Skip to main content
SpringerLink
Log in

Coefficient Random Permutation Based Compressed Sensing for Medical Image Compression

  • Conference paper
  • First Online:
Advances in Electronics, Communication and Computing

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 443))

Abstract

Compression of medical data remains challenging because of the loss in clarity of compressed images. In medical field, it is necessary to have high image quality in region of interest. This paper presents a Compressed Sensing (CS) method for the compression of medical images. Coefficient random permutation (CRP) based CS is used in this paper for compression of medical images. The different image block has different sparsity. If the nearby pixel values in a block have stronger correlation, then they are strongly sparse, otherwise they are said to be weakly sparse. The main objective of using this method is to provide high quality compressed images thereby maintaining a balanced sparsity of the reconstructed images. As a result performance gain would be high. Experimental results show that CRP based CS helps achieving better PSNR values even with lesser number of measurement values.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Similar content being viewed by others

References

  1. Candes, E.J., Wakin, M.B.: An introduction to compressive sampling. IEEE Signal Process. Mag. 25(2), 21–30 (2008)

    Article  Google Scholar 

  2. Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)

    Article  MathSciNet  Google Scholar 

  3. Gao, Z., Xiong, C., Zhou, C., Wang, H.: Compressive sampling with coefficients random permutations for image compression. In: Proceedings of International Conference on Multimedia and Signal Processing, pp. 321–324 (2011)

    Google Scholar 

  4. Gao, Z., Xiong, C., Ding, L., Zhou, C.: Image representation using block compressive sensing for compression applications. J. Vis. Commun. Image Represent. 24(7), 885–894 (2013)

    Article  Google Scholar 

  5. Tropp, J.A., Gilbert, A.C.: Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans. Inform. Theory 53(12), 4655–4666 (2007)

    Article  MathSciNet  Google Scholar 

  6. Deng, C., Lin, W., Lee, B.S., Lau, C.T.: Robust image compression based on compressive sensing. In: Proceedings of International Conference on Multimedia and Expo (ICME), pp. 462–467 (2010)

    Google Scholar 

  7. Gan, L.: Block compressed sensing of natural images. In: Proceedings of International Conference on Digital Signal Processing, pp. 403–406 (2007)

    Google Scholar 

  8. Sanei, S., Phan, A.H., Lo, J.L., Abolghasemi, V., Cichocki, A.: Compressive sensing approach for progressive transmission of images. In: Proceedings of International Conference on DSP, pp. 1–5 (2009)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. ECE Department, SRM University, Kattankulathur, Chennai, India

    R. Monika & S. Dhanalakshmi

  2. School of Electrical Engineering, VIT, Vellore, India

    S. Sreejith

Authors
  1. R. Monika
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. Dhanalakshmi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. S. Sreejith
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. Monika .

Editor information

Editors and Affiliations

  1. Smart Energy Research Unit, College of Engineering and Science, Victoria University, Melbourne, VIC, Australia

    Akhtar Kalam

  2. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

  3. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Rangpo, Sikkim, India

    Kalpana Sharma

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Monika, R., Dhanalakshmi, S., Sreejith, S. (2018). Coefficient Random Permutation Based Compressed Sensing for Medical Image Compression. In: Kalam, A., Das, S., Sharma, K. (eds) Advances in Electronics, Communication and Computing. Lecture Notes in Electrical Engineering, vol 443. Springer, Singapore. https://doi.org/10.1007/978-981-10-4765-7_56

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-4765-7_56

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-4764-0

  • Online ISBN: 978-981-10-4765-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation