Abstract
AbstractAn extension of the mutual information metric to a three-variate cost function for driving the registration of a volume to pair of co-registered volumes is presented. While mutual information has typically been applied to pairs of variables, it is possible to compute multi-variate mutual information. The implementation of multi-variate mutual information is described. This metric is demonstrated using the problem of registering a deformed t2 slice of the visible male magnetic resonance data set to either a single t1 slice or a pair of co-registered t1 and proton density slices. Two-variable and three-variable metric registration results are compared. Adding the extra proton density information to the registration cost metric leads to faster optimization convergence and better final accuracy. Multi-variate mutual information has potential application in problems where the addition of more information can lead to solution convergence or improve accuracy.KeywordsMutual InformationProton DensityAverage Mutual InformationOptimization CycleRegistration ProblemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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