In many clinical procedures, live X-ray images are used in order to guide the surgeon. As important anatomical structures may not be visible in these images, pre-operatively acquired 3-D images as CT- or MRI-scans can be overlaid with the 2-D image in order to visualize such structures. 2-D/3-D registration methods are used in order to estimate the pose of the 3-D image for the overlay. A feature-based registration method has been developed on the LME which can cope especially well with registration using a single 2-D image. This method makes use of a motion model which is able to estimate rigid 3-D transformations from 2-D displacements of a set of points.

In this research project, feature selection methods and motion model modifications are explored which can further improve and extend the scope of this registration method. Although different feature extraction methods are described in the literature, the setting in medical 2-D/3-D registration is unique in that the imaged objects are translucent for the X-ray imaging system. Extensions of the motion model are also considered to make optimal use of the displacement information which can be gained depending on the feature properties. The best features depending on the use cases are investigated. This includes feature selection depending on the anatomical structures which are registered as well as multi-modal registration, where the feature-matching is more challenging due to different intensity distributions for the same anatomical structures.