Multi-Modal Partial Surface Matching for Intra-Operative Registration

An important task for computer-assisted surgical interventions is the alignment of pre- and intra-operative spaces allowing the transfer of pre-operative information to the current patient situation, known as intra-operative registration. Registration is usually performed by using markers or image-based techniques. Another approach is the intra-operative acquisition of organ surfaces by 3D range scanners, which are then matched to pre-operatively generated surfaces. However, this approach is not trivial, as methods for intra-operative surface matching must be able to deal with noise, distortions, deformations, and the availability of only partially overlapping, nearly flat surfaces. For these reasons, surface matching for intra-operative registration has so far only been used to account for displacements that occur in local scales, while the actual alignment is still performed manually. The main contributions of this thesis are two different approaches for automatic surface matching in intra-operative environments. The focus here is the registration of surfaces acquired by different modalities, dealing with the aforementioned issues and without relying on unique landmarks. For the first approach, surfaces are converted to graph representations and correspondences between them are identified by means of graph matching. Graphs are obtained automatically by segmenting the surfaces into regions with similar properties. As the graph matching problem is known to be NP-hard, it was solved by iteratively computing node similarity scores, and converting it to a linear assignment problem. In the second approach, correspondences are identified by the selection of two spatial configurations of landmarks that can be better fitted to each other, according to an error metric. This error metric does not only incorporate a fitting error, but also a new measure for spatial configuration reliability. The optimization problem is solved by means of a greedy algorithm. Evaluation of the two approaches was performed with several experiments, simulating intra-operative conditions. While the graph matching approach proved to be robust for the registration of small partial data, the point-based approach proved to be more reliable for noisy surfaces. Apart from being a significant contribution to the field of feature-less partial surface matching, this work represents a great effort towards the achievement of a fully automatic, marker-less, registration system for computer-assisted surgery guidance