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

Research output: Contribution to journalArticle

Authors

  • Jinming Duan
  • Christopher Tench
  • Irene Gottlob
  • Frank Proudlock
  • Li Bai

Colleges, School and Institutes

External organisations

  • University of Nottingham
  • University of Leicester

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.

Details

Original languageEnglish
Pages (from-to)158-175
Number of pages18
JournalPattern Recognition
Volume72
Early online date6 Jul 2017
Publication statusPublished - 1 Dec 2017

Keywords

  • Eikonal equation, Fast sweeping, Geodesic distance, Optical coherence tomography, Ordinary differential equation, Partial differential equation, Segmentation