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.masking.apply_mask#
- 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.
- Parameters
- imgs
list
of 4D Niimg-like objects See https://nilearn.github.io/stable/manipulating_images/input_output.html # noqa:E501 Images to be masked. list of lists of 3D images are also accepted.
- mask_imgNiimg-like object
See https://nilearn.github.io/stable/manipulating_images/input_output.html # noqa:E501 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.
- smoothing_fwhm
float
, optional. If
smoothing_fwhm
is notNone
, it gives the full-width at half maximum in millimeters of the spatial smoothing to apply to the signal.Note
Implies ensure_finite=True.
- ensure_finite
bool
If ensure_finite is True, the non-finite values (NaNs and infs) found in the images will be replaced by zeros. Default=True.
- imgs
- Returns
- session_series
numpy.ndarray
2D array of series with shape (image number, voxel number)
- session_series
Notes
When using smoothing,
ensure_finite
is set to True, as non-finite values would spread across the image.
Examples using nilearn.masking.apply_mask
#
NeuroImaging volumes visualization