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Hybrid Lossless Coder of Medical Images with Statistical Data Modelling

  • Artur Przelaskowski
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2124)

Abstract

Methods of lossless compression of medical image data are considered in this paper. Chosen classes of efficient algorithms were constructed, examined and optimised to conclude the most useful tools for creation of medical image representation. 2-D context-based prediction schemes, and statistical models of entropy coder were fitted to different characteristics of US, MR and CT images. The SSM technique of suitable-to-image characteristics scanning followed by statistical modelling of the context in arithmetic coder was found out as the most effective in most cases. Average bit rate value over test images is equal to 2.54 bpp for SSM coder and significantly overcomes 2.92 bpp achieved for CALIC. Efficient hybrid encoding method (SHEC) was proposed as a complex tool for medical image archiving and transmission. SHEC develops SSM by including CALIC-like coder for archiving the highest quality images and JPEG2000-like wavelet coder for transmission of high and middle quality images in telemedicine systems.

Keywords

medical image compression reversible hybrid coders statistical modeling 

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References

  1. 1.
    Wu, X.: Lossless Compression of Continuous-tone Images via Context Selection, Quantization, and Modelling. IEEE Trans. Image Process. 6 (1997) 656–664CrossRefGoogle Scholar
  2. 2.
    Meyer, B., Tischer, P.: TMW-a New Method for Lossless Image Compression. In: International Picture Coding Symposium PCS97-Conference Proceedings (1997)Google Scholar
  3. 3.
    Motta, G., Storer, J.A., Carpentieri, B.: Adaptive Linear Prediction Lossless Image Coding. In: Proc. of IEEE Data Compression Conference (1999)Google Scholar
  4. 4.
    Weinberger, M., Seroussi, G., Sapiro, G.: The LOCO-I Lossless Image Compression Algorithm: Principles and Standarization into JPEG-LS. IEEE Trans. Image Process., 9 (2000) 1309–1324CrossRefGoogle Scholar
  5. 5.
    Robinson, J.A.: Efficient General-purpose Image Compression with Binary Tree Predictive Coding. IEEE Trans. Image Process. 6 (1997) 601–608CrossRefGoogle Scholar
  6. 6.
    Lempel, A., Ziv, J.: Compression of Two-dimensional Data. IEEE Trans. Inform. Theory, 32 (1986) 2–8CrossRefGoogle Scholar
  7. 7.
    Zhang, Ya-Qin, Loew, M.H., Pickholtz, R.L.: A Methodology for Modeling the Distributions of Medical Images and Their Stochastic Properties. IEEE Trans. Medical Imaging 9 (1990) 376–383CrossRefGoogle Scholar
  8. 8.
    Memon, N., Neuhoff, D.L., Shende, S.: An analysis of some common scanning techniques for lossless image coding. IEEE Trans. Image Process. 9 (2000) 1837–1848zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Ross, M., Viergever, M.A.: Reversible interframe compression based on HINT (hierarchical interpolation) decorrelation and arithmetic coding. Proc. SPIE, 1444 (1991) 283–290CrossRefGoogle Scholar
  10. 10.
    Marcellin, M. W., Gormish, M.J., Bilgin, A., Boliek, M.P.: An Overview of JPEG-2000. In: Proc. of IEEE Data Compression Conference (2000) 523–544Google Scholar
  11. 11.
    Said, A., Pearlman, W.A.: A new fast and efficient image codec based on set partitioning in hierarchical trees. IEEE Trans. Circ. & Syst. Video. Tech. 6 (1996) 243–250CrossRefGoogle Scholar
  12. 12.
    Sweldens, W.: The lifting scheme: a custom-design construction of biorthogonal wavelets. Appl. Comput. Harmonic. Analysis, 3 (1996) 186–200zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Adams, M.D., Kossentini, F.: Reversible Integer-to-Integer Wavelet Transforms for Image Compression: Performance Evaluation and Analysis. IEEE Trans. Image Process. 9 (2000) 1010–1024zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Calderbank, R.C., Daubechies, I., Sweldens, W., Yeo, Boon-Lock: Wavelet Transforms that Map Integers to Integers. Applied and Computational Harmonic Analysis 5 (1998) 332–369zbMATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Przelaskowski, A.: Performance evaluation of JPEG2000-like data decomposition schemes in wavelet codec. To be presented at ICIP 2001.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Artur Przelaskowski
    • 1
  1. 1.Institute of RadioelectronicsWarsaw University of TechnologyWarszawaPoland

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