Image registration by maximization of combined mutual information and gradient information.
Mutual information has developed into an accurate measure for rigid and affine monomodality and multimodality image registration. The robustness of the measure is questionable, however. A possible reason for this is 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 as a normalized measure are presented for rigid registration of three-dimensional clinical images [magnetic resonance (MR), computed tomography (CT), and positron emission tomography (PET)]. The results indicate that the combined measures yield a better registration function does mutual information or normalized mutual information per se. The registration functions are less sensitive to low sampling resolution, do not contain incorrect global maxima that are sometimes found in the mutual information function, and interpolation-induced local minima can be reduced. These characteristics yield the promise of more robust registration measures. The accuracy of the combined measures is similar to that of mutual information-based methods.
Pluim
J P
JP
Image Sciences Institute, University Medical Center Utrecht, The Netherlands. josien@isi.uu.nl
Maintz
J B
JB
Viergever
M A
MA
D019277:Entropy; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods* / Q000706:statistics & numerical data; D008279:Magnetic Resonance Imaging / Q000379:methods / Q000706:statistics & numerical data; D011336:Probability; D015203:Reproducibility of Results; D014055:Tomography, Emission-Computed / Q000379:methods / Q000706:statistics & numerical data; D014057:Tomography, X-Ray Computed / Q000379:methods / Q000706:statistics & numerical data