ANTsR (version 0.4.0)

vwnrfs: voxelwise neighborhood random forest segmentation and prediction

Description

Represents feature images as a neighborhood and uses the features to build a random forest prediction from an image population

Usage

vwnrfs(y, x, labelmasks, rad = NA, nsamples = 8, ntrees = 500,
  asFactors = TRUE, reduceFactor = 1)

Arguments

y

list of training label images, can be a factor or numeric vector this can also be a regular old vector

x

a list of lists where each list contains feature images

labelmasks

a list of masks where each mask defines the image space for the given list. that is, the nth mask indexes the nth feature set. multi-label masks will try to balance sampling for each label.

rad

vector of dimensionality d define nhood radius

nsamples

(per subject to enter training)

ntrees

(for the random forest model)

asFactors

boolean - treat the y entries as factors

reduceFactor

integer factor by which to reduce (imaging) data resolution

Value

list a 4-list with the rf model, training vector, feature matrix and the random mask

Examples

# NOT RUN {
mask<-makeImage( c(10,10), 0 )
mask[ 3:6, 3:6 ]<-1
mask[ 5, 5:6]<-2
ilist<-list()
lablist<-list()
masklist<-list()
inds<-1:50
scl<-0.33 # a noise parameter
for ( predtype in c("label","scalar") )
{
for ( i in inds ) {
  img<-antsImageClone(mask)
  imgb<-antsImageClone(mask)
  limg<-antsImageClone(mask)
  if ( predtype == "label") {  # 4 class prediction
    img[ 3:6, 3:6 ]<-rnorm(16)*scl+(i %% 4)+scl*mean(rnorm(1))
    imgb[ 3:6, 3:6 ]<-rnorm(16)*scl+(i %% 4)+scl*mean(rnorm(1))
    limg[ 3:6, 3:6 ]<-(i %% 4)+1  # the label image is constant
    }
    if ( predtype == "scalar") {
      img[ 3:6, 3:6 ]<-rnorm(16,1)*scl*(i)+scl*mean(rnorm(1))
      imgb[ 3:6, 3:6 ]<-rnorm(16,1)*scl*(i)+scl*mean(rnorm(1))
      limg<-i^2.0  # a real outcome
      }
    ilist[[i]]<-list(img,imgb)  # two features
    lablist[[i]]<-limg
    masklist[[i]] = mask
  }
  rfm<-vwnrfs( lablist , ilist, masklist[[1]], rad=c(2,2) ) # use single mask
  rfm<-vwnrfs( lablist , ilist, masklist, rad=c(2,2) )
  if ( predtype == "label" )
    print(  sum( rfm$tv != predict(rfm$rfm) ) )
  if ( predtype == "scalar" )
    print( cor(as.numeric(rfm$tv) , as.numeric(predict(rfm$rfm) ) ) )
} # end predtype loop

# }