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' )
concatx2 = treg['invtransforms'] + t1reg['invtransforms']
pts2bold = ants.apply_transforms_to_points( 3, powers_areal_mni_itk, concatx2,whichtoinvert = ( True, False, True, False ) )
locations = pts2bold.iloc[:,:3].values
ptImg = ants.make_points_image( locations, bmask, radius = 3 )
networks = powers_areal_mni_itk['SystemName'].unique()
dfnpts = np.where( powers_areal_mni_itk['SystemName'] == networks[5] )
dfnImg = ants.mask_image( ptImg, ptImg, level = dfnpts[0].tolist(), binarize=False )
# plot( und, ptImg, axis=3, window.overlay = range( ptImg ) )
bold2ch2 = ants.apply_transforms( ch2, und, concatx2, whichtoinvert = ( True, False, True, False ) )
# Extracting canonical functional network maps
## preprocessing
csfAndWM = ( ants.threshold_image( boldseg, 1, 1 ) +
ants.threshold_image( boldseg, 3, 3 ) ).morphology("erode",1)
bold = ants.image_read( boldfnsR )
boldList = ants.ndimage_to_list( bold )
avgBold = ants.get_average_of_timeseries( bold, range( 5 ) )
boldUndTX = ants.registration( und, avgBold, "SyN", regIterations = (15,4),
def __init__(self, ants_image=[[0.0]], mask=[[0.0]], level=1, **options):
import ants
self.masking = ants.mask_image(ants_image, mask, level, **options)
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_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))