geoSeg | R Documentation |
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
)
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 |
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 ) |
list of segmentation result images
Brian B. Avants
## 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)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.