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Journal of Biomedical Optics

Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images
Author(s): Brenton Keller; David Cunefare; Dilraj S. Grewal; Tamer H. Mahmoud; Joseph A. Izatt; Sina Farsiu
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Paper Abstract

We introduce a metric in graph search and demonstrate its application for segmenting retinal optical coherence tomography (OCT) images of macular pathology. Our proposed “adjusted mean arc length” (AMAL) metric is an adaptation of the lowest mean arc length search technique for automated OCT segmentation. We compare this method to Dijkstra’s shortest path algorithm, which we utilized previously in our popular graph theory and dynamic programming segmentation technique. As an illustrative example, we show that AMAL-based length-adaptive segmentation outperforms the shortest path in delineating the retina/vitreous boundary of patients with full-thickness macular holes when compared with expert manual grading.

Paper Details

Date Published: 28 July 2016
PDF: 9 pages
J. Biomed. Opt. 21(7) 076015 doi: 10.1117/1.JBO.21.7.076015
Published in: Journal of Biomedical Optics Volume 21, Issue 7
Show Author Affiliations
Brenton Keller, Duke Univ. (United States)
David Cunefare, Carl E. Ravin Advanced Imaging Labs. (United States)
Dilraj S. Grewal, Duke Univ. School of Medicine (United States)
Tamer H. Mahmoud
Joseph A. Izatt, Duke Univ. (United States)
Sina Farsiu, Duke Univ. (United States)

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