Ulcer detection and classification of wireless capsule endoscopy images using RGB masking

Wireless Capsule Endoscopy (WCE) is becoming an extensively accepted method used by physicians to diagnose diseases which affect small intestine. During the WCE examination of patients, a series of images are acquired to generate a video footage. Inspecting a WCE video is laborious and time-consumin...

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Main Authors: Suman, S., Hussi, F.A., Nicolas, W., Malik, A.S.
Format: Article
Published: American Scientific Publishers 2016
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009090585&doi=10.1166%2fasl.2016.7099&partnerID=40&md5=73772df1ac1aa7cb46072c7759821b2c
http://eprints.utp.edu.my/25679/
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spelling my.utp.eprints.256792021-08-27T09:39:59Z Ulcer detection and classification of wireless capsule endoscopy images using RGB masking Suman, S. Hussi, F.A. Nicolas, W. Malik, A.S. Wireless Capsule Endoscopy (WCE) is becoming an extensively accepted method used by physicians to diagnose diseases which affect small intestine. During the WCE examination of patients, a series of images are acquired to generate a video footage. Inspecting a WCE video is laborious and time-consuming that results in a limited application of WCE. Therefore, it would be extremely advantageous to develop an intelligent algorithm to inspect the WCE images. This research aims to develop an algorithm to enhance wireless capsule endoscopy images and analyses them to detect Ulcer located in small intestine. This algorithm of capsule images can be clinically very useful as it can discriminate between normal and abnormal frame. It also provides correlative information for huge set of data. We have proposed color detection method in RGB color space as it is a key technique of color image interpretation and recognition, as well as suitable for Ulcer detection in WCE images. Support Vector Machine (SVM) used to classify frames according to findings. Analysis is done on basis of pixel and frame information to check whether it is detected correctly or not. © 2016 American Scientific Publishers. All rights reserved. American Scientific Publishers 2016 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009090585&doi=10.1166%2fasl.2016.7099&partnerID=40&md5=73772df1ac1aa7cb46072c7759821b2c Suman, S. and Hussi, F.A. and Nicolas, W. and Malik, A.S. (2016) Ulcer detection and classification of wireless capsule endoscopy images using RGB masking. Advanced Science Letters, 22 (10). pp. 2764-2768. http://eprints.utp.edu.my/25679/
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Wireless Capsule Endoscopy (WCE) is becoming an extensively accepted method used by physicians to diagnose diseases which affect small intestine. During the WCE examination of patients, a series of images are acquired to generate a video footage. Inspecting a WCE video is laborious and time-consuming that results in a limited application of WCE. Therefore, it would be extremely advantageous to develop an intelligent algorithm to inspect the WCE images. This research aims to develop an algorithm to enhance wireless capsule endoscopy images and analyses them to detect Ulcer located in small intestine. This algorithm of capsule images can be clinically very useful as it can discriminate between normal and abnormal frame. It also provides correlative information for huge set of data. We have proposed color detection method in RGB color space as it is a key technique of color image interpretation and recognition, as well as suitable for Ulcer detection in WCE images. Support Vector Machine (SVM) used to classify frames according to findings. Analysis is done on basis of pixel and frame information to check whether it is detected correctly or not. © 2016 American Scientific Publishers. All rights reserved.
format Article
author Suman, S.
Hussi, F.A.
Nicolas, W.
Malik, A.S.
spellingShingle Suman, S.
Hussi, F.A.
Nicolas, W.
Malik, A.S.
Ulcer detection and classification of wireless capsule endoscopy images using RGB masking
author_facet Suman, S.
Hussi, F.A.
Nicolas, W.
Malik, A.S.
author_sort Suman, S.
title Ulcer detection and classification of wireless capsule endoscopy images using RGB masking
title_short Ulcer detection and classification of wireless capsule endoscopy images using RGB masking
title_full Ulcer detection and classification of wireless capsule endoscopy images using RGB masking
title_fullStr Ulcer detection and classification of wireless capsule endoscopy images using RGB masking
title_full_unstemmed Ulcer detection and classification of wireless capsule endoscopy images using RGB masking
title_sort ulcer detection and classification of wireless capsule endoscopy images using rgb masking
publisher American Scientific Publishers
publishDate 2016
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85009090585&doi=10.1166%2fasl.2016.7099&partnerID=40&md5=73772df1ac1aa7cb46072c7759821b2c
http://eprints.utp.edu.my/25679/
_version_ 1738656764516106240
score 13.211869