Color based stool region detection in colonoscopy videos for quality measurements

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

doi:10.1007/978-3-642-25367-6_6

Color based stool region detection in colonoscopy videos for quality measurements

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

Medicine - Gastro, Hepatology, Nutrition Division

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

15 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 language	English (US)
Title of host publication	Advances in Image and Video Technology - 5th Pacific Rim Symposium, PSIVT 2011, Proceedings
Pages	61-72
Number of pages	12
Edition	PART1
DOIs	https://doi.org/10.1007/978-3-642-25367-6_6
State	Published - 2011
Event	5th Pacific-Rim Symposium on Video and Image Technology, PSIVT 2011 - Gwangju, Korea, Republic of Duration: Nov 20 2011 → Nov 23 2011

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART1
Volume	7087 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	5th Pacific-Rim Symposium on Video and Image Technology, PSIVT 2011
Country/Territory	Korea, Republic of
City	Gwangju
Period	11/20/11 → 11/23/11

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Colonoscopy
Image Classification
Medical Image Analysis
Region of Interest Detection

Publisher link

10.1007/978-3-642-25367-6_6

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Cite this

Muthukudage, J., Oh, J., Tavanapong, W., Wong, J., & De Groen, P. C. (2011). Color based stool region detection in colonoscopy videos for quality measurements. In Advances in Image and Video Technology - 5th Pacific Rim Symposium, PSIVT 2011, Proceedings (PART1 ed., pp. 61-72). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7087 LNCS, No. PART1). https://doi.org/10.1007/978-3-642-25367-6_6

Color based stool region detection in colonoscopy videos for quality measurements. / Muthukudage, Jayantha; Oh, Junghwan; Tavanapong, Wallapak et al.
Advances in Image and Video Technology - 5th Pacific Rim Symposium, PSIVT 2011, Proceedings. PART1. ed. 2011. p. 61-72 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7087 LNCS, No. PART1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Muthukudage, J, Oh, J, Tavanapong, W, Wong, J & De Groen, PC 2011, Color based stool region detection in colonoscopy videos for quality measurements. in Advances in Image and Video Technology - 5th Pacific Rim Symposium, PSIVT 2011, Proceedings. PART1 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART1, vol. 7087 LNCS, pp. 61-72, 5th Pacific-Rim Symposium on Video and Image Technology, PSIVT 2011, Gwangju, Korea, Republic of, 11/20/11. https://doi.org/10.1007/978-3-642-25367-6_6

Muthukudage J, Oh J, Tavanapong W, Wong J, De Groen PC. Color based stool region detection in colonoscopy videos for quality measurements. In Advances in Image and Video Technology - 5th Pacific Rim Symposium, PSIVT 2011, Proceedings. PART1 ed. 2011. p. 61-72. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART1). doi: 10.1007/978-3-642-25367-6_6

Muthukudage, Jayantha ; Oh, Junghwan ; Tavanapong, Wallapak et al. / Color based stool region detection in colonoscopy videos for quality measurements. Advances in Image and Video Technology - 5th Pacific Rim Symposium, PSIVT 2011, Proceedings. PART1. ed. 2011. pp. 61-72 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART1).

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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.",

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N2 - 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.

AB - 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.

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Color based stool region detection in colonoscopy videos for quality measurements

Abstract

Publication series

Other

UN SDGs

Keywords

Publisher link

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