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2014 | OriginalPaper | Chapter

Compression of CT Images with Branched Inverse Pyramidal Decomposition

Authors : Ivo R. Draganov, Roumen K. Kountchev, Veska M. Georgieva

Published in: Advanced Intelligent Computational Technologies and Decision Support Systems

Publisher: Springer International Publishing



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.

  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)View Article
  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)
  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)View Article
  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)View Article
  5. Karadimitriou, K., Tyler, J.M.: Min-max compression methods for medical image databases. ACM SIGMOD Rec. 26, 47–52 (1997)View Article
  6. Wu, Y.G.: Medical image compression by sampling DCT coefficients. IEEE Trans. Inf. Technol. Biomed. 6(1), 86–94 (2002)View Article
  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)
  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)View Article
  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)
  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)
  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)
  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)
Compression of CT Images with Branched Inverse Pyramidal Decomposition
Ivo R. Draganov
Roumen K. Kountchev
Veska M. Georgieva
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