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- W2592975584 abstract "In this paper, a novel approach combining the active appearance model (AAM) and graph search is proposed to segmentretinal layers for optic nerve head(ONH) centered optical coherence tomography(OCT) images. The method includestwo 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 segmentationresults. Then a multi-resolution GS–AAM algorithm is applied to further refine the results, in which AAM is efficientlyintegrated into the graph search segmentation process. The proposed method was tested on a dataset whichcontained113-D SD-OCT images, and compared to the manual tracings of two observers on all the volumetric scans. Theoverall mean border positioning error for layer segmentation was found to be 7.09 ± 6.18μm for normal subjects. It wascomparable 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." @default.
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- W2592975584 date "2017-02-24" @default.
- W2592975584 modified "2023-09-22" @default.
- W2592975584 title "Graph search: active appearance model based automated segmentation of retinal layers for optic nerve head centered OCT images" @default.
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- W2592975584 doi "https://doi.org/10.1117/12.2250168" @default.
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