engleski

Voting Based Automatic Exudate Detection in Color Fundus Photographs

Diabetic retinopathy is one of the leading causes of preventable blindness. Screening programs using color fundus photographs enable early diagnosis of diabetic retinopathy, which enables timely treatment of the disease. Exudate detection algorithms are important for development of automatic screening systems and in this paper we present a method for detection of exudate regions in color fundus photographs. The method combines different preprocessing and candidate extraction algorithms to increase the exudate detection accuracy. First, we form an ensemble of different candidate extraction algorithms, which are used to increase the accuracy. After extracting the potential exudate regions we apply machine learning based classification for detection of exudate regions. For experimental validation we use the DRiDB color fundus image set where the presented method achieves higher accuracy in comparison to other state-of-the art methods.

diabetic retinopathy; exudate detection; machine learning; ensemble; image processing and analysis

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