摘要
距离规则水平集存在对噪声、初始轮廓敏感、收敛速度慢以及容易从弱边缘处泄露等不稳定问题.结合待分割目标灰度统计信息和图像梯度信息,提出了一种自适应初始轮廓的水平集演化方法,利用图像信息构成的自带符号目标信息函数代替面积项中的边缘指示函数,解决水平集方法对初始轮廓敏感问题.另外,还设计一个自我调整的面积项系数解决水平集方法对收敛速度慢以及弱边缘处泄露问题.实验结果表明:本文方法不仅可以减少图像分割时间,提高了分割质量,同时能够解决对初始轮廓敏感问题.
The Distance Regularized Level Set Evolution(DRLSE) model is sensitive to initial contour and weak boundary images.Meanwhile,it is easy to leak from the weak edges and the curve sometimes converges slowly.To these issues,an improved level set evolution method is proposed in the paper,which combines target gray level statistical information with image gradient information.The boundary stopping function in the model of DRLSE is replaced by a target information function based on image information.And the constant coefficient associated with the weighted area was modified as an adaptive variable sign coefficient to deal with slowconvergence and weak edge leakage.Experiments showthat this method is free of initial contour.Besides,it reduces the time of image segmentation and improves the quality of segmentation.
出处
《电子学报》
EI
CAS
CSCD
北大核心
2017年第11期2728-2734,共7页
Acta Electronica Sinica
关键词
水平集
距离规则
主动轮廓模型
图像分割
level set
distance regularized
active contour model
image segmentation
