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28 July 2016 Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images
Brenton Keller, David Cunefare, Dilraj S. Grewal, Tamer H. Mahmoud, Joseph A. Izatt, Sina Farsiu
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Funded by: US National Institutes of Health (NIH), U.S. Army Medical Research Acquisition Activity
Journal of Biomedical Optics, Vol. 21, Issue 7, 076015 (July 2016). https://doi.org/10.1117/1.JBO.21.7.076015
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.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE) 1083-3668/2016/$25.00 © 2016 SPIE
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Brenton Keller, David Cunefare, Dilraj S. Grewal, Tamer H. Mahmoud, Joseph A. Izatt, and Sina Farsiu "Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images," Journal of Biomedical Optics 21(7), 076015 (28 July 2016). https://doi.org/10.1117/1.JBO.21.7.076015
Published: 28 July 2016
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Cited by 33 scholarly publications.
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KEYWORDS
Image segmentation
Optical coherence tomography
Image processing algorithms and systems
Retina
Biomedical optics
Algorithm development
Pathology
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