Skip to main content
SpringerLink
Log in

Significance of Magnetic Resonance Image Details in Sparse Representation Based Super Resolution

  • Conference paper
  • First Online:
Medical Image Understanding and Analysis (MIUA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 723))

Included in the following conference series:

  • 3083 Accesses

Abstract

Diverse constraints on image acquisition environment often limit the resolution in cross-slice direction of Magnetic Resonance (MR) image volume, which does not meet the requirement of isotropic 3D MR images in accurate medical diagnosis. This paper proposes an algorithm to restore isotropic 3D MR images from anisotropic 2D multi-slice volumes, by preserving the MR details that play significant role in medical diagnosis. The MR image details are preserved using dictionaries, which are learned using fine to coarse patch details, extracted from different scales of MR image. Learned dictionaries provide detail information for restoring MR patch details. Furthermore, a constraint is used to preserve edges within the restored MR image by minimizing an energy cost. Here, the constraint is weighted adaptively according to the dominant edge orientation of the image, to preserve the details along different orientations effectively. Experimental results demonstrate the ability of our approach to preserve MR image details.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Available at http://personales.upv.es/jmanjon/demo2.zip.

References

  1. Van Reeth, E., Tham, I.W.K., Tan, C.H., Poh, C.L.: Super-resolution in magnetic resonance imaging: a review. Concepts Magn. Resona.-Part A 40(6), 306–325 (2012)

    Article  Google Scholar 

  2. Dagia, C., Ditchfield, M.: 3T MRI in paediatrics: challenges and clinical applications. Eur. J. Radiol. 68(2), 309–319 (2008)

    Article  Google Scholar 

  3. Resnick, S.M., Goldszal, A.F., Davatzikos, C., Golski, S., Kraut, M.A., Metter, E.J., Zonderman, A.B.: One-year age changes in MRI brain volumes in older adults. Cereb. Cortex 10(5), 464 (2000)

    Article  Google Scholar 

  4. Zhang, M., Nie, H., Pei, Y., Tao, L.: Volume reconstruction for MRI. In: Proceedings of International Conference on Pattern Recognition, pp. 3351–3356, August 2014

    Google Scholar 

  5. Iwamoto, Y., Han, X.H., Sasatani, S., Taniguchi, K., Xiong, W., Chen, Y.W.: Super-resolution of MR volumetric images using sparse representation and self-similarity. In: Proceedings of the 21st International Conference on Pattern Recognition, pp. 3758–3761, November 2012

    Google Scholar 

  6. Greenspan, H., Oz, G., Kiryati, N., Peled, S.: MRI inter-slice reconstruction using super-resolution. Magn. Reson. Imaging 20(5), 437–446 (2002)

    Article  MATH  Google Scholar 

  7. Greenspan, H., Oz, G., Kiryati, N., Peled, S.: Super-resolution in MRI. In: Proceedings IEEE International Symposium on Biomedical Imaging, pp. 943–946 (2002)

    Google Scholar 

  8. Hefnawy, A.A.: An efficient super-resolution approach for obtaining isotropic 3-D imaging using 2-D multi-slice MRI. Egypt. Inform. J. 14(2), 117–123 (2013)

    Article  Google Scholar 

  9. Rueda, A., Malpica, N., Romero, E.: Single-image super-resolution of brain MR images using overcomplete dictionaries. Med. Image Anal. 17(1), 113–132 (2013)

    Article  Google Scholar 

  10. Bahrami, K., Shi, F., Zong, X., Shin, H.W., An, H., Shen, D.: Reconstruction of 7T-like images from 3T MRI. IEEE Trans. Med. Imaging 35(9), 2085–2097 (2016)

    Article  Google Scholar 

  11. Rousseau, F.: Brain hallucination. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5302, pp. 497–508. Springer, Heidelberg (2008). doi:10.1007/978-3-540-88682-2_38

    Chapter  Google Scholar 

  12. Manjn, J.V., Coup, P., Buades, A., Fonov, V., Collins, D.L., Robles, M.: Non-local MRI upsampling. Med. Image Anal. 14(6), 784–792 (2010)

    Article  Google Scholar 

  13. Mandal, S., Bhavsar, A., Sao, A.K.: Super-resolving a single intensity/range image via non-local means and sparse representation. In: Proceedings of the Indian Conference on Computer Vision Graphics and Image Processing, (ICVGIP), pp. 1–8, December 2014

    Google Scholar 

  14. Mandal, S., Sao, A.K.: Edge preserving single image super resolution in sparse environment. In: IEEE International Conference on Image Processing, pp. 967–971, September 2013

    Google Scholar 

  15. Yang, J., Wang, Z., Lin, Z., Cohen, S., Huang, T.: Coupled dictionary training for image super-resolution. IEEE Trans. Image Process. 21(8), 3467–3478 (2012)

    Article  MathSciNet  Google Scholar 

  16. Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010)

    Article  MathSciNet  Google Scholar 

  17. Elad, M., Figueiredo, M.A., Ma, Y.: On the role of sparse and redundant representations in image processing. Proc. IEEE 98(6), 972–982 (2010)

    Article  Google Scholar 

  18. Dong, W., Li, X., Zhang, L., Shi, G.: Sparsity-based image denoising via dictionary learning and structural clustering. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 457–464 (2011)

    Google Scholar 

  19. Elad, M., Figueiredo, M.A.T., Ma, Y.: On the role of sparse and redundant representations in image processing. Proc. IEEE 98(6), 972–982 (2010)

    Article  Google Scholar 

  20. Bai, Y., Han, X., Prince, J.L.: Super-resolution reconstruction of MR brain images. In: Proceedings of the 38th Annual Conference on Information Sciences and Systems (CISS 2004) (2004)

    Google Scholar 

  21. Kwan, R.K.S., Evans, A.C., Pike, G.B.: MRI simulation-based evaluation of image-processing and classification methods. IEEE Trans. Med. Imaging 18(11), 1085–1097 (1999)

    Article  Google Scholar 

  22. Daubechies, I., Defrise, M., De Mol, C.: An iterative thresholding algorithm for linear inverse problems with a sparsity constraint. Commun. Pure Appl. Math. 57(11), 1413–1457 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  23. Cocosco, C.A., Kollokian, V., Kwan, R.K.S., Pike, G.B., Evans, A.C.: Brainweb: online interface to a 3D MRI simulated brain database. NeuroImage 5, 425 (1997)

    Google Scholar 

  24. Hore, A., Ziou, D.: Image quality metrics: PSNR vs. SSIM. In: 20th International Conference on Pattern Recognition, pp. 2366–2369, August 2010

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Technology Mandi, Kamand, India

    Prabhjot Kaur, Srimanta Mandal & Anil K. Sao

Authors
  1. Prabhjot Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Srimanta Mandal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anil K. Sao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Prabhjot Kaur .

Editor information

Editors and Affiliations

  1. Department of Neuroimaging Sciences, University of Edinburgh, Edinburgh, United Kingdom

    María Valdés Hernández

  2. University of León, León, Spain

    Víctor González-Castro

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Kaur, P., Mandal, S., Sao, A.K. (2017). Significance of Magnetic Resonance Image Details in Sparse Representation Based Super Resolution. In: Valdés Hernández, M., González-Castro, V. (eds) Medical Image Understanding and Analysis. MIUA 2017. Communications in Computer and Information Science, vol 723. Springer, Cham. https://doi.org/10.1007/978-3-319-60964-5_53

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-60964-5_53

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60963-8

  • Online ISBN: 978-3-319-60964-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation