Ch. P. Poojitha, B. Nalini and Dr.N. Balaji
Jawaharlal Nehru Technological University, India

Glaucoma is an eye disease which damages the optic nerve and or loss of the field of vision which leads to complete blindness caused by the pressure buildup by the fluid of the eye i.e. the intraocular pressure (IOP). This optic disorder with a gradual loss of the field of vision leads to progressive and irreversible blindness, so it should be diagnosed and treated properly at an early stage. In this paper, thedaubechies(db3) or symlets (sym3)and reverse biorthogonal (rbio3.7) wavelet filters are employed for obtaining average and energy texture feature which are used to classify glaucoma disease with high accuracy. The Feed-Forward neural network classifies the glaucoma disease with an accuracy of 96.67%. In this work, the computational complexity is minimized by reducing the number of filters while retaining the same accuracy.

Glaucoma, IOP, Wavelet transform, Texture features, Feed Forward neural network, Fundus images