Examples/Statistics/BayesianClassifierInitializer.cxx

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 *  Copyright NumFOCUS
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 *  you may not use this file except in compliance with the License.
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//
// This is an example of the itk::BayesianClassifierInitializationImageFilter.
// The example's goal is to serve as an initializer for the
// BayesianClassifier.cxx example also found in this directory.
//
// This example takes an input image (to be classified) and generates
// membership images. The membership images determine the degree to which each
// pixel belongs to a class.
//
// The membership image generated by the filter is an
// itk::VectorImage, (with pixels organized as follows: For a 2D image,
// its essentially a 3D array on file with DataType[y][x][c] where c is the
// number of classes and DataType is the template parameter of the filter
// (defaults to float). For a 3D image, it will be organized as
// Datatype[z][y][x][c])
//
// The example also optionally takes in two more arguments, as a convenience
// to the user. These arguements extract the specified component 'c' from the
// membership image and rescale, so the user can fire up a typical image
// viewer and see the relative pixel memberships to class 'c'.
//
// Example args:
//   BrainProtonDensitySlice.png Memberships.mhd 4  2 Class2.png
//
// Here Memberships.mhd will be a 2x2x4 image containing pixel memberships
// Class2.png shows pixel memberships to the third class, (rescaled for
// display)
//
// Notes:
//   The default behaviour of the filter is to generate memberships by
//   centering
// gaussian density functions around K-means of the pixel intensities in the
// image. The filter allows you to specify your own membership functions as
// well.
//
 
#include "itkImage.h"
#include "itkBayesianClassifierInitializationImageFilter.h"
#include "itkImageFileReader.h"
#include "itkImageFileWriter.h"
#include "itkRescaleIntensityImageFilter.h"
#include "itkImageRegionConstIterator.h"
 
int
main(int argc, char * argv[])
{
 
  constexpr unsigned int Dimension = 2;
  if (argc < 4)
  {
    std::cerr
      << "Usage arguments: InputImage MembershipImage numberOfClasses "
         "[componentToExtract ExtractedImage]"
      << std::endl;
    std::cerr
      << "  The MembershipImage image written is a VectorImage, ( an image "
         "with multiple components ) ";
    std::cerr << "Given that most viewers can't see vector images, we will "
                 "optionally "
                 "extract a component and ";
    std::cerr << "write it out as a scalar image as well." << std::endl;
    return EXIT_FAILURE;
  }
 
  using ImageType = itk::Image<unsigned char, Dimension>;
  using BayesianInitializerType =
    itk::BayesianClassifierInitializationImageFilter<ImageType>;
  BayesianInitializerType::Pointer bayesianInitializer =
    BayesianInitializerType::New();
 
  using ReaderType = itk::ImageFileReader<ImageType>;
  ReaderType::Pointer reader = ReaderType::New();
  reader->SetFileName(argv[1]);
 
  try
  {
    reader->Update();
  }
  catch (const itk::ExceptionObject & excp)
  {
    std::cerr << "Exception thrown " << std::endl;
    std::cerr << excp << std::endl;
    return EXIT_FAILURE;
  }
 
  bayesianInitializer->SetInput(reader->GetOutput());
  bayesianInitializer->SetNumberOfClasses(std::stoi(argv[3]));
 
  // TODO add test where we specify membership functions
 
  using WriterType =
    itk::ImageFileWriter<BayesianInitializerType::OutputImageType>;
  WriterType::Pointer writer = WriterType::New();
  writer->SetInput(bayesianInitializer->GetOutput());
  writer->SetFileName(argv[2]);
 
  try
  {
    bayesianInitializer->Update();
  }
  catch (const itk::ExceptionObject & excp)
  {
    std::cerr << "Exception thrown " << std::endl;
    std::cerr << excp << std::endl;
    return EXIT_FAILURE;
  }
 
  try
  {
    writer->Update();
  }
  catch (const itk::ExceptionObject & excp)
  {
    std::cerr << "Exception thrown " << std::endl;
    std::cerr << excp << std::endl;
    return EXIT_FAILURE;
  }
 
  if (argv[4] && argv[5])
  {
    using MembershipImageType = BayesianInitializerType::OutputImageType;
    using ExtractedComponentImageType =
      itk::Image<MembershipImageType::InternalPixelType, Dimension>;
    ExtractedComponentImageType::Pointer extractedComponentImage =
      ExtractedComponentImageType::New();
    extractedComponentImage->CopyInformation(
      bayesianInitializer->GetOutput());
    extractedComponentImage->SetBufferedRegion(
      bayesianInitializer->GetOutput()->GetBufferedRegion());
    extractedComponentImage->SetRequestedRegion(
      bayesianInitializer->GetOutput()->GetRequestedRegion());
    extractedComponentImage->Allocate();
    using ConstIteratorType =
      itk::ImageRegionConstIterator<MembershipImageType>;
    using IteratorType =
      itk::ImageRegionIterator<ExtractedComponentImageType>;
    ConstIteratorType cit(
      bayesianInitializer->GetOutput(),
      bayesianInitializer->GetOutput()->GetBufferedRegion());
    IteratorType it(extractedComponentImage,
                    extractedComponentImage->GetLargestPossibleRegion());
 
    const unsigned int componentToExtract = std::stoi(argv[4]);
    cit.GoToBegin();
    it.GoToBegin();
    while (!cit.IsAtEnd())
    {
      it.Set(cit.Get()[componentToExtract]);
      ++it;
      ++cit;
    }
 
    // Write out the rescaled extracted component
    using OutputImageType = itk::Image<unsigned char, Dimension>;
    using RescalerType =
      itk::RescaleIntensityImageFilter<ExtractedComponentImageType,
                                       OutputImageType>;
    RescalerType::Pointer rescaler = RescalerType::New();
    rescaler->SetInput(extractedComponentImage);
    rescaler->SetOutputMinimum(0);
    rescaler->SetOutputMaximum(255);
    using ExtractedComponentWriterType =
      itk::ImageFileWriter<OutputImageType>;
    ExtractedComponentWriterType::Pointer rescaledImageWriter =
      ExtractedComponentWriterType::New();
    rescaledImageWriter->SetInput(rescaler->GetOutput());
    rescaledImageWriter->SetFileName(argv[5]);
    rescaledImageWriter->Update();
  }
 
  return EXIT_SUCCESS;
}