nilearn.regions: Operating on Regions#

The nilearn.regions class module includes region extraction procedure on a 4D statistical/atlas maps and its function.


connected_regions(maps_img[, ...])

Extraction of brain connected regions into separate regions.

connected_label_regions(labels_img[, ...])

Extract connected regions from a brain atlas image defined by labels (integers).

img_to_signals_labels(imgs, labels_img[, ...])

Extract region signals from image.

signals_to_img_labels(signals, labels_img[, ...])

Create image from region signals defined as labels.

img_to_signals_maps(imgs, maps_img[, mask_img])

Extract region signals from image.

signals_to_img_maps(region_signals, maps_img)

Create image from region signals defined as maps.

recursive_neighbor_agglomeration(X, ...[, ...])

Recursive neighbor agglomeration (ReNA): it performs iteratively the nearest neighbor grouping.


RegionExtractor(maps_img[, mask_img, ...])

Class for brain region extraction.

Parcellations(method[, n_parcels, ...])

Learn parcellations on fMRI images.

ReNA(mask_img[, n_clusters, scaling, ...])

Recursive Neighbor Agglomeration (ReNA): Recursively merges the pair of clusters according to 1-nearest neighbors criterion.

HierarchicalKMeans(n_clusters[, init, ...])

Hierarchical KMeans: First clusterize the samples into big clusters.