nipy.labs.utils.mask.compute_mask_sessions¶
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nipy.labs.utils.mask.
compute_mask_sessions
(session_images, m=0.2, M=0.9, cc=1, threshold=0.5, exclude_zeros=False, return_mean=False, opening=2)¶ Compute a common mask for several sessions of fMRI data.
Uses the mask-finding algorithmes to extract masks for each session, and then keep only the main connected component of the a given fraction of the intersection of all the masks.Parameters: session_images : list of (list of strings) or nipy image objects
A list of images/list of nifti filenames. Each inner list/image represents a session.
m : float, optional
lower fraction of the histogram to be discarded.
M: float, optional :
upper fraction of the histogram to be discarded.
cc: boolean, optional :
if cc is True, only the largest connect component is kept.
threshold : float, optional
the inter-session threshold: the fraction of the total number of session in for which a voxel must be in the mask to be kept in the common mask. threshold=1 corresponds to keeping the intersection of all masks, whereas threshold=0 is the union of all masks.
exclude_zeros: boolean, optional :
Consider zeros as missing values for the computation of the threshold. This option is useful if the images have been resliced with a large padding of zeros.
return_mean: boolean, optional :
if return_mean is True, the mean image accross subjects is returned.
opening: int, optional, :
size of the morphological opening
Returns: mask : 3D boolean ndarray
The brain mask
mean : 3D float array
The mean image