Note
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.
6.6.4. nilearn.masking.compute_multi_background_mask¶
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nilearn.masking.compute_multi_background_mask(data_imgs, border_size=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 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.
Parameters: data_imgs: list of Niimg-like objects :
See http://nilearn.github.io/building_blocks/manipulating_mr_images.html#niimg. A list of arrays, each item being a subject or a session. 3D and 4D images are accepted. If 3D images is given, we suggest to use the mean image of each session
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.
border_size: integer, optional :
The size, in voxel of the border used on the side of the image to determine the value of the background.
connected: boolean, optional :
if connected is True, only the largest connect component is kept.
target_affine: 3x3 or 4x4 matrix, optional :
This parameter is passed to image.resample_img. Please see the related documentation for details.
target_shape: 3-tuple of integers, optional :
This parameter is passed to image.resample_img. Please see the related documentation for details.
memory: instance of joblib.Memory or string :
Used to cache the function call.
n_jobs: integer, optional :
The number of CPUs to use to do the computation. -1 means ‘all CPUs’.
Returns: mask : 3D nibabel.Nifti1Image
The brain mask.