A training based Support Vector Machine technique for blood detection in wireless capsule endoscopy images

Jie Li*, Jinwen Ma, Tammam Tillo, Bailing Zhang, Eng Gee Lim

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

5 Citations (Scopus)

Abstract

Wireless capsule endoscopy (WCE) is a non-invasive technique which could detect variety of abnormalities in the small bowel. However, in many cases it is difficult for doctors to distinguish obscure gastrointestinal bleeding of patients; moreover, the diagnosis process of the obtained video could take long time due to the huge number of generated frames. This project provides a method to automatically detect bleeding areas in the WCE images by using Support Vector Machine (SVM) classifier as the main engine with learning based mechanism in order to increase the accuracy. The experiment results show that this could not only advance the accuracy of WCE diagnosis, but also reduce the diagnosis time effectively. To further improve the performance of the proposed approach the length of the learning-based database was also tuned1.

Original languageEnglish
Title of host publication2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2012
Pages826-830
Number of pages5
DOIs
Publication statusPublished - 2012
Event2012 2nd IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2012 - Langkawi, Malaysia
Duration: 17 Dec 201219 Dec 2012

Publication series

Name2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2012

Conference

Conference2012 2nd IEEE-EMBS Conference on Biomedical Engineering and Sciences, IECBES 2012
Country/TerritoryMalaysia
CityLangkawi
Period17/12/1219/12/12

Keywords

  • Support Vector Machine
  • Wireless capsule endoscopy
  • bleeding detection
  • learning based mechanism

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