Automated segmentation of retinal layers from optical coherence tomography images using geodesic distance
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....
| Main Authors: | , , , , |
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| Format: | Article |
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Elsevier
2017
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| Online Access: | https://eprints.nottingham.ac.uk/44624/ |
| _version_ | 1848796960019972096 |
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| author | Duan, Jinming Tench, Christopher Gottlob, Irene Proudlock, Frank Bai, Li |
| author_facet | Duan, Jinming Tench, Christopher Gottlob, Irene Proudlock, Frank Bai, Li |
| author_sort | Duan, Jinming |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:56:16Z |
| format | Article |
| id | nottingham-44624 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:56:16Z |
| publishDate | 2017 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-446242020-05-04T19:20:22Z https://eprints.nottingham.ac.uk/44624/ Automated segmentation of retinal layers from optical coherence tomography images using geodesic distance Duan, Jinming Tench, Christopher Gottlob, Irene Proudlock, Frank Bai, Li 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. Elsevier 2017-12-01 Article PeerReviewed Duan, Jinming, Tench, Christopher, Gottlob, Irene, 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 Optical coherence tomography ; Segmentation ; Geodesic distance ; Eikonal equation ; Partial differential equation ; Ordinary differential equation ; Fast sweeping http://www.sciencedirect.com/science/article/pii/S0031320317302650 doi:10.1016/j.patcog.2017.07.004 doi:10.1016/j.patcog.2017.07.004 |
| spellingShingle | Optical coherence tomography ; Segmentation ; Geodesic distance ; Eikonal equation ; Partial differential equation ; Ordinary differential equation ; Fast sweeping Duan, Jinming Tench, Christopher Gottlob, Irene Proudlock, Frank Bai, Li Automated segmentation of retinal layers from optical coherence tomography images using geodesic distance |
| title | Automated segmentation of retinal layers from optical coherence tomography images using geodesic distance |
| title_full | Automated segmentation of retinal layers from optical coherence tomography images using geodesic distance |
| title_fullStr | Automated segmentation of retinal layers from optical coherence tomography images using geodesic distance |
| title_full_unstemmed | Automated segmentation of retinal layers from optical coherence tomography images using geodesic distance |
| title_short | Automated segmentation of retinal layers from optical coherence tomography images using geodesic distance |
| title_sort | automated segmentation of retinal layers from optical coherence tomography images using geodesic distance |
| topic | Optical coherence tomography ; Segmentation ; Geodesic distance ; Eikonal equation ; Partial differential equation ; Ordinary differential equation ; Fast sweeping |
| url | https://eprints.nottingham.ac.uk/44624/ https://eprints.nottingham.ac.uk/44624/ https://eprints.nottingham.ac.uk/44624/ |