"""
Test ants.registration module
nptest.assert_allclose
self.assertEqual
self.assertTrue
class TestModule_geo_seg(unittest.TestCase):
def setUp(self):
pass
def tearDown(self):
pass
def test_example(self):
image = ants.image_read(ants.get_ants_data('simple'))
image = ants.n3_bias_field_correction(image, 4)
image = ants.n3_bias_field_correction(image, 2)
bmk = ants.get_mask(image)
segs = ants.kmeans_segmentation(image, 3, bmk)
priors = segs['probabilityimages']
seg = ants.geo_seg(image, bmk, priors)
"""
import os
import unittest
from common import run_tests
from tempfile import mktemp
import numpy as np
import numpy.testing as nptest
import ants
class TestModule_atropos(unittest.TestCase):
def setUp(self):
pass
def tearDown(self):
pass
def test_example(self):
# test ANTsPy/ANTsR example
img = ants.image_read(ants.get_ants_data('r16'))
img = ants.resample_image(img, (64,64), 1, 0)
mask = ants.get_mask(img)
ants.atropos( a = img, m = '[0.2,1x1]', c = '[2,0]', i = 'kmeans[3]', x = mask )
def test_multiple_inputs(self):
img = ants.image_read( ants.get_ants_data("r16"))
img = ants.resample_image( img, (64,64), 1, 0 )
mask = ants.get_mask(img)
segs1 = ants.atropos( a = img, m = '[0.2,1x1]',
c = '[2,0]', i = 'kmeans[3]', x = mask )
# Use probabilities from k-means seg as priors
segs2 = ants.atropos( a = img, m = '[0.2,1x1]',
c = '[2,0]', i = segs1['probabilityimages'], x = mask )
# multiple inputs
feats = [img, ants.iMath(img,"Laplacian"), ants.iMath(img,"Grad") ]
segs3 = ants.atropos( a = feats, m = '[0.2,1x1]',
c = '[2,0]', i = segs1['probabilityimages'], x = mask )
class TestModule_joint_label_fusion(unittest.TestCase):
def setUp(self):
pass
def tearDown(self):
pass
def test_example(self):
ref = ants.image_read( ants.get_ants_data('r16'))
ref = ants.resample_image(ref, (50,50),1,0)
ref = ants.iMath(ref,'Normalize')
mi = ants.image_read( ants.get_ants_data('r27'))
mi2 = ants.image_read( ants.get_ants_data('r30'))
mi3 = ants.image_read( ants.get_ants_data('r62'))
mi4 = ants.image_read( ants.get_ants_data('r64'))
mi5 = ants.image_read( ants.get_ants_data('r85'))
refmask = ants.get_mask(ref)
refmask = ants.iMath(refmask,'ME',2) # just to speed things up
ilist = [mi,mi2,mi3,mi4,mi5]
seglist = [None]*len(ilist)
for i in range(len(ilist)):
ilist[i] = ants.iMath(ilist[i],'Normalize')
mytx = ants.registration(fixed=ref , moving=ilist[i] ,
type_of_transform = ('Affine') )
mywarpedimage = ants.apply_transforms(fixed=ref,moving=ilist[i],
transformlist=mytx['fwdtransforms'])
ilist[i] = mywarpedimage
seg = ants.threshold_image(ilist[i],'Otsu', 3)
seglist[i] = ( seg ) + ants.threshold_image( seg, 1, 3 ).morphology( operation='dilate', radius=3 )
r = 2
pp = ants.joint_label_fusion(ref, refmask, ilist, r_search=2,
label_list=seglist, rad=[r]*ref.dimension )
pp = ants.joint_label_fusion(ref,refmask,ilist, r_search=2, rad=2 )
def test_max_lab_plus_one(self):
ref = ants.image_read( ants.get_ants_data('r16'))
ref = ants.resample_image(ref, (50,50),1,0)
ref = ants.iMath(ref,'Normalize')
mi = ants.image_read( ants.get_ants_data('r27'))
mi2 = ants.image_read( ants.get_ants_data('r30'))
mi3 = ants.image_read( ants.get_ants_data('r62'))
mi4 = ants.image_read( ants.get_ants_data('r64'))
mi5 = ants.image_read( ants.get_ants_data('r85'))
refmask = ants.get_mask(ref)
refmask = ants.iMath(refmask,'ME',2) # just to speed things up
ilist = [mi,mi2,mi3,mi4,mi5]
seglist = [None]*len(ilist)
for i in range(len(ilist)):
ilist[i] = ants.iMath(ilist[i],'Normalize')
mytx = ants.registration(fixed=ref , moving=ilist[i] ,
type_of_transform = ('Affine') )
mywarpedimage = ants.apply_transforms(fixed=ref,moving=ilist[i],
transformlist=mytx['fwdtransforms'])
ilist[i] = mywarpedimage
seg = ants.threshold_image(ilist[i],'Otsu', 3)
seglist[i] = ( seg ) + ants.threshold_image( seg, 1, 3 ).morphology( operation='dilate', radius=3 )
r = 2
pp = ants.joint_label_fusion(ref, refmask, ilist, r_search=2,
label_list=seglist, rad=[r]*ref.dimension, max_lab_plus_one=True )
pp = ants.joint_label_fusion(ref,refmask,ilist, r_search=2, rad=2,
max_lab_plus_one=True)
class TestModule_kelly_kapowski(unittest.TestCase):
def setUp(self):
pass
def tearDown(self):
pass
def test_example(self):
img = ants.image_read( ants.get_ants_data('r16') ,2)
img = ants.resample_image(img, (64,64),1,0)
mask = ants.get_mask( img )
segs = ants.kmeans_segmentation( img, k=3, kmask = mask)
thick = ants.kelly_kapowski(s=segs['segmentation'], g=segs['probabilityimages'][1],
w=segs['probabilityimages'][2], its=45,
r=0.5, m=1)
class TestModule_kmeans(unittest.TestCase):
def setUp(self):
pass
def tearDown(self):
pass
def test_example(self):
fi = ants.image_read(ants.get_ants_data('r16'))
fi = ants.n3_bias_field_correction(fi, 2)
seg = ants.kmeans_segmentation(fi, 3)
class TestModule_label_geometry_measures(unittest.TestCase):
def setUp(self):
pass
def tearDown(self):
pass
def test_example(self):
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):
def setUp(self):
pass
def tearDown(self):
pass
def test_example(self):
fi = ants.image_read(ants.get_ants_data('r16'))
seg = ants.kmeans_segmentation(fi,3)
mask = ants.threshold_image(seg['segmentation'], 1, 1e15)
priorseg = ants.prior_based_segmentation(fi, seg['probabilityimages'], mask, 0.25, 0.1, 3)
class TestModule_random(unittest.TestCase):
def setUp(self):
pass
def tearDown(self):
pass
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)
mask_aa = ants.anti_alias(mask)
if __name__ == '__main__':
run_tests()
