Glaucoma is the world?s second largest reason for blindness worldwide as a results in the neuro degeneration of the optic nerve. The recovery of the degenerated optic nerve fibers is not medically feasible and Glaucoma is often undetected till its later stages. The objective of this study is to classify the given retinal image as Glaucoma image or healthy image using texture classification. Once the image is identified as a Glaucoma image, exudates are detected using Fuzzy c- means clustering. Texture classification plays a vital role in biomedical imaging, document processing, fault identification and other fields. For the past three decades, many models have been used for clinical image classification and identification or segmentation of abnormal tissues in the images. Texture features within images are actively pursued for accurate and efficient glaucoma classification. The R, G and B components of glaucoma images are considered as input for network formation. After that, the exudates are segmented in the given image.
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