Abstract
Performances of multi-modality image registration methods that are based on information-theoretic registration criteria crucially depend on the specific computational implementation. We proposed a new implementation based on the improved fast Gauss transform so as to estimate, from all available intensity samples, the intensity density functions needed to compute the information-theoretic criteria. The proposed and several other state-of-the-art implementations were tested and compared in 3-D rigid-body registration of multi-modal brain volumes. Experimental results indicate that the proposed implementation achieves the most consistent spatial alignment of brain volumes at a subpixel accuracy.
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References
Ma, B., Hero, A., Gorman, J., Michel, O.: Image registration with minimum spanning tree algorithm. In: Proceeding of the IEEE International Conference on Image Processing, vol. 1, pp. 481–484 (2000)
Maes, F., Collignon, A., Vadermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE T. Med. Imag. 16(2), 187–198 (1997)
Orchard, J., Mann, R.: Registering a multisensor ensemble of images. IEEE T. Image Process. 19(5), 1236–1247 (2010)
Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Mutual-information-based registration of medical images: a survey. IEEE T. Med. Imag. 22(8), 986–1004 (2003)
Raykar, V.C., Duraiswami, R., Zhao, L.H.: Fast Computation of Kernel Estimators. J. Comput. Graph. Stat. 19(1), 205–220 (2010)
Sabuncu, M.R., Ramadge, P.: Using spanning graphs for efficient image registration. IEEE T. Image Process. 17(5), 788–797 (2008)
Studholme, C., Cardenas, V.: A template free approach to volumetric spatial normalization of brain anatomy. Pattern Recogn. Lett. 25(10), 1191–1202 (2004)
Studholme, C., Hill, D.L.G., Hawkes, D.J.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recogn. 32(1), 71–86 (1999)
Terrell, G.R.: The Maximal Smoothing Principle in Density Estimation. J. Amer. Statistical Assoc. 85(410), 470–477 (1990)
Thevenaz, P., Unser, M.: Optimization of mutual information for multiresolution image registration. IEEE T. Image Process. 9(12), 2083–2099 (2000)
Viola, P.A.: Alignment by maximization of mutual information, Ph. D. thesis, Massachusetts Institute of Technology, Boston, MA, USA (1995)
West, J., et al.: Comparison and evaluation of retrospective intermodality brain image registration techniques. J. Comput. Assist. Tomogr. 21(4), 554–566 (1997)
Zhang, J., Rangarajan, A.: Multimodality image registration using an extensible information metric and high dimensional histogramming. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 725–737. Springer, Heidelberg (2005)
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Špiclin, Ž., Likar, B., Pernuš, F. (2011). Information-Theoretic Multi-modal Image Registration Based on the Improved Fast Gauss Transform: Application to Brain Images. In: Liu, T., Shen, D., Ibanez, L., Tao, X. (eds) Multimodal Brain Image Analysis. MBIA 2011. Lecture Notes in Computer Science, vol 7012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24446-9_17
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DOI: https://doi.org/10.1007/978-3-642-24446-9_17
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