eng Oriental Scientific Publishing Company Biosciences Biotechnology Research Asia 0973-1245 2015-12-25 12 3 2821 2828 10.13005/bbra/1966 3685 article A. Rajan 1 G. P Ramesh 2 Department of Electronics and communication, St.Peter’s University, Tamilnadu, India. Department of Electronics and communication, St.peters’s University, Tamilnadu, India. 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%. https://www.biotech-asia.org/vol12no3/automated-early-detection-of-glaucoma-in-wavelet-domain-using-optical-coherence-tomography-images/ Discrete wavelet transform; optical coherence tomography; support vector machine; feature selection; glaucoma diagnosis