Bright lesion detection in retinal images

Abstract

Digital retinal images are widely used as effective means of screening medical conditions such as diabetic retinopathy. The presence of bright lesions such as hard exudates and cotton wool spots is an indicator of diabetic retinopathy and automated detection of these bright lesions in retinal images is useful to reduce the cost of screening process.This work is focused on automatic detection of two types of bright lesions, namely hard exudates and cotton wool spots in retinal images. Hard exudates appear as yellow-white small spots in retinal images. We developed a technique that utilize wavelet analysis to localize the hard exudates. Cotton wool spots are yellowish fluffy patches in retinal images. We used intensity difference map of contrast-enhanced retinal images to localize cotton wool spots. Then we validated the candidate cotton wool spots regions with two methods. The first method is eigenimages and the second method is Support Vector Machine(SVM) classification. We evaluated our algorithms with 1198 retinal images collected from local clinics. Our hard exudates detection algorithm achieved 97.9% sensitivity and 78.2% specificity. The SVM classification approach outperformed eigenimages and achieved 100% sensitivity and 82.8% specificity. With the high sensitivity and specificity, our proposed approach will be able to facilitate the automated screening in clinics.