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%Reading Images image = imread('C:\Users\hp\Desktop\Other Works\NewDataSet\4.jpeg'); subplot(4,4,1); imshow(image); title('Original'); %Resizing Images resize = imresize(image,[500 700]); subplot(4,4,2); imshow(resize); title('Resized'); %RGB to Gray Conversion BW = rgb2gray(resize); subplot(4,4,3); imshow(BW); title('Black & White'); subplot(4,4,4); imhist(BW); title('Histogram'); %Equalizing Histogram new = histeq(BW); subplot(4,4,5); imshow(new); subplot(4,4,6); imhist(new,64); title('Equalized Histogram'); %Converting to Binary binary = im2bw(new,0.07); subplot(4,4,7); imshow(binary); title('Binary Image'); %Canny Filter Application BW2 = edge(binary,'canny',0.99); subplot(4,4,8); imshow(BW2); title('canny Filter'); %%Image Segmentation se0 = strel('square',9); BWsdil = imdilate(BW2,se0); subplot(4,4,9); imshow(BWsdil) title('Dilated Gradient Mask') BWdfill = imfill(BWsdil,'holes'); subplot(4,4,10); imshow(BWdfill) title('Binary Image with Filled Holes') seD = strel('disk',1,4); BWfinal = imerode(BWdfill,seD); BWfinal = imerode(BWfinal,seD); subplot(4,4,11); imshow(BWfinal) title('Segmented Image'); % Binarize the image. mask = imbinarize(double(BWfinal)); % Fill holes. mask = imfill(mask, 'holes'); % Take largest blob only. mask = bwareafilt(mask, 1); subplot(4, 4, 12); imshow(mask) title('Mask') % Compute the skeleton skelImage = bwmorph(mask,'skel',Inf); subplot(4, 4, 13); imshow(skelImage) title('Thinned') % Compute the Euclidean distance image. edtImage = bwdist(~mask); subplot(4, 4, 14); imshow(edtImage) title('Distance Transform Image'); diameterImage = 2 * edtImage .* single(skelImage); subplot(4, 4, 15); imshow(diameterImage) title('Diameter Image'); % Get the widths. These will be where the image is not zero. widths = diameterImage(diameterImage > 0); % Show histogram of widths. subplot(4, 4, 16); histogram(widths); grid on; xlabel('Width in Pixels'); ylabel('Count'); % Calculating mean width (1st parameter) widthmean = mean(widths) % Drawing line on mean width of red color line([widthmean, widthmean], ylim, 'LineWidth', 2, 'Color', 'r'); caption = sprintf('Histogram of Widths. Mean Width = %.1f Pixels', widthmean); title(caption); %Calculation of Ratio (2nd parameter) ratio = nnz(mask)/numel(mask); %Calculation of Gaussian (3rd Parameter) - to be done [fx,fy] = gradient(diameterImage); [fxx,fxy] = gradient(fx); [~,fyy] = gradient(fy); K = (fxx.*fyy - fxy.^2)./((1 + fx.^2 + fy.^2).^2); figure;mesh(K); title('Gaussian Curvature','FontSize',20); These two parameters are required to calculate gaussian parameters. Once mean and SD have been calculated try to execute the given code to generate a distribution curve. So you just need to calculate SD from your last code. Update me about the results. mean = 10; sd = 4; x = linspace(-4*sd,4*sd,1000); y = 1/(2*pi*sd)*exp(-(x-mu).^2/(2*sd^2)); plot(x,y);