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.
8.7.4. nilearn.masking.compute_multi_gray_matter_mask¶
nilearn.masking.compute_multi_gray_matter_mask(target_imgs, threshold=0.5, connected=True, opening=2, memory=None, verbose=0, n_jobs=1, **kwargs)¶Compute a mask corresponding to the gray matter part of the brain for a list of images. The gray matter part is calculated through the resampling of MNI152 template gray matter mask onto the target image
Parameters: target_imgs: list of Niimg-like object
See http://nilearn.github.io/manipulating_images/input_output.html Images used to compute the mask. 3D and 4D images are accepted. The images in this list must be of same shape and affine. The mask is calculated with the first element of the list for only the shape/affine of the image is used for this masking strategy
threshold: float, optional
The value under which the MNI template is cut off. Default value is 0.5
connected: bool, optional
if connected is True, only the largest connect component is kept. Default is True
opening: bool or int, optional
if opening is True, a morphological opening is performed, to keep only large structures. If opening is an integer n, it is performed via n erosions. After estimation of the largest connected constituent, 2`n` closing operations are performed followed by n erosions. This corresponds to 1 opening operation of order n followed by a closing operator of order n.
memory: instance of joblib.Memory or str
Used to cache the function call.
n_jobs: integer, optional
Argument not used but kept to fit the API
**kwargs: optional arguments
arguments such as ‘target_affine’ are used in the call of other masking strategies, which then would raise an error for this function which does not need such arguments.
verbose: int, optional
Controls the amount of verbosity: higher numbers give more messages
Returns: mask: nibabel.Nifti1Image
The brain mask (3D image)