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.

nilearn.masking.compute_multi_brain_mask#

nilearn.masking.compute_multi_brain_mask(target_imgs, threshold=0.5, connected=True, opening=2, memory=None, verbose=0, n_jobs=1, mask_type='whole-brain', **kwargs)[source]#

Compute the whole-brain, grey-matter or white-matter mask for a list of images. The mask is calculated through the resampling of the corresponding MNI152 template mask onto the target image.

New in version 0.8.1.

Parameters

target_imgslist of Niimg-like object

See https://nilearn.github.io/stable/manipulating_images/input_output.html # noqa:E501 Images used to compute the mask. 3D and 4D images are accepted.

Note

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.

thresholdfloat, optional

The value under which the MNI template is cut off. Default=0.5.

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:

A boolean : If False, no opening is performed. If True, it is equivalent to opening=1.

An integer n: The opening is performed via n erosions (see scipy.ndimage.binary_erosion). The largest connected component is then estimated if connected is 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.

Note

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

Default=2.

mask_type{‘whole-brain’, ‘gm’, ‘wm’}, optional

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.

Default = ‘whole-brain’.

memoryinstance of joblib.Memory or str

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, optional

Verbosity level (0 means no message). Default=0.

n_jobsint, optional.

The number of CPUs to use to do the computation. -1 means ‘all CPUs’. Default=1.

Note

Argument not used but kept to fit the API

**kwargsoptional 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.

Returns

masknibabel.nifti1.Nifti1Image: The brain mask (3D image).