def seperate_lungs(image):
#Creation of the markers as shown above:
marker_internal, marker_external, marker_watershed = generate_markers(image)
#Creation of the Sobel-Gradient
sobel_filtered_dx = ndimage.sobel(image, 1)
sobel_filtered_dy = ndimage.sobel(image, 0)
sobel_gradient = np.hypot(sobel_filtered_dx, sobel_filtered_dy)
sobel_gradient *= 255.0 / np.max(sobel_gradient)
#Watershed algorithm
watershed = morphology.watershed(sobel_gradient, marker_watershed)
#Reducing the image created by the Watershed algorithm to its outline
outline = ndimage.morphological_gradient(watershed, size=(3,3))
outline = outline.astype(bool)
#Performing Black-Tophat Morphology for reinclusion
#Creation of the disk-kernel and increasing its size a bit
blackhat_struct = [[0, 0, 1, 1, 1, 0, 0],
[0, 1, 1, 1, 1, 1, 0],
[1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1],
[0, 1, 1, 1, 1, 1, 0],
[0, 0, 1, 1, 1, 0, 0]]
blackhat_struct = ndimage.iterate_structure(blackhat_struct, 8)
#Perform the Black-Hat
outline += ndimage.black_tophat(outline, structure=blackhat_struct)
#Use the internal marker and the Outline that was just created to generate the lungfilter
lungfilter = np.bitwise_or(marker_internal, outline)
#Close holes in the lungfilter
#fill_holes is not used here, since in some slices the heart would be reincluded by accident
##structure = np.ones((BINARY_CLOSING_SIZE,BINARY_CLOSING_SIZE)) # 5 is not enough, 7 is
structure = morphology.disk(BINARY_CLOSING_SIZE) # better , 5 seems sufficient, we use 7 for safety/just in case
lungfilter = ndimage.morphology.binary_closing(lungfilter, structure=structure, iterations=3) #, iterations=3) # was structure=np.ones((5,5))
### NOTE if no iterattions, i.e. default 1 we get holes within lungs for the disk(5) and perhaps more
#Apply the lungfilter (note the filtered areas being assigned -2000 HU)
segmented = np.where(lungfilter == 1, image, -2000*np.ones((512, 512))) ### was -2000
return segmented, lungfilter, outline, watershed, sobel_gradient, marker_internal, marker_external, marker_watershed
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