Skip to main content

Texture Analysis Using Gabor Filter Based on Transcranial Sonography Image

  • Chapter
  • First Online:
Bildverarbeitung für die Medizin 2011

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

Abstract

Transcranial sonography (TCS) is a new tool for the diagnosis of Parkinson’s disease (PD) at a very early state. The TCS image of the mesencephalon shows a distinct hyperechogenic pattern in about 90% PD patients. This pattern is usually manually segmented and the substantia nigra (SN) region can be used as an early PD indicator. However this method is based on manual evaluation of examined images. We propose a texture analysis method using Gabor filters for the early PD risk assessment. The features are based on the local spectrum, which is obtained by a bank of Gabor filters, and the performance of these features is evaluated by feature selection method. The results show that the accuracy of the classification with the feature subset is reaching 92.73%.

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 99.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
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. Kier C, Seidel G, Bregemann N, et al. Transcranial sonography as early indicator for genetic Parkinson’s disease. Proc IFMBE. 2009; p. 456–9.

    Google Scholar 

  2. Spiegel J, Storch A, Jost WH. Early diagnosis of Parkinson’s disease. J Neurol. 2006;253[Suppl 4].

    Google Scholar 

  3. Behnke S, Berg D, Becker G. Does ultrasound disclose a vulnerability factor for Parkinson’s disease? J Neurol. 2006;250:24–7.

    Google Scholar 

  4. Vlaar AMM, Bouwmans A, Mess WH, et al. Transcranial duplex in the differential diagnosis of parkinsonian syndromes. Neurology. 2009;256:530–8.

    Article  Google Scholar 

  5. Chen L, Seidel G, Mertins A. Multiple feature extraction for early parkinson risk assessment based on transcranial sonography image. In: Proc ICIP; 2010.

    Google Scholar 

  6. Fogel I, Sagi D. Gabor filters as texture discriminator. Biol Cybern. 1989;61(2):103–13.

    Article  Google Scholar 

  7. Haralick RM, Shanmugan K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern. 1973;SMC-3:610–21.

    Article  Google Scholar 

  8. Pudil P, Novovicova J, Kittler J. Floating search methods in feature selection. Patt Recogn Lett. 1994;15:1119–25.

    Article  Google Scholar 

  9. Hu, K M. Visual pattern recognition by moments invariants. IEEE Trans Inf Theory. 1962;8:456–9.

    Google Scholar 

  10. Manjunath BS, Ma WY. Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell. 1996;18(8).

    Google Scholar 

  11. Devendran V, Thiagarajan H, Wahi A. SVM based hybrid moment features for natural scene categorization. Int Conf Comput Sci Eng. 2009;1:356–61.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lei Chen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Chen, L., Hagenah, J., Mertins, A. (2011). Texture Analysis Using Gabor Filter Based on Transcranial Sonography Image. In: Handels, H., Ehrhardt, J., Deserno, T., Meinzer, HP., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2011. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19335-4_52

Download citation

Publish with us

Policies and ethics