrinkFactorsArrayType shrinkFactorsPerLevel;
  shrinkFactorsPerLevel.SetSize(1);
  shrinkFactorsPerLevel[0] = 1;
 
  RegistrationType::SmoothingSigmasArrayType smoothingSigmasPerLevel;
  smoothingSigmasPerLevel.SetSize(1);
  smoothingSigmasPerLevel[0] = 0;
 
  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");
 
    std::cout << "Optimizer stop condition = "
              << registration->GetOptimizer()->GetStopConditionDescription()
              << std::endl;
  }
  catch (const itk::ExceptionObject & err)
  {
    std::cerr << "ExceptionObject caught !" << std::endl;
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
  }
 
  // Report the time and memory taken by the registration
  chronometer.Report(std::cout);
  memorymeter.Report(std::cout);
 
  //  Software Guide : BeginLatex
  //
  //  Let's execute this example using the rat lung images from the previous
  //  examples.
  //
  // \begin{itemize}
  // \item \code{RatLungSlice1.mha}
  // \item \code{RatLungSlice2.mha}
  // \end{itemize}
  //
  //  The \emph{transform} object is updated during the registration process
  //  and is passed to the resampler to map the moving image space onto the
  //  fixed image space.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  const OptimizerType::ParametersType finalParameters =
    transform->GetParameters();
  // Software Guide : EndCodeSnippet
 
  std::cout << "Last Transform Parameters" << std::endl;
  std::cout << finalParameters << std::endl;
 
  using ResampleFilterType =
    itk::ResampleImageFilter<MovingImageType, FixedImageType>;
 
  auto 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());
  resample->SetDefaultPixelValue(100);
 
  using OutputPixelType = unsigned char;
 
  using OutputImageType = itk::Image<OutputPixelType, ImageDimension>;
 
  using CastFilterType =
    itk::CastImageFilter<FixedImageType, OutputImageType>;
 
  using WriterType = itk::ImageFileWriter<OutputImageType>;
 
 
  auto writer = WriterType::New();
  auto caster = CastFilterType::New();
 
 
  writer->SetFileName(argv[3]);
 
 
  caster->SetInput(resample->GetOutput());
  writer->SetInput(caster->GetOutput());
 
 
  try
  {
    writer->Update();
  }
  catch (const itk::ExceptionObject & err)
  {
    std::cerr << "ExceptionObject caught !" << std::endl;
    std::cerr << err << std::endl;
    return EXIT_FAILURE;
  }
 
  using DifferenceFilterType =
    itk::SquaredDifferenceImageFilter<FixedImageType,
                                      FixedImageType,
                                      OutputImageType>;
 
  auto difference = DifferenceFilterType::New();
 
  auto writer2 = WriterType::New();
  writer2->SetInput(difference->GetOutput());
 
 
  // Compute the difference image between the
  // fixed and resampled moving image.
  if (argc >= 5)
  {
    difference->SetInput1(fixedImageReader->GetOutput());
    difference->SetInput2(resample->GetOutput());
    writer2->SetFileName(argv[4]);
    try
    {
      writer2->Update();
    }
    catch (const 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 >= 6)
  {
    writer2->SetFileName(argv[5]);
    difference->SetInput1(fixedImageReader->GetOutput());
    difference->SetInput2(movingImageReader->GetOutput());
    try
    {
      writer2->Update();
    }
    catch (const 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.
  using VectorPixelType = itk::Vector<float, ImageDimension>;
  using DisplacementFieldImageType =
    itk::Image<VectorPixelType, ImageDimension>;
 
  using DisplacementFieldGeneratorType =
    itk::TransformToDisplacementFieldFilter<DisplacementFieldImageType,
                                            CoordinateRepType>;
 
  auto dispfieldGenerator = DisplacementFieldGeneratorType::New();
  dispfieldGenerator->UseReferenceImageOn();
  dispfieldGenerator->SetReferenceImage(fixedImage);
  dispfieldGenerator->SetTransform(transform);
  try
  {
    dispfieldGenerator->Update();
  }
  catch (const itk::ExceptionObject & err)
  {
    std::cerr << "Exception detected while generating deformation field";
    std::cerr << " : " << err << std::endl;
    return EXIT_FAILURE;
  }
  using FieldWriterType = itk::ImageFileWriter<DisplacementFieldImageType>;
  auto fieldWriter = FieldWriterType::New();
 
  fieldWriter->SetInput(dispfieldGenerator->GetOutput());
 
  if (argc >= 7)
  {
    fieldWriter->SetFileName(argv[6]);
    try
    {
      fieldWriter->Update();
    }
    catch (const itk::ExceptionObject & excp)
    {
      std::cerr << "Exception thrown " << std::endl;
      std::cerr << excp << std::endl;
      return EXIT_FAILURE;
    }
  }
 
  return EXIT_SUCCESS;
}