Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2017, Article ID 3969152, 10 pages
https://doi.org/10.1155/2017/3969152
Research Article

Method of Improved Fuzzy Contrast Combined Adaptive Threshold in NSCT for Medical Image Enhancement

1College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
2Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200400, China
3Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland 1020, New Zealand

Correspondence should be addressed to ZhenHong Jia; moc.uhos@9009hhzj

Received 3 November 2016; Revised 6 April 2017; Accepted 14 May 2017; Published 28 June 2017

Academic Editor: Ayache Bouakaz

Copyright © 2017 Fei Zhou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Y. Yang, Y. Que, S. Huang, and P. Lin, “Multimodal Sensor Medical Image Fusion Based on Type-2 Fuzzy Logic in NSCT Domain,” IEEE Sensors Journal, vol. 16, no. 10, pp. 3735–3745, 2016. View at Publisher · View at Google Scholar · View at Scopus
  2. H. Lidong, Z. Wei, W. Jun, and S. Zebin, “Combination of contrast limited adaptive histogram equalisation and discrete wavelet transform for image enhancement,” IET Image Processing, vol. 9, no. 10, pp. 908–915, 2015. View at Publisher · View at Google Scholar · View at Scopus
  3. T. Celik and T. Tjahjadi, “Automatic image equalization and contrast enhancement using Gaussian mixture modeling,” IEEE Transactions on Image Processing, vol. 21, no. 1, pp. 145–156, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  4. C. Lee, C. Lee, Y. Y. Lee, and C. S. Kim, “Power-constrained contrast enhancement for emissive displays based on histogram equalization,” IEEE Transactions on Image Processing, vol. 21, no. 1, pp. 80–93, 2012. View at Publisher · View at Google Scholar · View at MathSciNet
  5. Z. Wei, H. Lidong, W. Jun, and S. Zebin, “Entropy maximisation histogram modification scheme for image enhancement,” IET Image Processing, vol. 9, no. 3, pp. 226–235, 2015. View at Publisher · View at Google Scholar · View at Scopus
  6. Y. Li, C. Z. Hou, F. Tian, H. L. Yu, L. Guo, G. Z. Xu et al., “Enhancement of infrared image based on the retinex theory,” in Proceedings of the 29th Annual International Conference of the IEEE Engineering in Medicine Biology Society, pp. 3315–3318, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. J. H. Jang, S. D. Kim, and J. B. Ra, “Enhancement of optical remote sensing images by subband-decomposed multiscale retinex with hybrid intensity transfer function,” IEEE Geoscience and Remote Sensing Letters, vol. 8, no. 5, pp. 983–987, 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. H. S. Bhadauria and M. L. Dewal, “Medical image denoising using adaptive fusion of curvelet transform and total variation,” Computers and Electrical Engineering, vol. 39, no. 5, pp. 1451–1460, 2013. View at Publisher · View at Google Scholar · View at Scopus
  9. M. H. Asmare, V. S. Asirvadam, and A. F. M. Hani, “Image enhancement based on contourlet transform,” Signal, Image and Video Processing, vol. 9, no. 7, pp. 1679–1690, 2014. View at Publisher · View at Google Scholar · View at Scopus
  10. A. L. da Cunha, J. Zhou, and M. N. Do, “Nonsubsampled contourlet transform: filter design and applications in denoising,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '05), vol. 1, pp. 749–752, September 2005. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Sajjadi, R. Amirfattahi, and M. R. Ahmadzadeh, “A new NSCT based contrast enhancement algorithm for amplification of early signs of ischemic stroke in brain CT images,” in Proceedings of the 7th Iranian conference on Machine Vision and Image Processing (MVIP), 2011. View at Publisher · View at Google Scholar · View at Scopus
  12. L. Liu, Z. Jia, J. Yang et al., “A medical image enhancement method using adaptive thresholding in NSCT domain combined unsharp masking,” International Journal of Imaging Systems & Technology, vol. 25, no. 3, pp. 199–205, 2015. View at Publisher · View at Google Scholar
  13. S. A. Pal and R. A. King, “Image enhancement using smoothing with fuzzy sets,” IEEE Transactions on Systems, Man and Cybernetics, vol. 11, no. 7, pp. 494–501, 1981. View at Publisher · View at Google Scholar
  14. L. J. Wang and T. Yan, “A new approach of image enhancement based on improved fuzzy domain algorithm,” in Proceedings of the International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI), pp. 1–5, 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. K. Hasikin and N. A. M. Isa, “Enhancement of the low contrast image using fuzzy set theory,” in Proceedings of the UKSim 14th Conference on Modelling and Simulation, pp. 371–386, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Liu, X. Sun, H. Deng et al., “Image enhancement based on intuitionistic fuzzy sets theory,” Iet Image Processing, vol. 10, no. 10, 2016. View at Google Scholar
  17. J. J. Wang, Z. H. Jia, X. Z. Qin, J. Yang, and N. Kasabov, “Medical image enhancement algorithm based on NSCT and the improved fuzzy contrast,” International Journal of Imaging Systems and Technology, vol. 25, no. 1, pp. 7–14, 2014. View at Publisher · View at Google Scholar
  18. A. L. da Cunha, J. Zhou, and M. N. Do, “The nonsubsampled contourlet transform: theory, design, and applications,” IEEE Transactions on Image Processing, vol. 15, no. 10, pp. 3089–3101, 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. A. P. Dhawan, G. Buelloni, and R. Gordon, “Enhancement of Mammographic Features by Optimal Adaptive Neighborhood Image Processing,” IEEE Transactions on Medical Imaging, vol. 5, no. 1, pp. 8–15, 1986. View at Publisher · View at Google Scholar · View at Scopus
  20. A. Thakur and D. Mishra, “Fuzzy contrast mapping for image enhancement,” in Proceedings of the 2nd International Conference on Signal Processing and Integrated Networks (SPIN), pp. 549–552, 2015. View at Publisher · View at Google Scholar · View at Scopus
  21. H. Wang, H. Cai, and W. Lu, “Locally adaptive bivariate shrinkage algorithm for image denoising based on nonsubsampled contourlet transform,” in Proceedings of the International Conference on Computer, Mechatronics, Control and Electronic Engineering, pp. 33–36, 2010. View at Publisher · View at Google Scholar · View at Scopus
  22. C. H. Yu and W. L. Feng, “Image detection method based on fuzzy set theory,” in Proceedings of the 2nd International Conference on Multimedia Technology, pp. 364–367, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. T. Chaira, “An improved medical image enhancement scheme using Type II fuzzy set,” Applied Soft Computing Journal, vol. 25, pp. 293–308, 2014. View at Publisher · View at Google Scholar · View at Scopus