A Custom GVFsnake Image Segmentation Method Based on Local Contrast Enhancement

This paper introduces a GVFsnake-based image segmentation method enhanced with local contrast improvement. Building upon Xu's foundational Gradient Vector Flow (GVF) framework, this implementation achieves enhanced segmentation accuracy through strategic modifications. The core innovation lies in implementing local contrast amplification prior to snake evolution, which significantly improves boundary detection in heterogeneous regions. Key algorithmic enhancements include preprocessing steps that calculate localized contrast maps using sliding window operations, followed by adaptive weighting of gradient magnitudes. The implementation modifies the standard GVF force field calculation by incorporating these enhanced gradients, resulting in improved snake convergence toward true boundaries. This method demonstrates particular effectiveness in handling images with weak edges or uneven illumination conditions. The technical implementation involves MATLAB-based coding with optimized convolution operations for efficient gradient computation and iterative snake evolution. Validation tests across diverse image datasets confirm superior performance compared to conventional GVF approaches, making it suitable for various medical imaging and computer vision applications requiring precise boundary delineation.