Detection and classification of bleeding using statistical color features for wireless capsule endoscopy images

Wireless capsule endoscopy (WCE) is an immense discovery for Gastrointestinal Tract (GIT) diagnosis and it can visualize complete area in GIT. However, A severe problem associated with this new technology is that there are huge amount of images to be inspected by clinician through naked eyes which c...

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Main Authors: Suman, S., Hussin, F.A.B., Walter, N., Malik, A.S., Ho, S.H., Goh, K.L.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.20133 /
Published: Institute of Electrical and Electronics Engineers Inc. 2017
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016123838&doi=10.1109%2fICONSIP.2016.7857440&partnerID=40&md5=b0b97d9464e5b183ae7c9d21f1b79972
http://eprints.utp.edu.my/20133/
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id utp-eprints.20133
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spelling utp-eprints.201332018-04-22T14:42:17Z Detection and classification of bleeding using statistical color features for wireless capsule endoscopy images Suman, S. Hussin, F.A.B. Walter, N. Malik, A.S. Ho, S.H. Goh, K.L. Wireless capsule endoscopy (WCE) is an immense discovery for Gastrointestinal Tract (GIT) diagnosis and it can visualize complete area in GIT. However, A severe problem associated with this new technology is that there are huge amount of images to be inspected by clinician through naked eyes which causes visual fatigue often and it leads to false detection. Therefore an automatic platform is much needed to find significant disease detection more accurately. This approach focuses on various color features which are also quite important and concerned criteria for clinicians. Here we propose five color features in HSV color space to differentiate between bleeding and non-bleeding frames. Support vector machine (SVM) is used as classifier to validate the performance of the proposed method and authorize the frames status. The result outcome shows that proposed method for feature and classification is quite effective and achieve high performance classifier. © 2016 IEEE. Institute of Electrical and Electronics Engineers Inc. 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016123838&doi=10.1109%2fICONSIP.2016.7857440&partnerID=40&md5=b0b97d9464e5b183ae7c9d21f1b79972 Suman, S. and Hussin, F.A.B. and Walter, N. and Malik, A.S. and Ho, S.H. and Goh, K.L. (2017) Detection and classification of bleeding using statistical color features for wireless capsule endoscopy images. 2016 International Conference on Signal and Information Processing, IConSIP 2016 . http://eprints.utp.edu.my/20133/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Wireless capsule endoscopy (WCE) is an immense discovery for Gastrointestinal Tract (GIT) diagnosis and it can visualize complete area in GIT. However, A severe problem associated with this new technology is that there are huge amount of images to be inspected by clinician through naked eyes which causes visual fatigue often and it leads to false detection. Therefore an automatic platform is much needed to find significant disease detection more accurately. This approach focuses on various color features which are also quite important and concerned criteria for clinicians. Here we propose five color features in HSV color space to differentiate between bleeding and non-bleeding frames. Support vector machine (SVM) is used as classifier to validate the performance of the proposed method and authorize the frames status. The result outcome shows that proposed method for feature and classification is quite effective and achieve high performance classifier. © 2016 IEEE.
format Article
author Suman, S.
Hussin, F.A.B.
Walter, N.
Malik, A.S.
Ho, S.H.
Goh, K.L.
spellingShingle Suman, S.
Hussin, F.A.B.
Walter, N.
Malik, A.S.
Ho, S.H.
Goh, K.L.
Detection and classification of bleeding using statistical color features for wireless capsule endoscopy images
author_sort Suman, S.
title Detection and classification of bleeding using statistical color features for wireless capsule endoscopy images
title_short Detection and classification of bleeding using statistical color features for wireless capsule endoscopy images
title_full Detection and classification of bleeding using statistical color features for wireless capsule endoscopy images
title_fullStr Detection and classification of bleeding using statistical color features for wireless capsule endoscopy images
title_full_unstemmed Detection and classification of bleeding using statistical color features for wireless capsule endoscopy images
title_sort detection and classification of bleeding using statistical color features for wireless capsule endoscopy images
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85016123838&doi=10.1109%2fICONSIP.2016.7857440&partnerID=40&md5=b0b97d9464e5b183ae7c9d21f1b79972
http://eprints.utp.edu.my/20133/
_version_ 1741196323334389760
score 11.62408

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