活动轮廓模型用于图像分割一般分为两种基于参数的模型和基于几何特征的模型.Snake模型可以快速地分割目标,但不能处理拓扑结构复杂的情况且对初值位置过于敏感.水平集模型具有拓扑可变性,但其时间效率较低,在分析这两种模型优缺点的基础上,提出了一种新的活动轮廓模型,该模型兼具有上述两种方法的优点快速性、拓扑可变性.在模型中用Snake模型的能量方程控制曲线的演化并提出一种基于水平集思想的符号表法来改变演化过程中曲线的拓扑结构.为了降低噪音的影响,用区域信息构造新的外力,在外力的作用下可以使初始曲线有更大的选择空间.对左心室MR图像的分割实验结果表明,该模型得到的分割结果与Level Set模型相似,但所用时间远比Level Set模型少.

In recent years,the fields of active-contour based image segmentation have seen the emergence of two competing approaches.The first is based on parameter models and the other is based on geography models.Snake model can segment objects quickly,but can not deal with topological changes and sensitive to initialization.Level Set model can deal with topological changes but it's time efficiency is low.After comparing these two approaches,this paper presents a new active contour model:S-L model,which combines the virtues of Snake model and Level Set model.The new model uses the energy equation of Snake model to evolve the curve and uses a symbol table,which is based on the soul of Level Set model,to change the topology of the curve.To reduce the effect of the noise,the new model constructs a new outer force on the basis of the region information.With the new outer force,the initial curve can be made in a large space. With the region information,the new model can find the edges powerfully,even if in case of complex topology,avoid local minima from Snake model.The experiments to segment cardiac magnetic resonance images show that comparing with Level Set model the new model can get the similar results which has mach higher speed.