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 and output: neuroimaging data representation. Input images.
labels_imgNiimg-like object: See Input and output: neuroimaging data representation. 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 Input and output: neuroimaging data representation. 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].