Multi-resolution three-dimensional multi-modality image registration by maximization of mutual information

Maximization of mutual information is a very powerful criterion for 3D medical image registration, allowing robust and accurate fully automated rigid registration of multi-modal images in various applications. In this paper, we presented a method based on normalized mutual information with sub-sampling of the images for 3D image registration on the images of CT, MR and PET. Powell´s direction set method and Brent´s one-dimensional optimization algorithm were used as optimization strategy. A multi-resolution approach was applied to speedup the matching process. For PET images, pre-processing of segmentation was performed to reduce the background artifacts. According to the evaluation by Vanderbilt University, the average of mean of registration error for CT-MR task was 1.47 mm and for MR-PET task was 3.22 mm. The registration images with edge extraction showed good matches by visual inspection. Sub-voxel accuracy in multi-modality registration has been achieved with this algorithm.