During orthopaedic surgery, preoperative CT scans can be aligned to the patient to assist the guidance of surgical instruments and the placement of implants. Registration (i.e. alignment) can be accomplished in many ways: by registering implanted fiducial markers, by touching a probe to the bone surface, or by aligning intraoperative two dimensional flouro images with the the three dimensional CT data. These approaches have problems: They require exposure of the bone, subject the patient and surgeons to ionizing radiation, or do both. Ultrasound can also be used to register a preoperative CT scan to the patient. The ultrasound probe is tracked as it passes over the patient and the ultrasound images are aligned to the CT data. This method eliminates the problems of bone exposure and ionizing radiation, but is computationally more difficult because the ultrasound images contain incomplete and unclear bone surfaces. In this work, we compare three methods to register a set of ultrasound images to a CT scan: Iterated Closest Point, Mutual Information and a novel method Points-to-Image. The average Target Registration Error and speed of each method is presented along with a brief summary of their strengths and weaknesses. / Thesis (Master, Computing) -- Queen's University, 2009-01-22 04:21:22.569

The Iterative Closest Point (ICP) algorithm has been an extremely popular method for 3D points or surface registration. Given two point sets, it simultaneously solves for correspondences and estimates the motion between these two point sets. However, by only registering two such views at a time, ICP fails to exploit the redundant information available in multiple views that have overlapping regions. In this thesis, a multiview extension of the ICP algorithm is provided that simultaneously averages the redundant information available in the views with overlapping regions. Variants of this method that carry out such simultaneous registration in a causal manner and that utilize the transitivity property of point correspondences are also provided. The improved accuracy in registration of these motion averaged approaches in comparison with the conventional ICP method is established through extensive experiments. In addition, the motion averaged approaches are compared with the existing multiview techniques of Bergevin et. al. and Benjemaa et. al. The results of the methods applied to the Happy Buddha and the Stanford Bunny datasets of 3D Stanford repository and to the Pooh and the Bunny datasets of the Ohio (MSU/WSU) Range Image database are also presented.