INV

Abstract

Image registration and segmentation tasks lie in the heart of Medical Imaging. In registration, our concern is to align two or more images using deformable transforms that have desirable regularities. In a multimodal image registration scenario, where two given images have similar features, but non-comparable intensity variations, the sum of squared differences is not suitable to measure image similarities.

In this talk, we first propose a new variational model based on combining intensity and geometric transformations, as an alternative to using mutual information and an improvement to the work by Modersitzki and Wirtz (2006, LNCS, vol.4057), and then develop a fast multigrid algorithm for solving the underlying system of fourth order and nonlinear partial differential equations. We can demonstrate the effective smoothing property of the adopted primal-dual smoother by a local Fourier analysis. An earlier use of mean curvature to regulairse image denosing models was in T F Chan and W Zhu (2008) and the previous work of developing a multigrid algorithm for the Chan-Zhu model was by Brito-Chen (2010). Numerical tests will be presented to show both the improvements achieved in image registration quality as well as multigrid efficiency. Joint work with Dr Noppadol Chumchob.