New Format for Coding of Single and Sequences of Medical Images

  • Roumen Kountchev
  • Vladimir Todorov
  • Roumiana Kountcheva
Part of the Studies in Computational Intelligence book series (SCI, volume 486)


The recent development and use of huge image databases creates various problems concerning their efficient archiving and content protection. A wide variety of standards, methods and formats have been created, most of them aimed at the efficient compression of still images. Each standard and method has its specific advantages and demerits, and the best image compression solution is still to come. This chapter presents new format for archiving of still images and sequences of medical images, based on the Inverse Pyramid Decomposition, whose compression efficiency is comparable to that of JPEG. Main advantages of the new format are the comparatively low computational complexity and the ability to insert resistant and fragile watermarks in same digital image.


Image archiving Lossy and lossless image compression Image formats Coding of medical image sequences 



This chapter was supported by the Joint Research Project Bulgaria-Romania (2010–2012): “Electronic Health Records for the Next Generation Medical Decision Support in Romanian and Bulgarian National Healthcare Systems”, DNTS 02/09.


  1. 1.
    Huang, H., Taira, R.: Infrastructure design of a picture archiving and communication system. Am. J. Roentgenol. 158, 743–749 (1992)CrossRefGoogle Scholar
  2. 2.
    Taubman, D., Marcellin, M.: JPEG 2000: Image Compression Fundamentals, Standards and Practice. Kluwer Academic Publishers, Boston (2002)CrossRefGoogle Scholar
  3. 3.
    Information Technology–JPEG 2000 Image Coding System: Part 9–Interactivity tools, APIs and protocols, no. 15444-9, ISO/IEC JTC1/SC29/WG1 IS, Rev. 3 (2003)Google Scholar
  4. 4.
    Pianykh, O.: Digital Imaging and Communications in Medicine (DICOM). Springer, Berlin (2008)Google Scholar
  5. 5.
    TIFF—Revision 6.0. Adobe Developers Association (1992)
  6. 6.
    Miano, J.: Compressed Image File Formats: JPEG, PNG, GIF, XBM, BMP. Addison Wesley Professional, Reading (1999)Google Scholar
  7. 7.
    Kountchev, R., Kountcheva, R.: Image representation with reduced spectrum pyramid. In: Tsihrintzis, G., Virvou, M., Howlett, R., Jain, L. (eds.) New Directions in Intelligent Interactive Multimedia, pp. 275–284. Springer, Berlin (2008)CrossRefGoogle Scholar
  8. 8.
    Kountchev, R., Kountcheva, R.: Compression of multispectral and multi-view images with inverse pyramid decomposition. Int. J. Reasoning-based Intell. Syst. 3(2), 124–131 (2011)Google Scholar
  9. 9.
    Kountchev, R., Todorov, V.L., Kountcheva, R.: New method for lossless data compression based on adaptive run-length coding. In: Enachescu, C., Filip, F., Iantovics, B. (eds.) Advanced Computational Technologies, Romanian Academy Publishing House, (in press) Google Scholar
  10. 10.
    Kountchev, R., Todorov, V.L., Kountcheva, R., Milanova, M.: Lossless compression of biometric image data. Proceedings of 5th WSEAS International Conference on Signal Processing, pp. 185–190. Istanbul, Turkey (2006)Google Scholar
  11. 11.
    Kountchev, R., Todorov, V.L., Kountcheva, R.: Fragile and resistant image watermarking based on inverse difference pyramid decomposition. WSEAS Trans. Sig. Process. 6(3), 101–112 (2010)Google Scholar
  12. 12.
    Kountchev, R., Milanova, M., Ford, C., Kountcheva, R.: Multi-layer image transmission with inverse pyramidal decomposition. In: Halgamuge, S., Wang, L. (eds.) Computational intelligence for modeling and predictions, Ch. 13, vol. 2, pp. 179–196. Springer, Berlin (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Roumen Kountchev
    • 1
  • Vladimir Todorov
    • 2
  • Roumiana Kountcheva
    • 2
  1. 1.Department of Radio Communications and Video TechnologiesTechnical University—SofiaSofiaBulgaria
  2. 2.T and K EngineeringSofiaBulgaria

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