A spatially adaptive hierarchical stochastic model for non-rigid image registration

In this paper, we propose a method for non-rigid image registration based on a spatially adaptive stochastic model. A smoothness constraint is imposed on the deformation field between the two images which is assumed to be a random variable following a Gaussian distribution, conditioned on the observations and maximum a posteriori (MAP) estimation is employed to evaluate the model parameters. Furthermore, the model is enriched by considering the deformation field to be spatially adaptive by assuming different density parameters for each image location. These parameters are assumed random variables generated by a Gamma distribution, which is conjugate to the Gaussian, leading to a model that can be estimated. Numerical experiments are presented that demonstrate the advantages of this model.

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