MATLAB Implementation of Adaptive Image Threshold Segmentation using Maximum Class Variance Method

This text presents MATLAB code implementation for adaptive image threshold segmentation using the maximum class variance method (commonly known as Otsu's method). This technique enables improved image processing by automatically adjusting the threshold based on image characteristics to achieve optimal segmentation results. The implementation utilizes MATLAB's Image Processing Toolbox functions, where the graythresh function calculates the adaptive threshold using Otsu's algorithm by maximizing the inter-class variance between foreground and background pixels. The computed threshold can then be applied using the imbinarize function for image binarization. Through this code implementation, users can gain deeper understanding of adaptive threshold segmentation principles and modify the algorithm according to specific requirements. The Otsu method works by iterating through all possible threshold values to find the optimal one that maximizes the separation between classes based on their intensity distributions. Key implementation steps include converting images to grayscale, calculating normalized histograms, computing probability distributions, and determining the threshold that yields maximum inter-class variance.