Article

Automatic classification of digestive organs in wireless capsule endoscopy videos

Authors:

Subodh Kumar Shah,

Shou Jiang TangAuthors Info & Claims

SAC '07: Proceedings of the 2007 ACM symposium on Applied computing

Pages 1041 - 1045

https://doi.org/10.1145/1244002.1244230

Published: 11 March 2007 Publication History

Abstract

Wireless Capsule Endoscopy (WCE) allows a physician to examine the entire small intestine without any surgical operation. With the miniaturization of wireless and camera technologies the ability comes to view the entire gestational track with little effort. Although WCE is a technical break-through that allows us to access the entire intestine without surgery, it is reported that a medical clinician spends one or two hours to assess a WCE video, It limits the number of examinations possible, and incur considerable amount of costs. To reduce the assessment time, it is critical to develop a technique to automatically discriminate digestive organs such as esophagus, stomach, small intestinal (i.e., duodenum, jejunum, and ileum) and colon. In this paper, we propose a novel technique to segment a WCE video into these anatomic parts based on color change pattern analysis. The basic idea is that the each digestive organ has different patterns of intestinal contractions that are quantified as the features. We present the experimental results that demonstrate the effectiveness of the proposed method.

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Cited By

Fan SWei SWei KLiu SFan YLv BLi L(2025)A novel automatic locating method for pylorus and ileocecal valve in wireless capsule endoscopyBiomedical Signal Processing and Control10.1016/j.bspc.2024.106969100(106969)Online publication date: Feb-2025
https://doi.org/10.1016/j.bspc.2024.106969
Chung JOh DPark JKim SLim Y(2023)Automatic Classification of GI Organs in Wireless Capsule Endoscopy Using a No-Code Platform-Based Deep Learning ModelDiagnostics10.3390/diagnostics1308138913:8(1389)Online publication date: 11-Apr-2023
https://doi.org/10.3390/diagnostics13081389
Sedighipour Chafjiri FMohebbian MWahid KBabyn P(2023)Classification of endoscopic image and video frames using distance metric-based learning with interpolated latent featuresMultimedia Tools and Applications10.1007/s11042-023-14982-182:23(36577-36598)Online publication date: 17-Mar-2023
https://doi.org/10.1007/s11042-023-14982-1
Show More Cited By

Index Terms

Automatic classification of digestive organs in wireless capsule endoscopy videos
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Video segmentation
      2. Computer vision tasks
        Scene understanding
        Video summarization

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Information & Contributors

Information

Published In

cover image ACM Conferences

SAC '07: Proceedings of the 2007 ACM symposium on Applied computing

March 2007

1688 pages

ISBN:1595934804

DOI:10.1145/1244002

Conference Chairs:
Yookun Cho
Seoul National University, Seoul, Korea
,
Roger L. Wainwright
University of Tulsa, Tulsa, Oklahoma
,
Hisham M. Haddad
Kennesaw State University, Kennesaw, Georgia
,
Sung Y. Shin
South Dakota State University, Brookings, South Dakota
,
Program Chair:
Yong Wan Koo
The University of Suwon, Gyeongggi-do, Korea

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 March 2007

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SAC07: The 2007 ACM Symposium on Applied Computing

March 11 - 15, 2007

Seoul, Korea

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

Fan SWei SWei KLiu SFan YLv BLi L(2025)A novel automatic locating method for pylorus and ileocecal valve in wireless capsule endoscopyBiomedical Signal Processing and Control10.1016/j.bspc.2024.106969100(106969)Online publication date: Feb-2025
https://doi.org/10.1016/j.bspc.2024.106969
Chung JOh DPark JKim SLim Y(2023)Automatic Classification of GI Organs in Wireless Capsule Endoscopy Using a No-Code Platform-Based Deep Learning ModelDiagnostics10.3390/diagnostics1308138913:8(1389)Online publication date: 11-Apr-2023
https://doi.org/10.3390/diagnostics13081389
Sedighipour Chafjiri FMohebbian MWahid KBabyn P(2023)Classification of endoscopic image and video frames using distance metric-based learning with interpolated latent featuresMultimedia Tools and Applications10.1007/s11042-023-14982-182:23(36577-36598)Online publication date: 17-Mar-2023
https://doi.org/10.1007/s11042-023-14982-1
Choi JShin KJung JBae HKim DByeon JKim N(2020)Convolutional Neural Network Technology in Endoscopic Imaging: Artificial Intelligence for EndoscopyClinical Endoscopy10.5946/ce.2020.05453:2(117-126)Online publication date: 30-Mar-2020
https://doi.org/10.5946/ce.2020.054
Ashour ADey NMohamed WTromp JSherratt RShi FMoraru L(2020)Colored Video Analysis in Wireless Capsule Endoscopy: A Survey of State-of-the-ArtCurrent Medical Imaging Formerly Current Medical Imaging Reviews10.2174/157340561666620012414091516:9(1074-1084)Online publication date: 16-Dec-2020
https://doi.org/10.2174/1573405616666200124140915
Yen SHuang HChen HHuang FSuk FLien GLiu C(2020)A Novel Method for Locating a Magnetic-Assisted Capsule Endoscope SystemIEEE Transactions on Magnetics10.1109/TMAG.2020.301540956:10(1-6)Online publication date: Oct-2020
https://doi.org/10.1109/TMAG.2020.3015409
Adewole SYeghyayan MHyatt DEhsan LJablonski JCopland ASyed SBrown D(2020)Deep Learning Methods for Anatomical Landmark Detection in Video Capsule Endoscopy ImagesProceedings of the Future Technologies Conference (FTC) 2020, Volume 110.1007/978-3-030-63128-4_32(426-434)Online publication date: 31-Oct-2020
https://doi.org/10.1007/978-3-030-63128-4_32
Barbi MGarcia-Pardo CNevarez APons Beltran VCardona N(2019) UWB RSS-Based Localization for Capsule Endoscopy Using a Multilayer Phantom and In Vivo Measurements IEEE Transactions on Antennas and Propagation10.1109/TAP.2019.291662967:8(5035-5043)Online publication date: Aug-2019
https://doi.org/10.1109/TAP.2019.2916629
Ali HSharif MYasmin MRehmani MRiaz F(2019)A survey of feature extraction and fusion of deep learning for detection of abnormalities in video endoscopy of gastrointestinal-tractArtificial Intelligence Review10.1007/s10462-019-09743-2Online publication date: 3-Aug-2019
https://doi.org/10.1007/s10462-019-09743-2
Barbi MPerez-Simbor SGarcia-Pardo CAndreu CCardona N(2018)Localization for capsule endoscopy at UWB frequencies using an experimental multilayer phantom2018 IEEE Wireless Communications and Networking Conference Workshops (WCNCW)10.1109/WCNCW.2018.8369015(390-395)Online publication date: Apr-2018
https://doi.org/10.1109/WCNCW.2018.8369015
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