R/priorBasedSegmentation.R

In neuroconductor-devel/ANTsR: ANTs in R: Quantification Tools for Biomedical Images

#' spatial prior-based image segmentation.
#'
#' markov random field regularized, prior-based image segmentation that is a
#' wrapper around atropos (see ANTs and related publications).
#'
#' @param img input image or image list for multivariate segmentation
#' @param priors list of priors that cover the number of classes
#' @param mask segment inside this mask
#' @param priorWeight usually 0 (priors used for initialization only), 0.25 or 0.5.
#' @param mrf regularization, higher is smoother, a numerical value in range 0.0 to 0.2
#' @param iterations maximum number of iterations.  could be a large value eg 25.
#' @return segmentation and probability images
#' @author Brian B. Avants
#' @examples
#'
#' fi = antsImageRead( getANTsRData( "r16" ) )
#' seg = kmeansSegmentation( fi, 3 )
#' msk = thresholdImage( seg$segmentation, 1, Inf )
#' pseg = priorBasedSegmentation( fi, seg$probabilityimages, msk, 0.25, 0.1, 3 )
#'
#' @export priorBasedSegmentation
priorBasedSegmentation <- function(
  img,
  priors,
  mask,
  priorWeight = 0.25,
  mrf = 0.1,
  iterations = 25
  ) {
  if ( class( img )[1] == 'antsImage' ) dim <- img@dimension
  if ( class( img )[1] == 'list' ) dim <- img[[1]]@dimension
  nhood <- paste( rep( 1, dim ), collapse = "x")
  mrf <- paste("[", mrf, ",", nhood, "]")
  conv <- paste("[", iterations, ",", 0, "]")
  pseg <- atropos( a = img, m = mrf,
      c = conv,  i = priors, x = mask, priorweight = priorWeight )
  return( pseg )
}