R/getCentroids.R

In stnava/ANTsR: ANTs in R: Quantification Tools for Biomedical Images

#' Convert an image to the geometric centroids of its signal
#'
#' Reduces a variate/statistical/network image to a set of centroids describing
#' the center of each stand-alone non-zero component in the image.
#'
#' @param img the image to reduce to centroids - presumably some kind of
#' statistical or network map
#' @param clustparam look at regions greater than or equal to this size
#' @return the centroids are output in matrix of size npoints by 3
#' @author Avants BB
#' @examples
#' img <- antsImageRead(getANTsRData("r16"))
#' img <- thresholdImage(img, 90, 120)
#' img <- labelClusters(img, 10)
#' cents <- getCentroids(img)
#'
#' @export getCentroids
getCentroids <- function(img, clustparam = 0) {
  if (nargs() == 0 | missing(img)) {
    print(args(getCentroids))
    return(1)
  }
  img <- check_ants(img)
  error_not_antsImage(img, "img")

  imagedim <- img@dimension
  if (clustparam > 0) {
    mypoints <- labelClusters(img, clustparam, maxThresh = Inf)
  }
  if (clustparam == 0) {
    mypoints <- antsImageClone(img)
  }
  mypoints <- data.frame(labelStats(mypoints, mypoints))
  mypoints <- mypoints[-1, ] # remove 0 label
  x <- mypoints$x
  y <- mypoints$y
  if (imagedim == 3) z <- mypoints$z else z <- rep(0, nrow(mypoints))
  if (imagedim == 4) t <- mypoints$t else t <- rep(0, nrow(mypoints))
  centroids <- as.matrix(
    data.frame(
      x = x,
      y = y,
      z = z,
      t = t
    )
  )
  return(centroids)
}