Adaptive threshold based automated identification of severity of diabetic macular edema from retinal images

Abstract

In this paper an algorithm is proposed for detection of severity of Diabetic Macular Edema (DME) from Retinal Images, which are invariant to image rotation. The proposed work includes detection of optic disc, macula, exudates, region of interest localization and a level indicator which indicates the severity of disease as severe, moderate or normal DME. Rotation Invariant detection of macula makes this method efficient. A set of NPDR data of fundus image of an eye is used to test the proposed algorithm. The results show a better comprehensive performance of disease severity checker and computationally efficient.