Clustering mechanism for electric tomography imaging

查看全文 作　　者：YUE [1]ShiHong;[2]WUTeresa;CUI [1]LiJun;WANG [1]HuaXiang

机构地区：[1]School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China;[2]School of Computing, Informatics, Decision Systems Engineering, Arizona State University, Tempe 85287-0112, USA

出　　处：《Science China(Information Sciences)》索引2012年第55卷第12期,共16页

基　　金：supported by National Science Foundation of China(Grant Nos.61174014,60572065,60772080);National Science Foundation of Tianjin(Grant No.08JCYBJC13800) 摘　　要：Electrical tomography(ET) imaging,developed in the 1980s,has attracted much industrial and research attentions owing to its low cost,quick response,lack of radiation exposure,and non-intrusiveness compared to other tomography modalities.However,to date applications thereof have been limited owing to its low imaging resolution.The issue with space resolution in existing ET imaging reconstruction methods is that they employ a mathematical approach based on an ill-posed equation with inconsistent solutions.In this paper,we propose a novel ET imaging method based on a data-driven approach.By recovering the cluster structures hidden in the ET imaging process followed by the application of a fuzzy clustering algorithm to identify the cluster structures,there is no need to study the ill-posed mathematical formulation.The proposed method has been tested by means of three experiments,including image reconstructions of a human lung image and plastic rode shape,as well as two simulations executed on the Comsol platform.The results show that the proposed method can reconstruct ET images with much higher space resolution more quickly than the existing algorithms. 关键词：模糊聚类算法断层扫描扫描成像空间分辨率图像重建机制电子应用程序

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Clustering mechanism for electric tomography imaging

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