Stool detection in colonoscopy videos

Sae Hwang, JungHwan Oh, Wallapak Tavanapong, Johnny Wong, Piet C. De Groen

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

10 Citations (Scopus)

Abstract

Colonoscopy is the accepted screening method for detection of colorectal cancer or its precursor lesions, colorectal polyps. Indeed, colonoscopy has contributed to a decline in the number of colorectal cancer related deaths. However, not all cancers or large polyps are detected at the time of colonoscopy, and methods to investigate why this occurs are needed. One of the main factors affecting the diagnostic accuracy of colonoscopy is the quality of bowel preparation. The quality of bowel cleansing is generally assessed by the quantity of solid or liquid stool in the lumen. 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, a machine learning approach to the detection of stool in images of digitized colonoscopy video files is presented. The method involves the classification based on color features using a support vector machine (SVM) classifier. Our experiments show that the proposed stool image classification method is very accurate.

Original languageEnglish (US)
Title of host publicationProceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"
Pages3004-3007
Number of pages4
StatePublished - 2008
Event30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - Vancouver, BC, Canada
Duration: Aug 20 2008Aug 25 2008

Other

Other30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08
CountryCanada
CityVancouver, BC
Period8/20/088/25/08

Fingerprint

Image classification
Colonoscopy
Support vector machines
Learning systems
Screening
Classifiers
Color
Liquids
Polyps
Experiments
Colorectal Neoplasms
Randomized Controlled Trials
Neoplasms

Keywords

  • Colonoscopy
  • Image classification
  • Support vector machines

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

Hwang, S., Oh, J., Tavanapong, W., Wong, J., & De Groen, P. C. (2008). Stool detection in colonoscopy videos. In Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology" (pp. 3004-3007). [4649835]

Stool detection in colonoscopy videos. / Hwang, Sae; Oh, JungHwan; Tavanapong, Wallapak; Wong, Johnny; De Groen, Piet C.

Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology". 2008. p. 3004-3007 4649835.

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

Hwang, S, Oh, J, Tavanapong, W, Wong, J & De Groen, PC 2008, Stool detection in colonoscopy videos. in Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"., 4649835, pp. 3004-3007, 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08, Vancouver, BC, Canada, 8/20/08.
Hwang S, Oh J, Tavanapong W, Wong J, De Groen PC. Stool detection in colonoscopy videos. In Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology". 2008. p. 3004-3007. 4649835
Hwang, Sae ; Oh, JungHwan ; Tavanapong, Wallapak ; Wong, Johnny ; De Groen, Piet C. / Stool detection in colonoscopy videos. Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology". 2008. pp. 3004-3007
@inproceedings{4c86258523de49ee860d2ef10129046c,
title = "Stool detection in colonoscopy videos",
abstract = "Colonoscopy is the accepted screening method for detection of colorectal cancer or its precursor lesions, colorectal polyps. Indeed, colonoscopy has contributed to a decline in the number of colorectal cancer related deaths. However, not all cancers or large polyps are detected at the time of colonoscopy, and methods to investigate why this occurs are needed. One of the main factors affecting the diagnostic accuracy of colonoscopy is the quality of bowel preparation. The quality of bowel cleansing is generally assessed by the quantity of solid or liquid stool in the lumen. 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, a machine learning approach to the detection of stool in images of digitized colonoscopy video files is presented. The method involves the classification based on color features using a support vector machine (SVM) classifier. Our experiments show that the proposed stool image classification method is very accurate.",
keywords = "Colonoscopy, Image classification, Support vector machines",
author = "Sae Hwang and JungHwan Oh and Wallapak Tavanapong and Johnny Wong and {De Groen}, {Piet C.}",
year = "2008",
language = "English (US)",
isbn = "9781424418152",
pages = "3004--3007",
booktitle = "Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - {"}Personalized Healthcare through Technology{"}",

}

TY - GEN

T1 - Stool detection in colonoscopy videos

AU - Hwang, Sae

AU - Oh, JungHwan

AU - Tavanapong, Wallapak

AU - Wong, Johnny

AU - De Groen, Piet C.

PY - 2008

Y1 - 2008

N2 - Colonoscopy is the accepted screening method for detection of colorectal cancer or its precursor lesions, colorectal polyps. Indeed, colonoscopy has contributed to a decline in the number of colorectal cancer related deaths. However, not all cancers or large polyps are detected at the time of colonoscopy, and methods to investigate why this occurs are needed. One of the main factors affecting the diagnostic accuracy of colonoscopy is the quality of bowel preparation. The quality of bowel cleansing is generally assessed by the quantity of solid or liquid stool in the lumen. 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, a machine learning approach to the detection of stool in images of digitized colonoscopy video files is presented. The method involves the classification based on color features using a support vector machine (SVM) classifier. Our experiments show that the proposed stool image classification method is very accurate.

AB - Colonoscopy is the accepted screening method for detection of colorectal cancer or its precursor lesions, colorectal polyps. Indeed, colonoscopy has contributed to a decline in the number of colorectal cancer related deaths. However, not all cancers or large polyps are detected at the time of colonoscopy, and methods to investigate why this occurs are needed. One of the main factors affecting the diagnostic accuracy of colonoscopy is the quality of bowel preparation. The quality of bowel cleansing is generally assessed by the quantity of solid or liquid stool in the lumen. 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, a machine learning approach to the detection of stool in images of digitized colonoscopy video files is presented. The method involves the classification based on color features using a support vector machine (SVM) classifier. Our experiments show that the proposed stool image classification method is very accurate.

KW - Colonoscopy

KW - Image classification

KW - Support vector machines

UR - http://www.scopus.com/inward/record.url?scp=61849162884&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=61849162884&partnerID=8YFLogxK

M3 - Conference contribution

C2 - 19163338

AN - SCOPUS:61849162884

SN - 9781424418152

SP - 3004

EP - 3007

BT - Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"

ER -