ants.segmentation.kelly_kapowski

kelly_kapowski(s, g, w, its=45, r=0.025, m=1.5, gm_label=2, wm_label=3, **kwargs)[source]

Compute cortical thickness using the DiReCT algorithm.

Diffeomorphic registration-based cortical thickness based on probabilistic segmentation of an image. This is an optimization algorithm.

Parameters:

  • s (ANTsimage) – segmentation image

  • g (ants.core.ANTsImage) – gray matter probability image

  • w (ants.core.ANTsImage) – white matter probability image

  • its (int) – convergence params - controls iterations

  • r (scalar) – gradient descent update parameter

  • m (scalar) – gradient field smoothing parameter

  • gm_label (int) – label for gray matter in the segmentation image

  • wm_label (int) – label for white matter in the segmentation image

  • kwargs (keyword arguments) – anything else, see KellyKapowski help in ANTs

Return type:

ants.core.ANTsImage

Example

>>> import ants
>>> 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)