MachineVision/colorsegmentation_HomeAssignment.m at main · AHamzaCodex/MachineVision · GitHub

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% Main script to process the cancer images
clear all;
close all;

% Load images
image1 = imread('Images/cancer_image_1.png');
image2 = imread('Images/cancer_image_2.png');

% Generate binary masks using color-based segmentation
mask1 = ColorBasedSegmentation(image1);
mask2 = ColorBasedSegmentation(image2);

% Display results for first image
figure(1);
subplot(1,3,1); imshow(image1); title('Tissue Image 1');
subplot(1,3,2); imshow(mask1); title('Cancer Binary Mask 1');

% Display results for second image
figure(2);
subplot(1,3,1); imshow(image2); title('Tissue Image 2');
subplot(1,3,2); imshow(mask2); title('Cancer Binary Mask 2');


% Save masks
imwrite(mask1, 'Images/Co_cancer_mask_1.png');
imwrite(mask2, 'Images/Co_cancer_mask_2.png');

function [binaryMask] = ColorBasedSegmentation(inputImage)
    % seperate channels
    hsvImage = rgb2hsv(inputImage);
    h = hsvImage(:,:,1);
    s = hsvImage(:,:,2);
    v = hsvImage(:,:,3);

    % Otsu's method to compute a threshold on the hue and saturation channels.
    hueThresh = graythresh(h);
    satThresh = graythresh(s);

    % cancer areas tend to have lower hue values (more purple)
    cancerMask_hsv = (h < hueThresh) & (s > satThresh) & (v < 0.8);

    % Cancerous regions tend to have a high density of cell boundaries (Edge Detector).
    grayImage = rgb2gray(inputImage);
    edgeImage = edge(grayImage, 'Sobel');
    se_edge = strel('disk', 2);
    dilatedEdges = imdilate(edgeImage, se_edge);

    % combinee the color mask with the edge infor.
    enhancedMask = cancerMask_hsv | dilatedEdges;

    %refine with morphological operations.
    binaryMask = bwareaopen(enhancedMask, 200);
    binaryMask = imfill(binaryMask, 'holes');
    se = strel('disk', 3);
    binaryMask = imclose(binaryMask, se);
    binaryMask = imopen(binaryMask, strel('disk', 1));
end