An Efficient Local Chan-Vese Model for Image Segmentation (LCV Model)

In this article, I provide MATLAB source code for "An Efficient Local Chan-Vese Model for Image Segmentation" (abbreviated as LCV model). The LCV model serves as a crucial local region active contour model with extensive applications across multiple domains. For instance, in MRI brain image segmentation, the LCV model employs localized intensity fitting functions to accurately extract regions of interest in cerebral imaging. For vascular image segmentation, the implementation utilizes adaptive contour initialization and gradient-based stopping functions to precisely identify vascular structures. Furthermore, in image bias field correction applications, the model incorporates bias estimation components within its energy functional to correct illumination artifacts. The MATLAB implementation includes core functions for: - Level set initialization and re-initialization procedures - Localized mean separation calculations using kernel functions - Euler-Lagrange equation discretization for curve evolution - Regularization terms for smoothing and boundary constraints By utilizing this MATLAB source code, researchers can gain deeper understanding of the model's numerical implementation and apply it effectively to achieve improved results in related research fields. The code structure follows modular design principles, separating energy computation, partial differential equation solving, and visualization components for enhanced readability and customization.