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.

8.5.19. nilearn.image.smooth_img

nilearn.image.smooth_img(imgs, fwhm)[source]

Smooth images by applying a Gaussian filter.

Apply a Gaussian filter along the three first dimensions of arr. In all cases, non-finite values in input image are replaced by zeros.

Parameters
imgsNiimg-like object or iterable of Niimg-like objects

Image(s) to smooth (see http://nilearn.github.io/manipulating_images/input_output.html for a detailed description of the valid input types).

fwhmscalar, numpy.ndarray, or tuple, or list,or ‘fast’ or None, optional

Smoothing strength, as a full-width at half maximum, in millimeters:

  • If a nonzero scalar is given, width is identical in all 3 directions.

  • If a numpy.ndarray, tuple, or list is given, it must have 3 elements, giving the FWHM along each axis. If any of the elements is zero or None, smoothing is not performed along that axis.

  • If fwhm=’fast’, a fast smoothing will be performed with a filter [0.2, 1, 0.2] in each direction and a normalisation to preserve the local average value.

  • If fwhm is None, no filtering is performed (useful when just removal of non-finite values is needed).

Note

In corner case situations, fwhm is simply kept to None when fwhm is specified as fwhm=0.

Returns
nibabel.nifti1.Nifti1Image or list of

Filtered input image. If imgs is an iterable, then filtered_img is a list.