Research Papers: Imaging

Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images

[+] Author Affiliations
Brenton Keller, David Cunefare

Duke University, Department of Biomedical Engineering, 101 Science Drive, Campus Box 90281, Durham, North Carolina 27708, United States

Dilraj S. Grewal, Tamer H. Mahmoud

Duke University, Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina 27710, United States

Joseph A. Izatt, Sina Farsiu

Duke University, Department of Biomedical Engineering, 101 Science Drive, Campus Box 90281, Durham, North Carolina 27708, United States

Duke University, Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina 27710, United States

J. Biomed. Opt. 21(7), 076015 (Jul 28, 2016). doi:10.1117/1.JBO.21.7.076015
History: Received March 3, 2016; Accepted July 11, 2016
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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.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Brenton Keller ; David Cunefare ; Dilraj S. Grewal ; Tamer H. Mahmoud ; Joseph A. Izatt, et al.
"Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images", J. Biomed. Opt. 21(7), 076015 (Jul 28, 2016). ; http://dx.doi.org/10.1117/1.JBO.21.7.076015


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