TY - JOUR T1 - Automated identification of diabetic retinal exudates in digital colour images JF - British Journal of Ophthalmology JO - Br J Ophthalmol SP - 1220 LP - 1223 DO - 10.1136/bjo.87.10.1220 VL - 87 IS - 10 AU - A Osareh AU - M Mirmehdi AU - B Thomas AU - R Markham Y1 - 2003/10/01 UR - http://bjo.bmj.com/content/87/10/1220.abstract N2 - Aim: To identify retinal exudates automatically from colour retinal images. Methods: The colour retinal images were segmented using fuzzy C-means clustering following some key preprocessing steps. To classify the segmented regions into exudates and non-exudates, an artificial neural network classifier was investigated. Results: The proposed system can achieve a diagnostic accuracy with 95.0% sensitivity and 88.9% specificity for the identification of images containing any evidence of retinopathy, where the trade off between sensitivity and specificity was appropriately balanced for this particular problem. Furthermore, it demonstrates 93.0% sensitivity and 94.1% specificity in terms of exudate based classification. Conclusions: This study indicates that automated evaluation of digital retinal images could be used to screen for exudative diabetic retinopathy. ER -