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

Enting Gao; Fei Shi; Weifang Zhu; Chao Jin; Min Sun; Haoyu Chen; Xinjian Chen

doi:10.1117/12.2250168

24 February 2017 Graph search: active appearance model based automated segmentation of retinal layers for optic nerve head centered OCT images

Enting Gao, Fei Shi, Weifang Zhu, Chao Jin, Min Sun, Haoyu Chen, Xinjian Chen

Author Affiliations +

Proceedings Volume 10133, Medical Imaging 2017: Image Processing; 101331Q (2017) https://doi.org/10.1117/12.2250168
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

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.

Citation Download Citation

Enting Gao, Fei Shi, Weifang Zhu, Chao Jin, Min Sun, Haoyu Chen, and Xinjian Chen "Graph search: active appearance model based automated segmentation of retinal layers for optic nerve head centered OCT images", Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101331Q (24 February 2017); https://doi.org/10.1117/12.2250168

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