Robust abnormal Wireless Capsule Endoscopy frames detection based on least squared density ratio algorithm

Haibin Wang, Dongmei Chen, M. Meng, Chao Hu, Zhiyong Liu
{"title":"Robust abnormal Wireless Capsule Endoscopy frames detection based on least squared density ratio algorithm","authors":"Haibin Wang, Dongmei Chen, M. Meng, Chao Hu, Zhiyong Liu","doi":"10.1109/ICINFA.2011.5949010","DOIUrl":null,"url":null,"abstract":"Wireless Capsule Endoscopy (WCE) constitutes a recent technological breakthrough that enables the observation of the gastrointestinal tract (GT) and especially the entire small bowel in a non-invasive way compared to the traditional imaging techniques. A primary difficulty with the management of WCE videos is that reviewing capsule endoscopic video is a labour intensive task and very time consuming. Also the diagnosis process by WCE videos is not real-time. In order to address those difficulties and limitations, we propose a new framework by defining Frame Abnormality Index (FAI) using the ratio of training and testing data densities, where training dataset only consist of normal samples and testing dataset consist of both normal and abnormal samples. In this paper, we use Least Square-based algorithm to estimate density ratio parameters without involving density estimation. Actual clinical patient frames including various abnormal frames are used to evaluate the performance of the proposed method. Experiments show that our proposed method is efficient and effective to detect abnormal frames in WCE videos.","PeriodicalId":299418,"journal":{"name":"2011 IEEE International Conference on Information and Automation","volume":"213 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 IEEE International Conference on Information and Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICINFA.2011.5949010","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

Wireless Capsule Endoscopy (WCE) constitutes a recent technological breakthrough that enables the observation of the gastrointestinal tract (GT) and especially the entire small bowel in a non-invasive way compared to the traditional imaging techniques. A primary difficulty with the management of WCE videos is that reviewing capsule endoscopic video is a labour intensive task and very time consuming. Also the diagnosis process by WCE videos is not real-time. In order to address those difficulties and limitations, we propose a new framework by defining Frame Abnormality Index (FAI) using the ratio of training and testing data densities, where training dataset only consist of normal samples and testing dataset consist of both normal and abnormal samples. In this paper, we use Least Square-based algorithm to estimate density ratio parameters without involving density estimation. Actual clinical patient frames including various abnormal frames are used to evaluate the performance of the proposed method. Experiments show that our proposed method is efficient and effective to detect abnormal frames in WCE videos.
基于最小二乘密度比算法的无线胶囊内窥镜鲁棒异常帧检测
无线胶囊内窥镜(WCE)是一项最新的技术突破,与传统的成像技术相比,它可以以一种无创的方式观察胃肠道(GT),特别是整个小肠。管理WCE视频的主要困难是回顾胶囊内窥镜视频是一项劳动密集型任务,非常耗时。WCE视频的诊断过程也不具有实时性。为了解决这些困难和限制,我们提出了一个新的框架,通过使用训练和测试数据密度的比率来定义帧异常指数(FAI),其中训练数据集仅由正常样本组成,测试数据集由正常样本和异常样本组成。在本文中,我们使用基于最小二乘的算法来估计密度比参数,而不涉及密度估计。实际临床病人帧包括各种异常帧被用来评估所提出的方法的性能。实验结果表明,该方法能够有效检测WCE视频中的异常帧。
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:604180095
Book学术官方微信