Reliability-Driven, Spatially-Adaptive Regularization for Deformable Registration

We propose a reliability measure that identifies informative image cues useful for registration, and present a novel, data-driven approach to spatially adapt regularization to the local image content via use of the proposed measure. We illustrate the generality of this adaptive regularization approach within a powerful discrete optimization framework and present various ways to construct a spatially varying regularization weight based on the proposed measure. We evaluate our approach within the registration process using synthetic experiments and demonstrate its utility in real applications. As our results demonstrate, our approach yielded higher registration accuracy than non-adaptive approaches and the proposed reliability measure performed robustly even in the presences of noise and intensity inhomogenity.