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Superpixel guided active contour segmentation of retinal layers in OCT volumes

Bai, Fangliang and Gibson, Stuart J. and Marques, Manuel J. and Podoleanu, Adrian G.H. (2018) Superpixel guided active contour segmentation of retinal layers in OCT volumes. In: Podoleanu, Adrian G.H. and Bang, Ole, eds. 2nd Canterbury Conference on OCT with Emphasis on Broadband Optical Sources. Proceedings of SPIE . SPIE. ISBN 978-1-5106-1674-5. E-ISBN 978-1-5106-1675-2. (doi:10.1117/12.2282326) (KAR id:66338)

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Retinal OCT image segmentation is a precursor to subsequent medical diagnosis by a clinician or machine learning algorithm. In the last decade, many algorithms have been proposed to detect retinal layer boundaries and simplify the image representation. Inspired by the recent success of superpixel methods for pre-processing natural images, we present a novel framework for segmentation of retinal layers in OCT volume data. In our framework, the region of interest (e.g. the fovea) is located using an adaptive-curve method. The cell layer boundaries are then robustly detected firstly using 1D superpixels, applied to A-scans, and then fitting active contours in B-scan images. Thereafter the 3D cell layer surfaces are efficiently segmented from the volume data. The framework was tested on healthy eye data and we show that it is capable of segmenting up to 12 layers. The experimental results imply the effectiveness of proposed method and indicate its robustness to low image resolution and intrinsic speckle noise.

Item Type: Book section
DOI/Identification number: 10.1117/12.2282326
Uncontrolled keywords: Optical coherence tomography, superpixel, active contour, retina segmentation, 3D model, retinal thickness
Divisions: Divisions > Division of Natural Sciences > Physics and Astronomy
Depositing User: Stuart Gibson
Date Deposited: 09 Mar 2018 14:25 UTC
Last Modified: 09 Dec 2022 01:44 UTC
Resource URI: (The current URI for this page, for reference purposes)
Gibson, Stuart J.:
Marques, Manuel J.:
Podoleanu, Adrian G.H.:
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