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Process brain image prior to segmentation

Source: R/preprocess_brain_image.R
antspynet_preprocess_brain_image.Rd

Strip skulls, normalize intensity, align and re-sample to template. This procedure is needed for many antspynet functions since the deep neural networks are trained in template spaces

Usage

antspynet_preprocess_brain_image(
  x,
  truncate_intensity = c(0.01, 0.99),
  brain_extraction_modality = c("none", "t1", "t1v0", "t1nobrainer", "t1combined",
    "flair", "t2", "bold", "fa", "t1infant", "t2infant"),
  template_transform_type = c("None", "Affine", "Rigid"),
  template = c("biobank", "croppedMni152"),
  do_bias_correction = TRUE,
  return_bias_field = FALSE,
  do_denoising = TRUE,
  intensity_matching_type = c("regression", "histogram"),
  reference_image = NULL,
  intensity_normalization_type = NULL,
  verbose = TRUE
)

Arguments

x

'ANTsImage' or path to image to process

truncate_intensity

defines the quantile threshold for truncating the image intensity

brain_extraction_modality

character of length 1, perform brain extraction modality

template_transform_type

either 'Rigid' or 'Affine' align to template brain

template

template image (not skull-stripped) or string, e.g. 'biobank', 'croppedMni152'

do_bias_correction

whether to perform bias field correction

return_bias_field

return bias field as an additional output without bias correcting the image

do_denoising

whether to remove noises using non-local means

intensity_matching_type

either 'regression' or 'histogram'; only is performed if reference_image is not NULL.

reference_image

'ANTsImage' or path to image, or NULL

intensity_normalization_type

either re-scale the intensities to c(0, 1) ('01'), or for zero-mean, unit variance ('0mean'); if NULL normalization is not performed

verbose

print progress to the screen

Value

Dictionary with images after process. The images are registered and re-sampled into template.

See also

antspynet$preprocess_brain_image

Examples


library(rpyANTs)
if(interactive() && ants_available("antspynet")) {
  image_path <- ants$get_ants_data('r30')
  preprocessed <- antspynet_preprocess_brain_image(
    image_path, verbose = FALSE
  )

  # Compare
  orig_img <- as_ANTsImage(image_path)
  new_img <- preprocessed$preprocessed_image
  pal <- grDevices::gray.colors(256, start = 0, end = 1)

  par(mfrow = c(1, 2), mar = c(0.1, 0.1, 0.1, 0.1),
      bg = "black", fg = "white")
  image(orig_img[], asp = 1, axes = FALSE,
        col = pal, ylim = c(1, 0))
  image(new_img[], asp = 1, axes = FALSE,
        col = pal, ylim = c(1, 0))

}