def get_protocol(self):
super(BatchFitProtocolLoader, self).get_protocol()
if self._protocol_fname and os.path.isfile(self._protocol_fname):
return load_protocol(self._protocol_fname)
return auto_load_protocol(self._base_dir, protocol_columns=self._protocol_columns,
bvec_fname=self._bvec_fname, bval_fname=self._bval_fname)
def get_protocol(self):
if self._protocol_fname and os.path.isfile(self._protocol_fname):
return load_protocol(self._protocol_fname)
return auto_load_protocol(self._base_dir,
protocol_columns=self._protocol_columns,
bvec_fname=self._bvec_fname,
bval_fname=self._bval_fname)
def create_median_otsu_brain_mask(dwi_info,
protocol,
mask_threshold=0,
fill_holes=True,
**kwargs):
"""Create a brain mask using the given volume.
Args:
dwi_info (string or tuple or image): The information about the volume, either:
- the filename of the input file
- or a tuple with as first index a ndarray with the DWI and as second index the header
- or only the image as an ndarray
protocol (string or :class:`~mdt.protocols.Protocol`): The filename of the protocol file or a Protocol object
mask_threshold (float): everything below this b-value threshold is masked away (value in s/m^2)
fill_holes (boolean): if we will fill holes after the median otsu algorithm and before the thresholding
**kwargs: the additional arguments for median_otsu.
Returns:
ndarray: The created brain mask
"""
logger = logging.getLogger(__name__)
logger.info('Starting calculating a brain mask')
if isinstance(dwi_info, str):
signal_img = load_nifti(dwi_info)
dwi = signal_img.get_data()
elif isinstance(dwi_info, (tuple, list)):
dwi = dwi_info[0]
else:
dwi = dwi_info
if isinstance(protocol, str):
protocol = load_protocol(protocol)
if len(dwi.shape) == 4:
unweighted_ind = protocol.get_unweighted_indices()
if len(unweighted_ind):
unweighted = np.mean(dwi[..., unweighted_ind], axis=3)
else:
unweighted = np.mean(dwi, axis=3)
else:
unweighted = dwi.copy()
brain_mask = median_otsu(unweighted, **kwargs)
brain_mask = brain_mask > 0
if fill_holes:
brain_mask = binary_fill_holes(brain_mask)
if mask_threshold:
brain_mask = np.mean(dwi[..., protocol.get_weighted_indices()],
axis=3) * brain_mask > mask_threshold
logger.info('Finished calculating a brain mask')
return brain_mask
def get_protocol(self):
if self._protocol is None:
self._protocol = load_protocol(self._filename)
return self._protocol
