Advertisement

Lossless Compression of Multidimensional Medical Images for Augmented Reality Applications

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8853)

Abstract

Medical digital imaging technologies produce daily a huge amount of data (data obtained by magnetic resonance, computed tomography and ultrasound examinations, functional resonance magnetic acquisitions, etc.), which is generally stored in ad-hoc repositories or it is transmitted to other entities, such as research centers, hospital structures, etc.. These data need efficient compression, in order to optimize memory space and transmission costs. In this work, we introduce an efficient lossless algorithm that can be used for the compression of volumetric multidimensional medical image sequences. This approach can be also used, in conjunction with Augmented Reality techniques, to save in a database or to transmit on a communication line the outcomes of surgical decisions or medical applications. We experimentally test our approach on a test set of 3-D computed tomography (CT), 3-D magnetic resonance (MR) images, and of 5-D functional Magnetic Resonance Images (fMRI). The achieved results outperform the other state-of-the-art approaches.

Keywords

Multidimensional medical images compression Multidimensional medical images coding Multidimensional data compression 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ait-Aoudia, S., Benhamida, F., Yousfi, M.: Lossless Compression of Volumetric Medical Data. In: Levi, A., Savaş, E., Yenigün, H., Balcısoy, S., Saygın, Y. (eds.) ISCIS 2006. LNCS, vol. 4263, pp. 563–571. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  2. 2.
    Aron, A.R., Behrens, T.E., Smith, S., Frank, M.J., Poldrack, R.A.: Triangulating a Cognitive Control Network Using Diffusion-Weighted Magnetic Resonance Imaging (MRI) and Functional MRI. The Journal of Neuroscience 27(14), 3743–3752 (2007)CrossRefGoogle Scholar
  3. 3.
    Bilgin, A., Zweig, G., Marcellin, M.W.: Three-Dimensional Image Compression with Integer Wavelet. Applied Optics 39(11), 1799–1814 (2000)CrossRefGoogle Scholar
  4. Carpentieri, B., Weinberger, M., Seroussi, G.: Lossless Compression of Continuous Tone Images. Proceeding of IEEE 88(11), 1797–1809 (2000)CrossRefGoogle Scholar
  5. 5.
    Cho, S., Kim, D., Pearlman, W.A.: Lossless Compression of Volumetric Medical Images with Improved Three-Dimensional SPIHT Algorithm. Journal of Digital Imaging 17(1), 57–63 (2004)CrossRefGoogle Scholar
  6. 6.
    De Paolis, L.T., Pulimeno, M., Aloisio, G.: Advanced Visualization and Interaction Systems for Surgical Pre-operative Planning. CIT 18(4) (2010)Google Scholar
  7. 8.
    fMRI Wikipedia English Page. http://en.wikipedia.org/wiki/Fmri (accessed on July 2014)
  8. 9.
    Golub, G.H., Van Loan, C.F.: Matrix Computations, 3rd ed. Baltimore. MD: The Johns Hopkins Univ. Press (1996)Google Scholar
  9. 10.
    Knoll, B., De Freitas, N.: A Machine Learning Perspective on Predictive Coding with PAQ8. Data Compression Conference (DCC) 24(8), 377–386 (2012)Google Scholar
  10. 11.
    Lalgudi, H.G., Bilgin, A., Marcellin, M.W., Nadar, M.S.: Compression of Multidimensional Images Using JPEG2000. IEEE Signal Processing Letters 15, 393–396 (2008)CrossRefGoogle Scholar
  11. 12.
    Motta, G., Storer, J.A., Carpentieri, B.: Lossless Image Coding via Adaptive Linear Prediction and Classification. Proceedings of the IEEE 88(11), 1790–1796 (2000)CrossRefGoogle Scholar
  12. 13.
    Motta, G., Rizzo, F., Storer, J.A.: Hyperspectral Data Compression. Springer Science, Berlin (2006)CrossRefzbMATHGoogle Scholar
  13. 14.
    OpenfMRI Site. https://openfmri.org (accessed on July 2014)
  14. 15.
    Pizzolante, R., Carpentieri, B.: Lossless, low-complexity, compression of three-dimensional volumetric medical images via linear prediction. Digital Signal Processing (DSP), 1–6 (July 1-3, 2013)Google Scholar
  15. 16.
    Pizzolante, R., Carpentieri, B.: Visualization, Band Ordering and Compression of Hyperspectral Images. Algorithms 5, 76–97 (2012)CrossRefGoogle Scholar
  16. 17.
    Rizzo, F., Carpentieri, B., Motta, G., Storer, J.A.: Low-complexity lossless compression of hyperspectral imagery via linear prediction. IEEE Signal Processing Letters 12(2), 138–141 (2005)CrossRefGoogle Scholar
  17. 18.
    Salomon, D., Motta, G.: Handbook of Data Compression, 5 edn. Springer (2010) ISBN: 978-1-84882-902-2Google Scholar
  18. 19.
    Xiong, Z., Wu, X., Cheng, S., Jianping, H.: Lossy-to-lossless compression of medical volumetric data using three-dimensional integer wavelet transforms. IEEE Trans. on Medical Imaging 22(3), 459–470 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Dipartimento di InformaticaUniversità degli Studi di SalernoFiscianoItaly

Personalised recommendations