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Abstract: The diagnostic potential of brain positron emission tomography (PET) imaging is limited by low spatial resolution. For solving this problem we propose a technique for the fusion of PET and MRI images. This fusion is a trade-off between the spectral information extracted from PET images and the spatial information extracted from high spatial resolution MRI. The proposed method can control this trade-off. To achieve this goal, it is necessary to build a multiscale fusion model, based on the retinal cell photoreceptors model. This paper introduces general prospects of this model, and its application in multispectral medical image fusion. Results showed that the proposed method preserves more spectral features with less spatial distortion. Comparing with hue-intensity-saturation (HIS), discrete wavelet transform (DWT), wavelet-based sharpening and wavelet-à trous transform methods, the best spectral and spatial quality is only achieved simultaneously with the proposed feature-based data fusion method. This method does not require resampling images, which is an advantage over the other methods, and can perform in any aspect ratio between the pixels of MRI and PET images.

[1] Burt, P.J., 1984. The Pyramid as a Structure for Efficient Computation. In: Rosenfeld, A. (Ed.), Multiresolution Image Processing and Analysis. Springer-Verlag, Berlin, p.6-35.

[2] Burt, P.J., Kolczynski, R.J., 1993. Enhanced Image Capture Through Fusion. Int. Conf. on Computer Vision, p.173-182.

[3] Choi, M., 2006. A new intensity-hue-saturation fusion approach to image fusion with a tradeoff parameter. IEEE Trans. on Geosci. Remote Sensing, 44(6):1672-1682.

[4] Garzelli, A., Nencini, F., 2005. Interband structure modeling for Pan-sharpening of very high-resolution multispectral images. Information Fusion, 6:213-224.

[5] Ghassemian, H., 2001a. Multisensor Image Fusion by Multiscale Filter Banks. Proc. IEEE Int. Conf. on Image Processing.

[6] Ghassemian, H., 2001b. A Retina Based Multi-resolution Image Fusion. Proc. IEEE Int. Geoscience and Remote Sensing Symp.

[7] Goshtasby, A., 2005. 2-D and 3-D Image Registration for Medical, Remote Sensing, and Industrial Applications. Wiley Press.

[8] Goshtasby, A., Nikolov, S., 2007. Image fusion: advances in the state of the art. Information Fusion, 8:114-118.

[9] Joshi, M.V., Bruzzone, L., Chaudhuri, S., 2006. A model-based approach to multiresolution fusion in remotely sensed images. IEEE Trans. on Geosci. Remote Sensing, 44(9):2549-2562.

[10] Kingsbury, N., 1999. Image Processing with Complex Wavelets. In: Silverman, B., Vassilicos, J., (Eds.), Wavelets: The Key to Intermittent Information. Oxford University Press, p.165-185.

[11] Kolb, H., 1991. The Neural Organization of the Human Retina. In: Heckenlively, J.R., Arden, G.B. (Eds.), Principles and Practices of Clinical Electrophysiology of Vision. Mosby Year Book Inc., St. Louis, p.25-52.

[12] Lewis, J.J., O′Callaghan, R.J., Nikolov, S.G., Bull, D.R., Canagarajah, C.N., 2004. Region-based Image Fusion Using Complex Wavelets. Proc. 7th Int. Conf. on Information Fusion. Stockholm, Sweden, p.555-562.

[13] Li, Z., Jing, Z., Yang, X., 2005. Color transfer based remote sensing image fusion using non-separable wavelet frame transform. Pattern Recog. Lett., 26:2006-2014.

[14] Mallat, S., Zhong, S., 1992. Characterization of signals from multiscale edges. IEEE Trans. on Pattern Anal. Machine Intell., 14(7):710-732.

[15] Nikolov, S.G., Bull, D.R., Canagarajah, C.N., 2000. 2-D Image Fusion by Multiscale Edge Graph Combination. Proc. 3rd Int. Conf. on Information Fusion. Paris, France, p.16-22.

[16] Nikolov, S.G., Hill, P., Bull, D.R., Canagarajah, C.N., 2001. Wavelets for Image Fusion. In: Petrosian, A., Meyer, F. (Eds.), Wavelets in Signal and Image Analysis. Kluwer Academic Publishers, the Netherlands, p.213-244.

[17] Nunez, J., Otazu, X., Fors, O.I., Prades, A., Pala, V., Arbiol, R., 1999. Multiresolution-based image fusion with additive wavelet decomposition. IEEE Trans. on Geosci. Remote Sensing, 37(3):1204-1212.

[18] Petrovic, V.S., Xydeas, C.S., 2004. Gradient-based multiresolution image fusion. IEEE Trans. on Image Processing, 13(2):228-237.

[19] Piella, G., 2003. A general framework for multiresolution image fusion: from pixels to regions. Information Fusion, 4:259-280.

[20] Toet, A., 1990. Hierarchical image fusion. Machine Vision Appl., 3:1-11.

[21] Wang, Z., Ziou, D., Armenakis, C., Li, D., Li, Q., 2005. A comparative analysis of image fusion methods. IEEE Trans. on Geosci. Remote Sensing, 43(6):1391-1402.

[22] Zheng, Y., Essock, E.A., Hansen, B.C., Haun, A.M., 2007. A new metric based on extended spatial frequency and its application to DWT based fusion algorithms. Information Fusion, 8:177-192.

[23] Zhou, J., Civco, D.L., Silander, J.A., 1998. A wavelet transform method to merge Landsat TM and SPOT panchromatic data. Int. J. Remote Sensing, 19:743-757.