Sparsity-based retinal layer segmentation of optical coherence tomography images

Jason Tokayer, Antonio Ortega, David Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

A novel method for optical coherence tomography retinal image segmentation utilizing sparsity constraints is demonstrated. Retinal images are sparse in the layer domain. The algorithm thus transforms an input retinal image into a layer-like domain, and then uses graph theory and dynamic programming to extract the retinal layers from the sparse representation. The number of identified boundaries is not fixed and is determined by the algorithm at run-time. Results show that this method can segment up to nine layer boundaries without making overly restrictive assumptions about anatomic structure.

Original languageEnglish (US)
Title of host publicationICIP 2011
Subtitle of host publication2011 18th IEEE International Conference on Image Processing
Pages449-452
Number of pages4
DOIs
StatePublished - Dec 1 2011
Event2011 18th IEEE International Conference on Image Processing, ICIP 2011 - Brussels, Belgium
Duration: Sep 11 2011Sep 14 2011

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2011 18th IEEE International Conference on Image Processing, ICIP 2011
CountryBelgium
CityBrussels
Period9/11/119/14/11

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Keywords

  • optical coherence tomography
  • segmentation
  • sparsity

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Tokayer, J., Ortega, A., & Huang, D. (2011). Sparsity-based retinal layer segmentation of optical coherence tomography images. In ICIP 2011: 2011 18th IEEE International Conference on Image Processing (pp. 449-452). [6116547] (Proceedings - International Conference on Image Processing, ICIP). https://doi.org/10.1109/ICIP.2011.6116547