A Robust and Accurate Non-rigid Medical Image Registration Algorithm Based on Multi-level Deformable Model

Background: Compared to the rigid image registration task, the non-rigid image registration task faces much more challenges due to its high degree of freedom and inherent requirement of smoothness in the deformation field. The purpose was to propose an efficient coarse-to-fine non-rigid medical image registration algorithm based on a multilevel deformable model. Methods: In this paper, a robust and efficient coarse-to-fine non-rigid medical image registration algorithm is proposed. It contains three level deformation models, i.e., the global homography model, the local mesh-level homography model, and the local B-spline FFD (Free-Form Deformation) model. The coarse registration is achieved by the first two level models. In the global homography model, a robust algorithm for simultaneous outliers (error matched feature points) removal and model estimation is applied. In the local mesh-level homography model, a new similarity measure is proposed to improve the robustness and accuracy of local mesh based registration. In the fine registration, a local B-spline FFD model with normalized mutual information gradient is employed. Results: We verified the effectiveness of each stage of the proposed registration algorithm with many non-rigid transformation image pairs, and quantitatively compared our proposed registration algorithm with the HBFFD method which is based on the control points of multi-resolution. The experimental results show that our algorithm is more accurate than the hierarchical local B-spline FFD method. Conclusion: Our algorithm can achieve high precision registration by coarse-to-fine process based on multi-level deformable model, which ourperforms the state-of-the-art methods.