Motion Field Regularization for Sliding Objects using Global Linear Optimization

In image registration it is often necessary to employ regularization in one form or another to be able to find a plausible displacement field. In medical applications, it is useful to define different constraints for different areas of the data. For instance to measure if organs have moved as expected after a finished treatment. One common problem is how to find plausible motion vectors far away from known motion. This paper introduces a new method to build and solve a Global Linear Optimizations (GLO) problem with a novel set of terms which enable specification of border areas to allow a sliding motion. The GLO approach is important especially because it allows simultaneous incorporation of several different constraints using information from medical atlases such as localization and properties of organs. The power and validity of the method is demonstrated using two simple, but relevant 2D test images. Conceptual comparisons with previous methods are also made to highlight the contributions made in this paper. The discussion explains important future work and experiments as well as exciting future improvements to the GLO framework.Dynamic Context Atlases for Image Denoising and Patient SafetyGlobal Linear Optimizatio