Matching of Feature Points Based on TSSC Method from MR Images of Nonrigid Deformed Tissues
Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 2nd International Conference on Biomedical Engineering and Biotechnology, 11–13 October 2013, Wuhan, China
Affiliations: School of Public Administration, China University of Geosciences, Wuhan 430073, China | Department of Robotics, Faculty of Science and Engineering, Ritsumeikan University, Kusatsu, Shiga, 525-8577, Japan | School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Abstract: Due to the nonlinear and nonuniform local deformation of nonrigid tissues, it is difficult to match a number of feature points distributing somewhat uniform in the tissues from MR images for deformation measurement. This paper proposes TSSC (TPS-SURF-SAC-Clustering) based method of feature point matching and elimination of mismatching. First, Fast-Hessian and Harris operator are utilized to extract the feature points in the initial MR image, and the matching region is identified by TPS transformation model for every query point in the deformed image. Then the SURF descriptors and the proposed Spatial Association Correspondence (SAC) method are combined to match the feature points. Finally, by clustering the coordinate differences between the matching points obtained by TPS-SURF-SAC and the matching points calculated by TPS model, most incorrectly matched points are eliminated. After every iterative processing of matching and mismatching elimination, the updated TPS model becomes more accurate and more correctly so that the matched points can be identified than those of last iteration. The experimental results show that the proposed SAC was efficient and that TSSC based method outperformed the single SURF or SIFT method.
Keywords: Feature point, extraction, matching, deformation
