def tissue_classification(img,
mask=None,
niters=25,
beta=0.5,
ngb_size=6,
probc=None,
probg=None,
probw=None):
import numpy as np
from nipy import load_image, save_image
from nipy.core.image.image_spaces import (make_xyz_image, xyz_affine)
from nipy.algorithms.segmentation import BrainT1Segmentation
import os
# Input image
img = load_image(img)
# Input mask image
mask_img = mask
if mask_img == None:
mask_img = img
else:
mask_img = load_image(mask_img)
# Other optional arguments
#niters = int(get_argument('niters', 25))
#beta = float(get_argument('beta', 0.5))
#ngb_size = int(get_argument('ngb_size', 6))
# Perform tissue classification
mask = mask_img.get_data() > 0
S = BrainT1Segmentation(img.get_data(),
mask=mask,
model='5k',
niters=niters,
beta=beta,
ngb_size=ngb_size)
# Save label image
outfile = os.path.abspath('hard_classif.nii')
save_image(make_xyz_image(S.label, xyz_affine(img), 'scanner'), outfile)
print('Label image saved in: %s' % outfile)
# Compute fuzzy Dice indices if a 3-class fuzzy model is provided
if not probc == None and\
not probg == None and\
not probw == None:
print('Computing Dice index')
gold_ppm = np.zeros(S.ppm.shape)
gold_ppm_img = (probc, probg, probw)
for k in range(3):
img = load_image(gold_ppm_img[k])
gold_ppm[..., k] = img.get_data()
d = fuzzy_dice(gold_ppm, S.ppm, np.where(mask_img.get_data() > 0))
print('Fuzzy Dice indices: %s' % d)
def tissue_classification(img,mask=None,niters=25,beta=0.5,ngb_size=6,probc=None,probg=None,probw=None):
import numpy as np
from nipy import load_image, save_image
from nipy.core.image.image_spaces import (make_xyz_image,
xyz_affine)
from nipy.algorithms.segmentation import BrainT1Segmentation
import os
# Input image
img = load_image(img)
# Input mask image
mask_img = mask
if mask_img == None:
mask_img = img
else:
mask_img = load_image(mask_img)
# Other optional arguments
#niters = int(get_argument('niters', 25))
#beta = float(get_argument('beta', 0.5))
#ngb_size = int(get_argument('ngb_size', 6))
# Perform tissue classification
mask = mask_img.get_data() > 0
S = BrainT1Segmentation(img.get_data(), mask=mask, model='5k',
niters=niters, beta=beta, ngb_size=ngb_size)
# Save label image
outfile = os.path.abspath('hard_classif.nii')
save_image(make_xyz_image(S.label, xyz_affine(img), 'scanner'),
outfile)
print('Label image saved in: %s' % outfile)
# Compute fuzzy Dice indices if a 3-class fuzzy model is provided
if not probc == None and\
not probg == None and\
not probw == None:
print('Computing Dice index')
gold_ppm = np.zeros(S.ppm.shape)
gold_ppm_img = (probc, probg, probw)
for k in range(3):
img = load_image(gold_ppm_img[k])
gold_ppm[..., k] = img.get_data()
d = fuzzy_dice(gold_ppm, S.ppm, np.where(mask_img.get_data() > 0))
print('Fuzzy Dice indices: %s' % d)
def change_vox(t_shape, img):
t_aff = adjust_affine(xyz_affine(img), img.shape, t_shape)
img2 = resample(img, reference=(t_shape, t_aff))
zooms = np.asarray(img.header.get_zooms())*np.asarray(img.shape)/np.asarray(t_shape)
return img2, t_aff, zooms
else:
mask_img = load_image(mask_img)
# Other optional arguments
niters = int(get_argument('niters', 25))
beta = float(get_argument('beta', 0.5))
ngb_size = int(get_argument('ngb_size', 6))
# Perform tissue classification
mask = mask_img.get_data() > 0
S = BrainT1Segmentation(img.get_data(), mask=mask, model='5k',
niters=niters, beta=beta, ngb_size=ngb_size)
# Save label image
outfile = 'hard_classif.nii'
save_image(make_xyz_image(S.label, xyz_affine(img), 'scanner'),
outfile)
print('Label image saved in: %s' % outfile)
# Compute fuzzy Dice indices if a 3-class fuzzy model is provided
if args.probc is not None and \
args.probg is not None and \
args.probw is not None:
print('Computing Dice index')
gold_ppm = np.zeros(S.ppm.shape)
gold_ppm_img = (args.probc, args.probg, args.probw)
for k in range(3):
img = load_image(gold_ppm_img[k])
gold_ppm[..., k] = img.get_data()
d = fuzzy_dice(gold_ppm, S.ppm, np.where(mask_img.get_data() > 0))
print('Fuzzy Dice indices: %s' % d)
niters = int(get_argument('niters', 25))
beta = float(get_argument('beta', 0.5))
ngb_size = int(get_argument('ngb_size', 6))
# Perform tissue classification
mask = mask_img.get_data() > 0
S = BrainT1Segmentation(img.get_data(),
mask=mask,
model='5k',
niters=niters,
beta=beta,
ngb_size=ngb_size)
# Save label image
outfile = 'hard_classif.nii'
save_image(make_xyz_image(S.label, xyz_affine(img), 'scanner'), outfile)
print('Label image saved in: %s' % outfile)
# Compute fuzzy Dice indices if a 3-class fuzzy model is provided
if args.probc is not None and \
args.probg is not None and \
args.probw is not None:
print('Computing Dice index')
gold_ppm = np.zeros(S.ppm.shape)
gold_ppm_img = (args.probc, args.probg, args.probw)
for k in range(3):
img = load_image(gold_ppm_img[k])
gold_ppm[..., k] = img.get_data()
d = fuzzy_dice(gold_ppm, S.ppm, np.where(mask_img.get_data() > 0))
print('Fuzzy Dice indices: %s' % d)
