Examples/Statistics/ScalarImageKmeansModelEstimator.cxx

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 *  Copyright NumFOCUS
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//  Software Guide : BeginCommandLineArgs
//    INPUTS: {BrainT1Slice.png}
//  Software Guide : EndCommandLineArgs
 
// Software Guide : BeginLatex
//
// This example shows how to compute the KMeans model of a Scalar Image.
//
// The  \subdoxygen{Statistics}{KdTreeBasedKmeansEstimator} is used for taking
// a scalar image and applying the K-Means algorithm in order to define
// classes that represents statistical distributions of intensity values in
// the pixels. In the context of Medical Imaging, each class is typically
// associated to a particular type of tissue and can therefore be used as a
// form of image segmentation. One of the drawbacks of this technique is that
// the spatial distribution of the pixels is not considered at all. It is
// common therefore to combine the classification resulting from K-Means with
// other segmentation techniques that will use the classification as a prior
// and add spatial information to it in order to produce a better
// segmentation.
//
// Software Guide : EndLatex
 
#include "itkKdTree.h"
#include "itkKdTreeBasedKmeansEstimator.h"
#include "itkWeightedCentroidKdTreeGenerator.h"
#include "itkImageToListSampleAdaptor.h"
 
#include "itkImage.h"
#include "itkImageFileReader.h"
 
int
main(int argc, char * argv[])
{
 
  if (argc < 2)
  {
    std::cerr << "Missing parameters." << std::endl;
    std::cerr << "Usage: " << argv[0] << " inputImageFileName" << std::endl;
    return EXIT_FAILURE;
  }
 
  using PixelType = unsigned char;
  constexpr unsigned int Dimension = 2;
 
  using ImageType = itk::Image<PixelType, Dimension>;
 
  using ReaderType = itk::ImageFileReader<ImageType>;
 
  auto reader = ReaderType::New();
 
  reader->SetFileName(argv[1]);
 
  try
  {
    reader->Update();
  }
  catch (const itk::ExceptionObject & excp)
  {
    std::cerr << "Problem encountered while reading image file : " << argv[1]
              << std::endl;
    std::cerr << excp << std::endl;
    return EXIT_FAILURE;
  }
 
 
  // Software Guide : BeginCodeSnippet
 
  // Create a List from the scalar image
  using AdaptorType = itk::Statistics::ImageToListSampleAdaptor<ImageType>;
 
  auto adaptor = AdaptorType::New();
 
  adaptor->SetImage(reader->GetOutput());
 
  // Create the K-d tree structure
  using TreeGeneratorType =
    itk::Statistics::WeightedCentroidKdTreeGenerator<AdaptorType>;
  auto treeGenerator = TreeGeneratorType::New();
 
  treeGenerator->SetSample(adaptor);
  treeGenerator->SetBucketSize(16);
  treeGenerator->Update();
 
 
  using TreeType = TreeGeneratorType::KdTreeType;
  using EstimatorType = itk::Statistics::KdTreeBasedKmeansEstimator<TreeType>;
 
  auto estimator = EstimatorType::New();
 
  constexpr unsigned int numberOfClasses = 3;
 
  EstimatorType::ParametersType initialMeans(numberOfClasses);
  initialMeans[0] = 25.0;
  initialMeans[1] = 125.0;
  initialMeans[2] = 250.0;
 
  estimator->SetParameters(initialMeans);
 
  estimator->SetKdTree(treeGenerator->GetOutput());
  estimator->SetMaximumIteration(200);
  estimator->SetCentroidPositionChangesThreshold(0.0);
  estimator->StartOptimization();
 
  EstimatorType::ParametersType estimatedMeans = estimator->GetParameters();
 
  for (unsigned int i = 0; i < numberOfClasses; ++i)
  {
    std::cout << "cluster[" << i << "] " << std::endl;
    std::cout << "    estimated mean : " << estimatedMeans[i] << std::endl;
  }
 
  // Software Guide : EndCodeSnippet
 
  //  Software Guide : BeginLatex
  //
  // \begin{figure} \center
  // \includegraphics[width=0.44\textwidth]{BrainT1Slice}
  // \itkcaption[Output of the ScalarImageKmeansModelEstimator]{Test image for
  // the KMeans model estimator.}
  // \label{fig:ScalarImageKmeansModelEstimatorTestImage}
  // \end{figure}
  //
  //  The example produces means of 14.8, 91.6, 134.9 on
  // Figure \ref{fig:ScalarImageKmeansModelEstimatorTestImage}
  //
  //  Software Guide : EndLatex
 
  return EXIT_SUCCESS;
}