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      Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation

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          Abstract

          Segmentation of anatomical and pathological structures in ophthalmic images is crucial for the diagnosis and study of ocular diseases. However, manual segmentation is often a time-consuming and subjective process. This paper presents an automatic approach for segmenting retinal layers in Spectral Domain Optical Coherence Tomography images using graph theory and dynamic programming. Results show that this method accurately segments eight retinal layer boundaries in normal adult eyes more closely to an expert grader as compared to a second expert grader.

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          Most cited references 21

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          A Threshold Selection Method from Gray-Level Histograms

           Nobuyuki Otsu (1979)
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            A note on two problems in connexion with graphs

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              Normalized cuts and image segmentation

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                Author and article information

                Affiliations
                [1 ]Department of Biomedical Engineering, Duke University, Durham, NC, 27708, USA
                [2 ]Department of Ophthalmology, Duke University Medical Center, Durham, NC, 27710, USA
                Journal
                Opt Express
                Opt Express
                OE
                Optics Express
                Optical Society of America
                1094-4087
                30 August 2010
                27 August 2010
                27 August 2011
                : 18
                : 18
                : 19413-19428
                20940837
                3408910
                131667
                10.1364/OE.18.019413
                ©2010 Optical Society of America

                This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 Unported License, which permits download and redistribution, provided that the original work is properly cited. This license restricts the article from being modified or used commercially.

                Funding
                Funded by: NIH
                Award ID: R21 EY019411
                Funded by: The American Health Assistance Foundation
                Funded by: The Hartwell Foundation
                Funded by: Genentech
                Funded by: Duke Pratt Fellowship Program
                Categories
                Research-Article

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