Examples/RegistrationITKv4/ImageRegistration20.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.
 *  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: {brainweb1e1a10f20.mha}
//    INPUTS: {brainweb1e1a10f20Rot10Tx15.mha}
//    ARGUMENTS:   ImageRegistration20Output.mhd
//  Software Guide : EndCommandLineArgs
 
// Software Guide : BeginLatex
//
// This example illustrates the use of the \doxygen{AffineTransform}
// for performing registration in $3D$.
//
// \index{itk::AffineTransform}
//
// Software Guide : EndLatex
 
#include "itkImageRegistrationMethod.h"
#include "itkMeanSquaresImageToImageMetric.h"
#include "itkRegularStepGradientDescentOptimizer.h"
#include "itkCenteredTransformInitializer.h"
 
//  Software Guide : BeginLatex
//
//  Let's start by including the header file of the AffineTransform.
//
//  \index{itk::AffineTransform!header}
//
//  Software Guide : EndLatex
 
// Software Guide : BeginCodeSnippet
#include "itkAffineTransform.h"
// Software Guide : EndCodeSnippet
 
 
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
 
#include "itkResampleImageFilter.h"
#include "itkCastImageFilter.h"
#include "itkSubtractImageFilter.h"
#include "itkRescaleIntensityImageFilter.h"
 
 
//
//  The following piece 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() = default;
 
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() << 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::endl;
    std::cerr << "   outputImagefile  [differenceBeforeRegistration] "
              << std::endl;
    std::cerr << "   [differenceAfterRegistration] " << std::endl;
    std::cerr << "   [stepLength] [maxNumberOfIterations] " << std::endl;
    return EXIT_FAILURE;
  }
 
 
  //  Software Guide : BeginLatex
  //
  //  We define then the types of the images to be registered.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  constexpr unsigned int Dimension = 3;
  using PixelType = float;
 
  using FixedImageType = itk::Image<PixelType, Dimension>;
  using MovingImageType = itk::Image<PixelType, Dimension>;
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  The transform type is instantiated using the code below. The template
  //  parameters of this class are the representation type of the space
  //  coordinates and the space dimension.
  //
  //  \index{itk::AffineTransform!Instantiation}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using TransformType = itk::AffineTransform<double, Dimension>;
  // Software Guide : EndCodeSnippet
 
 
  using OptimizerType = itk::RegularStepGradientDescentOptimizer;
  using MetricType =
    itk::MeanSquaresImageToImageMetric<FixedImageType, MovingImageType>;
  using InterpolatorType =
    itk::LinearInterpolateImageFunction<MovingImageType, double>;
  using RegistrationType =
    itk::ImageRegistrationMethod<FixedImageType, MovingImageType>;
 
  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::AffineTransform!New()}
  //  \index{itk::AffineTransform!Pointer}
  //  \index{itk::RegistrationMethod!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]);
 
 
  registration->SetFixedImage(fixedImageReader->GetOutput());
  registration->SetMovingImage(movingImageReader->GetOutput());
  fixedImageReader->Update();
 
  registration->SetFixedImageRegion(
    fixedImageReader->GetOutput()->GetBufferedRegion());
 
 
  //  Software Guide : BeginLatex
  //
  //  In this example, we again use the
  //  \doxygen{CenteredTransformInitializer} helper class in order to compute
  //  a reasonable value for the initial center of rotation and the
  //  translation. The initializer is set to use the center of mass of each
  //  image as the initial correspondence correction.
  //
  //  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();
  // Software Guide : EndCodeSnippet
 
  //  Software Guide : BeginLatex
  //
  //  Now we pass the parameters of the current transform 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 scaling, rotation and
  //  translation are quite different, we take advantage of the scaling
  //  functionality provided by the optimizers. We know that the first $N
  //  \times N$ elements of the parameters array correspond to the rotation
  //  matrix factor, and the last $N$ are the components of the translation to
  //  be applied after multiplication with the matrix is performed.
  //
  //  Software Guide : EndLatex
 
  double translationScale = 1.0 / 1000.0;
  if (argc > 8)
  {
    translationScale = std::stod(argv[8]);
  }
 
  // Software Guide : BeginCodeSnippet
  using OptimizerScalesType = OptimizerType::ScalesType;
  OptimizerScalesType optimizerScales(transform->GetNumberOfParameters());
  optimizerScales[0] = 1.0;
  optimizerScales[1] = 1.0;
  optimizerScales[2] = 1.0;
  optimizerScales[3] = 1.0;
  optimizerScales[4] = 1.0;
  optimizerScales[5] = 1.0;
  optimizerScales[6] = 1.0;
  optimizerScales[7] = 1.0;
  optimizerScales[8] = 1.0;
  optimizerScales[9] = translationScale;
  optimizerScales[10] = translationScale;
  optimizerScales[11] = translationScale;
  optimizer->SetScales(optimizerScales);
  // Software Guide : EndCodeSnippet
 
  //  Software Guide : BeginLatex
  //
  //  We also set the usual parameters of the optimization method. In this
  //  case we are using an
  //  \doxygen{RegularStepGradientDescentOptimizer}. Below, we define the
  //  optimization parameters like initial step length, minimal step length
  //  and number of iterations. These last two act as stopping criteria for
  //  the optimization.
  //
  //  Software Guide : EndLatex
 
  double steplength = 0.1;
  if (argc > 6)
  {
    steplength = std::stod(argv[6]);
  }
  unsigned int maxNumberOfIterations = 300;
  if (argc > 7)
  {
    maxNumberOfIterations = std::stoi(argv[7]);
  }
  // Software Guide : BeginCodeSnippet
  optimizer->SetMaximumStepLength(steplength);
  optimizer->SetMinimumStepLength(0.0001);
  optimizer->SetNumberOfIterations(maxNumberOfIterations);
  // Software Guide : EndCodeSnippet
 
  //  Software Guide : BeginLatex
  //
  //  We also set the optimizer to do minimization by calling the
  //  \code{MinimizeOn()} method.
  //
  //  \index{itk::Regular\-Step\-Gradient\-Descent\-Optimizer!MinimizeOn()}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  optimizer->MinimizeOn();
  // Software Guide : EndCodeSnippet
 
  // Create the Command observer and register it with the optimizer.
  //
  CommandIterationUpdate::Pointer observer = CommandIterationUpdate::New();
  optimizer->AddObserver(itk::IterationEvent(), observer);
 
  //  Software Guide : BeginLatex
  //
  //  Finally we trigger the execution of the registration method by calling
  //  the \code{Update()} method. The call is placed in a \code{try/catch}
  //  block in case any exceptions are thrown.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  try
  {
    registration->Update();
    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;
  }
  // Software Guide : EndCodeSnippet
 
  //  Software Guide : BeginLatex
  //
  //  Once the optimization converges, we recover the parameters from the
  //  registration method. This is done with the
  //  \code{GetLastTransformParameters()} method. We can also recover the
  //  final value of the metric with the \code{GetValue()} method and the
  //  final number of iterations with the \code{GetCurrentIteration()}
  //  method.
  //
  //  \index{itk::RegistrationMethod!GetValue()}
  //  \index{itk::RegistrationMethod!GetCurrentIteration()}
  //  \index{itk::RegistrationMethod!GetLastTransformParameters()}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  OptimizerType::ParametersType finalParameters =
    registration->GetLastTransformParameters();
 
  const unsigned int numberOfIterations = optimizer->GetCurrentIteration();
  const double       bestValue = optimizer->GetValue();
  // Software Guide : EndCodeSnippet
 
 
  // Print out results
  //
  std::cout << "Result = " << std::endl;
  std::cout << " Iterations    = " << numberOfIterations << std::endl;
  std::cout << " Metric value  = " << bestValue << std::endl;
 
 
  //  The following code is used to dump output images to files.
  //  They illustrate the final results of the registration.
  //  We will resample the moving image and write out the difference image
  //  before and after registration. We will also rescale the intensities of
  //  the difference images, so that they look better!
  using ResampleFilterType =
    itk::ResampleImageFilter<MovingImageType, FixedImageType>;
 
  TransformType::Pointer finalTransform = TransformType::New();
 
  finalTransform->SetParameters(finalParameters);
  finalTransform->SetFixedParameters(transform->GetFixedParameters());
 
  ResampleFilterType::Pointer resampler = ResampleFilterType::New();
 
  resampler->SetTransform(finalTransform);
  resampler->SetInput(movingImageReader->GetOutput());
 
  FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
 
  resampler->SetSize(fixedImage->GetLargestPossibleRegion().GetSize());
  resampler->SetOutputOrigin(fixedImage->GetOrigin());
  resampler->SetOutputSpacing(fixedImage->GetSpacing());
  resampler->SetOutputDirection(fixedImage->GetDirection());
  resampler->SetDefaultPixelValue(100);
 
  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(resampler->GetOutput());
  writer->SetInput(caster->GetOutput());
  writer->Update();
 
 
  using DifferenceFilterType =
    itk::SubtractImageFilter<FixedImageType, FixedImageType, FixedImageType>;
 
  DifferenceFilterType::Pointer difference = DifferenceFilterType::New();
 
  difference->SetInput1(fixedImageReader->GetOutput());
  difference->SetInput2(resampler->GetOutput());
 
  WriterType::Pointer writer2 = WriterType::New();
 
  using RescalerType =
    itk::RescaleIntensityImageFilter<FixedImageType, OutputImageType>;
 
  RescalerType::Pointer intensityRescaler = RescalerType::New();
 
  intensityRescaler->SetInput(difference->GetOutput());
  intensityRescaler->SetOutputMinimum(0);
  intensityRescaler->SetOutputMaximum(255);
 
  writer2->SetInput(intensityRescaler->GetOutput());
  resampler->SetDefaultPixelValue(1);
 
  // Compute the difference image between the
  // fixed and resampled moving image.
  if (argc > 5)
  {
    writer2->SetFileName(argv[5]);
    writer2->Update();
  }
 
 
  using IdentityTransformType = itk::IdentityTransform<double, Dimension>;
  IdentityTransformType::Pointer identity = IdentityTransformType::New();
 
  // Compute the difference image between the
  // fixed and moving image before registration.
  if (argc > 4)
  {
    resampler->SetTransform(identity);
    writer2->SetFileName(argv[4]);
    writer2->Update();
  }
 
  return EXIT_SUCCESS;
}