def get_training_data( file_img, file_mask, r ):
# create mask
input_mask = irtk.imread( file_mask, force_neurological=False )
x_min, y_min, z_min, x_max, y_max, z_max = (input_mask == 0).bbox()
background = irtk.zeros( input_mask.get_header(), dtype='uint8' )
background[z_min:z_max+1,
y_min:y_max+1,
x_min:x_max+1] = 1
background = nd.morphological_gradient( background, size=7)
n = background[z_min+1:z_max,
y_min+1:y_max,
x_min+1:x_max].sum()
z = np.random.randint(low=0, high=input_mask.shape[0],size=int(1.25*n))
y = np.random.randint(low=0, high=input_mask.shape[1],size=int(1.25*n))
x = np.random.randint(low=0, high=input_mask.shape[2],size=int(1.25*n))
background[z,y,x] = 1
background[z_min+1:z_max,
y_min+1:y_max,
x_min+1:x_max] = 0
foreground = (input_mask == 1).astype('uint8')
new_mask = irtk.zeros( input_mask.get_header(), dtype='uint8' )
new_mask[foreground == 1] = 1
new_mask[background != 0] = 2
img = irtk.imread( file_img, dtype='float32', force_neurological=False )
X = []
Y = []
for z in xrange(img.shape[0]):
YX = np.transpose( np.nonzero( foreground[z] ) )
if DEBUG:
YX = YX[::10]
else:
YX = YX[::2]
if YX.shape[0] == 0:
continue
patches = extract_patches2D( img[z], r, YX )
patches = np.reshape( patches, (patches.shape[0],patches.shape[1]*patches.shape[2]) )
#print patches.shape, YX.shape
X.extend( patches )
Y.extend( [1]*len(YX) )
for z in xrange(img.shape[0]):
YX = np.transpose( np.nonzero( background[z] ) )
if DEBUG:
YX = YX[::10]
else:
YX = YX[::2]
if YX.shape[0] == 0:
continue
patches = extract_patches2D( img[z], r, YX )
patches = np.reshape( patches, (patches.shape[0],patches.shape[1]*patches.shape[2]) )
#print patches.shape, YX.shape
X.extend( patches )
Y.extend( [0]*len(YX) )
return X, Y
def mask_image( file_img, file_mask, ga, r, neigh, output_dir ):
img = irtk.imread( file_img, dtype='float32', force_neurological=False )
input_mask = irtk.imread( file_mask, force_neurological=False )
print "predicting..."
res = irtk.zeros( img.get_header(), dtype='float32' )
res2 = irtk.zeros( img.get_header(), dtype='float32' )
res3 = irtk.zeros( img.get_header(), dtype='float32' )
res4 = irtk.zeros( img.get_header(), dtype='uint8' )
mask = irtk.ones( input_mask.get_header(), dtype='uint8' )
mask[input_mask == 2] = 0
for z in xrange(img.shape[0]):
#print z
YX = np.transpose( np.nonzero( mask[z] ) )
if YX.shape[0] == 0:
continue # this slice does not intersect the box
patches = extract_patches2D( img[z], r, YX )
patches = np.reshape( patches, (patches.shape[0],patches.shape[1]*patches.shape[2]) )
predictions = neigh.predict_proba(patches)[:,1]
res[z,YX[:,0],YX[:,1]] = predictions
x_min, y_min, z_min, x_max, y_max, z_max = mask.bbox()
proba = res[z_min:z_max+1,
y_min:y_max+1,
x_min:x_max+1]
irtk.imwrite("proba_before_rescaling.nii.gz", proba)
if args.mass:
BV = get_BV( args.ga )
box_volume = (z_max-z_min)*img.header['pixelSize'][2]*(y_max-y_min)*img.header['pixelSize'][1]*(x_max-x_min)*img.header['pixelSize'][0]
ratio = float(BV) / float(box_volume)
print "ratio", ratio
q0,q1 = mquantiles( proba.view(np.ndarray).flatten(), prob=[0.5*(1.0-ratio),
1.0-0.5*ratio] )
print "threshold", q0,q1
#threshold = max(0.5,threshold)
# labels = res[z_min:z_max+1,
# y_min:y_max+1,
# x_min:x_max+1] > threshold
#res = 1 / (np.exp(-(res-threshold)/(res.max()-res.min())))
res[res<q0] = q0
res[res>q1] = q1
res -= res.min()
res /= res.max()
labels = ( res[z_min:z_max+1,
y_min:y_max+1,
x_min:x_max+1] > 0.5 ).astype('int32')
proba = res[z_min:z_max+1,
y_min:y_max+1,
x_min:x_max+1]
irtk.imwrite("proba_after_rescaling.nii.gz", proba)
header = proba.get_header()
header['dim'][3] = 2
proba_crf = irtk.zeros( header, dtype='float32' )
proba_crf[0] = 1.0 - proba
proba_crf[1] = proba
cropped_img = img[z_min:z_max+1,
y_min:y_max+1,
x_min:x_max+1]
if args.do_3D:
labels = irtk.crf( cropped_img,
labels,
proba_crf,
l=args.l,
sigma=get_noiseXY(cropped_img),
sigmaZ=get_noiseZ(cropped_img)
)
else:
for z in xrange(cropped_img.shape[0]):
labels[z] = irtk.crf( cropped_img[z].as3D(),
labels[z].as3D(),
proba_crf[:,z].as4D(),
l=args.l,
sigma=get_noiseXY(cropped_img),
sigmaZ=get_noiseZ(cropped_img) )
print "MAX LABEL:", labels.max()
irtk.imwrite(output_dir + "/bare_"+os.path.basename(file_img), labels )
tmp = irtk.zeros( img.get_header(), dtype='uint8' )
tmp[z_min:z_max+1,
y_min:y_max+1,
x_min:x_max+1] = labels
( min_x_bare, min_y_bare, min_z_bare,
max_x_bare, max_y_bare, max_z_bare ) = tmp.bbox()
if not args.no_cleaning:
# clean by fitting ellipses enlarged of 10%
for z in xrange(labels.shape[0]):
edges = nd.morphological_gradient( labels[z] > 0,size=5 )
points = np.transpose(edges.nonzero())[:,::-1]
if len(points) == 0:
continue
points = np.array(map(lambda x:[x],points),dtype='int32')
ellipse = cv2.fitEllipse(points)
cv2.ellipse( labels[z], (ellipse[0],
(1.1*ellipse[1][0],1.1*ellipse[1][1]),
ellipse[2]) , 1, -1 )
irtk.imwrite(output_dir + "/seg_"+os.path.basename(file_img), labels )
irtk.imwrite(output_dir + "/res_"+os.path.basename(file_img), res )
# re-read the image in case we processed it
img = irtk.imread( file_img, dtype='float32', force_neurological=False )
cropped_img = img[z_min:z_max+1,
y_min:y_max+1,
x_min:x_max+1]
cropped_img[labels==0] = -1
masked = cropped_img.bbox(crop=True)
irtk.imwrite(output_dir + "/masked_"+os.path.basename(file_img), masked )
# re-read the image in case we processed it
img = irtk.imread( file_img, dtype='float32', force_neurological=False )
x0 = min_x_bare + (max_x_bare - min_x_bare) / 2
y0 = min_y_bare + (max_y_bare - min_y_bare) / 2
ofd = get_OFD(ga,centile=95)/img.header['pixelSize'][0]
cropped_img = img[min_z_bare:max_z_bare+1,
max(0,int(round(y0-ofd/2))):min(img.shape[1],int(round(y0+ofd/2+1))),
max(0,int(round(x0-ofd/2))):min(img.shape[2],int(round(x0+ofd/2+1)))].copy()
irtk.imwrite(output_dir + "/very_large_"+os.path.basename(file_img),
cropped_img )
cropped_proba = res[min_z_bare:max_z_bare+1,
max(0,int(round(y0-ofd/2))):min(img.shape[1],int(round(y0+ofd/2+1))),
max(0,int(round(x0-ofd/2))):min(img.shape[2],int(round(x0+ofd/2+1)))].copy()
irtk.imwrite(output_dir + "/proba_"+os.path.basename(file_img),
cropped_proba )