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Proceedings Paper

Graph search: active appearance model based automated segmentation of retinal layers for optic nerve head centered OCT images
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Paper Abstract

In this paper, a novel approach combining the active appearance model (AAM) and graph search is proposed to segment retinal layers for optic nerve head(ONH) centered optical coherence tomography(OCT) images. The method includes two parts: preprocessing and layer segmentation. During the preprocessing phase, images is first filtered for denoising, then the B-scans are flattened. During layer segmentation, the AAM is first used to obtain the coarse segmentation results. Then a multi-resolution GS–AAM algorithm is applied to further refine the results, in which AAM is efficiently integrated into the graph search segmentation process. The proposed method was tested on a dataset which contained113-D SD-OCT images, and compared to the manual tracings of two observers on all the volumetric scans. The overall mean border positioning error for layer segmentation was found to be 7.09 ± 6.18μm for normal subjects. It was comparable to the results of traditional graph search method (8.03±10.47μm) and mean inter-observer variability (6.35±6.93μm).The preliminary results demonstrated the feasibility and efficiency of the proposed method.

Paper Details

Date Published: 24 February 2017
PDF: 8 pages
Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101331Q (24 February 2017); doi: 10.1117/12.2250168
Show Author Affiliations
Enting Gao, Soochow Univ. (China)
Suzhou Univ. of Science and Technology (China)
Fei Shi, Soochow Univ. (China)
Weifang Zhu, Soochow Univ. (China)
Chao Jin, Soochow Univ. (China)
Min Sun, Soochow Univ. (China)
Haoyu Chen, Shantou Univ. (China)
Chinese Univ. of Hong Kong (China)
Xinjian Chen, Soochow Univ. (China)

Published in SPIE Proceedings Vol. 10133:
Medical Imaging 2017: Image Processing
Martin A. Styner; Elsa D. Angelini, Editor(s)

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