Use of Varying Constraints in Optimal 3-D Graph Search for Segmentation of Macular Optical Coherence Tomography Images
- 1 January 2007
- book chapter
- Published by Springer Nature
- Vol. 10, 244-251
- https://doi.org/10.1007/978-3-540-75757-3_30
Abstract
An optimal 3-D graph search approach designed for simultaneous multiple surface detection is extended to allow for varying smoothness and surface interaction constraints instead of the traditionally used constant constraints. We apply the method to the intraretinal layer segmentation of 24 3-D optical coherence tomography (OCT) images, learning the constraints from examples in a leave-one-subject-out fashion. Introducing the varying constraints decreased the mean unsigned border positioning errors (mean error of 7.3 ± 3.7 μm using varying constraints compared to 8.3 ± 4.9 μm using constant constraints and 8.2 ± 3.5 μm for the inter-observer variability).Keywords
This publication has 5 references indexed in Scilit:
- Incorporation of Regional Information in Optimal 3-D Graph Search with Application for Intraretinal Layer Segmentation of Optical Coherence Tomography ImagesPublished by Springer Nature ,2007
- Automated detection of retinal layer structures on optical coherence tomography imagesOptics Express, 2005
- Optimal Surface Segmentation in Volumetric Images-A Graph-Theoretic ApproachIEEE Transactions on Pattern Analysis and Machine Intelligence, 2005
- Optimal Net Surface Problems with ApplicationsPublished by Springer Nature ,2002
- Interactive Organ Segmentation Using Graph CutsPublished by Springer Nature ,2000