Abstract：The limitation of the conventional multiresolution registration framework is analyzed from the perspective of scale space filtering. Edge-preserved scale space is proposed for multi-scale registration to improve the accuracy and speed and avoid local extreme. The proposed framework has a good edge preserved property which provides more spatial information for mutual information based registration. To achieve automatic registration, a method is proposed to obtain the smoothing parameter λ for non-linear diffusion model. The experimental results show that the proposed framework is superior to other traditional frameworks and suitable for 3-D medical image registration. The registration results have higher accuracy with less numbers of iteration. Furthermore, when traditional frameworks fail to register the images, the proposed framework still has accurate registration results, thus the proposed framework has better robustness.
李登旺，王洪君，尹勇. 基于边缘保护多尺度空间的医学图像配准方法[J]. 模式识别与人工智能, 2011, 24(1): 117-122.
LI Deng-Wang, WANG Hong-Jun, YIN Yong. Multiscale Registration Based on Edge-Preserved Scale Space for Medical Images. , 2011, 24(1): 117-122.
[1] Goshtasby A. 2-D and 3-D Image Registration for Medical, Remote Sensing, and Industrial Applications. New York, USA: John Wiley Sons, 2005 [2] Dana P, Levy D, Xing Lei. Multiscale Registration of Planning CT and Daily Cone Beam CT Images for Adaptive Radiation Therapy. Medical Physics, 2009, 36(4): 4-11 [3] Klein S, van der Heide V A, Lips I M, et al. Automatic Segmentation of the Prostate in 3D MR Images by Atlas Matching Using Localized Mutual Information. Medical Physics, 2008, 35(4): 1407-1417 [4] Staring M, van der Heide V A, Klein S, et al. Registration of Cervical MRI Using Multifeature Mutual Information. IEEE Trans on Medical Imaging, 2009, 28(9): 1412-1421 [5] Pluim J P, Maintz J B, Viergever M A, et al. Mutual-Information-Based Registration of Medical Images: A Survey. IEEE Trans on Medical Imaging, 2003, 22(8): 986-1004 [6] Cole-Rhodes A A, Johnson K L, LeMoingne J, et al. Multiresolution Registration of Remote Sensing Imagery by Optimization of Mutual Information Using a Stochastic Gradient. IEEE Trans on Image Processing, 2003, 12(2): 1495-1511 [7] Lester H, Arridge S R. A Survey of Hierarchical Non-Linear Medical Image Registration. Pattern Recognition, 1999, 32(1): 129-149 [8] Pluim J P W, Maintz J B A, Viergever M A, et al. Mutual Information Matching in Multiresolution Contexts. Image and Vision Computing, 2001, 19(1/2): 45-52 [9] Chen T, Huang T S, Yin W, et al. A New Coarse-to-Fine Framework for 3D Brain MR Image Registration // Proc of the 1st International Workshop on Computer Vision for Biomedical Image Applications. Beijing, China, 2005: 114-124 [10] Perona P, Malik J. Scale-Space and Edge Detection Using Anisotropic Diffusion. IEEE Trans on Pattern Analysis and Machine Intelligence, 1990, 12(7): 629-639 [11] Pluim J P W, Maintz J B, Viergever M A. Image Registration by Maximization of Combined Mutual Information and Gradient Information. IEEE Trans on Medical Imaging, 2000, 19(8): 809-814 [12] Jenkinson M, Bannister P, Brady M, et al. Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images. NeuroImage, 2002, 17(2): 825-841 [13] Tony F C, Selim E. Aspects of Total Variation Regularized L′ Function Approximation. SIAM Journal on Applied Mathematics, 2005, 65(5): 1817-1837 [14] Studholme C, Hill D L G, Hawkes D J. An Overlap Invariant Entropy Measure of 3D Medical Image Alignment. Pattern Recognition, 1999, 32(1): 71-86
2011, Vol. 24 
