R/labels2matrix.R

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

#' Convert label image to a matrix
#'
#' Convert a labeled image to an n x m binary matrix where n = number of voxels
#' and m = number of labels. Only includes values inside the provided mask while
#' including background ( img == 0 ) for consistency with timeseries2matrix and
#' other image to matrix operations.
#'
#' @param img input label image
#' @param mask defines domain of interest
#' @param targetLabels defines target regions to be returned.  if the target
#' label does not exist in the input label image, then the matrix will contain
#' a constant value of missingVal (default NA) in that row.
#' @param missingVal for missing label values.
#' @return matrix is output
#' @author Avants BB
#' @examples
#'
#' fi <- antsImageRead(getANTsRData("r16"), 2) %>% resampleImage(c(60, 60), 1, 0)
#' mask <- getMask(fi)
#' labs <- kmeansSegmentation(fi, 3)$segmentation
#' labmat <- labels2matrix(labs, mask)
#'
#' @export labels2matrix
labels2matrix <- function(img, mask, targetLabels = NULL, missingVal = NA) {
  if (any(dim(img) != dim(mask))) {
    stop("image and mask must be same size")
  }
  # vec <- subset( img, mask > 0 )
  vec <- img[mask > 0]
  theLabels <- sort(unique(vec))
  if (!is.null(targetLabels)) theLabels <- targetLabels
  mylabelnames <- as.character(theLabels)
  nLabels <- length(theLabels)
  labels <- matrix(0, nrow = nLabels, ncol = length(vec))
  for (i in 1:nLabels)
  {
    lab <- as.numeric(theLabels[i])
    filler <- as.numeric(vec == lab)
    if (sum(filler) == 0) filler <- rep(missingVal, length(vec))
    labels[i, ] <- filler
  }
  return(labels)
}