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=0.2, M=0.9, cc=True, opening=2, exclude_zeros=False)

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: int, optional

if opening is larger than 0, 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.

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.

Returns:

mask : 3D boolean ndarray

The brain mask

nipy.labs.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.

nipy.labs.mask.compute_mask_sessions(session_images, m=0.2, M=0.9, cc=1, threshold=0.5, exclude_zeros=False, return_mean=False, opening=2)

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_images : list of (list of strings) or nipy image objects

A list of images/list of nifti filenames. Each inner list/image represents a session.

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.

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.

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.

return_mean: boolean, optional

if return_mean is True, the mean image accross subjects is returned.

opening: int, optional,

size of the morphological opening

Returns:

mask : 3D boolean ndarray

The brain mask

mean : 3D float array

The mean image

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, ensure_finite=True)

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.

ensure_finite: boolean, optional

If ensure_finite is True, the non-finite values (NaNs and infs) found in the images will be replaced by zeros

Returns:

session_series: ndarray

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

header: header object

The header of the first file.

Notes

When using smoothing, ensure_finite should be True: as elsewhere non finite values will spread accross the image.

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 spatial map to segment

threshold: scalar,

The minimum number of voxels to keep a cluster.

copy: bool, optional

If copy is false, the input array is modified inplace

Returns:

map: ndarray,

the map with connected components removed