Mr. Omer Sezer Profile

Omer Sezer

at Univ of Tennessee

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Proceedings Article | 10 March 2006 Paper

Characterization of the optic disc in retinal imagery using a probabilistic approach

Kenneth Tobin, Edward Chaum, V. Govindasamy, Thomas Karnowski, Omer Sezer

Proceedings Volume 6144, 61443F (2006) https://doi.org/10.1117/12.641670

KEYWORDS: Image segmentation, Optic nerve, Retina, Image filtering, Image analysis, Pathology, Image processing, Eye, Visualization, Binary data

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The application of computer based image analysis to the diagnosis of retinal disease is rapidly becoming a reality due to the broad-based acceptance of electronic imaging devices throughout the medical community and through the collection and accumulation of large patient histories in picture archiving and communications systems. Advances in the imaging of ocular anatomy and pathology can now provide data to diagnose and quantify specific diseases such as diabetic retinopathy (DR). Visual disability and blindness have a profound socioeconomic impact upon the diabetic population and DR is the leading cause of new blindness in working-age adults in the industrialized world. To reduce the impact of diabetes on vision loss, robust automation is required to achieve productive computer-based screening of large at-risk populations at lower cost. Through this research we are developing automation methods for locating and characterizing important structures in the human retina such as the vascular arcades, optic nerve, macula, and lesions. In this paper we present results for the automatic detection of the optic nerve using digital red-free fundus photography. Our method relies on the accurate segmentation of the vasculature of the retina along with spatial probability distributions describing the luminance across the retina and the density, average thickness, and average orientation of the vasculature in relation to the position of the optic nerve. With these features and other prior knowledge, we predict the location of the optic nerve in the retina using a two-class, Bayesian classifier. We report 81% detection performance on a broad range of red-free fundus images representing a population of over 345 patients with 19 different pathologies associated with DR.

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