一种基于期望最大化条件的电容层析成像图像重建算法  

Image Reconstruction Algorithm Based on Expectation Maximization in Electrical Capacitance Tomography

作　　者：张云龙[1] 陈德运[1] 王莉莉[1] 

机构地区：[1]哈尔滨理工大学测控技术与仪器黑龙江省高校重点实验室,黑龙江哈尔滨150080

出　　处：《哈尔滨理工大学学报》2016年第2期13-19,共7页Journal of Harbin University of Science and Technology

基　　金：国家自然科学基金(60572153;60972127);高等学校博士学科点专项科研基金(200802140001);黑龙江省自然科学基金(QC2012C059);哈尔滨市科技创新人才研究专项资金(2014RFXXJ022);黑龙江省教育厅计划项目(11541040;12511097)

摘　　要：针对电容层析成像系统(electrical capacitance tomography,ECT)的"软场"效应和病态特性对重建图像精度的影响,同时在分析了成像算法和电容层析成像基本原理的基础上,提出了一种基于期望最大化电容层析成像图像重建算法(expectation maximization,EM),采用最大似然分析的方法对本文算法进行了证明和推导,算法的重建图像误差较小,并能满足收敛条件.仿真实验结果表明,该方法与CG算法、Tikhonov算法相比,在成像精度有明显提高,为ECT图像重建提供了一种新方法 .Aiming at the ＂ soft field＂ effect and ill-posed characteristics in electrical capacitance tomography system of the reconstructed image accuracy,based on the analysis of basic principles and imaging algorithm of electrical capacitance tomography,an image reconstruction algorithm based on expectations maximization is proposed in this paper. Maximum likelihood method is adopted to derive and prove for this algorithm. The result shows that the proposed algorithm meets the convergence condition and the error of image reconstruction is small. Experiment and simulation results show that this method has significantly improved precision in imaging compared with Tikhonov algorithm and CG algorithm,which presents a feasible way to the research on image reconstruction for electrical capacitance tomography system.

关 键 词：电容层析成像 图像重建 期望最大化 牛顿迭代法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]