Locating the optic nerve in retinal images: comparing model-based and Bayesian decision methods.

Thomas P. Karnowski, V. Priya Govindasamy, Kenneth W. Tobin, Edward Chaum

Research output: Contribution to journalArticle

Abstract

In this work we compare two methods for automatic optic nerve (ON) localization in retinal imagery. The first method uses a Bayesian decision theory discriminator based on four spatial features of the retina imagery. The second method uses a principal component-based reconstruction to model the ON. We report on an improvement to the model-based technique by incorporating linear discriminant analysis and Bayesian decision theory methods. We explore a method to combine both techniques to produce a composite technique with high accuracy and rapid throughput. Results are shown for a data set of 395 images with 2-fold validation testing.

Original languageEnglish (US)
Pages (from-to)4436-4439
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
StatePublished - 2006
Externally publishedYes

Fingerprint

Bayes Theorem
Decision theory
Optic Nerve
Optics
Discriminators
Decision Theory
Discriminant analysis
Imagery (Psychotherapy)
Throughput
Composite materials
Testing
Discriminant Analysis
Retina

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Biomedical Engineering
  • Health Informatics

Cite this

Locating the optic nerve in retinal images : comparing model-based and Bayesian decision methods. / Karnowski, Thomas P.; Govindasamy, V. Priya; Tobin, Kenneth W.; Chaum, Edward.

In: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 2006, p. 4436-4439.

Research output: Contribution to journalArticle

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