Split time series image into k distinct images

Uses clustering methods to split a time series into similar subsets.

clusterTimeSeries(mat, krange = 2:10, nsvddims = NA, criterion = "asw")

Arguments

mat	input time series matrix
krange	k cluster range to explore
nsvddims	eg 2
criterion	for clustering see pamk

Value

matrix is output

Examples

if (FALSE) {
  if (!exists("fn") ) fn<-getANTsRData("pcasl")
   # high motion subject
  asl<-antsImageRead(fn,4)
  tr<-antsGetSpacing(asl)[4]
  aslmean<-getAverageOfTimeSeries( asl )
  aslmask<-getMask(aslmean,lowThresh=mean(aslmean),cleanup=TRUE)
  omat<-timeseries2matrix(asl, aslmask )
  clustasl<-clusterTimeSeries( omat, krange=4:10 )
  for ( ct in 1:max(clustasl$clusters) )
    {
    sel<-clustasl$clusters != ct
    img<-matrix2timeseries( asl, aslmask, omat[sel,] )
    perf <- aslPerfusion( img,
      dorobust=0.9, useDenoiser=4, skip=10, useBayesian=0,
      moreaccurate=0, verbose=F, mask=aslmask )
    perfp <- list( sequence="pcasl", m0=perf$m0 )
    cbf <- quantifyCBF( perf$perfusion, perf$mask, perfp )
    }
  }