Abstract
We present an approach to elastic registration of tomographic brain images which is based on thin-plate splines and takes into account landmark errors. The inclusion of error information is important in clinical applications since landmark extraction is always prone to error. In comparison to previous work, our approach can cope with anisotropic errors, which is significantly more realistic than dealing only with isotropic errors. In particular, it is now possible to include different types of landmarks, e.g., quasi-landmarks at the outer contour of the brain. Also, we introduce an approach to estimate landmark localization uncertainties directly from the image data. Experimental results are presented for the registration of 2D and 3D MR images.
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References
N. Arad, N. Dyn, D. Reisfeld, and Y. Yeshurun, “Image warping by radial basis functions: Application to facial expressions”, Computer Vision, Graphics, and Image Processing 56:2 (1994) 161–172
F.L. Bookstein, “Principal Warps: Thin-Plate Splines and the Decomposition of Deformations”, IEEE Trans. on Pattern Anal. and Machine Intell. 11:6 (1989) 567–585
F.L. Bookstein, “Four metrics for image variation”, Proc. IPMI’89, In Progress in Clinical and Biological Research, Vol. 363, D. Ortendahl and J. Llacer (Eds.), Wiley-Liss New York, 1991, 227–240
F.L. Bookstein, “Landmark methods for forms without landmarks: morphometrics of group differences in outline shape”, Medical Image Analysis 1:3 (1996) 225–243
T.E. Boult and J.R. Render, “Visual surface reconstruction using sparse depth data”, Proc. CVPR’86, June 22–26, Miami Beach, FL, 1986, 68–76
A.C. Evans, W. Dai, L. Collins, P. Neelin, and S. Marrett, “Warping of a computerized 3-D atlas to match brain image volumes for quantitative neuroanatomical and functional analysis”, Medical Imaging V: Image Processing, 1991, San Jose, CA, Proc. SPIE 1445, M.H. Loew (Ed.), 236–246
W. Förstner, “A Feature Based Correspondence Algorithm for Image Matching”, Intern. Arch. of Photogrammetry and Remote Sensing 26-3/3 (1986) 150–166
W.E.L. Grimson, “An Implementation of a Computational Theory of Visual Surface Interpolation”, Computer Vision, Graphics, and Image Processing 22 (1983) 39–69
D.L.G. Hill, D.J. Hawkes, J.E. Crossman, M.J. Gleeson, T.C.S. Cox, E.E.C.M. Bracey, A.J. Strong, and P. Graves, “Registration of MR and CT images for skull base surgery using point-like anatomical features”, The British J. of Radiology 64:767 (1991) 1030–1035
J.B.A. Maintz, P.A. van den Elsen, and M.A. Viergever, “Evaluation of ridge seeking operators for multimodality medical image matching”, IEEE Trans. on Pattern Anal. and Machine Intell. 18:4 (1996) 353–365
K. Mardia and J. Little, “Image warping using derivative information”, In Mathematical Methods in Medical Imaging III, 25–26 July 1994, San Diego, CA, Proc. SPIE 2299, F.L. Bookstein, J. Duncan, N. Lange, and D. Wilson (Eds.), 16–31
K. Rohr, “On 3D Differential Operators for Detecting Point Landmarks”, Image and Vision Computing 15:3 (1997) 219–233
K. Rohr, H.S. Stiehl, R. Sprengel, W. Beil, T.M. Buzug, J. Weese, and M.H. Kuhn, “Point-Based Elastic Registration of Medical Image Data Using Approximating Thin-Plate Splines”, Proc. VBC’96, Hamburg, Germany, Sept. 22–25, 1996, Lecture Notes in Computer Science 1131, K.H. Höhne and R. Kikinis (Eds.), Springer Berlin Heidelberg 1996, 297–306
K. Rohr, R. Sprengel, and H.S. Stiehl, “Incorporation of Landmark Error Ellipsoids for Image Registration based on Approximating Thin-Plate Splines”, Proc. CAR’97, Berlin, Germany, June 25–28, 1997, H.U. Lemke, M.W. Vannier, and K. Inamura (Eds.), Elsevier Amsterdam Lausanne 1997, 234–239
J. Talairach and P. Tornoux, Co-planar Stereotactic Atlas of the Human Brain, Georg Thieme Verlag Stuttgart New York 1988
D. Terzopoulos, “Regularization of Inverse Visual Problems Involving Discontinuities”, IEEE Trans. on Pattern Anal. and Machine Intell. 8:4 (1986) 413–424
J.-P. Thirion, “New Feature Points based on Geometric Invariants for 3D Image Registration”, Intern. J. of Computer Vision 18:2 (1996) 121–137
H.L. van Trees, Detection, Estimation, and Modulation Theory, Part I, John Wiley and Sons, New York London 1968
G. Wahba, Spline Models for Observational Data, Society for Industrial and Applied Mathematics, Philadelphia, Pennsylvania, 1990
G. Wahba, “Multivariate function and operator estimation, based on smoothing splines and reproducing kernels”, in Nonlinear Modeling and Forecasting, SFI Studies in the Sciences of Complexity, Vol. XII, M. Casdagli and S. Eubank (Eds.), Addison-Wesley 1992, 95–112
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Rohr, K. (1998). Image registration based on thin-plate splines and local estimates of anisotropic landmark localization uncertainties. In: Wells, W.M., Colchester, A., Delp, S. (eds) Medical Image Computing and Computer-Assisted Intervention — MICCAI’98. MICCAI 1998. Lecture Notes in Computer Science, vol 1496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056307
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DOI: https://doi.org/10.1007/BFb0056307
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