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.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, SurfaceImage, or iterable of Niimg-like objects or SurfaceImage.

Image(s) to smooth (see Input and output: neuroimaging data representation 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.

For surface data, only scalar and None are supported.

For volume data, several options are possible:

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 0 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 normalization 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:

Nifti1Image, SurfaceImage, list of Nifti1Image, or list of SurfaceImage: Smoothed input image(s) or surface(s). A list is returned if imgs was passed as an iterable.

Examples

Let’s first create a Nifti1Image:

>>> import numpy as np
>>> from nibabel import Nifti1Image
>>> data = np.array([[[0.0, 0.0, 0.0],
...                   [0.0, 3.0, 0.0],
...                   [0.0, 0.0, 0.0]]])
>>> img = Nifti1Image(data, affine=np.eye(4))

Now we can smooth the image:

>>> from nilearn.image import smooth_img, get_data
>>> smoothed_img = smooth_img(img, fwhm=2)
>>> data = get_data(smoothed_img)
>>> data
array([[[0.20943692, 0.37378672, 0.20943692],
    [0.37378672, 0.66710546, 0.37378672],
    [0.20943692, 0.37378672, 0.20943692]]])

Examples using `nilearn.image.smooth_img`¶

Basic nilearn example: manipulating and looking at data

Predicted time series and residuals

Computing a Region of Interest (ROI) mask manually

Smoothing an image

NeuroVault cross-study ICA maps

nilearn.image.smooth_img¶

Examples using nilearn.image.smooth_img¶

Examples using `nilearn.image.smooth_img`¶