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山东大学学报(工学版) ›› 2011, Vol. 41 ›› Issue (2): 40-45.

• 机器学习与数据挖掘 • 上一篇    下一篇

多相图像分割的交替凸松弛优化及其Split Bregman算法

王丽娅, 潘振宽, 魏伟波*, 刘存良, 张志梅, 王钰   

  1. 青岛大学信息工程学院, 山东 青岛 266071
  • 收稿日期:2010-12-01 出版日期:2011-04-16 发布日期:2010-12-01
  • 通讯作者: 魏伟波(1981- ),男,山东潍坊人,副教授,博士,主要研究方向为图像处理. E-mail:njustwwb@163.com
  • 作者简介:王丽娅(1986- ),女,山东菏泽人,硕士研究生,主要研究方向为图像分割.E-mail:wangliya402@163.com
  • 基金资助:

    山东省自然科学基金资助项目(Y2008G17)

Alternating convex relaxation minimization of the multiphase image
segmentation model and its Split Bregman algorithm

WANG Liya, PAN Zhenkuan, WEI Weibo*, LIU Cunliang, ZHANG Zhimei, WANG Yu   

  1. College of Information Engineering, Qingdao University, Qingdao 266071, China
  • Received:2010-12-01 Online:2011-04-16 Published:2010-12-01

摘要:

变分水平集模型已成为多相图像分割的基本框架,其求解过程通常为计算水平集函数演化方程的稳态解,计算效率不高。为提高计算效率,本研究采用n个水平集函数划分n个区域的分段常值多相图像分割变分模型,将对水平集函数的优化问题转化为对离散的二值水平集函数的优化问题;然后将其转化为凸优化问题,再通过对结果阈值化得到原问题的解,并且本研究设计了相应的Split Bregman算法;最后通过多个平面图像分割实例说明了本文模型与传统模型相比在计算效率上的提升,并且通过三维多相图像分割实例验证了本文方法的有效性。

关键词: 多相图像分割, 变分方法, 凸优化方法, Split Bregman算法

Abstract:

The variational level set model has been a fundamental framework of multiphase segmentation of images, which is usually solved by computing steady solutions of evolution equations of level set functions. The computing efficiency is low. For the model of piecewise constant image segmentation using n level set functions for n regions, the optimization problem was transformed to discrete binary value level set functions. Then they were transformed to a convex optimization problem. The solutions of the original problems were obtained from threshold solutions of the convex optimization problem. The Split Bregman algorithm was designed for the proposed problem. Some image segmentation examples were presented to prove that the method proposed in this paper improved computing efficiency compared to the traditional method, and one 3D image segmentation example was presented to prove the effectiveness of this method.

Key words:  multiphase image segmentation, variational method, convex optimization method, Split Bregman algorithm

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