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Detection of anatomic structures in human retinal imagery.

Tobin KW, Chaum E, Govindasamy VP, Karnowski TP
IEEE Trans Med Imaging. 2007 26 (12): 1729-39

PMID: 18092741 · DOI:10.1109/tmi.2007.902801

The widespread availability of electronic imaging devices throughout the medical community is leading to a growing body of research on image processing and analysis to diagnose retinal disease such as diabetic retinopathy (DR). Productive computer-based screening of large, at-risk populations at low cost requires robust, automated image analysis. In this paper we present results for the automatic detection of the optic nerve and localization of the macula using digital red-free fundus photography. Our method relies on the accurate segmentation of the vasculature of the retina followed by the determination of spatial features describing the density, average thickness, and average orientation of the vasculature in relation to the position of the optic nerve. Localization of the macula follows using knowledge of the optic nerve location to detect the horizontal raphe of the retina using a geometric model of the vasculature. We report 90.4% detection performance for the optic nerve and 92.5% localization performance for the macula for red-free fundus images representing a population of 345 images corresponding to 269 patients with 18 different pathologies associated with DR and other common retinal diseases such as age-related macular degeneration.

MeSH Terms (15)

Algorithms Artificial Intelligence Fluorescein Angiography Fundus Oculi Humans Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Macula Lutea Optic Nerve Pattern Recognition, Automated Photography Retina Retinal Diseases Retinal Vessels Sensitivity and Specificity

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