How can I access a matrix within a matrix?

I have a grayscale image that has been split up into 4 separate equally sized parts (for image analysis) from which I am trying to remove pixels that have an intensity less than the mean. To make the code short I have decided to make a for loop within a for loop that first accesses the image that I want (top right, top left ect) then an if loop to carry out the test regarding the mean. Currently I am having issues with getting each list as a whole, instead I get the first 4 values from bac_tl. Is there any way I can resolved this?

bac_list = [bac_tl, bac_tr, bac_bl, bac_br]; %this is a list of the values above

tl_mean = mean(bac_tl(:)); %the means of each separate image are found
tr_mean = mean(bac_tr(:));
bl_mean = mean(bac_bl(:));
br_mean = mean(bac_br(:));

mean_list = [tl_mean, tr_mean, bl_mean, br_mean]; %this is a list of the means

for i = 1:4
    list = bac_list(i)
    for z = 1:numel(list(i))
        if list(z) < mean_list(i)
           list(z) = 0; 
        end
    end
end

Answer 1

[Image Analyst]

Process each quadrant one at a time. Then do

meanGL = mean2(thisQuadrant);
thisQuadrant(thisQuadrant < meanGL) = 0; % Blacken pixels less than the mean gray level.

Repeat 3 more times for each quadrant. For example to get upper right quadrant:

thisQuadrant = grayImage(1:rows/2, columns/2+1:end); % Extract upper right.

Then do the code I gave. No need for "for" loops at all.

[Nathan Costin]

Ok thank you!

[Guillaume]

As per Image Analyst answer, you do not need to loop over the pixels. You can do the comparison for all of them at once.

Here is how I would code the whole thing:

%input: greyimage, the image to filter
rowdist = [floor(size(greyimage, 1)/2), ceil(size(greyimage, 1)/2)]; %calculate height of each subimage. If height is not divisible by 2, bottom image is 1 pixel taller
coldist = [floor(size(greyimage, 2)/2), ceil(size(greyimage, 2)/2)]; %%calculate width of each subimage. If width is not divisible by 2, right image is 1 pixel wider
splitimages = mat2cell(greyimage, rowdist, coldist); %split into 4 images
masks = cellfun(@(subimg) subimg < mean2(subimg), splitimages, 'UniformOutput', false);  %create mask for each subimage. logical 1 (true) is where the pixel is smaller than the mean
greyimage(cell2mat(masks)) = 0; %rejoin masks into one matrix and set true pixels to 0.