Unifying Energy Minimization and Mutual Information Maximization for Robust 2D/3D Registration of X-Ray and CT Images
Similarity measure is one of the main factors that affect the accuracy of intensity-based 2D/3D registration of X-ray fluoroscopy to CT images. Information theory has been used to derive similarity measure for image registration leading to the introduction of mutual information, an accurate similarity measure for multi-modal and mono-modal image registration tasks. However, it is known that the standard mutual information measure only takes intensity values into account without considering spatial information and its robustness is questionable. Previous attempt to incorporate spatial information into mutual information either requires computing the entropy of higher dimensional probability distributions, or is not robust to outliers. In this paper, we show how to incorporate spatial information into mutual information without suffering from these problems. Using a variational approximation derived from the Kullback-Leibler bound, spatial information can be effectively incorporated into mutual information via energy minimization. The resulting similarity measure has a least-squares form and can be effectively minimized by a multi-resolution Levenberg-Marquardt optimizer. Experimental results are presented on datasets of two applications: (a) intra-operative patient pose estimation from a few (e.g. 2) calibrated fluoroscopic images, and (b) post-operative cup alignment estimation from single X-ray radiograph with gonadal shielding.
Keywordssimilarity measure mutual information 2D/3D registration X-ray CT Markov random field Kullback-Leibler bound
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- 3.Wells, W., Viola, P., et al.: Multi-modal volume registration by maximization of mutual information. MedIA 1, 35–51 (1996)Google Scholar
- 6.Brown, L.M.G., Boult, T.E.: Registration of planar film radiographs with computed tomography. In: MMBIA 1996, pp. 42–51 (1996)Google Scholar
- 8.Rueckert, D., Clarkson, M.J., et al.: Non-rigid registration using higher-order mutual information. SPIE Medical Imaging: image processing 3979, 438–447 (2000)Google Scholar
- 9.Sabuncu, M.R., Ramadge, P.J.: Spatial information in entropy-based image registration. In: Gee, J.C., Maintz, J.B.A., Vannier, M.W. (eds.) WBIR 2003. LNCS, vol. 2717, pp. 132–141. Springer, Heidelberg (2003)Google Scholar
- 10.Russakoff, D.B., Tomasi, C., et al.: Image similarity using mutual information of regions. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3023, pp. 596–607. Springer, Heidelberg (2004)Google Scholar
- 11.Gan, R., Chung, A.C.S.: Multi-dimensional mutual information based robust image registration using maximum distance-gradient-magnitude. In: Christensen, G.E., Sonka, M. (eds.) IPMI 2005. LNCS, vol. 3565, pp. 210–221. Springer, Heidelberg (2005)Google Scholar
- 12.Barber, D., Agakov, F.V.: The IM algorithm: a variational approach to information maximization. In: NIPS’03, vol. 16, MIT Press, Cambridge (2004)Google Scholar
- 13.Li, S.Z.: Markov random field modeling in computer vision. Springer, Heidelberg (1995)Google Scholar
- 16.LaRose, D., et al.: Post-operative measurement of acetabular cup position using X-ray/CT registration. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 1104–1113. Springer, Heidelberg (2000)Google Scholar