摘要
水平集分割方法中的Chan-Vese模型能够处理具有模糊边界和复杂拓扑结构的图像,但没有充分利用图像局部灰度的变化信息,致使其不能准确分割强度不均匀物体。针对这一问题对模型做了改进,引入局部灰度均值替换全局均值,以边界指示函数作权进行加权长度积分,加入使用双阱势的距离正则项来避免水平集重新初始化。试验结果表明:改进后的模型能够有效提高分割精度与效率,可以有效应用在医学图像的分割领域。
Although the conventional Chan-Vese model could process images with fuzzy boundary and complex topology,it could not segment objects with nonuniform grayscale accurately for the underutilization of the local diversity in an image.In this paper,a local weighted mean grayscale was brought in to replace the globle one. A boundary indicator was used to make the weighted length integral,and a distance regulation term with double-well potential was added to avoid the reinitialization,which makes some improvements to the CV model.Experiments show that the proposed model can effectively improve the segmentation precision and efficiency,and it is useful for medical image segmentation.
出处
《河南科技大学学报（自然科学版）》
CAS
北大核心
2012年第2期30-33,6,共4页
Journal of Henan University of Science And Technology:Natural Science
基金
国家自然科学基金项目(61165002)
关键词
水平集方法
CV模型
距离正则化
图像分割
Level set method
CV model
Distance regularization
Image segmentation
