DocumentCode :
2203269
Title :
An effective approach to detect lesions in color retinal images
Author :
Wang, Huan ; Hsu, Wynne ; Goh, Kheng Guan ; Lee, Mong Li
Author_Institution :
Sch. of Comput., Nat. Univ. of Singapore, Singapore
Volume :
2
fYear :
2000
fDate :
2000
Firstpage :
181
Abstract :
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
Keywords :
diseases; eye; image classification; image colour analysis; learning (artificial intelligence); medical image processing; object detection; abnormal signs; abnormalities; automatic detection; blindness; brightness adjustment procedure; color retinal images; diabetic-related eye diseases; exudates; lesion detection; lesions; local-window-based verification strategy; medical professionals; normal retinal images; regular screening; retinal images; state-of-the-art image processing techniques; statistical classification method; Art; Biomedical imaging; Costs; Diabetes; Diseases; Ear; Image analysis; Lesions; Medical diagnostic imaging; Retina;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision and Pattern Recognition, 2000. Proceedings. IEEE Conference on
Conference_Location :
Hilton Head Island, SC
ISSN :
1063-6919
Print_ISBN :
0-7695-0662-3
Type :
conf
DOI :
10.1109/CVPR.2000.854775
Filename :
854775
Link To Document :
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