Non rigid medical image registration based on the maximization of quadratic mutual information

We present a new highly-accurate similarity criterion for medical image registration, inspired from mutual information. The novelty of our approach lies first, in the use of quadratic mutual information, and second, in a new adaptive kernel density estimation which does not require any prior information on the image. Practically, our method exhibits a drastic decrease of the computation cost compared to convention adaptive kernels.