R syntactic sugar

SimpleITK in R attempts to make all the common R array operators and arithmetic work on images.

First we load SimpleITK and set up a viewer for notebooks, using the jet colourmap.

In [1]:

Dwidth <- grid::unit(10, "cm")
## version of show using a jet colourmap.
setMethod('show', '_p_itk__simple__Image',
            jet.colors <- colorRampPalette(c("#00007F", "blue", "#007FFF", "cyan", "#7FFF7F", "yellow", "#FF7F00", "red", "#7F0000"))
            a <- t(as.array(object))
            rg <- range(a)
            A <- (a-rg[1])/(rg[2]-rg[1])
            dd <- dim(a)
            sp <- object$GetSpacing()
            sz <- object$GetSize()
            worlddim <- sp*sz
            worlddim <- worlddim/worlddim[1]
            W <- Dwidth
            H <- Dwidth * worlddim[2]
            jc <- jet.colors(101)
            A <- jc[A*100 + 1]               
            dim(A) <- dim(a)
            grid::grid.raster(A,default.units="mm", width=W, height=H)


## Make a smaller plot size
options(repr.plot.height = 3, repr.plot.width=3)
In [2]:
img = GaussianSource("sitkUInt8", size=c(64,64)) 

Here we note a problem with the swig bindings to the procedural interface - dispatching and defaults are not as nice as the python version. This means that we can't say

GaborSource("sitkFloat32", size=c(64,64), frequency=.03)

because there are arguments missing before frequency and swig can't generate default arguments from c++ code.

Image slicing and indexing

Lets start by accessing a single pixel - note that indexing starts from 1:

In [3]:


In [4]:
In [5]:
## Don't have the "from the end" slicing - negatives mean remove 

img[,16:(img$GetHeight() - 16)]
In [6]:
img = GaborSource("sitkFloat32", size=c(64,64), sigma=c(16, 16), mean=c(32,32), frequency=.03)
In [7]:
img[, -(1:16)]
In [8]:
img[1:32, 1:32]


In [9]:
corner=img[1:32, 1:32]

Tiling (using slicing)

We cannot perform tiling via slicing directly on an image. The reason for this restriction is that an image is not equivalent to an array. Unlike an array, an image enforces the concept of physical spacing between pixels/voxels, this spacing has to be uniform for each of the axes. Thus a non-uniform slicing, e.g. (1,2,3,3,2,1), will generate an error.

In [10]:
corner[c(1:corner$GetWidth(), corner$GetWidth():1), c(1:corner$GetHeight(), corner$GetHeight():1)]
Error in corner[c(1:corner$GetWidth(), corner$GetWidth():1), c(1:corner$GetHeight(), : X spacing is not uniform


1. corner[c(1:corner$GetWidth(), corner$GetWidth():1), c(1:corner$GetHeight(), 
 .     corner$GetHeight():1)]
2. corner[c(1:corner$GetWidth(), corner$GetWidth():1), c(1:corner$GetHeight(), 
 .     corner$GetHeight():1)]
3. stop("X spacing is not uniform\n")

We can still easily perform the tiling operation we want by slightly abusing the system, converting the image to an array, performing the tiling and then converting the array back to an image. Note that the new image will have unit spacings for each axis, the concept of physical spacing is lost once we convert to an array.

The correct, maintaining the data as an image, way of performing this operation is to use a SimpleITK filter as shown below.

In [11]:
arr = as.array(corner)
as.image(arr[c(1:corner$GetWidth(), corner$GetWidth():1), c(1:corner$GetHeight(), corner$GetHeight():1)])

Tiling (with SimpleITK filters)

In [12]:
Tile(corner, corner[corner$GetWidth():1,],corner[,corner$GetHeight():1],corner[corner$GetWidth():1, corner$GetHeight():1], c(2,2))