Registration of Brain MRI/PET Images Based on Adaptive Combination of Intensity and Gradient Field Mutual Information

Liu, Jiangang; Tian, Jie

doi:https://doi.org/10.1155/2007/93479

International Journal of Biomedical Imaging

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Multimodal Imaging and Hybrid Scanners

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Research Article | Open Access

Volume 2007 | Article ID 093479 | https://doi.org/10.1155/2007/93479

Registration of Brain MRI/PET Images Based on Adaptive Combination of Intensity and Gradient Field Mutual Information

Jiangang Liu¹and Jie Tian^1,2

Academic Editor: Robert R. Edelman

Received10 May 2006

Revised13 Jan 2007

Accepted22 Jan 2007

Published18 Mar 2007

Abstract

Traditional mutual information (MI) function aligns two multimodality images with intensity information, lacking spatial information, so that it usually presents many local maxima that can lead to inaccurate registration. Our paper proposes an algorithm of adaptive combination of intensity and gradient field mutual information (ACMI). Gradient code maps (GCM) are constructed by coding gradient field information of corresponding original images. The gradient field MI, calculated from GCMs, can provide complementary properties to intensity MI. ACMI combines intensity MI and gradient field MI with a nonlinear weight function, which can automatically adjust the proportion between two types MI in combination to improve registration. Experimental results demonstrate that ACMI outperforms the traditional MI and it is much less sensitive to reduced resolution or overlap of images.

References

F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, “Multimodality image registration by maximization of mutual information,” IEEE Transactions on Medical Imaging, vol. 16, no. 2, pp. 187–198, 1997.
View at: Publisher Site | Google Scholar
P. Viola and W. M. Wells III, “Alignment by maximization of mutual information,” in Proceedings of the 5th IEEE International Conference on Computer Vision (ICCV '95), pp. 16–23, Cambridge, Mass, USA, June 1995.
View at: Publisher Site | Google Scholar
F. Maes, D. Vandermeulen, and P. Suetens, “Medical image registration using mutual information,” Proceedings of the IEEE, vol. 91, no. 10, pp. 1699–1721, 2003.
View at: Publisher Site | Google Scholar
J. P. W. Pluim, J. B. A. Maintz, and M. A. Viergever, “Interpolation artefacts in mutual information-based image registration,” Computer Vision and Image Understanding, vol. 77, no. 2, pp. 211–232, 2000.
View at: Publisher Site | Google Scholar
J. Tsao, “Interpolation artifacts in multimodality image registration based on maximization of mutual information,” IEEE Transactions on Medical Imaging, vol. 22, no. 7, pp. 854–864, 2003.
View at: Publisher Site | Google Scholar
T. Butz and J.-P. Thiran, “Affine registration with feature space mutual information,” in Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '01), vol. 2208 of Lecture Notes in Computer Science, pp. 549–556, Utrecht, The Netherlands, October 2001.
View at: Google Scholar
J. P. W. Pluim, J. B. A. Maintz, and M. A. Viergever, “Image registration by maximization of combined mutual information and gradient information,” IEEE Transactions on Medical Imaging, vol. 19, no. 8, pp. 809–814, 2000.
View at: Publisher Site | Google Scholar
E. Haber and J. Modersitzki, “Intensity gradient based registration and fusion of multi-modal images,” Tech. Rep. TR-2004-027-A, Department of Mathematics and Computer Science, Emory University, Atlanta, Ga, USA, June 2004.
View at: Google Scholar
J. B. A. Maintz, P. A. van den Elsen, and M. A. Viergever, “Comparison of edge-based and ridge-based registration of CT and MR brain images,” Medical Image Analysis, vol. 1, no. 2, pp. 151–161, 1996.
View at: Publisher Site | Google Scholar
F. Maes, Segmentation and registration of multimodal medical images: from theoty, implementation and validation to a useful tool in clinical practice, Ph.D. thesis, Catholic University of Leuven, Leuven, Belgium, 1998.
C. Studholme, D. L. G. Hill, and D. J. Hawkes, “An overlap invariant entropy measure of 3D medical image alignment,” Pattern Recognition, vol. 32, no. 1, pp. 71–86, 1999.
View at: Publisher Site | Google Scholar
X. Wang and J. Tian, “Image registration based on maximization of gradient code mutual information,” Image Analysis and Stereology, vol. 24, pp. 1–7, 2005.
View at: Google Scholar
R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, China Machine Press, Beijing, China, 2nd edition, 2004.
J. A. Nelder and R. Mead, “A simplex method for function minimization,” Computer Journal, vol. 7, pp. 308–313, 1965.
View at: Google Scholar
F. Maes, D. Vandermeulen, and P. Suetens, “Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information,” Medical Image Analysis, vol. 3, no. 4, pp. 373–386, 1999.
View at: Publisher Site | Google Scholar
J. C. Lagarias, J. A. Reeds, M. H. Wright, and P. E. Wright, “Convergence properties of the Nelder-Mead simplex method in low dimensions,” SIAM Journal on Optimization, vol. 9, no. 1, pp. 112–147, 1998.
View at: Publisher Site | Google Scholar
J. West, J. M. Fitzpatrick, M. Y. Wang et al., “Comparison and evaluation of retrospective intermodality brain image registration techniques,” Journal of Computer Assisted Tomography, vol. 21, no. 4, pp. 554–566, 1997.
View at: Publisher Site | Google Scholar
R. C. Gonzalez and R. E. Woods, Digital Image Processing, Publishing House of Electronics Industry, Beijing, China, 2nd edition, 2002.
T. Hartkens, D. L. G. Hill, A. D. Castellano-Smith et al., “Using points and surfaces to improve voxel-based non-rigid registration,” in Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '02), vol. 2489 of Lecture Notes in Computer Science, pp. 565–572, Tokyo, Japan, September 2002.
View at: Google Scholar
P. Hellier and C. Barillot, “Coupling dense and landmark-based approaches for nonrigid registration,” IEEE Transactions on Medical Imaging, vol. 22, no. 2, pp. 217–227, 2003.
View at: Publisher Site | Google Scholar
H. J. Johnson and G. E. Christensen, “Consistent landmark and intensity-based image registration,” IEEE Transactions on Medical Imaging, vol. 21, no. 5, pp. 450–461, 2002.
View at: Publisher Site | Google Scholar
P. Thevenaz and M. Unser, “A pyramid approach to sub-pixel image fusion based on mutual information,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '96), vol. 1, pp. 265–268, Lausanne, Switzerland, September 1996.
View at: Publisher Site | Google Scholar
E. H. W. Meijering, W. J. Niessen, and M. A. Viergever, “Quantitative evaluation of convolution-based methods for medical image interpolation,” Medical Image Analysis, vol. 5, no. 2, pp. 111–126, 2001.
View at: Publisher Site | Google Scholar
R. He and P. A. Narayana, “Global optimization of mutual information: application to three-dimensional retrospective registration of magnetic resonance images,” Computerized Medical Imaging and Graphics, vol. 26, no. 4, pp. 277–292, 2002.
View at: Publisher Site | Google Scholar
T. Yokoi, T. Soma, H. Shinohara, and H. Matsuda, “Accuracy and reproducibility of co-registration techniques based on mutual information and normalized mutual information for MRI and SPECT brain images,” Annals of Nuclear Medicine, vol. 18, no. 8, pp. 659–667, 2004.
View at: Google Scholar
J. P. W. Pluim, J. B. A. Maintz, and M. A. Viergever, “Mutual-information-based registration of medical images: a survey,” IEEE Transactions on Medical Imaging, vol. 22, no. 8, pp. 986–1004, 2003.
View at: Publisher Site | Google Scholar
M. Jenkinson and S. Smith, “A global optimisation method for robust affine registration of brain images,” Medical Image Analysis, vol. 5, no. 2, pp. 143–156, 2001.
View at: Publisher Site | Google Scholar
C. Grova, A. Biraben, J. M. Scarabin et al., “A methodology to validate MRI/SPECT registration methods using realistic simulated spect data,” in Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI '01), vol. 2208 of Lecture Notes in Computer Science, pp. 275–282, Utrecht, The Netherlands, October 2001.
View at: Google Scholar
J. P. W. Pluim, J. B. A. Maintz, and M. A. Viergever, “Mutual information matching in multiresolution contexts,” Image and Vision Computing, vol. 19, no. 1-2, pp. 45–52, 2001.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2007 Jiangang Liu and Jie Tian. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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