def hard_thresholding( self, proba, smoothing=None ):
#print "MAX proba:",proba.max(), self.name
res = proba > 0.5
res = irtk.largest_connected_component(res,fill_holes=False)
if smoothing is None:
return res
else:
return res.gaussianBlurring( sigma=smoothing ) >= 0.5
def soft_thresholding( self, proba ):
res = proba > 0.5
res = irtk.largest_connected_component(res,fill_holes=False)
proba[res==0] = 0
return proba
max(0,x_min-args.narrow_band-1):min(seg.shape[2],x_max+args.narrow_band+1+1)]
tmp_img = img[max(0,z_min-args.narrow_band-1):min(img.shape[0],z_max+args.narrow_band+1+1),
max(0,y_min-args.narrow_band-1):min(img.shape[1],y_max+args.narrow_band+1+1),
max(0,x_min-args.narrow_band-1):min(img.shape[2],x_max+args.narrow_band+1+1)]
background = (nd.binary_dilation( tmp_seg>0,
structure=morphology.ball(args.narrow_band) ) == 0).astype('int32')
if args.thorax:
tmp_seg[background>0] = 4
else:
tmp_seg[background>0] = 5
if args.debug:
debug_seg = tmp_seg.transform(target=img,interpolation='nearest')
debug_seg[irtk.largest_connected_component(debug_seg==0)>0] = debug_seg.max()
irtk.imwrite("debug_seg.nii.gz",debug_seg)
irtk.imwrite("debug_background.nii.gz",debug_seg!=5)
tmp_img = tmp_img.rescale(-1,1)
labels = random_walker( tmp_img.view(np.ndarray),
tmp_seg.view(np.ndarray),
beta=1000,
mode='cg_mg',
return_full_prob=False )
header = tmp_img.get_header()
#header['dim'][3] = 5
if args.thorax:
labels[labels==4] = 0
