发布时间： 2014-03-03 摘要点击次数：  4400 全文下载次数： 599 DOI: 10.11834/jig.20140305 2014 | Volume 19 | Number 3 双重轮廓演化曲线的图像分割水平集模型 王相海1,2, 李明3(1.辽宁师范大学计算机与信息技术学院, 大连 116029;2.湘潭大学智能计算与信息处理教育部重点实验室, 湘潭 411105;3.辽宁师范大学数学学院, 大连 116029) 摘 要 目的 几何活动轮廓模型的标志性模型C-V模型及其改进LBF模型受到关注，然而这两个模型对初始轮廓曲线较强的依赖性使得模型在实际图像目标分割中表现出不稳定性或具有较高的时间复杂性。本文在对C-V模型及LBF模型的原理及对初始轮廓曲线的依赖特性进行分析的基础上，提出一种基于双重轮廓演化曲线的图像分割水平集模型。方法 所提出模型的主要过程如下：1）通过设置内、外两条轮廓线，使模型在演化过程中分别从目标的内部和外部向目标边界逼近，两条轮廓线的设计原则简单，其分别位于目标的外部和与目标有重叠；2）两条轮廓线的演化走向是通过在模型中设置相关项自动控制的，即演化过程中通过最小化内、外轮廓之间的差异来自动控制两条轮廓曲线的演化趋向，使之同时从目标的内部和外部向目标边界逼近，并逐渐稳定于目标的边界。结果 所提出的模型通过设置内部能量泛函项，避免了对符号距离函数的重新初始化；通过采用全局化的正则函数，增加了模型对复杂异质区域边界的捕捉能力；通过采用内、外轮廓线同时演化机制，避免了模型对初始轮廓线的过依赖性。结论 所提出的模型很好地解决了传统基于区域的分割模型对轮廓曲线初始化的过依赖问题，对初始轮廓线的设置较为简单且具有较强的鲁棒性，对图像目标的分割较为准确和稳定。 关键词 Level set model for image segmentation based on dual contour evolutional curve Wang Xianghai1,2, Li Ming3(1.College of Computer and Information Technology, Liaoning Normal University, Dalian 116029, China;2.Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education, Xiangtan University, Xiangtan 411105, China;3.College of Mathematics, Liaoning Normal University, Dalian 116029, China) Abstract Objective As the representative of geometric active contour model,the C-V model as well as its improved LBF model has attracted much attention. However,the C-V model and LBF model have strong dependence on the initial contour curve, so that they are instable or have high computational complexity in the process of image segmentation. In this study,we first analyze the principle of the two models and their characteristics of dependence on initial contours. Based on our analysis,we address a novel level set model for image segmentation using dual contour evolutional curve.Method The process of the proposed model is as follows:1)By setting the inner and outer contours,the model can approximate the target boundary from both, intern and extern of an object. The design principle of two contours is simple,and two contours are selected to be external and overlap with the object. 2)The evolution of two contours is controlled automatically through setting related terms of the model. The evolution controls the evolutionary trend of two contours automatically by minimizing the difference between internal and external contours,and stabilizes gradually at the boundary of the target from the internal and external. Result The proposed model avoids the re-initialization of signed distance function by setting an internal energy functional in our model. In addition,the proposed model enhances the capability of capturing the boundary in complex heterogeneous areas by applying the global regular function. By adopting the evolution mechanism of the internal and external contour at the same time,the proposed model avoids the dependence on initial contour curve. Conclusion The proposed model avoids strong dependence on the initial contour of the traditional region-based segmentation model,and the initial contour is easy and robust to be selected. The segmentation results of objects are accurate and stable. Keywords