Abstract
The process of constructing an atlas typically involves selecting one individual from a sample on which to base or root the atlas. If the individual selected is far from the population mean, then the resulting atlas is biased towards this individual. This, in turn, can bias any inferences made with the atlas. Unbiased atlas construction addresses this issue by either basing the atlas on the individual which is the median of the sample or by an iterative technique whereby the atlas converges to the unknown population mean. In this paper, we explore the question of whether a single atlas is appropriate for a given sample or whether there is sufficient image based evidence from which we can infer multiple atlases, each constructed from a subset of the data. We refer to this process as atlas stratification. Essentially, we determine whether the sample, and hence the population, is multi-modal and is best represented by an atlas per mode. We use the mean shift algorithm to identify the modes of the sample and multidimensional scaling to visualize the clustering process.
This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149. Information on the National Centers for Biomedical Computing can be obtained from http://nihroadmap.nih.gov/bioinformatics.
Chapter PDF
Similar content being viewed by others
References
Marsland, S., Twining, C., Taylor, C.: Groupwise non-rigid registration using polyharmonic clamped-plate splines. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 771–779. Springer, Heidelberg (2003)
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)
Studholme, C., Cardenas, V.: A template free approach to volumetric spatial normalization of brain anatomy. Pattern Recogn. Lett. 25(10), 1191–1202 (2004)
Bhatia, K.K., Hajnal, J.V., Puri, B.K., Edwards, A.D., Rueckert, D.: Consistent groupwise non-rigid registration for atlas construction. In: ISBI, pp. 908–911 (2004)
De Craene, M., du Bois d’Aische, A., Macq, B., Warfield, S.K.: Multi-subject registration for unbiased statistical atlas construction. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 655–662. Springer, Heidelberg (2004)
Lorenzen, P.J., Davis, B.C., Joshi, S.: Unbiased atlas formation via large deformations metric mapping. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3750, pp. 411–418. Springer, Heidelberg (2005)
Zollei, L., Learned-Miller, E., Grimson, E., Wells, W.: Efficient population registration of 3d data. In: ICCV 2005 (2005)
Fukunaga, K., Hostetler, L.D.: The estimation of the gradient of a density function with applications in pattern recognition. T-IT 21(1), 32–40 (1975)
Cheng, Y.: Mean shift, mode seeking and clustering. T-PAMI 17(8), 790–799 (1995)
Comaniciu, D., Meer, P.: Mean shift: A robust approach toward featue space analysis. T-PAMI 24(5), 603–619 (2002)
Comaniciu, D., Ramesh, V., Meer, P.: Real-time tracking of non-rigid objects using mean shift. In: CVPR 2000, vol. 2, pp. 142–149 (2000)
Silverman, B.W.: Density estimation for statistics and data analysis. Chapman and Hall, Boca Raton (1992)
Wells, W., Viola, P., Atsumi, H., Nakajima, S., Kikinis, R.: Multi-modal volume registration by maximization of mutual information. Medical Image Analysis 1(1), 35–51 (1996)
Collignon, A., Maes, F., Delaere, D., Vandermeulen, D., Suetens, P., Marchal, G.: Automated multimodality medical image registration using information theory. In: Proc. 14th Int. Conf. Information Processing in Medical Imaging; Computational Imaging and Vision 3, pp. 263–274 (1995)
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging 16(2), 187–198 (1997)
Mattes, D., Haynor, D.R., Vesselle, H., Lewellen, T.K., Eubank, W.: Pet-ct image registration in the chest using free-form deformations. IEEE Transactions on Medical Imaging 22(1), 120–128 (2003)
Ibanez, L., Schroeder, W., Ng, L., Cates, J.: The ITK Software Guide, 2nd edn. Kitware, Inc. (2005) ISBN 1-930934-15-7, http://www.itk.org/ItkSoftwareGuide.pdf
Everitt, B.S., Landau, S., Leese, M.: Cluster Analysis. Arnold (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Blezek, D.J., Miller, J.V. (2006). Atlas Stratification. In: Larsen, R., Nielsen, M., Sporring, J. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006. MICCAI 2006. Lecture Notes in Computer Science, vol 4190. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11866565_87
Download citation
DOI: https://doi.org/10.1007/11866565_87
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44707-8
Online ISBN: 978-3-540-44708-5
eBook Packages: Computer ScienceComputer Science (R0)