Robust rigid registration of CT to MRI brain volumes using the SCV similarity measure

Multi-modal medical image registration is an important processing step for extracting the maximum amount of information from multi-modal medical images. In this paper, to perform image registration of CT and MRI data volumes, we use the sum-of-conditional variance (SCV) similarity measure which utilizes the joint probability distribution of two images and allows Gauss-Newton optimization to be used. We compare the results from experiments on clinical CT and MRI datasets obtained using the SCV similarity measure, the entropy images on sum-of-squared-difference (eSSD) method and the mutual information (MI) approach. Our results indicate that the proposed SCV approach outperforms the eSSD and MI similarity measure approaches.