def image_matrix(imgs,
contrast,
masks=None,
do_whitestripe=True,
return_ctrl_matrix=False,
membership_thresh=0.99,
smoothness=0.25,
max_ctrl_vox=10000,
do_registration=False,
ctrl_prob=1,
use_fcm=False):
"""
creates an matrix of images where the rows correspond the the voxels of
each image and the columns are the images
Args:
imgs (list): list of paths to MR images of interest
contrast (str): contrast of the set of imgs (e.g., T1)
masks (list or str): list of corresponding brain masks or just one (template) mask
do_whitestripe (bool): do whitestripe on the images before storing in matrix or nah
return_ctrl_matrix (bool): return control matrix for imgs (i.e., a subset of V's rows)
membership_thresh (float): threshold of membership for control voxels (want this very high)
this option is only used if the registration is turned off
smoothness (float): smoothness parameter for segmentation for control voxels
this option is only used if the registration is turned off
max_ctrl_vox (int): maximum number of control voxels (if too high, everything
crashes depending on available memory) only used if do_registration is false
do_registration (bool): register the images together and take the intersection of the csf
masks (as done in the original paper, note that this takes much longer)
ctrl_prob (float): given all data, proportion of data labeled as csf to be
used for intersection (i.e., when do_registration is true)
use_fcm (bool): use FCM for segmentation instead of atropos (may be less accurate)
Returns:
V (np.ndarray): image matrix (rows are voxels, columns are images)
Vc (np.ndarray): image matrix of control voxels (rows are voxels, columns are images)
Vc only returned if return_ctrl_matrix is True
"""
img_shape = io.open_nii(imgs[0]).get_data().shape
V = np.zeros((int(np.prod(img_shape)), len(imgs)))
if return_ctrl_matrix:
ctrl_vox = []
if masks is None and return_ctrl_matrix:
raise NormalizationError(
'Brain masks must be provided if returning control memberships')
if masks is None:
masks = [None] * len(imgs)
for i, (img_fn, mask_fn) in enumerate(zip(imgs, masks)):
_, base, _ = io.split_filename(img_fn)
img = io.open_nii(img_fn)
mask = io.open_nii(mask_fn) if mask_fn is not None else None
# do whitestripe on the image before applying RAVEL (if desired)
if do_whitestripe:
logger.info('Applying WhiteStripe to image {} ({:d}/{:d})'.format(
base, i + 1, len(imgs)))
inds = whitestripe(img, contrast, mask)
img = whitestripe_norm(img, inds)
img_data = img.get_data()
if img_data.shape != img_shape:
raise NormalizationError(
'Cannot normalize because image {} needs to have same dimension '
'as all other images ({} != {})'.format(
base, img_data.shape, img_shape))
V[:, i] = img_data.flatten()
if return_ctrl_matrix:
if do_registration and i == 0:
logger.info(
'Creating control mask for image {} ({:d}/{:d})'.format(
base, i + 1, len(imgs)))
verbose = True if logger.getEffectiveLevel(
) == logging.getLevelName('DEBUG') else False
ctrl_masks = []
reg_imgs = []
reg_imgs.append(csf.nibabel_to_ants(img))
ctrl_masks.append(
csf.csf_mask(img,
mask,
contrast=contrast,
csf_thresh=membership_thresh,
mrf=smoothness,
use_fcm=use_fcm))
elif do_registration and i != 0:
template = ants.image_read(imgs[0])
tmask = ants.image_read(masks[0])
img = csf.nibabel_to_ants(img)
logger.info(
'Starting registration for image {} ({:d}/{:d})'.format(
base, i + 1, len(imgs)))
reg_result = ants.registration(template,
img,
type_of_transform='SyN',
mask=tmask,
verbose=verbose)
img = reg_result['warpedmovout']
mask = csf.nibabel_to_ants(mask)
reg_imgs.append(img)
logger.info(
'Creating control mask for image {} ({:d}/{:d})'.format(
base, i + 1, len(imgs)))
ctrl_masks.append(
csf.csf_mask(img,
mask,
contrast=contrast,
csf_thresh=membership_thresh,
mrf=smoothness,
use_fcm=use_fcm))
else:
logger.info(
'Finding control voxels for image {} ({:d}/{:d})'.format(
base, i + 1, len(imgs)))
ctrl_mask = csf.csf_mask(img,
mask,
contrast=contrast,
csf_thresh=membership_thresh,
mrf=smoothness,
use_fcm=use_fcm)
if np.sum(ctrl_mask) == 0:
raise NormalizationError(
'No control voxels found for image ({}) at threshold ({})'
.format(base, membership_thresh))
elif np.sum(ctrl_mask) < 100:
logger.warning(
'Few control voxels found ({:d}) (potentially a problematic image ({}) or '
'threshold ({}) too high)'.format(
int(np.sum(ctrl_mask)), base, membership_thresh))
ctrl_vox.append(img_data[ctrl_mask == 1].flatten())
if return_ctrl_matrix and not do_registration:
min_len = min(min(map(len, ctrl_vox)), max_ctrl_vox)
logger.info('Using {:d} control voxels'.format(min_len))
Vc = np.zeros((min_len, len(imgs)))
for i in range(len(imgs)):
ctrl_voxs = ctrl_vox[i][:min_len]
logger.info(
'Image {:d} control voxel stats - mean: {:.3f}, std: {:.3f}'.
format(i + 1, np.mean(ctrl_voxs), np.std(ctrl_voxs)))
Vc[:, i] = ctrl_voxs
elif return_ctrl_matrix and do_registration:
ctrl_sum = reduce(
add,
ctrl_masks) # need to use reduce instead of sum b/c data structure
intersection = np.zeros(ctrl_sum.shape)
intersection[ctrl_sum >= np.floor(len(ctrl_masks) * ctrl_prob)] = 1
num_ctrl_vox = int(np.sum(intersection))
Vc = np.zeros((num_ctrl_vox, len(imgs)))
for i, img in enumerate(reg_imgs):
ctrl_voxs = img.numpy()[intersection == 1]
logger.info(
'Image {:d} control voxel stats - mean: {:.3f}, std: {:.3f}'.
format(i + 1, np.mean(ctrl_voxs), np.std(ctrl_voxs)))
Vc[:, i] = ctrl_voxs
del ctrl_masks, reg_imgs
import gc
gc.collect(
) # force a garbage collection, since we just used the majority of the system memory
return V if not return_ctrl_matrix else (V, Vc)
def run_intensity_ravel(outfolder):
from intensity_normalization.normalize import ravel
from intensity_normalization.utilities import io, csf
try:
images = []
brainMasks = []
csfMasks = []
_, _, filenames = next(walk(outfolder))
for f in filenames:
filename = f.split(sep)[-1].split(".")[0]
images.append(io.open_nii(join(outfolder, f.split(sep)[-1])))
brainMasks.append(
io.open_nii(
join(outfolder, 'robex_masks', filename + "_mask.nii.gz")))
if not exists(join(outfolder, "Robex")):
makedirs(join(outfolder, "Robex"))
if not exists(join(outfolder, "csf_masks")):
makedirs(join(outfolder, "csf_masks"))
print("creating csf masks...")
for image, brainMask, f in zip(images, brainMasks, filenames):
filename = f.split(sep)[-1].split(".")[0]
csfMask = csf.csf_mask(image,
brainMask,
contrast='T1',
csf_thresh=0.9,
return_prob=False,
mrf=0.25,
use_fcm=False)
output = nib.Nifti1Image(csfMask, None)
io.save_nii(
output,
join(outfolder, 'csf_masks', filename + "_csfmask.nii.gz"))
shutil.move(join(outfolder,
f.split(sep)[-1]), join(outfolder, "Robex"))
print('running intensity ravel...')
ravel.ravel_normalize(join(outfolder, 'Robex'),
join(outfolder, 'csf_masks'),
'T1',
output_dir=outfolder,
write_to_disk=True,
do_whitestripe=True,
b=1,
membership_thresh=0.99,
segmentation_smoothness=0.25,
do_registration=False,
use_fcm=True,
sparse_svd=False,
csf_masks=True)
for i in filenames:
rename(
join(outfolder,
i.split(sep)[-1]),
join(outfolder,
i.split(sep)[-1].split(".")[0] + "_RAVEL.nii.gz"))
except:
e = sys.exc_info()
print("Error: ", str(e[0]))
print("Error: ", str(e[1]))
print("Error: executing ravel method")
sys.exit(2)