We propose a meta-algorithm for registration improvement by combining deformable image registrations (MetaReg). It is inspired by a well-established method from machine learning, the combination of classifiers. MetaReg consists of two main components: (1) A strategy for composing an improved registration by combining deformation fields from different registration algorithms. (2) A method for regularization of deformation fields post registration (UnfoldReg). In order to compare and combine different registrations, MetaReg utilizes a landmark-based classifier for assessment of local registration quality. We present preliminary results of MetaReg, evaluated on five CT pulmonary breathhold inspiration and expiration scan pairs, employing a set of three registration algorithms (NiftyReg, Demons, Elastix). MetaReg generated for each scan pair a registration that is better than any registration obtained by each registration algorithm separately. On average, 10% improvement is achieved, with a reduction of 30% of regions with misalignments larger than 5mm, compared to the best single registration algorithm.
On Combining Algorithms for Deformable Image Registration
S. Muenzing, B. van Ginneken and J. Pluim
Biomedical Image Registration 2012:256-265.