Abstract
In this article, we propose a novel multimodal Medical Image Fusion (MIF) method based on a neuro-fuzzy technique in the transform (Non-Subsampled Shearlet Transform (NSST)) domain for spatially registered, multi-modal medical images. The source medical images are first decomposed by NSST. The low-frequency subbands (LFSs) are fused using the Max-selection rule. Fuzzy triangular memberships are derived from a specific neighborhood-region of each high-frequency coefficient. Then they (high-frequency subbands, HFSs) are fused using a biologically inspired neural network (Pulse Coupled Neural Network (PCNN)) according to our newly proposed rule. Then inverse NSST (INSST) is applied to the fused coefficients to get the fused image. Visual and quantitative analysis and comparisons with state-of-the-art MIF techniques show the effectiveness of the proposed scheme in fusing multimodality medical images.
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References
Barra, V., Boire, J.Y.: A general framework for the fusion of anatomical and functional medical images. NeuroImage 13(3), 410–424 (2001)
Bezdek, J.C.: Fuzzy models-what are they, and why? [editorial]. IEEE Trans. Fuzzy Syst. 1(1), 1–6 (1993)
da Cunha, A.L., Zhou, J., Do, M.N.: The nonsubsampled contourlet transform: theory, design, and applications. IEEE Trans. Image Process. 15(10), 3089–3101 (2006)
Das, S., Kundu, M.K.: NSCT-based multimodal medical image fusion using pulse-coupled neural network and modified spatial frequency. Med. Biol. Eng. Comput. 50(10), 1105–1114 (2012)
Easley, G., Labate, D., Lim, W.Q.: Sparse directional image representations using the discrete shearlet transform. Appl. Comput. Harm. Anal. 25, 25–46 (2008)
James, A.P., Dasarathy, B.V.: Medical image fusion: a survey of the state of the art. Inf. Fusion 19, 4–19 (2014)
Johnson, J.L., Padgett, M.L.: PCNN models and applications. IEEE Trans. Neural Netw. 10(3), 480–498 (1999)
Pedrycz, W.: Why triangular membership functions? Fuzzy Sets Syst. 64(1), 21–30 (1994)
Singh, S., Gupta, D., Anand, R.S., Kumar, V.: Nonsubsampled shearlet based CT and MR medical image fusion using biologically inspired spiking neural network. Biomed. Sig. Process. Control 18, 91–101 (2015)
Wang, Z., Ma, Y.: Medical image fusion using m-PCNN. Inf. Fusion 9(2), 176–185 (2008)
Wang, Z., Ma, Y., Cheng, F., Yang, L.: Review of pulse-coupled neural networks. Image Vis. Comput. 28(1), 5–13 (2010)
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)
Yang, Y., Park, D.S., Huang, S., Rao, N.: Medical image fusion via an effective wavelet-based approach. EURASIP J. Adv. Sig. Process. 2010, 44:1–44:13 (2010)
Yue, S., Wu, T., Pan, J., Wang, H.: Fuzzy clustering based ET image fusion. Inf. Fusion 14, 487–497 (2013)
Acknowledgement
The authors would like to thank the anonymous referees for their constructive criticism and valuable suggestions. They also like to thank http://www.imagefusion.org/ and http://www.med.harvard.edu/aanlib/home.html for providing the source medical images. Prof. (Dr.) Amlan Chakrabarti likes to thank “CoE in Systems Biology and Biomedical Engineering, University of Calcutta supported by TEQIP-II Project” for supporting the research work.
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Mishra, N.S., Das, S., Chakrabarti, A. (2017). Shearlet Based Medical Image Fusion Using Pulse-Coupled Neural Network with Fuzzy Memberships. In: Mukherjee, S., et al. Computer Vision, Graphics, and Image Processing. ICVGIP 2016. Lecture Notes in Computer Science(), vol 10481. Springer, Cham. https://doi.org/10.1007/978-3-319-68124-5_29
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DOI: https://doi.org/10.1007/978-3-319-68124-5_29
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