Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Three-dimensional separate descendant-based SPIHT algorithm for fast compression of high-resolution medical image sequences

© The Institution of Engineering and Technology
Received 11/07/2016, Accepted 02/10/2016, Revised 01/09/2016, Published 11/10/2016

To provide a fast compression algorithm for high-resolution medical image sequences, an efficient three-dimensional (3D) separate descendant-based (SBD) set partitioning in hierarchical trees (SPIHT) algorithm (3D SDB-SPIHT) is proposed in this study. To accelerate the transformation, 3D integer wavelet transform is used first. Based on an efficient spatial–temporal tree structure, which is designed for the transformed coefficients, the authors propose a fast coding scheme by separating the descendant set into offspring set and leaves set. The proposed algorithm has more selectivity in deciding the scanning and coding of the descendant sets and hence the coding time is accelerated. Experimental results demonstrate that 3D SDB-SPIHT compresses medical images faster compared with traditional 3D SPIHT and other variations of 3D SPIHT.

Inspec keywords: wavelet transforms; data compression; medical image processing; image scanners; trees (mathematics); image coding; image sequences

Other keywords: spatial-temporal tree structure; fast image coding scheme; high-resolution medical image sequence; 3D SBD set partitioning in hierarchical tree algorithm; three-dimensional separate descendant based SPIRT algorithm; leaves set; 3D integer wavelet transform; offspring set; image compression algorithm

Subjects: Computer vision and image processing techniques; Biology and medical computing; Combinatorial mathematics; Integral transforms; Function theory, analysis; Algebra, set theory, and graph theory; Patient diagnostic methods and instrumentation; Image and video coding; Combinatorial mathematics; Biomedical measurement and imaging; Integral transforms; Medical and biomedical uses of fields, radiations, and radioactivity; health physics

References

    1. 1)
      • 17. Xiong, Z., Wu, X., Cheng, S., et al: ‘Lossy-to-lossless compression of medical volumetric data using three-dimensional integer wavelet transforms’, IEEE Trans. Med. Imaging, 2003, 22, (3), pp. 459–470.
    2. 2)
      • 6. Wu, J., Wu, Z., Wu, C.: ‘Lossy to lossless compressions of hyperspectral images using three-dimensional set partitioning algorithm’. Proc. of SPIE, 2005, p. 5637.
    3. 3)
      • 8. Kim, Y., Pearlman, W.A.: ‘Stripe-based SPIHT lossy compression of volumetric medical images for low memory usage and uniform reconstruction quality’. Proc. of ICASSP, 2000, pp. 2031–2034.
    4. 4)
      • 2. Said, A., Pearlman, W.A.: ‘A new, fast, and efficient image codec based on set partitioning in hierarchical trees’, IEEE Trans. Circuits Syst. Video Technol., 1996, 6, (3), pp. 243–250.
    5. 5)
      • 10. Beladgham, M., Bessaid, A., Taleb-Ahmed, A., et al: ‘Medical image compression using quincunx wavelets and SPIHT coding’, J. Electr. Eng. Technol., 2012, 7, (2), pp. 264–272.
    6. 6)
      • 21. Kim, B.J., Xiong, Z., Pearlman, W.A.: ‘Low bit-rate scalable video coding with 3-D set partitioning in hierarchical trees (3-D SPIHT)’, IEEE Trans. Circuits Syst. Video Technol., 2000, 10, (8), pp. 1374–1387.
    7. 7)
      • 16. Zhong, C., Huaung, M.: ‘An effective improvement on 3D SPIHT’. in Proc. IASP, 2012, pp. 1–4.
    8. 8)
      • 13. Jin, Y., Lee, H.J.: ‘A block-based pass-parallel SPIHT algorithm’, IEEE Trans. Circuits Syst. Video Technol., 2012, 22, (7), pp. 1064–1075.
    9. 9)
      • 7. Xu, R., Li, H., Xie, S.: ‘A new video codec based on 3D-DTCWT and vector SPIHT’. Proc. of SPIE, 2011, p. 8285.
    10. 10)
      • 3. Pearlman, W.A., Islam, A., Nagaraj, N., et al: ‘Efficient, low-complexity image coding with a set-partitioning embedded block coder’, IEEE Trans. Circuits Syst. Video Technol., 2004, 14, (11), pp. 1219–1235.
    11. 11)
      • 4. Taubman, D.: ‘High performance scalable image compression with EBCOT’, IEEE Trans. Image Process., 2000, 9, (7), pp. 1158–1170.
    12. 12)
      • 12. Cho, S., Kim, D., Pearlman, W.A.: ‘Lossless compression of volumetric medical images with improved three-dimensional SPIHT algorithm’, J. Digit. Imaging, 2004, 17, (1), pp. 57–63.
    13. 13)
      • 9. Ginesu, G., Giusto, D.D., Pearlman, W.A.: ‘Lossy to lossless SPIHT-based volumetric image compression’. Proc. of ICASSP, 2004, pp. 693–696.
    14. 14)
      • 5. Kim, B.J., Pearlman, W.A.: ‘An embedded wavelet video coder using three-dimensional set partitioning in hierarchical trees (SPIHT)’. Proc. of DCC'97, 1997, pp. 251–260.
    15. 15)
      • 18. Xu, S., Hu, B., Gao, J.: ‘Realization of 3-D DWT-SPIHT video compression algorithm’. Proc. of SPIE, 2005, vol. 5960, pp. 1949–1956.
    16. 16)
      • 22. Wang, Z., Bovik, A.C., Sheikh, H.R., et al: ‘Image quality assessment: from error visibility to structural similarity’, IEEE Trans. Image Process., 2004, 13, (4), pp. 600–612.
    17. 17)
      • 15. Kabir, M.A., Khan, M.M., Islam, M.T., et al: ‘Image compression using lifting based wavelet transform coupled with SPIHT algorithm’. ICIEV, 2013, pp. 17–18.
    18. 18)
      • 19. He, C., Dong, J., Zheng, Y.F., et al: ‘Optimal 3-D coefficient tree structure for 3-D wavelet video coding’, IEEE Trans. Circuits Syst. Video Technol., 2003, 13, (10), pp. 961–972.
    19. 19)
      • 23. Hwang, Y.T., Lyu, M.W., Lin, C.C.: ‘A low-complexity embedded compression codec design with rate control for high-definition video’, IEEE Trans. Circuits Syst. Video Technol., 2015, 25, (4), pp. 674–687.
    20. 20)
      • 11. Danyali, H., Mertins, A.: ‘Volumetric medical image coding: an object-based, lossy-to-lossless and fully scalable approach’, J. Med. Signals Sens., 2011, 1, (1), pp. 1–11.
    21. 21)
      • 14. Cavero, E., Alesanco, A., Castro, L., et al: ‘SPIHT-based echocardiogram compression: clinical evaluation and recommendations of use’, IEEE J. Biomed. Health Inf., 2013, 17, (1), pp. 103–112.
    22. 22)
      • 20. Available at http://www.datatang.com.
    23. 23)
      • 1. Shapiro, J.M.: ‘Embedded image coding using zerotrees of wavelet coefficients’, IEEE Trans. Signal Process., 1993, 41, (12), pp. 3445–3462.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2016.0564
Loading

Related content

content/journals/10.1049/iet-ipr.2016.0564
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address