def test_median_otsu():
fname = get_data('S0_10')
img = nib.load(fname)
data = img.get_data()
data = np.squeeze(data)
dummy_mask = data > data.mean()
data_masked, mask = median_otsu(data, median_radius=3, numpass=2,
autocrop=False, vol_idx=None,
dilate=None)
assert_equal(mask.sum() < dummy_mask.sum(), True)
data2 = np.zeros(data.shape + (2,))
data2[..., 0] = data
data2[..., 1] = data
data2_masked, mask2 = median_otsu(data2, median_radius=3, numpass=2,
autocrop=False, vol_idx=[0, 1],
dilate=None)
assert_equal(mask.sum() == mask2.sum(), True)
_, mask3 = median_otsu(data2, median_radius=3, numpass=2,
autocrop=False, vol_idx=[0, 1],
dilate=1)
assert_equal(mask2.sum() < mask3.sum(), True)
_, mask4 = median_otsu(data2, median_radius=3, numpass=2,
autocrop=False, vol_idx=[0, 1],
dilate=2)
assert_equal(mask3.sum() < mask4.sum(), True)
def test_median_otsu():
fname = get_fnames('S0_10')
img = nib.load(fname)
data = img.get_data()
data = np.squeeze(data.astype('f8'))
dummy_mask = data > data.mean()
data_masked, mask = median_otsu(data, median_radius=3, numpass=2,
autocrop=False, vol_idx=None,
dilate=None)
assert_equal(mask.sum() < dummy_mask.sum(), True)
data2 = np.zeros(data.shape + (2,))
data2[..., 0] = data
data2[..., 1] = data
data2_masked, mask2 = median_otsu(data2, median_radius=3, numpass=2,
autocrop=False, vol_idx=[0, 1],
dilate=None)
assert_almost_equal(mask.sum(), mask2.sum())
_, mask3 = median_otsu(data2, median_radius=3, numpass=2,
autocrop=False, vol_idx=[0, 1],
dilate=1)
assert_equal(mask2.sum() < mask3.sum(), True)
_, mask4 = median_otsu(data2, median_radius=3, numpass=2,
autocrop=False, vol_idx=[0, 1],
dilate=2)
assert_equal(mask3.sum() < mask4.sum(), True)
def test_median_otsu():
fname = get_fnames('S0_10')
data = load_nifti_data(fname)
data = np.squeeze(data.astype('f8'))
dummy_mask = data > data.mean()
data_masked, mask = median_otsu(data, median_radius=3, numpass=2,
autocrop=False, vol_idx=None,
dilate=None)
assert_equal(mask.sum() < dummy_mask.sum(), True)
data2 = np.zeros(data.shape + (2,))
data2[..., 0] = data
data2[..., 1] = data
data2_masked, mask2 = median_otsu(data2, median_radius=3, numpass=2,
autocrop=False, vol_idx=[0, 1],
dilate=None)
assert_almost_equal(mask.sum(), mask2.sum())
_, mask3 = median_otsu(data2, median_radius=3, numpass=2,
autocrop=False, vol_idx=[0, 1],
dilate=1)
assert_equal(mask2.sum() < mask3.sum(), True)
_, mask4 = median_otsu(data2, median_radius=3, numpass=2,
autocrop=False, vol_idx=[0, 1],
dilate=2)
assert_equal(mask3.sum() < mask4.sum(), True)
# For 4D volumes, can't call without vol_idx input:
assert_raises(ValueError, median_otsu, data2)
def exampleDipy():
# example obtained from: http://nipy.org/dipy/examples_built/syn_registration_2d.html
import ssl
if hasattr(ssl, '_create_unverified_context'):
ssl._create_default_https_context = ssl._create_unverified_context
from dipy.data import fetch_stanford_hardi, read_stanford_hardi
fetch_stanford_hardi()
nib_stanford, gtab_stanford = read_stanford_hardi()
stanford_b0 = np.squeeze(nib_stanford.get_data())[..., 0]
from dipy.data.fetcher import fetch_syn_data, read_syn_data
fetch_syn_data()
nib_syn_t1, nib_syn_b0 = read_syn_data()
syn_b0 = np.array(nib_syn_b0.get_data())
from dipy.segment.mask import median_otsu
stanford_b0_masked, stanford_b0_mask = median_otsu(stanford_b0, 4, 4)
syn_b0_masked, syn_b0_mask = median_otsu(syn_b0, 4, 4)
static = stanford_b0_masked
static_affine = nib_stanford.affine
moving = syn_b0_masked
moving_affine = nib_syn_b0.affine
pre_align = np.array(
[[1.02783543e+00, -4.83019053e-02, -6.07735639e-02, -2.57654118e+00],
[4.34051706e-03, 9.41918267e-01, -2.66525861e-01, 3.23579799e+01],
[5.34288908e-02, 2.90262026e-01, 9.80820307e-01, -1.46216651e+01],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])
from dipy.align.imaffine import AffineMap
affine_map = AffineMap(pre_align,
static.shape, static_affine,
moving.shape, moving_affine)
resampled = affine_map.transform(moving)
metric = CCMetric(3)
level_iters = [10, 10, 5]
sdr = SymmetricDiffeomorphicRegistration(metric, level_iters)
mapping = sdr.optimize(static, moving, static_affine, moving_affine,
pre_align)
warped_moving = mapping.transform(moving)
for slice in range(41 - 12, 41 + 13):
regtools.overlay_slices(static, resampled, slice, 1, 'Static',
'Pre Moving',
'GIFexample1/' + str(slice) + 'T1pre.png')
regtools.overlay_slices(static, warped_moving, slice, 1, 'Static',
'Post moving',
'GIFexample1/' + str(slice) + 'T1post.png')
def main(dti_file, bvals_file, bvecs_file, b_ss=1000):
# Load the image data
nii = nib.load(dti_file)
img_data = nii.get_data()
# Read in the b-shell values and gradient directions
bvals, bvecs = read_bvals_bvecs(bvals_file, bvecs_file)
# Boolean array to identify entries with either b = 0 or b = b_ss
bvals_eq_0_b_ss = (bvals == 0) | (bvals == b_ss)
# Extract info needed to run single-compartment dti model
dti_bvals = bvals[bvals_eq_0_b_ss].copy()
dti_bvecs = bvecs[bvals_eq_0_b_ss].copy()
dti_img_data = img_data[:, :, :, bvals_eq_0_b_ss].copy()
# Compute gradient table
grad_table = gradient_table(dti_bvals, dti_bvecs)
# Extract brain so we don't fit the background
brain_img_data, brain_mask = median_otsu(dti_img_data, 2, 1)
# Run the dti model and fit it to the brain extracted image data
ten_model = dti.TensorModel(grad_table)
ten_fit = ten_model.fit(brain_img_data)
def test_median_otsu_flow():
with nib.tmpdirs.InTemporaryDirectory() as out_dir:
data_path, _, _ = get_data('small_25')
volume = nib.load(data_path).get_data()
save_masked = True
median_radius = 3
numpass = 3
autocrop = False
vol_idx = [0]
dilate = 0
median_otsu_flow(data_path, out_dir, save_masked, median_radius,
numpass, autocrop, vol_idx, dilate)
masked, mask = median_otsu(volume, median_radius, numpass, autocrop,
vol_idx, dilate)
fname, _ = splitext(splitext(basename(data_path))[0])
mask_fname = fname + '_mask.nii.gz'
result_mask_data = nib.load(join(out_dir, mask_fname)).get_data()
npt.assert_array_equal(result_mask_data, mask)
masked_fname = fname + '_bet.nii.gz'
result_masked_data = nib.load(join(out_dir, masked_fname)).get_data()
npt.assert_array_equal(result_masked_data, masked)
def test_dwi_sh_conversion(data_dwi, gtab):
rtol = 0.05
atol = 1e-8
b0_mask_crop, mask_crop = median_otsu(data_dwi,
vol_idx=gtab.b0s_mask,
autocrop=False)
mask_crop = mask_crop[..., None]
data_dwi_true = shm.normalize_data(data_dwi, gtab.b0s_mask)
data_dwi_true *= mask_crop
sh_order = 4
data_sh = shm.dwi_to_sh(data_dwi_true,
gtab,
smooth=0.006,
mask=mask_crop,
sh_order=sh_order)
data_dwi_reconst = shm.sh_to_dwi(data_sh,
gtab,
mask=mask_crop,
add_b0=False)
data_dwi_true = data_dwi_true[..., ~gtab.b0s_mask]
print(
np.isclose(data_dwi_true, data_dwi_reconst, rtol=rtol,
atol=atol)[mask_crop[..., 0]].astype(int).sum() /
(data_dwi_true.shape[-1] * np.sum(mask_crop)))
assert data_dwi_reconst.shape == data_dwi_true.shape
assert np.isclose(data_dwi_true, data_dwi_reconst, rtol=rtol,
atol=atol).all()
def measure_denoising(n_datasets, ip_file_path):
data_ids = range(n_datasets)
ip_ports, ips = dh.read_ip_file(ip_file_path)
tf_cluster = dt.TfCluster(ip_ports[:])
datasets = []
masks = []
for data_id in data_ids:
data, bvals_path, bvecs_path = dh.download(data_id)
datasets.append(data)
gtab = dpg.gradient_table(bvals_path, bvecs_path, b0_threshold=10)
mean_b0 = np.mean(data[..., gtab.b0s_mask], -1)
_, mask = median_otsu(mean_b0,
4,
2,
False,
vol_idx=np.where(gtab.b0s_mask),
dilate=1)
masks.append(mask)
with tf.Session("grpc://%s" % tf_cluster.host) as sess:
dt.parallel_denoise(sess, tf_cluster, datasets, masks, depth=1)
def test_median_otsu_flow():
with nib.tmpdirs.InTemporaryDirectory() as out_dir:
data_path, _, _ = get_data('small_25')
volume = nib.load(data_path).get_data()
save_masked = True
median_radius = 3
numpass = 3
autocrop = False
vol_idx = [0]
dilate = 0
median_otsu_flow(data_path, out_dir, save_masked, median_radius,
numpass, autocrop, vol_idx, dilate)
masked, mask = median_otsu(volume, median_radius,
numpass, autocrop,
vol_idx, dilate)
fname, _ = splitext(splitext(basename(data_path))[0])
mask_fname = fname + '_mask.nii.gz'
result_mask_data = nib.load(join(out_dir, mask_fname)).get_data()
npt.assert_array_equal(result_mask_data, mask)
masked_fname = fname + '_bet.nii.gz'
result_masked_data = nib.load(join(out_dir, masked_fname)).get_data()
npt.assert_array_equal(result_masked_data, masked)
def test_median_otsu_flow():
with TemporaryDirectory() as out_dir:
data_path, _, _ = get_data('small_25')
volume = nib.load(data_path).get_data()
save_masked = True
median_radius = 3
numpass = 3
autocrop = False
vol_idx = [0]
dilate = 0
mo_flow = MedianOtsuFlow()
mo_flow.run(data_path, out_dir=out_dir, save_masked=save_masked,
median_radius=median_radius, numpass=numpass,
autocrop=autocrop, vol_idx=vol_idx, dilate=dilate)
mask_name = mo_flow.last_generated_outputs['out_mask']
masked_name = mo_flow.last_generated_outputs['out_masked']
masked, mask = median_otsu(volume, median_radius,
numpass, autocrop,
vol_idx, dilate)
result_mask_data = nib.load(join(out_dir, mask_name)).get_data()
npt.assert_array_equal(result_mask_data, mask)
result_masked_data = nib.load(join(out_dir, masked_name)).get_data()
npt.assert_array_equal(result_masked_data, masked)
def createB0_ColorFA_Mask_Sprites(b0_file, colorFA_file, mask_file):
colorfa = make_a_square(load_and_reorient(colorFA_file),
include_last_dim=False)
b0 = make_a_square(load_and_reorient(b0_file)[:, :, :, 0])
anat_mask = make_a_square(load_and_reorient(mask_file))
# make a b0 sprite
_, mask = median_otsu(b0)
outb0 = create_sprite_from_tiles(b0, as_bytes=True)
outb0['img_type'] = 'brainsprite'
# make a colorFA sprite, masked by b0
Q = make_a_square(colorfa, include_last_dim=False)
Q[np.logical_not(mask)] = np.nan
Q = np.moveaxis(Q, -2, -1)
outcolorFA = create_sprite_from_tiles(Q, as_bytes=True)
outcolorFA['img_type'] = 'brainsprite'
# make an anat mask contour sprite
outmask = create_sprite_from_tiles(
make_a_square(anat_mask, include_last_dim=False))
img = mplfigcontour(outmask.pop("mosaic"), as_bytes=True)
outmask['img'] = img
return outb0, outcolorFA, outmask
def _brain_mask(self,
row,
median_radius=4,
numpass=1,
autocrop=False,
vol_idx=None,
dilate=10):
brain_mask_file = self._get_fname(row, '_brain_mask.nii.gz')
if self.force_recompute or not op.exists(brain_mask_file):
b0_file = self._b0(row)
mean_b0_img = nib.load(b0_file)
mean_b0 = mean_b0_img.get_fdata()
_, brain_mask = median_otsu(mean_b0,
median_radius,
numpass,
autocrop,
dilate=dilate)
be_img = nib.Nifti1Image(brain_mask.astype(int),
mean_b0_img.affine)
nib.save(be_img, brain_mask_file)
meta = dict(source=b0_file,
median_radius=median_radius,
numpass=numpass,
autocrop=autocrop,
vol_idx=vol_idx)
meta_fname = self._get_fname(row, '_brain_mask.json')
afd.write_json(meta_fname, meta)
return brain_mask_file
def process(self, ds: Dataset = None, *args, **kwargs):
median_radius = kwargs.get('median_radius', 5)
num_pass = kwargs.get('num_pass', 4)
brain, mask = median_otsu(ds.pixel_array,
median_radius=median_radius,
numpass=num_pass)
return brain
def auto_mask(data, raw_d=None, nskip=3, mask_bad_end_vols=False):
from dipy.segment.mask import median_otsu
mn = data[:, :, :, nskip:].mean(3)
_, mask = median_otsu(mn, 3, 2) # oesteban: masked_data was not used
mask = np.concatenate((
np.tile(True, (data.shape[0], data.shape[1], data.shape[2], nskip)),
np.tile(np.expand_dims(mask == 0, 3), (1, 1, 1, data.shape[3]-nskip))),
axis=3)
mask_vols = np.zeros((mask.shape[-1]), dtype=int)
if mask_bad_end_vols:
# Some runs have corrupt volumes at the end (e.g., mux scans that are stopped prematurely). Mask those too.
# But... motion correction might have interpolated the empty slices such that they aren't exactly zero.
# So use the raw data to find these bad volumes.
# 2015.10.29 RFD: this caused problems with some non-mux EPI scans that (inexplicably)
# have empty slices at the top of the brain. So we'll disable it for
# now.
if raw_d is None:
slice_max = data.max(0).max(0)
else:
slice_max = raw_d.max(0).max(0)
bad = np.any(slice_max == 0, axis=0)
# We don't want to miss a bad volume somewhere in the middle, as that could be a valid artifact.
# So, only mask bad vols that are contiguous to the end.
mask_vols = np.array([np.all(bad[i:]) for i in range(bad.shape[0])])
# Mask out the skip volumes at the beginning
mask_vols[0:nskip] = True
mask[..., mask_vols] = True
brain = np.ma.masked_array(data, mask=mask)
good_vols = np.logical_not(mask_vols)
return brain, mask, good_vols
def process(self, tup):
if tup.values[0] == 0:
image_data = tup.values[1]
_, mask = median_otsu(image_data, 4, 2, False, dilate=1)
self.log("emitting mask with shap {} edge: {} center: {}".format(
mask.shape, mask[0][0][0], mask[32, 32, 15]))
self.emit(['reference', image_data, mask])
def createB0_ColorFA_Mask_Sprites(b0_file, colorFA_file, mask_file):
colorfa = load_and_reorient(colorFA_file)
b0 = load_and_reorient(b0_file)[:, :, :, 0]
anat_mask = load_and_reorient(mask_file)
N = max(*b0.shape[:2])
# make a b0 sprite
b0 = reshape3D(b0, N)
_, mask = median_otsu(b0)
outb0 = create_sprite_from_tiles(b0, as_bytes=True)
outb0['img_type'] = 'brainsprite'
# make a colorFA sprite, masked by b0
Q = reshape4D(colorfa, N)
Q[np.logical_not(mask)] = np.nan
Q = np.moveaxis(Q, -2, -1)
outcolorFA = create_sprite_from_tiles(Q, as_bytes=True)
outcolorFA['img_type'] = 'brainsprite'
# make an anat mask contour sprite
outmask = create_sprite_from_tiles(reshape3D(anat_mask, N))
img = mplfigcontour(outmask.pop("mosaic"), as_bytes=True)
outmask['img'] = img
return outb0, outcolorFA, outmask
def process(self, ds: Dataset = None, *args, **kwargs):
mode = kwargs.get('mode', 'crop')
# if mode == 'crop':
median_radius = kwargs.get('median_radius', 5)
num_pass = kwargs.get('num_pass', 4)
threshold = kwargs.get('threshold', THRESHOLD)
window = kwargs.get('window', (5, 5))
iterations = kwargs.get('iterations', 3)
brain, mask = median_otsu(ds.pixel_array,
median_radius=median_radius,
numpass=num_pass)
flatten = brain.reshape((-1))
for i in range(flatten.shape[0]):
if flatten[i] < threshold:
flatten[i] = 0
# ret, thresh = cv.threshold(brain, threshold, MAX_BIT, cv.THRESH_BINARY)
thresh = flatten.reshape(brain.shape)
kernel = np.ones(window, dtype=np.uint16)
res = cv.morphologyEx(thresh,
cv.MORPH_OPEN,
kernel,
iterations=iterations)
res = res.reshape((-1))
original = brain.reshape((-1))
for i in range(res.shape[0]):
if res[i] == 0:
original[i] = 0
return original.reshape(brain.shape)
def process(self, img, **kwargs):
n_components = kwargs.get('n_components', 3)
numpass = kwargs.get('numpass', 5)
median_radius = kwargs.get('median_radius', 10)
if isinstance(img, Dataset):
img = img.pixel_array
w, h = img.shape[1], img.shape[0]
img, _ = median_otsu(img, numpass=numpass, median_radius=median_radius)
img = (img - np.min(img)) / (np.max(img) - np.min(img))
x = img.reshape((-1, 1))
model = GaussianMixture(n_components=n_components,
covariance_type='spherical')
model.fit(x)
labels = model.predict(x)
# c_index = np.argmax(k_means.cluster_centers_.reshape((-1)))
# flat = np.full(img.shape[0] * img.shape[1], 0, dtype=np.uint8)
# flat[k_means.labels_ == c_index] = 1
# mask = flat.reshape(img.shape)
# k1 = np.ones((3, 3), np.uint16)
# k2 = np.ones((5, 5), np.uint16)
# mask = cv.erode(mask, k2, iterations=1)
# mask = cv.dilate(mask, k1, iterations=1)
# mask = cv.erode(mask, k2, iterations=2)
# mask = cv.dilate(mask, k1, iterations=5)
return labels.reshape((h, w))
def apply_mask(self):
"""
If self.mask is not None, will mask the raw_data with the mask provided.
If self.mask is None, median_otsu is used to generate those files.
"""
if self.mask == None:
print("Generating mask with median_otsu.")
raw_data = self.raw_data.get_data()
masked_data, mask = median_otsu(raw_data, 2,2)
#Update the instance
self.data = nib.nifti1.Nifti1Image(masked_data.astype(np.float32), self.raw_data.get_affine())
self.mask = nib.nifti1.Nifti1Image(mask.astype(np.int_), self.data.get_affine())
else:
print("Masking data with provided mask.")
raw_data = self.raw_data.get_data()
mask_data = self.mask.get_data()
masked_data = raw_data * mask_data
#Update the instance
self.data = nib.nifti1.Nifti1Image(masked_data.astype(np.float32), self.raw_data.get_affine())
#Regenerate an average B0 image
values = np.array(self.gradient_table.bvals)
ii = np.where(values == self.gradient_table.bvals.min())[0]
self.b0_average = np.mean(self.data.get_data()[:,:,:,ii], axis=3)
def load_mask(self, make_mask):
if make_mask is None:
# empty mask
self.mask = np.zeros_like(self.volume)
else:
# use dipy to isolate brain as an initial mask
if make_mask.lower() == 'auto':
print('creating brain mask...')
_, self.mask = median_otsu(self.volume, median_radius=1,
numpass=2)
print('done.')
elif make_mask.lower() == 'none':
# empty mask
self.mask = np.zeros_like(self.volume)
else:
print('reading mask file...')
# same initial axes swap as for volume
self.mask = nib.load(make_mask).get_fdata()
print('done.')
if config['remove small clusters'] and (np.sum(self.mask) > 0):
# only keep biggest data cluster (brain) - removes small clusters
label_image = label(self.mask)
max_label = sorted([[np.sum(label_image == val), val]
for val in np.unique(label_image)[1:]])[-1][1]
self.mask[label_image != max_label] = 0
def mask(d, raw_d=None, nskip=3):
mn = d[:, :, :, nskip:].mean(3)
masked_data, mask = median_otsu(mn, 3, 2)
mask = np.concatenate(
(np.tile(True, (d.shape[0], d.shape[1], d.shape[2], nskip)),
np.tile(np.expand_dims(mask == False, 3),
(1, 1, 1, d.shape[3] - nskip))),
axis=3)
# Some runs have corrupt volumes at the end (e.g., mux scans that are stopped prematurely). Mask those too.
# But... motion correction might have interpolated the empty slices such that they aren't exactly zero.
# So use the raw data to find these bad volumes.
if raw_d != None:
slice_max = raw_d.max(0).max(0)
else:
slice_max = d.max(0).max(0)
bad = np.any(slice_max == 0, axis=0)
# We don't want to miss a bad volume somewhere in the middle, as that could be a valid artifact.
# So, only mask bad vols that are contiguous to the end.
mask_vols = np.array([np.all(bad[i:]) for i in range(bad.shape[0])])
# Mask out the skip volumes at the beginning
mask_vols[0:nskip] = True
mask[:, :, :, mask_vols] = True
brain = np.ma.masked_array(d, mask=mask)
good_vols = np.logical_not(mask_vols)
return brain, good_vols
def process(self, img, **kwargs):
quantile = kwargs.get('quantile', 0.1)
n_samples = kwargs.get('n_samples', 100)
numpass = kwargs.get('numpass', 5)
median_radius = kwargs.get('median_radius', 10)
high_intensity_threshold = kwargs.get('high_intensity_threshold', 0.1)
blur_radius = kwargs.get('blur_radius', 9)
dilation_radius = kwargs.get('dilation_radius', 5)
dilation_iterations = kwargs.get('dilation_iterations', 1)
if isinstance(img, Dataset):
img = img.pixel_array
img, _ = median_otsu(img, numpass=numpass, median_radius=median_radius)
original_shape = img.shape
img = (img - np.min(img)) / (np.max(img) - np.min(img))
blurred = cv.blur(img, (15, 15))
edges = np.clip(img - blurred, 0.0, 1.0)
edges[edges > high_intensity_threshold] = 1.0
edges[edges <= high_intensity_threshold] = 0.0
edges = cv.dilate(edges, np.ones((3, 3)), iterations=1)
img = np.clip(img - edges, 0.0, 1.0)
img = cv.erode(img, np.ones((3, 3)), iterations=1)
img = cv.blur(img, (blur_radius, blur_radius))
# Flatten image.
x = np.reshape(img, [-1, 1])
bandwidth = estimate_bandwidth(x, quantile=quantile, n_samples=n_samples)
mean_shift = MeanShift(bandwidth=bandwidth, bin_seeding=True)
mean_shift.fit(x)
# c_index = np.argmax(mean_shift.cluster_centers_.reshape((-1)))
# flat = np.full(original_shape[0] * original_shape[1], 0, dtype=np.uint8)
# flat[mean_shift.labels_ == c_index] = 1
mask = mean_shift.labels_.reshape(original_shape)
# mask = cv.dilate(mask, np.ones((dilation_radius, dilation_radius)), iterations=dilation_iterations)
return mask
def createBrainMaskFromb0Data(b0Data, affineMatrix=None, saveDir=None):
"""Creates a mask of the brain from a b0 volume.
The output is written to file if affineMatrix and saveDir are provided.
Parameters:
----------
b0Data : 3D ndarray
MRI scan of the head without diffusion weighting
affineMatrix : 4x4 array
Affine transformation matrix as in Nifti
saveDir : string
Directory where the created mask will be saved as 'brainMask.nii.gz'.
Returns:
--------
mask : 3D ndarray
The binary brain mask.
"""
# Call median_otsu and discard first return value
masked_b0, mask = median_otsu(b0Data, median_radius=3, numpass=5, dilate=2)
mask = np.logical_and(mask == 1, masked_b0 > 0)
if affineMatrix is not None and saveDir is not None:
try:
maskNifti = nib.Nifti1Image(mask.astype(np.float32), affineMatrix)
nib.save(maskNifti, join(saveDir, 'brainMask.nii.gz'))
except Exception as e:
print('Saving of the brain mask \
failed with message {}'.format(e.message))
return masked_b0, mask
def _run_interface(self, runtime):
in_file = self.inputs.in_file
b0_img = nb.load(in_file)
b0_data = b0_img.get_fdata()
masked_data, data_mask = median_otsu(
b0_data,
median_radius=self.inputs.median_radius,
numpass=self.inputs.num_pass,
autocrop=False,
dilate=self.inputs.dilate)
self._results['out_mask'] = fname_presuffix(in_file,
suffix='_mask',
newpath=runtime.cwd)
self._results['masked_input'] = fname_presuffix(in_file,
suffix='_brain_masked',
newpath=runtime.cwd)
masked_img = nb.Nifti1Image(masked_data, b0_img.affine, b0_img.header)
masked_img.to_filename(self._results['masked_input'])
mask_img = nb.Nifti1Image(data_mask.astype('f8'), b0_img.affine,
b0_img.header)
mask_img.to_filename(self._results['out_mask'])
return runtime
def auto_mask(data, raw_d=None, nskip=3, mask_bad_end_vols=False):
from dipy.segment.mask import median_otsu
mn = data[:, :, :, nskip:].mean(3)
_, mask = median_otsu(mn, 3, 2) # oesteban: masked_data was not used
mask = np.concatenate((
np.tile(True, (data.shape[0], data.shape[1], data.shape[2], nskip)),
np.tile(np.expand_dims(mask == 0, 3), (1, 1, 1, data.shape[3]-nskip))),
axis=3)
mask_vols = np.zeros((mask.shape[-1]), dtype=int)
if mask_bad_end_vols:
# Some runs have corrupt volumes at the end (e.g., mux scans that are stopped prematurely). Mask those too.
# But... motion correction might have interpolated the empty slices such that they aren't exactly zero.
# So use the raw data to find these bad volumes.
# 2015.10.29 RFD: this caused problems with some non-mux EPI scans that (inexplicably)
# have empty slices at the top of the brain. So we'll disable it for
# now.
if raw_d is None:
slice_max = data.max(0).max(0)
else:
slice_max = raw_d.max(0).max(0)
bad = np.any(slice_max == 0, axis=0)
# We don't want to miss a bad volume somewhere in the middle, as that could be a valid artifact.
# So, only mask bad vols that are contiguous to the end.
mask_vols = np.array([np.all(bad[i:]) for i in range(bad.shape[0])])
# Mask out the skip volumes at the beginning
mask_vols[0:nskip] = True
mask[..., mask_vols] = True
brain = np.ma.masked_array(data, mask=mask)
good_vols = np.logical_not(mask_vols)
return brain, mask, good_vols
def apply_mask(self):
"""
If self.mask is not None, will mask the raw_data with the mask provided.
If self.mask is None, median_otsu is used to generate those files.
"""
if self.mask == None:
print("Generating mask with median_otsu.")
raw_data = self.raw_data.get_data()
masked_data, mask = median_otsu(raw_data, self.median_radius, self.numpass)
#Update the instance
self.data = nib.nifti1.Nifti1Image(masked_data.astype(np.float32), self.raw_data.get_affine())
self.mask = nib.nifti1.Nifti1Image(mask.astype(np.int_), self.data.get_affine())
else:
print("Masking data with provided mask.")
raw_data = self.raw_data.get_data()
mask_data = self.mask.get_data()
masked_data = raw_data * mask_data
#Update the instance
self.data = nib.nifti1.Nifti1Image(masked_data.astype(np.float32), self.raw_data.get_affine())
#Regenerate an average B0 image
values = np.array(self.gradient_table.bvals)
ii = np.where(values == self.gradient_table.bvals.min())[0]
self.b0_average = np.mean(self.data.get_data()[:,:,:,ii], axis=3)
def _get_from_file_mapping(self, path, file_mapping: dict, b0_threshold: float = 10.0):
path_mapping = {key: os.path.join(path, file_mapping[key]) for key in file_mapping}
bvals, bvecs = read_bvals_bvecs(path_mapping['bvals'],
path_mapping['bvecs'])
# img, t1, gradient table, affine and dwi
img = nb.load(path_mapping['img'])
t1 = nb.load(path_mapping['t1']).get_data()
dwi = img.get_data().astype("float32")
aff = img.affine
# binary mask
if 'mask' in path_mapping:
binary_mask = nb.load(path_mapping['mask']).get_data()
else:
_, binary_mask = median_otsu(dwi[..., 0], 2, 1)
# calculating b0
b0 = dwi[..., bvals < b0_threshold].mean(axis=-1)
# Do not generate fa yet
fa = None
gtab = gradient_table(bvals, bvecs)
data_container = DataContainer(bvals, bvecs, gtab, t1, dwi, aff, binary_mask, b0, fa)
return self._preprocess(data_container)
def medianOtsu(file_in, outPath, median_radius=4, num_pass=4):
print(' - running Median Otsu algoritm...')
finalFileName = outPath + utils.to_extract_filename(
file_in) + d.id_median_otsu + '_maskedVolume' + d.extension
binaryMaskFileName = outPath + utils.to_extract_filename(
file_in) + d.id_median_otsu + '_binaryMask' + d.extension
b0MaskedFileName = outPath + utils.to_extract_filename(
file_in) + d.id_median_otsu + '_b0Masked' + d.extension
if not (os.path.exists(finalFileName)):
img = nib.load(file_in)
data = img.get_data()
maskedvolume, mask = median_otsu(data, median_radius, num_pass)
nib.save(nib.Nifti1Image(maskedvolume.astype(np.float32), img.affine),
finalFileName)
nib.save(nib.Nifti1Image(mask.astype(np.float32), img.affine),
binaryMaskFileName)
nib.save(
nib.Nifti1Image(
maskedvolume[:, :, :, d.default_b0_ref].astype(np.float32),
img.affine), b0MaskedFileName)
return finalFileName, binaryMaskFileName
def process(self, img, **kwargs):
n_clusters = kwargs.get('n_clusters', 3)
numpass = kwargs.get('numpass', 5)
median_radius = kwargs.get('median_radius', 10)
high_intensity_threshold = kwargs.get('high_intensity_threshold', 0.1)
blur_radius = kwargs.get('blur_radius', 5)
dilation_radius = kwargs.get('dilation_radius', 5)
dilation_iterations = kwargs.get('dilation_iterations', 1)
if isinstance(img, Dataset):
img = img.pixel_array
img, _ = median_otsu(img, numpass=numpass, median_radius=median_radius)
original_shape = img.shape
img = (img - np.min(img)) / (np.max(img) - np.min(img))
blurred = cv.blur(img, (15, 15))
edges = np.clip(img - blurred, 0.0, 1.0)
edges[edges > high_intensity_threshold] = 1.0
edges[edges <= high_intensity_threshold] = 0.0
edges = cv.dilate(edges, np.ones((3, 3)), iterations=1)
img = np.clip(img - edges, 0.0, 1.0)
img = cv.erode(img, np.ones((3, 3)), iterations=1)
img = cv.blur(img, (blur_radius, blur_radius))
# Flatten image.
x = np.reshape(img, [-1, 1])
k_means = KMeans(n_clusters=n_clusters, random_state=0).fit(x)
c_index = np.argmax(k_means.cluster_centers_.reshape((-1)))
flat = np.full(original_shape[0] * original_shape[1], 0, dtype=np.uint8)
flat[k_means.labels_ == c_index] = 1
mask = flat.reshape(original_shape)
mask = cv.dilate(mask, np.ones((dilation_radius, dilation_radius)), iterations=dilation_iterations)
return mask
def test_median_otsu_flow():
with TemporaryDirectory() as out_dir:
data_path, _, _ = get_data('small_25')
volume = nib.load(data_path).get_data()
save_masked = True
median_radius = 3
numpass = 3
autocrop = False
vol_idx = [0]
dilate = 0
mo_flow = MedianOtsuFlow()
mo_flow.run(data_path, out_dir=out_dir, save_masked=save_masked,
median_radius=median_radius, numpass=numpass,
autocrop=autocrop, vol_idx=vol_idx, dilate=dilate)
mask_name = mo_flow.last_generated_outputs['out_mask']
masked_name = mo_flow.last_generated_outputs['out_masked']
masked, mask = median_otsu(volume, median_radius,
numpass, autocrop,
vol_idx, dilate)
result_mask_data = nib.load(join(out_dir, mask_name)).get_data()
npt.assert_array_equal(result_mask_data, mask)
result_masked_data = nib.load(join(out_dir, masked_name)).get_data()
npt.assert_array_equal(result_masked_data, masked)
def process_data(path_dict, gtab, signal_parameters):
processing_parameters = signal_parameters["processing_params"]
try:
data, affine = load_nifti(path_dict["dwi"])
except Exception as e:
print(path_dict['name'])
raise e
data = data[:].copy()
# Crop the MRI
try:
mask, _ = load_nifti(path_dict["mask"])
except FileNotFoundError:
print('No mask found, generating one, may be erroneous')
data, mask = median_otsu(data,
vol_idx=gtab.b0s_mask,
autocrop=True,
**processing_parameters['median_otsu_params'])
mask = np.expand_dims(mask.astype(int), axis=-1)
mean_b0 = data[..., gtab.b0s_mask].mean(-1)
mean_b0 = np.expand_dims(mean_b0, axis=-1)
mean_b0 *= mask
data = normalize_data(data, gtab.b0s_mask)
sh_coeff = dwi_to_sh(data,
gtab,
mask=mask,
sh_order=signal_parameters['sh_order'],
**processing_parameters["sh_params"])
# Pad the x,y,z axes so they can be divided by the respective patch size
patch_size = np.array(signal_parameters["patch_size"])
pad_needed = patch_size - sh_coeff.shape[:3] % patch_size
pad_needed = [(x // 2, x // 2 + x % 2) for x in pad_needed] + [(0, 0)]
sh_coeff = np.pad(sh_coeff, pad_width=pad_needed)
mask = np.pad(mask, pad_width=pad_needed)
mean_b0 = np.pad(mean_b0, pad_width=pad_needed)
real_size = sh_coeff.shape[:3]
sh_coeff = sh_coeff.astype(np.float32)
mean_b0 = mean_b0.astype(np.float32)
data = {
'sh': sh_coeff,
'mask': mask,
'mean_b0': mean_b0,
'real_size': real_size,
'gtab': gtab
}
if 'site' in path_dict.keys():
data['site'] = [path_dict['site']]
return data
def run_dmri_pipeline(subject_session, do_topup=True, do_edc=True):
subject, session = subject_session
data_dir = os.path.join(source_dir, subject, session, 'dwi')
write_dir = os.path.join(derivatives_dir, subject, session)
dwi_dir = os.path.join(write_dir, 'dwi')
# Apply topup to the images
input_imgs = sorted(glob.glob('%s/sub*.nii.gz' % data_dir))
dc_imgs = sorted(glob.glob(os.path.join(dwi_dir, 'dcsub*run*.nii.gz')))
mem = Memory('/neurospin/tmp/bthirion/cache_dir')
if len(dc_imgs) < len(input_imgs):
se_maps = [
os.path.join(source_dir, subject, session, 'fmap',
'%s_%s_dir-ap_epi.nii.gz' % (subject, session)),
os.path.join(source_dir, subject, session, 'fmap',
'%s_%s_dir-pa_epi.nii.gz' % (subject, session))]
if do_topup:
fsl_topup(se_maps, input_imgs, mem, write_dir, 'dwi')
# Then proceeed with Eddy current correction
# get the images
dc_imgs = sorted(glob.glob(os.path.join(dwi_dir, 'dc*run*.nii.gz')))
dc_img = os.path.join(dwi_dir, 'dc%s_%s_dwi.nii.gz' % (subject, session))
concat_images(dc_imgs, dc_img)
# get the bvals/bvec
file_bvals = sorted(glob.glob('%s/sub*.bval' % data_dir))
bvals = np.concatenate([np.loadtxt(fbval) for fbval in sorted(file_bvals)])
bvals_file = os.path.join(dwi_dir, 'dc%s_%s_dwi.bval' % (subject, session))
np.savetxt(bvals_file, bvals)
file_bvecs = sorted(glob.glob('%s/sub*.bvec' % data_dir))
bvecs = np.hstack([np.loadtxt(fbvec) for fbvec in sorted(file_bvecs)])
bvecs_file = os.path.join(dwi_dir, 'dc%s_%s_dwi.bvec' % (subject, session))
np.savetxt(bvecs_file, bvecs)
# Get eddy-preprocessed images
# eddy_img = nib.load(glob.glob(os.path.join(dwi_dir, 'eddc*.nii*'))[-1])
# Get eddy-preprocessed images
eddy_img = mem.cache(eddy_current_correction)(
dc_img, bvals_file, bvecs_file, dwi_dir, mem)
# load the data
gtab = gradient_table(bvals, bvecs, b0_threshold=10)
# Create a brain mask
from dipy.segment.mask import median_otsu
b0_mask, mask = median_otsu(eddy_img.get_data(), 2, 1)
if subject == 'sub-13':
from nilearn.masking import compute_epi_mask
from nilearn.image import index_img
imgs_ = [index_img(eddy_img, i)
for i in range(len(bvals)) if bvals[i] < 50]
mask_img = compute_epi_mask(imgs_, upper_cutoff=.8)
mask_img.to_filename('/tmp/mask.nii.gz')
mask = mask_img.get_data()
# do the tractography
streamlines = tractography(eddy_img, gtab, mask, dwi_dir)
return streamlines
def brainMask(dwi):
img = nib.load(dwi['denoised'])
raw = img.get_data()
b0_raw = raw[:, :, :, dwi['shellind'] == 0]
if b0_raw.shape[3] > 0:
b0_raw = np.mean(b0_raw, axis=3)
raw = np.transpose(raw, np.hstack((dwi['perm'], 3)))
if dwi['flip_sign'][0] < 0:
raw = raw[::-1, :, :, :]
if dwi['flip_sign'][1] < 0:
raw = raw[:, ::-1, :, :]
if dwi['flip_sign'][2] < 0:
raw = raw[:, :, ::-1, :]
b0 = raw[:, :, :, dwi['shellind'] == 0]
b0 = np.median(b0, axis=3)
mds = raw[:, :, :, dwi['shellind'] != 0]
mds = np.median(mds, axis=3)
# do bfc correction for better brainMasks
import SimpleITK as sitk
corrector = sitk.N4BiasFieldCorrectionImageFilter()
inputImage = sitk.GetImageFromArray(b0)
inputImageMask = sitk.Cast(inputImage, sitk.sitkInt32)
maskImage = sitk.OtsuThreshold(inputImageMask, 0, 1, 200)
inputImage = sitk.Cast(inputImage, sitk.sitkFloat32)
b0ImageBfc = corrector.Execute(inputImage, maskImage)
b0 = sitk.GetArrayFromImage(b0ImageBfc)
inputImage = sitk.GetImageFromArray(mds)
inputImageMask = sitk.Cast(inputImage, sitk.sitkInt32)
maskImage = sitk.OtsuThreshold(inputImageMask, 0, 1, 200)
inputImage = sitk.Cast(inputImage, sitk.sitkFloat32)
mdsImageBfc = corrector.Execute(inputImage, maskImage)
mds = sitk.GetArrayFromImage(mdsImageBfc)
_, b0_mask = median_otsu(b0, 4, 4)
_, mds_mask = median_otsu(mds, 4, 4)
dwi['b0'] = b0_raw
dwi['mask'] = np.bitwise_or(b0_mask, mds_mask)
def mask_getter(subses_dict, dwi_affine, data_imap):
mean_b0_img = nib.load(data_imap["b0_file"])
mean_b0 = mean_b0_img.get_fdata()
_, mask_data = median_otsu(mean_b0, **self.median_otsu_kwargs)
return mask_data, dict(
source=data_imap["b0_file"],
technique="median_otsu applied to b0",
median_otsu_kwargs=self.median_otsu_kwargs)
def remove_background(image):
maskdata, mask = median_otsu(image,
vol_idx=range(image.shape[-1]),
median_radius=3,
numpass=1,
autocrop=True,
dilate=2)
return maskdata
def smooth_mask(subject, run):
""" Applies smoothing and computes mask. Applies mask to smoothed data """
data = bold_data(subject, run)
mean_data = np.mean(data,axis=-1)
masked, mask = median_otsu(mean_data,2,1)
smooth_data = gaussian_filter(data,[2,2,2,0])
smooth_masked = smooth_data[mask]
return smooth_masked.T
def handle(self):
img = nib.load(self.dmri_file)
data = img.get_data()
bvals, bvecs = read_bvals_bvecs(self.fbvals, self.fbvecs)
gtab = gradient_table(bvals, bvecs)
maskdata, mask = median_otsu(data, 3, 1, True,\
vol_idx=range(10, 50), dilate=2)
#print('maskdata.shape (%d, %d, %d, %d)' % maskdata.shape)
tenmodel = dti.TensorModel(gtab)
self.tenfit = tenmodel.fit(maskdata)
def brain_extraction(file):
from dipy.segment.mask import median_otsu
from os.path import splitext
img = nib.load(file)
data = img.get_data()
masked_data, mask = median_otsu(data, 2, 1)
mask_img = nib.Nifti1Image(mask.astype(np.int), img.get_affine())
masked_img = nib.Nifti1Image(masked_data, img.get_affine())
root, ext = splitext(file)
nib.save(mask_img, root + '_binary_mask.nii')
nib.save(masked_img, root + '_masked.nii')
def design_matrix(subject, run, TR = 2.5):
data = bold_data(subject,run)
vol_shape, n_trs = data.shape[:-1], data.shape[-1]
tr_times = np.arange(0,30,TR)
hrf_at_trs = hrf(tr_times)
col = 0
X = np.ones((n_trs,14))
#Smoothed and masked data
mean_data = np.mean(data,axis=-1)
masked, mask = median_otsu(mean_data,2,1)
# smooth_data = gaussian_filter(data,[2,2,2,0])
# Y = smooth_data[mask].T
#omitted smoothing for now
Y = data[mask].T
#Adding onsets to design matrix
for i in list_cond_file(subject,run):
neural_prediction = events2neural_fixed(i, TR, n_trs)
convolved = convolve(neural_prediction, hrf_at_trs)
X[:,col] = convolved
col = col+1
##PCA
Y_demeaned = Y - np.mean(Y,axis=1).reshape([-1,1])
unscaled_cov = Y_demeaned.dot(Y_demeaned.T)
U, S, V = npl.svd(unscaled_cov)
X[:,8] = U[:,0]
X[:,9:11] = U[:,6:8]
linear_drift = np.linspace(-1,1,n_trs)
X[:,11] = linear_drift
quadratic_drift = linear_drift ** 2
quadratic_drift -= np.mean(quadratic_drift)
X[:,12]= quadratic_drift
betas = npl.pinv(X).dot(Y)
betas_vols = np.zeros(vol_shape+(14,))
betas_vols[mask,:] = betas.T
projections = U.T.dot(Y_demeaned)
projection_vols = np.zeros(data.shape)
projection_vols[mask,:] = projections.T
return X, Y, betas_vols, mask, U, Y_demeaned, mean_data, projection_vols
def _brain_mask(row, median_radius=4, numpass=4, autocrop=False,
vol_idx=None, dilate=None, force_recompute=False):
brain_mask_file = _get_fname(row, '_brain_mask.nii.gz')
if not op.exists(brain_mask_file) or force_recompute:
img = nib.load(row['dwi_file'])
data = img.get_data()
gtab = row['gtab']
mean_b0 = np.mean(data[..., ~gtab.b0s_mask], -1)
_, brain_mask = median_otsu(mean_b0, median_radius, numpass,
autocrop, dilate=dilate)
be_img = nib.Nifti1Image(brain_mask.astype(int),
img.affine)
nib.save(be_img, brain_mask_file)
return brain_mask_file
def peaks_from_nifti(fdwi, fbvec=None, fbval=None, mask=None):
if '.' not in fdwi:
fbase = fdwi
fdwi = fdwi+".nii.gz"
if not fbval:
fbval = fbase+".bval"
if not fbvec:
fbvec = fbase+".bvec"
print fdwi
img = nib.load(fdwi)
data = img.get_data()
zooms = img.get_header().get_zooms()[:3]
affine = img.get_affine()
bval, bvec = dio.read_bvals_bvecs(fbval, fbvec)
gtab = dgrad.gradient_table(bval, bvec)
if not mask:
print 'generate mask'
maskdata, mask = median_otsu(data, 3, 1, False, vol_idx=range(10, 50), dilate=2)
else:
mask_img = nib.load(mask)
mask = mask_img.get_data()
from dipy.segment.mask import applymask
maskdata = applymask(data, mask)
print maskdata.shape, mask.shape
from dipy.reconst.shm import QballModel, CsaOdfModel
model = QballModel(gtab, 6)
sphere = get_sphere('symmetric724')
print "fit Qball peaks"
proc_num = multiprocessing.cpu_count()-1
print "peaks_from_model using core# =" + str(proc_num)
peaks = peaks_from_model(model=model, data=maskdata, relative_peak_threshold=.5,
min_separation_angle=25,
sphere=sphere, mask=mask, parallel=True, nbr_processes=proc_num)
return peaks
def mask(d, raw_d=None, nskip=3):
mn = d[:,:,:,nskip:].mean(3)
masked_data, mask = median_otsu(mn, 3, 2)
mask = np.concatenate((np.tile(True, (d.shape[0], d.shape[1], d.shape[2], nskip)),
np.tile(np.expand_dims(mask==False, 3), (1,1,1,d.shape[3]-nskip))),
axis=3)
# Some runs have corrupt volumes at the end (e.g., mux scans that are stopped prematurely). Mask those too.
# But... motion correction might have interpolated the empty slices such that they aren't exactly zero.
# So use the raw data to find these bad volumes.
if raw_d!=None:
slice_max = raw_d.max(0).max(0)
else:
slice_max = d.max(0).max(0)
bad = np.any(slice_max==0, axis=0)
# We don't want to miss a bad volume somewhere in the middle, as that could be a valid artifact.
# So, only mask bad vols that are contiguous to the end.
mask_vols = np.array([np.all(bad[i:]) for i in range(bad.shape[0])])
# Mask out the skip volumes at the beginning
mask_vols[0:nskip] = True
mask[:,:,:,mask_vols] = True
brain = np.ma.masked_array(d, mask=mask)
good_vols = np.logical_not(mask_vols)
return brain,good_vols
def dodata(f_name,data_path):
dipy_home = pjoin(os.path.expanduser('~'), 'dipy_data')
folder = pjoin(dipy_home, data_path)
fraw = pjoin(folder, f_name+'.nii.gz')
fbval = pjoin(folder, f_name+'.bval')
fbvec = pjoin(folder, f_name+'.bvec')
flabels = pjoin(folder, f_name+'.nii-label.nii.gz')
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs)
img = nib.load(fraw)
data = img.get_data()
affine = img.get_affine()
label_img = nib.load(flabels)
labels=label_img.get_data()
lap=through_label_sl.label_position(labels, labelValue=1)
dataslice = data[40:80, 20:80, lap[2][2] / 2]
#print lap[2][2]/2
#get_csd_gfa(f_name,data,gtab,dataslice)
maskdata, mask = median_otsu(data, 2, 1, False, vol_idx=range(10, 50), dilate=2) #不去背景
""" get fa and tensor evecs and ODF"""
from dipy.reconst.dti import TensorModel,mean_diffusivity
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
sphere = get_sphere('symmetric724')
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
np.save(os.getcwd()+'\zhibiao'+f_name+'_FA.npy',FA)
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
nib.save(fa_img,os.getcwd()+'\zhibiao'+f_name+'_FA.nii.gz')
print('Saving "DTI_tensor_fa.nii.gz" sucessful.')
evecs_img = nib.Nifti1Image(tenfit.evecs.astype(np.float32), affine)
nib.save(evecs_img, os.getcwd()+'\zhibiao'+f_name+'_DTI_tensor_evecs.nii.gz')
print('Saving "DTI_tensor_evecs.nii.gz" sucessful.')
MD1 = mean_diffusivity(tenfit.evals)
nib.save(nib.Nifti1Image(MD1.astype(np.float32), img.get_affine()), os.getcwd()+'\zhibiao'+f_name+'_MD.nii.gz')
#tensor_odfs = tenmodel.fit(data[20:50, 55:85, 38:39]).odf(sphere)
#from dipy.reconst.odf import gfa
#dti_gfa=gfa(tensor_odfs)
wm_mask = (np.logical_or(FA >= 0.4, (np.logical_and(FA >= 0.15, MD >= 0.0011))))
response = recursive_response(gtab, data, mask=wm_mask, sh_order=8,
peak_thr=0.01, init_fa=0.08,
init_trace=0.0021, iter=8, convergence=0.001,
parallel=False)
from dipy.reconst.csdeconv import ConstrainedSphericalDeconvModel
csd_model = ConstrainedSphericalDeconvModel(gtab, response)
#csd_fit = csd_model.fit(data)
from dipy.direction import peaks_from_model
csd_peaks = peaks_from_model(model=csd_model,
data=data,
sphere=sphere,
relative_peak_threshold=.5,
min_separation_angle=25,
parallel=False)
GFA = csd_peaks.gfa
nib.save(GFA, os.getcwd()+'\zhibiao'+f_name+'_MSD.nii.gz')
print('Saving "GFA.nii.gz" sucessful.')
from dipy.reconst.shore import ShoreModel
asm = ShoreModel(gtab)
print('Calculating...SHORE msd')
asmfit = asm.fit(data,mask)
msd = asmfit.msd()
msd[np.isnan(msd)] = 0
#print GFA[:,:,slice].T
print('Saving msd_img.png')
nib.save(msd, os.getcwd()+'\zhibiao'+f_name+'_GFA.nii.gz')
import numpy as np
from dipy.segment.mask import median_otsu
import utils
if __name__ == "__main__":
img = utils.load_nifti('b0.nii.gz')
data = img.get_data()
# b0_mask, mask = median_otsu(data, 2, 1)
# b0_mask, mask = median_otsu(data, 3, 1)
# b0_mask, mask = median_otsu(data, 1, 2)
# b0_mask, mask = median_otsu(data, 1, 3)
# b0_mask, mask = median_otsu(data, 3, 2)
# b0_mask, mask = median_otsu(data, 2, 3)
b0_mask, mask1 = median_otsu(data, 1, 1)
b0_mask, mask2 = median_otsu(data, 4, 2)
# parametres retenues
b0_mask, mask = median_otsu(data, 2, 2)
fig = plt.figure()
fig.add_subplot(221)
plt.title("Image")
plt.axis('off')
plt.imshow(data[data.shape[0]/2, :, :], cmap=cm.gray)
fig.add_subplot(222)
plt.title("radius 2, numpass 2")
plt.axis('off')
plt.imshow(mask[mask.shape[0]/2, :, :], cmap=cm.gray)
img, gtab = read_cenir_multib(bvals) data = img.get_data() affine = img.get_affine() """ Function ``read_cenir_multib`` return img and gtab which contains respectively a nibabel Nifti1Image object (where the data can be extracted) and a GradientTable object with information about the b-values and b-vectors. Before fitting the data, we preform some data pre-processing. We first compute a brain mask to avoid unnecessary calculations on the background of the image. """ maskdata, mask = median_otsu(data, 4, 2, False, vol_idx=[0, 1], dilate=1) """ Since the diffusion kurtosis models involves the estimation of a large number of parameters [TaxCMW2015]_ and since the non-Gaussian components of the diffusion signal are more sensitive to artefacts [NetoHe2012]_, a fundamental data pre-processing step for diffusion kurtosis fitting is to denoise our data. For this, we use Dipy's non-local mean filter (see :ref:`example-denoise-nlmeans`). Note that, since the HCP-like data has a large number of diffusion-weigthed volumes, this procedure can take a couple of hours to compute the entire dataset. Therefore, to speed the run time in this example we only denoise an axial slice of the data. """ axial_slice = 40
fval = sys.argv[7]+'.bval'
fvec = sys.argv[7]+'.bvec'
bvals,bvecs = read_bvals_bvecs(fval,fvec)
fname = sys.argv[7]+'.nii.gz'
gtab=gradient_table(bvals,bvecs)
img=nib.load(fname)
data = img.get_data()
data_b0=np.squeeze(data)[...,0]
from dipy.data.fetcher import fetch_syn_data, read_syn_data
fetch_syn_data()
nib_syn_t1, nib_syn_b0 = read_syn_data()
syn_b0 = np.array(nib_syn_b0.get_data())
from dipy.segment.mask import median_otsu
data_b0_masked, data_b0_mask = median_otsu(data_b0, 4, 4)
syn_b0_masked, syn_b0_mask = median_otsu(syn_b0, 4, 4)
static = data_b0_masked
static_affine = nib_stanford.get_affine()
moving = syn_b0_masked
moving_affine = nib_syn_b0.get_affine()
pre_align = np.array([[1.02783543e+00, -4.83019053e-02, -6.07735639e-02, -2.57654118e+00],
[4.34051706e-03, 9.41918267e-01, -2.66525861e-01, 3.23579799e+01],
[5.34288908e-02, 2.90262026e-01, 9.80820307e-01, -1.46216651e+01],
[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.00000000e+00]])
import dipy.align.vector_fields as vfu
transform = np.linalg.inv(moving_affine).dot(pre_align.dot(static_affine))
resampled = vfu.warp_3d_affine(moving.astype(np.float32),
def run(self, data_files, bvals_files, bvecs_files, mask_files,
bbox_threshold=[0.6, 1, 0, 0.1, 0, 0.1], out_dir='',
out_file='product.json', out_mask_cc='cc.nii.gz',
out_mask_noise='mask_noise.nii.gz'):
"""Compute the signal-to-noise ratio in the corpus callosum.
Parameters
----------
data_files : string
Path to the dwi.nii.gz file. This path may contain wildcards to
process multiple inputs at once.
bvals_files : string
Path of bvals.
bvecs_files : string
Path of bvecs.
mask_files : string
Path of brain mask
bbox_threshold : variable float, optional
Threshold for bounding box, values separated with commas for ex.
[0.6,1,0,0.1,0,0.1]. (default (0.6, 1, 0, 0.1, 0, 0.1))
out_dir : string, optional
Where the resulting file will be saved. (default '')
out_file : string, optional
Name of the result file to be saved. (default 'product.json')
out_mask_cc : string, optional
Name of the CC mask volume to be saved (default 'cc.nii.gz')
out_mask_noise : string, optional
Name of the mask noise volume to be saved
(default 'mask_noise.nii.gz')
"""
io_it = self.get_io_iterator()
for dwi_path, bvals_path, bvecs_path, mask_path, out_path, \
cc_mask_path, mask_noise_path in io_it:
data, affine = load_nifti(dwi_path)
bvals, bvecs = read_bvals_bvecs(bvals_path, bvecs_path)
gtab = gradient_table(bvals=bvals, bvecs=bvecs)
logging.info('Computing brain mask...')
_, calc_mask = median_otsu(data)
mask, affine = load_nifti(mask_path)
mask = np.array(calc_mask == mask.astype(bool)).astype(int)
logging.info('Computing tensors...')
tenmodel = TensorModel(gtab)
tensorfit = tenmodel.fit(data, mask=mask)
logging.info(
'Computing worst-case/best-case SNR using the CC...')
if np.ndim(data) == 4:
CC_box = np.zeros_like(data[..., 0])
elif np.ndim(data) == 3:
CC_box = np.zeros_like(data)
else:
raise IOError('DWI data has invalid dimensions')
mins, maxs = bounding_box(mask)
mins = np.array(mins)
maxs = np.array(maxs)
diff = (maxs - mins) // 4
bounds_min = mins + diff
bounds_max = maxs - diff
CC_box[bounds_min[0]:bounds_max[0],
bounds_min[1]:bounds_max[1],
bounds_min[2]:bounds_max[2]] = 1
if len(bbox_threshold) != 6:
raise IOError('bbox_threshold should have 6 float values')
mask_cc_part, cfa = segment_from_cfa(tensorfit, CC_box,
bbox_threshold,
return_cfa=True)
save_nifti(cc_mask_path, mask_cc_part.astype(np.uint8), affine)
logging.info('CC mask saved as {0}'.format(cc_mask_path))
mean_signal = np.mean(data[mask_cc_part], axis=0)
mask_noise = binary_dilation(mask, iterations=10)
mask_noise[..., :mask_noise.shape[-1]//2] = 1
mask_noise = ~mask_noise
save_nifti(mask_noise_path, mask_noise.astype(np.uint8), affine)
logging.info('Mask noise saved as {0}'.format(mask_noise_path))
noise_std = np.std(data[mask_noise, :])
logging.info('Noise standard deviation sigma= ' + str(noise_std))
idx = np.sum(gtab.bvecs, axis=-1) == 0
gtab.bvecs[idx] = np.inf
axis_X = np.argmin(
np.sum((gtab.bvecs-np.array([1, 0, 0])) ** 2, axis=-1))
axis_Y = np.argmin(
np.sum((gtab.bvecs-np.array([0, 1, 0])) ** 2, axis=-1))
axis_Z = np.argmin(
np.sum((gtab.bvecs-np.array([0, 0, 1])) ** 2, axis=-1))
SNR_output = []
SNR_directions = []
for direction in ['b0', axis_X, axis_Y, axis_Z]:
if direction == 'b0':
SNR = mean_signal[0]/noise_std
logging.info("SNR for the b=0 image is :" + str(SNR))
else:
logging.info("SNR for direction " + str(direction) +
" " + str(gtab.bvecs[direction]) + "is :" +
str(SNR))
SNR_directions.append(direction)
SNR = mean_signal[direction]/noise_std
SNR_output.append(SNR)
data = []
data.append({
'data': str(SNR_output[0]) + ' ' + str(SNR_output[1]) +
' ' + str(SNR_output[2]) + ' ' + str(SNR_output[3]),
'directions': 'b0' + ' ' + str(SNR_directions[0]) +
' ' + str(SNR_directions[1]) + ' ' +
str(SNR_directions[2])
})
with open(os.path.join(out_dir, out_path), 'w') as myfile:
json.dump(data, myfile)
def run(
self,
input_files,
save_masked=False,
median_radius=2,
numpass=5,
autocrop=False,
vol_idx=None,
dilate=None,
out_dir="",
out_mask="brain_mask.nii.gz",
out_masked="dwi_masked.nii.gz",
):
"""Workflow wrapping the median_otsu segmentation method.
Applies median_otsu segmentation on each file found by 'globing'
``input_files`` and saves the results in a directory specified by
``out_dir``.
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to
process multiple inputs at once.
save_masked : bool
Save mask
median_radius : int, optional
Radius (in voxels) of the applied median filter (default 2)
numpass : int, optional
Number of pass of the median filter (default 5)
autocrop : bool, optional
If True, the masked input_volumes will also be cropped using the
bounding box defined by the masked data. For example, if diffusion
images are of 1x1x1 (mm^3) or higher resolution auto-cropping could
reduce their size in memory and speed up some of the analysis.
(default False)
vol_idx : string, optional
1D array representing indices of ``axis=3`` of a 4D `input_volume`
'None' (the default) corresponds to ``(0,)`` (assumes first volume
in 4D array)
dilate : string, optional
number of iterations for binary dilation (default 'None')
out_dir : string, optional
Output directory (default input file directory)
out_mask : string, optional
Name of the mask volume to be saved (default 'brain_mask.nii.gz')
out_masked : string, optional
Name of the masked volume to be saved (default 'dwi_masked.nii.gz')
"""
io_it = self.get_io_iterator()
for fpath, mask_out_path, masked_out_path in io_it:
logging.info("Applying median_otsu segmentation on {0}".format(fpath))
data, affine, img = load_nifti(fpath, return_img=True)
masked_volume, mask_volume = median_otsu(data, median_radius, numpass, autocrop, vol_idx, dilate)
save_nifti(mask_out_path, mask_volume.astype(np.float32), affine)
logging.info("Mask saved as {0}".format(mask_out_path))
if save_masked:
save_nifti(masked_out_path, masked_volume, affine, img.header)
logging.info("Masked volume saved as {0}".format(masked_out_path))
return io_it
Load the raw diffusion data and the affine.
"""
data = img.get_data()
print('data.shape (%d, %d, %d, %d)' % data.shape)
"""
data.shape ``(81, 106, 76, 160)``
Remove most of the background using dipy's mask module.
"""
from dipy.segment.mask import median_otsu
maskdata, mask = median_otsu(data, 3, 2, True, range(0, 10))
"""
We instantiate our CSA model with spherical harmonic order of 4
"""
csamodel = CsaOdfModel(gtab, 4)
"""
`Peaks_from_model` is used to calculate properties of the ODFs (Orientation
Distribution Function) and return for
example the peaks and their indices, or GFA which is similar to FA but for ODF
based models. This function mainly needs a reconstruction model, the data and a
sphere as input. The sphere is an object that represents the spherical discrete
grid where the ODF values will be evaluated.
"""
from dipy.reconst.dti import TensorModel, fractional_anisotropy
from dipy.reconst.csdeconv import (ConstrainedSphericalDeconvModel,
auto_response)
from dipy.reconst.peaks import peaks_from_model
from dipy.tracking.eudx import EuDX
from dipy.data import get_sphere
from dipy.segment.mask import median_otsu
from dipy.viz import fvtk
from dipy.viz.colormap import line_colors
from dipy.core.gradients import gradient_table
from dipy.io import read_bvals_bvecs
fval = sys.argv[1]+'.bval'
fvec = sys.argv[1]+'.bvec'
bvals,bvecs = read_bvals_bvecs(fval,fvec)
fname = sys.argv[1]+'.nii.gz'
gtab=gradient_table(bvals,bvecs)
img=nib.load(fname)
data = img.get_data()
print(data.shape)
maskdata, mask = median_otsu(data, 3, 1, False,
vol_idx=range(1, 9), dilate=2)
response, ratio = auto_response(gtab, data, roi_radius=5, fa_thr=0.7)
input_folder = '/Users/arokem/tmp/input/'
fdata = op.join(input_folder, "Ax DTI 30 DIRECTIONAL_aligned_trilin.nii.gz")
fbval = op.join(input_folder, "Ax DTI 30 DIRECTIONAL_aligned_trilin.bvals")
fbvec = op.join(input_folder, "Ax DTI 30 DIRECTIONAL_aligned_trilin.bvecs")
img = nib.load(fdata)
data = img.get_data()
gtab = dpg.gradient_table(fbval, fbvec)
mean_b0 = np.mean(data[..., gtab.b0s_mask], -1)
print("Calculating brain-mask")
if not op.exists('./brain_mask.nii.gz'):
_, brain_mask = median_otsu(mean_b0, median_radius=4, numpass=4)
nib.save(nib.Nifti1Image(brain_mask.astype(int),
img.affine), './brain_mask.nii.gz')
else:
brain_mas = nib.load('./brain_mask.nii.gz').get_data().astype(bool)
print("Calculating DTI...")
if not op.exists('./dti_FA.nii.gz'):
dti_params = dti.fit_dti(fdata, fbval, fbvec,
out_dir='.', mask=brain_mask)
else:
dti_params = {'FA': './dti_FA.nii.gz',
'MD': './dti_MD.nii.gz',
'RD': './dti_RD.nii.gz',
'AD': './dti_AD.nii.gz',
'params': './dti_params.nii.gz'}
""" ``img`` contains a nibabel Nifti1Image object. Data is the actual brain data as a numpy ndarray. Segment the brain using dipy's mask module. ``median_otsu`` returns the segmented brain data and a binary mask of the brain. It is possible to fine tune the parameters of ``median_otsu`` (``median_radius`` and ``num_pass``) if extraction yields incorrect results but the default parameters work well on most volumes. For this example, default parameters (4, 4) will be used. """ from dipy.segment.mask import median_otsu b0_mask, mask = median_otsu(data, 4, 4) """ Saving the segmentation results is very easy using nibabel. We need the b0_mask, and the binary mask volumes. The affine matrix which transform the image's coordinates to the world coordinates is also needed. Here, we choose to save both images in float32. """ mask_img = nib.Nifti1Image(mask.astype(np.float32), img.get_affine()) b0_img = nib.Nifti1Image(b0_mask.astype(np.float32), img.get_affine()) fname = 'ge_3t' nib.save(mask_img, fname + '_binary_mask.nii.gz') nib.save(b0_img, fname + '_mask.nii.gz')
The selected b-values and gradient directions are then converted to Dipy's
GradientTable format.
"""
from dipy.core.gradients import gradient_table
gtab = gradient_table(selected_bvals, selected_bvecs)
"""
Before fitting the data some data pre-processing is done. First, we mask and
crop the data to avoid calculating Tensors on the background of the image.
"""
from dipy.segment.mask import median_otsu
maskdata, mask = median_otsu(selected_data, 3, 1, True,
vol_idx=range(10, 50), dilate=2)
"""
Now that we have prepared the datasets we can go forward with the voxel
reconstruction. This can be done by first instantiate the DiffusinKurtosisModel
in the following way.
"""
dkimodel = dki.DiffusionKurtosisModel(gtab)
"""
Fitting the data is very simple. We just need to call the fit method of the
DiffusinKurtosisModel in the following way:
"""
dkifit = dkimodel.fit(maskdata)
def otsu_median(data, size, n_iter):
from dipy.segment.mask import median_otsu
data, mask = median_otsu(data, size, n_iter)
return mask
"""
The second one will be the same b0 we used for the 2D registration tutorial
"""
from dipy.data.fetcher import fetch_syn_data, read_syn_data
fetch_syn_data()
nib_syn_t1, nib_syn_b0 = read_syn_data()
syn_b0 = np.array(nib_syn_b0.get_data())
"""
We first remove the skull from the b0's
"""
from dipy.segment.mask import median_otsu
stanford_b0_masked, stanford_b0_mask = median_otsu(stanford_b0, 4, 4)
syn_b0_masked, syn_b0_mask = median_otsu(syn_b0, 4, 4)
static = stanford_b0_masked
static_affine = nib_stanford.affine
moving = syn_b0_masked
moving_affine = nib_syn_b0.affine
"""
Suppose we have already done a linear registration to roughly align the two
images
"""
pre_align = np.array([[1.02783543e+00, -4.83019053e-02, -6.07735639e-02, -2.57654118e+00],
[4.34051706e-03, 9.41918267e-01, -2.66525861e-01, 3.23579799e+01],
[5.34288908e-02, 2.90262026e-01, 9.80820307e-01, -1.46216651e+01],
"""
from __future__ import division, print_function
import nibabel as nib
import numpy as np
from dipy.data import fetch_stanford_hardi, read_stanford_hardi
from dipy.segment.mask import median_otsu
from dipy.reconst.dti import TensorModel
fetch_stanford_hardi()
img, gtab = read_stanford_hardi()
data = img.get_data()
affine = img.get_affine()
print("Computing brain mask...")
b0_mask, mask = median_otsu(data)
print("Computing tensors...")
tenmodel = TensorModel(gtab)
tensorfit = tenmodel.fit(data, mask=mask)
"""Next, we set our red-green-blue thresholds to (0.6, 1) in the x axis
and (0, 0.1) in the y and z axes respectively.
These values work well in practice to isolate the very RED voxels of the cfa map.
Then, as assurance, we want just RED voxels in the CC (there could be
noisy red voxels around the brain mask and we don't want those). Unless the brain
acquisition was badly aligned, the CC is always close to the mid-sagittal slice.
The following lines perform these two operations and then saves the computed mask.
"""
bvals, bvecs = read_bvals_bvecs(fbval, fbvec)
gtab = gradient_table(bvals, bvecs)
img = nib.load(fraw)
data = img.get_data()
affine = img.get_affine()
label_img = nib.load(flabels)
labels=label_img.get_data()
lap=through_label_sl.label_position(labels, labelValue=1)
dataslice = data[40:80, 20:80, lap[2][2] / 2]
#print lap[2][2]/2
#get_csd_gfa(f_name,data,gtab,dataslice)
maskdata, mask = median_otsu(data, 2, 1, False, vol_idx=range(10, 50), dilate=2) #不去背景
""" get fa and tensor evecs and ODF"""
from dipy.reconst.dti import TensorModel,mean_diffusivity
tenmodel = TensorModel(gtab)
tenfit = tenmodel.fit(data, mask)
sphere = get_sphere('symmetric724')
FA = fractional_anisotropy(tenfit.evals)
FA[np.isnan(FA)] = 0
np.save(os.getcwd()+'\zhibiao'+f_name+'_FA.npy',FA)
fa_img = nib.Nifti1Image(FA.astype(np.float32), affine)
print FA.shape
nib.save(fa_img,os.getcwd()+'/zhibiao/'+f_name+'_FA.nii.gz')
##############
#Loading Data and HDR
data = bold_data(subject, 1)
vol_shape, n_trs = data.shape[:-1], data.shape[-1]
TR = 2.5
tr_times = np.arange(0,30,TR)
all_tr_times = np.arange(n_trs) * TR
hrf_at_trs = hrf(tr_times)
X = np.ones((n_trs,14))
X_np = np.ones((n_trs,14))
mean_data = np.mean(data,axis=-1)
masked, mask = median_otsu(mean_data,2,1)
Y = data[mask].T
col = 0
pred = 0
#Adding onsets to design matrix
for i in list_cond_file(subject,run):
neural_prediction = events2neural_fixed(i, TR, n_trs)
convolved = convolve(neural_prediction, hrf_at_trs)
X[:,col] = convolved
X_np[:,pred] = neural_prediction
col = col + 1
pred = pred + 1
plt.plot(all_tr_times ,X_np[:,:8])
Load the raw diffusion data and the affine.
"""
data = img.get_data()
print('data.shape (%d, %d, %d, %d)' % data.shape)
"""
data.shape ``(81, 106, 76, 160)``
Remove most of the background using dipy's mask module.
"""
from dipy.segment.mask import median_otsu
maskdata, mask = median_otsu(data, 3, 1, True,
vol_idx=range(10, 50), dilate=2)
"""
We instantiate our CSA model with spherical harmonic order of 4
"""
csamodel = CsaOdfModel(gtab, 4)
"""
`Peaks_from_model` is used to calculate properties of the ODFs (Orientation
Distribution Function) and return for
example the peaks and their indices, or GFA which is similar to FA but for ODF
based models. This function mainly needs a reconstruction model, the data and a
sphere as input. The sphere is an object that represents the spherical discrete
grid where the ODF values will be evaluated.
"""
def median_otsu_flow(input_files, out_dir='', save_masked=False,
median_radius=4, numpass=4, autocrop=False,
vol_idx=None, dilate=None):
""" Workflow wrapping the median_otsu segmentation method.
It applies median_otsu segmentation on each file found by 'globing'
``input_files`` and saves the results in a directory specified by
``out_dir``.
Parameters
----------
input_files : string
Path to the input volumes. This path may contain wildcards to process
multiple inputs at once.
out_dir : string, optional
Output directory (default input file directory)
save_masked : bool
Save mask
median_radius : int, optional
Radius (in voxels) of the applied median filter(default 4)
numpass : int, optional
Number of pass of the median filter (default 4)
autocrop : bool, optional
If True, the masked input_volumes will also be cropped using the
bounding box defined by the masked data. Should be on if DWI is
upsampled to 1x1x1 resolution. (default False)
vol_idx : string, optional
1D array representing indices of ``axis=3`` of a 4D `input_volume`
'None' (the default) corresponds to ``(0,)`` (assumes first volume in
4D array)
dilate : string, optional
number of iterations for binary dilation (default 'None')
Outputs
-------
mask : Nifti File
Binary volume representing the computed mask.
masked : Nifti File
Volume representing the masked input. This file is saved
save_masked is True.
"""
for fpath in glob(input_files):
print('')
print('Applying median_otsu segmentation on {0}'.format(fpath))
img = nib.load(fpath)
volume = img.get_data()
masked, mask = median_otsu(volume, median_radius,
numpass, autocrop,
vol_idx, dilate)
fname, ext = splitext(basename(fpath))
if fname.endswith('.nii'):
fname, _ = splitext(fname)
ext = '.nii.gz'
mask_fname = fname + '_mask' + ext
out_dir_path = choose_create_out_dir(out_dir, fpath)
mask_img = nib.Nifti1Image(mask.astype(np.float32), img.get_affine())
mask_out_path = join(out_dir_path, mask_fname)
mask_img.to_filename(mask_out_path)
print('Mask saved as {0}'.format(mask_out_path))
if save_masked:
masked_fname = fname + '_bet' + ext
masked_img = nib.Nifti1Image(
masked, img.get_affine(), img.get_header())
masked_out_path = join(out_dir_path, masked_fname)
masked_img.to_filename(masked_out_path)
print('Masked volume saved as {0}'.format(masked_out_path))