MATLAB Image Segmentation Using Threshold Method: Complete Source Code and Lab Report

This undergraduate image processing assignment demonstrates MATLAB implementation of threshold-based image segmentation accompanied by a comprehensive lab report. The project serves as an excellent reference for beginners to understand and master thresholding techniques in image segmentation. The report details the fundamental principles of threshold methods, including global thresholding using Otsu's algorithm and adaptive thresholding approaches. Implementation covers key MATLAB functions such as graythresh() for automatic threshold calculation and imbinarize() for image binarization. The experimental section presents step-by-step procedures: image preprocessing, threshold selection methods (manual and automated), segmentation execution, and result evaluation using quantitative metrics like Dice coefficient. Code examples illustrate histogram analysis for threshold determination and morphological operations for post-processing. Results showcase original images alongside segmented outputs, comparing different thresholding strategies' effectiveness on various image types. This work provides practical foundation for beginners exploring image processing applications in medical imaging, object detection, and computer vision tasks.