[Insight-users] ExpectationMaximizationMixtureModelEstimator use of WeightedMeanSampleFilter

[smrolfe at u.washington.edu]

Hello,

I am trying to resolve the error described below in my implementation of the ExpectationMaximizationMixtureModelEstimator for a 3D data set.  I'm trying to find out the relationship between the ExpectationMaximizationMixtureModelEstimator and the WeightedMeanSampleFilter.  I read through the source code and it is not explicitly called in the .hxx file.  If you have any leads on the use of this filter, please let me know.

Thanks,
Sara

> On Mon, 1 Jul 2013, smrolfe at u.washington.edu wrote:
>
>> Hello,
>> 
>> I am using the ExpectationMaximizationMixtureModelEstimator filter to 
>> estimate a mixture model from a set of observations.  I am closely 
>> following the example
>> ExpectationMaximizationMixtureModelEstimator_2D.cxx
>> 
>> When I run the program, I am getting the error:
>> 
>> Description: itk::ERROR: WeightedMeanSampleFilter(00000000038A66C0): Total 
>> weight was too close to zero. Value = 0
>> 
>> It looks like this filter computes the mean of a set of weighted 
>> measurement vectors, but I'm not clear on what might cause the total weight 
>> to be zero.  I'd appreciate any thoughts.
>> An excerpt of my code is below.
>> 
>> Thanks!
>> Sara
>> 
>> 
>> typedef itk::Array< double > ParametersType;
>> typedef itk::Statistics::GaussianMixtureModelComponent< SampleType > 
>> ComponentType;
>> typedef itk::Statistics::ExpectationMaximizationMixtureModelEstimator< 
>> SampleType > EstimatorType;
>> 
>> SampleType::Pointer sample = SampleType::New();
>> 
>> for (int pointIter = 0; pointIter < pointArray[i].size(); pointIter++){ 
>> //put all points in current histogram bin in the sample vector
>>              sample->PushBack( pointArray[i][pointIter] ); //pointArray[i] 
>> is a vector of measurement vectors
>> }
>> int distributionNumber = initialParameters.meanVectors[i].size();
>> int paramNumber = 
>> (MeasurementVectorLength)+(MeasurementVectorLength*MeasurementVectorLength);
>> ParametersType params( paramNumber );
>> std::vector< ParametersType > inputParameters( distributionNumber );
>> itk::Array< double > initialProportions(distributionNumber);
>> 
>> for(int j = 0; j < distributionNumber; j++){         // for each 
>> distribution in initial estimate
>> initialProportions[j] = initialParameters.subHistogram[i][j];   // set 
>> initial proportions to the sub-bin size
>> for(int k = 0; k < MeasurementVectorLength; k++){ // assign values in 
>> distribution parameter matrix
>> params[k] = initialParameters.meanVectors[i][j][k];
>> }
>> int paramCount = MeasurementVectorLength;
>> for(int l=0; l < MeasurementVectorLength; l++){
>> for(int m=0; m < MeasurementVectorLength; m++){
>> params[paramCount] = initialParameters.covarianceMats[i][j][l][m];
>> paramCount++;
>> }
>> }
>> 
>> inputParameters[j] = params; // push parameters for current distribution 
>> into parameter vector
>> }
>> 
>> 
>> std::vector< ComponentType::Pointer > components;                       // 
>> Create the Gausian model components
>> for ( unsigned int k = 0 ; k < distributionNumber ; k++ ){
>> components.push_back( ComponentType::New() );
>> (components[k])->SetSample( sample );
>> (components[k])->SetParameters( inputParameters[k] );
>> }
>> 
>> EstimatorType::Pointer estimator = EstimatorType::New();         // set up 
>> estimator for bin i
>> estimator->SetSample( sample );
>> estimator->SetMaximumIteration( 200 );
>> estimator->SetInitialProportions( initialProportions );
>> 
>> for ( unsigned int k = 0 ; k < distributionNumber ; k++){
>> estimator->AddComponent( 
>> (ComponentType::Superclass*)(components[k]).GetPointer() );
>> }
>> 
>> try{
>> estimator->Update();
>> }catch( itk::ExceptionObject & err ){
>> std::cerr << "ExceptionObject caught !" << std::endl;
>> std::cerr << err << std::endl;
>> }
>> 
>> 
>
>