By Topic

Classification of endoscopic images based on texture and neural network

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Wang, P. ; Biomed. Eng. Res. Center, Nanyang Technol. Univ., Singapore ; Krishnan, S.M. ; Kugean, C. ; Tjoa, M.P.

Computerized processing of medical images can ease the search of the representative features in the images. The endoscopic images possess rich information expressed by texture. Regions affected by diseases, such as ulcer or coli, may have different texture features. The texture model implemented in this study is Local Binary Pattern (LBP) and a log-likelihood-ratio, called the G-statistic, is used to evaluate the similarity of regions based on LBP. The neural network is used in the classification. SOM and BP are applied and compared. The texture model and classification algorithm are implemented and tested with clinically obtained colonoscopic data. For a large number of colonoscopic images, proper classification results corresponding with unique medical features can be acquired, which suggests that the unsupervised endoscopic image classification is applicable.

Published in:

Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE  (Volume:4 )

Date of Conference:

2001