3D Rigid Registration of Patient Body Surface Point Clouds by Integer Linear Programming

Abstract

Surface point cloud matching is a useful technique for patient positioning during radiation therapy, system registering of surgical navigation system, etc. A common method for 3D point cloud registration is to estimate the registration function based on the 3D keypoint feature correspondences. However, the feature distance of correct correspondence is usually not the closest, but it is hidden in the k-nearest correspondences generally. This makes the rate of false correspondences too high for registration. In this paper, we convert the 3D point clouds rigid registration problem to a new graph matching model which combines the k-nearest feature information and the geometric constraints. The key idea is that the distance between 2 model feature points does not change after rigid transformation. This is a quadratic programming problem which could be transformed into an integer linear programming problem and thus could be efficiently solved. The final output is the 3D keypoint correspondences set which are accurate enough for registration. Our experimental results on point cloud datasets, generated from TCIA CT images datasets, show the advantages of the proposed algorithm.