Multimodal Image Fusion Based on Non-subsampled Shearlet Transform and Neuro-Fuzzy

  • Haithem HermessiEmail author
  • Olfa Mourali
  • Ezzeddine Zagrouba
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 684)


Due to the appealing advantages in term of medical decision making, the problem of multimodal medical image fusion has received focused research over the recent years. Moreover, complimentary imaging modalities such as CT and MRI are able to improve medical reliability by reducing uncertainty. In this paper, we propose a new algorithm for multimodal medical image fusion based on non-subsampled shearlet transform (NSST) and neuro-fuzzy. Firstly, CT and MR source images are decomposed using the NSST to obtain low and high frequency sub-bands. Maximization of absolute value is performed to fuse low frequency coefficients while high frequency coefficients are fused using the neuro-fuzzy approach. Finally, the inverse NSST is performed to gain the fused image. To assess the performance of the proposed method, several experiments are carried on different medical CT and MR image datasets. Subjective and objective assessments reveal that the proposed scheme produces better results in various quantitative criterions compared to other existing methods.


Multimodal image fusion Non-subsampled shearlet transform Neuro-fuzzy 



The authors would like to thank Dr S. Rajkuma, the VIT University, Vellore-India, for providing image datasets of patients at different modalities.


  1. 1.
    James, A.P., Belur, V.D.: Medical image fusion: a survey of the state of the art. Inf. Fusion 19, 4–19 (2014)CrossRefGoogle Scholar
  2. 2.
    Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)CrossRefzbMATHGoogle Scholar
  3. 3.
    Wang, A., Sun, H., Guan, Y.: The application of wavelet transform to multi-modality medical image fusion. In: IEEE International Conference on Networking, Sensing and Control, Ft. Lauderdale, FL, pp. 270–274 (2006)Google Scholar
  4. 4.
    Yang, L., Guo, B.L., Ni, W.: Multimodality medical image fusion based on multiscale geometric analysis of contourlet transform. Neurocomputing 72(1–3), 203–211 (2008)CrossRefGoogle Scholar
  5. 5.
    Mankar, R., Daimiwal, N.: Multimodal medical image fusion under non-subsampled contourlet transform domain. In: International Conference on Communications and Signal Processing (ICCSP), 2015, Melmaruvathur, pp. 0592–0596 (2015)Google Scholar
  6. 6.
    Jothi, C., Elvina, N., Vetrivelan, P.: Medical image fusion using ridgelet transform. In: International Conference on Innovations in Intelligent Instrumentation, Optimization and Signal Processing, pp. 21–25 (2013)Google Scholar
  7. 7.
    Lu, H.M., Nakashima, S., Li, Y.J., Zhang, L.F., Yang, S.Y., Seiichi, S.: An improved method for CT/MRI image fusion on bandelets transform domain. Appl. Mech. Mater. 103, 700–704 (2012)CrossRefGoogle Scholar
  8. 8.
    Ali, F.E., El-Dokany, I.M., Saad, A.A., Abd El-Samie, F.E.: A curvelet transform approach for the fusion of MR and CT images. J. Modern Optics 57(4), 273–286 (2010)CrossRefzbMATHGoogle Scholar
  9. 9.
    Kutyniok, G., Labate, D.: Introduction to shearlets. In: Kutyniok, G., Labate, D. (eds.) Shearlets: Multiscale Analysis for Multivariate Data, Birkhäuser, Boston (2012)Google Scholar
  10. 10.
    Do, M.N., Vetterli, M.: The contourlet transform: an efficient directional multiresolution image representation. IEEE Trans. Image Process. 14(12), 2091–2106 (2005)CrossRefGoogle Scholar
  11. 11.
    Easley, G., Labate, D., Lim, W.Q.: Sparse directional image representations using the discrete shearlet transform. Appl. Comput. Harmonic Anal. 25(1), 25–46 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Guo, K., Labate, D., Lim, W.Q.: Edge analysis and identification using the continuous shearlet transform. Appl. Comput. Harmonic Anal. 27(1), 24–46 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Miao, Q.G., Shi, C., Xu, P.F., Yang, M., Shi, Y.B.: A novel algorithm of image fusion using shearlets. Optics Commun. 284(6), 1540–1547 (2011)CrossRefGoogle Scholar
  14. 14.
    Deng, C., Wang, S., Chen, X.: Remote sensing images fusion algorithm base on shearlet transform. In: Proceeding of International Conference on Environmental Science and Information Application Technology, pp. 451–454. ACM, Wu Han, China, (2009)Google Scholar
  15. 15.
    Wang, L., Li, B., Tian, L.F.: EGGDD: an explicit dependency model for multi-modal medical image fusion in shift-invariant shearlet transform domain. Inf. Fusion 19, 29–37 (2014)CrossRefGoogle Scholar
  16. 16.
    Singh, S., Gupta, D., Anand, R.S., Kumar, V.: Non-subsampled shearlet based CT and MR medical image fusion using biologically inspired spiking neural network. Biomed. Signal Process. Control 18, 91–101 (2015)CrossRefGoogle Scholar
  17. 17.
    Teng, J., Wang, S., Zhang, J., Wang, X.: Neuro-fuzzy logic based fusion algorithm of medical images. In: 3rd International Congress on Image and Signal Processing (CISP), 2010, Yantai, pp. 1552–1556 (2010)Google Scholar
  18. 18.
    Das, S., Kundu, M.K.: A neuro-fuzzy approach for medical image fusion. IEEE Trans. Biomed. Eng. 60(12), 3347–3353 (2013)CrossRefGoogle Scholar
  19. 19.
    Kavitha, C.T., Chellamuthu, C.: Multimodal medical image fusion based on Integer Wavelet Transform and Neuro-Fuzzy. In: International Conference on Signal and Image Processing (ICSIP), 2010, pp. 296–300 (2010)Google Scholar
  20. 20.
    Rajkumar, S., Bardhan, P., Akkireddy, S.K., Munshi, C.: CT and MRI image fusion based on Wavelet Transform and Neuro-Fuzzy concepts with quantitative analysis. In: International Conference on Electronics and Communication Systems (ICECS), 2014, Coimbatore, pp. 1–6 (2014)Google Scholar
  21. 21.
    Guo, K., Lim, W., Labate, D., Weiss, G., Wilson, E.: Wavelets with composite dilation s and their MRA properties. Appl. Comput. Harmonic Anal. 20(2), 231–249 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Liu, S., Shi, M., Zhu, Z., Zhao, J.: Image fusion based on complex-shearlet domain with guided filtering. Multidimension. Syst. Signal Process. 20(2), 1–18 (2015)Google Scholar
  23. 23.
    Kong, W.W., Liu, J.P.: Technique for image fusion based on non-subsampled shearlet transform and improved pulse-coupled neural network. Optical Eng. 52(1), 017001/1–12 (2013)Google Scholar
  24. 24.
    Balasubramaniam, P., Ananthi, V.P.: Image fusion using intuitionistic fuzzy sets. Inf. Fusion 20, 21–30 (2014)CrossRefGoogle Scholar
  25. 25.
    Rao, D.S., Seetha, M., Hazarath, M.: Iterative image fusion using neuro fuzzy logic and applications. In: International Conference on Machine Vision and Image Processing (MVIP), 2012, Taipei, pp. 121–124 (2012)Google Scholar
  26. 26.
    Geng, P., Wang, Z., Zhang, Z., Xiao, Z.: Image fusion by pulse couple neural network with shearlet. Optical Eng. 51, 067005 (2012)CrossRefGoogle Scholar
  27. 27.
    Zagrouba, E., Barhoumi, W.: Semiautomatic detection of tumoral zone. Image Anal. Stereology 21(1), 13–18 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  28. 28.
    Shearlet webpage.
  29. 29.

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Haithem Hermessi
    • 1
    Email author
  • Olfa Mourali
    • 1
  • Ezzeddine Zagrouba
    • 1
  1. 1.Research Team SIIVA - LIMTIC Laboratory, Higher Institute of Computer ScienceUniversity of Tunis El ManarTunisTunisia

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