Automated diagnosis of glaucoma using digital fundus images

Jagadish Nayak, Rajendra Acharya U., P. Subbanna Bhat, Nakul Shetty, Teik Cheng Lim

Research output: Contribution to journalArticle

180 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)337-346
Number of pages10
JournalJournal of Medical Systems
Volume33
Issue number5
DOIs
Publication statusPublished - 01-10-2009
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Medicine (miscellaneous)
  • Information Systems
  • Health Informatics
  • Health Information Management

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    Nayak, J., Acharya U., R., Bhat, P. S., Shetty, N., & Lim, T. C. (2009). Automated diagnosis of glaucoma using digital fundus images. Journal of Medical Systems, 33(5), 337-346. https://doi.org/10.1007/s10916-008-9195-z