Research Papers: Imaging

Two-dimensional segmentation of the retinal vascular network from optical coherence tomography

[+] Author Affiliations
Pedro Rodrigues, Sílvia Simão, Telmo Miranda

AIBILI—Association for Innovation and Biomedical Research on Light and Image, Azinhaga de Santa Comba, Celas, 3000-548 Coimbra, Portugal

Pedro Guimarães

University of Coimbra, IBILI—Institute for Biomedical Imaging and Life Sciences, Faculty of Medicine, Azinhaga de Santa Comba, Celas, 3000-548 Coimbra, Portugal

Torcato Santos, Rui Bernardes

AIBILI—Association for Innovation and Biomedical Research on Light and Image, Azinhaga de Santa Comba, Celas, 3000-548 Coimbra, Portugal

University of Coimbra, IBILI—Institute for Biomedical Imaging and Life Sciences, Faculty of Medicine, Azinhaga de Santa Comba, Celas, 3000-548 Coimbra, Portugal

Pedro Serranho

University of Coimbra, IBILI—Institute for Biomedical Imaging and Life Sciences, Faculty of Medicine, Azinhaga de Santa Comba, Celas, 3000-548 Coimbra, Portugal

Open University, Campus TagusPark, Department of Science and Technology, Mathematics Section, Av. Dr. Jacques Delors, 2740-122 Porto Salvo, Oeiras, Portugal

J. Biomed. Opt. 18(12), 126011 (Dec 16, 2013). doi:10.1117/1.JBO.18.12.126011
History: Received August 23, 2013; Revised October 23, 2013; Accepted November 6, 2013
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Abstract.  The automatic segmentation of the retinal vascular network from ocular fundus images has been performed by several research groups. Although different approaches have been proposed for traditional imaging modalities, only a few have addressed this problem for optical coherence tomography (OCT). Furthermore, these approaches were focused on the optic nerve head region. Compared to color fundus photography and fluorescein angiography, two-dimensional ocular fundus reference images computed from three-dimensional OCT data present additional problems related to system lateral resolution, image contrast, and noise. Specifically, the combination of system lateral resolution and vessel diameter in the macular region renders the process particularly complex, which might partly explain the focus on the optic disc region. In this report, we describe a set of features computed from standard OCT data of the human macula that are used by a supervised-learning process (support vector machines) to automatically segment the vascular network. For a set of macular OCT scans of healthy subjects and diabetic patients, the proposed method achieves 98% accuracy, 99% specificity, and 83% sensitivity. This method was also tested on OCT data of the optic nerve head region achieving similar results.

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© 2013 Society of Photo-Optical Instrumentation Engineers

Citation

Pedro Rodrigues ; Pedro Guimarães ; Torcato Santos ; Sílvia Simão ; Telmo Miranda, et al.
"Two-dimensional segmentation of the retinal vascular network from optical coherence tomography", J. Biomed. Opt. 18(12), 126011 (Dec 16, 2013). ; http://dx.doi.org/10.1117/1.JBO.18.12.126011


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