Locally Weighted Multi-atlas Construction

  • Junning Li
  • Yonggang Shi
  • Ivo D. Dinov
  • Arthur W. Toga
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8159)


In image-based medical research, atlases are widely used in many tasks, for example, spatial normalization and segmentation. If atlases are regarded as representative patterns for a population of images, then multiple atlases are required for a heterogeneous population. In conventional atlas construction methods, the “unit” of representative patterns is images. Every input image is associated with its most similar atlas. As the number of subjects increases, the heterogeneity increases accordingly, and a big number of atlases may be needed. In this paper, we explore using region-wise, instead of image-wise, patterns to represent a population. Different parts of an input image is fuzzily associated with different atlases according to voxel-level association weights. In this way, regional structure patterns from different atlases can be combined together. Based on this model, we design a variational framework for multi-atlas construction. In the application to two T1-weighted MRI data sets, the method shows promising performance, in comparison with a conventional unbiased atlas construction method.


Input Image Template Image Variational Framework Representative Pattern Warped Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Joshi, S., Davis, B., Jomier, M., Gerig, G.: Unbiased diffeomorphic atlas construction for computational anatomy. NeuroImage 23(Suppl. 1), S151–S160 (2004)Google Scholar
  2. 2.
    Park, H., Bland, P.H., Hero III, A.O., Meyer, C.R.: Least biased target selection in probabilistic atlas construction. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 419–426. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Seghers, D., D’Agostino, E., Maes, F., Vandermeulen, D., Suetens, P.: Construction of a brain template from Mr images using state-of-the-art registration and segmentation techniques. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 696–703. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Hamm, J., Ye, D.H., Verma, R., Davatzikos, C.: Gram: A framework for geodesic registration on anatomical manifolds. Med. Image Anal. 14(5), 633–642 (2010)CrossRefGoogle Scholar
  5. 5.
    Jia, H., Wu, G., Wang, Q., Shen, D.: Absorb: Atlas building by self-organized registration and bundling. Neuroimage 51(3), 1057–1070 (2010)CrossRefGoogle Scholar
  6. 6.
    Wang, Q., Chen, L., Yap, P.-T., Wu, G., Shen, D.: Groupwise registration based on hierarchical image clustering and atlas synthesis. Hum. Brain Mapp. 31(8), 1128–1140 (2010)Google Scholar
  7. 7.
    Wu, G., Jia, H., Wang, Q., Shen, D.: Sharpmean: groupwise registration guided by sharp mean image and tree-based registration. Neuroimage 56(4), 1968–1981 (2011)CrossRefGoogle Scholar
  8. 8.
    Blezek, D.J., Miller, J.V.: Atlas stratification. Med. Image Anal. 11(5), 443–457 (2007)CrossRefGoogle Scholar
  9. 9.
    Sabuncu, M.R., Balci, S.K., Shenton, M.E., Golland, P.: Image-driven population analysis through mixture modeling. IEEE Transactions on Medical Imaging 28(9), 1473–1487 (2009)CrossRefGoogle Scholar
  10. 10.
    Xie, Y., Ho, J., Vemuri, B.: Multiple atlas construction from a heterogeneous brain MR image collection. IEEE Transactions on Medical Imaging PP(99), 1 (2013)Google Scholar
  11. 11.
    Shattuck, D.W., Mirza, M., Adisetiyo, V., Hojatkashani, C., Salamon, G., Narr, K.L., Poldrack, R.A., Bilder, R.M., Toga, A.W.: Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage 39(3), 1064–1080 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Junning Li
    • 1
  • Yonggang Shi
    • 1
  • Ivo D. Dinov
    • 1
  • Arthur W. Toga
    • 1
  1. 1.Laboratory of Neuro Imaging, Department of NeurologyUniversity of CaliforniaLos AngelesUSA

Personalised recommendations