Abstract
Diabetic retinopathy is the leading cause of blindness in the working age population of the world. There is evidence that the digital image of the eye fundus is sensitive and specific for early signs of diabetic retinopathy. An automated image analysis method to detect early signs of diabetic retinopathy is highly desired for screening. The optic disc is one of the main structures visible in the eye fundus image that can be used as a reference to detect other abnormalities. In this paper, we propose a new cascade classifier based method for online optic disc detection. The cascade classifiers are trained using segmented images of optic discs and non-optic discs obtained from a training database (STARE). The method extracts Haar features from rectangular windows that are used to scan the digital image of the eye fundus. After training with segmented images from the STARE database, our optic disc detection method was tested with the DIARETDB1 database. Results showed high classification accuracy and fast detection capacity for online optic disc detection.
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