Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

A Fast and Efficient Method for Compressing fMRI Data Sets

  • Conference paper
Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005 (ICANN 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3697))

Included in the following conference series:

  • 3669 Accesses

Abstract

We present a new lossless compression method named FTTcoder, which compresses images and 3d sequences collected during a typical functional MRI experiment. The large data sets involved in this popular medical application necessitate novel compression algorithms to take into account the structure of the recorded data as well as the experimental conditions, which include the 4d recordings, the used stimulus protocol and marked regions of interest (ROI). We propose to use simple temporal transformations and entropy coding with context modeling to encode the 4d scans after preprocessing with the ROI masking. Experiments confirm the superior performance of FTTcoder in contrast to previously proposed algorithms both in terms of speed and compression.

An erratum to this chapter can be found at http://dx.doi.org/10.1007/11550907_163 .

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

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Keck, I., Theis, F., Gruber, P., Lang, E., Specht, K., Puntonet, C.: 3D spatial analysis of fMRI data - a comparison of ICA and GLM analysis on a word perception task. In: Proc. IJCNN 2004, Budapest, Hungary, pp. 2495–2500 (2004)

    Google Scholar 

  2. Klappenecker, A., May, F., Beth, T.: Lossless compression of 3D MRI and CT data. In: Laine, A., Unser, M., Aldroubi, A. (eds.): Proc. SPIE, Wavelet Applications in Signal and Imaging Processing VI, vol. 3458, pp. 140–149 (1998)

    Google Scholar 

  3. Wu, M., Forchhammer, S.: Medical image sequence coding. In: Proc. DSAGM, Copenhagen, Denmark, pp. 77–87 (2004)

    Google Scholar 

  4. Cohen, M.: A data compression method for image time series. Human Brain Mapping 12, 20–24 (2001)

    Article  Google Scholar 

  5. Weinberger, M., Seroussi, G., Sapiro, G.: The LOCO-I lossless image compression algorithm: Principles and standardization into JPEG-LS. IEEE Transactions on Image Processing 9, 1309–1324 (2000)

    Article  Google Scholar 

  6. Adamson, C.: Lossless compression of magnetic resonance imaging data. Master’s thesis, Monash University, Melbourne, Australia (2002)

    Google Scholar 

  7. Taswell, C.: Wavelet transform compression of functional magnetic resonance image sequences. In: Proc. SIP, Las Vegas, USA, pp. 725–728 (1998)

    Google Scholar 

  8. Taubman, D.S., Marcellin, M.W.: JPEG 2000 Image Compression Fundamentals, Standards and Practice. Kluwer Academic Publishers, Massachusetts (2002)

    Google Scholar 

  9. Golomb, S.: Run-length encodings. IEEE Transactions on Information Theory 12, 399–401 (1966)

    Article  MATH  MathSciNet  Google Scholar 

  10. Woods, R., Cherry, S., Mazziotta, J.: Rapid automated algorithm for aligning and reslicing pet images. Journal of Computer Assisted Tomography 16, 620–633 (1992)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, Tokyo University of Agriculture and Technology, Tokyo, 184-8588, Japan

    Fabian J. Theis & Toshihisa Tanaka

  2. Institute of Biophysics, University of Regensburg, 93040, Regensburg, Germany

    Fabian J. Theis

Authors
  1. Fabian J. Theis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Toshihisa Tanaka
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

    Włodzisław Duch

  2. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01–447, Warsaw, Poland

    Janusz Kacprzyk

  3. Adaptive Informatics Research Centre, Helsinki University of Technology, P.O. Box 5400, 02015, HUT, Finland

    Erkki Oja

  4. Systems Research Institute, Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Sławomir Zadrożny

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Theis, F.J., Tanaka, T. (2005). A Fast and Efficient Method for Compressing fMRI Data Sets. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550907_122

Download citation

  • DOI: https://doi.org/10.1007/11550907_122

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-28755-1

  • Online ISBN: 978-3-540-28756-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation