NIPY logo

Site Navigation

NIPY Community

Table Of Contents

Next topic

labs.spatial_models.bayesian_structural_analysis

This Page

labs.mask

Module: labs.mask

Utilities for extracting masks from EPI images and applying them to time series.

Functions

nipy.labs.mask.compute_mask(mean_volume, reference_volume=None, m=0.20000000000000001, M=0.90000000000000002, cc=True, opening=True)

Compute a mask file from fMRI data in 3D or 4D ndarrays.

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 :

mean_volume : 3D ndarray

mean EPI image, used to compute the threshold for the mask.

reference_volume: 3D ndarray, optional :

reference volume used to compute the mask. If none is give, the mean volume is used.

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.

opening: boolean, optional :

if opening is True, an morphological opening is performed, to keep only large structures. This step is useful to remove parts of the skull that might have been included.

Returns :

mask : 3D boolean ndarray

The brain mask

nipy.labs.mask.compute_mask_files(input_filename, output_filename=None, return_mean=False, m=0.20000000000000001, M=0.90000000000000002, cc=1)

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.

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.

nipy.labs.mask.compute_mask_sessions(session_files, m=0.20000000000000001, M=0.90000000000000002, cc=1, threshold=0.5)

Compute a common mask for several sessions of fMRI data.

Uses the mask-finding algorithmes to extract masks for each session, and then keep only the main connected component of the a given fraction of the intersection of all the masks.
Parameters :

session_files : list of list of strings

A list of list of nifti filenames. Each inner list represents a session.

threshold : float, optional

the inter-session threshold: the fraction of the total number of session in for which a voxel must be in the mask to be kept in the common mask. threshold=1 corresponds to keeping the intersection of all masks, whereas threshold=0 is the union of all masks.

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.

Returns :

mask : 3D boolean ndarray

The brain mask

nipy.labs.mask.intersect_masks(input_masks, output_filename=None, threshold=0.5, cc=True)

Given a list of input mask images, generate the output image which is the the threshold-level intersection of the inputs

Parameters :

input_masks: list of strings or ndarrays :

paths of the input images nsubj set as len(input_mask_files), or individual masks.

output_filename, string: :

Path of the output image, if None no file is saved.

threshold: float within [0, 1[, optional :

gives the level of the intersection. threshold=1 corresponds to keeping the intersection of all masks, whereas threshold=0 is the union of all masks.

cc: bool, optional :

If true, extract the main connected component

Returns :

grp_mask, boolean array of shape the image shape :

nipy.labs.mask.largest_cc(mask)

Return the largest connected component of a 3D mask array.

Parameters :

mask: 3D boolean array :

3D array indicating a mask.

Returns :

mask: 3D boolean array :

3D array indicating a mask, with only one connected component.

nipy.labs.mask.series_from_mask(filenames, mask, dtype=<type 'numpy.float32'>, smooth=False)

Read the time series from the given sessions filenames, using the mask.

Parameters :

filenames: list of 3D nifti file names, or 4D nifti filename. :

Files are grouped by session.

mask: 3d ndarray :

3D mask array: true where a voxel should be used.

smooth: False or float, optional :

If smooth is not False, it gives the size, in voxel of the spatial smoothing to apply to the signal.

Returns :

session_series: ndarray :

3D array of time course: (session, voxel, time)

header: header object :

The header of the first file.

nipy.labs.mask.threshold_connect_components(map, threshold, copy=True)

Given a map with some coefficients set to zero, segment the connect components with number of voxels smaller than the threshold and set them to 0.

Parameters :

map: ndarray :

The map to segment

threshold: :

The minimum number of voxels to keep a cluster.

copy: bool, optional :

If copy is false, the input array is modified inplace