中文摘要：

针对快速水平集算法用于图像分割时,存在水平集初始化和阈值设置的困难,该文提出一种融合金字塔模型、随机游走及水平集（PYR-RW-LS）的新算法。首先将多尺度分析引入随机游走算法,把分割结果作为快速水平集算法的初始化曲线,解决其初始化问题;接着把水平集演化看成对曲线上的点不断进行模式分类的过程,引入贝叶斯分类决策和最小距离分类决策交替工作,产生曲线演化所需的驱动力,同时将两种分类决策的失效条件作为新算法迭代停止的条件,解决了快速水平集算法阈值设置的困难。仿真实验结果表明：PYR-RW-LS算法比只采用模式分类思想的快速水平集算法拥有更高的计算效率,且在抗噪性方面亦优于随机游走算法,同时保留了随机游走算法对弱边缘不敏感的优点,尤其适用于大尺寸,高清晰度的图像处理。

英文摘要：

In the application of image segmentation based on fast level set algorithm, there exist difficulties in level set initialization and setting thresholds, so a new algorithm which combining PYRamid model, Random Walk and Level Set （PYR-RW-LS） is proposed. First, the multi-scale analysis technique is introduced into Random Walk （RW） algorithm, and its partition result is taken as the initialized curve of the fast level set algorithm, so the fast level set algorithm＇s initialization problem is solved; Then the evolution of the level set can be seen as the constant pattern classification of the points on the curve. Both Bayesian classification rule and minimal distance classification rule were introduced by this new algorithm to work alternatively, in order to acquire the driving force for curve evolution. And the invalidation conditions for both of the classification rules are set as the iteration stop conditions in this new algorithm, thus solving the difficulties in setting thresholds. Simulating experimental results show that PYR-RW-LS not only runs faster than the fast level set algorithm, which only adopts pattern classification ideas, but also has better capabilities than RW algorithm in terms of anti-noise capabilities; And the advantages of being insensitive to blurry boundaries remains with the RW algorithm. PYR-RW-LS algorithm, therefore, is good in particular, for images with large size and high resolution.