def _sim_mask(in_file):
import nibabel as nb
import numpy as np
import os.path as op
import scipy.ndimage as sn
from scipy.ndimage.morphology import (binary_opening, binary_dilation)
from pyacwereg.misc import ball as gen_ball
if not isinstance(in_file, basestring):
in_file = in_file[2]
out_file = op.abspath('sim_mask.nii.gz')
im = nb.load(in_file)
data = im.get_data()
data[data > 1.0e-4] = 1
data[data < 1] = 0
ball1 = gen_ball(4, 1.9)
data = binary_opening(data.astype(np.uint8), structure=ball1,
iterations=1).astype(np.uint8)
# Get largest object
label_im, nb_labels = sn.label(data)
sizes = sn.sum(data, label_im, range(nb_labels + 1))
larger = np.squeeze(np.argwhere(sizes == sizes.max()))
data[label_im != larger] = 0
# Dilate
# data = binary_dilation(data, structure=ball1,
# iterations=1).astype(np.uint8)
nb.Nifti1Image(
data, im.get_affine(), im.get_header()).to_filename(out_file)
return out_file
def compute_fractions(sf_vfs, tissue_vfs, max_fa=0.80):
import os.path as op
import nibabel as nb
import numpy as np
import scipy.ndimage as sn
from pyacwereg.misc import ball as gen_ball
from scipy.ndimage.morphology import binary_erosion
# Load tissue fractions
ntissues = len(tissue_vfs)
timgs = [nb.load(f) for f in tissue_vfs]
tvfs = np.nan_to_num(nb.concat_images(timgs).get_data())
tissue_totals = np.sum(tvfs, axis=3)
tvfs[tissue_totals > 0, ...] /= tissue_totals[tissue_totals > 0,
np.newaxis]
wmvf = tvfs[..., 2].copy()
tvfs[..., 2] *= .05
# Load single-fiber fractions
nfibers = len(sf_vfs)
sfimgs = [nb.load(f) for f in sf_vfs]
data = np.nan_to_num(nb.concat_images(sfimgs).get_data())
data = np.sort(data)[..., ::-1]
data *= np.array([1.5, 1., .95])[np.newaxis, np.newaxis, np.newaxis, ...]
total_fibers = np.sum(data, axis=3)
data[total_fibers > 0, ...] /= total_fibers[total_fibers > 0, np.newaxis]
data *= wmvf[..., np.newaxis] * .95
total_fibers = np.sum(data, axis=3)
tvfs[..., 2] += wmvf - total_fibers
tvfs = np.clip(tvfs, 0.0, 1.0)
# Save volume fractions
out_ts = [op.abspath('ts_vf%02d.nii.gz' % i) for i in range(ntissues)]
for i, f in enumerate(out_ts):
nb.Nifti1Image(tvfs[..., i], timgs[i].get_affine(),
timgs[i].get_header()).to_filename(f)
out_sf = [op.abspath('sf_vf%02d.nii.gz' % i) for i in range(nfibers)]
for i, f in enumerate(out_sf):
nb.Nifti1Image(data[..., i], sfimgs[i].get_affine(),
sfimgs[i].get_header()).to_filename(f)
# Compute single fiber mask
wmmask = np.zeros_like(total_fibers)
wmmask[total_fibers > 0.85] = 1
wmmask = binary_erosion(wmmask, structure=gen_ball(5, 2.4),
iterations=1).astype(np.uint8)
# Get largest connected object
label_im, nb_labels = sn.label(wmmask)
sizes = sn.sum(wmmask, label_im, range(nb_labels + 1))
larger = np.squeeze(np.argwhere(sizes == sizes.max()))
wmmask[label_im != larger] = 0
hdr = sfimgs[0].get_header().copy()
hdr.set_data_dtype(np.uint8)
out_wmmsk = op.abspath('wmmsk.nii.gz')
nb.Nifti1Image(wmmask, sfimgs[0].get_affine(), hdr).to_filename(out_wmmsk)
return out_sf, out_ts, out_wmmsk
