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.masking.apply_mask(imgs, mask_img, dtype='f', smoothing_fwhm=None, ensure_finite=True)[source]#
Extract signals from images using specified mask.
Read the time series from the given Niimg-like object, using the mask.
listof 4D Niimg-like objects
See Input and output: neuroimaging data representation. Images to be masked. list of lists of 3D images are also accepted.
- mask_imgNiimg-like object
See Input and output: neuroimaging data representation. 3D mask array: True where a voxel should be used.
- dtype: numpy dtype or ‘f’
The dtype of the output, if ‘f’, any float output is acceptable and if the data is stored on the disk as floats the data type will not be changed.
None, it gives the full-width at half maximum in millimeters of the spatial smoothing to apply to the signal.
If ensure_finite is True, the non-finite values (NaNs and infs) found in the images will be replaced by zeros. Default=True.
2D array of series with shape (image number, voxel number)
When using smoothing,
ensure_finiteis set to True, as non-finite values would spread across the image.
NeuroImaging volumes visualization