Examples/Statistics/ScalarImageKmeansModelEstimator.cxx

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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 command line arguments" << std::endl;
    std::cerr << "Usage :  " << argv[0] << "  inputImageFileName " << std::endl;
    return -1;
    }

  typedef unsigned char       PixelType;
  const unsigned int          Dimension = 2;

  typedef itk::Image<PixelType, Dimension > ImageType;

  typedef itk::ImageFileReader< ImageType > ReaderType;

  ReaderType::Pointer reader = ReaderType::New();

  reader->SetFileName( argv[1] );

  try
    {
    reader->Update();
    }
  catch( itk::ExceptionObject & excp )
    {
    std::cerr << "Problem encoutered while reading image file : " << argv[1] << std::endl;
    std::cerr << excp << std::endl;
    return -1;
    }


  // Software Guide : BeginCodeSnippet

  // Create a List from the scalar image
  typedef itk::Statistics::ImageToListSampleAdaptor< ImageType >   AdaptorType;

  AdaptorType::Pointer adaptor = AdaptorType::New();

  adaptor->SetImage(  reader->GetOutput() );

  // Create the K-d tree structure
  typedef itk::Statistics::WeightedCentroidKdTreeGenerator<
                                      AdaptorType > TreeGeneratorType;
  TreeGeneratorType::Pointer treeGenerator = TreeGeneratorType::New();

  treeGenerator->SetSample( adaptor );
  treeGenerator->SetBucketSize( 16 );
  treeGenerator->Update();


  typedef TreeGeneratorType::KdTreeType TreeType;
  typedef itk::Statistics::KdTreeBasedKmeansEstimator< TreeType >
                                        EstimatorType;

  EstimatorType::Pointer estimator = EstimatorType::New();

  const 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;
}