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.
6.7.1. nilearn.region.img_to_signals_labels¶
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nilearn.region.img_to_signals_labels(imgs, labels_img, mask_img=None, background_label=0, order='F')¶ 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: imgs: 4D Niimg-like object :
See http://nilearn.github.io/building_blocks/manipulating_mr_images.html#niimg. input images.
labels_img: Niimg-like object :
See http://nilearn.github.io/building_blocks/manipulating_mr_images.html#niimg. regions definition as labels. By default, the label zero is used to denote an absence of region. Use background_label to change it.
mask_img: Niimg-like object :
See http://nilearn.github.io/building_blocks/manipulating_mr_images.html#niimg. Mask to apply to labels before extracting signals. Every point outside the mask is considered as background (i.e. no region).
background_label: number :
number representing background in labels_img.
order: str :
ordering of output array (“C” or “F”). Defaults to “F”.
Returns: signals: numpy.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)
labels: list or tuple :
corresponding labels for each signal. signal[:, n] was extracted from the region with label labels[n].