Three-dimensional cardiac point cloud registration by improved iterative closest point method

In medical multi-atlas registration, to improve the limitations of low efficiency and poor accuracy caused by large initial position differences, complex shapes, and local residual differences, a fine registration algorithm that used Iterative Closest Point (ICP) was proposed based on the bidirectional distance ratio. The proposed algorithm was based on the coarse registration method followed by fine registration, where the former was processed by principal registration analysis. In the fine registration algorithm, the K-Dimensional tree was initially used to perform a nearest-neighbor search to improve the searching speed of corresponding point pairs. A bidirectional matching method was then proposed for each point, and the bidirectional distance and ratio were calculated. To further improve the accuracy of the registration, an exponential function was introduced to determine the probability that the point pair belongs to the correct match. The final transformation matrix was then obtained using Singular Value Decomposition. To evaluate the feasibility and effectiveness of the algorithm, experiments were designed using Stanford point cloud data and two sets of CT cardiac point cloud data registration. The results show that the average error during registration is reduced by 21% using this method compared to the classical ICP algorithm, which is 13% lower than the error obtained using the trimmed ICP (TrICP)algorithm. In the cardiac point cloud data registration experiment, this method is accelerated to 1.77 s compared to the TrICP algorithm, which has a value of 15.5 s.Therefore, the proposed method has high efficiency, accuracy, and stability in solving the registration problem associated with the three-dimensional cardiac point cloud data. © 2020, Science Press. All right reserved.