Elastic image registration using subspace constraints

Image registration is the process of aligning two images taken from different views, at different times, or by different modalities. In this article, we propose a new framework that incorporates prior deformation knowledge in the registration process. First, an elastic image registration method is used to obtain deformation fields by modeling the nonrigid deformations as locally affine and globally smooth flow fields. Next, the estimated geometric transformation maps are used to train a prior deformation model using two subspace projection techniques, namely principle component analysis (PCA) and independent component analysis (ICA). A smooth deformation is now guaranteed by projecting the locally calculated deformation onto a subspace of allowed deformations. One advantage of our approach is in its ability to guarantee smoothness without the need for iterative regularization. The new algorithms were validated using the Amsterdam library of images (ALOI). Our experiments demonstrate promising results in terms of mean square error.