def main(args=None):
args = arg_parser().parse_args(args)
if args.verbosity == 1:
level = logging.getLevelName('INFO')
elif args.verbosity >= 2:
level = logging.getLevelName('DEBUG')
else:
level = logging.getLevelName('WARNING')
logging.basicConfig(
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
level=level)
logger = logging.getLogger(__name__)
try:
if not args.single_img:
if not os.path.isdir(args.image) or not os.path.isdir(
args.brain_mask):
raise NormalizationError(
'if single-img option off, then image and brain-mask must be directories'
)
img_fns = io.glob_nii(args.image)
mask_fns = io.glob_nii(args.brain_mask)
if len(img_fns) != len(mask_fns) and len(img_fns) > 0:
raise NormalizationError(
'input images and masks must be in correspondence and greater than zero '
'({:d} != {:d})'.format(len(img_fns), len(mask_fns)))
for i, (img, mask) in enumerate(zip(img_fns, mask_fns), 1):
_, base, _ = io.split_filename(img)
logger.info('Normalizing image {} ({:d}/{:d})'.format(
img, i, len(img_fns)))
process(img, mask, args, logger)
else:
if not os.path.isfile(args.image) or not os.path.isfile(
args.brain_mask):
raise NormalizationError(
'if single-img option on, then image and brain-mask must be files'
)
process(args.image, args.brain_mask, args, logger)
if args.plot_hist:
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=FutureWarning)
from intensity_normalization.plot.hist import all_hists
import matplotlib.pyplot as plt
ax = all_hists(args.output_dir, args.brain_mask)
ax.set_title('GMM')
plt.savefig(os.path.join(args.output_dir, 'hist.png'))
return 0
except Exception as e:
logger.exception(e)
return 1
def whitestripe(img,
contrast,
mask=None,
width=0.05,
width_l=None,
width_u=None):
"""
find the "(normal appearing) white (matter) stripe" of the input MR image
and return the indices
Args:
img (nibabel.nifti1.Nifti1Image): target MR image
contrast (str): contrast of img (e.g., T1)
mask (nibabel.nifti1.Nifti1Image): brainmask for img (None is default, for skull-stripped img)
width (float): width quantile for the "white (matter) stripe"
width_l (float): lower bound for width (default None, derives from width)
width_u (float): upper bound for width (default None, derives from width)
Returns:
ws_ind (np.ndarray): the white stripe indices (boolean mask)
"""
if width_l is None and width_u is None:
width_l = width
width_u = width
img_data = img.get_data()
if mask is not None:
mask_data = mask.get_data()
masked = img_data * mask_data
voi = img_data[mask_data == 1]
else:
masked = img_data
voi = img_data[img_data > img_data.mean()]
if contrast.lower() in ['t1', 'last']:
mode = hist.get_last_mode(voi)
elif contrast.lower() in ['t2', 'flair', 'largest']:
mode = hist.get_largest_mode(voi)
elif contrast.lower() in ['md', 'first']:
mode = hist.get_first_mode(voi)
else:
raise NormalizationError(
'Contrast {} not valid, needs to be `t1`,`t2`,`flair`,`md`,`first`,`largest`,`last`'
.format(contrast))
img_mode_q = np.mean(voi < mode)
ws = np.percentile(voi, (max(img_mode_q - width_l, 0) * 100,
min(img_mode_q + width_u, 1) * 100))
ws_ind = np.logical_and(masked > ws[0], masked < ws[1])
if len(ws_ind) == 0:
raise NormalizationError(
'WhiteStripe failed to find any valid indices!')
return ws_ind
def kde_normalize(img, mask=None, contrast='t1', norm_value=1):
"""
use kernel density estimation to find the peak of the white
matter in the histogram of a skull-stripped image. Normalize
the WM of the non-skull-stripped image to norm_value
Args:
img (nibabel.nifti1.Nifti1Image): target MR image
mask (nibabel.nifti1.Nifti1Image): brain mask of img
contrast (str): contrast of img (T1,T2,FA,MD)
norm_value (float): value at which to place WM peak
Returns:
normalized (nibabel.nifti1.Nifti1Image): WM normalized img
"""
if mask is not None:
voi = img.get_fdata()[mask.get_fdata() == 1].flatten()
else:
voi = img.get_fdata()[img.get_fdata() > img.get_fdata().mean()].flatten()
if contrast.lower() in ['t1', 'flair', 'last']:
wm_peak = hist.get_last_mode(voi)
elif contrast.lower() in ['t2', 'largest']:
wm_peak = hist.get_largest_mode(voi)
elif contrast.lower() in ['md', 'first']:
wm_peak = hist.get_first_mode(voi)
else:
raise NormalizationError(
'Contrast {} not valid, needs to be `t1`,`t2`,`flair`,`md`,`first`,`largest`,`last`'.format(contrast))
normalized = nib.Nifti1Image((img.get_fdata() / wm_peak) * norm_value,
img.affine, img.header)
return normalized
def csf_mask_intersection(img_dir, masks=None, prob=1):
"""
use all nifti T1w images in data_dir to create csf mask in common areas
Args:
img_dir (str): directory containing MR images to be normalized
masks (str or ants.core.ants_image.ANTsImage): if images are not skull-stripped,
then provide brain mask as either a corresponding directory or an individual mask
prob (float): given all data, proportion of data labeled as csf to be
used for intersection
Returns:
intersection (np.ndarray): binary mask of common csf areas for all provided imgs
"""
if not (0 <= prob <= 1):
raise NormalizationError(
'prob must be between 0 and 1. {} given.'.format(prob))
data = io.glob_nii(img_dir)
masks = io.glob_nii(masks) if isinstance(masks,
str) else [masks] * len(data)
csf = []
for i, (img, mask) in enumerate(zip(data, masks)):
_, base, _ = io.split_filename(img)
logger.info('Creating CSF mask for image {} ({:d}/{:d})'.format(
base, i + 1, len(data)))
imgn = ants.image_read(img)
maskn = ants.image_read(mask) if isinstance(mask, str) else mask
csf.append(csf_mask(imgn, maskn))
csf_sum = reduce(
add, csf) # need to use reduce instead of sum b/c data structure
intersection = np.zeros(csf_sum.shape)
intersection[csf_sum >= np.floor(len(data) * prob)] = 1
return intersection
def get_tissue_mode(data: Array, modality: str) -> float:
"""Find the appropriate tissue mode given a modality"""
modality_ = modality.lower()
if modality_ in PEAK["last"]:
mode = get_last_tissue_mode(data)
elif modality_ in PEAK["largest"]:
mode = get_largest_tissue_mode(data)
elif modality_ in PEAK["first"]:
mode = get_first_tissue_mode(data)
else:
modalities = ", ".join(VALID_PEAKS)
msg = f"Modality {modality} not valid. Needs to be one of {modalities}."
raise NormalizationError(msg)
return mode
def main(args=None):
args = arg_parser().parse_args(args)
if args.verbosity == 1:
level = logging.getLevelName('INFO')
elif args.verbosity >= 2:
level = logging.getLevelName('DEBUG')
else:
level = logging.getLevelName('WARNING')
logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', level=level)
logger = logging.getLogger(__name__)
try:
img_fns = glob_nii(args.img_dir)
if not os.path.exists(args.output_dir):
logger.info('Making Output Directory: {}'.format(args.output_dir))
os.mkdir(args.output_dir)
if args.template_dir is None:
logger.info('Registering image to MNI template')
template = ants.image_read(ants.get_ants_data('mni')).reorient_image2(args.orientation)
orientation = args.orientation
else:
template_fns = glob_nii(args.template_dir)
if len(template_fns) != len(img_fns):
raise NormalizationError('If template images are provided, they must be in '
'correspondence (i.e., equal number) with the source images')
for i, img in enumerate(img_fns):
_, base, _ = split_filename(img)
logger.info('Registering image to template: {} ({:d}/{:d})'.format(base, i+1, len(img_fns)))
if args.template_dir is not None:
template = ants.image_read(template_fns[i])
orientation = template.orientation if hasattr(template, 'orientation') else None
input_img = ants.image_read(img)
input_img = input_img.reorient_image2(orientation) if orientation is not None else input_img
if not args.no_rigid:
logger.info('Starting rigid registration: {} ({:d}/{:d})'.format(base, i+1, len(img_fns)))
mytx = ants.registration(fixed=template, moving=input_img, type_of_transform="Rigid")
tx = mytx['fwdtransforms'][0]
else:
tx = None
logger.info('Starting {} registration: {} ({:d}/{:d})'.format(args.registration, base, i+1, len(img_fns)))
mytx = ants.registration(fixed=template, moving=input_img, initial_transform=tx, type_of_transform=args.registration)
logger.debug(mytx)
moved = ants.apply_transforms(template, input_img, mytx['fwdtransforms'], interpolator='bSpline')
registered = os.path.join(args.output_dir, base + '_reg.nii.gz')
ants.image_write(moved, registered)
return 0
except Exception as e:
logger.exception(e)
return 1
def pairwise_jsd(img_dir, mask_dir, nbins=200):
"""
Calculate the Jensen-Shannon Divergence for all pairs of images in the image directory
Args:
img_dir (str): path to directory of images
mask_dir (str): path to directory of masks
nbins (int): number of bins to use in the histograms
Returns:
pairwise_jsd (np.ndarray): array of pairwise Jensen-Shannon divergence
"""
eps = np.finfo(np.float32).eps
img_fns = io.glob_nii(img_dir)
mask_fns = io.glob_nii(mask_dir)
if len(img_fns) != len(mask_fns):
raise NormalizationError(
f'Number of images ({len(img_fns)}) must be equal to the number of masks ({len(mask_fns)}).'
)
min_intensities, max_intensities = [], []
for img_fn, mask_fn in zip(img_fns, mask_fns):
data = nib.load(img_fn).get_fdata()[nib.load(mask_fn).get_fdata() == 1]
min_intensities.append(np.min(data))
max_intensities.append(np.max(data))
intensity_range = (min(min_intensities), max(max_intensities))
hists = []
for img_fn, mask_fn in zip(img_fns, mask_fns):
data = nib.load(img_fn).get_fdata()[nib.load(mask_fn).get_fdata() == 1]
hist, _ = np.histogram(data.flatten(),
nbins,
range=intensity_range,
density=True)
hists.append(hist + eps)
pairwise_jsd = []
for i in range(len(hists)):
for j in range(i + 1, len(hists)):
pairwise_jsd.append(jsd(hists[i], hists[j]))
return np.array(pairwise_jsd)
def all_hists(img_dir, mask_dir=None, alpha=0.8, figsize=(12, 10), **kwargs):
"""
plot all histograms over one another to get an idea of the
spread for a sample/population
note that all histograms are for the intensities within a given brain mask
or estimated foreground mask (the estimate is just all intensities above the mean)
Args:
img_dir (str): path to images
mask_dir (str): path to corresponding masks of imgs
alpha (float): controls alpha parameter of individual line plots (default: 0.8)
figsize (tuple): size of figure (default: (12,10))
**kwargs: for numpy histogram routine
Returns:
ax (matplotlib.axes.Axes): plotted on ax obj
"""
imgs = glob_nii(img_dir)
if mask_dir is not None:
masks = glob_nii(mask_dir)
else:
masks = [None] * len(imgs)
if len(imgs) != len(masks):
raise NormalizationError(
'Number of images and masks must be equal ({:d} != {:d})'.format(
len(imgs), len(masks)))
_, ax = plt.subplots(figsize=figsize)
for i, (img_fn, mask_fn) in enumerate(zip(imgs, masks), 1):
logger.info('Creating histogram for image {:d}/{:d}'.format(
i, len(imgs)))
img = nib.load(img_fn)
if mask_fn is not None:
mask = nib.load(mask_fn)
else:
mask = None
_ = hist(img, mask, ax=ax, alpha=alpha, **kwargs)
ax.set_xlabel('Intensity')
ax.set_ylabel(r'Log$_{10}$ Count')
ax.set_ylim((0, None))
return ax
def ws_normalize(img_dir,
contrast,
mask_dir=None,
output_dir=None,
write_to_disk=True):
"""
Use WhiteStripe normalization method ([1]) to normalize the intensities of
a set of MR images by normalizing an area around the white matter peak of the histogram
Args:
img_dir (str): directory containing MR images to be normalized
contrast (str): contrast of MR images to be normalized (T1, T2, or FLAIR)
mask_dir (str): if images are not skull-stripped, then provide brain mask
output_dir (str): directory to save images if you do not want them saved in
same directory as img_dir
write_to_disk (bool): write the normalized data to disk or nah
Returns:
normalized (np.ndarray): last normalized image data from img_dir
I know this is an odd behavior, but yolo
References:
[1] R. T. Shinohara, E. M. Sweeney, J. Goldsmith, N. Shiee,
F. J. Mateen, P. A. Calabresi, S. Jarso, D. L. Pham,
D. S. Reich, and C. M. Crainiceanu, “Statistical normalization
techniques for magnetic resonance imaging,” NeuroImage Clin.,
vol. 6, pp. 9–19, 2014.
"""
# grab the file names for the images of interest
data = io.glob_nii(img_dir)
# define and get the brain masks for the images, if defined
if mask_dir is None:
masks = [None] * len(data)
else:
masks = io.glob_nii(mask_dir)
if len(data) != len(masks):
raise NormalizationError(
'Number of images and masks must be equal, Images: {}, Masks: {}'
.format(len(data), len(masks)))
# define the output directory and corresponding output file names
if output_dir is None:
output_files = [None] * len(data)
else:
output_files = []
for fn in data:
_, base, ext = io.split_filename(fn)
output_files.append(os.path.join(output_dir, base + '_ws' + ext))
if not os.path.exists(output_dir):
os.mkdir(output_dir)
# do whitestripe normalization and save the results
for i, (img_fn, mask_fn,
output_fn) in enumerate(zip(data, masks, output_files), 1):
logger.info('Normalizing image: {} ({:d}/{:d})'.format(
img_fn, i, len(data)))
img = io.open_nii(img_fn)
mask = io.open_nii(mask_fn) if mask_fn is not None else None
indices = whitestripe(img, contrast, mask=mask)
normalized = whitestripe_norm(img, indices)
if write_to_disk:
logger.info('Saving normalized image: {} ({:d}/{:d})'.format(
output_fn, i, len(data)))
io.save_nii(normalized, output_fn)
# output the last normalized image (mostly for testing purposes)
return normalized
def main(args=None):
args = arg_parser().parse_args(args)
if not (args.brain_mask is None) ^ (args.tissue_mask is None):
raise NormalizationError(
'Only one of {brain mask, tissue mask} should be given')
if args.verbosity == 1:
level = logging.getLevelName('INFO')
elif args.verbosity >= 2:
level = logging.getLevelName('DEBUG')
else:
level = logging.getLevelName('WARNING')
logging.basicConfig(
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
level=level)
logger = logging.getLogger(__name__)
try:
if not args.single_img:
if not os.path.isdir(
args.image) or (False if args.brain_mask is None else
not os.path.isdir(args.brain_mask)):
raise NormalizationError(
'if single-img option off, then image and brain-mask must be directories'
)
img_fns = io.glob_nii(args.image)
mask_fns = io.glob_nii(
args.brain_mask
) if args.brain_mask is not None else [None] * len(img_fns)
if len(img_fns) != len(mask_fns) and len(img_fns) > 0:
raise NormalizationError(
'input images and masks must be in correspondence and greater than zero '
'({:d} != {:d})'.format(len(img_fns), len(mask_fns)))
args.output_dir = args.output_dir or 'fcm'
output_dir_base = os.path.abspath(
os.path.join(args.output_dir, '..'))
if args.contrast.lower() == 't1' and args.tissue_mask is None:
tissue_mask_dir = os.path.join(output_dir_base, 'tissue_masks')
if os.path.exists(tissue_mask_dir):
logger.warning(
'Tissue mask directory already exists, may overwrite existing tissue masks!'
)
else:
logger.info('Creating tissue mask directory: {}'.format(
tissue_mask_dir))
os.mkdir(tissue_mask_dir)
for i, (img, mask) in enumerate(zip(img_fns, mask_fns), 1):
_, base, _ = io.split_filename(img)
_, mask_base, _ = io.split_filename(mask)
logger.info(
'Creating tissue mask for {} ({:d}/{:d})'.format(
base, i, len(img_fns)))
logger.debug('Tissue mask {} ({:d}/{:d})'.format(
mask_base, i, len(img_fns)))
process(img, mask, None, tissue_mask_dir, args, logger)
elif os.path.exists(args.tissue_mask):
tissue_mask_dir = args.tissue_mask
else:
raise NormalizationError(
'If contrast is not t1, then tissue mask directory ({}) '
'must already be created!'.format(args.tissue_mask))
tissue_masks = io.glob_nii(tissue_mask_dir)
for i, (img, tissue_mask) in enumerate(zip(img_fns, tissue_masks),
1):
dirname, base, _ = io.split_filename(img)
_, tissue_base, _ = io.split_filename(tissue_mask)
logger.info('Normalizing image {} ({:d}/{:d})'.format(
base, i, len(img_fns)))
logger.debug('Tissue mask {} ({:d}/{:d})'.format(
tissue_base, i, len(img_fns)))
if args.output_dir is not None:
dirname = args.output_dir
process(img, None, tissue_mask, dirname, args, logger)
else:
if not os.path.isfile(args.image):
raise NormalizationError(
'if single-img option on, then image must be a file')
if args.tissue_mask is None and args.contrast.lower() == 't1':
logger.info('Creating tissue mask for {}'.format(args.image))
process(args.image, args.brain_mask, None, args.output_dir,
args, logger)
elif os.path.isfile(args.tissue_mask):
pass
else:
raise NormalizationError(
'If contrast is not t1, then tissue mask must be provided!'
)
logger.info('Normalizing image {}'.format(args.image))
dirname, base, _ = io.split_filename(args.image)
dirname = args.output_dir or dirname
if args.tissue_mask is None:
tissue_mask = os.path.join(
dirname, base + '_{}_mask.nii.gz'.format(args.tissue_type))
else:
tissue_mask = args.tissue_mask
process(args.image, args.brain_mask, tissue_mask, dirname, args,
logger)
if args.plot_hist:
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=FutureWarning)
from intensity_normalization.plot.hist import all_hists
import matplotlib.pyplot as plt
ax = all_hists(args.output_dir, args.brain_mask)
ax.set_title('Fuzzy C-Means')
plt.savefig(os.path.join(args.output_dir, 'hist.png'))
return 0
except Exception as e:
logger.exception(e)
return 1
def gmm_class_mask(img,
brain_mask=None,
contrast='t1',
return_wm_peak=True,
hard_seg=False):
"""
get a tissue class mask using gmms (or just the WM peak, for legacy use)
Args:
img (nibabel.nifti1.Nifti1Image): target img
brain_mask (nibabel.nifti1.Nifti1Image): brain mask for img
(none if already skull-stripped)
contrast (str): string to describe img's MR contrast
return_wm_peak (bool): if true, return only the wm peak
hard_seg (bool): if true and return_wm_peak false, then return
hard segmentation of tissue classes
Returns:
if return_wm_peak true:
wm_peak (float): represents the mean intensity for WM
else:
mask (np.ndarray):
if hard_seg, then mask is the same size as img
else, mask is the same size as img * 3, where
the new dimensions hold the probabilities of tissue class
"""
img_data = img.get_data()
if brain_mask is not None:
mask_data = brain_mask.get_data() > 0
else:
mask_data = img_data > img_data.mean()
brain = np.expand_dims(img_data[mask_data].flatten(), 1)
gmm = GaussianMixture(3)
gmm.fit(brain)
if return_wm_peak:
means = sorted(gmm.means_.T.squeeze())
if contrast.lower() == 't1':
wm_peak = means[2]
elif contrast.lower() == 'flair':
wm_peak = means[1]
elif contrast.lower() == 't2':
wm_peak = means[0]
else:
raise NormalizationError(
'Invalid contrast type: {}. Must be t1, t2, or flair.'.format(
contrast))
return wm_peak
else:
classes = np.argsort(gmm.weights_)
if hard_seg:
tmp_predicted = gmm.predict(brain)
predicted = np.zeros(tmp_predicted.shape)
for i, c in enumerate(classes):
predicted[tmp_predicted == c] = i + 1
mask = np.zeros(img_data.shape)
mask[mask_data] = predicted + 1
else:
predicted_proba = gmm.predict_proba(brain)
mask = np.zeros((*img_data.shape, 3))
for i, c in enumerate(classes):
mask[mask_data, i] = predicted_proba[:, c]
return mask
def main(args=None):
args = arg_parser().parse_args(args)
if args.verbosity == 1:
level = logging.getLevelName('INFO')
elif args.verbosity >= 2:
level = logging.getLevelName('DEBUG')
else:
level = logging.getLevelName('WARNING')
logging.basicConfig(
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
level=level)
logger = logging.getLogger(__name__)
try:
img_fns = io.glob_nii(args.img_dir)
mask_fns = io.glob_nii(args.mask_dir)
if len(img_fns) != len(mask_fns) or len(img_fns) == 0:
raise NormalizationError(
'Image directory ({}) and mask directory ({}) must contain the same '
'(positive) number of images!'.format(args.img_dir,
args.mask_dir))
logger.info('Normalizing the images according to RAVEL')
Z, _ = ravel.ravel_normalize(
args.img_dir,
args.mask_dir,
args.contrast,
do_whitestripe=args.no_whitestripe,
b=args.num_unwanted_factors,
membership_thresh=args.control_membership_threshold,
do_registration=args.no_registration,
segmentation_smoothness=args.segmentation_smoothness,
use_fcm=not args.use_atropos,
sparse_svd=args.sparse_svd,
csf_masks=args.csf_masks)
V = ravel.image_matrix(img_fns, args.contrast, masks=mask_fns)
V_norm = ravel.ravel_correction(V, Z)
normalized = ravel.image_matrix_to_images(V_norm, img_fns)
# save the normalized images to disk
output_dir = os.getcwd(
) if args.output_dir is None else args.output_dir
out_fns = []
for fn in img_fns:
_, base, ext = io.split_filename(fn)
out_fns.append(os.path.join(output_dir, base + '_ravel' + ext))
if not os.path.exists(output_dir):
os.mkdir(output_dir)
for norm, out_fn in zip(normalized, out_fns):
norm.to_filename(out_fn)
if args.plot_hist:
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=FutureWarning)
from intensity_normalization.plot.hist import all_hists
import matplotlib.pyplot as plt
ax = all_hists(output_dir, args.mask_dir)
ax.set_title('RAVEL')
plt.savefig(os.path.join(output_dir, 'hist.png'))
return 0
except Exception as e:
logger.exception(e)
return 1
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)