Image registration is now a well understood problem and several techniques using a combination of cost functions,
transformation models and optimizers have been reported in medical imaging literature. Parametric methods
often rely on the efficient placement of control points in the images, that is, depending on the location and scale
at which images are mismatched. Poor choice of parameterization results in deformations not being modeled
accurately or over parameterization, where control points may lie in homogeneous regions with low sensitivity to
cost. This lowers computational efficiency due to the high complexity of the search space and might also provide
transformations that are not physically meaningful, and possibly folded.
Adaptive methods that parameterize based on mismatch in images have been proposed. In such methods, the
cost measure must be normalized, heuristics such as how many points to pick, resolution of the grids, choosing
gradient thresholds and when to refine scale would have to be ascertained in addition to the limitation of working
only at a few discrete scales.
In this paper we identify mismatch by searching the entire image and a wide range of smooth spatial scales.
The mismatch vector, containing location and scale of mismatch is computed from peaks in the local joint
entropy. Results show that this method can be used to quickly and effectively locate mismatched regions in
images where control points can be placed in preference to other regions speeding up registration.