Skip to main content

Towards Automated OCT-based Identification of White Brain Matter

  • Conference paper
Book cover Bildverarbeitung für die Medizin 2007

Part of the book series: Informatik aktuell ((INFORMAT))

Abstract

A novel model-based identification of white brain matter in OCT A-scans is proposed. Based on nonlinear energy operators used in the classification of neural activity, candidates for white matter structures are extracted from a baseline-corrected signal. Validation of candidates is done by evaluating the correspondence to a simplified intensity model which is parametrized beforehand. Results for identification of white matter in rat brain in vitro show the capability of the proposed algorithm.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bouma BE, Tearney GJ. Handbook Of Optical Coherence Tomography. Marcel Dekker, Inc.; 2002.

    Google Scholar 

  2. Boehringer HJ, Boller D, Leppert J, et al. Time-domain and spectral-domain optical coherence tomography in the analysis of brain tumor tissue. Lasers in Surgery and Medicine 2006;38:588–597.

    Article  Google Scholar 

  3. Jeon SW, Shure MA, Baker KB, etal. Optical coherence tomography and optical coherence domain reflectometry for deep brain stimulation probe guidance. Procs SPIE 2005;5686:487–494.

    Article  Google Scholar 

  4. Safri MS, Farhang S, Tang RS, et al. Optical coherence tomography in the diagnosis and treatment of neurological disorders. J Biomed Opt 2005;10(5):1–11.

    Google Scholar 

  5. Kim KW, Kim SJ. Neural spike sorting under nearly 0-dB signal-to-noise ratio using nonlinear energy operator and artificial neural-network classifier. IEEE Trans Biomed Eng 2000;47:1406–1411.

    Article  Google Scholar 

  6. Menne KL. Computerassistenz zur Implantation von Tiefenhirnstimulatoren. Ph.D. thesis. University of Luebeck; 2005.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ramrath, L., Hofmann, U.G., Huettmann, G., Moser, A., Schweikard, A. (2007). Towards Automated OCT-based Identification of White Brain Matter. In: Horsch, A., Deserno, T.M., Handels, H., Meinzer, HP., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2007. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71091-2_83

Download citation

Publish with us

Policies and ethics