JBiSE  Vol.5 No.12 A , December 2012
Medical image fusion based on pulse coupled neural networks and multi-feature fuzzy clustering
Abstract: Medical image fusion plays an important role in clinical applications such as image-guided surgery, image-guided radiotherapy, noninvasive diagnosis, and treatment planning. In order to retain useful information and get more reliable results, a novel medical image fusion algorithm based on pulse coupled neural networks (PCNN) and multi-feature fuzzy clustering is proposed, which makes use of the multi-feature of image and combines the advantages of the local entropy and variance of local entropy based PCNN. The results of experiments indicate that the proposed image fusion method can better preserve the image details and robustness and significantly improve the image visual effect than the other fusion methods with less information distortion.
Cite this paper: Luo, X. and Wu, X. (2012) Medical image fusion based on pulse coupled neural networks and multi-feature fuzzy clustering. Journal of Biomedical Science and Engineering, 5, 878-883. doi: 10.4236/jbise.2012.512A111.

[1]   Bhatnagar, G., Wu, J.Q.M. and Liu, Z. (2012) Human visual system inspired multi-modal medical image fusion framework. Expert Systems with Applications, in press. doi:10.1016/j.eswa.2012.09.011

[2]   Pajares, G. and Cruz, J. (2004) A wavelet-based image fusion tutorial. Pattern Recognition, 37, 1855-1872. doi:10.1016/j.patcog.2004.03.010

[3]   Wang, Z.B. and Ma Y.D. (2008) Medical image fusion using m-PCNN. Information Fusion, 9, 176-185. doi:10.1016/j.inffus.2007.04.003

[4]   Burt, P.J. (1992) A gradient pyramid basis for pattern- selective image fusion, Society for Information Displays (SID) International Symposium Digest of Technical Pa- pers, 23, 467-470.

[5]   Toet, A. (1989) A morphological pyramidal image de- composition. Pattern Recognition Letter, 9, 255-261. doi:10.1016/0167-8655(89)90004-4

[6]   Li, H., Manjunath, B.S. and Mitra, S.K. (1995) Multi- sensor image fusion using the wavelet transform. Graphi- cal Models and Image Processing, 57, 235-245. doi:10.1006/gmip.1995.1022

[7]   Toet, A., van Ruyven, L.J. and Valeton, J.M. (1989) Mer- ging thermal and visual images by a contrast pyramid. Optical Engineering, 28, 789-792. doi:10.1117/12.7977034

[8]   Wang, Z.B., Ma, Y.D. and Gu, J.S. (2010) Multi-focus image fusion using PCNN. Pattern Recognition, 43, 2003- 2016. doi:10.1016/j.patcog.2010.01.011

[9]   Su, D.X. and Wu, X.J. (2006) Image fusion based on multi-feature fuzzy clustering. Journal of CAD and Gra- phics, 18, 838-843.

[10]   Eckhorn, R., Reitboeck, H.J., Arndt, M. and Dicke, P.W. (1990) Feature linking via synchronization among dis- tributed assemblies: Simulation of results from cat cortex. Neural Computation, 2, 293-307. doi:10.1162/neco.1990.2.3.293

[11]   Lindblad, T. and Kinser, J.M. (2005) Image processing using pulse-coupled neural networks. 2nd Edition, Sprin- ger, New York

[12]   Zhang,Y.F. and He, M.Y. (2006) Image fusion algorithm based on local information entropy and its distribution property. Computer Engineering, 32, 22-30.

[13]   Nikhil, R.P., Kuhu, P. and James, M.K. (2005) A possi- bilistic fuzzy c means clustering algorithm. IEEE Trans- actions on Fuzzy Systems, 13, 517-530. doi:10.1109/TFUZZ.2004.840099

[14]   Zhang, Z. and Blum, R.S. (1999) A categorization of multiscale-decomposition-based image fusion schemes with a performance study for a digital camera application. Proceedings of the IEEE, 87, 1315-1326. doi:10.1109/5.775414

[15]   Luo, X.Q. and Wu, X.J. (2010) New metric of image fusion based on region similarity. Optical Engineering, 49, 1-13. doi:10.1117/1.3394086