"""
Morphology operations on multi-label ANTsImage types
"""
__all__ = ['multi_label_morphology']
import numpy as np
from ants.decorators import image_method
@image_method
def multi_label_morphology(image, operation, radius, dilation_mask=None, label_list=None, force=False):
"""
Morphology on multi label images.
Wraps calls to iMath binary morphology. Additionally, dilation and closing operations preserve
pre-existing labels. The choices of operation are:
Dilation: dilates all labels sequentially, but does not overwrite original labels.
This reduces dependence on the intensity ordering of adjoining labels. Ordering dependence
can still arise if two or more labels dilate into the same space - in this case, the label
with the lowest intensity is retained. With a mask, dilated labels are multiplied by the
mask and then added to the original label, thus restricting dilation to the mask region.
Erosion: Erodes labels independently, equivalent to calling iMath iteratively.
Closing: Close holes in each label sequentially, but does not overwrite original labels.
Opening: Opens each label independently, equivalent to calling iMath iteratively.
Arguments
---------
image : ANTsImage
Input image should contain only 0 for background and positive integers for labels.
operation : string
One of MD, ME, MC, MO, passed to iMath.
radius : integer
radius of the morphological operation.
dilation_mask : ANTsImage
Optional binary mask to constrain dilation only (eg dilate cortical label into WM).
label_list : list or tuple or numpy.ndarray
Optional list of labels, to perform operation upon. Defaults to all unique
intensities in image.
Returns
-------
ANTsImage
Example
-------
>>> import ants
>>> img = ants.image_read(ants.get_data('r16'))
>>> labels = ants.get_mask(img,1,150) + ants.get_mask(img,151,225) * 2
>>> labels_dilated = ants.multi_label_morphology(labels, 'MD', 2)
>>> # should see original label regions preserved in dilated version
>>> # label N should have mean N and 0 variance
>>> print(ants.label_stats(labels_dilated, labels))
"""
if (label_list is None) or (len(label_list) == 1):
label_list = np.sort(np.unique(image[image > 0]))
if (len(label_list) > 200) and (not force):
raise ValueError('More than 200 labels... Make sure the image is discrete'
' and call this function again with `force=True` if you'
' really want to do this.')
image_binary = image.clone()
image_binary[image_binary > 1] = 1
# Erosion / opening is simply a case of looping over the input labels
if (operation == 'ME') or (operation == 'MO'):
output = image.clone()
for current_label in label_list:
output = output.iMath(operation, radius, current_label)
return output
if dilation_mask is not None:
if int(dilation_mask.max()) != 1:
raise ValueError('Mask is either empty or not binary')
output = image.clone()
for current_label in label_list:
current_label_region = image.threshold_image(current_label, current_label)
other_labels = output - current_label_region
clab_binary_morphed = current_label_region.iMath(operation, radius, 1)
if (operation == 'MD') and (dilation_mask is not None):
clab_binary_morphed_nooverlap = current_label_region + dilation_mask * clab_binary_morphed - other_labels
clab_binary_morphed_nooverlap = clab_binary_morphed_nooverlap.threshold_image(1, 2)
else:
clab_binary_morphed_nooverlap = clab_binary_morphed - other_labels
clab_binary_morphed_nooverlap = clab_binary_morphed_nooverlap.threshold_image(1, 1)
output = output + clab_binary_morphed_nooverlap * current_label
return output
