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.

4.3.3.1.1. nilearn.masking.compute_epi_mask

nilearn.masking.compute_epi_mask(epi_img, lower_cutoff=0.2, upper_cutoff=0.85, connected=True, opening=2, exclude_zeros=False, ensure_finite=True, target_affine=None, target_shape=None, memory=None, verbose=0)

Compute a brain mask from fMRI data in 3D or 4D ndarrays.

This is based on an heuristic proposed by T.Nichols: find the least dense point of the histogram, between fractions lower_cutoff and upper_cutoff of the total image histogram.

In case of failure, it is usually advisable to increase lower_cutoff.

Parameters:

epi_img: Niimg-like object :

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

lower_cutoff: float, optional :

lower fraction of the histogram to be discarded.

upper_cutoff: float, optional :

upper fraction of the histogram to be discarded.

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

ensure_finite: bool :

If ensure_finite is True, the non-finite values (NaNs and infs) found in the images will be replaced by zeros

exclude_zeros: bool, optional :

Consider zeros as missing values for the computation of the threshold. This option is useful if the images have been resliced with a large padding of zeros.

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: if this is a string, it specifies the directory where the cache will be stored.

verbose: int, optional :

Controls the amount of verbosity: higher numbers give more messages

Returns:

mask: nibabel.Nifti1Image :

The brain mask (3D image)