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_brain_mask(target_img, threshold=0.5, connected=True, opening=2, memory=None, verbose=0, mask_type='whole-brain')[source]#

Compute the whole-brain, grey-matter or white-matter mask.

This mask is calculated using MNI152 1mm-resolution template mask onto the target image.

target_imgNiimg-like object

See Input and output: neuroimaging data representation. Images used to compute the mask. 3D and 4D images are accepted. Only the shape and affine of target_img will be used here.

thresholdfloat, default=0.5

The value under which the MNI template is cut off.

connectedbool, optional

If connected is True, only the largest connect component is kept. Default=True.

openingbool or int, optional

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. opening can be:


Turning off opening (opening=False) will also prevent any smoothing applied to the image during the mask computation.


memoryinstance of joblib.Memory, str, or pathlib.Path

Used to cache the masking process. By default, no caching is done. If a str is given, it is the path to the caching directory.

verboseint, default=0

Verbosity level (0 means no message).

mask_type{“whole-brain”, “gm”, “wm”}, default=”whole-brain”

Type of mask to be computed:

  • “whole-brain”: Computes the whole-brain mask.

  • “gm”: Computes the grey-matter mask.

  • “wm”: Computes the white-matter mask.

New in version 0.8.1.


The whole-brain mask (3D image).