__all__ = ['label_geometry_measures']
from tempfile import mktemp
import pandas as pd
import numpy as np
from ants.internal import get_lib_fn, process_arguments
from ants.decorators import image_method
[docs]
@image_method
def label_geometry_measures(label_image, intensity_image=None):
"""
Wrapper for the ANTs funtion LabelGeometryMeasures
ANTsR function: `labelGeometryMeasures`
Arguments
---------
label_image : ANTsImage
image on which to compute geometry. Labels must be representable as uint32.
intensity_image : ANTsImage (optional)
image with intensity values
Returns
-------
pandas.DataFrame
Example
-------
>>> import ants
>>> fi = ants.image_read( ants.get_ants_data('r16') )
>>> seg = ants.kmeans_segmentation( fi, 3 )['segmentation']
>>> geom = ants.label_geometry_measures(seg,fi)
"""
if intensity_image is None:
intensity_image = label_image.clone()
outcsv = mktemp(suffix='.csv')
# Library function requires unsigned int labels
if label_image.pixeltype != 'unsigned int':
label_image_int = label_image.clone('unsigned int')
if not np.all(label_image.numpy() == label_image_int.numpy()):
raise ValueError('Input label values must be representable as uint32.')
label_image = label_image.clone('unsigned int')
else:
label_image_int = label_image
veccer = [label_image.dimension, label_image_int, intensity_image, outcsv]
veccer_processed = process_arguments(veccer)
libfn = get_lib_fn('LabelGeometryMeasures')
pp = libfn(veccer_processed)
pp = pd.read_csv(outcsv)
if 'VolumeInVoxels' in pp.columns and not 'VolumeInMillimeters' in pp.columns:
spc = np.prod(label_image.spacing)
pp['VolumeInMillimeters'] = pp['VolumeInVoxels'] * spc
# Ensure that the label column is of integer type - if there is any NaN, it will be float
# Something has gone seriously wrong if the labels are not interpreted as integers
if not np.issubdtype(pp['Label'].dtype, np.integer):
raise ValueError('Label column not integer type, label values may be invalid')
return pp