Automated layer segmentation of macular OCT images via graph-based SLIC superpixels and manifold ranking approach



Gao, Zhijun, Bu, Wei, Zheng, Yalin ORCID: 0000-0002-7873-0922 and Wu, Xiangqian
(2017) Automated layer segmentation of macular OCT images via graph-based SLIC superpixels and manifold ranking approach. COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 55. pp. 42-53.

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Abstract

Using the graph-based a simple linear iterative clustering (SLIC) superpixels and manifold ranking technology, a novel automated intra-retinal layer segmentation method is proposed in this paper. Eleven boundaries of ten retinal layers in optical coherence tomography (OCT) images are exactly, fast and reliably quantified. Instead of considering the intensity or gradient features of the single-pixel in most existing segmentation methods, the proposed method focuses on the superpixels and the connected components-based image cues. The image is represented as some weighted graphs with superpixels or connected components as nodes. Each node is ranked with the gradient and spatial distance cues via graph-based Dijkstra's method or manifold ranking. So that it can effectively overcome speckle noise, organic texture and blood vessel artifacts issues. Segmentation is carried out in a three-stage scheme to extract eleven boundaries efficiently. The segmentation algorithm is validated on 2D and 3D OCT images in three databases, and is compared with the manual tracings of two independent observers. It demonstrates promising results in term of the mean unsigned boundaries errors, the mean signed boundaries errors, and layers thickness errors.

Item Type: Article
Uncontrolled Keywords: Optical coherence tomography (OCT), Segmentation, Graph, SLIC superpixels, Manifold ranking
Depositing User: Symplectic Admin
Date Deposited: 22 Sep 2016 16:04
Last Modified: 19 Jan 2023 07:29
DOI: 10.1016/j.compmedimag.2016.07.006
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3003431