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_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)[source]#

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

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.

Note

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

Parameters:
epi_imgNiimg-like object

See Input and output: neuroimaging data representation. EPI image, used to compute the mask. 3D and 4D images are accepted.

Note

If a 3D image is given, we suggest to use the mean image.

lower_cutofffloat, optional

Lower fraction of the histogram to be discarded. Default=0.2.

upper_cutofffloat, optional

Upper fraction of the histogram to be discarded. Default=0.85.

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:

Note

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

Default=2.

ensure_finitebool

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

exclude_zerosbool, 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. Default=False.

target_affinenumpy.ndarray, optional.

If specified, the image is resampled corresponding to this new affine. target_affine can be a 3x3 or a 4x4 matrix. Default=None.

Note

This parameter is passed to nilearn.image.resample_img.

target_shapetuple or list, optional.

If specified, the image will be resized to match this new shape. len(target_shape) must be equal to 3.

Note

If target_shape is specified, a target_affine of shape (4, 4) must also be given.

Default=None.

Note

This parameter is passed to nilearn.image.resample_img.

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, optional

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

Returns:
masknibabel.nifti1.Nifti1Image

The brain mask (3D image).

Examples using nilearn.masking.compute_epi_mask#

NeuroImaging volumes visualization

NeuroImaging volumes visualization

NeuroImaging volumes visualization
Visualizing global patterns with a carpet plot

Visualizing global patterns with a carpet plot

Visualizing global patterns with a carpet plot
Predicted time series and residuals

Predicted time series and residuals

Predicted time series and residuals
Understanding NiftiMasker and mask computation

Understanding NiftiMasker and mask computation

Understanding NiftiMasker and mask computation