CODEWITHSUNDEEP
c++image-processingfeature-extractionitk
blackbishop
blackbishop

Reputation: 32690

ITK - Calculate texture features for segmented 3D brain MRI

I'm trying to calculate texture features for a segmented 3D brain MRI using ITK library with C++. So I followed this example. The example takes a 3D image, and extracts 3 different features for all 13 possible spatial directions. In my program, I just want for a given 3D image to get :

Here is what I have so far :

//definitions of used types
typedef itk::Image<float, 3> InternalImageType;
typedef itk::Image<unsigned char, 3> VisualizingImageType;
typedef itk::Neighborhood<float, 3> NeighborhoodType;
typedef itk::Statistics::ScalarImageToCooccurrenceMatrixFilter<InternalImageType>
Image2CoOccuranceType;
typedef Image2CoOccuranceType::HistogramType HistogramType;
typedef itk::Statistics::HistogramToTextureFeaturesFilter<HistogramType> Hist2FeaturesType;
typedef InternalImageType::OffsetType OffsetType;
typedef itk::AddImageFilter <InternalImageType> AddImageFilterType;
typedef itk::MultiplyImageFilter<InternalImageType> MultiplyImageFilterType;

void calcTextureFeatureImage (OffsetType offset, InternalImageType::Pointer inputImage)
{
// principal variables
//Gray Level Co-occurance Matrix Generator
Image2CoOccuranceType::Pointer glcmGenerator=Image2CoOccuranceType::New();
glcmGenerator->SetOffset(offset);
glcmGenerator->SetNumberOfBinsPerAxis(16); //reasonable number of bins
glcmGenerator->SetPixelValueMinMax(0, 255); //for input UCHAR pixel type
Hist2FeaturesType::Pointer featureCalc=Hist2FeaturesType::New();
//Region Of Interest
typedef itk::RegionOfInterestImageFilter<InternalImageType,InternalImageType> roiType;
roiType::Pointer roi=roiType::New();
roi->SetInput(inputImage);



InternalImageType::RegionType window;
InternalImageType::RegionType::SizeType size;
size.Fill(50);
window.SetSize(size);

window.SetIndex(0,0);
window.SetIndex(1,0);
window.SetIndex(2,0);

roi->SetRegionOfInterest(window);
roi->Update();

glcmGenerator->SetInput(roi->GetOutput());
glcmGenerator->Update();

featureCalc->SetInput(glcmGenerator->GetOutput());
featureCalc->Update();

std::cout<<"\n Entropy : ";
std::cout<<featureCalc->GetEntropy()<<"\n Energy";
std::cout<<featureCalc->GetEnergy()<<"\n Correlation";
std::cout<<featureCalc->GetCorrelation()<<"\n Inertia";             
std::cout<<featureCalc->GetInertia()<<"\n HaralickCorrelation";
std::cout<<featureCalc->GetHaralickCorrelation()<<"\n InverseDifferenceMoment";
std::cout<<featureCalc->GetInverseDifferenceMoment()<<"\nClusterProminence";
std::cout<<featureCalc->GetClusterProminence()<<"\nClusterShade";
std::cout<<featureCalc->GetClusterShade();
}

The program works. However I have this problem : it gives the same results for different 3D images, even when I change the window size.

Does any one used ITK to do this ? If there is any other method to achieve that, could anyone point me to a solution please ?

Any help will be much apreciated.

Upvotes: 5

Views: 1532

Answers (2)

M&#39;henni Koroghli
M&#39;henni Koroghli

Reputation: 212

I think that your images have only one gray scale level. For example, if you segment your images using itk-snap tool, when you save the result of the segmentation, itk-snap save it with one gray scale level. So, if you try to calculate texture features for images segmented with itk-snap you'll always have the same results even if you change the images or the window size because you have only one gray scale level in the co-occurrence matrix. Try to run your program using unsegmented images, you'll certainly have different results.

EDIT :

To calculate texture features for segmented images, try another segmentation method which saves the original gray scale levels of the unsegmented image.

Upvotes: 2

lib
lib

Reputation: 2956

Something strange in your code is size.Fill(50), while in the example they show it should hold the image dimension:

 size.Fill(3); //window size=3x3x3

Upvotes: 0

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