An Efficient Algorithm for Medical Image Fusion Using Nonsubsampled Shearlet Transform

  • Amit Vishwakarma
  • M. K. Bhuyan
  • Yuji Iwahori
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 703)


Multimodal medical image fusion techniques are utilized to fuse two images obtained from dissimilar sensors for obtaining additional information. These methods are used to fuse computed tomography (CT) images with magnetic resonance images (MRI), MR-T1 images with MR-T2 images, and MR images with single photon emission computed tomography (SPECT) images. In proposed method, nonsubsampled shearlet transform (NSST) is used for decomposition of source images to attain the low-frequency and high-frequency bands. The low-frequency bands are fused using weighted saliency-based fusion criteria, and high-frequency bands are fused with the help of phase stretch transform (PST) features. Applying inverse NSST operation, fused image is obtained. The results show the proposed method produces better results compared to state-of-the-art methods.


Medical image fusion Nonsubsampled shearlet transform (NSST) Phase stretch transform (PST) 


  1. 1.
    Asghari, M.H., Jalali, B.: Edge detection in digital images using dispersive phase stretch transform. Journal of Biomedical Imaging 2015, 6 (2015)Google Scholar
  2. 2.
    Bhateja, V., Patel, H., Krishn, A., Sahu, A., Lay-Ekuakille, A.: Multimodal medical image sensor fusion framework using cascade of wavelet and contourlet transform domains. IEEE Sensors Journal 15(12), 6783–6790 (2015)Google Scholar
  3. 3.
    Bhatnagar, G., Wu, Q.J., Liu, Z.: Directive contrast based multimodal medical image fusion in NSCT domain. IEEE transactions on multimedia 15(5), 1014–1024 (2013)Google Scholar
  4. 4.
    Burt, P.J., Kolczynski, R.J.: Enhanced image capture through fusion. In: Computer Vision, 1993. Proceedings., Fourth International Conference on. pp. 173–182. IEEE (1993)Google Scholar
  5. 5.
    Choi, M., Kim, R.Y., Kim, M.G.: The curvelet transform for image fusion. International Society for Photogrammetry and Remote Sensing, ISPRS 2004 35, 59–64 (2004)Google Scholar
  6. 6.
    Collignon, A., Maes, F., Delaere, D., Vandermeulen, D., Suetens, P., Marchal, G.: Automated multi-modality image registration based on information theory. In: Information processing in medical imaging. vol. 3, pp. 263–274 (1995)Google Scholar
  7. 7.
    Easley, G., Labate, D., Lim, W.Q.: Sparse directional image representations using the discrete shearlet transform. Applied and Computational Harmonic Analysis 25(1), 25–46 (2008)Google Scholar
  8. 8.
    Grohs, P., Keiper, S., Kutyniok, G., Schaefer, M.: Alpha molecules: curvelets, shearlets, ridgelets, and beyond. In: SPIE Optical Engineering + Applications. pp. 885804–885804. International Society for Optics and Photonics (2013)Google Scholar
  9. 9.
    Guo, K., Labate, D.: Optimally sparse multidimensional representation using shearlets. SIAM journal on mathematical analysis 39(1), 298–318 (2007)Google Scholar
  10. 10.
    Guorong, G., Luping, X., Dongzhu, F.: Multi-focus image fusion based on non-subsampled shearlet transform. IET Image Processing 7(6), 633–639 (2013)Google Scholar
  11. 11.
    Kaplan, I., Oldenburg, N.E., Meskell, P., Blake, M., Church, P., Holupka, E.J.: Real time MRI-ultrasound image guided stereotactic prostate biopsy. Magnetic resonance imaging 20(3), 295–299 (2002)Google Scholar
  12. 12.
    Kong, W., Liu, J.: Technique for image fusion based on nonsubsampled shearlet transform and improved pulse-coupled neural network. Optical Engineering 52(1), 017001–017001 (2013)Google Scholar
  13. 13.
    Li, H., Manjunath, B., Mitra, S.K.: Multisensor image fusion using the wavelet transform. Graphical models and image processing 57(3), 235–245 (1995)Google Scholar
  14. 14.
    Liu, Y., Liu, S., Wang, Z.: Multi-focus image fusion with dense sift. Information Fusion 23, 139–155 (2015)Google Scholar
  15. 15.
    Mitianoudis, N., Stathaki, T.: Optimal contrast correction for ICA-based fusion of multimodal images. IEEE sensors journal 8(12), 2016–2026 (2008)Google Scholar
  16. 16.
    Petrovic, V.S., Xydeas, C.S.: Gradient-based multiresolution image fusion. IEEE Transactions on Image processing 13(2), 228–237 (2004)Google Scholar
  17. 17.
    Prabhakar, S., Jain, A.K.: Decision-level fusion in fingerprint verification. Pattern Recognition 35(4), 861–874 (2002)Google Scholar
  18. 18.
    Schoder, H., Yeung, H.W., Gonen, M., Kraus, D., Larson, S.M.: Head and neck cancer: Clinical usefulness and accuracy of pet/ct image fusion 1. Radiology 231(1), 65–72 (2004)Google Scholar
  19. 19.
    Shih, P., Liu, C.: Comparative assessment of content-based face image retrieval in different color spaces. International Journal of Pattern Recognition and Artificial Intelligence 19(07), 873–893 (2005)Google Scholar
  20. 20.
    Summers, D.: Harvard whole brain atlas: Journal of neurology, neurosurgery, and psychiatry 74(3), 288 (2003)
  21. 21.
    Wang, T., Zhu, Z., Blasch, E.: Bio-inspired adaptive hyperspectral imaging for real-time target tracking. IEEE Sensors Journal 10(3), 647–654 (2010)Google Scholar
  22. 22.
    Xiao-Bo, Q., Jing-Wen, Y., Hong-Zhi, X., Zi-Qian, Z.: Image fusion algorithm based on spatial frequency-motivated pulse coupled neural networks in nonsubsampled contourlet transform domain. Acta Automatica Sinica 34(12), 1508–1514 (2008)Google Scholar
  23. 23.
    Xydeas, C., Petrovic, V.: Objective image fusion performance measure. Electronics letters 36(4), 308–309 (2000)Google Scholar
  24. 24.
    Yang, C., Zhang, J.Q., Wang, X.R., Liu, X.: A novel similarity based quality metric for image fusion. Information Fusion 9(2), 156–160 (2008)Google Scholar
  25. 25.
    Yang, Y., Que, Y., Huang, S., Lin, P.: Multimodal sensor medical image fusion based on type-2 fuzzy logic in nsct domain. IEEE Sensors Journal 16(10), 3735–3745 (2016)Google Scholar

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© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Indian Institute of Technology GuwahatiGuwahatiIndia
  2. 2.Department of Computer ScienceChubu UniversityKasugaiJapan

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