Abstract
The paper introduces the development process of the mutual information for medical image registration, and analyzes the local maximum problems existence of information medical image registration based on the traditional mutual ,and using the SNI information get from the I-alpha information instead of the traditional mutual information to improve the speed and the accuracy of registration; Combining the improvement of spatial information to depict the gradient information with the SNI information. Use the new measure for the registration of different modes medical image from The Whole Brain Atlas database ( MRI, CT, SPECT). Results proved that the convergence and registration precision are greatly improved, and solution the robustness problems of the traditional mutual information for medical image registration commendably.
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References
Yang, J.: Based on gray level similarity measure of medical image registration technology research. Biomedical Engineering, Shandong University (2008)
Tian, J.: Medical image processing and analysis, pp. 54–59. The Electronic Industry Press, Beijing (2003)
Liao, X., Liang, L.: Medical image registration technology. Journal of Modern Electronic Technology (16), 107–112 (2009)
Huang, Y., Wang, S., Zhang, S.: Based on the regional characteristics of image registration method of automatic. Computer Engineering and Project 30(16), 3850–3855 (2009)
Peng, X., Chen, Q., Wei, B.: An efficient medical image registration method based on mutual information model. Fuzzy Systems and Knowledge Discovery 5, 2168–2172 (2010)
Tang, M.: Image Registration Based on Improved Mutual Information with Hybrid Optimizer. Chinese J. Biomed. Eng. 17(1) (March 2008)
Shi, H., Luo, S.: Image registration using the shift-insensitive discrete wavelet transform. Medical Image Analysis and Clinical Applications, 46–49 (2010)
Woods, R.P., Maziotta, J.C., Cherry, S.R.: MRI-PET registration with automated algorithm. Journal of Computer Assisted Tomography 17(4), 536–546 (1993)
Maes, E., Collignon, A., Vandermeulen, D., et al.: Multimodality image registration by maximization of mutual information. IEEE Trans. Med. Image 16(2), 187–198 (1997)
Studholme, C., Ct Hill, D.L., Hawkes, D.J.: Anovedap invariant entropy measure of 3D medical image alignment. Pattern Recognition 32, 71–86 (1999)
Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Image registration by maximization of combined mutual information and gradient information. IEEE Trans. Med. Image 19(8), 809–814 (2000)
Rueckert, D., Clarkson, M.J., Hill, D.L.G., et al.: Non-rigid registration using higher-order mutual information. In: SPIE Medical Imaging: Image Processing, vol. 3979, pp. 438–447 (2000)
Al-Azzawi, N.A., Sakim, H.A.M., Abdullah, W.A.K.W.: MR image monomodal registration based on the nonsubsampledcontourlet transform and mutual information. In: International Conference on Computer Applications and Industrial Electronics, ICCAIE, pp. 481–485 (2010)
Gorbunova, V., Durrleman, S., Lo, P., Pennec, X., de Bruijne, M.: Lung CT registration combining intensity, curves and surfaces. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 340–343 (2010)
Chenoune, Y., Constantinides, C., El Berbari, R., Roullot, E., Frouin, F., Herment, A., Mousseaux, E.: Rigid registration of Delayed-Enhancement and Cine Cardiac MR images using 3D Normalized Mutual Information. Computing in Cardiology, 161–164 (2010)
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Tan, T., Liu, H., Zhan, Y., Jin, Y. (2012). Multi-modality Medical Image Registration Based on Improved I-alpha Information (SNI) with Gradient. In: Lei, J., Wang, F.L., Deng, H., Miao, D. (eds) Artificial Intelligence and Computational Intelligence. AICI 2012. Lecture Notes in Computer Science(), vol 7530. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33478-8_8
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DOI: https://doi.org/10.1007/978-3-642-33478-8_8
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