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 nyul_normalize(img_dir, mask_dir=None, output_dir=None, standard_hist=None, write_to_disk=True):
"""
Use Nyul and Udupa method ([1,2]) to normalize the intensities of a set of MR images
Args:
img_dir (str): directory containing MR images
img_dir (str): directory containing masks for MR images
output_dir (str): directory to save images if you do not want them saved in
same directory as data_dir
standard_hist (str): path to output or use standard histogram landmarks
write_to_disk (bool): write the normalized data to disk or nah
Returns:
normalized (np.ndarray): last normalized image from img_dir
References:
[1] N. Laszlo G and J. K. Udupa, “On Standardizing the MR Image
Intensity Scale,” Magn. Reson. Med., vol. 42, pp. 1072–1081,
1999.
[2] M. Shah, Y. Xiao, N. Subbanna, S. Francis, D. L. Arnold,
D. L. Collins, and T. Arbel, “Evaluating intensity
normalization on MRIs of human brain with multiple sclerosis,”
Med. Image Anal., vol. 15, no. 2, pp. 267–282, 2011.
"""
input_files = io.glob_nii(img_dir)
if output_dir is None:
out_fns = [None] * len(input_files)
else:
out_fns = []
for fn in input_files:
_, base, ext = io.split_filename(fn)
out_fns.append(os.path.join(output_dir, base + '_hm' + ext))
if not os.path.exists(output_dir):
os.mkdir(output_dir)
mask_files = [None] * len(input_files) if mask_dir is None else io.glob_nii(mask_dir)
if standard_hist is None:
logger.info('Learning standard scale for the set of images')
standard_scale, percs = train(input_files, mask_files)
elif not os.path.isfile(standard_hist):
logger.info('Learning standard scale for the set of images')
standard_scale, percs = train(input_files, mask_files)
np.save(standard_hist, np.vstack((standard_scale, percs)))
else:
logger.info('Loading standard scale ({}) for the set of images'.format(standard_hist))
standard_scale, percs = np.load(standard_hist)
for i, (img_fn, mask_fn, out_fn) in enumerate(zip(input_files, mask_files, out_fns)):
_, base, _ = io.split_filename(img_fn)
logger.info('Transforming image {} to standard scale ({:d}/{:d})'.format(base, i+1, len(input_files)))
img = io.open_nii(img_fn)
mask = io.open_nii(mask_fn) if mask_fn is not None else None
normalized = do_hist_norm(img, percs, standard_scale, mask)
if write_to_disk:
io.save_nii(normalized, out_fn, is_nii=True)
return normalized
def lsq_normalize(img_dir, mask_dir=None, output_dir=None, write_to_disk=True):
"""
normalize intensities of a set of MR images by minimizing the squared distance
between CSF, GM, and WM means within the set
Args:
img_dir (str): directory containing MR images
mask_dir (str): directory containing masks for MR images
output_dir (str): directory to save images if you do not want them saved in
same directory as data_dir
write_to_disk (bool): write the normalized data to disk or nah
Returns:
normalized (np.ndarray): last normalized image from img_dir
"""
input_files = io.glob_nii(img_dir)
if output_dir is None:
out_fns = [None] * len(input_files)
else:
out_fns = []
for fn in input_files:
_, base, ext = io.split_filename(fn)
out_fns.append(os.path.join(output_dir, base + '_lsq' + ext))
if not os.path.exists(output_dir):
os.mkdir(output_dir)
mask_files = [None] * len(
input_files) if mask_dir is None else io.glob_nii(mask_dir)
standard_tissue_means = None
normalized = None
for i, (img_fn, mask_fn,
out_fn) in enumerate(zip(input_files, mask_files, out_fns)):
_, base, _ = io.split_filename(img_fn)
logger.info(
'Transforming image {} to standard scale ({:d}/{:d})'.format(
base, i + 1, len(input_files)))
img = io.open_nii(img_fn)
mask = io.open_nii(mask_fn) if mask_fn is not None else None
tissue_mem = mask_util.fcm_class_mask(img, mask)
if standard_tissue_means is None:
csf_tissue_mask = find_tissue_mask(img, mask, tissue_type='csf')
csf_normed_data = fcm_normalize(img, csf_tissue_mask).get_fdata()
standard_tissue_means = calc_tissue_means(csf_normed_data,
tissue_mem)
del csf_tissue_mask, csf_normed_data
img_data = img.get_fdata()
tissue_means = calc_tissue_means(img_data, tissue_mem)
sf = find_scaling_factor(tissue_means, standard_tissue_means)
logger.debug('Scaling factor for {}: {:0.3e}'.format(base, sf))
normalized = nib.Nifti1Image(sf * img_data, img.affine, img.header)
if write_to_disk:
io.save_nii(normalized, out_fn, is_nii=True)
return normalized
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 os.path.isdir(args.img_dir):
raise ValueError(
'(-i / --img-dir) argument needs to be a directory of NIfTI images.'
)
if args.mask_dir is not None:
if not os.path.isdir(args.mask_dir):
raise ValueError(
'(-m / --mask-dir) argument needs to be a directory of NIfTI images.'
)
img_fns = io.glob_nii(args.img_dir)
if args.mask_dir is not None:
mask_fns = io.glob_nii(args.mask_dir)
else:
mask_fns = [None] * len(img_fns)
if not os.path.exists(args.output_dir):
logger.info('Making Output Directory: {}'.format(args.output_dir))
os.mkdir(args.output_dir)
hard_seg = not args.memberships
for i, (img_fn, mask_fn) in enumerate(zip(img_fns, mask_fns), 1):
_, base, _ = io.split_filename(img_fn)
logger.info('Creating Mask for Image: {}, ({:d}/{:d})'.format(
base, i, len(img_fns)))
img = io.open_nii(img_fn)
mask = io.open_nii(mask_fn)
tm = fcm_class_mask(img, mask,
hard_seg) if not args.gmm else gmm_class_mask(
img, mask, 't1', False, hard_seg)
tissue_mask = os.path.join(args.output_dir, base + '_tm')
if args.memberships:
classes = ('csf', 'gm', 'wm')
for j, c in enumerate(classes):
io.save_nii(img, tissue_mask + '_' + c + '.nii.gz', tm[...,
j])
else:
io.save_nii(img, tissue_mask + '.nii.gz', tm)
return 0
except Exception as e:
logger.exception(e)
return 1
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 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 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.mask_dir):
logger.info('Making Output Mask Directory: {}'.format(args.mask_dir))
os.mkdir(args.mask_dir)
for i, img in enumerate(img_fns, 1):
_, base, _ = split_filename(img)
logger.info('Creating Mask for Image: {}, ({:d}/{:d})'.format(base, i, len(img_fns)))
mask = os.path.join(args.mask_dir, base + '_mask.nii.gz')
_ = robex(os.path.abspath(img), os.path.abspath(mask), args.return_skull_stripped)
return 0
except Exception as e:
logger.exception(e)
return 1
def preprocess(img_dir,
out_dir,
mask_dir=None,
res=(1., 1., 1.),
orientation='RAI',
n4_opts=None):
"""
preprocess.py MR images according to a simple scheme,
that is:
1) N4 bias field correction
2) resample to x mm x y mm x z mm
3) reorient images to RAI
Args:
img_dir (str): path to directory containing images
out_dir (str): path to directory for output preprocessed files
mask_dir (str): path to directory containing masks
res (tuple): resolution for resampling (default: (1,1,1) in mm)
n4_opts (dict): n4 processing options. See ANTsPy for details. (default: None)
Returns:
None, outputs preprocessed images to file in given out_dir
"""
if n4_opts is None:
n4_opts = {'iters': [200, 200, 200, 200], 'tol': 0.0005}
logger.debug('N4 Options are: {}'.format(n4_opts))
# get and check the images and masks
img_fns = glob_nii(img_dir)
mask_fns = glob_nii(
mask_dir) if mask_dir is not None else [None] * len(img_fns)
assert len(img_fns) == len(mask_fns), 'Number of images and masks must be equal ({:d} != {:d})' \
.format(len(img_fns), len(mask_fns))
# create the output directory structure
out_img_dir = os.path.join(out_dir, 'imgs')
out_mask_dir = os.path.join(out_dir, 'masks')
if not os.path.exists(out_dir):
logger.info('Making output directory structure: {}'.format(out_dir))
os.mkdir(out_dir)
if not os.path.exists(out_img_dir):
logger.info('Making image output directory: {}'.format(out_img_dir))
os.mkdir(out_img_dir)
if not os.path.exists(out_mask_dir) and mask_dir is not None:
logger.info('Making mask output directory: {}'.format(out_mask_dir))
os.mkdir(out_mask_dir)
# preprocess the images by n4 correction, resampling, and reorientation
for i, (img_fn, mask_fn) in enumerate(zip(img_fns, mask_fns), 1):
_, img_base, img_ext = split_filename(img_fn)
logger.info('Preprocessing image: {} ({:d}/{:d})'.format(
img_base, i, len(img_fns)))
img = ants.image_read(img_fn)
if mask_dir is not None:
_, mask_base, mask_ext = split_filename(mask_fn)
mask = ants.image_read(mask_fn)
smoothed_mask = ants.smooth_image(mask, 1)
# this should be a second n4 after an initial n4 (and coregistration), once masks are obtained
img = ants.n4_bias_field_correction(img,
convergence=n4_opts,
weight_mask=smoothed_mask)
if res is not None:
if res != img.spacing:
mask = ants.resample_image(mask, res, False, 1)
mask = mask.reorient_image2(orientation) if hasattr(img, 'reorient_image2') else \
mask.reorient_image((1, 0, 0))['reoimage']
out_mask = os.path.join(out_mask_dir, mask_base + mask_ext)
ants.image_write(mask, out_mask)
else:
img = ants.n4_bias_field_correction(img, convergence=n4_opts)
if res is not None:
if res != img.spacing:
img = ants.resample_image(img, res, False, 4)
if hasattr(img, 'reorient_image2'):
img = img.reorient_image2(orientation)
else:
logger.info(
'Cannot reorient image to a custom orientation. Update ANTsPy to a version >= 0.1.5.'
)
img = img.reorient_image((1, 0, 0))['reoimage']
logger.info('Writing preprocessed image: {} ({:d}/{:d})'.format(
img_base, i, len(img_fns)))
out_img = os.path.join(out_img_dir, img_base + img_ext)
ants.image_write(img, out_img)
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 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 ravel_normalize(img_dir,
mask_dir,
contrast,
output_dir=None,
write_to_disk=False,
do_whitestripe=True,
b=1,
membership_thresh=0.99,
segmentation_smoothness=0.25,
do_registration=False,
use_fcm=True):
"""
Use RAVEL [1] to normalize the intensities of a set of MR images to eliminate
unwanted technical variation in images (but, hopefully, preserve biological variation)
this function has an option that is modified from [1] in where no registration is done,
the control mask is defined dynamically by finding a tissue segmentation of the brain and
thresholding the membership at a very high level (this seems to work well and is *much* faster)
but there seems to be some more inconsistency in the results
Args:
img_dir (str): directory containing MR images to be normalized
mask_dir (str): brain masks for imgs
contrast (str): contrast of MR images to be normalized (T1, T2, or FLAIR)
output_dir (str): directory to save images if you do not want them saved in
same directory as data_dir
write_to_disk (bool): write the normalized data to disk or nah
do_whitestripe (bool): whitestripe normalize the images before applying RAVEL correction
b (int): number of unwanted factors to estimate
membership_thresh (float): threshold of membership for control voxels
segmentation_smoothness (float): segmentation smoothness parameter for atropos ANTsPy
segmentation scheme (i.e., mrf parameter)
do_registration (bool): deformably register images to find control mask
use_fcm (bool): use FCM for segmentation instead of atropos (may be less accurate)
Returns:
Z (np.ndarray): unwanted factors (used in ravel correction)
normalized (np.ndarray): set of normalized images from data_dir
References:
[1] J. P. Fortin, E. M. Sweeney, J. Muschelli, C. M. Crainiceanu,
and R. T. Shinohara, “Removing inter-subject technical variability
in magnetic resonance imaging studies,” Neuroimage, vol. 132,
pp. 198–212, 2016.
"""
img_fns = io.glob_nii(img_dir)
mask_fns = io.glob_nii(mask_dir)
if output_dir is None or not write_to_disk:
out_fns = None
else:
out_fns = []
for fn in img_fns:
_, base, ext = io.split_filename(fn)
out_fns.append(os.path.join(output_dir, base + ext))
if not os.path.exists(output_dir):
os.mkdir(output_dir)
# get parameters necessary and setup the V array
V, Vc = image_matrix(img_fns,
contrast,
masks=mask_fns,
do_whitestripe=do_whitestripe,
return_ctrl_matrix=True,
membership_thresh=membership_thresh,
do_registration=do_registration,
smoothness=segmentation_smoothness,
use_fcm=use_fcm)
# estimate the unwanted factors Z
_, _, vh = np.linalg.svd(Vc)
Z = vh.T[:, 0:b]
# perform the ravel correction
V_norm = ravel_correction(V, Z)
# save the results to disk if desired
if write_to_disk:
for i, (img_fn, out_fn) in enumerate(zip(img_fns, out_fns)):
img = io.open_nii(img_fn)
norm = V_norm[:, i].reshape(img.get_data().shape)
io.save_nii(img, out_fn, data=norm)
return Z, V_norm
