Automated Diagnosis of Glaucoma Using Digital Fundus Images |
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Authors: | Jagadish Nayak Rajendra Acharya U P Subbanna Bhat Nakul Shetty Teik-Cheng Lim |
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Institution: | (1) Department of E&C Engineering, Manipal Institute of Technology, Manipal, 576104, India;(2) ECE Department, NGEE ANN Polytechnic, Singapore, Singapore;(3) Department of E&C Engineering, BVB College of Engineering and Technology, Hubli, India;(4) School of Science and Technology, SIM University (UniSIM), Singapore, Singapore; |
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Abstract: | Glaucoma is a disease of the optic nerve caused by the increase in the intraocular pressure of the eye. Glaucoma mainly affects
the optic disc by increasing the cup size. It can lead to the blindness if it is not detected and treated in proper time.
The detection of glaucoma through Optical Coherence Tomography (OCT) and Heidelberg Retinal Tomography (HRT) is very expensive. This paper presents
a novel method for glaucoma detection using digital fundus images. Digital image processing techniques, such as preprocessing,
morphological operations and thresholding, are widely used for the automatic detection of optic disc, blood vessels and computation
of the features. We have extracted features such as cup to disc (c/d) ratio, ratio of the distance between optic disc center
and optic nerve head to diameter of the optic disc, and the ratio of blood vessels area in inferior-superior side to area
of blood vessel in the nasal-temporal side. These features are validated by classifying the normal and glaucoma images using neural network classifier. The results presented in this paper indicate that the features are clinically significant
in the detection of glaucoma. Our system is able to classify the glaucoma automatically with a sensitivity and specificity
of 100% and 80% respectively. |
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