NDLI: Stool detection in colonoscopy videos

Content Provider	IEEE Xplore Digital Library
Author	Sae Hwang, JungHwan Oh, Tavanapong, Wallapak Wong, Johnny de Groen, Piet C.
Copyright Year	2008
Description	Author affiliation: Mayo Clinic College of Medicine, Rochester, MN 55905, USA (de Groen, Piet C.) \|\| Computer Science Department, Iowa State University, Ames, 50011, USA (Tavanapong, Wallapak; Wong, Johnny) \|\| Department of Computer Science and Engineering, University of North Texas, Denton, 76203, USA (Sae Hwang,; JungHwan Oh,)
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.
Starting Page	3004
Ending Page	3007
File Size	150689
Page Count	4
File Format	PDF
ISBN	9781424418145
ISSN	1557170X
DOI	10.1109/IEMBS.2008.4649835
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-08-20
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support Vector Machines Image Classification Colonoscopy
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Automatic polyp detection: A comparative study

Reduction of False Positives in Polyp Detection Using Weighted Support Vector Machines

Automatic Classification of Images with Appendiceal Orifice in Colonoscopy Videos

Features for cells and nuclei classification

Neurotransmitter vesicle movement dynamics in living neurons

Speech Sound Classification and Detection of Articulation Disorders with Support Vector Machines and Wavelets

Arteriovenous fistula stenosis detection using wavelets and support vector machines

Edge cross-section features for detection of appendiceal orifice appearance in colonoscopy videos

Support vector machine classification of complex fMRI data

Stool detection in colonoscopy videos

Similar Documents

Automatic polyp detection: A comparative study

Reduction of False Positives in Polyp Detection Using Weighted Support Vector Machines

Automatic Classification of Images with Appendiceal Orifice in Colonoscopy Videos

Features for cells and nuclei classification

Neurotransmitter vesicle movement dynamics in living neurons

Speech Sound Classification and Detection of Articulation Disorders with Support Vector Machines and Wavelets

Arteriovenous fistula stenosis detection using wavelets and support vector machines

Edge cross-section features for detection of appendiceal orifice appearance in colonoscopy videos

Support vector machine classification of complex fMRI data

Stool detection in colonoscopy videos