MATLAB Code Implementation for Image Segmentation with Algorithm Explanations

This repository provides MATLAB implementations for image segmentation along with other related image processing programs. These codes are designed to be educational and practical resources for researchers and developers working in computer vision. The image segmentation MATLAB programs utilize advanced algorithms to partition images into distinct regions for enhanced analysis and processing. Key implementations include: - Region-based segmentation using watershed transformation with marker-controlled approaches - Thresholding methods with automatic level detection using Otsu's algorithm - Edge detection techniques incorporating Canny and Sobel operators with customizable parameters - Clustering-based segmentation (K-means, Fuzzy C-means) with color space transformations All programs feature modular design with configurable parameters allowing users to adjust segmentation criteria based on specific requirements. The code includes preprocessing steps for noise reduction and contrast enhancement to improve segmentation accuracy. In addition to core segmentation algorithms, this collection includes supplementary programs covering various image processing domains: - Morphological operations for post-processing segmented regions - Feature extraction modules for calculating region properties - Image registration and fusion techniques - Performance evaluation metrics (Dice coefficient, Jaccard index) These implementations serve as valuable learning resources for understanding image processing fundamentals while providing practical tools for research applications. The code follows MATLAB best practices with comprehensive comments and example usage scenarios. We welcome feedback and suggestions for improvement to enhance the functionality and educational value of these programs. Your contributions help maintain and expand this resource for the image processing community. Thank you for your support and understanding.