Automatic polyp detection in endoscopy videos: A survey

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

35 Scopus citations

Abstract

Early detection of polyps play an essential role for the prevention of colorectal cancer. Manual clinical inspection have many limitations and could result to either false or missed polyps. Computer aided diagnosis system has been used to help the medical expert and to provide more accurate diagnosis. Since their introduction, many types of algorithms have been proposed in the literature using different types of features and classifiers. This paper provides a state-of-the-art for the automatic detection of polyps using endoscopic videos. Given the increasing evolution of medical imaging technologies and algorithms, it is important to have a recent review in order to know the current state of the art, and the opportunities for improving existing algorithms, or developing innovative ones. The paper divides the work done on this research area according to the type of features and classification methods implemented. The features have been divided into shape, texture or fusion features. Future directions and challenges for more accurate polyp detection in endoscopy videos are also discussed.

Original languageBritish English
Title of host publicationProceedings of the 13th IASTED International Conference on Biomedical Engineering, BioMed 2017
EditorsRita Kiss, Philipp J. Thurner
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages233-240
Number of pages8
ISBN (Electronic)9780889869905
DOIs
StatePublished - 5 Apr 2017
Event13th IASTED International Conference on Biomedical Engineering, BioMed 2017 - Innsbruck, Austria
Duration: 20 Feb 201721 Feb 2017

Publication series

NameProceedings of the 13th IASTED International Conference on Biomedical Engineering, BioMed 2017

Conference

Conference13th IASTED International Conference on Biomedical Engineering, BioMed 2017
Country/TerritoryAustria
CityInnsbruck
Period20/02/1721/02/17

Keywords

  • Deep learning
  • Endoscopy videos
  • Feature fusion
  • Polyp detection
  • Shape features
  • Texture features

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