How to arbitrarily extract a specific subset of images from a dataset? - indexing

Question

Recently I'm planning to manipulate a stack of images and the goal is to extract a specific subset of slices from there, for example only even or odd or arbitrary indexes, and then save them into another dataset.
In DM, there are a number of helpful functions in the Volume menu but unfortunately, they cannot really fullfill what I want to do.
I am just wondering whether this idea can be realized via scripting.
Many thanks for your help in advance.

Answer 1

There are two ways you can go about it, one of them only suitable for data up to 3D and generally slower than the other, but more flexible.
As you have been asking for arbitrary subsampling, I'm starting with that option, but it is more likely that the second option gives you what you want: orthogonal, regular subsampling.
If you are in a hurry, the short answer is: Use the SliceN command.
1) Using expressions (arbitrary subsampling)
Individual pixel positions in an Image data (img) can be addressed
using the notations
img[ X, 0 ] ... for 1D data at position X
img[ X, Y ] ... for 2D data at position X/Y
img[ X, Y, Z ] ... for 3D data at position X/Y/Z
Note that even if this addresses a single number, the result is an expression of size 1x1 or 1x1x1 and not a scalar number, therefore you can not do: number num = img[10,4]
However, you can use a little trick to use any of the functions that convert an expression to a single number like f.e. summation. So you can do: number num = sum(img[10,4])
So how does this relate to your question? Well, in the expressions above, we used scalar values as X, Y and Z, and the resulting expressions were expressions of size 1x1 and 1x1x1, but
You can use expressions of any size as X, Y, Z in this notations, as long as all of them are expressions of same size. The resulting addressed data is of this size with values references by the according coordinates.
This will become clearer with the examples below. Starting out with a simple 1D example:
image img1D := RealImage( "TestData", 4, 100 )
image coord := RealImage( "Coordinates", 4, 10 )
img1D = 1000 + icol // Just sum test data
coord = trunc(100*Random()) // random integer 0-99
image subImg := img1D[coord,0]
img1D.ShowImage()
coord.ShowImage()
subImg.ShowImage()
Our testdata (img1D) here is just a linear graph from 1000 to 1099 using the icol expression which, at each pixel, represents that pixels X coordinate.
The coordinate image (coord) is containing random integer values between 0 and 99.
The 'magic' happens in the subImg. We use an expression with the coord image as X coordinates. That images is of size 10(x1), so the outcoming expression is of size 10(x1) which we assign to the image subImg before showing it.
Note, that the expression we have built is really just pointing to that data of the image. Instead of showing it as a new image, we could have use that expression to change these points in the data instead, using:
img1D[coord,0] = 0
Taking it from here, it is straight forward to extend the example to 2D:
image img2D := RealImage( "TestData", 4, 30, 30 )
image coordX := RealImage( "Coordinates X", 4, 10 )
image coordY := RealImage( "Coordinates Y", 4, 10 )
img2D = 10000 + icol + irow * 100
coordX = trunc(30*Random())
coordY = trunc(30*Random())
img2D[coordX,coordY] = 0
coordX.ShowImage()
coordY.ShowImage()
img2D.ShowImage()
...and 3D:
image img3D := RealImage( "TestData", 4, 30, 30, 30 )
image coordX := RealImage( "Coordinates X", 4, 10 )
image coordY := RealImage( "Coordinates Y", 4, 10 )
image coordZ := RealImage( "Coordinates Y", 4, 10 )
img3D = 10000 + icol + irow * 100 + iplane * 1000
coordX = trunc(30*Random())
coordY = trunc(30*Random())
coordZ = trunc(30*Random())
img3D[coordX,coordY,coordZ] = 0
coordX.ShowImage()
coordY.ShowImage()
coordZ.ShowImage()
img3D.ShowImage()
Unfortunately, it ends here.
You can no longer do this type of addressing in 4D or 5D data, because expression with 4 parameters are already defined to address a rectangle region in 2D data as img[T,L,B,R]
2) Using SliceN (orthogonal subsampling)
Data subsets along the dimension directions of data can be addressed using the command SliceN and its simplified variants Slice1, Slice2 and Slice3.
The SliceN command is maybe one of my favourite commands in the language when dealing with data. It looks intimidating at first, but it is straight forward.
Lets start with its simplified version for 1D extraction, Slice1.
To extract 1D data from any data up to 3D with the Slice1 command, you need the following (-and these are exactly the 7 parameters used by the command-):
data source
start point in the source
sampling direction
sampling length
sampling step-size
The only thing you need to know on top of that is:
The start point is always defined as a X,Y,Z triplet, even if the data source is only 2D or 1D. 0 is used for the not needed
dimensions.
Directions are given as dimension index: 0 = X, 1 = Y, 2 = Z
Step-size can be negative to indicate opposite directions
The specified sampling must be contained within the source data.(You can not 'extrapolate')
So a very simple example of extracting a 1D data of a 3D dataset would be:
number sx = 20
number sy = 20
number sz = 20
image img3D := RealImage( "Spectrum Image", 4, sx, sy, sz )
img3D = 5000000 + icol + irow * 100 + iplane * 10000
number px = 5
number py = 7
image spec1D := Slice1( img3D, px,py,0, 2,sz,1 )
ShowImage( img3D )
ShowImage( spec1D )
This example showed a quite typical situation in analytical microscopy when dealing with "3D Spectrum Image" data: Extracting a "1D Spectrum" at a specific spatial position.
The example did that for the spatial point px,py. Starting at the point at that position (px,py,0), it samples along the Z direction (2) for all pixels of the data (sz) with a step-size of 1.
Note, that the command again returns an expression within the source data, and that you can use this to set values as well, just using f.e.:
Slice1( img3D, px,py,0, 2,sz,1 ) = 0
The extension for 2D and 3D data using the commands Slice2 and Slice3 is straight forward. Instead of defining one output direction, you define two or three, respectively. Each with a triplet of numbers: direction, length, step-size.
The following example extracts an "image plane" of a "3D Spectrum image":
number sx = 20
number sy = 20
number sz = 20
image img3D := RealImage( "Spectrum Image", 4, sx, sy, sz )
img3D = 5000000 + icol + irow * 100 + iplane * 10000
number pz = 3
image plane2D := Slice2( img3D, 0,0,pz, 0,sx,1, 1,sy,1 )
ShowImage( img3D )
ShowImage( plane2D )
And the following example "rotates" a 3D image:
number sx = 6
number sy = 4
number sz = 3
image img3D := RealImage( "Spectrum Image", 4, sx, sy, sz )
img3D = 1000 + icol + irow * 10 + iplane * 100
image rotated := Slice3( img3D, 0,0,0, 0,sx,1, 2,sz,1, 1,sy,1 )
ShowImage( img3D )
ShowImage( rotated )
You can get all sorts of rotations, mirroring, binning with these
commands. If you want the full flexibility to get any expression up to
5D from any source data up to 5D, then you need the most versatile
SliceN command.
It works exactly the same, but you need to specify both the dimensionality of the source data, and the dimensionality of the output expression. Then, the 'starting' point needs to be defined with as many coordinates as the source data dimension suggests, and you need one triplet of specification for each output dimension.
For a source data of N dimensions and want an output of M dimensions you need: 2 + N + 3*M parameters.
As an example, lets extract the "plane" at specific spatial position from a "4D Diffraction image" data, which stores a 2D image at each spatial location of a 2D scan:
number sx = 9
number sy = 9
number kx = 9
number ky = 9
image img4D := RealImage( "Diffraction Image", 4, sx, sy, kx, ky )
img4D = 50000 + icol + irow * 10 + idimindex(2)*100 + idimindex(3)*1000
number px = 3
number py = 4
image img2D := SliceN( img4D, 4, 2, px,py,0,0, 2,kx,1, 3,ky,1 )
ShowImage( img4D )
ShowImage( img2D )