def test_brainMaskVolume(self):
b0Img = read_siemens_scil_b0()
b0Data = np.squeeze(b0Img.get_data())
voxelSizes = b0Img.header.get_zooms()[:3]
voxelVolumeInCubicCentimeters = 1e-3*np.prod(voxelSizes)
brainMask = createBrainMaskFromb0Data(b0Data)[1]
brainVolume = voxelVolumeInCubicCentimeters*np.sum(brainMask)
self.assertTrue(brainVolume > 500 and brainVolume < 1500)
First import the necessary modules: """ import numpy as np import nibabel as nib """ Download and read the data for this tutorial. The scil_b0 dataset contains different data from different companies and models. For this example, the data comes from a 1.5 tesla Siemens MRI. """ from dipy.data.fetcher import fetch_scil_b0, read_siemens_scil_b0 fetch_scil_b0() img = read_siemens_scil_b0() data = np.squeeze(img.get_data()) """ ``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, we used 2 as ``median_radius`` and 1 as ``num_pass`` """ from dipy.segment.mask import median_otsu
First import the necessary modules: """ import numpy as np import nibabel as nib """ Download and read the data for this tutorial. The scil_b0 dataset contains different data from different companies and models. For this example, the data comes from a 1.5 tesla Siemens MRI. """ from dipy.data.fetcher import fetch_scil_b0, read_siemens_scil_b0 fetch_scil_b0() img = read_siemens_scil_b0() data = np.squeeze(img.get_data()) """ ``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, we used 2 as ``median_radius`` and 1 as ``num_pass`` """
def load_data(name="sherbrooke", a=.5, flat=True):
""" Loads the raw data
:param name: str
options: "sherbrooke", "hardi", "siemens" (all MRI)
"j0126" (EM)
:param a: float
specifies the angle of the used MRI data
0 <= a < 1.
:param flat: bool
True: Transform data in the format used along with spark
:return:
either:
new_data: flatted raw data
or:
data: 3d raw data
sigma: noise estimation
"""
if name == "sherbrooke":
img, gtab = read_sherbrooke_3shell()
elif name == "hardi":
img, gtab = read_stanford_hardi()
elif name == "siemens":
img = read_siemens_scil_b0()
elif name == "j0126":
data = np.load(path_to_folder + "/data/j0126_sample.npy")
else:
raise Exception()
if not "j0126" in name:
data = img.get_data()
d_type = np.int
else:
d_type = np.uint8
print("Original shape:", data.shape)
z = int((data.shape[2]-sh[2])/2)
if not "j0126" in name:
a = int(data.shape[3]*a)
data = data[: sh[0], : sh[1], z: z+sh[2], a].astype(np.int32)
else:
data = data[: sh[0], : sh[1], z: z+sh[2]].astype(np.int32)
sigma = noise_estimate.estimate_sigma(data, N=4)
print(np.mean(sigma))
if flat:
if len(sigma.shape) == 1:
sigma = np.ones_like(data)*sigma[0]
new_data = []
for x in range(0, data.shape[0], DB):
for y in range(0, data.shape[1], DB):
for z in range(0, data.shape[2], DB):
data_block = np.zeros([DB, DB, DB], dtype=d_type)
sigma_block = np.ones([DB, DB, DB], dtype=d_type)
d_sh = data[x: x+DB, y: y+DB, z: z+DB].shape
data_block[0: d_sh[0], 0: d_sh[1], 0: d_sh[2]] = data[x: x+DB, y: y+DB, z: z+DB]
sigma_block[0: d_sh[0], 0: d_sh[1], 0: d_sh[2]] = sigma[x: x+DB, y: y+DB, z: z+DB]
new_data.append([_coordinate_to_index([x, y, z]),
[data_block, sigma_block]])
return new_data
else:
return data, sigma
