A rfSegmentation function.

Unsupervised image segmentation via random forests. An example.

rfSegmentation(
  featureMatrix,
  mask,
  labelimg = NULL,
  ntrees = 100,
  verbose = FALSE
)

Arguments

featureMatrix

input matrix of features matched to mask size n predictors in rf

mask

input antsImage mask

labelimg

input antsImage labelimage, optional for supervised seg

ntrees

number of rf trees

verbose

boolean

Value

list of n-probability images is output where n is number of classes

See also

mrvnrfs

Author

Tustison NJ, Avants BB

Examples

if (FALSE) { # \dontrun{
if (usePkg("randomForest")) {
  img <- antsImageRead(getANTsRData("r16")) %>% iMath("Normalize")
  mask <- getMask(img)
  segs <- kmeansSegmentation(img, k = 3, kmask = mask)
  fmat0 <- t(antsrimpute(getNeighborhoodInMask(img, mask, c(2, 2))))
  fmat1 <- t(antsrimpute(getNeighborhoodInMask(
    segs$probabilityimages[[1]], mask, c(2, 2)
  )))
  fmat2 <- t(antsrimpute(getNeighborhoodInMask(
    segs$probabilityimages[[2]], mask, c(2, 2)
  )))
  fmat3 <- t(antsrimpute(getNeighborhoodInMask(
    segs$probabilityimages[[3]], mask, c(2, 2)
  )))
  fmat <- cbind(fmat0, fmat1, fmat2, fmat3)
  # produces proximity between all voxel pairs
  rfsegs <- rfSegmentation(fmat, verbose = FALSE)
  lrr <- lowrankRowMatrix(rfsegs, 10, faster = TRUE)
  nv <- eanatSelect(
    inmat = lrr, mask = mask, selectorScale = 1.2, cthresh = 50,
    verbose = T, smooth = 1
  )
  ee <- eanatDef(lrr, mask,
    nvecs = nv, smooth = 0., cthresh = 50,
    its = 2, verbose = TRUE
  )
  eseg <- eigSeg(mask, ee)
  plot(img, eseg)
}
} # }