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Glaucoma is a chronic eye disease now-a-days that leads to visual impairment and blindness worldwide. Neurodegeneration of the optic nerve in association with changes in the parapapillary region induces glaucoma. Hence, the premature detection of optic nerve damage may permit early detection of glaucoma. In this paper, an image classification system is developed for early stage glaucoma diagnosis based on Discrete Wavelet Transform (DWT) using Optical Coherence Tomography (OCT) images. After DWT decomposition, significant wavelet coefficients are selected using t-test class separability criteria and fed into the Support Vector Machine (SVM) classifier for automated diagnosis. The proposed approach effectively classifies the glaucomatous and non glaucomatous image with accuracy of 90.75%, sensitivity of 91.79%, and specificity of 89.71%.

Automated Early Detection of Glaucoma in Wavelet Domain Using Optical Coherence Tomography Images