Examples/RegistrationITKv4/ImageRegistration10.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
 *
 *         https://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
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 *=========================================================================*/
 
// Software Guide : BeginLatex
//
// This example illustrates the use of the image registration framework in
// Insight to align two label maps. Common structures are assumed to
// use the same label.  The registration metric simply counts the
// number of corresponding pixels that have the same label.
//
//
// Software Guide : EndLatex
 
 
// Software Guide : BeginCodeSnippet
#include "itkImageRegistrationMethod.h"
#include "itkTranslationTransform.h"
#include "itkMatchCardinalityImageToImageMetric.h"
#include "itkNearestNeighborInterpolateImageFunction.h"
#include "itkAmoebaOptimizer.h"
// Software Guide : EndCodeSnippet
 
 
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
 
#include "itkResampleImageFilter.h"
#include "itkCastImageFilter.h"
#include "itkSquaredDifferenceImageFilter.h"
#include "itkFileOutputWindow.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::AmoebaOptimizer;
  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->GetCachedValue() << "   ";
    std::cout << optimizer->GetCachedCurrentPosition() << std::endl;
  }
};
 
 
int
main(int argc, char * argv[])
{
  if (argc < 3)
  {
    std::cerr << "Missing Parameters " << std::endl;
    std::cerr << "Usage: " << argv[0];
    std::cerr << " fixedImageFile  movingImageFile ";
    std::cerr << " outputImagefile [differenceImage]" << std::endl;
    std::cerr << " [initialTx] [initialTy]" << std::endl;
    return EXIT_FAILURE;
  }
 
  auto fow = itk::FileOutputWindow::New();
  fow->SetInstance(fow);
 
  // The types of each one of the components in the registration methods
  // should be instantiated. First, we select the image dimension and the type
  // for representing image pixels.
  //
  constexpr unsigned int Dimension = 2;
  using PixelType = float;
 
 
  //  The types of the input images are instantiated by the following lines.
  //
  using FixedImageType = itk::Image<PixelType, Dimension>;
  using MovingImageType = itk::Image<PixelType, Dimension>;
 
  //  Software Guide : BeginLatex
  //
  //  The transform that will map one image space into the other is defined
  //  below.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using TransformType = itk::TranslationTransform<double, Dimension>;
  // Software Guide : EndCodeSnippet
 
  //  Software Guide : BeginLatex
  //
  //  An optimizer is required to explore the parameter space of the transform
  //  in search of optimal values of the metric. The metric selected
  //  does not require analytical derivatives of its cost function.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using OptimizerType = itk::AmoebaOptimizer;
  // Software Guide : EndCodeSnippet
 
  //  Software Guide : BeginLatex
  //
  //  The metric will compare how well the two images match each
  //  other. Metric types are usually parameterized by the image types
  //  as can be seen in the following type declaration. The metric
  //  selected here is suitable for comparing two label maps where the
  //  labels are consistent between the two maps.  This metric
  //  measures the percentage of pixels that exactly match or
  //  mismatch.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using MetricType =
    itk::MatchCardinalityImageToImageMetric<FixedImageType, MovingImageType>;
  // Software Guide : EndCodeSnippet
 
  //  Finally, the type of the interpolator is declared. The
  //  interpolator will evaluate the moving image at non-grid
  //  positions.
 
  //  Software Guide : BeginLatex
  //
  //  Since we are registering label maps, we use a
  //  NearestNeighborInterpolateImageFunction to ensure subpixel
  //  values are not interpolated (to labels that do not exist).
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  using InterpolatorType =
    itk::NearestNeighborInterpolateImageFunction<MovingImageType, double>;
  // Software Guide : EndCodeSnippet
 
  //  The registration method type is instantiated using the types of the
  //  fixed and moving images. This class is responsible for interconnecting
  //  all the components we have described so far.
  using RegistrationType =
    itk::ImageRegistrationMethod<FixedImageType, MovingImageType>;
  //  Software Guide : BeginLatex
  //
  //  Each one of the registration components is created using its
  //  \code{New()} method and is assigned to its respective
  //  \doxygen{SmartPointer}.
  //
  // Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  auto metric = MetricType::New();
  auto transform = TransformType::New();
  auto optimizer = OptimizerType::New();
  auto interpolator = InterpolatorType::New();
  auto registration = RegistrationType::New();
  // Software Guide : EndCodeSnippet
 
  //  Software Guide : BeginLatex
  //
  // We are using a MatchCardinalityImageToImageMetric to compare two
  // label maps.  This metric simple counts the percentage of
  // corresponding pixels that have the same label.  This metric does
  // not provide analytical derivatives, so we will use an
  // AmoebaOptimizer to drive the registration.  The AmoebaOptimizer
  // can only minimize a cost function, so we set the metric to count
  // the percentages of mismatches.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  metric->MeasureMatchesOff();
  // Software Guide : EndCodeSnippet
 
 
  //  Each component is now connected to the instance of the registration
  //  method. \index{itk::RegistrationMethod!SetMetric()}
  //  \index{itk::RegistrationMethod!SetOptimizer()}
  //  \index{itk::RegistrationMethod!SetTransform()}
  //  \index{itk::RegistrationMethod!SetFixedImage()}
  //  \index{itk::RegistrationMethod!SetMovingImage()}
  //  \index{itk::RegistrationMethod!SetInterpolator()}
  //
  registration->SetMetric(metric);
  registration->SetOptimizer(optimizer);
  registration->SetTransform(transform);
  registration->SetInterpolator(interpolator);
 
  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]);
 
 
  //  In this example, the fixed and moving images are read from files. This
  //  requires the \doxygen{ImageRegistrationMethod} to acquire its inputs to
  //  the output of the readers.
  //
  registration->SetFixedImage(fixedImageReader->GetOutput());
  registration->SetMovingImage(movingImageReader->GetOutput());
 
  //  Software Guide : BeginLatex
  //
  //  The registration can be restricted to consider only a particular region
  //  of the fixed image as input to the metric computation. This region is
  //  defined by the \code{SetFixedImageRegion()} method.  You could use this
  //  feature to reduce the computational time of the registration or to avoid
  //  unwanted objects present in the image affecting the registration
  //  outcome. In this example we use the full available content of the image.
  //  This region is identified by the \code{BufferedRegion} of the fixed
  //  image. Note that for this region to be valid the reader must first
  //  invoke its \code{Update()} method.
  //
  //  \index{itk::ImageRegistrationMethod!SetFixedImageRegion()}
  //  \index{itk::Image!GetBufferedRegion()}
  //
  //  Software Guide : EndLatex
 
  fixedImageReader->Update();
  movingImageReader->Update();
 
  registration->SetFixedImageRegion(
    fixedImageReader->GetOutput()->GetBufferedRegion());
 
  //  Software Guide : BeginLatex
  //
  //  The parameters of the transform are initialized by passing them in an
  //  array. This can be used to setup an initial known correction of the
  //  misalignment. In this particular case, a translation transform is
  //  being used for the registration. The array of parameters for this
  //  transform is simply composed of the translation values along each
  //  dimension. Setting the values of the parameters to zero
  //  initializes the transform as an \emph{identity} transform. Note that the
  //  array constructor requires the number of elements as an argument.
  //
  //  \index{itk::TranslationTransform!GetNumberOfParameters()}
  //  \index{itk::RegistrationMethod!SetInitialTransformParameters()}
  //
  //  Software Guide : EndLatex
 
  using ParametersType = RegistrationType::ParametersType;
  ParametersType initialParameters(transform->GetNumberOfParameters());
 
  double tx = 0.0;
  double ty = 0.0;
 
  if (argc > 6)
  {
    tx = std::stod(argv[5]);
    ty = std::stod(argv[6]);
  }
 
  initialParameters[0] = tx; // Initial offset in mm along X
  initialParameters[1] = ty; // Initial offset in mm along Y
 
  registration->SetInitialTransformParameters(initialParameters);
 
  //  Software Guide : BeginLatex
  //
  //  At this point the registration method is ready for execution. The
  //  optimizer is the component that drives the execution of the
  //  registration.  However, the ImageRegistrationMethod class
  //  orchestrates the ensemble to make sure that everything is in place
  //  before control is passed to the optimizer.
  //
  //  Software Guide : EndLatex
 
  //  Software Guide : BeginLatex
  //
  //  It is usually desirable to fine tune the parameters of the optimizer.
  //  Each optimizer has particular parameters that must be interpreted in the
  //  context of the optimization strategy it implements.
  //
  //  The AmoebaOptimizer moves a simplex around the cost surface.
  //  Here we set the initial size of the simplex (5 units in each of
  //  the parameters).
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  OptimizerType::ParametersType simplexDelta(
    transform->GetNumberOfParameters());
  simplexDelta.Fill(5.0);
 
  optimizer->AutomaticInitialSimplexOff();
  optimizer->SetInitialSimplexDelta(simplexDelta);
  // Software Guide : EndCodeSnippet
 
 
  //  Software Guide : BeginLatex
  //
  //  We also adjust the tolerances on the optimizer to define
  //  convergence.  Here, we used a tolerance on the parameters of
  //  0.25 (which will be a quarter of image unit, in this case
  //  pixels). We also set the tolerance on the cost function value to
  //  define convergence.  The metric we are using returns the
  //  percentage of pixels that mismatch.  So we set the function
  //  convergence to be 0.1%
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  optimizer->SetParametersConvergenceTolerance(0.25); // quarter pixel
  optimizer->SetFunctionConvergenceTolerance(0.001);  // 0.1%
  // Software Guide : EndCodeSnippet
 
  //  Software Guide : BeginLatex
  //
  //  In the case where the optimizer never succeeds in reaching the desired
  //  precision tolerance, it is prudent to establish a limit on the number of
  //  iterations to be performed. This maximum number is defined with the
  //  method \code{SetMaximumNumberOfIterations()}.
  //
  //  \index{itk::Amoeba\-Optimizer!SetMaximumNumberOfIterations()}
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  optimizer->SetMaximumNumberOfIterations(200);
  // Software Guide : EndCodeSnippet
 
  //
  // Create the Command observer and register it with the optimizer.
  //
  auto observer = CommandIterationUpdate::New();
  optimizer->AddObserver(itk::IterationEvent(), observer);
 
  //  Software Guide : BeginLatex
  //
  //  The registration process is triggered by an invocation of the
  //  \code{Update()} method. If something goes wrong during the
  //  initialization or execution of the registration an exception will be
  //  thrown. We should therefore place the \code{Update()} method
  //  in a \code{try/catch} block as illustrated in the following lines.
  //
  //  Software Guide : EndLatex
 
  // Software Guide : BeginCodeSnippet
  try
  {
    // print out the initial metric value.  need to initialize the
    // registration method to force all the connections to be established.
    registration->Initialize();
    std::cout << "Initial Metric value  = "
              << metric->GetValue(initialParameters) << std::endl;
 
    // run the registration
    registration->Update();
    std::cout << "Optimizer stop condition = "
              << registration->GetOptimizer()->GetStopConditionDescription()
              << std::endl;
  }
  catch (const itk::ExceptionObject & err)
  {
    std::cout << "ExceptionObject caught !" << std::endl;
    std::cout << err << std::endl;
    return EXIT_FAILURE;
  }
  // Software Guide : EndCodeSnippet
 
  //  Software Guide : BeginLatex
  //
  // In a real application, you may attempt to recover from the error in the
  // catch block. Here we are simply printing out a message and then
  // terminating the execution of the program.
  //
  //  Software Guide : EndLatex
 
  //  Software Guide : BeginLatex
  //
  //  The result of the registration process is an array of parameters that
  //  defines the spatial transformation in an unique way. This final result
  //  is obtained using the \code{GetLastTransformParameters()} method.
  //
  //  \index{itk::RegistrationMethod!GetLastTransformParameters()}
  //
  //  Software Guide : EndLatex
  ParametersType finalParameters = registration->GetLastTransformParameters();
 
  //  Software Guide : BeginLatex
  //
  //  In the case of the \doxygen{TranslationTransform}, there is a
  //  straightforward interpretation of the parameters.  Each element of the
  //  array corresponds to a translation along one spatial dimension.
  //
  //  Software Guide : EndLatex
  const double TranslationAlongX = finalParameters[0];
  const double TranslationAlongY = finalParameters[1];
 
  //  The optimizer can be queried for the actual number of iterations
  //  performed to reach convergence.
  //
  const unsigned int numberOfIterations =
    optimizer->GetOptimizer()->get_num_evaluations();
 
  //  Software Guide : BeginLatex
  //
  //  The value of the image metric corresponding to the last set of
  //  parameters can be obtained with the \code{GetValue()} method of the
  //  optimizer. Since the AmoebaOptimizer does not yet support a call
  //  to \code{GetValue()}, we will simply re-evaluate the metric at the
  //  final parameters.
  //
  //  Software Guide : EndLatex
 
  //  Software Guide : BeginCodeSnippet
  const double bestValue = metric->GetValue(finalParameters);
  //  Software Guide : EndCodeSnippet
 
  // 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
  //
  //  It is common, as the last step of a registration task, to use the
  //  resulting transform to map the moving image into the fixed image space.
  //  This is easily done with the \doxygen{ResampleImageFilter}. Please
  //  refer to Section~\ref{sec:ResampleImageFilter} for details on the use
  //  of this filter.  First, a ResampleImageFilter type is instantiated
  //  using the image types. It is convenient to use the fixed image type as
  //  the output type since it is likely that the transformed moving image
  //  will be compared with the fixed image.
  //
  //  Software Guide : EndLatex
 
  using ResampleFilterType =
    itk::ResampleImageFilter<MovingImageType, FixedImageType>;
 
  //  Software Guide : BeginLatex
  //
  //  A transform of the same type used in the registration process should be
  //  created and initialized with the parameters resulting from the
  //  registration process.
  //
  //  \index{itk::ImageRegistrationMethod!Resampling image}
  //
  //  Software Guide : EndLatex
 
  //  Software Guide : BeginCodeSnippet
  auto finalTransform = TransformType::New();
  finalTransform->SetParameters(finalParameters);
  finalTransform->SetFixedParameters(transform->GetFixedParameters());
  //  Software Guide : EndCodeSnippet
 
  //  Software Guide : BeginLatex
  //
  //  Then a resampling filter is created and the corresponding transform and
  //  moving image connected as inputs.
  //
  //  Software Guide : EndLatex
 
  //  Software Guide : BeginCodeSnippet
  auto resample = ResampleFilterType::New();
  resample->SetTransform(finalTransform);
  resample->SetInput(movingImageReader->GetOutput());
  //  Software Guide : EndCodeSnippet
 
  //  Software Guide : BeginLatex
  //
  //  As described in Section \ref{sec:ResampleImageFilter}, the
  //  ResampleImageFilter requires additional parameters to be
  //  specified, in particular, the spacing, origin and size of the output
  //  image. The default pixel value is also set to the standard label
  //  for "unknown" or background. Finally, we need to set the
  //  interpolator to be the same type of interpolator as the
  //  registration method used (nearest neighbor).
  //
  //  Software Guide : EndLatex
 
  //  Software Guide : BeginCodeSnippet
  FixedImageType::Pointer fixedImage = fixedImageReader->GetOutput();
  resample->SetSize(fixedImage->GetLargestPossibleRegion().GetSize());
  resample->SetOutputOrigin(fixedImage->GetOrigin());
  resample->SetOutputSpacing(fixedImage->GetSpacing());
  resample->SetOutputDirection(fixedImage->GetDirection());
  resample->SetDefaultPixelValue(0);
  resample->SetInterpolator(interpolator);
  //  Software Guide : EndCodeSnippet
 
  //  Software Guide : BeginLatex
  //
  //  The output of the filter is passed to a writer that will store the
  //  image in a file. An \doxygen{CastImageFilter} is used to convert the
  //  pixel type of the resampled image to the final type used by the
  //  writer. The cast and writer filters are instantiated below.
  //
  //  Software Guide : EndLatex
 
  //  Software Guide : BeginCodeSnippet
  using OutputPixelType = unsigned short;
  using OutputImageType = itk::Image<OutputPixelType, Dimension>;
  using CastFilterType =
    itk::CastImageFilter<FixedImageType, OutputImageType>;
  using WriterType = itk::ImageFileWriter<OutputImageType>;
  //  Software Guide : EndCodeSnippet
 
  //  SoftwareGuide : BeginLatex
  //
  //  The filters are created by invoking their \code{New()}
  //  method.
  //
  // SoftwareGuide : EndLatex
 
  // SoftwareGuide : BeginCodeSnippet
  auto writer = WriterType::New();
  auto caster = CastFilterType::New();
  // SoftwareGuide : EndCodeSnippet
 
  writer->SetFileName(argv[3]);
 
  //  SoftwareGuide : BeginLatex
  //
  //  The \code{Update()} method of the writer is invoked in order to trigger
  //  the execution of the pipeline.
  //
  //  SoftwareGuide : EndLatex
 
  // SoftwareGuide : BeginCodeSnippet
  caster->SetInput(resample->GetOutput());
  writer->SetInput(caster->GetOutput());
  writer->Update();
  // SoftwareGuide : EndCodeSnippet
 
  // SoftwareGuide : BeginLatex
  //
  //  The fixed image and the transformed moving image can easily be compared
  //  using the \code{SquaredDifferenceImageFilter}. This pixel-wise
  //  filter computes the squared value of the difference between homologous
  //  pixels of its input images.
  //
  // SoftwareGuide : EndLatex
 
  // SoftwareGuide : BeginCodeSnippet
  using DifferenceFilterType =
    itk::SquaredDifferenceImageFilter<FixedImageType,
                                      FixedImageType,
                                      OutputImageType>;
 
  auto difference = DifferenceFilterType::New();
  difference->SetInput1(fixedImageReader->GetOutput());
  difference->SetInput2(resample->GetOutput());
  // SoftwareGuide : EndCodeSnippet
 
  //  Its output can be passed to another writer.
  //
  auto writer2 = WriterType::New();
  writer2->SetInput(difference->GetOutput());
 
  if (argc > 4)
  {
    writer2->SetFileName(argv[4]);
    writer2->Update();
  }
 
 
  return EXIT_SUCCESS;
}
 
// SoftwareGuide : BeginLatex
//
// The example was run on two binary images. The first binary image was
// generated by running the confidence connected image filter (section
// \ref{sec:ConfidenceConnected}) on the MRI slice of the brain. The second
// was generated similarly after shifting the slice by 13 pixels horizontally
// and 17 pixels vertically. The Amoeba optimizer converged after 34
// iterations and produced the following results:
//
// \begin{verbatim}
// Translation X = 12.5
// Translation Y = 16.77
// \end{verbatim}
// These results are a close match to the true misalignment.
//
// SoftwareGuide : EndLatex