MATLAB: Calculate mean value on a skeleton of an image

Image Processing Toolboximage skeletonmean value

Hi everyone! I'm working on a skeleton of an image and I found some errors on it. I marked them with ones, so I can identify them easily. I had introduced data in the positions of the original skeleton, that's why most of the values are different to 1 and 0. I'd like to change the value of the ones by the mean value of the 3×3 neighbours of that position, that are different to 0 or 1. For example:

A = [0 0 0 265 0 ; 0 1 260 0 0  ; 0 250 0 0 0  ; 251 250 0 0 0 ; 251 0 0 0 0];

I'd like to change the 1 by 255, the mean (average) value of 260 and 250 (neighbours of 1 and with a different value of 1 and 0).

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What is A? The modified skeleton or the original image? A skeleton is a binary image of type logical. Ignoring your A, what I'd do is this:

% Blur image
blurredImage = conv2(double(grayImage), ones(3)/9, 'same');
% Replace image "B" with values of the blurred image at the location of the skeleton 1 values:
B(skelImage) = blurredImage(skelImage);

That's the best I can do because I can't follow what you're saying. You have a gray scale image, a logical skeleton image, and you even have a third image that I guess is the skeleton image converted from logical to gray scale and some "data" from the gray scale image has replaced some pixels (or something like that). So, with these 3 images, you only give the one, very-badly-named "A" image, and don't say exactly what it is. Please give all 3 matrices plus a fourth one that is your desired output image.

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MATLAB: Is masking,ROI and contour the same

Masking and ROI are pretty similar. ROI is the pixels in the image that you're interested in analyzing. Those outside the ROI you want to ignore. One way to do this is by masking. In masking you create a mask, a binary image. You can then use that to set all pixels outside the ROI to black for example. Then you can process the whole image and perhaps your algorithm will ignore the black pixels, like if you just summed all the pixel gray levels for example. So somehow you can create a mask and ROI, for example by intensity thresholding to find all bright pixels,

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maskedImage(mask) = 0;

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Contours are isolines of constant gray level, just like contours on topographic maps are lines of constant altitude. It doesn't necessarily have anything to do with ROIs or masking, though if you were to threshold at, say, 200 gray levels, the white blobs you get from that would have perimeters that are the same as the contours you'd get at 200 gray levels. But normally, just ignore contours and the contour() function as far as setting ROIs/masks goes.

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if not does registered image has target image pixel values or moving image pixel values?

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