Cross Correlation Map for stack of images

Question

João Paulo Castro Zerba 2019-8-20

0
链接

此问题的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/476777-cross-correlation-map-for-stack-of-images

回答： Subhadeep Koley 2019-8-23

采纳的回答： Subhadeep Koley

在 MATLAB Online 中打开

Hello guys,

I am wondering if someone here could help me with correlation map.

Basically I have some stacks of images and I would like to cross correlate them using the Pearson

Correlation coefficient formula below.

Ai and Bi are the intensity values for a particular pixel in the pattern, Abar and Bbar are the

mean values of the intensities and sigmaA and sigmaB are the standard deviations for the two CXD

patterns, A and B. The sum is evaluated over the 3D stack of images.

Anything would be nice. I have ran out of clues to implement this properly.

Basically in my script I have the following, sending to my pearson correlation function the stack of pixels:

for i=1:size(crop_cell,2)%Running over the cell stacks
    for j=1:size(crop_cell,2)%Running over the cell stacks
        for k=1:size(crop_cell{1},1)%Running over the rows
            for L=1:size(crop_cell{1},2)%Running over the columns
                corr_matrix{i,j}(k,L) = pearson_corr_scores_pix(crop_cell{i}(k,L,:),crop_cell{j}(k,L,:));
            end
        end
    end
end

And the correlation function:

function [p] = pearson_corr_scores_pix(a,b)
mean_a = mean(a);
mean_b = mean(b);
fac_a = a - mean_a;
fac_b = b - mean_b;
std_a = std2(a);
std_b = std2(b);
score_a = fac_a/std_a;
score_b = fac_b/std_b;
p = (1/(length(a)-1))*sum(score_a.*score_b);
end

Attached is my result(first) and a reference result (second) I would like to reach.

Mine is somehow not taking into account the correlation with all the stack of pixels

in the image? I don't know.

Thanks

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Answer 1

Subhadeep Koley 2019-8-23

0
链接

此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/476777-cross-correlation-map-for-stack-of-images#answer_388750

在 MATLAB Online 中打开

myPCC.m

Find the attached Pearson Correlation Coefficient function myPCC.m implemented in terms of covariance according to the documentation.

Run the following script to get the PCC Map.

close all;clc; 
% read all images from your folder and making them of same dimension 
myFolder = 'your_path'; 
if ~isfolder(myFolder) 
  errorMessage = sprintf('Error: The following folder does not exist:\n%s', myFolder); 
  uiwait(warndlg(errorMessage)); 
  return; 
end 
filePattern = fullfile(myFolder, '*.tiff');% change the extension according to your file extension 
tiffFiles = dir(filePattern); 
for k = 1:length(tiffFiles) 
  baseFileName = tiffFiles(k).name; 
  fullFileName = fullfile(myFolder, baseFileName); 
  fprintf(1, 'Now reading %s\n', fullFileName); 
  temp = double(imread(fullFileName)); 
  [r,c,d]=size(temp); 
  if d==3 
     temp=rgb2gray(temp); 
  end 
  if r~=256 && c~=256 
     temp=imresize(temp,[256,256],'bicubic'); 
  end 
  imageArray{k} =temp; 
end 
% calculate PCC for every image combinations 
for i=1:size(imageArray,2) 
    for j=1:size(imageArray,2) 
        corr_cell(i,j)=myPCC(imageArray{i},imageArray{j}); 
    end 
end 
% plotting the Pearson Cross Correlation Coefficient Matrix 
imagesc(rot90(flipud(corr_cell))); 
colormap(parula); 
set(gca,'YDir','normal'); 
set(gca, 'XTick', 1:2:length(tiffFiles)); 
set(gca, 'YTick', 1:2:length(tiffFiles)); 
title('Pearson Cross Correlation Coefficient Matrix'); 
colorbar; axis square;