Towards Robust and Accurate Image Registration by Incorporating Anatomical and Appearance Priors

Abstract

Image registration is one of the fundamental computer vision problems, with applications ranging from motion modelling, image fusion, shape analysis, to medical image analysis. The process finds the spatial correspondences between different images that may be taken at different time or by modalities of acquisition. Recently, it has been shown that incorporating prior knowledge into the registration process has the potential to significantly improve the image registration results. Therefore, many researchers have been putting lots of effort in this field.

In this thesis, we investigate the possibility of improving the robust- ness and accuracy of image registration, by incorporating anatomical and appearance priors. We explored and formulated several methods to incorporate anatomical and appearance prior knowledge into image registration process explicitly and implicitly.