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A Novel Subsampling Method for 3D Multimodality Medical Image Registration Based on Mutual Information | ||
| AUT Journal of Electrical Engineering | ||
| مقاله 2، دوره 41، شماره 1، تیر 2009، صفحه 11-19 اصل مقاله (313.08 K) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22060/eej.2009.273 | ||
| نویسندگان | ||
| Maryam Zibaeifardi1؛ Mohammad Rahmatiii2 | ||
| 1Maryam Zibaeifard is with Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran (email: zibaeifard@ce.aut.ac.ir). | ||
| 2Mohammad Rahmati is with Department of Computer Engineering and Information Technology, Amirkabir University of Technology, Tehran, Iran (email: rahmati@aut.ac.ir), Corresponding author. | ||
| چکیده | ||
| Mutual information (MI) is a widely used similarity metric for multimodality image registration. However, it involves an extremely high computational time especially when it is applied to volume images. Moreover, its robustness is affected by existence of local maxima. The multi-resolution pyramid approaches have been proposed to speed up the registration process and increase the accuracy of the result. In this paper, we present a new improved method of sample selection for multi-stage registration based on mutual information. Instead of down-sampling of the whole image as it is done in the pyramid methods, we propose a new technique to find a suitable subset of image samples based on image information content, which results in a better estimate of the optimal transformation. A comparison for MR images indicates that our proposed method yields better registration than subsampling method, especially when subsampling factor is low. The experimental results involving three-dimensional clinical images of CT, MR and PET are presented for rigid registration. | ||
| کلیدواژه‌ها | ||
| Image registration؛ Mutual information؛ Simulated Annealing | ||
| عنوان مقاله [English] | ||
| A Novel Subsampling Method for 3D Multimodality Medical Image Registration Based on Mutual Information | ||
| چکیده [English] | ||
| Mutual information (MI) is a widely used similarity metric for multimodality image registration. However, it involves an extremely high computational time especially when it is applied to volume images. Moreover, its robustness is affected by existence of local maxima. The multi-resolution pyramid approaches have been proposed to speed up the registration process and increase the accuracy of the result. In this paper, we present a new improved method of sample selection for multi-stage registration based on mutual information. Instead of down-sampling of the whole image as it is done in the pyramid methods, we propose a new technique to find a suitable subset of image samples based on image information content, which results in a better estimate of the optimal transformation. A comparison for MR images indicates that our proposed method yields better registration than subsampling method, especially when subsampling factor is low. The experimental results involving three-dimensional clinical images of CT, MR and PET are presented for rigid registration. | ||
| کلیدواژه‌ها [English] | ||
| Image registration, Mutual information, Simulated Annealing | ||
| مراجع | ||
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[1] Lisa G. Brown, “A survey of image registration techniques,” ACM Computing Surveys, vol. 24, no. 4, pp. 325-376, 1992. [2] B. A. Maintz, M. A. Viergever, “A survey of medical image registration,” Medical Image Analysis, vol. 2, no. 1, pp. 1-36, 1998. [3] V. Noblet, C. Heinrich, F. Heitz, J. P. Armspach, “3-d deformable image registration: a topology preservation scheme based on hierarchical deformation models and interval analysis optimization,” IEEE Transactions on Image Processing, vol. 14, no. 5, pp. 553-566, 2005. [4] Hua mei Chen, Pramod K. Varshney, “A pyramid approach for multimodality image registration based on mutual information,” Proceedings of 3rd international conference on information fusion, vol. I, pp. 9-15, 2000. [5] F. Maes, D. Vandermeulen, P. Suetens, “Comparative evaluation of multiresolution optimization strategies for multimodalitiy image registration by maximization of mutual information,” Medical Image Analysis, vol. 3, no. 4, pp. 373–386, 1999. [6] S. A. Teukolsky, B. P. Flannery, W. T. Vetterling, Numerical recipes in C, Cambridge University Press, Cambridge, UK, 1992. [7] R. Shekhar, V. Zagrodsky, “Mutual information-based rigid and nonrigid registration of ultrasound volumes,” IEEE Transactions on Medical Imaging, vol. 21, no. 1, pp. 9-22, 2002. [8] Josien P. W. Pluim, J. B. Antoine Maintz, Max A. Viergever, “Mutual information based registration of medical images: a survey,” IEEE Transactions on Medical Imaging, vol. 22, no. 8, pp. 986-1004, 2003. [9] C. Studholme, D. L. G. Hill, D. J. Hawkes, “An overlap invariant entropy measure of 3D medical image alignment,” Pattern Recognition, vol. 32, no. 1, pp. 71–86, 1999. [10] F. Maes, A. Collignon, “Multimodality image registration by maximization of mutual information,” IEEE Transactions on Medical Imaging, vol. 16, no. 2, pp. 187-198, 1997. | ||
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