Automated glaucoma detection system based on wavelet energy features and ANN

Glaucoma is an eye disease which damages the optic nerve of the eye and becomes severe over time. It is caused due to buildup of pressure inside the eye. Glaucoma tends to be inherited and may not show up until later in life. The detection of glaucomatous progression is one of the most important and most challenging aspects of primary open angle glaucoma (OAG) management. The early detection of glaucoma is important in order to enable appropriate monitoring, treatment and to minimize the risk of irreversible visual field loss. Although advances in ocular imaging offer the potential for earlier diagnosis, the best method is to involve a combination of information from structural and functional tests. In this proposed method both structural and energy features are considered then analyzed to classify as glaucomatous image. Energy distribution over wavelet sub bands were applied to find these important texture energy features. Finally extracted energy features are applied to Multilayer Perceptron (MLP) and Back Propagation (BP) neural network for effective classification by considering normal subject´s extracted energy features. Naive Bayes classifies the images in the database with the accuracy of 89.6%. MLP-BP Artificial Neural Network (ANN) algorithm classifies the images in the database with the accuracy of 97.6%.