Image registration based on thin-plate splines and local estimates of anisotropic landmark localization uncertainties

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1496, Issue , 1998, Pages 1174-1183

  Rohr, Karl ^a  

^a UNIVERSITY OF HAMBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ANISOTROPY; BRAIN MAPPING; ERRORS; MAGNETIC RESONANCE IMAGING; MEDICAL COMPUTING; PORE PRESSURE;

ANISOTROPIC ERRORS; BRAIN IMAGES; CLINICAL APPLICATION; ELASTIC REGISTRATION; ERROR INFORMATION; LANDMARK LOCALIZATION UNCERTAINTIES; THIN PLATE SPLINE; TOMOGRAPHIC;

UNCERTAINTY ANALYSIS;

EID: 84947730461     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/bfb0056307     Document Type: Conference Paper

References (20)

1. N. Arad, N. Dyn, D. Reisfeld, and Y. Yeshurun, “Image warping by radial ba-sis functions: Application to facial expressions”, Computer Vision, Graphics, and Image Processing 56:2 (1994) 161-172
2. 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
3. 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
4. F. L. Bookstein, “Landmark methods for forms without landmarks: morphometries of group differences in outline shape”, Medical Image Analysis 1:3 (1996/7) 225-243
5. 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
6. A. C. Evans, W. Dai, L. Collins, P. Neelin, and S. Marrett, “Warping of a comput-erized 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
7. W. Förstner, “A Feature Based Correspondence Algorithm for Image Matching”, Intern. Arch, of Photogrammetry and Remote Sensing 26-3/3 (1986) 150-166
8. W. E. L. Grimson, “An Implementation of a Computational Theory of Visual Sur-face Interpolation”, Computer Vision, Graphics, and Image Processing 22 (1983) 39-69
9. 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
10. J. B. A. Maintz, P. A. van den Elsen, and M. A. Viergever, “Evaluation of ridge seek-ing operators for multimodality medical image matching”, IEEE Trans, on Pattern Anal, and Machine Intell. 18:4 (1996) 353-365
11. K. Mardia and J. Little, “Image warping using derivative information”, In Math-ematical 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
12. K. Rohr, “On 3D Differential Operators for Detecting Point Landmarks”, Image and Vision Computing 15:3 (1997) 219-233
13. 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
14. K. Rohr, R. Sprengel, and H. S. Stiehl, “Incorporation of Landmark Error Ellip-soids 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
15. J. Talairach and P. Tornoux, Co-planar Stereotactic Atlas of the Human Brain, Georg Thieme Verlag Stuttgart New York 1988
16. D. Terzopoulos, “Regularization of Inverse Visual Problems Involving Discontinu-ities”, IEEE Trans, on Pattern Anal, and Machine Intell. 8:4 (1986) 413-424
17. J.-P. Thirion, “New Feature Points based on Geometric Invariants for 3D Image Registration”, Intern. J. of Computer Vision 18:2 (1996) 121-137
18. H. L. van Trees, Detection, Estimation, and Modulation Theory, Part I, John Wiley and Sons, New York London 1968
19. G. Wahba, Spline Models for Observational Data, Society for Industrial and Applied Mathematics, Philadelphia, Pennsylvania, 1990
20. G. Wahba, “Multivariate function and operator estimation, based on smoothing splines and reproducing kernels”, in Nonlinear Modeling and Forecasting, SFI Stud-ies in the Sciences of Complexity, Vol. XII, M. Casdagli and S. Eubank (Eds.), Addison-Wesley 1992, 95-112