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

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Chanille 2023-4-27

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https://ww2.mathworks.cn/matlabcentral/answers/1953709-can-someone-help-me-to-measure-the-avgferet-avgminferet-and-area-of-the-edges-in-my-image-in-this-c

评论： Chanille 2023-4-28

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!

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John D'Errico 2023-4-27

Please don't post the same question repeatedly.

Chanille 2023-4-27

编辑：Chanille 2023-4-27

@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.

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Answer 1

Kevin Holly 2023-4-27

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此回答的直接链接

https://ww2.mathworks.cn/matlabcentral/answers/1953709-can-someone-help-me-to-measure-the-avgferet-avgminferet-and-area-of-the-edges-in-my-image-in-this-c#answer_1224284

在 MATLAB Online 中打开

C1_L1_WDa.jpg

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

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Kevin Holly 2023-4-27

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 2023-4-28

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.

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

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

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