A gradient method used to identify object boundary in EIT image

Ruofan Wang, Liwen Miao, Yixin Ma
{"title":"A gradient method used to identify object boundary in EIT image","authors":"Ruofan Wang, Liwen Miao, Yixin Ma","doi":"10.1109/IST.2013.6729726","DOIUrl":null,"url":null,"abstract":"With the development of electronic technology and image reconstruction algorithm, the quality of EIT image is improved significantly. EIT technology has the potential to be of great value in medical and industrial applications. However, there is very little research about the estimation of anomaly's boundary from EIT image. The gradient method is proposed in this paper to identify object boundary from EIT image reconstructed by sensitivity conjugate gradient (SCG) algorithm. The performance of gradient method in terms of position error and size error is compared with traditional threshold method at two threshold levels through computer simulation and phantom experiments. The results show that the result of gradient method can cover the anomaly region in investigated situations and rely on very little priori-knowledge, while threshold method can estimate more accurately if the threshold value is set properly based on priori-knowledge. Improper threshold definition may lead to significant error in boundary identification. The pilot study to estimate the location and size of the anomaly from EIT image further completes EIT research and promotes its practical application.","PeriodicalId":448698,"journal":{"name":"2013 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE International Conference on Imaging Systems and Techniques (IST)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IST.2013.6729726","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

With the development of electronic technology and image reconstruction algorithm, the quality of EIT image is improved significantly. EIT technology has the potential to be of great value in medical and industrial applications. However, there is very little research about the estimation of anomaly's boundary from EIT image. The gradient method is proposed in this paper to identify object boundary from EIT image reconstructed by sensitivity conjugate gradient (SCG) algorithm. The performance of gradient method in terms of position error and size error is compared with traditional threshold method at two threshold levels through computer simulation and phantom experiments. The results show that the result of gradient method can cover the anomaly region in investigated situations and rely on very little priori-knowledge, while threshold method can estimate more accurately if the threshold value is set properly based on priori-knowledge. Improper threshold definition may lead to significant error in boundary identification. The pilot study to estimate the location and size of the anomaly from EIT image further completes EIT research and promotes its practical application.
一种用于EIT图像中目标边界识别的梯度方法
随着电子技术和图像重建算法的发展,EIT图像的质量得到了显著提高。EIT技术在医疗和工业应用方面具有巨大的潜力。然而,关于从EIT图像中估计异常边界的研究却很少。针对灵敏度共轭梯度(SCG)算法重构的EIT图像,提出了一种梯度识别目标边界的方法。通过计算机仿真和仿真实验,在两个阈值水平上比较了梯度法在位置误差和尺寸误差方面与传统阈值法的性能。结果表明,梯度法对优先级知识的依赖很小,能较好地覆盖被调查情况下的异常区域,而阈值法在优先级知识的基础上合理设置阈值,能较准确地估计出异常区域。阈值定义不当会导致边界识别出现较大误差。从EIT图像中估计异常位置和大小的初步研究进一步完善了EIT研究,促进了EIT的实际应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信