knitr::opts_chunk$set(collapse = T, comment = "#>") library(neuroim)
The easiest way to read an image file is to use loadVolume
:
fileName <- system.file("extdata", "global_mask.nii", package="neuroim") vol <- loadVolume(fileName)
Information about the geometry of the image volume is easily accessed:
print(vol)
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 array
or numeric
vector:
# 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
writeVolume(vol2, "output.nii") ## adding a '.gz' extension results ina gzipped file. writeVolume(vol2, "output.nii.gz")
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