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

Examples

if (FALSE) {
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 )
}
}