Abstract
The performances of information-theoretic multi-modality image registration methods crucially depend on the model representing the joint density function of the co-occurring image intensities and on the implementation of the entropy estimator. We proposed an entropy estimator for image registration based on quad-tree (QT) that is essentially an entropic graph entropy estimator, but can be adapted to work as a plug-in entropy estimator. This duality was achieved by incorporating the Hilbert kernel density estimator. Results of 3-D rigid-body registration of multi-modal brain volumes indicate that the proposed methods achieve similar accuracies as the registration method based on minimal spanning tree (MST), but have a higher success rate and a higher capture range. Although the MST and QT have similar computational complexities, the QT-based methods had about 50% shorter registration times.
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References
Devroye, L., Krzyżak, A.: On the Hilbert kernel density estimate. Stat. Probabil. Lett. 44(3), 299–308 (1999)
Kwan, R.K., Evans, A.C., Pike, G.B.: MRI simulation-based evaluation of image-processing and classification methods. IEEE T. Med. Imag. 18(11), 1085–1097 (1999)
Kybic, J., Vnučko, I.: Approximate all nearest neighbor search for high dimensional entropy estimation for image registration. Signal Process. 92(5), 1302–1316 (2012)
Ma, B., Hero, A., Gorman, J., Michel, O.: Image registration with minimum spanning tree algorithm. In: Dubois, E., Konrad, J. (eds.) Proc. IEEE ICIP, vol. 1, pp. 481–484. IEEE, Vancouver (2000)
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE T. Med. Imag. 16(2), 187–198 (1997)
Miller, E.G.: A new class of entropy estimators for multi-dimensional densities. In: Proc. IEEE ICASSP, vol. 3, pp. 297–300. IEEE, Hong Kong (2003)
Neemuchwala, H., Hero, A., Carson, P.: Image matching using alpha-entropy measures and entropic graphs. Signal Process. 85(2), 277–296 (2005)
Neemuchwala, H., Hero, A., Zabuawala, S., Carson, P.: Image registration methods in high-dimensional space. Int. J. Imag. Syst. Tech. 16(5), 130–145 (2006)
Špiclin, Ž., Likar, B., Pernuš, F.: Groupwise registration of multi-modal images by an efficient joint entropy minimization scheme. IEEE T. Image Process 21(5), 2546–2558 (2012)
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)
Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: f-information measures in medical image registration. IEEE T. Med. Imag. 23(12), 1508–1516 (2004)
Sabuncu, M.R., Ramadge, P.: Using spanning graphs for efficient image registration. IEEE T. Image Process. 17(5), 788–797 (2008)
Sheather, S.J.: Density estimation. Stat. Sci. 19(4), 588–597 (2004)
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. of Comput. Assist. Tomogr. 21(4), 554–566 (1997)
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Špiclin, Ž., Likar, B., Pernuš, F. (2012). Quad-tree Based Entropy Estimator for Fast and Robust Brain Image Registration. In: Dawant, B.M., Christensen, G.E., Fitzpatrick, J.M., Rueckert, D. (eds) Biomedical Image Registration. WBIR 2012. Lecture Notes in Computer Science, vol 7359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31340-0_17
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DOI: https://doi.org/10.1007/978-3-642-31340-0_17
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