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.regions.signals_to_img_labels(signals, labels_img, mask_img=None, background_label=0, order='F')[source]#
Create image from region signals defined as labels.
The same region signal is used for each voxel of the corresponding 3D volume.
labels_img, mask_img must have the same shapes and affines.
Changed in version 0.9.2: Support 1D signals.
1D or 2D array. If this is a 1D array, it must have as many elements as there are regions in the labels_img. If it is 2D, it should have the shape (number of scans, number of regions in labels_img).
- labels_imgNiimg-like object
See Input and output: neuroimaging data representation. Region definitions using labels.
- mask_imgNiimg-like object, optional
See Input and output: neuroimaging data representation. Boolean array giving voxels to process. integer arrays also accepted, In this array, zero means False, non-zero means True.
- background_labelnumber, optional
Label to use for “no region”. Default=0.
Ordering of output array (“C” or “F”). Default=”F”.
Reconstructed image. dtype is that of “signals”, affine and shape are those of labels_img.
Signal extraction on labels images e.g. clusters