Entropic Smoothing of 3D Volumetric Medical Images
We propose an information-theoretic variational model for volumetric medical image smoothing. It is a result of minimizing a functional subject to some noise constraints, and takes a hybrid form of a negative-entropy variational integral for small gradient magnitudes and a total variational integral for large gradient magnitudes. The core idea behind this approach is to use geometric insight in helping construct regularizing functionals and avoiding a subjective choice of a prior in maximum a posteriori estimation. Illustrating experimental results demonstrate a much improved performance of the approach in the presence of noise