Skip to main content

Compression of CT Images using Contextual Vector Quantization with Simulated Annealing for Telemedicine Application

Journal of Medical Systems volume 42, Article number: 218 (2018) Cite this article

Abstract

The role of compression is vital in telemedicine for the storage and transmission of medical images. This work is based on Contextual Vector Quantization (CVQ) compression algorithm with codebook optimization by Simulated Annealing (SA) for the compression of CT images. The region of interest (foreground) and background are separated initially by region growing algorithm. The region of interest is encoded with low compression ratio and high bit rate; the background region is encoded with high compression ratio and low bit rate. The codebook generated from foreground and background is merged, optimized by simulated annealing algorithm. The performance of CVQ-SA algorithm was validated in terms of metrics like Peak to Signal Noise Ratio (PSNR), Mean Square Error (MSE) and Compression Ratio (CR), the result was superior when compared with classical VQ, CVQ, JPEG lossless and JPEG lossy algorithms. The algorithms are developed in Matlab 2010a and tested on real-time abdomen CT datasets. The quality of reconstructed image was also validated by metrics like Structural Content (SC), Normalized Absolute Error (NAE), Normalized Cross Correlation (NCC) and statistical analysis was performed by Mann Whitney U Test. The outcome of this work will be an aid in the field of telemedicine for the transfer of medical images.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price includes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

References

  1. Linde, Y., Buzo, A., and Gray, R., An algorithm for vector quantizer design. IEEE Trans. Commun. 28(1):84–95, 1980.

    Article  Google Scholar 

  2. Cosman, P. C., Oehler, K. L., Riskin, E. A., and Gray, R. M., Using vector quantization for image processing. Proc. IEEE 81(9):1326–1341, 1993.

    CAS  Article  Google Scholar 

  3. Cosman, P. C., Tseng, C., Gray, R. M., Olshen, R. A., Moses, L. E., Davidson, H. C. et al., Tree-structured vector quantization of CT chest scans: image quality and diagnostic accuracy. IEEE Trans. Med. Imaging 12(4):727–739, 1993.

    CAS  Article  Google Scholar 

  4. Nakagaki, R., and Katsaggelos, A. K., A VQ-based blind image restoration algorithm. IEEE Trans. Image Process. 12(9):1044–1053, 2003.

    Article  Google Scholar 

  5. Kekre, H. B., and Shrinath, P., Tumour delineation using statistical properties of the breast us images and vector quantization based clustering algorithms. International Journal of Image, Graphics and Signal Processing 5(11):1, 2013.

    Article  Google Scholar 

  6. Chang, C. C., and Hu, Y. C., A fast LBG codebook training algorithm for vector quantization. IEEE Trans. Consum. Electron. 44(4):1201–1208, 1998.

    Article  Google Scholar 

  7. Said, A., and Pearlman, W. A., A new, fast, and efficient image codec based on set partitioning in hierarchical trees. IEEE Transactions on Circuits and Systems for Video Technology 6(3):243–250, 1996.

    Article  Google Scholar 

  8. Christopoulos, C., Askelof, J., and Larsson, M., Efficient methods for encoding regions of interest in the upcoming JPEG2000 still image coding standard. IEEE Signal Processing Letters 7(9):247–249, 2000.

    Article  Google Scholar 

  9. Park, K. H., and Park, H. W., Region-of-interest coding based on set partitioning in hierarchical trees. IEEE Transactions on Circuits and Systems for Video Technology 12(2):106–113, 2002.

    Article  Google Scholar 

  10. Chiu, E., Vaisey, J., and Atkins, M. S., Wavelet-based space-frequency compression of ultrasound images. IEEE Trans. Inf. Technol. Biomed. 5(4):300–310, 2001.

    CAS  Article  Google Scholar 

  11. Askelöf, J., Carlander, M. L., and Christopoulos, C., Region of interest coding in JPEG 2000. Signal Process. Image Commun. 17(1):105–111, 2002.

    Article  Google Scholar 

  12. Liu, L., and Fan, G., A new JPEG2000 region-of-interest image coding method: Partial significant bitplanes shift. IEEE Signal Processing Letters 10(2):35–38, 2003.

    Article  Google Scholar 

  13. Hosseini, S. M., and Naghsh-Nilchi, A. R., Medical ultrasound image compression using contextual vector quantization. Comput. Biol. Med. 42(7):743–750, 2012.

    Article  Google Scholar 

  14. Ansari, M. A., and Anand, R. S., Context-based medical image compression for ultrasound images with contextual set partitioning in hierarchical trees algorithm. Adv. Eng. Softw. 40(7):487–496, 2009.

    Article  Google Scholar 

  15. Huang, H. C., Pan, J. S., Lu, Z. M., Sun, S. H., and Hang, H. M., Vector quantization based on genetic simulated annealing. Signal Process. 81(7):1513–1523, 2001.

    Article  Google Scholar 

  16. Kamal, A. N. B., Iteration free fractal image compression for color images using vector quantization, genetic algorithm, and simulated annealing. Turkish Online Journal of Science & Technology 5(1):39–48, 2015.

    Google Scholar 

  17. Vallabhaneni, R. B., & Rajesh, V., On the Performance Characteristics of Embedded Techniques for Medical Image Compression. 76:662-665, 2017.

  18. Jiang, H., et al., Medical image compression based on vector quantization with variable block sizes in the wavelet domain. Computational Intelligence and Neuroscience, 5, 2012.

  19. Gaudeau, Y., and Moureaux, J. M., Lossy compression of volumetric medical images with 3D dead-zone lattice vector quantization. Annals of Telecommunications-annales des télécommunications 64(5-6):359–367, 2009.

    Article  Google Scholar 

  20. Ayoobkhan, M. U. A., Chikkannan, E., and Ramakrishnan, K., Lossy image compression based on prediction error and vector quantization. EURASIP Journal on Image and Video Processing 2017(1):35, 2017.

    Article  Google Scholar 

  21. Huang, Z., Zhang, X., Chen, L., Zhu, Y., An, F., Wang, H., and Feng, S., A Hardware-Efficient Vector Quantizer Based on Self-Organizing Map for High-Speed Image Compression. Appl. Sci. 7(11):1106, 2017.

    Article  Google Scholar 

  22. Nowaková, J., Prílepok, M., and Snášel, V., Medical image retrieval using vector quantization and fuzzy S-tree. J. Med. Syst. 41(2):18, 2017.

    Article  Google Scholar 

  23. Eben Sophia, P., and Anitha, J., Contextual Medical Image Compression using Normalized Wavelet-Transform Coefficients and Prediction. IETE J. Res. 63(5):671–683, 2017.

    Article  Google Scholar 

  24. Moreno-Bernal, P., Cruz-Chávez, M.A., Rodríguez-León, A., López, O., Malumbres, M.P., Martínez-Rangel, M.G., Martínez-Oropeza, A., Martínez-Bahena, B. and Juárez-Chávez, J.Y., Simulated annealing algorithm for 2D image compression. In Electronics, Robotics and Automotive Mechanics Conference (CERMA), IEEE Ninth, 129-134. 2012.

  25. Hopkins, M., Mitzenmacher, M., & Wagner-Carena, S., Simulated Annealing for JPEG Quantization. arXiv preprint arXiv:1709.00649. 2017.

  26. Chakrapani, Y., and Rajan, K. S., Hybrid genetic-simulated annealing approach for fractal image compression. Int. J. Comput. Intell. 4:308–313, 2008.

    Google Scholar 

  27. Demsar, J., Statistical comparisons of classifiers over multiple datasets. J. Mach. Learn. Res. 7:1–30, 2006.

    Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the support provided by DST under IDP scheme (No: IDP/MED/03/2015). We thank Dr. P. Sebastian Varghese (Consultant Radiologist, Metro Scans & Laboratory, Trivandrum) for providing the medical CT images and supporting us in the preparation of manuscript.

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Jeppiaar Nagar, Rajiv Gandhi Salai, Chennai, Tamil Nadu, 600 119, India

    S. N. Kumar

  2. School of Computer Science Engineering, Mar Ephraem College of Engineering and Technology, Elavuvilai, Kanyakumari, Tamil Nadu, 629171, India

    A. Lenin Fred

  3. Metro Scans & Laboratory, Trivandrum, Kerala, 695011, India

    P. Sebastin Varghese

Authors
  1. S. N. Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Lenin Fred
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. Sebastin Varghese
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. N. Kumar.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest.

Additional information

This article is part of the Topical Collection on Image & Signal Processing

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kumar, S.N., Lenin Fred, A. & Sebastin Varghese, P. Compression of CT Images using Contextual Vector Quantization with Simulated Annealing for Telemedicine Application. J Med Syst 42, 218 (2018). https://doi.org/10.1007/s10916-018-1090-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-018-1090-7

Keywords

  • Compression
  • Contextual vector quantization
  • Region growing
  • Code book
  • Telemedicine