The eye is an organ in human, responsible for the vision. However, it gets affected by the diseases. Glaucoma is a one such eye disease. It develops in the eye due to the increase in intra-ocular pressure. If glaucoma is not treated in its initial stage, leads for permanent vision loss. This work is aimed to develop a computer aided diagnosis system for glaucoma detection in fundus retinal images. In this paper, we presented a method to automatically outline the optic disc in a retinal image by automatic thresholding technique. The optic cup is segmented based on marker-controlled automatic watershed transformation. The optic cup to disc ratio (OCDR) is calculated, to show the presence of glaucoma. The proposed work is assessed with 15 normal retinal images and 15 retinal images with glaucoma, retrospectively collected from the Annai Eye Clinic, Chennai. To validate the system performance, obtained results were compared with the ophthalmologist results (taken as the gold standard). The experimental results show that, the proposed work is potential for the glaucoma detection.
Determination of Cup to Disc Ratio Using Unsupervised Machine Learning Techniques for Glaucoma Detection
