Classic CV Model in Level Set Methods with MATLAB Implementation

The CV (Chan-Vese) model represents one of the most fundamental and widely-used approaches in level set methods. This technique evolves curves to minimize energy functionals, making it particularly effective for image segmentation, shape reconstruction, and computer vision applications. The implementation typically involves solving partial differential equations through finite difference methods, where the central algorithm iteratively updates the level set function based on regional intensity statistics. Key implementation components include: - Initialization of the level set function (often using signed distance functions) - Calculation of regional mean intensities inside and outside the evolving curve - Regularization terms for curve length and area constraints - Time-dependent evolution equations discretized using finite difference schemes For researchers and practitioners requiring MATLAB source code, we provide complete implementation files containing the core CV model algorithm. The code includes comprehensive comments demonstrating how to handle image preprocessing, parameter tuning, and convergence criteria for optimal segmentation results. This implementation serves as both an educational tool for understanding level set fundamentals and a practical solution for real-world image analysis tasks.