Description

ABSTRACT:

    Glaucoma is one of the most common causes of blindness. Robust mass screening may help to

extend the symptom-free life for affected patients. To realize mass screening requires a cost-

effective glaucoma detection method which integrates well with digital medical and

administrative processes. To address these requirements, we propose a novel low cost automated

glaucoma diagnosis system based on hybrid feature extraction from digital fundus images. The

paper discusses a system for the automated identification of normal and glaucoma classes using

higher order spectra (HOS), trace transform (TT), and discrete wavelet transform (DWT). features The extracted features are fed to a support vector machine (SVM) classi¯er with linear,

polynomial order 1, 2, 3 and radial basis function (RBF) in order to select the best kernel

for automated decision making.