Rigid point feature registration using mutual information

We have developed a new mutual information-based registration method for matching unlabeled point features. In contrast to earlier mutual information-based registration methods, which estimate the mutual information using image intensity information, our approach uses the point feature location information. A novel aspect of our approach is the emergence of correspondence (between the two sets of features) as a natural by-product of joint density estimation. We have applied this algorithm to the problem of geometric alignment of primate autoradiographs. We also present preliminary results on three-dimensional robust matching of sulci derived from anatomical magnetic resonance images. Finally, we present an experimental comparison between the mutual information approach and other recent approaches which explicitly parameterize feature correspondence.