Evaluation of similarity measures for 3D/2D image registration

Abstract

Several 3D/2D registration algorithms for image-guided therapy have been introduced in the past years. Recently, we have proposed a method which first reconstructs a 3D image from a few intraoperative 2D X-ray images and then establishes the rigid transformation between the preoperative 3D CT or MR image and the 3D reconstructed image. The similarity measure applied in this registration method should be able to cope, among others, with the low quality of the reconstructed image. Using the recently proposed similarity measure evaluation protocol, we have evaluated the behavior of five similarity measures. The measures have been evaluated with respect to: a) preoperative imaging modalities (CT and MR); b) number of 2D images used for reconstruction; and c) number of reconstruction iterations. Increasing the number of 2D projections or reconstruction iterations improves the accuracy but slightly worsens the robustness. We have shown that almost all similarity measures have better properties if the optimal parameters are chosen. The most appropriate similarity measure for this type of registration is the asymmetric multi-feature mutual information.