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.image.mean_img

nilearn.image.mean_img(imgs, target_affine=None, target_shape=None, verbose=0, n_jobs=1)

Compute the mean of the images (in the time dimension of 4th dimension)

Note that if list of 4D images are given, the mean of each 4D image is computed separately, and the resulting mean is computed after.

Parameters:

imgs: Niimg-like object or iterable of Niimg-like objects :

See http://nilearn.github.io/building_blocks/manipulating_mr_images.html#niimg. Images to mean.

target_affine: numpy.ndarray, optional :

If specified, the image is resampled corresponding to this new affine. target_affine can be a 3x3 or a 4x4 matrix

target_shape: tuple or list, optional :

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

verbose: int, optional :

Controls the amount of verbosity: higher numbers give more messages (0 means no messages).

n_jobs: integer, optional :

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

Returns:

mean: nibabel.Nifti1Image :

mean image