geoSeg: brain segmentation based on geometric priors

In neuroconductor-devel/ANTsR: ANTs in R: Quantification Tools for Biomedical Images

Description

uses topological constraints to enhance accuracy of brain segmentation

Usage

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(s)

Brian B. Avants

Examples

## Not run: 
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 )

## End(Not run)

neuroconductor-devel/ANTsR documentation built on April 1, 2021, 1:02 p.m.