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Color Based Stool Region Detection in Colonoscopy Videos for Quality Measurements

  • Jayantha Muthukudage
  • JungHwan Oh
  • Wallapak Tavanapong
  • Johnny Wong
  • Piet C. de Groen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7087)

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.

Keywords

Image Classification Region of Interest Detection Colonoscopy Medical Image Analysis 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jayantha Muthukudage
    • 1
  • JungHwan Oh
    • 1
  • Wallapak Tavanapong
    • 2
  • Johnny Wong
    • 2
  • Piet C. de Groen
    • 3
  1. 1.Department of Computer Science and EngineeringUniversity of North TexasDentonUSA
  2. 2.Computer Science DepartmentIowa State UniversityAmesUSA
  3. 3.Mayo Clinic College of MedicineRochesterUSA

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