Date of Publication :22nd October 2020

Abstract: This review paper focuses on detection of glaucoma by learning and understanding different image processing techniques used till now. Glaucoma is disease related with human eyes. It is difficult to identify glaucoma until it reaches severe vision loss, because it shows zero symptoms at the early stage. Due to this factor this disease became the second leading cause of blindness after cataract in world wide. A comprehensive dilated eye exam can reveal the risk factors of glaucoma such as high eye pressure, thickness of cornea and abnormality in optic nerve. But, the challenging factor is functional changes in fundus of the eye cannot be easily tracked and hence the only way is identifying the structural changes of eye with the help of image processing technologies. This study would be helpful and applicable to both ophthalmologists in practice and researchers in the same field to enhance the diagnosis. This paper conclude that, combining most relevant features which are notable for structural changes of eye with Retinal Nerve Fiber Layer (RNFL) thickness alone can be more effective and provide promising accuracy in glaucoma detection.

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