Predict image segmentation via random forests.

rfSegmentationPredict(
  rfSegmentationModel,
  featureMatrix,
  mask,
  verbose = FALSE
)

Arguments

rfSegmentationModel

input rf model

featureMatrix

input feature matrix

mask

antsImage mask

verbose

bool

Value

segmentation is output

Examples

if ( usePkg('randomForest') ) { img<-antsImageRead( getANTsRData("r16")) mask<-getMask( img ) mask2<-getMask( img ) mask [ 129:255, 1:255 ]<-0 mask2[ 2:128, 1:255 ]<-0 segs<-kmeansSegmentation( img, k=3, kmask = mask) fmat = t( antsrimpute( getNeighborhoodInMask( img, mask, c(2,2) ) ) ) rfsegs<-rfSegmentation( fmat, mask, segs$segmentation, ntrees=100 ) fmat2 = t( antsrimpute( getNeighborhoodInMask( img, mask2, c(2,2) ) ) ) rfseg2<-rfSegmentationPredict( rfsegs$rfModel , fmat2 , mask2 ) }