ITK/Examples/Broken/Statistics/ExpectationMaximizationMixtureModelEstimator Image

The clusters are not computed correctly. The output is: <source lang="text"> Cluster[0]

   Parameters:
        [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5]
   Proportion:          -nan

Cluster[1]

   Parameters:
        [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5]
   Proportion:          -nan

Cluster[2]

   Parameters:
        [0, 0, 0, 5, 5, 5, 5, 5, 5, 5, 5, 5]
   Proportion:          -nan

</source>

ExpectationMaximizationMixtureModelEstimator_Image.cxx

<source lang="cpp">

1. include "itkVector.h"
2. include "itkListSample.h"
3. include "itkGaussianMixtureModelComponent.h"
4. include "itkExpectationMaximizationMixtureModelEstimator.h"
5. include "itkNormalVariateGenerator.h"
6. include "itkSampleClassifierFilter.h"
7. include "itkMaximumDecisionRule2.h"
8. include "itkImageToListSampleAdaptor.h"
9. include "itkCovariantVector.h"
10. include "itkImageRegionIterator.h"

typedef itk::CovariantVector<unsigned char, 3> PixelType; typedef itk::Image<PixelType, 2> ImageType;

void CreateImage(ImageType::Pointer image);

int main(int, char*[]) {

 ImageType::Pointer image = ImageType::New();
 CreateImage(image);

 typedef itk::Statistics::ImageToListSampleAdaptor<ImageType> ImageToListSampleAdaptorType;
 ImageToListSampleAdaptorType::Pointer imageToListSampleAdaptor = ImageToListSampleAdaptorType::New();
 imageToListSampleAdaptor->SetImage(image);
 imageToListSampleAdaptor->Update();
 
 unsigned int numberOfClasses = 3;

 typedef itk::Statistics::NormalVariateGenerator NormalGeneratorType;
 NormalGeneratorType::Pointer normalGenerator = NormalGeneratorType::New();

 typedef itk::Array< double > ParametersType;
 ParametersType params( numberOfClasses + numberOfClasses*numberOfClasses ); // 3 for means and 9 for 3x3 covariance

 // Create the first set (for the first cluster/model) of initial parameters
 std::vector< ParametersType > initialParameters( numberOfClasses );
 for(unsigned int i = 0; i < 3; i++)
   {
   params[i] = 5.0; // mean of dimension i
   }
 for(unsigned int i = 3; i < 12; i++)
   {
   params[i] = 5.0; // covariance
   }

 initialParameters[0] = params;

 // Create the second set (for the second cluster/model) of initial parameters
 params[0] = 210.0;
 params[1] = 5.0;
 params[2] = 5.0;
 for(unsigned int i = 3; i < 12; i++)
   {
   params[i] = 5.0; // covariance
   }
 initialParameters[1] = params;

 // Create the third set (for the third cluster/model) of initial parameters
 params[0] = 5.0;
 params[1] = 210.0;
 params[2] = 5.0;
 for(unsigned int i = 3; i < 12; i++)
   {
   params[i] = 5.0; // covariance
   }
 initialParameters[2] = params;

 std::cout << "Initial parameters: " << std::endl;
 for ( unsigned int i = 0 ; i < numberOfClasses ; i++ )
   {
   std::cout << initialParameters[i] << std::endl;
   }
   
 typedef itk::Statistics::GaussianMixtureModelComponent< ImageToListSampleAdaptorType >
   ComponentType;

 // Create the components
 std::vector< ComponentType::Pointer > components;
 for ( unsigned int i = 0 ; i < numberOfClasses ; i++ )
   {
   components.push_back( ComponentType::New() );
   (components[i])->SetSample( imageToListSampleAdaptor );
   (components[i])->SetParameters( initialParameters[i] );
   }

 typedef itk::Statistics::ExpectationMaximizationMixtureModelEstimator<
                          ImageToListSampleAdaptorType > EstimatorType;
 EstimatorType::Pointer estimator = EstimatorType::New();

 estimator->SetSample( imageToListSampleAdaptor );
 estimator->SetMaximumIteration( 200 );

 itk::Array< double > initialProportions(numberOfClasses);
 initialProportions[0] = 0.33;
 initialProportions[1] = 0.33;
 initialProportions[2] = 0.33;

 estimator->SetInitialProportions( initialProportions );

 for ( unsigned int i = 0 ; i < numberOfClasses ; i++)
   {
   estimator->AddComponent( (ComponentType::Superclass*)
                            (components[i]).GetPointer() );
   }

 estimator->Update();

 // Output the results
 for ( unsigned int i = 0 ; i < numberOfClasses ; i++ )
   {
   std::cout << "Cluster[" << i << "]" << std::endl;
   std::cout << "    Parameters:" << std::endl;
   std::cout << "         " << (components[i])->GetFullParameters()
             << std::endl;
   std::cout << "    Proportion: ";
   // Outputs: // mean of dimension 1, mean of dimension 2, covariance(0,0), covariance(0,1), covariance(1,0), covariance(1,1)
   std::cout << "         " << estimator->GetProportions()[i] << std::endl;
   }

 // Display the membership of each sample
 typedef itk::Statistics::SampleClassifierFilter< ImageToListSampleAdaptorType > FilterType;

 typedef itk::Statistics::MaximumDecisionRule2  DecisionRuleType;
 DecisionRuleType::Pointer    decisionRule = DecisionRuleType::New();

 typedef FilterType::ClassLabelVectorObjectType               ClassLabelVectorObjectType;
 typedef FilterType::ClassLabelVectorType                     ClassLabelVectorType;

 ClassLabelVectorObjectType::Pointer  classLabelsObject = ClassLabelVectorObjectType::New();
 ClassLabelVectorType & classLabelVector  = classLabelsObject->Get();

 typedef FilterType::ClassLabelType        ClassLabelType;

 ClassLabelType  class0 = 0;
 classLabelVector.push_back( class0 );

 ClassLabelType  class1 = 1;
 classLabelVector.push_back( class1 );

 ClassLabelType  class2 = 2;
 classLabelVector.push_back( class2 );

 FilterType::Pointer sampleClassifierFilter = FilterType::New();
 sampleClassifierFilter->SetInput( imageToListSampleAdaptor );
 sampleClassifierFilter->SetNumberOfClasses( numberOfClasses );
 sampleClassifierFilter->SetClassLabels( classLabelsObject );
 sampleClassifierFilter->SetDecisionRule( decisionRule );
 sampleClassifierFilter->SetMembershipFunctions( estimator->GetOutput() );
 sampleClassifierFilter->Update();

 const FilterType::MembershipSampleType* membershipSample = sampleClassifierFilter->GetOutput();
 FilterType::MembershipSampleType::ConstIterator iter = membershipSample->Begin();

 while ( iter != membershipSample->End() )
   {
   std::cout << (int)iter.GetMeasurementVector()[0] << " " << (int)iter.GetMeasurementVector()[1] << " " << (int)iter.GetMeasurementVector()[2]
             << " : " << iter.GetClassLabel() << std::endl;
   ++iter;
   }

 return EXIT_SUCCESS;

}

void CreateImage(ImageType::Pointer image) {

 // Create an image
 ImageType::RegionType region;
 ImageType::IndexType start;
 start[0] = 0;
 start[1] = 0;

 ImageType::SizeType size;
 size[0] = 10;
 size[1] = 10;

 region.SetSize(size);
 region.SetIndex(start);

 image->SetRegions(region);
 image->Allocate();

 // Make a red and a green square
 itk::CovariantVector<unsigned char, 3> green;
 green[0] = 0;
 green[1] = 255;
 green[2] = 0;

 itk::CovariantVector<unsigned char, 3> red;
 red[0] = 255;
 red[1] = 0;
 red[2] = 0;

 itk::CovariantVector<unsigned char, 3> black;
 black[0] = 0;
 black[1] = 0;
 black[2] = 0;
 
 itk::ImageRegionIterator<ImageType> imageIterator(image,region);
 imageIterator.GoToBegin();
 
 while(!imageIterator.IsAtEnd())
   {
   if(imageIterator.GetIndex()[0] > 2 && imageIterator.GetIndex()[0] < 5 &&
     imageIterator.GetIndex()[1] > 2 && imageIterator.GetIndex()[1] < 5)
     {
     imageIterator.Set(green);
     }
   else if(imageIterator.GetIndex()[0] > 6 && imageIterator.GetIndex()[0] < 9 &&
     imageIterator.GetIndex()[1] > 6 && imageIterator.GetIndex()[1] < 9)
     {
     imageIterator.Set(red);
     }
   else
     {
     imageIterator.Set(black);
     }
   ++imageIterator;
   }

} </source>

CMakeLists.txt

<source lang="cmake"> cmake_minimum_required(VERSION 2.6)

PROJECT(ExpectationMaximizationMixtureModelEstimator)

FIND_PACKAGE(ITK REQUIRED) INCLUDE(${ITK_USE_FILE})

ADD_EXECUTABLE(ExpectationMaximizationMixtureModelEstimator_Image ExpectationMaximizationMixtureModelEstimator_Image.cxx) TARGET_LINK_LIBRARIES(ExpectationMaximizationMixtureModelEstimator_Image ITKBasicFilters ITKCommon ITKIO ITKStatistics)

</source>