brain segmentation based on geometric priors

uses topological constraints to enhance accuracy of brain segmentation

geoSeg(
  img,
  brainmask,
  priors,
  seginit,
  vesselopt = "none",
  vesselk = 2,
  gradStep = 1.25,
  mrfval = 0.1,
  atroposits = 10,
  jacw = NULL,
  beta = 0.9
)

Arguments

img

input image or list of images (multiple features) where 1st image would typically be the primary constrast
brainmask

binary image
priors

spatial priors, assume first is csf, second is gm, third is wm
seginit

a previously computed segmentation which should have the structure of atropos or kmeansSegmentation output
vesselopt

one of bright, dark or none
vesselk

integer for kmeans vessel-based processing
gradStep

scalar for registration
mrfval

e.g. 0.05 or 0.1
atroposits

e.g. 5 iterations
jacw

precomputed diffeo jacobian
beta

for sigma transformation ( thksig output variable )

Value

list of segmentation result images

Author

Brian B. Avants

Examples


if (FALSE) {
img = antsImageRead( getANTsRData("simple") ,2)
img = n3BiasFieldCorrection( img , 4 )
img = n3BiasFieldCorrection( img , 2 )
bmk = getMask( img )
segs <- kmeansSegmentation( img, 3, bmk )
priors = segs$probabilityimages
seg = geoSeg( img, bmk, priors )
}