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.regions.img_to_signals_labels#

nilearn.regions.img_to_signals_labels(imgs, labels_img, mask_img=None, background_label=0, order='F', strategy='mean')[source]#

Extract region signals from image.

This function is applicable to regions defined by labels.

labels, imgs and mask shapes and affines must fit. This function performs no resampling.

Parameters

imgslist of Niimg-like objects: See input-output. Input images.
labels_imgNiimg-like object: See http://nilearn.github.io/manipulating_images/input_output.html regions definition as labels. By default, the label zero is used to denote an absence of region. Use background_label to change it.
mask_imgNiimg-like object, optional: See http://nilearn.github.io/manipulating_images/input_output.html Mask to apply to labels before extracting signals. Every point outside the mask is considered as background (i.e. no region).
background_labelnumber, optional: Number representing background in labels_img. Default=0.
orderstr, optional: Ordering of output array (“C” or “F”). Default=”F”.
strategystr, optional: The name of a valid function to reduce the region with. Must be one of: sum, mean, median, minimum, maximum, variance, standard_deviation. Default=’mean’.

Returns

signalsnumpy.ndarray: Signals extracted from each region. One output signal is the mean of all input signals in a given region. If some regions are entirely outside the mask, the corresponding signal is zero. Shape is: (scan number, number of regions)
labelslist or tuple: Corresponding labels for each signal. signal[:, n] was extracted from the region with label labels[n].