Subject-Matched Templates for Spatial Normalization

  • Torsten Rohlfing
  • Edith V. Sullivan
  • Adolf Pfefferbaum
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5762)


Spatial normalization of images from multiple subjects is a common problem in group comparison studies, such as voxel-based and deformation-based morphometric analyses. Use of a study-specific template for normalization may improve normalization accuracy over a study-independent standard template (Good et al., NeuroImage, 14(1):21-36, 2001). Here, we develop this approach further by introducing the concept of subject-matched templates. Rather than using a single template for the entire population, a different template is used for every subject, with the template matched to the subject in terms of age, sex, and potentially other parameters (e.g., disease). All subject-matched templates are created from a single generative regression model of atlas appearance, thus providing a priori template-to-template correspondence without registration. We demonstrate that such an approach is technically feasible and significantly improves spatial normalization accuracy over using a single template.


  1. 1.
    Ashburner, J., Friston, K.J.: Voxel-based morphometry — the methods. NeuroImage 11(6), 805–821 (2000)CrossRefGoogle Scholar
  2. 2.
    Ashburner, J., Hutton, C., Frackowiak, R., Johnsrude, I., Price, C., Friston, K.: Identifying global anatomical differences: Deformation-based morphometry. Hum. Brain Map. 6(5-6), 348–357 (1998)CrossRefGoogle Scholar
  3. 3.
    Good, C.D., Johnsrude, I.S., Ashburner, J., Henson, R.N.A., Friston, K.J., Frackowiak, R.S.J.: A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage 14(1), 21–36 (2001)CrossRefGoogle Scholar
  4. 4.
    Kochunov, P., Lancaster, J.L., Thompson, P., Woods, R., Mazziotta, J., Hardies, J., Fox, P.: Regional spatial normalization: toward an optimal target. J. Comput. Assist. Tomogr. 25(5), 805–816 (2001)CrossRefGoogle Scholar
  5. 5.
    Rohlfing, T., Sullivan, E.V., Pfefferbaum, A.: Regression models of atlas appearance. In: Prince, J.L., Pham, D.L., Myers, K.J. (eds.) IPMI 2009. LNCS, vol. 5636, pp. 151–162. Springer, Heidelberg (2009)Google Scholar
  6. 6.
    Davis, B.C., Fletcher, P.T., Bullitt, E., Joshi, S.: Population shape regression from random design data. In: IEEE 11th International Conference on Computer Vision, ICCV, October 2007, pp. 1–7 (2007)Google Scholar
  7. 7.
    Cootes, T.F., Beeston, C.J., Edwards, G.J., Taylor, C.J.: A unified framework for atlas matching using active appearance models. In: Kuba, A., Sámal, M., Todd-Pokropek, A. (eds.) IPMI 1999. LNCS, vol. 1613, pp. 322–333. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  8. 8.
    Likar, B., Viergever, M.A., Pernus, F.: Retrospective correction of MR intensity inhomogeneity by information minimization. IEEE Trans. Med. Imag. 20(12), 1398–1410 (2001)CrossRefGoogle Scholar
  9. 9.
    Battaglini, M., Smith, S.M., Brogi, S., De Stefano, N.: Enhanced brain extraction improves the accuracy of brain atrophy estimation. NeuroImage 40(2), 583–589 (2008)CrossRefGoogle Scholar
  10. 10.
    Zhang, Y., Brady, M., Smith, S.: Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imag. 20(1), 45–57 (2001)CrossRefGoogle Scholar
  11. 11.
    Balci, S.K., Golland, P., Shenton, M., Wells, W.M.: Free-form B-spline deformation model for groupwise registration. In: MICCAI 2007 Workshop Statistical Registration: Pair-wise and Group-wise Alignment and Atlas Formation, pp. 23–30 (2007)Google Scholar
  12. 12.
    Rueckert, D., Frangi, A.F., Schnabel, J.A.: Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration. IEEE Trans. Med. Imag. 22(8), 1014–1025 (2003)CrossRefGoogle Scholar
  13. 13.
    Guimond, A., Meunier, J., Thirion, J.P.: Average brain models: A convergence study. Comput. Vision Image Understanding 77(2), 192–210 (2000)CrossRefGoogle Scholar
  14. 14.
    Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Trans. Med. Imag. 18(8), 712–721 (1999)CrossRefGoogle Scholar
  15. 15.
    Klein, A., Andersson, J., Ardekani, B.A., Ashburner, J., Avants, B., Chiang, M.C., Christensen, G.E., Collins, L.D., Gee, J., Hellier, P., Song, J.H., Jenkinson, M., Lepage, C., Rueckert, D., Thompson, P., Vercauteren, T., Woods, R.P., Mann, J.J., Parsey, R.V.: Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. NeuroImage 46(3), 786–802 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Torsten Rohlfing
    • 1
  • Edith V. Sullivan
    • 2
  • Adolf Pfefferbaum
    • 1
    • 2
  1. 1.Neuroscience ProgramSRI InternationalMenlo ParkUSA
  2. 2.Department of Psychiatry and Behavioral SciencesStanford UniversityStanfordUSA

Personalised recommendations