Segmentation of three-dimensional retinal image data

Alfred R. Fuller, Robert Zawadzki, Stacey Choi, David F. Wiley, John S Werner, Bernd Hamann

Research output: Contribution to journalArticlepeer-review

66 Scopus citations


We have combined methods from volume visualization and data analysis to support better diagnosis and treatment of human retinal diseases. Many diseases can be identified by abnormalities in the thicknesses of various retinal layers captured using optical coherence tomography (OCT). We used a support vector machine (SVM) to perform semi-automatic segmentation of retinal layers for subsequent analysis including a comparison of layer thicknesses to known healthy parameters. We have extended and generalized an older SVM approach to support better performance in a clinical setting through performance enhancements and graceful handling of inherent noise in OCT data by considering statistical characteristics at multiple levels of resolution. The addition of the multi-resolution hierarchy extends the SVM to have "global awareness." A feature, such as a retinal layer, can therefore be modeled within the SVM as a combination of statistical characteristics across all levels; thus capturing high- and low-frequency information. We have compared our semi-automatically generated segmentations to manually segmented layers for verification purposes. Our main goals were to provide a tool that could (i) be used in a clinical setting; (ii) operate on noisy OCT data; and (iii) isolate individual or multiple retinal layers in both healthy and disease cases that contain structural deformities.

Original languageEnglish (US)
Pages (from-to)1719-1726
Number of pages8
JournalIEEE Transactions on Visualization and Computer Graphics
Issue number6
StatePublished - Nov 2007


  • Image analysis
  • Image processing
  • Optical coherence tomography
  • Retinal
  • Segmentation
  • Support vector machine
  • Volume visualization

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Software


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