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.

6.6.3. nilearn.masking.compute_background_mask

nilearn.masking.compute_background_mask(data_imgs, border_size=2, connected=False, opening=False, target_affine=None, target_shape=None, memory=None, verbose=0)

Compute a brain mask for the images by guessing the value of the background from the border of the image.

Parameters:

data_imgs: Niimg-like object :

See http://nilearn.github.io/building_blocks/manipulating_mr_images.html#niimg. Images used to compute the mask. 3D and 4D images are accepted. If a 3D image is given, we suggest to use the mean image

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: bool, optional :

if connected is True, only the largest connect component is kept.

opening: bool or int, optional :

if opening is True, 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. 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.

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.

verbose: int, optional :

Returns:

mask: nibabel.Nifti1Image :

The brain mask (3D image)