Dear Joost, Pranita,
I am trying the same thing, but then for the grey-level co-occurence (glcm) matrix features.
The code Joost suggested works for first order features with my RGB image (480x480x3) and mask (480x480).
Although trying to extract the glcm features, I keep getting:
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self.kwargs.get('force2Ddimension', 0))
RuntimeError: Expected a 3D array for image and mask.
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Where I tried using .png and .nrrd files for the images:
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imageName= r'C:\Users\ruben\Documents\Master\SCRIPTIE\Images\Images_batch_2\Image_Script\2637932_image_0.nrrd'
maskName= r'C:\Users\ruben\Documents\Master\SCRIPTIE\Images\Images_batch_2\Image_Script\2637932_mask_0.nrrd'
#imageName= r'C:\Users\ruben\Documents\Master\SCRIPTIE\Images\Images_batch_2\Image_Script\2637932_image_0.png'
#maskName= r'C:\Users\ruben\Documents\Master\SCRIPTIE\Images\Images_batch_2\Image_Script\2637932_mask_0.png'
image = sitk.ReadImage(imageName)
#image3d = sitk.JoinSeries(image)
mask = sitk.ReadImage(maskName)
#mask3d = sitk.JoinSeries(mask)
#glcmFeatures = glcm.RadiomicsGLCM(image3d,mask3d, label=255,binWidth=3)
glcmFeatures = glcm.RadiomicsGLCM(image,mask, label=255)
m = glcmFeatures.getAutocorrelationFeatureValue()
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I don't seem to get a solution...
Any help would be really appreciated!
Kind regards,
Ruben Hekster