Scheduled System Maintenance on December 17th, 2014:
IEEE Xplore will be upgraded between 2:00 and 5:00 PM EST (18:00 - 21:00) UTC. During this time there may be intermittent impact on performance. We apologize for any inconvenience.
By Topic

An effective approach to detect lesions in color retinal images

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)
Huan Wang ; Sch. of Comput., Nat. Univ. of Singapore, Singapore ; Hsu, W. ; Kheng Guan Goh ; Mong Li Lee

Diabetic-related eye diseases are the most common cause of blindness in the world. So far the most effective treatment for these eye diseases is early detection through regular screening. To lower the cost of such screenings, we employ state-of-the-art image processing techniques to automatically detect the presence of abnormalities in the retinal images obtained during the screenings. The authors focus on one of the abnormal signs: the presence of exudates/lesions in the retinal images. We propose a novel approach that combines brightness adjustment procedure with statistical classification method and local-window-based verification strategy. Experimental results indicate that we are able to achieve 100% accuracy in terms of identifying all the retinal images with exudates while maintaining a 70% accuracy in correctly classifying the truly normal retinal images as normal. This translates to a huge amount of savings in terms of the number of retinal images that need to be manually reviewed by the medical professionals each year

Published in:

Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on  (Volume:2 )

Date of Conference: