Skip to main content
SpringerLink
Log in

A novel medical image enhancement algorithm based on improvement correction strategy in wavelet transform domain

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

Since medical image is often corrupted by all kinds of noises during acquisition or transmission, this will lead to the degradation of the image quality, which further seriously affects the clinical diagnoses. In order to solve the degradation problem and enhance image quality, a novel medical image enhancement algorithm based on improvement correction strategy in wavelet transform domain is proposed in paper. Firstly, the image is decomposed into high-frequency component and low-frequency component by Shear wavelet transform. Secondly, the low-frequency component in wavelet domain is processed by the improved correction method so as to adjust the global contrast of the image, and the improved adaptive threshold function is adopted to reduce the high-frequency noise. Finally, the reconstructed image from inverse wavelets transform is proposed by fuzzy contrast enhancement to highlight the image detail while maintaining excellent spectral information. Many simulation experiments show that our proposed algorithm achieves more favorable performance for the non-reference evaluation and the reference evaluation than these existing state-of-the-art algorithms in handing medicine images, which can effectively improve the image quality. This performance increase is the most pronounced in indexes for our proposed enhancement algorithm, which can raise the rate of conformity in clinical diagnoses.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Li, B., Xie, W.: Adaptive fractional differential approach and its application to medical image enhancement. Comput. Electr. Eng. 45, 323–335 (2015)

    Article  Google Scholar 

  2. Huang, L.-D., Zhao, W., Wang, J.: Ombination of contrast limited adaptive histogram equalization and discrete wavelet transform for image enhancement. IET Image Process 9(10), 908–915 (2015)

    Article  Google Scholar 

  3. Dong, L.-L., Ding, C., Xu, W.-H.: An image enhancement method based on local gradient field equalization. J. Optoelectr. Laser 7(12), 557–565 (2016)

    Google Scholar 

  4. Sun, W.-Z., Li, F., Zhang, Q.-Z.: The applications of improved retinex algorithm for x-Ray medical image enhancement. In: Proceedings of International Conference on Computer Science & Service System, pp. 1655–1658 (2012)

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

    Article  Google Scholar 

  6. Wu, Y., Meng., T.-L., Wu, S.: Adaptive image enhancement based on NSSI and constraint of human eye perception information fidelity. J. Optoelectr. Laser 26(5), 978–985 (2015)

  7. Bhadauria, H.S., Dewal, M.L.: Medical image denoising using adaptive fusion of curvelet transform and total variation. Comput. Electr. Eng. 39(J), 151–160 (2013)

    Google Scholar 

  8. Asmare, M.H., Asirvadam, V.S., Hani, A.F.M.: Image enhancement based on contourlet transform. Signal Image Video Process. 9(7), 1–12 (2016)

    Google Scholar 

  9. Cunha, A.L.D., Jiamping, Z., DOM, N.: The nonsubsampled contourlet transform;theory,design,and applications. IEEE Trans. Image Process. A Publ. IEEE Signal Process. Soc. 15(10), 3089–3101 (2006)

    Article  Google Scholar 

  10. Guoi, K.-H., Labate, D.: Optimally sparse multidimensional reprentation using Shearlets. SLAM J. Math. Anal. 39(1), 298–318 (2007)

    Article  Google Scholar 

  11. Wang, L., Li, B., Tian, L.F.: Multi-modal medical image fusion using the inter-scale and intra-scale dependencies between image shift-invariant shearlet coefficients. Inf. Fusion 19(1), 20–28 (2014)

    Article  Google Scholar 

  12. Biswas, B., Chakrabarti, A., Dey, K N.: Medical image fusion using regional statistics of shif-invariant shearlet domain. In: Proceedings of IEEE Conference on Biomedical Engineering and Sciences, pp. 711–717 (2016)

  13. Yang, HY., Wang, XY., Niu, PP., et al.: Image denoising using nonsubsampled shearlet transform and twin support vector machines. Neural Netw. 57, 152–165 (2014)

  14. Liu, S.-Q., Hu, S.-H., Xiao, Y.: Shearlet domain SAR image de-noising via spare representation. J. Electron. Inf. Technol. 24(9), 2110–2115 (2012)

    Article  Google Scholar 

  15. Zhong, Q.-X., Xia, Q.-X.: Image fusion method based on dual-tree complex wavelet transform. Comput. Eng. Appl. 44(24), 89–95 (2008)

    Google Scholar 

  16. Tang, Z., Wang, S., et al.: Bayesian framework with non-local and low-rank constraint for image reconstruction. J. Phys. doi:10.1088/1742-6596/787/1/012008. CCISP2017 Conference Series

  17. Kingsbury, NG.: The dual-tree complex wavelet transform: a new technique for shift invariance and directional filters. In: Proceedings of 8th IEEE Digital Signal Processing Workshop, Bryce Canyon, Utah, USA. IEEE: IEEE, pp. 86–89 (1998)

  18. Li, J.-X., Sun, W., Xia, L.-Z.: Novel fuzzy contrast enhancement algorithm. J. S. Univ. 34(5), 671–677 (2009)

    MATH  Google Scholar 

  19. Yang, F., Wei, H.: Fusion of infrared polarization and intensity images using support value transform and fuzzy combination rules. Infrared Phys. Technol. 60, 235–243 (2013)

    Article  Google Scholar 

  20. Xu, G., Su, J., Pan, H. et al.: An image enhancement method based on Gamma correction. In: Proceedings of IEEE International Symposium on Computational Intelligence Design, pp. 60–63 (2009)

  21. Men, G.-Z., Yang, J., Zhao, J.: Fuzzy contrast enhancement for remote sensing image based on fuzzy set in non subsampled contourlet domain. In: Proceedings of Ninth International Conference on Machine Learning and Cybernetics, vol. 7, pp. 35–710 (2010)

  22. Wubuli, A., Zhenhong, J., Xizhong, O., et al.: Medical imageenhancement based on Shearlet transform and unsharpmasking. J. Med. Imaging Health Inf. 4(5), 812–818 (2014)

    Google Scholar 

Download references

Acknowledgements

This work was supported in part by the technology Projects of Suzhou science and technology development Project (No. SYSD2015014) and Science and Technology Development Project of Changshu Grant (No. CS201503).

Author information

Authors and Affiliations

  1. Changshu affiliated Hospital of Soochow University (Changshu No. 1 People’s Hospital), Changshu, 215500, Jiangsu, China

    Kai-jian Xia, Jian-qiang Wang & Jian Cai

  2. School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221008, Jiangsu, China

    Kai-jian Xia

Authors
  1. Kai-jian Xia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jian-qiang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jian Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kai-jian Xia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xia, Kj., Wang, Jq. & Cai, J. A novel medical image enhancement algorithm based on improvement correction strategy in wavelet transform domain. Cluster Comput 22 (Suppl 5), 10969–10977 (2019). https://doi.org/10.1007/s10586-017-1264-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-017-1264-y

Keywords

Search

Navigation