基于C-V模型的水平集方法在脑CT图像分割中的应用

阐述了C-V模型和多相水平集算法的原理。在分析了其优缺点后,引入了多阈值单水平集算法。并针对其不足,将李纯明惩罚函数项引入到多阈值单水平集算法,提出了无需重初始化的多阈值单水平集算法。实际结果表明,算法在保证分割精度的基础上,能够有效提高算法效率。

C-V model-based level set method in brain CT image segmentation

This paper focuses on the principle of the C-V model and multiphase level set algorithm. In light of the advantages and drawbacks of these two algorithms, we introduce multi-threshold single level set algorithm. And for its shortcoming, we introduce penalty-function to multi-threshold single level set algorithm, and proposed multi-threshold single level set algorithm without re-initialization. The experimental results show that the algorithm can effectively improve the efficiency of the algorithm based on ensuring the accuracy of segmentation.