Feature-space mutual information for multi-modal signal processing with application to medical image registration

A relatively large class of information theoretical measures, such as mutual information or normalized entropy, have been used in multi-modal medical image registration. Even though the mathematical foundations of the different measures were very similar, the final expressions turned out to be surprisingly different. Therefore one of the main aims of this paper is to enlighten the relationship of different, objective functions by introducing a mathematical framework from which several known optimization objectives can be deduced. Furthermore we will extend existing measures in order to be applicable on image features different, than image intensities and introduce “feature efficiency” as a very general concept, to qualify such features. The presented framework is very general and not at all restricted to medical images. Still we want, to discuss the possible impact, of our theoretical framework for the particular problem of medical image registration, where the feature space has traditionally been fixed to image intensities. Our theoretical approach though can be used for any kind of multi-modal signals, such as for the broad field of multi-media applications.