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Automatic Microaneurysm Detection and Characterization Through Digital Color Fundus Images [electronic resource].

Published
Berkeley, Calif. : Lawrence Berkeley National Laboratory, 2008.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
Physical Description
6 : digital, PDF file
Additional Creators
Lawrence Berkeley National Laboratory and United States. Department of Energy. Office of Scientific and Technical Information
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Summary
Ocular fundus images can provide information about retinal, ophthalmic, and even systemic diseases such as diabetes. Microaneurysms (MAs) are the earliest sign of Diabetic Retinopathy, a frequently observed complication in both type 1 and type 2 diabetes. Robust detection of MAs in digital color fundus images is critical in the development of automated screening systems for this kind of disease. Automatic grading of these images is being considered by health boards so that the human grading task is reduced. In this paper we describe segmentation and the feature extraction methods for candidate MAs detection.We show that the candidate MAs detected with the methodology have been successfully classified by a MLP neural network (correct classification of 84percent).
Report Numbers
E 1.99:lbnl-2856e
lbnl-2856e
Other Subject(s)
Note
Published through SciTech Connect.
08/29/2008.
"lbnl-2856e"
International Joint Conference - Brazilian Symposium on Artificial Intelligence and Brazilian Symposium on Neural Networks - II Workshop on Computational Intelligence, Salvador, Bahia- Brazil.
Ushizima, Daniela; Medeiros, Fatima; Martins, Charles; Veras, Rodrigo; Ramalho, Geraldo.
Computational Research Division
Funding Information
DE-AC02-05CH11231
View MARC record | catkey: 14654353