can someone help me to measure the avgferet, avgminferet and area of the edges in my image in this code

edges = edge(imgContrast,'Canny'); 
se = strel('disk', 1);
edgesClean = imclose(edges, se);
edgesClean = imfill(edgesClean, 'holes');
zoneMask = zoneMask .* im2double(edgesClean) .* DD;
% Calculate connected components
cc = bwconncomp(zoneMask);

% Calculate region properties for each connected component in this zone
statscc = regionprops(cc, 'Area', 'Centroid', 'Eccentricity', 'Perimeter', 'MajorAxisLength', 'MinorAxisLength','Circularity');

% Calculate features for this zone
numObjects = cc.NumObjects;
if numObjects > 0
    areas = [statscc.Area];
    profileCounts(k) = numObjects;
    totalArea(k) = sum(areas) * pixelSize^2;
    zoneArea(k) = sum(zoneMask, 'all') * pixelSize^2;
    % Calculate average size of mitochondria for this zone
    avgSize(k) = mean(areas) * pixelSize^2;
    circularities = [statscc.Circularity];
    circularities(circularities > 1) = 1; % Cap circularities greater than 1 at 1
    avgCircularity(k) = mean(circularities(isfinite(circularities)));
    ferets = [statscc.FeretDiameter];
    avgFeret(k) = mean(ferets) * pixelSize;
    minFerets = [statscc.MinFeretDiameter];
    avgMinFeret(k) = mean(minFerets) * pixelSize;
    
end

Does this function work? Is this how you properly calculate feret diameters and the other features? Can someone please confirm? Thank you!

[Chanille]

@Kevin Holly I was wondering if you can please help me to determine that this is accuracte measurment of the edges as determined by the code above. Thank you.

[John D'Errico]

Please don't post the same question repeatedly.

[Chanille]

@John D'Errico There was a wesite error and I accidentally posted a duplicate question, it was not intentional. Thank you for bringing this to my attention.

Answer 1

[Kevin Holly]

imageData = imread('C1_L1_WDa.jpg');
imshow(imageData)

figure
    [rows, columns, numberOfColorChannels] = size(imageData);
    if numberOfColorChannels > 1
        grayImage = rgb2gray(imageData); % Convert to color.
    end
    % Apply background subtraction.
    grayImage = imtophat(grayImage, strel('disk', 100));
    %the lower this number the less lipid droplets
    % Apply contrast correction.
    grayImage = imadjust(grayImage);
    % Apply Gaussian filtering.
    grayImage = imgaussfilt(grayImage, 3);
    % Apply thresholding.
    thresholdValue = graythresh(grayImage);
    binaryImage = imbinarize(grayImage, thresholdValue);
    % Remove black elongated shapes.
    binaryImage = bwareaopen(binaryImage, 200);
    binaryImage = imclearborder(binaryImage);
    binaryImage = imfill(binaryImage, 'holes');
    % mask
    % Threshold the image to create a binary mask
    gray_img = rgb2gray(imageData); % Convert to color.
    threshold = 5;
    mask = gray_img > threshold;
    % Remove small objects
    min_size = 1000;
    mask = bwareaopen(mask, min_size);
    % Fill holes
    mask = imfill(mask, 'holes');
    % Erode and dilate the mask to remove noise and smooth edges
    se = strel('disk', 5);
    mask = imerode(mask, se);
    mask = imdilate(mask, se);

% Define the pixel size in square microns
pixelSize = 1;
numZones = 12;
fontSize = 12;

    x = 4592.86363636364;
    y = 705.954545454545;

 % Find out what the max distance will be by computing the distance to each corner.
    distanceToUL = sqrt((1-y)^2 + (1-x)^2);
    distanceToUR = sqrt((1-y)^2 + (columns-x)^2);
    distanceToLL = sqrt((rows-y)^2 + (1-x)^2);
    distanceToLR= sqrt((rows-y)^2 + (columns-x)^2);
    maxDistance = ceil(max([distanceToUL, distanceToUR, distanceToLL, distanceToLR]));
    
    % Calculate the radius of each zone
    radius = linspace(0, maxDistance, numZones+1);
    
    % Preallocate
    zonemask = zeros(size(mask,1),size(mask,2),numZones+1);
    fig2 = figure;
    imshow(mask)
    figure(fig2)
    c(1) = drawcircle("Center",[x, y],"Radius",radius(1),"LineWidth",1,"Color","b","Visible","off");
    % Draw the circles for each zone
    for ii = 2:numZones+1
        figure(fig2)
        c(ii) = drawcircle("Center",[x, y],"Radius",radius(ii),"LineWidth",1,"Color","b","Visible","off");
        zonemask(:,:,ii) = createMask(c(ii))-createMask(c(ii-1));
        viscircles([x, y], radius(ii), 'LineStyle', '--', 'LineWidth', 1);
        total_intensity(ii) = sum(sum(gray_img.*uint8(mask).*uint8(zonemask(:,:,ii)))); % or replace binaryImage with mask
        total_zone_pixels(ii) = sum(sum(uint8(mask).*uint8(zonemask(:,:,ii))));
    end

    mean_intensity_zone = total_intensity./total_zone_pixels

mean_intensity_zone = 1×13

NaN 20.2110 18.5171 18.3373 16.1967 20.3758 21.0019 24.8756 23.1034 23.8200 22.3985 23.9102 16.3538

You need to add 'MaxFeretProperties' and 'MinFeretProperties' as inputs to regionprops

edges = edge(mask,'Canny'); 
se = strel('disk', 1);
edgesClean = imclose(edges, se);
edgesClean = imfill(edgesClean, 'holes');
zoneMask = mask;
zoneMask = zoneMask .* im2double(edgesClean);% .* DD;
% Calculate connected components
cc = bwconncomp(zoneMask);
% Calculate region properties for each connected component in this zone
statscc = regionprops(zoneMask, 'Area', 'Centroid', 'Eccentricity', 'Perimeter', 'MajorAxisLength', 'MinorAxisLength','Circularity','MaxFeretProperties','MinFeretProperties');
k=1;
statscc

statscc = struct with fields:
                   Area: 939999
               Centroid: [2.3244e+03 851.9382]
        MajorAxisLength: 5.1200e+03
        MinorAxisLength: 1.4472e+03
           Eccentricity: 0.9592
            Circularity: 1
              Perimeter: 0
       MaxFeretDiameter: 5.4429e+03
          MaxFeretAngle: -164.5824
    MaxFeretCoordinates: [2×2 double]
       MinFeretDiameter: 1462
          MinFeretAngle: -90
    MinFeretCoordinates: [2×2 double]

% Calculate features for this zone
numObjects = cc.NumObjects;
if numObjects > 0
    areas = [statscc.Area];
    % profileCounts(k) = numObjects;
    totalArea(k) = sum(areas) * pixelSize^2;
    zoneArea(k) = sum(zoneMask, 'all') * pixelSize^2;
    % Calculate average size of mitochondria for this zone
    avgSize(k) = mean(areas) * pixelSize^2;
    circularities = [statscc.Circularity];
    circularities(circularities > 1) = 1; % Cap circularities greater than 1 at 1
    avgCircularity(k) = mean(circularities(isfinite(circularities)));
    ferets = [statscc.MaxFeretDiameter];
    avgFeret(k) = mean(ferets) * pixelSize;
    minFerets = [statscc.MinFeretDiameter];
    avgMinFeret(k) = mean(minFerets) * pixelSize;
    
end

[Chanille]

@Kevin Holly Got it! Can you also confirm that these features are measuring accurately:

totalArea(k) = sum(areas) * pixelSize^2;

total area of the edges

zoneArea(k) = sum(zoneMask, 'all') * pixelSize^2;

zone area for each zone

avgSize(k) = mean(areas) * pixelSize^2;

average size of the edges detected

avgCircularity(k) = mean(circularities(isfinite(circularities)));

average circularity of the edges detected

i think the ferets should now be good.

[Kevin Holly]

It looks right to me. I am assuming pixelSize would be a unit conversion and each of these calcuations takes place within a for loop, where the zonemask changes every iteration.

[Chanille]

I changed how the zones are segmented I think that’s why the circularities are now 1 and the data is behaving stagnant. Can you suggest how to fix this? I’ve attached the full file with picture.