Principles of Otsu's Maximum Inter-class Variance Method

This text discusses an adaptive threshold determination method called Otsu's Maximum Inter-class Variance (OTSU), proposed by Japanese scholar Nobuyuki Otsu in 1979. The method divides images into background and foreground components based on grayscale characteristics. When the inter-class variance between background and foreground is large, it indicates significant differentiation between the two image components. However, misclassification of foreground pixels as background or background pixels as foreground reduces this distinction. Therefore, Otsu's method can determine optimal image thresholds for improved image analysis and information extraction. To implement Otsu's method, MATLAB code can be developed as follows. The key implementation involves using MATLAB's built-in graythresh() function which calculates the optimal threshold by maximizing inter-class variance: % Read input image img = imread('image.jpg'); % Convert to grayscale using rgb2gray() function gray_img = rgb2gray(img); % Calculate Otsu threshold - graythresh() returns normalized threshold [0,1] threshold = graythresh(gray_img); % Binarize image using imbinarize() with Otsu threshold binary_img = imbinarize(gray_img, threshold); % Display binary result imshow(binary_img); The algorithm works by iterating through all possible thresholds to find the value that maximizes the variance between two classes. The graythresh() function automatically handles histogram calculation and optimal threshold selection. Otsu's method enables effective image understanding and information extraction, and this MATLAB code example demonstrates practical implementation for image analysis tasks.