  In image registration, many efforts have been devoted to the development of
alternatives to the popular normalized mutual information criterion.
Concurrently to these efforts, an increasing number of works have demonstrated
that substantial gains in registration accuracy can also be achieved by
aligning structural representations of images rather than images themselves.
Following this research path, we propose a new method for mono- and multimodal
image registration based on the alignment of regularized vector fields derived
from structural information such as gradient vector flow fields, a technique we
call \textit{vector field similarity}. Our approach can be combined in a
straightforward fashion with any existing registration framework by
substituting vector field similarity to intensity-based registration. In our
experiments, we show that the proposed approach compares favourably with
conventional image alignment on several public image datasets using a diversity
of imaging modalities and anatomical locations.
