# Take average of the nearest n pixels

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Mike Rovan on 28 Oct 2019
Commented: Mike Rovan on 31 Oct 2019
I have a 512x512x100 matrix of an image. I want to replace every pixel in a certaint section of that matrix (i have a seperate 512x512x100 mask of that section to know the location) to become the average of the nearest n pixels which are not in that section. The mask of the section is different in each of the 100 images.
Thanks
Image Analyst on 30 Oct 2019
What is n = 2? Which 2 of the 4 closest neighbors do you pick?
So what you need to do is to make up a circular mask for each radius using strel('disk', r, 0) and note how many pixels are in that mask. Then pick the next largest n. So for a radius of 1, if you don't include the pixel itself, if you picked n=3, you can't get that (unless you somehow pick 3 out of the 4) but you can get 4. You can make up a look up table of n vs radius, or just loop until the number of pixels is equal to greater than your desired n.
Then just call conv2() on each slice with that kernel. Trivial, but let us know if you can't figure it out.

Matt J on 28 Oct 2019
If you have the Statistics Toolbox, you could use knnsearch,
Mike Rovan on 31 Oct 2019
Matt, thank you for mentioning this. Yes I realized that and tweaked the code to convert the 3D matrix to a cell array of the size of the 3rd dimension and performed what you mentioned individually through a for loop. Thanks.

Image Analyst on 29 Oct 2019
% Get the mean value not in the "section" (i.e., mask which is a logical image)
% Replace values in the section with the mean value outside the section:
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Image Analyst on 29 Oct 2019
When you said you "have a seperate 512x512x100 mask of that section" I thought you had a 3-D mask the same size as your image where the mask was true inside the "section" (what everybody else calls "mask").
Does your mask actually move with the pixel, like it's a 5-by-5-by-5 volumetric mask centered about the pixel of interest but taking a "shell" of pixels with a thickness of 2 pixels, and having some radius, and the mask marches along so that it visits every pixel in the image? If so, you can make a small kernel, like the 5x5x5 mask or whatever, and use convn() to get the local average at each pixel. Is that what you want to do?