Skip to main content

A 3D point matching algorithm for affine registration

International Journal of Computer Assisted Radiology and Surgery volume 6, pages 229–236 (2011)Cite this article

Abstract

Purpose

Landmark point–based registration is an important tool in medical image analysis and applications. An efficient and robust new method is sought that does not require optimization and is less susceptible to noise.

Methods

A new quaternion-based affine registration algorithm for matching 3D point sets, a generalization of a previously reported method for 2D point sets, was developed. The new algorithm computes the exact affine transformation and the unknown correspondence between two 3D point sets but does not require any optimization. The method performs robustly in the presence of noise for non-degenerate cases. The method performs a reduction of general affine case to an orthogonal case, and then computes the unknown rotation using the quaternion representation of the 3D points. The method assumes no prior knowledge of point-wise correspondence between the two point sets. The algebraic and geometric concepts underlying the method are shown to be both clear and intuitive.

Results

Experimental evaluation of the method was performed using both randomly generated synthetic 3D point sets and Stanford Bunny dataset. The algorithm performed well even for noisy data. A feasibility test was performed with medical MR scans showing promising results.

Conclusion

The algorithm for point-based correspondence registration demonstrated robust results, even in noisy cases, and was shown to be feasible for use with medical images.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

43,34 €

Price includes VAT (Finland)
Tax calculation will be finalised during checkout.

References

  1. Chrisochoides N, Fedorov A, Kot A, Archip N, Black P, Clatz O, Golby A, Kikinis R, Warfield SK (2006) Toward real-time, image guided neurosurgery using distributed and grid computing. In: Proceedings of the ACM/IEEE conference on supercomputing. Tampa, FL

  2. Lange T, Wenckebach T, Lamecker H, Martin Seeba MH, Eulenstein S, Schlag P-M (2005) Registration of portal and hepatic venous phase of mr/ct data for computer-assisted liver surgery planning. Comput Assist Radiol Surg 1281: 768–772

    Google Scholar 

  3. Shamir RR, Joskowicz L, Spektor S, Shoshan Y (2009) Localization and registration accuracy in image guided neurosurgery: a clinical study. Int J CARS 4: 45–52

    Article  Google Scholar 

  4. Papademetris X, Jackowski AP, Schultz RT, Staib LH, Duncan JS (2004) Integrated intensity and point-feature nonrigid registration. Medical Image Computing and Computer-Assisted Intervention (MICCAI) 3216: 763–770

    Google Scholar 

  5. Haker S, Warfield SK, Tempany CM (2004) Landmark-guided surface matching and volumetric warping for improved prostate biopsy targeting and guidance. Medical Image Computing and Computer-Assisted Intervention–MICCAI 3216: 853–861

    Google Scholar 

  6. Beg MF, Miller MI, Alain Trouvé LY (2003) The euler-lagrange equation for interpolating sequence of landmark datasets. Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2879: 918–925

    Article  Google Scholar 

  7. Fischer B, Modersitzki J (2003) Combination of automatic and landmark based registration:the best of both worlds. In: Proceedings of SPIE, vol 5032. pp 1037–1048

  8. Yang L, Gong L, Zhang H, Nosher JL, Foran DJ (2009) A method for parallel platform based acceleration of landmark based image registration. In: The European conference on parallel computing. Delft, Netherland, Aug

  9. Pluim JPW, Maintz JBA, Viergever MA (2003) Mutual information based registration of medical images: a survey. IEEE Trans Med Imaging 22: 986–1004

    PubMed  Article  Google Scholar 

  10. Maintz JBA, Viergever MA (1998) A survey of medical image registration. Med Image Anal 2(1): 1–36

    PubMed  Article  CAS  Google Scholar 

  11. Augustine K, Holmes, Robb R (2004) Itk and analyze: a synergistic integration. In: Proceedings of SPIE—medical imaging 2004, visualization, image-guided procedures, and display, vol 5367. pp 6–15

  12. Pieper S, Lorensen WE, Schroeder WJ, Kikinis R (2006) The na-mic kit: Itk, vtk, pipelines, grids and 3d slicer as an open platform for the medical image computing community. In: Proceedings of international symposium of biomedical imaging. pp 698–701

  13. Ibanez L, Schroeder W, Ng L, Cates J, Consortium T, Hamming R (2003) The itk software guide, vol 1. Kitware, Inc

  14. Ng L, Ibanez L (2004) Medical image registration: concepts and implementation. In: Yoo TS (eds) Insight into images. AK Peters, Wellesley, Massachusetts, pp 239–306

    Google Scholar 

  15. Rohrer J, Gong L (2009) Accelerating 3d nonrigid registration using the cell broadband engine processor. IBM J Res Dev 53(5): 12:1–12:10

    Article  Google Scholar 

  16. Meihe X, Srinivasan R, Nowinski WL (1999) A fast mutual information method for multi-modal registration. In: Proceedings of the 16th international conference on information processing, in medical imaging, Lecture notes in computer science, Vol 1613. pp 466–471

  17. Besl P, McKay N (1992) A method for registration of 3d shapes. IEEE Trans Pattern Anal Mach Intell 14(2): 239–266

    Article  Google Scholar 

  18. Zhang Z (1994) Iterative point matching for registration of free-form curves and surfaces. IJCV 13(2): 119–152

    Article  Google Scholar 

  19. Fitzgibbon AW (2003) Robust registration of 2d and 3d point sets. Image Vis Comput 21: 1145–1153

    Article  Google Scholar 

  20. Rusinkiewicz S, Levoy M (2001) Efficient variants of the icp algorithm. In: International conference on 3D digital imaging and modeling (3DIM). pp 145–152

  21. Jian B, Vemuri BC (2005) A robust algorithm for point set registration using mixture of gaussians. IEEE ICCV 2: 1246–1251

    Google Scholar 

  22. Ho J, Yang M-H, Ranganrajan A, Vemuri B (2007) A new affine registration algorithm for 2d point sets. Workshop Appl Comput Vis (WACV) 12: 234–778

    Google Scholar 

  23. Tsin Y, Kanade T (2004) A correlation based approach to robust point set registration. ECCV 3: 558–569

    Google Scholar 

  24. Myronenko A, Song X (2009) Point-set registration: coherent point drift. Technical Report, arXiv:0905.2635v1 [cs.CV], May

  25. Ho J, Peter A, Rangarajan A, Yang M-H (2009) An algebraic approach to affine registration of point sets. In: IEEE international conference on computer vision (ICCV)

  26. Chi Y-T, Shahed SN, Ho J, Yang M-H (2008) Higher dimensional affine registration and vision applications. In: Proceedings of the 10th European conference on computer vision (ECCV), vol 5303. pp 256–269

  27. Horn B (1987) Closed-form solution of absolute orientation using unit quaternions. J Opt Soc Am A Opt Image Sci Vis 4(4): 629–642

    Article  Google Scholar 

  28. Shapiro L, Brady J (1992) Feature-based correspondence-an eigenvector approach. Image Vis Comput 10: 283–288

    Article  Google Scholar 

  29. Carcassoni M, Hancock E (2003) Spectral correspondence for point pattern matching. Pattern Recognit 36: 193–204

    Article  Google Scholar 

  30. Stanford Computer Graphics Laboratory, The stanford 3d scanning repository. http://graphics.stanford.edu/data/3Dscanrep/

  31. Collins DL, Zijdenbos AP, Kollokian V, Sled JG, Kabani NJ, Holmes CJ, Evans AC (1998) Design and construction of a realistic digital brain phantom. IEEE Trans Med Imag 17(3): 463–468

    Article  CAS  Google Scholar 

  32. http://mouldy.bic.mni.mcgill.ca/brainweb/

Download references

Author information

Affiliations

  1. College of Computer Science, Jilin University, Changchun, Jilin, 130012, People’s Republic of China

    Jianqin Qu

  2. College of Engineering, Shantou University, Shantou, Guangdong, 515063, People’s Republic of China

    Jianqin Qu

  3. Watson Research Center, IBM, 19 Skyline Dr., Hawthorne, NY, 10532, USA

    Leiguang Gong

  4. Department of Radiology, UMDNJ-Robert Wood Johnson Medical School, Piscataway, NJ, 08854, USA

    Lin Yang

Authors
  1. Jianqin Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leiguang Gong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lin Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Leiguang Gong.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Qu, J., Gong, L. & Yang, L. A 3D point matching algorithm for affine registration. Int J CARS 6, 229–236 (2011). https://doi.org/10.1007/s11548-010-0503-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-010-0503-y

Keywords

  • Image registration
  • Affine transformation
  • Point matching