knitr::opts_chunk$set(collapse = T, comment = "#>") library(neuroim)
The easiest way to read an image file is to use
fileName <- system.file("extdata", "global_mask.nii", package="neuroim") vol <- loadVolume(fileName)
Information about the geometry of the image volume is easily accessed:
loadVolume returns an object of class
DenseBrainVolume which extends an R `array' and has 3 dimensions (x,y,z).
class(vol) is.array(vol) dim(vol) vol[1,1,1] vol[64,64,24]
Arithmetic can be performed on images as if they were ordinary arrays:
vol2 <- vol + vol sum(vol2) == 2 * sum(vol) vol3 <- vol2 - 2*vol all(vol3 == 0)
A numeric image volume can be converted to a binary image as follows:
vol2 <- as.logical(vol) print(vol2[1,1,1])
We can also create a
BrainVolume instance from an
# create an 64X64X64 array of zeros x <- array(0, c(64,64,64)) # create a 'BrainSpace' instance that describes the geometry of the image including, at minimu its dimensions and voxel spacing bspace <- BrainSpace(Dim=c(64,64,64), spacing=c(1,1,1)) vol <- BrainVolume(x, bspace) vol
We do not usually have to create
BrainSpace objects because this information is usually read from disk. Thus,
BrainSpace objects are usually copied from existing images using the
space extractor function when needed:
vol2 <- BrainVolume((vol+1)*25, space(vol)) max(vol2) space(vol2)
When we're ready to write an image volume to disk, we use
writeVolume(vol2, "output.nii") ## adding a '.gz' extension results ina gzipped file. writeVolume(vol2, "output.nii.gz")
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