An Unsupervised Method for Detection and Validation of The Optic Disc and The Fovea

In this work, we have presented a novel method for detection of retinal image features, the optic disc and the fovea, from colour fundus photographs of dilated eyes for Computer-aided Diagnosis(CAD) system. A saliency map based method was used to detect the optic disc followed by an unsupervised probabilistic Latent Semantic Analysis for detection validation. The validation concept is based on distinct vessels structures in the optic disc. By using the clinical information of standard location of the fovea with respect to the optic disc, the macula region is estimated. Accuracy of 100\% detection is achieved for the optic disc and the macula on MESSIDOR and DIARETDB1 and 98.8\% detection accuracy on STARE dataset.

A Gaussian Scale Space Approach For Exudates Detection, Classification And Severity Prediction

In the context of Computer Aided Diagnosis system for diabetic retinopathy, we present a novel method for detection of exudates and their classification for disease severity prediction. The method is based on Gaussian scale space based interest map and mathematical morphology. It makes use of support vector machine for classification and location information of the optic disc and the macula region for severity prediction. It can efficiently handle luminance variation and it is suitable for varied sized exudates. The method has been probed in publicly available DIARETDB1V2 and e-ophthaEX databases. For exudate detection the proposed method achieved a sensitivity of 96.54% and prediction of 98.35% in DIARETDB1V2 database.