Weighted ensemble based automatic detection of exudates in fundus photographs
Diabetic retinopathy (DR) is a visual complication of diabetes, which has become one of the leading causes of preventable blindness in the world. Exudate detection is an important problem in automatic screening systems for detection of diabetic retinopathy using color fundus photographs. In this pap...
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Published in | 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society Vol. 2014; pp. 138 - 141 |
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Main Authors | , |
Format | Conference Proceeding Journal Article |
Language | English |
Published |
United States
IEEE
01.01.2014
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Subjects | |
Online Access | Get full text |
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Summary: | Diabetic retinopathy (DR) is a visual complication of diabetes, which has become one of the leading causes of preventable blindness in the world. Exudate detection is an important problem in automatic screening systems for detection of diabetic retinopathy using color fundus photographs. In this paper, we present a method for detection of exudates in color fundus photographs, which combines several preprocessing and candidate extraction algorithms to increase the exudate detection accuracy. The first stage of the method consists of an ensemble of several exudate candidate extraction algorithms. In the learning phase, simulated annealing is used to determine weights for combining the results of the ensemble candidate extraction algorithms. The second stage of the method uses a machine learning-based classification for detection of exudate regions. The experimental validation was performed using the DRiDB color fundus image set. The validation has demonstrated that the proposed method achieved higher accuracy in comparison to state-of-the art methods. |
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ISSN: | 1094-687X 1557-170X 1558-4615 |
DOI: | 10.1109/EMBC.2014.6943548 |