Color based stool region detection in colonoscopy videos for quality measurements

Jayantha Muthukudage, Junghwan Oh, Wallapak Tavanapong, Johnny Wong, Piet C. De Groen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Colonoscopy is the accepted screening method for detecting colorectal cancer or colorectal polyps. One of the main factors affecting the diagnostic accuracy of colonoscopy is the quality of bowel preparation. Despite a large body of published data on methods that could optimize cleansing, a substantial level of inadequate cleansing occurs in 10% to 75% of patients in randomized controlled trials. In this paper, we propose a novel approach that automatically determines percentages of stool areas in images of digitized colonoscopy video files, and automatically computes an estimate of the BBPS (Boston Bowel Preparation Scale) score based on the percentages of stool areas. It involves the classification of image pixels based on their color features using a new method of planes on RGB (Red, Green and Blue) color space. Our experiments show that the proposed stool classification method is sound and very suitable for colonoscopy video analysis where variation of color features is considerably high.

Original languageEnglish (US)
Title of host publicationAdvances in Image and Video Technology - 5th Pacific Rim Symposium, PSIVT 2011, Proceedings
Pages61-72
Number of pages12
EditionPART1
DOIs
StatePublished - 2011
Event5th Pacific-Rim Symposium on Video and Image Technology, PSIVT 2011 - Gwangju, Korea, Republic of
Duration: Nov 20 2011Nov 23 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART1
Volume7087 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other5th Pacific-Rim Symposium on Video and Image Technology, PSIVT 2011
CountryKorea, Republic of
CityGwangju
Period11/20/1111/23/11

Keywords

  • Colonoscopy
  • Image Classification
  • Medical Image Analysis
  • Region of Interest Detection

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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