Compression of CT Images with Branched Inverse Pyramidal Decomposition

  • Ivo R. Draganov
  • Roumen K. Kountchev
  • Veska M. Georgieva
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 486)

Abstract

In this chapter a new approach is suggested for compression of CT images with branched inverse pyramidal decomposition. A packet of CT images is analyzed and the correlation between each couple inside it is found. Then the packet is split into groups of images with almost even correlation, typically into six or more. One is chosen as a referent being mostly correlated with all of the others. From the rest difference images with the referent are found. After the pyramidal decomposition a packet of spectral coefficients is formed and difference levels which are coded by entropy coder. Scalable high compression is achieved at higher image quality in comparison to that of the JPEG2000 coder. The proposed approach is considered perspective also for compression of MRI images.

Keywords

CT image Compression Branched inverse pyramidal decomposition 

Notes

Acknowledgments

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 02/19.

References

  1. 1.
    Graham, R.N.J., Perriss, R.W., Scarsbrook, A.F.: DICOM demystified: a review of digital file formats and their use in radiological practice. Clin. Radiol. 60, 1133–1140 (2005)CrossRefGoogle Scholar
  2. 2.
    Clunie, D.A.: Lossless compression of grayscale medical images: effectiveness of traditional and state of the art approaches. In: Proceedings of SPIE, vol. 3980, pp. 74–84 (2000)Google Scholar
  3. 3.
    Kivijarvi, J., Ojala, T., Kaukoranta, T., Kuba, A., Nyu′l, L., Nevalainen, O.: A comparison of lossless compression methods for medical images. Comput. Med. Imaging Graph. 22, 323–339 (1998)CrossRefGoogle Scholar
  4. 4.
    Ko, J.P., Chang, J., Bomsztyk, E., Babb, J.S., Naidich, D.P., Rusinek, H.: Effect of CT image compression on computer-assisted lung nodule volume measurement. Radiology 237, 83–88 (2005)CrossRefGoogle Scholar
  5. 5.
    Karadimitriou, K., Tyler, J.M.: Min-max compression methods for medical image databases. ACM SIGMOD Rec. 26, 47–52 (1997)CrossRefGoogle Scholar
  6. 6.
    Wu, Y.G.: Medical image compression by sampling DCT coefficients. IEEE Trans. Inf. Technol. Biomed. 6(1), 86–94 (2002)CrossRefGoogle Scholar
  7. 7.
    Erickson, B.J., Manduca, A., Palisson, P., Persons, K.R., Earnest, F., Savcenko, V., Hangiandreou, N.J.: Wavelet compression of medical images. Radiology 206, 599–607 (1998)Google Scholar
  8. 8.
    Buccigrossi, R.W., Simoncelli, E.P.: Image compression via joint statistical characterization in the wavelet domain. IEEE Trans Image Process 8(12), 1688–1701 (1999)CrossRefGoogle Scholar
  9. 9.
    Ramesh, S.M., Shanmugam, D.A.: Medical image compression using wavelet decomposition for prediction method. Int. J. Comput. Sci. Inf. Secur. (IJCSIS) 7(1), 262–265 (2010)Google Scholar
  10. 10.
    Gokturk, S.B., Tomasi, C., Girod, B., Beaulieu, C.: Medical image compression based on region of interest, with application to colon CT images. In: Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 3, pp. 2453–2456 (2001)Google Scholar
  11. 11.
    Lalitha, Y.S., Latte, M.V.: Image compression of MRI image using planar coding. Int. J. Adv. Comput. Sci. Appl. (IJACSA) 2(7), 23–33 (2011)Google Scholar
  12. 12.
    Kountchev, R.K., Kountcheva, R.A.: Image representation with reduced spectrum pyramid. In: Tsihrintzis, G., Virvou, M., Howlett, R., Jain, L. (eds.) New Directions in Intelligent Interactive Multimedia. Springer, Berlin (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ivo R. Draganov
    • 1
  • Roumen K. Kountchev
    • 1
  • Veska M. Georgieva
    • 1
  1. 1.Radio Communications and Video Technologies DepartmentTechnical University of SofiaSofiaBulgaria

Personalised recommendations