Multimodality image registration using ordinary procrustes analysis and entropy of bivariate normal kernel density

We present a registration method for medical images based on shape information and voxel intensities. First, we segment volume images using the Markov random field and the Gibbs distribution. We extract the 3D feature points of the shape from the surface of the segmented object. Then, we conduct first registration using ordinary Procrustes analysis for two sets of 3D feature points. For the second registration, we define the new optimization measure of registration as the entropy of the bivariate normal kernel density for pairs of intensities given from the extracted feature points as well as the transformed feature points. The final registration for two volume images is carried out by finding the appropriate transformation parameter yielding the minimum value of this optimization measure. To evaluate the performance of the proposed registration method, we conduct various experiments comparing our method with existing ones such as the Mutual Information measure.