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Medical image processing is a tool and technique for creating a visual image of inside of the body. The rapid advancement of digital imaging and computer vision has broadened the potential for the use of imaging technology in medicine. Image processing is especially useful in diagnostic medical systems. Reliable glaucoma detection in digital fundus images remains an open problem in biomedical image processing. Detection of glaucoma in the retinal fundus image is necessary to avoid loss of vision. Glaucoma is an irretrievable chronic eye condition that causes blindness due to damage to the optic nerves. The time of diagnosis of glaucoma is very critical for slowing down the adverse effect, since glaucoma can not be cured. Several studies have shown that glaucoma is detected or screened in a 2D retinal fundus image. This paper discusses the various methods of segmentation and classification techniques used to diagnose retinal glaucoma based on the Cup to Disk Ratio (CDR) assessment of the pre-processed image. This survey paper proposes an image processing technique for the segmentation of the optic disc and cup as well as the diagnosis of glaucoma using the features obtained from the image based on the study of the adaptive thresholding technique and the classification technique compared to the remaining or present algorithms.
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