Abstract
Despite generally good performance, mutual information has also been shown by several researchers to lack robustness for certain registration problems. A possible cause may be the absence of spatial information in the measure. The present paper proposes to include spatial information by combining mutual information with a term based on the image gradient of the images to be registered. The gradient term not only seeks to align locations of high gradient magnitude, but also aims for a similar orientation of the gradients at these locations.
Results of combining both standard mutual information as well a normalized measure are presented for rigid registration of three-dimensional clinical images (MR, CT and PET). The results indicate that the combined measures yield a better registration function than mutual information or normalized mutual information per se. The accuracy of the combined measures is compared against a screw marker based gold standard, revealing a similar accuracy for the combined measures to that of the standard measures. Experiments into the robustness of the measures with respect to starting position imply a clear improvement in robustness for the measures including spatial information.
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Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A. (2000). Image Registration by Maximization of Combined Mutual Information and Gradient Information. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2000. MICCAI 2000. Lecture Notes in Computer Science, vol 1935. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-40899-4_46
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DOI: https://doi.org/10.1007/978-3-540-40899-4_46
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