2D/3D registration is the process of aligning 3D volume data to a 2D image. Typically, during orthopedic trauma surgeries, 3D volume data is acquired prior to the surgery to visualize the extent of the injury and also plan the operation. During the surgery, 2D fluoroscopic images are acquired to visualize the position of surgical instruments and understand the progress of the operation. However, the depth perception from these 2D images becomes difficult. By employing 2D/3D registration to overlay pre-operative imaging modalities, doctors may be presented a better way to visualize the progress of the surgery. Although several approaches have been proposed in the literature to deal with 2D/3D registration, they are limited by the need for a good initial alignment. This requirement for accurate initial alignment is the main reason hindering the adoption of 2D/3D registration in clinical practice. In this thesis, we propose automated initialization strategies, the output of which can be used by any common 2D/3D registration algorithm for
further fine tuning.

A library based approach is to used to study the initialization problem. Four parameters of the six dimensional parameter space are sampled and their projections are stored prior to the surgery. We proposed frequency and feature based methods to retrieve the correct library match which is used for the initialization during the surgery. The feasibility of the approach is demonstrated by initializing an intensity based 2D/3D registration method with the automatically obtained estimation of the initialization transformation parameters. For fixed C-arm cases with available ground truth the initialization was successful for 87:41% for various anatomies such as the head, pelvis, spine, knees and feet. We further tested the approach in a completely un-calibrated scenario using a mobile C-arm. Visual evaluation of the results revealed a success rate of 88:4% for the initialization. Successful results were also obtained even in the presence of additional surgical instruments indicating the robustness of the proposed approach.