FLOOR: Fusing Locally Optimal Registrations

Most registration algorithms, such as Demons, align two scans by iteratively finding the deformation minimizing the image dissimilarity at each location and smoothing this minimum across the image domain. These methods generally get stuck in local minima, are negatively impacted by missing correspondences between the images, and require careful tuning of the smoothing parameters to achieve optimal results. In this paper, we propose to improve on those issues by choosing the minimum from a set of candidates. Our method generates such candidates by running the registration algorithm multiple times varying the setting of the smoothing and the image domain. We iteratively refine those candidates by fusing them with the outcome of alternative approaches and locally adapting the smoothing parameters. We implement our algorithm based on Demons and find alternative minima via manifold learning. Compared to those two methods, our 600 pairwise registrations of cardiac MRIs significantly better handle the large shape variations of the heart and the different field of views captured by scans.