# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
Utilities for extracting masks from EPI images and applying them to time
series.
"""
from __future__ import absolute_import
import math
# Major scientific libraries imports
import numpy as np
from scipy import ndimage
# Neuroimaging libraries imports
from nibabel import load, nifti1, save
from ..io.nibcompat import get_header, get_affine, get_unscaled_data
from ..externals.six import string_types
###############################################################################
# Operating on connect component
###############################################################################
[docs]def 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.
"""
# We use asarray to be able to work with masked arrays.
mask = np.asarray(mask)
labels, label_nb = ndimage.label(mask)
if not label_nb:
raise ValueError('No non-zero values: no connected components')
if label_nb == 1:
return mask.astype(np.bool)
label_count = np.bincount(labels.ravel().astype(np.int))
# discard 0 the 0 label
label_count[0] = 0
return labels == label_count.argmax()
[docs]def 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
"""
labels, _ = ndimage.label(map)
weights = np.bincount(labels.ravel())
if copy:
map = map.copy()
for label, weight in enumerate(weights):
if label == 0:
continue
if weight < threshold:
map[labels == label] = 0
return map
###############################################################################
# Utilities to calculate masks
###############################################################################
[docs]def 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.
"""
if isinstance(input_filename, string_types):
# One single filename or image
nim = load(input_filename) # load the image from the path
vol_arr = get_unscaled_data(nim)
header = get_header(nim)
affine = get_affine(nim)
if vol_arr.ndim == 4:
if isinstance(vol_arr, np.memmap):
# Get rid of memmapping: it is faster.
mean_volume = np.array(vol_arr, copy=True).mean(axis=-1)
else:
mean_volume = vol_arr.mean(axis=-1)
# Make a copy, to avoid holding a reference on the full array,
# and thus polluting the memory.
first_volume = vol_arr[..., 0].copy()
elif vol_arr.ndim == 3:
mean_volume = first_volume = vol_arr
else:
raise ValueError('Need 4D file for mask')
del vol_arr
else:
# List of filenames
if len(list(input_filename)) == 0:
raise ValueError('input_filename should be a non-empty '
'list of file names')
# We have several images, we do mean on the fly,
# to avoid loading all the data in the memory
# We do not use the unscaled data here?:
# if the scalefactor is being used to record real
# differences in intensity over the run this would break
for index, filename in enumerate(input_filename):
nim = load(filename)
if index == 0:
first_volume = nim.get_data().squeeze()
mean_volume = first_volume.copy().astype(np.float32)
header = get_header(nim)
affine = get_affine(nim)
else:
mean_volume += nim.get_data().squeeze()
mean_volume /= float(len(list(input_filename)))
del nim
if np.isnan(mean_volume).any():
tmp = mean_volume.copy()
tmp[np.isnan(tmp)] = 0
mean_volume = tmp
mask = compute_mask(mean_volume, first_volume, m, M, cc,
opening=opening, exclude_zeros=exclude_zeros)
if output_filename is not None:
header['descrip'] = 'mask'
output_image = nifti1.Nifti1Image(mask.astype(np.uint8),
affine=affine,
header=header)
save(output_image, output_filename)
if not return_mean:
return mask
else:
return mask, mean_volume
[docs]def 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
"""
if reference_volume is None:
reference_volume = mean_volume
sorted_input = np.sort(mean_volume.reshape(-1))
if exclude_zeros:
sorted_input = sorted_input[sorted_input != 0]
limiteinf = int(math.floor(m * len(sorted_input)))
limitesup = int(math.floor(M * len(sorted_input)))
delta = sorted_input[limiteinf + 1:limitesup + 1] \
- sorted_input[limiteinf:limitesup]
ia = delta.argmax()
threshold = 0.5 * (sorted_input[ia + limiteinf]
+ sorted_input[ia + limiteinf + 1])
mask = (reference_volume >= threshold)
if cc:
mask = largest_cc(mask)
if opening > 0:
mask = ndimage.binary_opening(mask.astype(np.int),
iterations=opening)
return mask.astype(bool)
[docs]def 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
"""
mask, mean = None, None
for index, session in enumerate(session_images):
if hasattr(session, 'get_data'):
mean = session.get_data()
if mean.ndim > 3:
mean = mean.mean(-1)
this_mask = compute_mask(mean, None, m=m, M=M, cc=cc,
opening=opening, exclude_zeros=exclude_zeros)
if return_mean:
this_mask = this_mask, mean
else:
this_mask = compute_mask_files(
session, m=m, M=M, cc=cc, exclude_zeros=exclude_zeros,
return_mean=return_mean, opening=opening)
if return_mean:
this_mask, this_mean = this_mask
if mean is None:
mean = this_mean.astype(np.float)
else:
mean += this_mean
this_mask = this_mask.astype(np.int8)
if mask is None:
mask = this_mask
else:
mask += this_mask
# Free memory early
del this_mask
# Take the "half-intersection", i.e. all the voxels that fall within
# 50% of the individual masks.
mask = (mask > threshold * len(list(session_images)))
if cc:
# Select the largest connected component (each mask is
# connect, but the half-interesection may not be):
mask = largest_cc(mask)
mask = mask.astype(np.bool)
if return_mean:
# Divide by the number of sessions
mean /= len(session_images)
return mask, mean
return mask
[docs]def 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
"""
grp_mask = None
if threshold > 1:
raise ValueError('The threshold should be < 1')
if threshold < 0:
raise ValueError('The threshold should be > 0')
threshold = min(threshold, 1 - 1.e-7)
for this_mask in input_masks:
if isinstance(this_mask, string_types):
# We have a filename
this_mask = load(this_mask).get_data()
if grp_mask is None:
grp_mask = this_mask.copy().astype(np.int)
else:
# If this_mask is floating point and grp_mask is integer, numpy 2
# casting rules raise an error for in-place addition.
# Hence we do it long-hand.
# XXX should the masks be coerced to int before addition?
grp_mask = grp_mask + this_mask
grp_mask = grp_mask > (threshold * len(list(input_masks)))
if np.any(grp_mask > 0) and cc:
grp_mask = largest_cc(grp_mask)
if output_filename is not None:
if isinstance(input_masks[0], string_types):
nim = load(input_masks[0])
header = get_header(nim)
affine = get_affine(nim)
else:
header = dict()
affine = np.eye(4)
header['descrip'] = 'mask image'
output_image = nifti1.Nifti1Image(grp_mask.astype(np.uint8),
affine=affine,
header=header,
)
save(output_image, output_filename)
return grp_mask > 0
###############################################################################
# Time series extraction
###############################################################################
[docs]def series_from_mask(filenames, mask, dtype=np.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.
"""
assert len(filenames) != 0, (
'filenames should be a file name or a list of file names, '
'%s (type %s) was passed' % (filenames, type(filenames)))
mask = mask.astype(np.bool)
if smooth:
# Convert from a sigma to a FWHM:
smooth /= np.sqrt(8 * np.log(2))
if isinstance(filenames, string_types):
# We have a 4D nifti file
data_file = load(filenames)
header = get_header(data_file)
series = data_file.get_data()
if ensure_finite:
# SPM tends to put NaNs in the data outside the brain
series[np.logical_not(np.isfinite(series))] = 0
series = series.astype(dtype)
affine = get_affine(data_file)[:3, :3]
del data_file
if isinstance(series, np.memmap):
series = np.asarray(series).copy()
if smooth:
vox_size = np.sqrt(np.sum(affine ** 2, axis=0))
smooth_sigma = smooth / vox_size
for this_volume in np.rollaxis(series, -1):
this_volume[...] = ndimage.gaussian_filter(this_volume,
smooth_sigma)
series = series[mask]
else:
nb_time_points = len(list(filenames))
series = np.zeros((mask.sum(), nb_time_points), dtype=dtype)
for index, filename in enumerate(filenames):
data_file = load(filename)
data = data_file.get_data()
if ensure_finite:
# SPM tends to put NaNs in the data outside the brain
data[np.logical_not(np.isfinite(data))] = 0
data = data.astype(dtype)
if smooth is not False:
affine = get_affine(data_file)[:3, :3]
vox_size = np.sqrt(np.sum(affine ** 2, axis=0))
smooth_sigma = smooth / vox_size
data = ndimage.gaussian_filter(data, smooth_sigma)
series[:, index] = data[mask]
# Free memory early
del data
if index == 0:
header = get_header(data_file)
return series, header