Grayscale Image Threshold Segmentation Using 2D Otsu Method

This document presents a MATLAB program that implements grayscale image threshold segmentation using the two-dimensional Otsu method. The program employs the Otsu algorithm, which is a widely-used image segmentation technique that converts grayscale images into binary images through optimal threshold selection. The implementation specifically utilizes the 2D Otsu method that considers both pixel intensity values and their local neighborhood information, providing more robust segmentation compared to the traditional 1D approach. The core algorithm calculates the optimal threshold by maximizing the between-class variance, effectively separating the image into foreground and background regions. Key programming aspects include: - Image preprocessing and grayscale conversion - Calculation of 2D grayscale histograms incorporating pixel intensity and local mean - Implementation of the between-class variance maximization algorithm - Automatic threshold determination and binary image generation The program allows users to perform threshold segmentation by automatically selecting the optimal threshold value that divides the image into two distinct regions. This implementation serves as a valuable tool for image processing tasks, enabling better understanding and analysis of image characteristics through effective segmentation. We hope this program proves beneficial for your image processing applications!