Diagnosing the level of Glaucoma from Fundus Image Using Empirical Wavelet Transform

Abstract

. An increased pressure of fluid in optic nerve can subsequently leads to permanent blindness are known as Glaucoma. The normal pressure of eye is 15mmHg or even lower, once it is higher than 30mmHg then there is risk in vision loss. There are many existing technique that require experienced clinicians and cost effective. These systems use higher order spectra and discrete wavelet transform features for extracting the values and fed to classifier for normaliza-tion and ranking the feature. In this paper presenting a new methodology for diagnosis of glaucoma based on EWT. Empirical wavelet transform is applied on image to format the sub band which is also called as decomposed image. These features are sustained into neural network system that produces ne value from n iteration and classify images into mild, intermediate and heavily affected eye using Fundus images.