R/getMask.R

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

#' Get Mask
#'
#' Get a binary mask image from the given image after thresholding.
#'
#' If \code{cleanup} is \code{>0}, the following steps are applied
#' \enumerate{ \item Erosion with radius 2 voxels \item Retain largest
#' component \item Dilation with radius 1 voxel \item Morphological closing }
#'
#' These functions are available in \link{iMath}, see the operations
#' \dQuote{ME}, \dQuote{GetLargestComponent}, \dQuote{MD}, \dQuote{FillHoles}.
#'
#' @param img Input image. Can be an \code{antsImage} of 2, 3 or 4 dimensions.
#' If \code{img} is NULL, a file chooser dialog will appear.
#' @param lowThresh An inclusive lower threshold for voxels to be included in
#' the mask.
#' @param highThresh An inclusive upper threshold for voxels to be included in
#' the mask.
#' @param cleanup If \code{>0}, morphological operations will be applied to
#' clean up the mask by eroding away small or weakly-connected areas, and
#' closing holes.
#' @return Object of type \code{antsImage} containing the mask image. The
#' voxel intensities will be binarized, 1 for voxels in the mask and 0 outside.
#' @author Shrinidhi KL, Cook PA, Avants BB
#' @keywords mask
#' @examples
#'
#' img <- ri(1)
#' mask <- getMask(img)
#' testthat::expect_error(getMask(img, lowThresh = "hey"))
#' pixeltype(img) <- "unsigned int"
#' mask2 <- getMask(img)
#'
#' @export getMask
getMask <- function(img, lowThresh, highThresh, cleanup = 2) {
  # Binarizes a mask between specified thresholds Input can be a file name or an
  # antsImage, if it is not specified, a file chooser is launched. Works on 3D
  # images only If cleanup == TRUE, small and weakly-connected elements are removed
  # by erosion, and then holes are filled.  Returns: a binary antsImage
  cleanup <- as.numeric(cleanup)
  if (!is.antsImage(img)) {
    img <- check_ants(img)
  }
  if (is.antsImage(img)) {
    if (img@pixeltype != "float") {
      img <- antsImageClone(img, "float")
    }
  }
  if (missing(lowThresh)) lowThresh <- mean(img)
  if (missing(highThresh)) highThresh <- max(img)
  if ((!is.numeric(lowThresh)) ||
    (!is.numeric(highThresh)) ||
    length(lowThresh) > 1 || length(highThresh) > 1) {
    stop("'lowthresh' and 'highthresh' must be numeric scalars")
  }

  mask_img <- thresholdImage(img, lowThresh, highThresh)

  if (cleanup > 0) {
    mask_img <- iMath(mask_img, "ME", cleanup)
    mask_img <- iMath(mask_img, "GetLargestComponent")
    mask_img <- iMath(mask_img, "MD", cleanup)
    mask_img <- iMath(mask_img, "FillHoles")
    mask_img <- thresholdImage(mask_img, 1, Inf)
    while ((min(mask_img) == max(mask_img)) & cleanup > 0) {
      cleanup <- cleanup - 1
      mask_img <- thresholdImage(img, lowThresh, highThresh)
      if (cleanup > 0) {
        mask_img <- morphology(mask_img, "erode", cleanup)
        mask_img <- morphology(mask_img, "dilate", cleanup)
        mask_img <- iMath(mask_img, "FillHoles")
        mask_img <- thresholdImage(mask_img, 1, Inf)
      }
      if (cleanup == 0) {
        clustlab <- labelClusters(mask_img, 1)
        mask_img <- thresholdImage(clustlab, 1, 1)
      }
    }
  }
  return(mask_img)
}