R/weingartenImageCurvature.R

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

#' image curvature for 2D or 3D
#'
#' uses the weingarten map to estimate image mean or gaussian curvature
#'
#' @param image antsImage
#' @param sigma smoothing parameter
#' @param opt mean by default, otherwise use string gaussian or characterize
#' @return image
#' @author Brian B. Avants
#' @references Avants, B, J. Gee, and B. Avants.
#' Shape operator for differential image analysis
#' Information Processing in Medical Imaging, 2003.
#' @keywords curvature
#' @examples
#' img = makeImage( c(10,10,10) , rnorm( 1000 ) )
#' fik <- weingartenImageCurvature( img )
#' \dontrun{
#' fi <- antsImageRead( getANTsRData( "mni" ) )
#' fik <- weingartenImageCurvature( fi )
#' }
#' @export weingartenImageCurvature
weingartenImageCurvature <- function( image, sigma=1.0, opt='mean' ) {
  if ( image@dimension != 3 & image@dimension != 2 ) {
    stop("input image must be 2D or 3D")
  }
  if ( ! opt %in% c('mean','gaussian','characterize' ) ) {
    stop("choose mean gaussian or characterize as option")
  }
  if ( image@dimension == 2 )
    {
    d = dim( image )
    temp = as.array( makeImage( c( d, 10 ) ) )
    for ( k in 2:8 ) {
      voxvals = image[ 1:d[1], 1:d[2] ]
      temp[ 1:d[1], 1:d[2], k ] = voxvals
        # + rnorm(  length( voxvals ), 0, 1.e-5 )
      }
    temp = as.antsImage( temp )
    myspc = antsGetSpacing( image )
    myspc = c( myspc, min(myspc) )
    x=antsSetSpacing( temp, myspc )
    } else temp = antsImageClone( image )
  optnum=0
  if ( opt == 'gaussian' ) optnum=6
  if ( opt == 'characterize' ) optnum=5
  mykout = .Call("weingartenImageCurvature",
    temp, sigma, optnum, PACKAGE = "ANTsR")
  if ( image@dimension == 3 ) return( mykout )
  if ( image@dimension == 2 )
  {
  subarr = as.antsImage( as.array( mykout )[,,5] )
  return( antsCopyImageInfo( image, subarr ) )
  }
}