Abstract
Diabetic retinopathy is caused by complications of diabetes, which can eventually lead to blindness. As new blood vessels form at the back of the eye as a part of diabetic retinopathy (DR), they can bleed and blur vision. Detection of these new vessels and their structure in retinal images is very important for diagnosis of diabetic retinopathy. In this paper two different techniques have been compared. First technique uses Gaussian filtering for preprocessing, LoG filtering for enhancement and adaptive thresholding for segmentation purpose. Second technique uses unsharp masking for preprocessing, Gabor wavelet for enhancement and global thresholding for segmentation. The performance of these systems is evaluated on publicly available DRIVE and STARE databases of manually labeled images. Experimental results show that Gabor wavelet method gives best results for vessel enhancement and global threshold gives good results for vessel segmentation in retinal images. Index Terms—Blood vessel, DIABETIC retinopathy (DR), retinal images, unsharp masking, gabor wavelet transform, adaptive thresholding, log filtering, enhancement, segmentation.
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