def test_initialize_eigenanatomy_example(self):
mat = np.random.randn(4, 100).astype('float32')
init = ants.initialize_eigenanatomy(mat)
img = ants.image_read(ants.get_ants_data('r16'))
segs = ants.kmeans_segmentation(img, 3)
init = ants.initialize_eigenanatomy(segs['segmentation'])
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)
t1maskFromBold = ants.apply_transforms( t1, bmask, t1reg['invtransforms'],
interpolator = 'nearestNeighbor' )
t1 = ants.n4_bias_field_correction( t1, t1mask, 8 ).n4_bias_field_correction( t1mask, 4 )
bmask = ants.apply_transforms( und, t1mask, t1reg['fwdtransforms'],
interpolator = 'nearestNeighbor' ).morphology("close",3)
ofn = rdir + "features/LS" + id + "_mask_py.nii.gz"
ants.image_write( bmask, ofn )
t1toBOLD = ants.apply_transforms( und, t1, t1reg['fwdtransforms'] )
ofn = rdir + "features/LS" + id + "_t1ToBold_py.nii.gz"
ants.image_write( t1toBOLD, ofn )
## Tissue segmentation
# a simple method
################################################
qt1 = ants.iMath_truncate_intensity( t1, 0, 0.95 )
t1seg = ants.kmeans_segmentation( qt1, 3, t1mask, 0.2 )
volumes = ants.label_stats( t1seg['segmentation'], t1seg['segmentation'] )
boldseg = ants.apply_transforms( und, t1seg['segmentation'],
t1reg['fwdtransforms'], interpolator = 'nearestNeighbor' )
## Template mapping
# include prior information e.g. from meta-analysis or anatomy
myvoxes = range(powers_areal_mni_itk.shape[0])
anat = powers_areal_mni_itk['Anatomy']
syst = powers_areal_mni_itk['SystemName']
Brod = powers_areal_mni_itk['Brodmann']
xAAL = powers_areal_mni_itk['AAL']
ch2 = ants.image_read( ants.get_ants_data( "ch2" ) )
treg = ants.registration( t1 * t1mask, ch2, 'SyN' )
def test_mask_image_example(self):
myimage = ants.image_read(ants.get_ants_data('r16'))
mask = myimage > myimage.mean()
myimage_mask = ants.mask_image(myimage, mask, 3)
seg = ants.kmeans_segmentation(myimage, 3)
myimage_mask = ants.mask_image(myimage, seg['segmentation'], (1, 3))
def test_labels_to_matrix_example(self):
fi = ants.image_read(ants.get_ants_data('r16')).resample_image(
(60, 60), 1, 0)
mask = fi > fi.mean()
labs = ants.kmeans_segmentation(fi, 3)['segmentation']
labmat = ants.labels_to_matrix(labs, mask)
def test_label_stats_example(self):
image = ants.image_read(ants.get_ants_data('r16'), 2)
image = ants.resample_image(image, (64, 64), 1, 0)
mask = image > image.mean()
segs1 = ants.kmeans_segmentation(image, 3)
stats = ants.label_stats(image, segs1['segmentation'])
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)
def test_example(self):
fi = ants.image_read(ants.get_ants_data('r16'))
seg = ants.kmeans_segmentation(fi, 3)['segmentation']
geom = ants.label_geometry_measures(seg, fi)
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)
def __init__(self, ants_image=[[0.0]], k=3, **options):
import ants
self.seg = ants.kmeans_segmentation(ants_image, k, **options)
print(self.seg)
def test_mask_image_example(self):
myimage = ants.image_read(ants.get_ants_data("r16"))
mask = ants.get_mask(myimage)
myimage_mask = ants.mask_image(myimage, mask, (2, 3))
seg = ants.kmeans_segmentation(myimage, 3)
myimage_mask = ants.mask_image(myimage, seg["segmentation"], (1, 3))
def test_labels_to_matrix_example(self):
fi = ants.image_read(ants.get_ants_data("r16")).resample_image(
(60, 60), 1, 0)
mask = ants.get_mask(fi)
labs = ants.kmeans_segmentation(fi, 3)["segmentation"]
labmat = ants.labels_to_matrix(labs, mask)
def test_label_stats_example(self):
image = ants.image_read(ants.get_ants_data("r16"), 2)
image = ants.resample_image(image, (64, 64), 1, 0)
segs1 = ants.kmeans_segmentation(image, 3)
stats = ants.label_stats(image, segs1["segmentation"])
def test_label_overlap_measures(self):
r16 = ants.image_read(ants.get_ants_data('r16'))
r64 = ants.image_read(ants.get_ants_data('r64'))
s16 = ants.kmeans_segmentation(r16, 3)['segmentation']
s64 = ants.kmeans_segmentation(r64, 3)['segmentation']
stats = ants.label_overlap_measures(s16, s64)
