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Issue No.11 - November (2010 vol.32)
pp: 1994-2005
Timothy F. Cootes , University of Manchester, Manchester
Carole J. Twining , University of Manchester, Manchester
Vladimir S. Petrović , University of Manchester, Manchester
Kolawole O. Babalola , University of Manchester, Manchester
Christopher J. Taylor , University of Manchester, Manchester
ABSTRACT
Groupwise image registration algorithms seek to establish dense correspondences between sets of images. Typically, they involve iteratively improving the registration between each image and an evolving mean. A variety of methods have been proposed, which differ in their choice of objective function, representation of deformation field, and optimization methods. Given the complexity of the task, the final accuracy is significantly affected by the choices made for each component. Here, we present a groupwise registration algorithm which can take advantage of the statistics of both the image intensities and the range of shapes across the group to achieve accurate matching. By testing on large sets of images (in both 2D and 3D), we explore the effects of using different image representations and different statistical shape constraints. We demonstrate that careful choice of such representations can lead to significant improvements in overall performance.
INDEX TERMS
Nonrigid registration, correspondence problem, appearance models.
CITATION
Timothy F. Cootes, Carole J. Twining, Vladimir S. Petrović, Kolawole O. Babalola, Christopher J. Taylor, "Computing Accurate Correspondences across Groups of Images", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 11, pp. 1994-2005, November 2010, doi:10.1109/TPAMI.2009.193
REFERENCES
[1] A. Guimond, J. Meunier, and J.-P. Thirion, "Automatic Computation of Average Brain Models," Proc. Medical Image Computing and Computer-Assisted Intervention, pp. 631-640, 1998.
[2] T.F. Cootes, G.J. Edwards, and C.J. Taylor, "Active Appearance Models," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 681-685, June 2001.
[3] S. Baker, I. Matthews, and J. Schneider, "Automatic Construction of Active Appearance Models as an Image Coding Problem," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 10, pp. 1380-1384, Oct. 2004.
[4] D. Rueckert, A. Frangi, and J. Schnabel, "Automatic Construction of 3D Statistical Deformation Models of the Brain Using Non-Rigid Registration," IEEE Trans. Medical Imaging, vol. 22, no. 8, pp. 1014-1025, Aug. 2003.
[5] S. Balci, P. Golland, M. Shenton, and W. Wells, "Free-Form B-Spline Deformation Model for Groupwise Registration," Proc. Medical Image Computing and Computer-Assisted Intervention Statistical Registration Workshop, pp. 23-30, 2007.
[6] T. Cootes, C. Twining, V. Petrović, R. Schestowitz, and C. Taylor, "Groupwise Construction of Appearance Models Using Piece-Wise Affine Deformations," Proc. 16th British Machine Vision Conf., vol. 2, pp. 879-888, 2005.
[7] S. Duchesne, J. Pruessner, and D. Collins, "Appearance-Based Segmentation of Medial Temporal Lobe Structures," NeuroImage, vol. 17, pp. 515-531, 2002.
[8] M.J. Jones and T. Poggio, "Multidimensional Morphable Models: A Framework for Representing and Matching Object Classes," Int'l J. Computer Vision, vol. 2, no. 29, pp. 107-131, 1998.
[9] S. Joshi, B. Davis, M. Jomier, and G. Gerig, "Unbiased Diffeomorphic Atlas Construction for Computational Anatomy," NeuroImage, vol. 23, pp. S151-S160, 2004.
[10] D. Rueckert, L.I. Sonoda, C. Hayes, D.L.G. Hill, M.O. Leach, and D.J. Hawkes, "Non-Rigid Registration Using Free-Form Deformations: Application to Breast MR Images," IEEE Trans. Medical Imaging, vol. 18, no. 8, pp. 712-721, Aug. 1999.
[11] A. Frangi, D. Rueckert, J. Schnabel, and W. Niessen, "Automatic Construction of Multiple-Object Three-Dimensional Statistical Shape Models: Application to Cardiac Modeling," IEEE Trans. Medical Imaging, vol. 21, no. 9, pp. 1151-1166, Sept. 2002.
[12] K. Messer, J. Matas, J. Kittler, J. Luettin, and G. Maitre, "XM2VTSDB: The Extended M2VTS Database," Proc. Second Conf. Audio and Video-Based Biometric Personal Verification, pp. 72-77, 1999.
[13] T. Vetter, M. Jones, and T. Poggio, "A Bootstrapping Algorithm for Learning Linear Models of Object Classes," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 40-46, 1997.
[14] M.J. Jones and T. Poggio, "Multidimensional Morphable Models," Proc. Sixth IEEE Int'l Conf. Computer Vision, pp. 683-688, 1998.
[15] J.V. Hajnal, D.L. Hill, and D.J. Hawkes, Medical Image Registration. CRC Press, 2001.
[16] B. Zitová and J. Flusser, "Image Registration Methods: A Survey," Image and Vision Computing, vol. 21, pp. 977-1000, 2003.
[17] J.B.A. Maintz and M.A. Viergever, "A Survey of Medical Image Registration," Medical Image Analysis, vol. 2, no. 1, pp. 1-36, 1998.
[18] J.P. Thirion, "Image Matching as a Diffusion Process: An Analogy with Maxwell's Demons," Medical Image Analysis, vol. 2, no. 3, pp. 243-260, 1998.
[19] D.L. Collins, C. Holmes, T. Peters, and A. Evans, "Automatic 3D Model-Based Neuroanatomical Segmentation," Human Brain Mapping, vol. 3, pp. 190-208, 1995.
[20] T. Cootes, C. Twining, K. Babalola, and C. Taylor, "Diffeomorphic Statistical Shape Models," Image and Vision Computing, vol. 26, pp. 326-332, 2008.
[21] P. Lorenzen, B. Davis, and S. Joshi, "Unbiased Atlas Formation via Large Deformations Metric Mapping," Proc. Medical Image Computing and Computer-Assisted Intervention, pp. 411-418, 2005.
[22] S. Marsland, C. Twining, and C. Taylor, "Groupwise Non-Rigid Registration Using Polyharmonic Clamped-Plate Splines," Proc. Medical Image Computing and Computer-Assisted Intervention, pp. 771-779, 2003.
[23] S. Marsland, C.J. Twining, and C.J. Taylor, "A Minimum Description Length Objective Function for Groupwise Non-Rigid Image Registration," Image and Vision Computing, vol. 26, no. 3, pp. 333-346, 2008.
[24] T. Cootes, S. Marsland, C. Twining, K. Smith, and C. Taylor, "Groupwise Diffeomorphic Non-Rigid Registration for Automatic Model Building," Proc. Eighth European Conf. Computer Vision, vol. 4, pp. 316-327, 2004.
[25] E. 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.
[26] R. Fergus, P. Perona, and A. Zisserman, "Weakly Supervised Scale Invariant Learning of Models for Visual Recognition," Int'l J. Computer Vision, vol. 71, pp. 273-303, 2007.
[27] Towards Category-Level Object Recognition. J. Ponce, M. Hebert, C. Schmid, and A. Zisserman, eds. Springer-Verlag, 2006.
[28] G. Langs, R. Donner, P. Peloschek, and H. Bischof, "Robust Autonomous Model Learning from 2D and 3D Data Sets," Proc. Medical Image Computing and Computer-Assisted Intervention, vol. 1, pp. 968-976, 2007.
[29] I. Kokkinos and A. Yuille, "Unsupervised Learning of Object Deformation Models," Proc. IEEE Int'l Conf. Computer Vision, 2007.
[30] F. Wang, B.V. Vemuri, A. Rangarajan, and S.J. Eisenschenk, "Simultaneous Nonrigid Registration of Multiple Point Sets and Atlas Construction," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 11, pp. 2011-2022, Nov. 2008.
[31] J. Rissanen, Stochastic Complexity in Statistical Inquiry. World Scientific Press, 1989.
[32] R. Davies, C. Twining, T. Cootes, and C. Taylor, "A Minimum Description Length Approach to Statistical Shape Modeling," IEEE Trans. Medical Imaging, vol. 21, no. 5, pp. 525-537, May 2002.
[33] R. Davies, C. Twining, T. Cootes, J. Waterton, and C. Taylor, "3D Statistical Shape Models Using Direct Optimisation of Description Length," Proc. European Conf. Computer Vision, vol. 3, pp. 3-20, 2002.
[34] C. Shannon, "A Mathematical Theory of Communication," Bell System Technical J., vol. 27, pp. 379-423, 623-656, 1948.
[35] C.J. Twining, S. Marsland, and C.J. Taylor, "A Unified Information-Theoretic Approach to the Correspondence Problem in Image Registration," Proc. Int'l Conf. Pattern Recognition, vol. 3, pp. 704-709, 2004.
[36] I. Matthews and S. Baker, "Active Appearance Models Revisited," Int'l J. Computer Vision, vol. 60, no. 2, pp. 135-164, Nov. 2004.
[37] I. Dryden and K.V. Mardia, The Statistical Analysis of Shape. Wiley, 1998.
[38] J. Lötjönen and T. Mäkelä, "Elastic Matching Using a Deformation Sphere," Proc. Medical Image Computing and Computer-Assisted Intervention, pp. 541-548, 2001.
[39] W. Press, S. Teukolsky, W. Vetterling, and B. Flannery, Numerical Recipes in C, second ed. Cambridge Univ. Press, 1992.
[40] F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, "Multimodality Image Registration by Maximization of Mutual Information," IEEE Trans. Medical Imaging, vol. 16, no. 2, pp. 187-198, Apr. 1997.
[41] P. Viola and W.M. WellsIII, "Alignment by Maximization of Mutual Information," Int'l J. Computer Vision, vol. 24, no. 2, pp. 137-154, 1997.
[42] T. Gaens, F. Maes, D. Vandermeulen, and P. Suetens, "Non-Rigid Multimodal Image Registration Using Mutual Information," Proc. Medical Image Computing and Computer-Assisted Intervention, pp. 1099-1106, 1998.
[43] D. Cristinacce and T.F. Cootes, "Automatic Feature Localisation with Constrained Local Models," Pattern Recognition, vol. 41, no. 10, pp. 3054-3067, 2008.
[44] P. Filipek, C. Richelme, D. Kennedy, and V. Caviness, "The Young Adult Human Brain: An MRI-Based Morphometric Analysis," Cerebral Cortex, vol. 4, pp. 344-360, 1994.
[45] M. Nishida, N. Makris, D.N. Kennedy, M. Vangel, B. Fischl, K.S. Krishnamoorthy, V.S. Caviness, and P.E. Grant, "Detailed Semiautomated MRI Based Morphometry of the Neonatal Brain: Preliminary Results," NeuroImage, vol. 32, pp. 1041-1049, 2006.
[46] L.R. Dice, "Measures of the Amount of Ecologic Association between Species," Ecology, vol. 26, pp. 297-302, 1945.
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