ANTsR (version 0.7.2.0)

mrvnrfs: multi-res voxelwise neighborhood random forest segmentation learning

Description

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

Usage

mrvnrfs(y, x, labelmasks, rad = NA, nsamples = 1, ntrees = 500,
  multiResSchedule = c(4, 2, 1), asFactors = TRUE, voxchunk = 50000)

Arguments

y

list of training labels. either an image or numeric value

x

a list of lists where each list contains feature images

labelmasks

a mask (or list of masks) used to define where the samples will come from. Note, two labels (e.g., GM and WM) will double the number of samples from each feature images. If the mask is binary, samples are selected randomly within 1 values.

rad

vector of dimensionality d define nhood radius

nsamples

(per subject to enter training)

ntrees

(for the random forest model)

multiResSchedule

an integer vector defining multi-res levels

asFactors

boolean - treat the y entries as factors

voxchunk

value of maximal voxels to predict at once. This value is used to split the prediction into smaller chunks such that memory requirements do not become too big

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()
inds<-1:5
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
  }
rad<-rep( 1, 2 )
mr <- c(1.5,1)
rfm<-mrvnrfs( lablist , ilist, mask, rad=rad, multiResSchedule=mr,
     asFactors = (  predtype == "label" ) )
rfmresult<-mrvnrfs.predict( rfm$rflist,
     ilist, mask, rad=rad, asFactors=(  predtype == "label" ),
     multiResSchedule=mr )
if ( predtype == "scalar" )
  print( cor( unlist(lablist) , unlist( rfmresult$seg ) ) )
} # end predtype loop


# }