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Spatial-Stiffness Analysis of Surface-Based Registration and Randy E. Ellis1

Summary: Spatial-Stiffness Analysis of Surface-Based Registration
Burton Ma1
and Randy E. Ellis1
School of Computing, Queen's University at Kingston, Canada K7L 3N6
Abstract. We have developed a new approach for preoperative selection of points
from a surface model for rigid shape-based registration. This approach is based
on an extension of our earlier spatial-stiffness model of fiducial registration. We
compared our approach with the maximization of the noise-amplification index
(NAI), using target registration accuracy (TRE) as our comparison measure, on
models derived from computed tomography scans of volunteers. In this study, our
approach was substantially less expensive to compute than maximizing the NAI
and produced similar TREs with smaller variances. Optimal incremental selection
shows promise for improving the preoperative selection of registration points for
image-guided surgical procedures.
1 Introduction
A patient's anatomy can be registered to preoperative 3D medical images for use in
image-guided surgery by digitizing anatomical registration points on the patient and
matching them to surface models derived from the images. We propose a method for
choosing model registration points from the preoperative medical image, based on an
extension of the method we described in Ma and Ellis [5] for fiducial registration. We


Source: Abolmaesumi, Purang - School of Computing, Queen's University (Kingston)


Collections: Computer Technologies and Information Sciences