R/abpN4.R

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

#' MR image bias correction based on the N4 algorithm.
#'
#' Truncate outlier intensities and bias correct with the N4 algorithm.
#'
#' @param img image to be bias corrected
#' @param intensityTruncation quantiles for image truncation.
#' @param mask optional antsImage mask
#' @param usen3 Use N3 algorithm instead of N4
#' @param ... additional arguments to pass to
#' \code{\link{n4BiasFieldCorrection}}
#' @return outputs a bias corrected image. 1 indicates failure.
#' @author Tustison N, Avants BB
#' @examples
#'
#' img <- antsImageRead(getANTsRData("r16"))
#' img2 <- abpN4(img)
#' img3 <- abpN4(img)
#' testthat::expect_equal(mean(img2), 82.6, tolerance = 1e-2)
#' testthat::expect_equal(img2, img3)
#'
#' @export abpN4
abpN4 <- function(
    img, intensityTruncation = c(0.025, 0.975, 256),
    mask, usen3 = FALSE,
    ...) {
  numargs <- nargs()
  if (numargs < 1 | missing(img) | !inherits(img, "antsImage")) {
    stop("Missing image.")
  }
  if (length(intensityTruncation) != 3) {
    stop("length( intensityTruncation ) should = 3 \n")
    return(1)
  }
  outimg <- iMath(
    img, "TruncateIntensity",
    intensityTruncation[1], intensityTruncation[2],
    intensityTruncation[3]
  )
  if (usen3 == TRUE) {
    outimg <- n3BiasFieldCorrection(outimg, 4)
    outimg <- n3BiasFieldCorrection(outimg, 2)
    return(outimg)
  }
  if (!missing(mask)) {
    outimg <- n4BiasFieldCorrection(img, mask = mask, ...)
  } else {
    outimg <- n4BiasFieldCorrection(img, ...)
  }

  return(outimg)
}