Automated identification of diabetic retinal exudates in digital colour images.
OBJECTIVE
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.
D003116:Color; D003930:Diabetic Retinopathy / Q000175:diagnosis*; D005122:Exudates and Transudates; D006801:Humans; D010781:Photography / Q000379:methods / Q000592:standards; D012372:ROC Curve; D012680:Sensitivity and Specificity