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Robust segmentation of intraretinal layers in the normal human fovea using a novel statistical model based on texture and shape analysis

Kajić, Vedran, Považay, Boris, Hermann, Boris, Hofer, Bernd, Marshall, Andrew, Rosin, Paul L. and Drexler, Wolfgang 2010. Robust segmentation of intraretinal layers in the normal human fovea using a novel statistical model based on texture and shape analysis. Optics Express 18 (14) , pp. 14730-14744. 10.1364/OE.18.014730

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Abstract

A novel statistical model based on texture and shape for fully automatic intraretinal layer segmentation of normal retinal tomograms obtained by a commercial 800nm optical coherence tomography (OCT) system is developed. While existing algorithms often fail dramatically due to strong speckle noise, non-optimal imaging conditions, shadows and other artefacts, the novel algorithm’s accuracy only slowly deteriorates when progressively increasing segmentation task difficulty. Evaluation against a large set of manual segmentations shows unprecedented robustness, even in the presence of additional strong speckle noise, with dynamic range tested down to 12dB, enabling segmentation of almost all intraretinal layers in cases previously inaccessible to the existing algorithms. For the first time, an error measure is computed from a large, representative manually segmented data set (466 B-scans from 17 eyes, segmented twice by different operators) and compared to the automatic segmentation with a difference of only 2.6% against the inter-observer variability.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Optometry and Vision Sciences
Computer Science & Informatics
Subjects: R Medicine > RE Ophthalmology
Additional Information: Pdf uploaded in accordance with publisher's policy at http://www.sherpa.ac.uk/romeo/issn/1094-4087/ and http://www.opticsinfobase.org/submit/review/copyright_permissions.cfm#posting (accessed 28/02/2014).
Publisher: Optical Society
ISSN: 1094-4087
Date of First Compliant Deposit: 30 March 2016
Last Modified: 15 Dec 2017 09:02
URI: http://orca-mwe.cf.ac.uk/id/eprint/14112

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