前向后向扩散的距离正则模型应用于图像分割  

Forward-and-backward diffusion-based distance regularized model for image segmentation

作　　者：李梦[1,2] 刘礼培[1,2] 

机构地区：[1]重庆文理学院数学与财经学院,重庆402160 [2]重庆文理学院群与图的理论及应用重点实验室,重庆402160

出　　处：《计算机应用研究》2016年第5期1596-1600,共5页Application Research of Computers

基　　金：国家自然科学基金资助项目(61202349);重庆市基础与前沿研究计划一般项目(cstc2013jcyj A40058);重庆高校创新团队建设计划资助项目(KJTD201321);重庆文理学院科研项目(R2012SC20;Y2012SC73)

摘　　要：针对在演化过程中水平集函数振荡问题,提出前向后向扩散的距离正则模型应用于图像分割。新的距离正则项由一个势函数定义,推导的演化方程以唯一的方式前向后向扩散,即水平集函数在其陡峭区域前向扩散,降低函数的梯度模直至为1,反之它后向扩散,提高梯度模直至1。演化结果是水平集函数收敛于符号距离函数,这是水平集函数稳定演化所希望保持的状态;为了阐述距离正则项的有效性,将其与基于边缘信息的外能量项相结合。实验结果表明,该模型能够更好地完成图像分割,对噪声和弱目标图像鲁棒。This paper introduced a forward-and-backward diffusion-based distance regularized model for image segmentation to deal with the problem of irregularities that commonly appeared in level set evolution. It defined the distance regularization term（ DRT） with a potential function such as the derived level set evolution had a unique forward-and-backward diffusion effect,i.e.,the diffusion was forward for steep shape region of the level set function（ LSE）,which kept decreasing the gradient magnitude until it approached 1,otherwise,the diffusion became backward and increased the gradient magnitude back to 1. As a result,the LSE converge to sign distance function which was a desired shape of level set evolution. To demonstrate the effectiveness of the DRT,this paper applied it to an edge-based external energy for image segmentation. Experimental results show a good performance of the distance regularized model,and the proposed method is robust for noisy and / or weak object images.

关 键 词：图像分割 距离正则 水平集演化 偏微分方程 前向后向扩散 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]