Registration of multiple medical images commonly comprises the steps feature extraction, correspondences search and transformation computation. In this paper, we present a new method for a fast and pose independent search of correspondences using as features anatomical trees such as the bronchial system in the lungs or the vessel system in the liver. Our approach scores the similarities between the trees' nodes (bifurcations) taking into account both, topological properties extracted from their graph representations and anatomical properties extracted from the trees themselves. The node assignment maximizes the global similarity (sum of the scores of each pair of assigned nodes), assuring that the matches are distributed throughout the trees. Furthermore, the proposed method is able to deal with distortions in the data, such as noise, motion, artifacts, and problems associated with the extraction method, such as missing or false branches. According to an evaluation on swine lung data sets, the method requires less than one second on average to compute the matching and yields a high rate of correct matches compared to state of the art work.
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