Skip to main content
SpringerLink
Log in

Rigid Registration

  • Chapter
Image-Guided Interventions

Rigid registration is a key component of all image-guided surgical applications, either as an end in itself or as a precursor to nonrigid registration. This chapter reviews common methods used for rigidly registering pairs of three-dimensional data sets (3D/3D registration), and three-dimensional data to two-dimensional data (2D/3D registration). The chapter defines five criteria that should be addressed when evaluating a registration algorithm. These include execution time, accuracy in the region of interest, breakdown point, automation, and reliability. On the basis of these criteria, one can assess whether an algorithm is applicable for a specific medical procedure, where acceptable bounds on algorithm performance are defined subjectively by physicians. Currently, the only registration algorithms that address these criteria analytically are the paired-point registration methods. All other algorithms have been evaluated empirically, usually using proprietary data sets whose transformations were estimated using paired-point registration. Future efforts should thus focus on addressing the evaluation criteria analytically, and on the establishment of publicly available data sets with known gold standard transformations, enabling objective empirical evaluations.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Arun KS, Huang TS, and Blostein SD. (1987). “Least-squares fitting of two 3D point sets.” IEEE Transact Pattern Anal Machine Intell, 9(5), 698-700.

    Article  Google Scholar 

  • Audette MA, Ferrie FP, and Peters TM. (2000). “An algorithmic overview of surface registration techniques for medical imaging.” Med Image Anal, 4(3), 201-217.

    Article  Google Scholar 

  • Balachandran R, Fitzpatrick JM, and Labadie RF. (2005). “Fiducial registration for tracking systems that employ coordinate reference frames.” SPIE Med Imaging: Visualization, Image-guided Procedures, Display, 5744, 134-145.

    Google Scholar 

  • Barratt DC, Penney GP, Chan CS, Slomczykowski M, Carter TJ, Edwards PJ, and Hawkes DJ. (2006). “Self-calibrating 3D-ultrasound based bone registration for minimally invasive orthopedic surgery.” IEEE Trans Med Imaging, 25(3), 312-323.

    Article  Google Scholar 

  • Besl PJ and McKay ND. (1992). “A method for registration of 3D shapes.” IEEE Transact Pattern Anal Machine Intell, 14(2), 239-255.

    Article  Google Scholar 

  • Bijhold J. (1993). “Three-dimensional verification of patient placement during radiotherapy using portal images.” Med Phys, 20(2), 347-356.

    Article  Google Scholar 

  • Birkfellner W, Seemann R, Figl M, Hummel J, Ede C, Homolka P, Yang X, Niederer P, and Bergmann H. (2005). “Wobbled splatting-a fast perspective volume rendering method for simulation of X-ray images from CT.” Phys Med Biol, 50(9), N73-84.

    Article  Google Scholar 

  • Burschka D, Li M, Ishii M, Taylor RH, and Hager GD. (2005). “Scale-invariant registration of monocular endoscopic images to CT-scans for sinus surgery.” Med Image Anal, 9(5), 413-426.

    Article  Google Scholar 

  • Buzug TM, Weese J, Fassnacht C, and Lorenz C. (1997). “Image registration: convex weighting functions for histogram-based similarity measures.” CVRMed-MRCAS 1997, Springer, Berlin Heidelberg New York, 203-212.

    Google Scholar 

  • Cabral B, Cam N, and Foran J. (1994). “Accelerated volume rendering and to-mographic reconstruction using texture mapping hardware.” Proceedings of the 1994 Symposium on Volume Visualization, ACM Press, New York, USA, 91-98.

    Google Scholar 

  • Chen Y and Medioni G. (1992). “Object modeling by registration of multiple range images.” Image Vis Comput, 10(3), 145-155.

    Article  Google Scholar 

  • Chetverikov D, Stepanov D, and Krsek P. (2005). “Robust Euclidean alignment of 3D point sets: the trimmed iterative closest point algorithm.” Image Vis Comput, 23(3), 299-309.

    Article  Google Scholar 

  • Collignon A, Vandermeulen D, Suetans P, and Marchal G. (1995). “3D multi modality medical image registration using feature space clustering.” Computer Vision, Virtual Reality and Robotics in Medicine, Springer, Berlin Heidelberg New York, 195-204.

    Google Scholar 

  • Deligianni F, Chung A, and Yang GZ. (2004). “Patient-specific bronchoscope simulation with pq-space-based 2D/3D registration.” Comput Aided Surg, 9(5), 215-226.

    Article  Google Scholar 

  • Duda RO, Hart PE, and Stork DG. (2001). Pattern Classification, 2nd Ed., Wiley, New York.

    MATH  Google Scholar 

  • Eggert DW, Lorusso A, and Fisher RB. (1997). “Estimating3-D rigid body transformations: a comparison of four major algorithms.” Machine Vis Appl, 9(5-6), 272-290.

    Article  Google Scholar 

  • Estépar RSJ, Brun A, and Westin C-F. (2004). “Robust generalized total least squares iterative closest point registration.” Seventh International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI’04), Springer-Verlag, St. Malo, France, 234-241.

    Google Scholar 

  • Faugeras OD and Hebert M. (1986). “The representation, recognition, and locating of 3-D objects.” Int J Robotics Res, 5(3), 27-52.

    Article  Google Scholar 

  • Fitzgibbon AW. (2003). “Robust registration of 2D and 3D point sets.” Image Vis Comput, 21(13-14), 1145-1153.

    Article  Google Scholar 

  • Fitzpatrick JM. (2007). “Retrospective image registration evaluation project.” http://www.insight-journal.org/rire/index.html, accessed February 2007.

  • Fitzpatrick JM and West JB. (2001). “The distribution of target registration error in rigid-body point-based registration.” IEEE Trans Med Imaging, 20(9), 917-927.

    Article  Google Scholar 

  • Fitzpatrick JM, West JB, and Maurer CR, Jr. (1998). “Predicting error in rigid-body point-based registration.” IEEE Trans Med Imaging, 17(5), 694-702.

    Article  Google Scholar 

  • Fletcher R. (1987). Practical Methods of Optimization, 2nd Ed., Wiley-Interscience, New York.

    MATH  Google Scholar 

  • Foley JD, van Dam A, Feiner SK, and Hughes JF. (1996). Computer graphics: Principles and practice, 2nd Ed., Addison-Wesley Longman, Boston, MA.

    Google Scholar 

  • Gong RH, Stewart AJ, and Abolmaesumi P. (2006). “A new method for CT to fluoroscope registration based on unscented Kalman filter.” Ninth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI’06), Springer-Verlag, Copenhagen, Denmark. (Pt 1), 891-898.

    Google Scholar 

  • Gortler S, Grzeszczuk R, Szeliski R, and Cohen M. (1996). “The Lumigraph.” Pro-ceedings of the 23rd Annual Conference on Computer Graphics, Annual Con-ference Series 1996 (SIGGRAPH ‘96), ACM press, New York, USA, 43-54.

    Google Scholar 

  • Goshtasby A. (2005). 2-D and 3-D Image Registration, Wiley, New York.

    Google Scholar 

  • Greenspan M and Yurick M. (2003). “Approximate K-D tree search for efficient ICP.” Proceedings of the Fourth International. Conference on 3-D Digital Imaging and Modeling, 442-448.

    Google Scholar 

  • Grimson WEL and Lozano-Perez T. (1987). “Localizing overlapping parts by searching the interpretation tree.” IEEE Transact Pattern Anal Machine Intell, 9 (4), 469-482.

    Article  Google Scholar 

  • Gueziec A, Kazanzides P, Williamson B, and Taylor RH. (1998). “Anatomy-based registration of CT-scan and intraoperative X-ray images for guiding a surgical robot.” IEEE Trans Med Imaging, 17(5), 715-728.

    Article  Google Scholar 

  • Hajnal JV, Hill DLG, and Hawkes DJ. (2001). Medical Image Registration, CRC Press.

    Google Scholar 

  • Hamadeh A, Lavallée S, and Cinquin P. (1998). “Automated 3-dimensional com-puted tomographic and fluoroscopic image registration.” Comput Aided Surg, 3 (1), 11-19.

    Article  Google Scholar 

  • Hill DL, Batchelor PG, Holden M, and Hawkes DJ. (2001). “Medical image registration.” Phys Med Biol, 46(3), R1-R45.

    Article  Google Scholar 

  • Hipwell JH, Penney GP, McLaughlin RA, Rhode K, Summers P, Cox TC, Byrne JV, Noble JA, and Hawkes DJ. (2003). “Intensity-based 2-D-3-D registration of cerebral angiograms.” IEEE Trans Med Imaging, 22(11), 1417-1426.

    Article  Google Scholar 

  • Ho YC and Pepyne DL. (2002). “Simple explanation of the no-free-lunch theorem and its implications.” J Optimization Theor Appl, 115(3), 549-570.

    Article  MathSciNet  MATH  Google Scholar 

  • Horn BKP. (1987). “Closed-form solution of absolute orientation using unit quaternions.” J Opt Soc Am A, 4(4), 629-642.

    Article  MathSciNet  Google Scholar 

  • Horn BKP, Hilden HM, and Negahdaripour S. (1988). “Closed-form solution of absolute orientation using orthonormal matrices.” J Opt Soc Am A, 5(7), 1127-1135.

    Article  MathSciNet  Google Scholar 

  • Jost T and Hugli H. (2003). “A multi-resolution ICP with heuristic closest point search for fast and robust 3D registration of range images.” Proceedings of the Fourth International Conference on 3-D Digital Imaging and Modeling (3DIM 2003), 427-433.

    Google Scholar 

  • Jumarie G. (1997). “A new information theoretic approach to the entropy of non-random discrete maps relation to fractal dimension and temperature of curves.” Chaos, Solitons and Fractals, 8(6), 953-970.

    Article  MathSciNet  MATH  Google Scholar 

  • Kaneko S, Kondo T, and Miyamoto A. (2003). “Robust matching of 3D contours using iterative closest point algorithm improved by M-estimation.” Pattern Recognition, 36(9), 2041-2047.

    Article  MATH  Google Scholar 

  • Khamene A, Bloch P, Wein W, Svatos M, and Saur F. (2006). “Automatic registration of portal images and volumetric CT for patient positioning in radiation therapy.” Med Image Anal, 10(1), 96-112.

    Article  Google Scholar 

  • Knaan D and Joskowicz L.(2003).“Effective intensity-based2D/3D rigid registration between fluoroscopic X-ray and CT.” Medical Image Computing and Computer-Assisted Intervention (MICCAI 2003), Springer-Verlag, Montreal, Canada, 351-358.

    Google Scholar 

  • Langis C, Greenspan M, and Godin G. (2001). “The parallel iterative closest point algorithm.” Proceedings of the Third International Conference on 3-D Digital Imaging and Modeling (3DIM 2001), 195-204.

    Google Scholar 

  • LaRose DA. (2001). “Iterative X-ray/CT Registration Using Accelerated Volume Rendering,” PhD, Carnegie Mellon University, Pittsburgh, PA.

    Google Scholar 

  • Lavallée S and Szeliski R. (1995). “Recovering the position and orientation of free-form objects from image contours using 3D distance maps.” IEEE Transact Pattern Anal Machine Intell, 17(4), 378-390.

    Article  Google Scholar 

  • Lehmann TM, Gonner C, and Spitzer K. (1999). “Survey: Interpolation methods in medical image processing.” IEEE Trans Med Imaging, 18(11), 1049-1075.

    Article  Google Scholar 

  • Lemieux L, Jagoe R, Fish DR, Kitchen ND, and Thomas GT. (1994). “A patient-to-computed-tomography image registration method based on digitally recon-structed radiographs.” Med Phys, 21(11), 1749-1760.

    Article  Google Scholar 

  • Levoy M and Hanrahan P. (1996). “Light field rendering.” Proceedings of the 23rd Annual Conference on Computer graphics and interactive techniques (SIGGRAPH 1996), ACM Press, New York, USA, 31-42.

    Book  Google Scholar 

  • Livyatan H, Yaniv Z, and Joskowicz L. (2003). “Gradient-based 2D/3D rigid registration of fluoroscopic X-ray to CT.” IEEE Trans Med Imaging, 22(11), 1395-1406.

    Article  Google Scholar 

  • Lorensen WE and Cline HE. (1987). “Marching cubes: A high resolution 3D surface construction algorithm.” Comput Graphics, 21(4), 163-169.

    Article  Google Scholar 

  • Luck JP, Little CQ, and Hoff W. (2000). “Registration of range data using a hybrid simulated annealing and iterative closest point algorithm.” Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2000), 4, 3739-3744.

    Google Scholar 

  • Ma B and Ellis R. (2006). “Analytic expressions for fiducial and surface target registration error.” Medical Image Computing and Computer-Assisted Intervention (MICCAI 2006), Springer-Verlag, Copenhagen, Denmark, 637-644.

    Google Scholar 

  • Ma B and Ellis RE. (2003). “Robust registration for computer-integrated orthopedic surgery: laboratory validation and clinical experience.” Med Image Anal, 7(3), 237-250.

    Article  Google Scholar 

  • Maes F, Collignon A, Vandermeulen D, Marchal G, and Suetens P. (1997). “Multimodality image registration by maximization of mutual information.” IEEE Trans Med Imaging, 16(2), 187-198.

    Article  Google Scholar 

  • Maintz JB, van den Elsen PA, and Viergever MA. (1996). “Comparison of edge-based and ridge-based registration of CT and MR brain images.” Med Image Anal, 1(2), 151-161.

    Article  Google Scholar 

  • Maintz JB and Viergever MA. (1998). “A survey of medical image registration.” Med Image Anal, 2(1), 1-36.

    Article  Google Scholar 

  • Masuda T and Yokoya N. (1995). “A robust method for registration and segmentation of multiple range images.” Comput Vis Image Understanding, 61(3), 295-307.

    Article  Google Scholar 

  • Matei B and Meer P. (1999). “Optimal rigid motion estimation and performance evaluation with bootstrap.” IEEE Computer Society conference on Computer ision and Pattern Recognition (CVPR 1999), 1339-1347.

    Google Scholar 

  • Maurer CR, Jr., Fitzpatrick JM, Wang MY, Galloway RL, Jr., Maciunas RJ, and Allen GS. (1997). “Registration of head volume images using implantable fiducial markers.” IEEE Trans Med Imaging, 16(4), 447-462.

    Article  Google Scholar 

  • Maurer CR, Jr., Maciunas RJ, and Fitzpatrick JM. (1998). “Registration of head CT images to physical space using a weighted combination of points and surfaces.” IEEE Trans Med Imaging, 17(5), 753-761.

    Article  Google Scholar 

  • Meijering EH, Niessen WJ, and Viergever MA. (2001). “Quantitative evaluation of convolution-based methods for medical image interpolation.” Med Image Anal, 5 (2), 111-126.

    Article  Google Scholar 

  • Moghari MH and Abolmaesumi P. (2005). “A novel incremental technique for ultrasound to CT bone surface registration using Unscented Kalman Filtering.” Eighth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2005), Springer-Verlag, Palm Springs, 8(Pt 2), 197-204.

    Google Scholar 

  • Moghari MH and Abolmaesumi P. (2006). “A high-order solution for the dis-tribution of target registration error in rigid-body point-based registration.” Ninth International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2006), Springer-Verlag, Copenhagen, Denmark, 603-611.

    Google Scholar 

  • Mori K, Deguchi D, Sugiyama J, Suenaga Y, Toriwaki J, Maurer CR, Jr., Takabatake H, and Natori H. (2002). “Tracking of a bronchoscope using epipolar geometry analysis and intensity-based image registration of real and virtual endoscopic images.” Med Image Anal, 6(3), 321-336.

    Article  Google Scholar 

  • Murphy MJ. (1997). “An automatic six-degree-of-freedom image registration algo-rithm for image-guided frameless stereotaxic radiosurgery.” Med Phys, 24(6), 857-866.

    Article  Google Scholar 

  • Ohta N and Kanatani K. (1998). “Optimal estimation of three-dimensional rotation and reliability evaluation.” Computer Vision - ECCV 1998, Springer, Heidel-berg, Germany, 175-187.

    Google Scholar 

  • Pelizzari CA, Chen GT, Spelbring DR, Weichselbaum RR, and Chen CT. (1989). “Accurate three-dimensional registration of CT, PET, and/or MR images of the brain.” J Comput Assist Tomogr, 13(1), 20-26.

    Article  Google Scholar 

  • Pennec X and Thirion J-P. (1997). “A framework for uncertainty and validation of 3-D registration methods based on points and frames.” Int J Comput Vision, 25 (3), 203-229.

    Article  Google Scholar 

  • Penney GP, Barratt DC, Chan CS, Slomczykowski M, Carter TJ, Edwards PJ, and Hawkes DJ. (2006). “Cadaver validation of intensity-based ultrasound to CT registration.” Med Image Anal, 10(3), 385-395.

    Article  Google Scholar 

  • Penney GP, Blackall JM, Hamady MS, Sabharwal T, Adam A, and Hawkes DJ. (2004). “Registration of freehand 3D ultrasound and magnetic resonance liver images.” Med Image Anal, 8(1), 81-91.

    Article  Google Scholar 

  • Penney GP, Edwards PJ, JKing AP, Blackall JM, Batchelor PG, and Hawkes DJ. (2001). “A stochastic iterative closest point algorithm (stochastICP).” Medical Image Computing and Computer-Assisted Intervention - MICCAI 2001, Springer-Verlag, Heidelberg, Germany, 762-769.

    Google Scholar 

  • Pluim JP, Maintz JB, and Viergever MA. (2003). “Mutual-information-based regis-tration of medical images: a survey.” IEEE Trans Med Imaging, 22(8), 986-1004.

    Article  Google Scholar 

  • Pluim JPW, Maintz JBA, and Viergever MA. (2000). “Interpolation artefacts in mutual information-based image registration.” Computer Vision and Image Understanding, 77(2), 211-223.

    Article  Google Scholar 

  • Press WH, Flannery BP, Teukolsky SA, and Vetterling WT. (1992). Numerical Recipes: The Art of Scientific Computing, 2nd Ed., Cambridge University Press, Cambridge.

    Google Scholar 

  • Robinson M, Eckhoff DG, Reinig KD, Bagur MM, and Bach JM. (2006). “Variability of landmark identification in total knee arthroplasty.” Clin Orthop Relat Res, 442, 57-62.

    Article  Google Scholar 

  • Roche A, Malandain G, Pennec X, and Ayache N. (1998). “The correlation ratio as a new similarity measure for multimodal image registration.” Medical Image Computing and Computer-Assisted Intervention - MICCAI 1998, Springer-Verlag, Heidelberg, Germany, 1115-1124.

    Google Scholar 

  • Rohlfing T, Russakoff DB, Denzler J, Mori K, and Maurer CR, Jr. (2005). “Progressive attenuation fields: fast2D-3D image registration without precomputation.” Med Phys, 32(9), 2870-2880.

    Article  Google Scholar 

  • Russakoff DB, Rohlfing T, Mori K, Rueckert D, Ho A, Adler JR, Jr., and Maurer CR, Jr. (2005). “Fast generation of digitally reconstructed radiographs using attenuation fields with application to 2D-3D image registration.” IEEE Trans Med Imaging, 24(11), 1441-1454.

    Article  Google Scholar 

  • Schönemann PH. (1966). “A generalized solution of the orthogonal procrustes problem.” Psychometrika, 31, 1-10.

    Article  MathSciNet  MATH  Google Scholar 

  • Simon D. (1996). “Fast and Accurate Shape-Based Registration,” PhD, Carnegie Mellon University. Pittsburgh, PA.

    Google Scholar 

  • Škerl D, Likar B, and Pernus F. (2006). “A protocol for evaluation of similarity measures for rigid registration.” IEEE Trans Med Imaging, 25(6), 779-791.

    Article  Google Scholar 

  • Studholme C, Hill DLG, and Hawkes DJ. (1999). “An overlap invariant entropy measure of 3D medical image alignment.” Pattern Recognition, 32(1), 71-86.

    Article  Google Scholar 

  • Tomaževič D, Likar B, and Pernus F. (2006). “3-D/2-D registration by integrating 2-D information in 3-D.” IEEE Trans Med Imaging, 25(1), 17-27.

    Article  Google Scholar 

  • Tomazežič D, Likar B, Slivnik T, and Pernus F. (2003). “3-D/2-D registration of CT and MR to X-ray images.” IEEE Trans Med Imaging, 22(11), 1407-1416.

    Article  Google Scholar 

  • Trucco E, Fusiello A, and Roberto V. (1999). “Robust motion and correspondence of noisy 3-D point sets with missing data.” Pattern Recognition Lett, 20(9), 889-898.

    Article  Google Scholar 

  • Turk G and Levoy M. (1994). “Zippered polygon meshes from range images.” Pro-ceedings of the 21st Annual Conference on Computer graphics and interactive techniques (SIGGRAPH 1994), ACM Press, New York, USA, 311-318.

    Google Scholar 

  • Umeyama S. (1991). “Least-squares estimation of transformation parameters bet-ween two point patterns.” IEEE Transact Pattern Anal Machine Intell, 13(4), 376-380.

    Article  Google Scholar 

  • van de Kraats EB. (2007). “Standardized evaluation methodology for 2D-3D regis-tration.” http://www.isi.uu.nl/Research/Databases/GS/, accessed February 2007.

  • van de Kraats EB, Penney GP, Tomazevic D, van Walsum T, and Niessen WJ. (2005). “Standardized evaluation methodology for 2-D-3-D registration.” IEEE Trans Med Imaging, 24(9), 1177-1189.

    Article  Google Scholar 

  • Viola P and Wells III WM. (1995). “Alignment by maximization of mutual information.” IEEE International Conference on Computer Vision, 16-23.

    Google Scholar 

  • Viola P and Wells III WM. (1997). “Alignment by maximization of mutual infor-mation.” Int J Comput Vision, 24(2), 137-154.

    Article  Google Scholar 

  • Walker MW, Shao L, and Volz RA. (1991). “Estimating 3-D location parameters using dual number quaternions.” CVGIP: Image Understanding, 54(3), 358-367.

    Article  MATH  Google Scholar 

  • Wang MY, Maurer CR, Jr., Fitzpatrick JM, and Maciunas RJ. (1996). “An automatic technique for finding and localizing externally attached markers in CT and MR volume images of the head.” IEEE Trans Biomed Eng, 43(6), 627-637.

    Article  Google Scholar 

  • Weese J, Rosch P, Netsch T, Blaffert T, and Quist M. (1999). “Gray-value based registration of CT and MR images by maximization of local correlation.” Medical Image Computing and Computer-Assisted Intervention (MICCAI 1999), Springer-Verlag, Heidelberg, Germany, 656-663.

    Google Scholar 

  • West J, Fitzpatrick JM, Wang MY, Dawant BM, Maurer CR, Jr., Kessler RM, Maciunas RJ, Barillot C, Lemoine D, Collignon A, Maes F, Suetens P, Vandermeulen D, van den Elsen PA, Napel S, Sumanaweera TS, Harkness B, Hemler PF, Hill DL, Hawkes DJ, Studholme C, Maintz JB, Viergever MA, Malandain G, Woods RP, et al. (1997). “Comparison and evaluation of retrospective intermodality brain image registration techniques.” J Comput Assist Tomogr, 21(4), 554-566.

    Article  Google Scholar 

  • Wörz S and Rohr K. (2006). “Localization of anatomical point landmarks in 3D medical images by fitting 3D parametric intensity models.” Med Image Anal, 10 (1), 41-58.

    Article  Google Scholar 

  • Wunsch P and Hirzinger G. (1996). “Registration of CAD-models to images by iterative inverse perspective matching.” Proceedings of the 13th International Conference on Pattern Recognition (ICPR 1996), Vienna, Austria, 78-83.

    Google Scholar 

  • Zhang R, Tsai P-S, Cryer JE, and Shah M. (1999). “Shape from shading: A survey.” IEEE Transact Pattern Anal Machine Intell, 21(2), 690-706.

    Article  Google Scholar 

  • Zhang Z. (1994). “Iterative point matching for registration of free-form curves and surfaces.” Int J Comput Vision, 13(2), 119-152.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Georgetown University, Washington, DC, USA

    Ziv Yaniv

Authors
  1. Ziv Yaniv
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. University of Western Ontario, 100 Perth Drive, N6A 5K8, London, ON, Canada

    Terry Peters

  2. Imaging Science and Information Systems (ISIS) Center, 2115 Wisconsin Ave, 20057, Washington, DC, USA

    Kevin Cleary

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer Science+Business Media, LLC

About this chapter

Cite this chapter

Yaniv, Z. (2008). Rigid Registration. In: Peters, T., Cleary, K. (eds) Image-Guided Interventions. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-73858-1_6

Download citation

  • DOI: https://doi.org/10.1007/978-0-387-73858-1_6

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-73856-7

  • Online ISBN: 978-0-387-73858-1

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation