Symmetric Diffeomorphic Image Registration with Multi-Label Segmentation Masks

Image registration aims to align two images through a spatial transformation. It plays a significant role in brain imaging analysis. In this research, we propose a symmetric diffeomorphic image registration model based on multi-label segmentation masks to solve the problems in brain MRI registration. We first introduce the similarity metric of the multi-label masks to the energy function, which improves the alignment of the brain region boundaries and the robustness to the noise. Next, we establish the model on the diffeomorphism group through the relaxation method and the inverse consistent constraint. The algorithm is designed through the local linearization and least-squares method. We then give spatially adaptive parameters to coordinate the descent of the energy function in different regions. The results show that our approach, compared with the mainstream methods, has better accuracy and noise resistance, and the transformations are more smooth and more reasonable.

Automated summary

Image registration, an important technique in brain imaging analysis, aims to align two images through a spatial transformation. This research proposes a symmetric diffeomorphic image registration model based on multi-label segmentation masks. By introducing a new similarity metric and adaptive parameters, the proposed model improves accuracy, robustness, and smoothness of the registration compared to mainstream methods.