Hybrid Surface- and Voxel-Based Registration for MR-PET Brain Fusion

In this paper, we propose a novel technique of registration using hybrid approach for MR-PET brain image fusion. Hybrid approach uses merits of surface- and voxel-based registration. Thus, our method measures similarities using voxel intensities in MR images corresponding to the feature points of the brain in PET images. Proposed method selects the brain threshold using histogram accumulation ratio in PET images. And then, we automatically segment the brain using the inverse region growing with pre-calculated threshold and extract the feature points of the brain using sharpening filter in PET images. In order to find the optimal location for registration, we evaluate the Hybrid-based Cross-Correlation using the voxel intensities in MR images corresponding to the feature points in PET images. In our experiments, we evaluate our method using software phantom and clinical datasets in the aspect of visual inspection, accuracy, robustness, and computation time. Experimental results show that our method is dramatically faster than the voxel-based registration and more accurate than the surface-based registration. In particular, our method can robustly align two datasets with large geometrical displacement and noise at optimal location.