MATLAB Implementation of Image Segmentation Using Level Set Evolution and GAC Model

This image segmentation program implements the Geometric Active Contour (GAC) model using level set evolution principles from partial differential equations. As a mathematically-grounded image processing approach, the program segments images into distinct regions through level set evolution. The implementation utilizes MATLAB's PDE solving capabilities to handle curve evolution equations. Level set evolution represents a mathematical framework for describing curve progression using partial differential equations. The algorithm works by evolving an initial contour based on image features such as intensity gradients, color variations, or texture patterns. In the code implementation, this involves solving the level set equation that governs how the contour moves according to image-derived speed functions. The GAC model represents a widely-used level set evolution algorithm that refines image segmentation by evolving boundaries according to geometric properties. The MATLAB implementation typically includes functions for initial contour setup, speed function calculation, and iterative evolution using finite difference methods. Key algorithmic components involve computing curvature-dependent flows and handling topological changes automatically during evolution. Through this program, users can explore fundamental image segmentation principles while gaining practical experience with PDE-based computer vision techniques. The code structure demonstrates efficient numerical implementation of level set methods, making it suitable for applications in medical imaging, object recognition, and other computer vision domains where precise boundary detection is crucial. The implementation includes optimization techniques for stable evolution and convergence monitoring to ensure accurate segmentation results.