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Label geom test #784

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Label geom test #784

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785e93d
ENH: More label_geometry_measures tests
cookpa Mar 2, 2025
120428f
ENH: Check label geometry return type for problems
cookpa Mar 3, 2025
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7 changes: 4 additions & 3 deletions ants/label/label_geometry_measures.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,6 @@ def label_geometry_measures(label_image, intensity_image=None):
>>> seg = ants.kmeans_segmentation( fi, 3 )['segmentation']
>>> geom = ants.label_geometry_measures(seg,fi)
"""
import numpy as np
if intensity_image is None:
intensity_image = label_image.clone()

Expand All @@ -49,9 +48,11 @@ def label_geometry_measures(label_image, intensity_image=None):
libfn = get_lib_fn('LabelGeometryMeasures')
pp = libfn(veccer_processed)
pp = pd.read_csv(outcsv)
import numpy as np
# pp['Label'] = np.sort(np.unique(label_image[label_image>0])).astype('int')
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
26 changes: 22 additions & 4 deletions tests/test_segmentation.py
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Expand Up @@ -177,9 +177,27 @@ def tearDown(self):
pass

def test_example(self):
fi = ants.image_read( ants.get_ants_data('r16') )
fi = ants.resample_image(ants.image_read( ants.get_ants_data('r16') ), (140,140),1,0)
seg = ants.kmeans_segmentation( fi, 3 )['segmentation']
geom = ants.label_geometry_measures(seg,fi)
expected_cols = ['Label', 'VolumeInVoxels', 'VolumeInMillimeters',
'SurfaceAreaInMillimetersSquared', 'Eccentricity', 'Elongation',
'Roundness', 'Flatness', 'Centroid_x', 'Centroid_y', 'AxesLength_x',
'AxesLength_y', 'BoundingBoxLower_x', 'BoundingBoxLower_y',
'BoundingBoxUpper_x', 'BoundingBoxUpper_y', 'MeanIntensity',
'SigmaIntensity', 'MinIntensity', 'MaxIntensity',
'IntegratedIntensity']
for col in expected_cols:
self.assertTrue(col in geom.columns)
# Label column should have int type
self.assertTrue(np.issubdtype(geom['Label'].dtype, np.integer))
# So should VolumeInVoxels
self.assertTrue(np.issubdtype(geom['VolumeInVoxels'].dtype, np.integer))
# Check math of VolumeInMillimeters
self.assertTrue(np.allclose(geom['VolumeInMillimeters'], geom['VolumeInVoxels'] * np.prod(fi.spacing), atol=1e-6))
# should be three rows
self.assertEqual(geom.shape[0], 3)



class TestModule_prior_based_segmentation(unittest.TestCase):
Expand All @@ -198,7 +216,7 @@ def test_example(self):


class TestModule_random(unittest.TestCase):

def setUp(self):
pass
def tearDown(self):
Expand All @@ -208,15 +226,15 @@ def test_fuzzy_cmeans(self):
image = ants.image_read(ants.get_ants_data('r16'))
mask = ants.get_mask(image)
fuzzy = ants.fuzzy_spatial_cmeans_segmentation(image, mask, number_of_clusters=3)

def test_functional_lung(self):
image = ants.image_read(ants.get_data("mni")).resample_image((4,4,4))
mask = image.get_mask()
seg = ants.functional_lung_segmentation(image, mask, verbose=True,
number_of_iterations=1,
number_of_clusters=2,
number_of_atropos_iterations=1)

def test_anti_alias(self):
img = ants.image_read(ants.get_data('r16'))
mask = ants.get_mask(img)
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