Skip to main content
Log in

Spinal cord MRI contrast enhancement using adaptive gamma correction for patient with multiple sclerosis

  • Original Article
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Magnetic resonance imaging (MRI) is a clinically important tool for diagnosing several neurological diseases such as the multiple sclerosis (MS). Brain MRI has always facilitated the examination of the MS pathology. Moreover, spinal cord MRI is greatly suggested for the management of such disease, even though the use of conventional spinal cord MRI can be a challenging task. In fact, it is a long and fine organ that has some mobility and that suffers from breathing artifacts, low contrast, heartbeat and cerebro-spinal fluid flows. In this study, to identify spinal cord damage in MS patient, an adaptive MRI contrast enhancement (CE), called the LL-GAGC method, is proposed. This novel technique is based on a combination of the adaptive gamma correction and the discrete wavelet transform with singular value decomposition algorithms. The main reason of this association is to enhance adaptively the contrast of dark MR images while preserving edge information from any distortion. A large database formed by 112 T2-w spinal cord MR images was examined for assessment of the proposed LL-GAGC CE method. Qualitative and quantitative evaluations demonstrated that our proposed algorithm performs well in enhancing the contrast of dark MR images with the benefit of preserving brightness information and edges details.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Polman, C.H., Reingold, S.C., Banwell, B., et al.: Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann. Neurol. 69, 292–302 (2011)

    Article  Google Scholar 

  2. Simon, J.H., Li, D., Traboulsee, A., et al.: Standardized MR imaging protocol for multiple sclerosis: consortium of MS Centers consensus guidelines. Am J Neuroradiol 27, 455–461 (2006)

    Google Scholar 

  3. Bot, J.C., Barkhof, F.: Spinal-cord MRI in multiple sclerosis: conventional and nonconventional MR techniques. Neuroimaging Clin. N. Am. 19, 81–99 (2009)

    Article  Google Scholar 

  4. McGowan, J.C.: Technical issues for MRI examination of the spinal cord. J. Neurol. Sci. 172, 27–31 (2000)

    Article  Google Scholar 

  5. Taber, K.H., Herrick, R.C., Weathers, S.W., Kumar, A.J., Schomer, D.F., Hayman, L.A.: Pitfalls and artifacts encountered in clinical MR imaging of the spine. Radiographics 18, 1499–1521 (1998)

    Article  Google Scholar 

  6. Liang, Y., Yang, L., Fan, H.: Image enhancement for liver CT images. In: Proceedings of the SPIE International Conference on Optical Instruments and Technology: Optoelectronic Imaging and Process Technology (2009)

  7. Somasundaram, K., Kalavathi, P.: Medical image contrast enhancement based on gamma correction. Int. J. Knowl. Manag. e-Learn. 3(1), 15–18 (2011)

    Google Scholar 

  8. Yang, Y., Su, Z., Sun, L.: Medical image enhancement algorithm based on wavelet transform. IEEE Electron. Lett. 46(2), 120–121 (2010)

    Article  Google Scholar 

  9. Wyatt, C., Wang, Y.-P., Freedman, M.T., Loew, M., Wang, Y.: Biomedical information technology. Data Process. Anal. 7, 165–169 (2007)

    Google Scholar 

  10. Fan, Y.-C., Taso, H.-W., Yang, H.-Y., Lee, Y.-C.: A novel algorithm of local contrast enhancement for medical image. In: IEEE Nuclear Science Symposium, pp. 3951–3954 (2007)

  11. Vidaurrazaga, M., Diago, L.A., Cruz, A.: Contrast enhancement with wavelet transform in radiololgical images. In: IEEE EMBS, pp. 1760–1763 (2000)

  12. Shanmugavadivu, P., Balasubramanian, K.: Particle swarm optimized multi-objective histogram equalization for image enhancement. Opt. Laser Technol. 57, 243–251 (2014)

    Article  Google Scholar 

  13. Kim, T.K., Paik, J.K., Kang, B.S.: Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering. IEEE Trans. Consum. Electron. 44, 1 (1998)

    Article  Google Scholar 

  14. Kim, J.Y., Kim, L.S., Hwang, S.: An advanced contrast enhancement using partially overlapped sub-block histogram equalization. IEEE Trans. Circuits Syst. Video Technol. 11, 475–484 (2001)

    Article  Google Scholar 

  15. Demirel, H., Anbarjafari, G., Jahromi, M.N.: Image equalization based on singular value decomposition. In: Proc. 23rd IEEE International Symposium on Computer and Information Sciences, pp. 1–5 (2008)

  16. Demirel, H., Ozcinar, C., Anbarjafari, G.: Satellite image contrast enhancement using discrete wavelet transform and singular value decomposition. IEEE Geosci. Remote Sens. 7, 333–337 (2010)

    Article  Google Scholar 

  17. Bhandari, A.K., Kumar, A., Padhy, P.K.: Enhancement of low contrast satellite images using discrete cosine transform and singular value decomposition. World Acad. Sci. Eng. Technol. 79, 35–41 (2011)

    Google Scholar 

  18. Bhandari, A.K., Kumar, A., Singh, G.K., et al.: Dark satellite image enhancement using knee transfer function and gamma correction based on DWT–SVD. Multidimens. Syst. Signal Process. 27, 453 (2016)

    Article  Google Scholar 

  19. Rahmen, S., Rahman, M.M., Abdullah Al Wadud, M., Al Quaderi, G.D., Shoyaib, M.: An adaptive gamma correction for image enhancement. EURASIP 35, 2–13 (2016)

    Google Scholar 

  20. Kallel, F., Ben Hamida, A.: A new adaptive gamma correction based algorithm using DWT–SVD for non-contrast CT image enhancement. IEEE Trans. NanoBiosci. 16(8), 666–675 (2017)

    Article  Google Scholar 

  21. Cao, G., et al.: Contrast enhancement of brightness-distorted images by improved adaptive gamma correction. Comput. Electr. Eng. 66, 569–582 (2017)

    Article  Google Scholar 

  22. Huang, Z., Fang, H., Li, Q., Li, Z., Zhang, T., Sang, N., Li, Y.: Optical remote sensing image enhancement with weak structure preservation via spatially adaptive gamma correction. Infrared Phys. Technol. 94, 38–47 (2018)

    Article  Google Scholar 

  23. Huang, Z., Zhang, T., Li, Q., Fang, H.: Adaptive gamma correction based on cumulative histogram for enhancing near-infrared images. Infrared Phys. Technol. 79, 205–215 (2016)

    Article  Google Scholar 

  24. Kim, Y.T.: Contrast enhancement using brightness preserving bi-histogram equalization. IEEE Trans. Consum. Electron. 43(1), 1–8 (1997)

    Article  Google Scholar 

  25. Ibrahim, H., Kong, N.S.P.: Brightness preserving dynamic histogram equalization for image contrast enhancement. IEEE Trans. Consum. Electron. 53(1), 1752–1758 (2007)

    Article  Google Scholar 

  26. Yao, Z., Lai, Z., Wang, C., Xia, W.: Brightness preserving and contrast limited bi-histogram equalization for image enhancement. In: 3rd International Conference on Systems and Informatics (ICSAI) Shanghai, pp. 866–870 (2016)

  27. Zarie, M., Hajghassem, H., Majd, A.E.: Contrast enhancement using triple dynamic clipped histogram equalization based on mean or median. Optik 175, 126–137 (2018)

    Article  Google Scholar 

  28. Bhandari, A., Soni, V., Kumar, A., Singh, G.: Artificial bee colony-based satellite image contrast and brightness enhancement technique using DWT–SVD. Int. J. Remote Sens. 35(5), 1601–1624 (2014)

    Article  Google Scholar 

  29. Atta, R., Abdel-Kader, R.F.: Brightness preserving based on singular value decomposition for image contrast enhancement. Optik Int. J. Light Electron Opt. 126(7–8), 799–803 (2015)

    Article  Google Scholar 

  30. Kallel, F., Sahnoun, M., Ben Hamida, A., et al.: CT scan contrast enhancement using singular value decomposition and adaptive gamma correction. SIViP 12(5), 905–913 (2018)

    Article  Google Scholar 

  31. Bhandari, A.K., Gadde, M., Kumar, A., Singh, G.K.: Comparative analysis of different wavelet filters for low contrast and brightness enhancement of multispectral remote sensing images. In: Proceedings of the IEEE International Conference on Machine Vision and Image Processing (MVIP), pp. 81–86 (2012)

  32. Mallat, S.G.: A theory for multi resolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)

    Article  Google Scholar 

  33. Bhandari, A.K., Soni, V., Kumar, A., Singh, G.K.: Cuckoo search algorithm based satellite image contrast and brightness enhancement using DWT–SVD. ISA Trans. 53(4), 1286–1296 (2014)

    Article  Google Scholar 

  34. Huang, S.-C., Cheng, F.-C., Chiu, Y.-S.: Efficient contrast enhancement using adaptive gamma correction with weighting distribution. IEEE Trans. Image Process. 22(3), 1032–1041 (2013)

    Article  MathSciNet  Google Scholar 

  35. Hasikin, K., Mat Isa, N.A.: Adaptive fuzzy contrast factor enhancement technique for low contrast and nonuniform illumination images. SIViP 8(8), 1591–1603 (2014)

    Article  Google Scholar 

  36. Kaur, A., Singh, C.: Contrast enhancement for cephalometric images using wavelet-based modified adaptive histogram equalization. Appl. Soft Comput. J. 51, 180–191 (2016)

    Article  Google Scholar 

  37. Santhi, K., Wahida Banu, R.S.D.: Contrast enhancement by modified octagon histogram equalization. SIViP 9(Suppl 1), 73 (2015)

    Article  Google Scholar 

  38. Wang, Z., Bovik, A.L., Sheikh, H.R., Simoncelli, R.E.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  39. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error measurement to structural similarity. IEEE Trans. Image Process. 13, 87–94 (2004)

    Article  Google Scholar 

  40. Oliva, D., Cuevas, E., Pajares, G., Zaldivar, D., Osuna, V.: A multilevel thresholding algorithm using electromagnetism optimization. Neurocomputing 139, 357–381 (2014)

    Article  Google Scholar 

  41. Garg, R., Mittal, B., Garg, S.: Histogram equalization techniques for image enhancement. IJECT 2(1), 107–111 (2011)

    Google Scholar 

  42. Chen, G.-H., et al.: Edge-based structural similarity for image quality assessment. In: 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings, vol. 2, pp. II–II (2006)

  43. Hiary, H., Zaghloul, R., Al-Adwan, A., et al.: Image contrast enhancement using geometric mean filter. SIViP 11, 833 (2017)

    Article  Google Scholar 

  44. Traboulsee, A.L., Li, D.K.: The role of MRI in the diagnosis of multiple sclerosis. Adv. Neurol. 98, 125 (2006)

    Google Scholar 

  45. Schneider, E., Zimmermann, H., Oberwahrenbrock, T., Kaufhold, F., Kadas, E.M., et al.: Optical coherence tomography reveals distinct patterns of retinal damage in neuromyelitis optica and multiple sclerosis. PLoS ONE 8, 1–10 (2013)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mouna Sahnoun.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (PDF 1062 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sahnoun, M., Kallel, F., Dammak, M. et al. Spinal cord MRI contrast enhancement using adaptive gamma correction for patient with multiple sclerosis. SIViP 14, 377–385 (2020). https://doi.org/10.1007/s11760-019-01561-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-019-01561-x

Keywords

Navigation