Rigid registration of 3-D medical image using convex hull matching

In this paper, a robust approach called convex hull matching (CHM) technique is proposed for registration of medical images that differ from each other with Euclidean transformation. Firstly, point sets on the surface of the medical image are extracted, and then the 3-D convex hull is constructed from the point sets and triangle patches on the surface of convex hulls are specified by predefining their normal vectors. Secondly, each edge of the referenced triangle is compared with all the edges of the triangle in other point set to find the congruent pair set and also to obtain the scaling factor. Thereafter, the transformation parameters of each triangle pairs including rotation and translation are optimized by minimizing the Euclidian distance between the corresponding vertex pairs. Hence, rigid transformation of the two point sets is obtained by iteratively enumerating and evaluating similarity measures of the triangle patches chosen. Global optimization is achieved through RANSAC optimization by removing the correspondence pairs that may lead to large matching errors of the whole point sets. The experiments evaluate the performance of the proposed algorithm on simulated data with the presence of outliers and noise. The results show the efficiency of CHM by quantitative analysis and comparative study with existing approaches like EM-ICP, LM-ICP and 4PCS. Finally, the real clinical data experiments confirm the proposed algorithm is a strong performer in medical image registration.