Journal
BIOMEDICAL OPTICS EXPRESS
Volume 5, Issue 2, Pages 348-365Publisher
OPTICAL SOC AMER
DOI: 10.1364/BOE.5.000348
Keywords
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Funding
- U.S. Army Medical Research Acquisition Activity [W81XWH-12-1-0397]
- North Carolina Biotechnology Center [IDG 2012-1015, NIH P30 EY005722, EY12859]
- Duke University Pratt Undergraduate Fellowship Program
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Accurate quantification of retinal layer thicknesses in mice as seen on optical coherence tomography (OCT) is crucial for the study of numerous ocular and neurological diseases. However, manual segmentation is time-consuming and subjective. Previous attempts to automate this process were limited to high-quality scans from mice with no missing layers or visible pathology. This paper presents an automatic approach for segmenting retinal layers in spectral domain OCT images using sparsity based denoising, support vector machines, graph theory, and dynamic programming (S-GTDP). Results show that this method accurately segments all present retinal layer boundaries, which can range from seven to ten, in wild-type and rhodopsin knockout mice as compared to manual segmentation and has a more accurate performance as compared to the commercial automated Diver segmentation software. (C) 2014 Optical Society of America
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