def match_histograms(reference_path, apply_to_path):
print('Start')
for name in ['InPhase', 'OutPhase']:
print(f'Working on: {name}')
ref_files = list()
for folder in os.listdir(reference_path):
ref_files.append(f'{reference_path}/{folder}/{name}.nii')
print('Training')
mask_files = [None] * len(ref_files)
standard_scale, percs = nyul.train(ref_files, mask_files)
input_files, output_files = list(), list()
for folder in os.listdir(apply_to_path):
input_files.append(f'{apply_to_path}/{folder}/{name}.nii')
output_files.append(f'{apply_to_path}/{folder}/{name}_HM.nii')
print('Normalizing')
for img_fn, out_fn in zip(input_files, output_files):
print(img_fn, '->', out_fn)
_, base, _ = io.split_filename(img_fn)
img = io.open_nii(img_fn)
normalized = nyul.do_hist_norm(img,
percs,
standard_scale,
mask=None)
io.save_nii(normalized, out_fn, is_nii=True)
print('Done')
def process(image, brain_mask, args, logger):
img = io.open_nii(image)
mask = io.open_nii(brain_mask)
dirname, base, ext = io.split_filename(image)
if args.output_dir is not None:
dirname = args.output_dir
if not os.path.exists(dirname):
logger.info('Making output directory: {}'.format(dirname))
os.mkdir(dirname)
if args.find_background_mask:
bg_mask = background_mask(img)
bgfile = os.path.join(dirname, base + '_bgmask' + ext)
io.save_nii(bg_mask, bgfile, is_nii=True)
if args.wm_peak is not None:
logger.info('Loading WM peak: ', args.wm_peak)
peak = float(np.load(args.wm_peak))
else:
peak = gmm_class_mask(img, brain_mask=mask, contrast=args.contrast)
if args.save_wm_peak:
np.save(os.path.join(dirname, base + '_wmpeak.npy'), peak)
normalized = gmm.gmm_normalize(img, mask, args.norm_value, args.contrast,
args.background_mask, peak)
outfile = os.path.join(dirname, base + '_gmm' + ext)
logger.info('Normalized image saved: {}'.format(outfile))
io.save_nii(normalized, outfile, is_nii=True)
def run_intensity_fcm(infile, outfolder):
from intensity_normalization.normalize import fcm
from intensity_normalization.utilities import io
try:
if not exists(join(outfolder, "Robex")):
makedirs(join(outfolder, "Robex"))
if not exists(join(outfolder, "wm_masks")):
makedirs(join(outfolder, "wm_masks"))
filename = infile.split(sep)[-1].split(".")[0]
i = io.open_nii(infile)
b_mask = io.open_nii(
join(outfolder, "robex_masks", filename + "_mask.nii.gz"))
wm_mask = fcm.find_tissue_mask(i, b_mask)
normalized = fcm.fcm_normalize(i, wm_mask)
io.save_nii(wm_mask,
join(outfolder, "wm_masks", filename + "_wmmask.nii.gz"))
io.save_nii(normalized, join(outfolder, filename + "_fcm.nii.gz"))
shutil.move(join(infile), join(outfolder, "Robex"))
except:
e = sys.exc_info()
print("Error: ", str(e[0]))
print("Error: ", str(e[1]))
print("Error: executing fcm method")
sys.exit(2)
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 process(image_fn, brain_mask_fn, args, logger):
img = io.open_nii(image_fn)
if args.brain_mask is not None:
mask = io.open_nii(brain_mask_fn)
else:
mask = None
dirname, base, _ = io.split_filename(image_fn)
if args.output_dir is not None:
dirname = args.output_dir
if not os.path.exists(dirname):
logger.info('Making output directory: {}'.format(dirname))
os.mkdir(dirname)
normalized = kde.kde_normalize(img, mask, args.contrast, args.norm_value)
outfile = os.path.join(dirname, base + '_kde.nii.gz')
logger.info('Normalized image saved: {}'.format(outfile))
io.save_nii(normalized, outfile, is_nii=True)
def process(image_fn, brain_mask_fn, output_dir, logger):
img = io.open_nii(image_fn)
dirname, base, _ = io.split_filename(image_fn)
if output_dir is not None:
dirname = output_dir
if not os.path.exists(dirname):
logger.info('Making output directory: {}'.format(dirname))
os.mkdir(dirname)
if brain_mask_fn is None:
mask = None
else:
if brain_mask_fn == 'nomask':
mask = 'nomask'
else:
mask = io.open_nii(brain_mask_fn)
normalized = zscore.zscore_normalize(img, mask)
outfile = os.path.join(dirname, base + '_zscore.nii.gz')
logger.info('Normalized image saved: {}'.format(outfile))
io.save_nii(normalized, outfile, is_nii=True)
def process(image_fn, brain_mask_fn, wm_mask_fn, output_dir, args, logger):
img = io.open_nii(image_fn)
dirname, base, _ = io.split_filename(image_fn)
if output_dir is not None:
dirname = output_dir
if not os.path.exists(dirname):
logger.info('Making output directory: {}'.format(dirname))
os.mkdir(dirname)
if brain_mask_fn is not None:
mask = io.open_nii(brain_mask_fn)
wm_mask = fcm.find_wm_mask(img, mask)
outfile = os.path.join(dirname, base + '_wmmask.nii.gz')
io.save_nii(wm_mask, outfile, is_nii=True)
if wm_mask_fn is not None:
wm_mask = io.open_nii(wm_mask_fn)
normalized = fcm.fcm_normalize(img, wm_mask, args.norm_value)
outfile = os.path.join(dirname, base + '_fcm.nii.gz')
logger.info('Normalized image saved: {}'.format(outfile))
io.save_nii(normalized, outfile, is_nii=True)
def run_intensity_gmm(infile, outfolder):
from intensity_normalization.normalize import gmm
from intensity_normalization.utilities import io
try:
if not exists(join(outfolder, "Robex")):
makedirs(join(outfolder, "Robex"))
filename = infile.split(sep)[-1].split(".")[0]
i = io.open_nii(join(outfolder, infile))
b_mask = io.open_nii(
join(outfolder, "robex_masks", filename + "_mask.nii.gz"))
normalized = gmm.gmm_normalize(i, b_mask)
io.save_nii(normalized, join(outfolder, filename + "_gmm.nii.gz"))
shutil.move(join(outfolder, infile), join(outfolder, "Robex"))
except:
e = sys.exc_info()
print("Error: ", str(e[0]))
print("Error: ", str(e[1]))
print("Error: executing gmm method")
sys.exit(2)
def run_intensity_ws(infile, outfolder):
from intensity_normalization.normalize import whitestripe
from intensity_normalization.utilities import io
try:
filename = infile.split(sep)[-1].split(".")[0]
if not exists(join(outfolder, "Robex")):
makedirs(join(outfolder, "Robex"))
print('running intensity white stripe...')
mask = whitestripe.whitestripe(io.open_nii(infile), "T1")
normalized = whitestripe.whitestripe_norm(io.open_nii(infile), mask)
io.save_nii(normalized, join(outfolder, filename + "_ws.nii.gz"))
shutil.move(infile, join(outfolder, "Robex"))
except:
e = sys.exc_info()
print("Error: ", str(e[0]))
print("Error: ", str(e[1]))
print("Error: executing white stripe method")
sys.exit(2)
def run_intensity_zscore(infile, outfolder):
from intensity_normalization.normalize import zscore
from intensity_normalization.utilities import io
try:
filename = infile.split(sep)[-1].split(".")[0]
i = io.open_nii(infile)
b_mask = io.open_nii(
join(outfolder, "robex_masks", filename + "_mask.nii.gz"))
if not exists(join(outfolder, "Robex")):
makedirs(join(outfolder, "Robex"))
print('running intensity zscore...')
normalized = zscore.zscore_normalize(i, b_mask)
io.save_nii(normalized, join(outfolder, filename + "_zscore.nii.gz"))
shutil.move(join(infile), join(outfolder, "Robex"))
except:
e = sys.exc_info()
print("Error: ", str(e[0]))
print("Error: ", str(e[1]))
print("Error: executing z-score method")
sys.exit(2)
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 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
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)
