I am working on 3-D numpy array in python and want to do post-processing on CNN output of Brain Tumor Segmentation images. We get a 3-D (208x208x155) numpy array with values as 0/1/2/4 for each pixel. I want to remove the connected components with a threshold less than 1000 for better results.
I tried erosion-dilation but don't get good results. Can anyone help me?
Ok, so shrink and grow will, as you realised yourself, not be the way to approach this problem. What you need to do is region labelling, and it seems that Scipy has a method that will let you do that for nd images.
I assume that by threshold less than 1000 you mean sum of the pixel values in the connected components.
Here is an outline of how I would do it.
from scipy.ndimage import label
segmentation_mask = [...] # This should be your 3D mask.
# Let us create a binary mask.
# It is 0 everywhere `segmentation_mask` is 0 and 1 everywhere else.
binary_mask = segmentation_mask.copy()
binary_mask[binary_mask != 0] = 1
# Now, we perform region labelling. This way, every connected component
# will have their own colour value.
labelled_mask, num_labels = label(binary_mask)
# Let us now remove all the too small regions.
refined_mask = segmentation_mask.copy()
minimum_cc_sum = 1000
for label in range(num_labels):
if np.sum(refined_mask[labelled_mask == label]) < minimum_cc_sum:
refined_mask[labelled_mask == label] = 0