__all__ = ['mask_image']
import numpy as np
import ants
from ants.decorators import image_method
[docs]
@image_method
def mask_image(image, mask, level=1, binarize=False):
"""
Mask an input image by a mask image. If the mask image has multiple labels,
it is possible to specify which label(s) to mask at.
ANTsR function: `maskImage`
Arguments
---------
image : ANTsImage
Input image.
mask : ANTsImage
Mask or label image.
level : scalar or tuple of scalars
Level(s) at which to threshold the mask. Can be a single value or an iterable of values.
binarize : boolean
whether to binarize the output image. This computes an intersection between (image != 0) and (mask == i), for
all i in level.
Returns
-------
ANTsImage
Example
-------
>>> import ants
>>> myimage = ants.image_read(ants.get_ants_data('r16'))
>>> mask = ants.get_mask(myimage)
>>> myimage_mask = ants.mask_image(myimage, mask, 3)
>>> seg = ants.kmeans_segmentation(myimage, 3)
>>> myimage_mask = ants.mask_image(myimage, seg['segmentation'], (1,3))
"""
leveluse = level
if type(leveluse) is np.ndarray:
leveluse = level.tolist()
if type(leveluse) is int or type(leveluse) is float:
leveluse = [level]
image_out = image.clone() * 0
mask_float = ants.image_clone(mask, 'float')
for mylevel in leveluse:
temp = ants.threshold_image(mask_float, mylevel, mylevel)
image_out = image_out + temp * image
if binarize:
image_out_b = image_out * 0.0
image_out_b[ image_out > 0.5 ] = 1.0
return image_out_b
return image_out