Abstract: Glaucoma is an eye disease that is the second most common cause of blindness in worldwide. The characteristic of glaucoma are high eye pressure, loss of vision gradually which can cause blindness and damage to the structure of retina. The damages which may occur for example are structural form changes of the Optic Nerve Head (ONH) and Retinal Nerve Fiber Layer (RNFL) thickness. Quantitative analysis of Retinal Nerve Fiber Layer (RNFL) via image processing of “Optical coherence tomography” images plays a major role in its early detection. The retinal ganglion axons are an important part of the visual system, which can be directly observed by OCT images. By using Entropy image processing method, extracted from the green and blue channel of OCT, images are correlated with corresponding RNFL thickness. Thus, this work aims to develop a system which will recognize the presence of glaucoma by the changes in the OCT image of an eye of a person and automatically quantify the RNFL defect using image processing techniques which aids in the diagnosis of glaucoma disease. Results show that , the performance of the Algorithm is appreciable compared with the clinical diagnosis.
Keywords: Glaucoma, Optic Nerve Head, Retinal Nerve Fiber Layer thickness, OCT images.
Title: DETECTION OF SEVERITYOF OPTICAL NERVE HEAD DAMAGE USING OPTICAL COHERENCE TOMOGRAPHY IMAGES
Author: Jyothirmai Joshi, Dr. R. Manjula sri, P. Sampurna Lakshmi
International Journal of Interdisciplinary Research and Innovations
ISSN 2348-1218 (print), ISSN 2348-1226 (online)
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