def border_distance(self):
border_ref = MorphologyOps(self.ref, self.neigh).border_map()
border_seg = MorphologyOps(self.seg, self.neigh).border_map()
oppose_ref = 1 - self.ref
oppose_seg = 1 - self.seg
distance_ref = ndimage.distance_transform_edt(oppose_ref)
distance_seg = ndimage.distance_transform_edt(oppose_seg)
distance_border_seg = border_ref * distance_seg
distance_border_ref = border_seg * distance_ref
return distance_border_ref, distance_border_seg
def borders(seg, ref, neigh=8):
"""
This function determines the points that lie on the border of the
inferred and reference segmentations
:param seg: numpy array with binary mask from inferred segmentation
:param ref: numpy array with binary mask from reference segmentation
:param neigh: connectivity 4 or 8
:return: numpy arrays of reference and inferred segmentation borders
"""
border_ref = MorphologyOps(ref[:, :, :, 0, 0], neigh).border_map()
border_seg = MorphologyOps(seg[:, :, :, 0, 0], neigh).border_map()
return border_ref, border_seg
def _connected_components(self):
"""
This function creates the maps of connected component for the
reference and the segmentation image using the neighborhood defined
in self.neigh
:return: blobs_ref: connected labeling for the reference image,
blobs_seg: connected labeling for the segmentation image,
init: intersection between segmentation and reference
"""
init = np.multiply(self.seg, self.ref)
blobs_ref = MorphologyOps(self.ref, self.neigh).foreground_component()
blobs_seg = MorphologyOps(self.seg, self.neigh).foreground_component()
return blobs_ref, blobs_seg, init
def border_distance(self):
"""
This functions determines the map of distance from the borders of the
segmentation and the reference and the border maps themselves
:return: distance_border_ref, distance_border_seg, border_ref,
border_seg
"""
border_ref = MorphologyOps(self.ref, self.neigh).border_map()
border_seg = MorphologyOps(self.seg, self.neigh).border_map()
oppose_ref = 1 - self.ref
oppose_seg = 1 - self.seg
# euclidean distance transform
distance_ref = ndimage.distance_transform_edt(oppose_ref)
distance_seg = ndimage.distance_transform_edt(oppose_seg)
distance_border_seg = border_ref * distance_seg
distance_border_ref = border_seg * distance_ref
return distance_border_ref, distance_border_seg, border_ref, border_seg
def connected_components(seg, ref, output_prob, neigh=8):
"""
Numbers connected components in the reference and inferred segmentations
:param seg: numpy array with binary mask from inferred segmentation
:param ref: numpy array with binary mask from reference segmentation
:param output_prob: Is the segmentation probabilistic (if so,
argmax is used first to compute a label map)
:param neigh: connectivity 4 or 8
:return: (cc_map_ref, count) numbered connected components from reference
:return: (cc_map_seg, count) numbered connected components from inferred
"""
if output_prob:
seg = np.argmax(seg, -1)
blobs_ref = MorphologyOps(ref[:, :, :, 0, 0], neigh).foreground_component()
blobs_seg = MorphologyOps(seg[:, :, :, 0, 0], neigh).foreground_component()
return (blobs_ref[0][:, :, :, np.newaxis, np.newaxis], blobs_ref[1]), \
(blobs_seg[0][:, :, :, np.newaxis, np.newaxis], blobs_seg[1]),
def surface(self):
"""
Similarly to the volume function, returns probabilistic sum, binary
number, probabilistic volume and binarised volume of the segmentation
border
:return:
"""
border_seg = MorphologyOps(self.seg, self.neigh).border_map()
numb_border_seg_bin = np.sum(border_seg > 0)
numb_border_seg = np.sum(border_seg)
return numb_border_seg, numb_border_seg_bin, \
numb_border_seg * self.vol_vox, numb_border_seg_bin * self.vol_vox
def test_3d_offset(self):
test_img = np.zeros([10, 10, 10])
test_img[5, 5, 5] = 1
# border is the same as the test image -- just the one positive voxel
calculated_border = MorphologyOps(test_img, 8).border_map()
self.assertTrue(np.array_equal(test_img, calculated_border))
def surface(self):
border_seg = MorphologyOps(self.seg, self.neigh).border_map()
numb_border_seg_bin = np.sum(border_seg > 0)
numb_border_seg = np.sum(border_seg)
return numb_border_seg, numb_border_seg_bin, \
numb_border_seg * self.vol_vox, numb_border_seg_bin * self.vol_vox
def _connected_components(self):
init = np.multiply(self.seg, self.ref)
blobs_ref = MorphologyOps(self.ref, self.neigh).foreground_component()
blobs_seg = MorphologyOps(self.seg, self.neigh).foreground_component()
return blobs_ref, blobs_seg, init