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Unifying Energy Minimization and Mutual Information Maximization for Robust 2D/3D Registration of X-Ray and CT Images

  • Guoyan Zheng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4713)

Abstract

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.

Keywords

similarity measure mutual information 2D/3D registration X-ray CT Markov random field Kullback-Leibler bound 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Guoyan Zheng
    • 1
  1. 1.MEM Research Center, University of Bern, Stauffacherstrasse 78, CH-3014, BernSwitzerland

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