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

TL;DR

This paper introduces a novel Gaussian scale space and morphological approach for detecting and classifying exudates in retinal images, aiding diabetic retinopathy diagnosis and severity prediction.

Contribution

It presents a new method combining Gaussian scale space, morphology, and SVM for exudate detection, classification, and severity prediction in retinal images.

Findings

Achieved 96.54% sensitivity in exudate detection

Achieved 98.35% accuracy in severity prediction

Validated on publicly available databases

Abstract

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.