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Issue No.05 - May (2013 vol.35)
pp: 1221-1233
Christian Wachinger , Dept. of Neurology, Massachusetts Inst. of Technol., Cambridge, MA, USA
N. Navab , Dept. of Inf., Tech. Univ. Munchen, Garching, Germany
ABSTRACT
We address the alignment of a group of images with simultaneous registration. Therefore, we provide further insights into a recently introduced framework for multivariate similarity measures, referred to as accumulated pair-wise estimates (APE), and derive efficient optimization methods for it. More specifically, we show a strict mathematical deduction of APE from a maximum-likelihood framework and establish a connection to the congealing framework. This is only possible after an extension of the congealing framework with neighborhood information. Moreover, we address the increased computational complexity of simultaneous registration by deriving efficient gradient-based optimization strategies for APE: Gauss-Newton and the efficient second-order minimization (ESM). We present next to SSD the usage of intrinsically nonsquared similarity measures in this least squares optimization framework. The fundamental assumption of ESM, the approximation of the perfectly aligned moving image through the fixed image, limits its application to monomodal registration. We therefore incorporate recently proposed structural representations of images which allow us to perform multimodal registration with ESM. Finally, we evaluate the performance of the optimization strategies with respect to the similarity measures, leading to very good results for ESM. The extension to multimodal registration is in this context very interesting because it offers further possibilities for evaluations, due to publicly available datasets with ground-truth alignment.
INDEX TERMS
Approximation methods, Optimization methods, Estimation, Joints, Density functional theory, Convergence,multimodal, Registration, groupwise, simultaneous, optimization, similarity measures
CITATION
Christian Wachinger, N. Navab, "Simultaneous Registration of Multiple Images: Similarity Metrics and Efficient Optimization", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.35, no. 5, pp. 1221-1233, May 2013, doi:10.1109/TPAMI.2012.196
REFERENCES
[1] E.G. Learned-Miller, "Data Driven Image Models through Continuous Joint Alignment," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 2, pp. 236-250, Feb. 2006.
[2] G. Huang, V. Jain, and E. Learned-Miller, "Unsupervised Joint Alignment of Complex Images," Proc. IEEE Int'l Conf. Computer Vision, pp. 1-8, 2007.
[3] M. Cox, S. Lucey, S. Sridharan, and J. Cohn, "Least Squares Congealing for Unsupervised Alignment of Images," Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 2008.
[4] M. Cox, S. Sridharan, S. Lucey, and J. Cohn, "Least-Squares Congealing for Large Numbers of Images," Proc. IEEE Int'l Conf. Computer Vision, pp. 1949-1956, 2009.
[5] L. Zöllei, E. Learned-Miller, E. Grimson, and W. Wells, "Efficient Population Registration of 3D Data," Proc. Int'l Conf. Computer Vision for Biomedical Image Applications, 2005.
[6] C. Wachinger, W. Wein, and N. Navab, "Three-Dimensional Ultrasound Mosaicing," Proc. Int'l Conf. Medical Image Computing and Computer-Assisted Intervention, Oct. 2007.
[7] C. Wachinger and N. Navab, "Structural Image Representation for Image Registration," Proc. IEEE CS Workshop Math. Methods in Biomedical Image Analysis, June 2010.
[8] C. Wachinger and N. Navab, "Manifold Learning for Multi-Modal Image Registration," Proc. 11th British Machine Vision Conf., 2010.
[9] J. West et al., "Comparison and Evaluation of Retrospective Intermodality Brain Image Registration Techniques," J. Computer Assisted Tomography, vol. 21, no. 4, pp. 554-566, 1997.
[10] C. Studholme and V. Cardenas, "A Template Free Approach to Volumetric Spatial Normalization of Brain Anatomy," Pattern Recognition Letters, vol. 25, no. 10, pp. 1191-1202, 2004.
[11] T. Cootes, S. Marsland, C. Twining, K. Smith, and C. Taylor, "Groupwise Diffeomorphic Non-Rigid Registration for Automatic Model Building," Proc. European Conf. Computer Vision, 2004.
[12] K. Sidorov, S. Richmond, and D. Marshall, "An Efficient Stochastic Approach to Groupwise Non-Rigid Image Registration," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[13] M. Yigitsoy, C. Wachinger, and N. Navab, "Temporal Groupwise Registration for Motion Modeling," Proc. Int'l Conf. Information Processing in Medical Imaging, 2011.
[14] C. Metz, S. Klein, M. Schaap, T. Van Walsum, and W. Niessen, "Nonrigid Registration of Dynamic Medical Imaging Data Using Nd+t b-Splines and a Groupwise Optimization Approach," Medical Image Analysis, vol. 15, no. 2, pp. 238-249, 2011.
[15] B. Lukas and T. Kanade, "An Iterative Image Registration Technique with an Application to Stereo Vision," Proc. Image Understanding Workshop, 1981.
[16] B. Horn and B. Schunck, "Determining Optical Flow," Artificial intelligence, vol. 17, nos. 1-3, pp. 185-203, 1981.
[17] S. Baker and I. Matthews, "Lucas-Kanade 20 Years On: A Unifying Framework," Int'l J. Computer Vision, vol. 56, no. 3, pp. 221-255, 2004.
[18] K. Madsen, H. Nielsen, and O. Tingleff, Methods for Non-Linear Least Squares Problems, second ed. Technical Univ. of Denmark, 2004.
[19] S. Benhimane and E. Malis, "Real-Time Image-Based Tracking of Planes Using Efficient Second-Order Minimization," Proc. IEEE/RSJ Int'l Conf. Intelligent Robots and Systems, pp. 943-948, 2004.
[20] T. Vercauteren, X. Pennec, E. Malis, A. Perchant, and N. Ayache, "Insight Into Efficient Image Registration Techniques and the Demons Algorithm," Proc. Int'l Conf. Information Processing in Medical Imaging, 2007.
[21] R. Stefanescu, X. Pennec, and N. Ayache, "Grid Powered Nonlinear Image Registration with Locally Adaptive Regularization," Medical Image Analysis, vol. 8, no. 3, pp. 325-342, 2004.
[22] C. Chefd'hotel, G. Hermosillo, and O. Faugeras, "Flows of Diffeomorphisms for Multimodal Image Registration," Proc. IEEE Int'l Symp. Biomedical Imaging, pp. 753-756, 2002.
[23] T. Vercauteren, X. Pennec, A. Perchant, and N. Ayache, "Diffeomorphic Demons: Efficient Non-Parametric Image Registration," NeuroImage, vol. 45, no. 1, pp. 61-72, 2009.
[24] C. Wachinger and N. Navab, "Similarity Metrics and Efficient Optimization for Simultaneous Registration," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[25] L. Zöllei, "A Unified Information Theoretic Framework for Pair- and Group-Wise Registration of Medical Images," PhD thesis, MIT-CSAIL, Massachusetts Inst. of Tech nology, 2006.
[26] P.A. Viola, "Alignment by Maximization of Mutual Information," PhD thesis, Massachusetts Inst. of Tech nology, 1995.
[27] A. Roche, G. Malandain, and N. Ayache, "Unifying Maximum Likelihood Approaches in Medical Image Registration," Int'l J. Imaging Systems and Technology, special issue on 3D imaging, vol. 11, no. 1, pp. 71-80, 2000.
[28] C. Wachinger and N. Navab, "A Contextual Maximum Likelihood Framework for Modeling Image Registration," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2012.
[29] S. Li, Markov Random Field Modeling in Image Analysis. Springer-Verlag, 2009.
[30] C. Wachinger, "Three-Dimensional Ultrasound Mosaicing," master's thesis, Technische Univ. München, Mar. 2007.
[31] C.M. Bishop, Pattern Recognition and Machine Learning. Springer-Verlag, 2006.
[32] P. Lee and J. Moore, "Gauss-Newton-on-Manifold for Pose Estimation," J. Industrial and Management Optimization, vol. 1, no. 4, pp. 565-587, 2005.
[33] R. Mahony and J. Manton, "The Geometry of the Newton Method on Non-Compact Lie Groups," J. Global Optimization, vol. 23, no. 3, pp. 309-327, 2002.
[34] M. Zefran, V. Kumar, and C. Croke, "On the Generation of Smooth Three-Dimensional Rigid Body Motions," IEEE Trans. Robotics and Automation, vol. 14, no. 4, pp. 576-589, Aug. 1998.
[35] R.M. Murray, Z. Li, and S.S. Sastry, A Mathematical Introduction to Robotic Manipulation. CRC Press, 1994.
[36] E. Malis, "Méthodologies d'Estimation et de Commande à Partir d'un Système de Vision," habilitation, Nice-Sophia Antipolis, 2008.
[37] S. Benhimane, "Vers une Approche Unifiee pour le Suivi Temps-Reel et l'Asservissement Visuel," Docteur en Sciences-Specialite: Informatique Temps-Reel, Automatique et Robotique, Ecole Nationale Superieure des Mines de Paris, 2006.
[38] G. Hermosillo, C. Chefd'Hotel, and O. Faugeras, "Variational Methods for Multimodal Image Matching," Int'l J. Computer Vision, vol. 50, no. 3, pp. 329-343, 2002.
[39] W. Wells, P. Viola, H. Atsumi, S. Nakajima, and R. Kikinis, "Multi-Modal Volume Registration by Maximization of Mutual Information," Medical Image Analysis, vol. 1, pp. 35-51, 1996.
[40] E. D'Agostino, F. Maes, D. Vandermeulen, and P. Suetens, "A Viscous Fluid Model for Multimodal Non-Rigid Image Registration Using Mutual Information," Medical Image Analysis, vol. 7, no. 4, pp. 565-575, 2003.
[41] J.P.W. Pluim, J.B.A. Maintz, and M.A. Viergever, "Mutual Information Based Registration of Medical Images: A Survey," IEEE Trans. Medical Imaging, vol. 22, no. 8, pp. 986-1004, Aug. 2003.
[42] E. Parzen, "On Estimation of a Probability Density Function and Mode," The Ann. of Math. Statistics, vol. 33, no. 3, pp. 1065-1076, 1962.
[43] B. Turlach, "Bandwidth Selection in Kernel Density Estimation: A Review," CORE and Institut de Statistique, vol. 19, no. 4, pp. 1-33, 1993.
[44] P. Thevenaz and M. Unser, "Optimization of Mutual Information for Multiresolution Image Registration," IEEE Trans. Image Processing, vol. 9, no. 12, pp. 2083-2099, Dec. 2000.
[45] G. Panin and A. Knoll, "Mutual Information-Based 3D Object Tracking," Int'l J. Computer Vision, vol. 78, no. 1, pp. 107-118, 2008.
[46] A. Dame and E. Marchand, "Accurate Real-Time Tracking Using Mutual Information," Proc. IEEE Int'l Symp. Mixed and Augmented Reality, pp. 47-56, Oct. 2010.
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