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

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Keller, B; Cunefare, D; Grewal, DS; Mahmoud, TH; Izatt, JA; Farsiu, S

Published Date

  • July 2016

Published In

Volume / Issue

  • 21 / 7

Start / End Page

  • 76015 -

PubMed ID

  • 27533243

Pubmed Central ID

  • 27533243

Electronic International Standard Serial Number (EISSN)

  • 1560-2281

International Standard Serial Number (ISSN)

  • 1083-3668

Digital Object Identifier (DOI)

  • 10.1117/1.JBO.21.7.076015

Language

  • eng