MATLAB Implementation of Otsu's Algorithm for Image Binarization

This content demonstrates how to implement Otsu's algorithm in MATLAB for image binarization. The algorithm automatically calculates the optimal threshold by maximizing the inter-class variance between foreground and background pixels. The implementation typically involves computing the image histogram, calculating probability distributions, and iterating through possible threshold values to find the one that maximizes the separation criterion. The code structure includes functions for histogram calculation, probability computation, and threshold optimization. Additionally, combining this with histogram equalization preprocessing can improve results by enhancing image contrast before binarization. The equalization process redistributes pixel intensities to achieve a more uniform histogram, which helps Otsu's algorithm perform better on low-contrast images. Key MATLAB functions involved may include graythresh for automatic threshold calculation and imbinarize for applying the threshold to create binary images.