Haralik features : Maximal Correlation Coefficient

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Hend Abouche 2022-4-10

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编辑： Serhat Yesilkoy 2022-12-14

Hi Guys, I am working on haralik features and so far I managed to find all the 13 features except for Maximal Correlation Coefficient, that should be calculated as:

I've already define the px and py:

p_x(i,k) = p_x(i,k) + glcm(i,j,k); 
p_y(i,k) = p_y(i,k) + glcm(j,i,k);
p(i,j)=glcm(i,j,k)

I ahve 4 offsets

thank u in advance for ur comments

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Image Analyst 2022-4-10

What are Q, p, px, and py? Which is your image? How did you get the glcm array? How does your equation compute Q(i, j)?

Hend Abouche 2022-4-10

编辑：Hend Abouche 2022-4-10

glcm is the results of graycomatrix(gry,'offset',offsets); using a gray image, p is co-occurence probability matrix,

px(i): i-th entry in the marginal-probability matrix obtained by summing the rows of p(i,j)

py(j): j-th entry in the marginal-probability matrix obtained by summing the columns of p(i,j)

I used this website equations https://earlglynn.github.io/RNotes/package/EBImage/Haralick-Textural-Features.html

PS: I normalized the data: glcm = glcm./sum(glcm(:));

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Answer 1

Image Analyst 2022-4-10

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在 MATLAB Online 中打开

You need to have 3 nested loops. The outer one would be over j (columns). The middle one would be over i (rows). The innermost one would be over k.

[rows, columns] = size(p)
for j = 1 : columns
    for i = 1 : rows
        numerator = 0;
        denominator = 0;
        for k = 1 : columns
            numerator = numerator + p(i, k) * p(j, k);
            denominator = denominator + px(i) * py(k);
        end
        Q(i, j) = sum(numerator / denominator);
    end
end

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Hend Abouche 2022-4-13

alright thank you so much

Serhat Yesilkoy 2022-12-14

编辑：Serhat Yesilkoy 2022-12-14

hi,I didn't understand the part of how the marginal probability functions px and py are defined.Unrecognized function or variable 'px'.Unrecognized function or variable 'px'. I am facing the problem.

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