__all__ = ['labels_to_matrix']
import numpy as np
import ants
from ants.decorators import image_method
[docs]
@image_method
def labels_to_matrix(image, mask, target_labels=None, missing_val=np.nan):
"""
Convert a labeled image to an n x m binary matrix where n = number of voxels
and m = number of labels. Only includes values inside the provided mask while
including background ( image == 0 ) for consistency with timeseries2matrix and
other image to matrix operations.
ANTsR function: `labels2matrix`
Arguments
---------
image : ANTsImage
input label image
mask : ANTsImage
defines domain of interest
target_labels : list/tuple
defines target regions to be returned. if the target label does not exist
in the input label image, then the matrix will contain a constant value
of missing_val (default None) in that row.
missing_val : scalar
value to use for missing label values
Returns
-------
ndarray
Example
-------
>>> import ants
>>> fi = ants.image_read(ants.get_ants_data('r16')).resample_image((60,60),1,0)
>>> mask = ants.get_mask(fi)
>>> labs = ants.kmeans_segmentation(fi,3)['segmentation']
>>> labmat = ants.labels_to_matrix(labs, mask)
"""
if (not ants.is_image(image)) or (not ants.is_image(mask)):
raise ValueError('image and mask must be ANTsImage types')
vec = image[mask > 0]
if target_labels is not None:
the_labels = target_labels
else:
the_labels = np.sort(np.unique(vec))
n_labels = len(the_labels)
labels = np.zeros((n_labels, len(vec)))
for i in range(n_labels):
lab = float(the_labels[i])
filler = (vec == lab).astype('float')
if np.sum(filler) == 0:
filler = np.asarray([np.nan]*len(vec))
labels[i,:] = filler
return labels