N4 bias field correction — n4BiasFieldCorrection • ANTsR

Perform N4 bias field correction on the given image

n4BiasFieldCorrection(
  img,
  mask,
  rescaleIntensities = FALSE,
  shrinkFactor = 4,
  convergence = list(iters = c(50, 50, 50, 50), tol = 1e-07),
  splineParam = 200,
  returnBiasField = FALSE,
  verbose = FALSE,
  weightMask = NULL
)

Arguments

img: input antsImage
mask: input mask, if one is not passed one will be made
rescaleIntensities: At each iteration, a new intensity mapping is calculated and applied but there is nothing which constrains the new intensity range to be within certain values. The result is that the range can "drift" from the original at each iteration. This option rescales to the [min,max] range of the original image intensities within the user-specified mask. A mask is required to perform rescaling. Default is FALSE in ANTsR/ANTsPy but TRUE in ANTs.
shrinkFactor: Shrink factor for multi-resolution correction, typically integer less than 4.
convergence: List of: iters, vector of maximum number of iterations for each shrinkage factor, and tol, the convergence tolerance. Default tolerance is 1e-7 in ANTsR/ANTsPy but 0.0 in ANTs.
splineParam: Parameter controlling number of control points in spline. Either single value, indicating how many control points, or vector with one entry per dimension of image, indicating the spacing in each direction. Default in ANTsR/ANTsPy is 200 mm per mesh element in each dimension. The ANTs default is a mesh size of 1 per dimension.
returnBiasField: bool, return the field instead of the corrected image.
verbose: enables verbose output.
weightMask: antsImage of weight mask

Value

bias corrected image or bias field

Author

BB Avants

Examples

dims <- c(50, 50)
img <- makeImage(imagesize = dims, rnorm(prod(dims)))
n4img <- n4BiasFieldCorrection(img)
n4img <- n4BiasFieldCorrection(img, mask = img > 0)
testthat::expect_error(n4BiasFieldCorrection(img, weightMask = "somepath"))
testthat::expect_error(n4BiasFieldCorrection(img, splineParam = rep(200, 3)))
n4img <- n4BiasFieldCorrection(img, splineParam = c(200, 20))

rm(img)
gc()
#>           used  (Mb) gc trigger  (Mb) limit (Mb) max used  (Mb)
#> Ncells 4011156 214.3    5925935 316.5         NA  5925935 316.5
#> Vcells 6719863  51.3   15907889 121.4     102400 15907845 121.4
rm(n4img)
gc()
#>           used  (Mb) gc trigger  (Mb) limit (Mb) max used  (Mb)
#> Ncells 4010654 214.2    5925935 316.5         NA  5925935 316.5
#> Vcells 6719058  51.3   15907889 121.4     102400 15907845 121.4
fname <- getANTsRData("r16")
in_img <- antsImageRead(fname)
n4 <- n4BiasFieldCorrection(in_img)
rm(n4)
gc()
#>           used  (Mb) gc trigger  (Mb) limit (Mb) max used  (Mb)
#> Ncells 4010714 214.2    5925935 316.5         NA  5925935 316.5
#> Vcells 6719154  51.3   15907889 121.4     102400 15907845 121.4
mask <- in_img > 0
mask2 <- antsImageClone(mask, out_pixeltype = "float")
# fails
mask
#> antsImage
#>   Pixel Type          : unsigned char 
#>   Components Per Pixel: 1 
#>   Dimensions          : 256x256 
#>   Voxel Spacing       : 1x1 
#>   Origin              : 0 0 
#>   Direction           : 1 0 0 1 
#> 
sum(mask)
#> [1] 19278
if (FALSE) { # \dontrun{
n4 <- n4BiasFieldCorrection(in_img, mask = mask, verbose = TRUE)
# fails
n4 <- n4BiasFieldCorrection(in_img, mask = mask2)
} # }