#
rm(list = ls())
library(neurobase)
library(extrantsr)
library(ANTsR)
library(fslr)
imgs = list.files(pattern = ".nii.gz")
df = data.frame(image = imgs,
id = gsub("(\\d*).*", "\\1", imgs),
stringsAsFactors = FALSE)
df$type = gsub(".*_(.*)[.]nii.gz", "\\1", df$image)
df$type[ !grepl("_", df$image)] = "original"
df = reshape(df, direction = "wide",
timevar = "type", idvar = "id")
colnames(df) = gsub("image[.]", "", colnames(df))
rownames(df) = NULL
iimg = 1
for (iimg in seq(nrow(df))) {
# for (iimg in 1:20) {
id_imgs = df[ iimg, ]
id_imgs$id = NULL
id_imgs = unlist(id_imgs)
mask_fname = id_imgs["mask"]
img_fname = id_imgs["original"]
print(iimg)
dims = lapply(id_imgs, dim_)
res = sapply(dims,
identical,
x = dims[[1]])
res = all(res)
if (!res) {
stop("dimensions are not equal!")
}
if (all(file.exists(id_imgs))) {
template.file = mni_fname(mm = 1, brain = TRUE)
template.mask = mni_fname(mm = 1, brain = TRUE, mask = TRUE)
img = remove_neck(file = img_fname,
template.file = template.file,
template.mask = template.mask)
omask = oMask(img)
mask = readnii(mask_fname)
drop_mask = omask | mask
rm(list = c("mask", "omask")); gc(); gc();
imgs = check_nifti(id_imgs)
inds = getEmptyImageDimensions(img = drop_mask)
imgs = lapply(imgs, applyEmptyImageDimensions, inds = inds)
if (!same_dims(imgs)) {
stop("Something has gone terribly wrong!")
}
mapply(writenii, nim = imgs, filename = id_imgs)
rm(list = c("imgs", "img"));
for (i in 1:10) gc();
}
}
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