Image Segmentation Algorithms with MATLAB Implementation

This documentation presents MATLAB implementations of image segmentation algorithms, including watershed algorithm and region growing methods. While the author confirms their practical effectiveness, we can further examine their advantages and limitations, along with optimization strategies for real-world applications. For instance, we can analyze computational efficiency when processing large-scale images and address challenges like image noise and complex backgrounds. From a code implementation perspective, the watershed algorithm typically utilizes gradient magnitude transformation and marker-controlled watershed transformation to prevent over-segmentation, while region growing methods employ seed point selection and similarity criteria for pixel aggregation. Additionally, we can introduce other commonly used segmentation approaches such as clustering-based methods (like k-means clustering using color/feature space partitioning) and edge detection-based techniques (employing operators like Sobel or Canny for boundary identification). This comprehensive overview helps readers better understand the development and practical applications in this field, including key MATLAB functions such as watershed(), regiongrowing(), and edge() for implementing these algorithms.