一种新的基于各向异性扩散的GVF模型  

New GVF Model Based on Non-linear Diffusion

作　　者：王宇[1] 张建伟[1] 陈允杰[1] 詹天明[1] 阮晶[1] 

机构地区：[1]南京信息工程大学数理学院,南京210044

出　　处：《计算机工程》2010年第4期215-217,共3页Computer Engineering

基　　金：国家自然科学基金青年基金资助项目(60805003);香港特区政府研究资助局基金资助项目(CUHK/4185/00E)

摘　　要：针对传统梯度矢量流(GVF)模型各向同性扩散分割图像时所导致的模糊边缘以及弱边界处的泄漏问题,提出一种新的GVF模型,该模型采用8个方向各向异性扩散策略以保持目标边界,并使用具有较快下降速度的保真项系数来增强Snake进入凹陷部分的能力。理论分析和实验结果表明,新方法能较准确地分割出目标凹陷部分,对于弱边界泄漏具有更强的鲁棒性。A new improvement based on Gradient Vector Flow（GVF） model is put forward, in order to solve the problem brought about by linear diffusion that the traditional GVF model will often blur the boundary of object map and weak boundary leaking. This improvement adopts eight directional non-linear diffusion to keep the boundary of object map, and makes use of a new coefficient of fidelity term which have a faster descent speed to strengthen Snake to segment the depression part. Theoretical analysis and experimental result show that, this new improvement has better ability to segment the depression, and has better robustness concerning weak boundary leaking.

关 键 词：图像分割 SNAKE模型 GVF模型 各向异性扩散 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]