Description

Glaucomatous image classification can be efficiently performed using the texture features of an image. These texture features are accurately obtained using the energy distributed over the different wavelet sub bands. The various wavelet filters used in this paper are daubechies (db3), symlet3 (sym3) and biorthogonal (bio3.3, bio3.5, bio3.7) filters. In this paper, we propose a novel technique to extract the energy signatures obtained using 2-D Discrete Wavelet Transform (DWT) and these coefficients are subjected to feature selection scheme. The selected features are fed to two types of classification algorithms namely, Support Vector Machine (SVM) classifier and Naïve Bayes classifier. In order to gauge the effectiveness of these methods, we used different classifiers and tenfold cross validations thereby resulting in a high accuracy.