Automated segmentation of retinal layers from optical coherence tomography images using geodesic distance

Duan, Jinming and Tench, Christopher and Gottlob, Irene and Proudlock, Frank and Bai, Li (2017) Automated segmentation of retinal layers from optical coherence tomography images using geodesic distance. Pattern Recognition, 72 . pp. 158-175. ISSN 0031-3203

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Abstract

Optical coherence tomography (OCT) is a noninvasive imaging technique that can produce images of the eye at the microscopic level. OCT image segmentation to detect retinal layer boundaries is a fundamental procedure for diagnosing and monitoring the progression of retinal and optical nerve diseases. In this paper, we introduce a novel and accurate segmentation method based on geodesic distance for both two and three dimensional OCT images. The geodesic distance is weighted by an exponential function, which takes into account both horizontal and vertical intensity variations in the image. The weighted geodesic distance is efficiently calculated from an Eikonal equation via the fast sweeping method. Segmentation then proceeds by solving an ordinary differential equation of the geodesic distance. The performance of the proposed method is compared with manual segmentation. Extensive experiments demonstrate that the proposed method is robust to complex retinal structures with large curvature variations and irregularities and it outperforms the parametric active contour algorithm as well as graph based approaches for segmenting retinal layers in both healthy and pathological images.

Item Type: Article
RIS ID: https://nottingham-repository.worktribe.com/output/897851
Keywords: Optical coherence tomography ; Segmentation ; Geodesic distance ; Eikonal equation ; Partial differential equation ; Ordinary differential equation ; Fast sweeping
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Identification Number: https://doi.org/10.1016/j.patcog.2017.07.004
Depositing User: Eprints, Support
Date Deposited: 03 Aug 2017 10:05
Last Modified: 04 May 2020 19:20
URI: http://eprints.nottingham.ac.uk/id/eprint/44624

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