http://iet.metastore.ingenta.com
1887

Multilevel magnetic resonance imaging compression using compressive sensing

Multilevel magnetic resonance imaging compression using compressive sensing

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Image Processing — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

In this study, a multilevel compressive sensing (CS) compression for magnetic resonance imaging (MRI) images is presented. The proposed algorithm divides the image into frames of equal size, transforms the pixels inside the frame into the sparse domain, and then applies the CS compression to each frame with different level of compression. Four levels of compression are suggested, based on how sparse is the information inside the frame. The proposed algorithm is evaluated using six real MRI images showing different parts of the human body. The experimental results show a significant improvement of 7.03 dB in peak-signal-to-noise ratio and 23.76% in compression level (CL) when compared with a uniform CL algorithm.

References

    1. 1)
      • 1. Candès, E.J.: ‘Compressive sampling’. Proc. Int. Congress of Mathematicians, Madrid, Spain, 2006, vol. 3, pp. 14331452.
    2. 2)
      • 2. Donoho, D.L.: ‘Compressed sensing’, IEEE Trans. Inf. Theory, 2006, 52, (4), pp. 12891306.
    3. 3)
      • 3. Kim, D.-O., Park, R.-H.: ‘Evaluation of image quality using dual-tree complex wavelet transform and compressive sensing’, Electron. Lett., 2010, 46, (7), pp. 494495.
    4. 4)
      • 4. Rontani, D., Choi, D., Chang, C.-Y., et al: ‘Compressive sensing with optical chaos’, Sci. Rep., 2016, 6, pp. 17.
    5. 5)
      • 5. Xu, J., Qiao, Y., Wen, Q., et al: ‘Perceptual rate-distortion optimized image compression based on block compressive sensing’, J. Electron. Imaging, 2016, 25, (5), p. 053004.
    6. 6)
      • 6. Unni, V., Mishra, D., Subrahmanyam, G.: ‘Adaptive multifocus image fusion using block compressed sensing with smoothed projected landweber integration in the wavelet domain’, J. Opt. Soc. Am. A, 2016, 33, (12), pp. 25162525.
    7. 7)
      • 7. Eslahi, N., Aghagolzadeh, A., Andargoli, S.M.H.: ‘Image/video compressive sensing recovery using joint adaptive sparsity measure’, Neurocomputing, 2016, 200, pp. 88109.
    8. 8)
      • 8. Zhou, N., Pan, S., Cheng, S., et al: ‘Image compression–encryption scheme based on hyper-chaotic system and 2D compressive sensing’, Opt. Laser Technol., 2016, 82, pp. 121133.
    9. 9)
      • 9. Qureshi, M.A., Deriche, M.: ‘A new wavelet based efficient image compression algorithm using compressive sensing’, Multimedia Tools Appl., 2016, 75, (12), pp. 67376754.
    10. 10)
      • 10. Liang, R., Kang, L., Huang, J., et al: ‘Reconstruction for infrared image based on block-sparse compressive sensing’. 2016 IEEE 13th Int. Conf. Signal Processing (ICSP), Chengdu, China, 2016, pp. 719722.
    11. 11)
      • 11. Manimala, M., Naidu, C., Giriprasad, M.: ‘Sparse recovery algorithms based on dictionary learning for MR image reconstruction’. Int. Conf. Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India, 2016, pp. 13541360.
    12. 12)
      • 12. Ragab, M., Omer, O.A., Hussien, H.S.: ‘Compressive sensing MRI using dual tree complex wavelet transform with wavelet tree sparsity’. 2017 34th National Radio Science Conf. (NRSC), Port Said, Egypt, 2017, pp. 481486.
    13. 13)
      • 13. Chang, C.-H., Yu, X., Ji, J.X.: ‘Compressed sensing MRI reconstruction from 3D multichannel data using GPUs’, Magn. Reson. Med., 2017, 22652274.
    14. 14)
      • 14. Lustig, M., Donoho, D.L., Santos, J.M., et al: ‘Compressed sensing MRI’, IEEE Signal Process. Mag., 2008, 25, (2), pp. 7282.
    15. 15)
      • 15. Babacan, S.D., Peng, X., Wang, X.-P., et al: ‘Reference-guided sparsifying transform design for compressive sensing MRI’. 2011 Annual Int. Conf. IEEE Engineering in Medicine and Biology Society EMBC, Boston, USA, 2011, pp. 57185721.
    16. 16)
      • 16. Roohi, S.F., Zonoobi, D., Kassim, A.A., et al: ‘Dynamic MRI reconstruction using low rank plus sparse tensor decomposition’. 2016 IEEE Int. Conf. Image Processing (ICIP), Phoenix, USA, 2016, pp. 17691773.
    17. 17)
      • 17. Razzaq, F.A., Mohamed, S., Bhatti, A., et al: ‘Locally sparsified compressive sensing for improved MR image quality’. 2013 IEEE Int. Conf. Systems, Man, and Cybernetics (SMC), Manchester, UK, 2013, pp. 21632167.
    18. 18)
      • 18. Razzaq, F.A., Mohamed, S., Bhatti, A., et al: ‘Non-uniform sparsity in rapid compressive sensing MRI’. 2012 IEEE Int. Conf. Systems, Man, and Cybernetics (SMC), Seoul, Korea, 2012, pp. 22532258.
    19. 19)
      • 19. Safavi, S.H., Torkamani-Azar, F.: ‘Sparsity-aware adaptive block-based compressive sensing’, IET Signal Process., 2016, 11, (1), pp. 3642.
    20. 20)
      • 20. Chartrand, R.: ‘Fast algorithms for non-convex compressive sensing: MRI reconstruction from very few data’. IEEE Int. Symp. Biomedical Imaging: From Nano to Macro, Boston, USA, 2009, pp. 262265.
    21. 21)
      • 21. Tropp, J.A., Gilbert, A.C.: ‘Signal recovery from random measurements via orthogonal matching pursuit’, IEEE Trans. Inf. Theory, 2007, 53, (12), pp. 46554666.
    22. 22)
      • 22. Mallat, S.G., Zhang, Z.: ‘Matching pursuits with time-frequency dictionaries’, IEEE Trans. Signal Process., 1993, 41, (12), pp. 33973415.
    23. 23)
      • 23. Chen, S.S., Donoho, D.L., Saunders, M.A.: ‘Atomic decomposition by basis pursuit’, SIAM Rev., 2001, 43, (1), pp. 129159.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-ipr.2018.5611
Loading

Related content

content/journals/10.1049/iet-ipr.2018.5611
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address