Using region trajectories to construct an accurate and efficient polyaffine transform model

Authors:
Gang Song;Yang Liu;Baohua Wu;Brian Avants;James C. Gee
Affiliations:
Penn Image Computing and Science Lab, University of Pennsylvania, Philadelphia, PA;Penn Image Computing and Science Lab, University of Pennsylvania, Philadelphia, PA;Penn Image Computing and Science Lab, University of Pennsylvania, Philadelphia, PA;Penn Image Computing and Science Lab, University of Pennsylvania, Philadelphia, PA;Penn Image Computing and Science Lab, University of Pennsylvania, Philadelphia, PA
Venue:
IPMI'13 Proceedings of the 23rd international conference on Information Processing in Medical Imaging
Year:
2013

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Abstract

In this paper we propose a novel way to construct a diffeomorphic polyaffine model. Each affine transform is defined on a local region and the resulting diffeomorphism encapsulates all the local transforms by a smooth and invertible displacement field. Compared with traditional weighting schemes used in combining local transforms, our new scheme guarantees that the resulting transform precisely preserves the value of each local affine transform. By introducing the trajectory of local regions instead of using regions themselves, the new approach encodes precisely each local affine transform using a diffeomorphism with one or more stationary velocity fields. Experiments show that our new polyaffine model is both accurate and efficient.