Minimum Spanning Tree Hierarchically Fusing Multi-feature Points and High-Dimensional Features for Medical Image Registration

In this paper, we propose a novel medical registration approach based on minimal spanning tree. The proposed approach has the following contributions. (1) Compared with single type of feature points, we extracted corner-like and edge-like points from image, and added a few random points to cover the low contrast regions. (2) Instead of fixing the multi-feature points in the whole procedure, they are hierarchically updated at different registration stages. (3) Based on the feature points, in addition to using pixel intensity, we also added region based feature to include more spatial information. The proposed method is evaluated by performing registration experiments on Brain Web. The experimental results show that the proposed method achieves better robustness while maintaining good registration accuracy, compared to the conventional normalized mutual information (NMI) based registration method.