nipy.labs.utils.mask.compute_mask_files

nipy.labs.utils.mask.compute_mask_files(input_filename, output_filename=None, return_mean=False, m=0.2, M=0.9, cc=1, exclude_zeros=False, opening=2)

Compute a mask file from fMRI nifti file(s)

Compute and write the mask of an image based on the grey level This is based on an heuristic proposed by T.Nichols: find the least dense point of the histogram, between fractions m and M of the total image histogram.

In case of failure, it is usually advisable to increase m.

Parameters:

input_filename : string

nifti filename (4D) or list of filenames (3D).

output_filename : string or None, optional

path to save the output nifti image (if not None).

return_mean : boolean, optional

if True, and output_filename is None, return the mean image also, as a 3D array (2nd return argument).

m : float, optional

lower fraction of the histogram to be discarded.

M: float, optional

upper fraction of the histogram to be discarded.

cc: boolean, optional

if cc is True, only the largest connect component is kept.

exclude_zeros: boolean, optional

Consider zeros as missing values for the computation of the threshold. This option is useful if the images have been resliced with a large padding of zeros.

opening: int, optional

Size of the morphological opening performed as post-processing

Returns:

mask : 3D boolean array

The brain mask

mean_image : 3d ndarray, optional

The main of all the images used to estimate the mask. Only provided if return_mean is True.