Learning-Based 2D/3D Rigid Registration Using Jensen-Shannon Divergence for Image-Guided Surgery
Registration of 3D volumetric data to 2D X-ray images has many applications in image-guided surgery, varying from verification of patient position to working projection searching. In this work, we propose a learning-based method that incorporates the prior information on the expected joint intensity histogram for robust real-time 2D/3D registration. Jensen-Shannon divergence (JSD) is used to quantify the statistical (dis)similarity between the observed and expected joint histograms, and is shown to be superior to Kullback-Leibler divergence (KLD) in its symmetry, being theoretically upper-bounded, and well-defined with histogram non-continuity. A nonlinear histogram mapping technique is proposed to handle the intensity difference between the observed data and the training data so that the learned prior can be used for registration of a wide range of data subject to intensity variations. We applied the proposed method on synthetic, phantom and clinical data. Experimental results demonstrated that a combination of the prior knowledge and the low-level similarity measure between the images being registered led to a more robust and accurate registration in comparison with the cases where either of the two factors was used alone as the driving force for registration.
KeywordsMutual Information Training Image Pattern Intensity Rigid Registration Training Pair
Unable to display preview. Download preview PDF.
- 1.Liu, A., Bullitt, E., Pizer, S.M.: 3D/2D Registration via Skeletal near Projective Invariance in Tubular Objects. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 952–963. Springer, Heidelberg (1998)Google Scholar
- 2.Kita, Y., Wilson, D.L., Nobel, J.A.: Real-Time Registration of 3D Cerebral Vessels to X-Ray Angiograms. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 1125–1133. Springer, Heidelberg (1998)Google Scholar
- 5.Leventon, M., Grimson, E.: Multi-modal Volume Registration Using Joint Intensity Distributions. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 1057–1066. Springer, Heidelberg (1998)Google Scholar
- 6.Chan, H.M., Chung, A.C.S., Yu, S.C.H., Norbash, A., Wells, W.M.: Multi-Modal Image Registration by Minimizing Kullback-Leibler Distance between Expected and Observed Joint Class Histograms. In: CVPR 2003, pp. 181–190 (2003)Google Scholar
- 8.Lee, L.: On the Effectiveness of the Skew Divergence for Statistical Language Analysis. Artificial Intelligence and Statistics, 65–72 (2001)Google Scholar
- 10.Grosse, I., et al.: Analysis of Symbolic Sequence Using the Jensen-Shannon Divergence. Physical Review E 65, 041905 (2002)Google Scholar