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How to calculate random shift intensity difference (RSID) feature?

Asked by Sara Salimi on 8 Dec 2018 at 17:40
Latest activity Edited by Sara Salimi on 11 Dec 2018 at 8:33
Hi,
I have 3D patients volumes saved in a matlab variable.
I need to extract random shift intensity difference (RSID) feature, this feature compares the intensity of the current voxel x and another
voxel x + u with random offset u, ` f(x, u) = I(x + u) − I(x)`, where u is a random offset vector.
My question is
  1. How many neighbors of a specific voxel needs to be considered to calculate RSID?
  2. How to find them with matlab code?
Your help is appreciated

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1 Answer

Answer by Image Analyst
on 9 Dec 2018 at 0:34

I think you'd have to scan your 3-D value one voxel at a time.
If you just want the intensity difference over all the neighbors, you can simply use convn with a kernel of all -1 excel the center being 26
kernel = -1 * ones(3,3,3);
kernel(2,2,2) = 26;
kernel = kernel / 26; % So output is in same intensity range as input.
output = convn(inputImage, kernel, 'same');
Why do you want the difference at just one randomly located pixel instead of the average over ALL neighbors?

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Hi again Sir,
I apologize because I missed out the last line of your question "Why do you want the difference at just one randomly locatlcued pixel instead of the average over ALL neighbors?"
I have calculated 138 different features, I saw in one paper that authors in this paper , mentioning about semantic feature (random shift intensity difference) and semantic context feature ( random shift probability difference (RSPD), which is the probability maps from a previous stage of voxel classification ). Which I do not understand how they are calculating for each voxel with 26 neighbors.
So let's see your for loop. Did it look something like this:
for slice = 1 : slices
for col = 1 : columns
for row = 1 : rows
ur = 2*randi([0, 1], 1, 1) - 1;
uc = 2*randi([0, 1], 1, 1) - 1;
us = 2*randi([0, 1], 1, 1) - 1;
randomPixel = double(image3d(row+ur, col+uc, slice+us));
thisPixel = double(image3d(row, col, slice))
diffmage(row, col, slice) = thisPixel - randomPixel;
end
end
end
If not, what did it look like?
Could I ask is image3d the input 3D array of image, and diffimage is the output? Once I am running the last code, I am getting an error:
Subscript indices must either be real positive integers or logicals.
Error in (line 10)
randomPixel = double(inputDataVol(row+ur, col+uc, slice+us));
The intensity range for the original image has been normalized between [0,1].
Once I am running the first code, After applying the following code,
kernel = -1 * ones(3,3,3);
kernel(2,2,2) = 26;
kernel = kernel / 26; % So output is in same intensity range as input.
output = convn(inputImage, kernel, 'same');
I get an image like for RSID that I have show for a specific slide and the ranges of values is between [-0.3517:0.6728]
and for RSPD I get the following image for a specific probability map of structure, the values ranging [-0.6063: 0.6893]

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