// Computes specificity of the model by comparing random samples of the model with the test mashes.
float specificity(Logger& logger, StatisticalModelType::Pointer model, const MeshDataList& testMeshes, unsigned numberOfSamples) {
// draw a number of samples and compute its distance to the closest training dataset
double accumulatedDistToClosestTrainingShape = 0;
for (unsigned i = 0; i < numberOfSamples; i++) {
MeshType::Pointer sample = model->DrawSample();
double minDist = std::numeric_limits<double>::max();
for (MeshDataList::const_iterator it = testMeshes.begin(); it != testMeshes.end(); ++it) {
MeshType::Pointer testMesh = it->first;
// before we compute the distances between the meshes, we normalize the scale by scaling them
// to optimally match the mean. This makes sure that models that include scale and those that have them normalized
// ar etreated the same.
MeshType::Pointer sampledScaledToMean = normalizeScale(sample, model->DrawMean());
MeshType::Pointer testScaledToMean = normalizeScale(testMesh, model->DrawMean());
double dist = computeAverageDistance(testScaledToMean, sampledScaledToMean, ConfigParameters::numSamplingPointsSpecificity);
logger.Get(logINFO) << "distance " << dist << std::endl;
if (dist < minDist) {
minDist = dist;
}
}
logger.Get(logINFO) << "closest distance for sample " << i << ": " << minDist << std::endl;
accumulatedDistToClosestTrainingShape += minDist;
}
double avgDist = accumulatedDistToClosestTrainingShape / numberOfSamples;
logger.Get(logINFO) << "average distance " << avgDist << std::endl;
return avgDist;
}
void buildImageIntensityModelOnROI(const char* referenceFilename, const char* maskFilename, const char* dir, const char* outputImageFilename) {
typedef itk::PCAModelBuilder<RepresenterType> ModelBuilderType;
typedef itk::StatisticalModel<RepresenterType> StatisticalModelType;
typedef std::vector<std::string> StringVectorType;
typedef itk::DataManager<RepresenterType> DataManagerType;
RepresenterType::Pointer representer = RepresenterType::New();
typedef itk::ImageFileReader< ImageType > MaskReaderType;
MaskReaderType::Pointer maskReader = MaskReaderType::New();
maskReader->SetFileName( maskFilename );
maskReader->Update();
representer->SetReference( ReadImageFromFile(referenceFilename), maskReader->GetOutput() );
StringVectorType filenames;
getdir(dir, filenames, ".vtk");
DataManagerType::Pointer dataManager = DataManagerType::New();
dataManager->SetRepresenter(representer);
for (StringVectorType::const_iterator it = filenames.begin(); it != filenames.end(); it++) {
std::string fullpath = (std::string(dir) + "/") + *it;
dataManager->AddDataset( ReadImageFromFile(fullpath), fullpath.c_str());
}
ModelBuilderType::Pointer pcaModelBuilder = ModelBuilderType::New();
StatisticalModelType::Pointer model = pcaModelBuilder->BuildNewModel(dataManager->GetSampleDataStructure(), 0);
std::cout<<"dimensionality of the data: "<<model->GetDomain().GetNumberOfPoints()<<", dimension of the images: "<<(*dataManager->GetSampleDataStructure().begin())->GetSample()->GetLargestPossibleRegion().GetNumberOfPixels()<<std::endl;
std::cout<<"writing the mean sample to a png file..."<<std::endl;
typedef itk::ImageFileWriter< ImageType > ImageWriterType;
ImageWriterType::Pointer writer = ImageWriterType::New();
writer->SetFileName( outputImageFilename );
writer->SetInput(model->DrawSample());
writer->Update();
}
