EMPIRICAL WAVELET TRANSFORM AND GLCM FEATURES BASED GLAUCOMA CLASSIFICATION FROM FUNDUS IMAGE
Glaucoma is an ocular disorder caused due to increased fluid pressure in the optic nerve. It damages the optic nerve subsequently causes loss of vision. There is a need to diagnose glaucoma accurately with low cost. In this paper, a new methodology for an automated diagnosis of glaucoma using digital fundus images based on Empirical Wavelet Transform (EWT) is proposed. The EWT is used to decompose the image and Gray Level Concurrence Matrix (GLCM) features are obtained from decomposed EWT components. Then, these features are used for the classification of normal and glaucoma images using Neural Network (NN) classifier. The evaluation of the system is carried on using 46 generated images from that 20 images are used for training purpose and 26 images are used for testing purpose. In this the classification rate of the proposed system is satisfied. Overall accuracy of the proposed system is 96%.
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