This page is a reference documentation. It only explains the function signature, and not how to use it. Please refer to the user guide for the big picture.
- nilearn.masking.compute_multi_epi_mask(epi_imgs, lower_cutoff=0.2, upper_cutoff=0.85, connected=True, opening=2, threshold=0.5, target_affine=None, target_shape=None, exclude_zeros=False, n_jobs=1, memory=None, verbose=0)#
Compute a common mask for several sessions or subjects of fMRI data.
Uses the mask-finding algorithms to extract masks for each session or subject, and then keep only the main connected component of the a given fraction of the intersection of all the masks.
listof Niimg-like objects
See Input and output: neuroimaging data representation. A list of arrays, each item being a subject or a session. 3D and 4D images are accepted.
If 3D images are given, we suggest to use the mean image of each session.
The inter-session threshold: the fraction of the total number of sessions 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.
Lower fraction of the histogram to be discarded. Default=0.2.
Upper fraction of the histogram to be discarded. Default=0.85.
If connected is True, only the largest connect component is kept. Default=True.
This parameter determines whether a morphological opening is performed, to keep only large structures. This step is useful to remove parts of the skull that might have been included.
A boolean : If False, no opening is performed. If True, it is equivalent to
An integer n: The opening is performed via n erosions (see
scipy.ndimage.binary_erosion). The largest connected component is then estimated if
connectedis set to True, and 2`n` dilation operations are performed (see
scipy.ndimage.binary_dilation) followed by n erosions. This corresponds to 1 opening operation of order n followed by a closing operator of order n.
Turning off opening (
opening=False) will also prevent any smoothing applied to the image during the mask computation.
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. Default=False.
If specified, the image is resampled corresponding to this new affine.
target_affinecan be a 3x3 or a 4x4 matrix. Default=None.
This parameter is passed to
If specified, the image will be resized to match this new shape.
len(target_shape)must be equal to 3.
target_shapeis specified, a
(4, 4)must also be given.
This parameter is passed to
- memoryinstance of
Used to cache the masking process. By default, no caching is done. If a
stris given, it is the path to the caching directory.
The number of CPUs to use to do the computation. -1 means ‘all CPUs’. Default=1.
The brain mask.