MATLAB Program and Experimental Report for Image Segmentation Using Threshold Method

During our undergraduate studies in image processing, we completed an assignment comprising a MATLAB program and experimental report for image segmentation using threshold methods. This project serves as a valuable reference for beginners to understand fundamental image processing concepts and techniques. We first introduced the theoretical principles of threshold segmentation, explaining how to implement the algorithm in MATLAB through functions like graythresh() for automatic threshold calculation or manual threshold selection using logical indexing operations. The implementation includes key steps such as image preprocessing, histogram analysis, and binary mask generation. Subsequently, we conducted systematic experiments testing various threshold values, comparing global thresholding approaches with adaptive methods. The experimental analysis involved quantitative evaluation metrics and visual result comparisons, discussing the impact of different thresholds on segmentation accuracy. The code demonstrates practical applications of MATLAB's image processing toolbox functions including im2bw() (for older versions) or imbinarize() (for newer versions). Through this project, we deepened our understanding of threshold-based segmentation algorithms while enhancing programming skills and experimental methodology. This work provides beginners with hands-on MATLAB coding examples, algorithm implementation details, and analytical frameworks to spark interest in digital image processing.