antspynet_preprocess_brain_image {rpyANTs} | R Documentation |
Process brain image prior to segmentation
Description
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 |
|
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 |
template |
template image (not skull-stripped) or string, e.g.
|
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 |
reference_image |
|
intensity_normalization_type |
either re-scale the intensities to
|
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))
}