基于Snake模型的图像分割新算法  

Novel image segmentation algorithm based on Snake model

作　　者：胡学刚[1,2] 邱秀兰 

机构地区：[1]重庆邮电大学通信与信息工程学院,重庆400065 [2]重庆邮电大学系统理论与应用研究中心,重庆400065

出　　处：《计算机应用》2017年第12期3523-3527,共5页journal of Computer Applications

基　　金：国家自然科学基金资助项目(61571017)~~

摘　　要：针对目前基于Snake模型的图像分割算法普遍存在噪声鲁棒性差、适用范围受限、易发生弱边缘泄露以及轮廓曲线难以收敛到细小深凹边界的缺陷,提出了一种基于Snake模型的图像分割新算法。首先,选取新的扩散项代替具有各向同性光滑作用的拉普拉斯算子;其次,引入p-拉普拉斯泛函到平滑能量项中强化法线方向外力;最后,利用边缘保护项使外力场方向与边缘方向一致,以防止弱边缘泄漏并促使轮廓线收敛到细小深凹边界。实验结果表明,所提模型不仅克服了现有基于Snake模型的图像分割算法的缺陷,具有更好的分割效果,明显提高了抗噪性能和角点定位精度,而且耗时更少,适用于噪声图像、医学图像以及含有很多弱边缘的自然图像分割。The existing image segmentation algorithms based on Snake model generally have the disadvantages of poor noise robustness, limited application range, easy leakage of weak edge and difficult to converge to small and deep concave boundary of contour curve. In order to solve the problems, a novel image segmentation algorithm based on Snake model was proposed. Firstly, the Laplaeian operator with isotropic smoothness was replaced by the new chosen diffusion term. Secondly, the p- Laplacian functional was introduced into the smooth energy term to strengthen the external force in the normal direction. Finally, the edge-preserving term was used to keep the external force field parallel to the edge direction, so as to prevent the weak edge from leaking and promote the contour curve to converge to the small and deep concave boundary. The experimental results show that, the proposed model not only overcomes the drawbacks of the existing image segmentation algorithms based on Snake model, possesses better segmentation effect, improves the anti-noise performance and comer positioning accuracy obviously, but also consumes less time. The proposed model is suitable for segmenting noise images, medical images, and natural images with many weak edges.

关 键 词：图像分割 SNAKE模型 梯度向量流 边缘保护 弱边缘 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]