Abstract
A method is presented to systematically encode brain shape variation, observed from actual image samples, in the form of empirical distributions that can be applied to guide the Bayesian analysis of future image studies. Unlike eigendecompositions based on intrinsic features of a physical model, our modal basis for describing anatomic variation is derived directly from spatial mappings which bring previous brain samples into alignment with a reference configuration. The resultant representation ensures parsimony, yet captures information about the variation across the entire volumetric extent of the brain samples, and facilitates analyses that are governed by the measured statistics of anatomic variability rather than by the physics of some assumed model.
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References
J. C. Gee and R. K. Bajcsy, “Elastic matching: Continuum mechanical and probabilistic analysis,” in Brain Warping, A. Toga, ed., Academic Press, San Diego, In preparation.
J. C. Gee, D. R. Haynor, M. Reivich, and R. Bajcsy, “Finite element approach to warping of brain images,” in Medical Imaging 1994: Image Processing, M. H. Loew, ed., SPIE, Bellingham, 1994.
T. F. Cootes, D. H. Cooper, C. J. Taylor, and J. Graham, “Trainable method of parametric shape description,” Image and Vision Computing, 10, (5), pp. 289–294, 1992.
A. Hill, T. F. Cootes, and C. J. Taylor, “A generic system for image interpretation using flexible templates,” in Proc. British Machine Vision Conference, pp. 276–285, 1992.
J. Martin, A. Pentland, S. Sclaroff, and R. Kikinis, “Characterization of neuropathological shape deformations,” IEEE Trans. Pattern Anal. Machine Intell, To appear.
L. Le Briquer and J. C. Gee, “Design of a statistical model of brain shape,” in Information Processing in Medical Imaging, J. S. Duncan and G. Gindi, eds., pp. 477–482, Springer-Verlag, Heidelberg, 1997.
J. C. Gee, “Atlas warping for brain morphometry,” in Medical Imaging 1998: Image Processing, K. M. Hanson, ed., SPIE, Bellingham, To appear, 1998.
D. Heeger and J. Bergen, “Pyramid-based texture analysis/synthesis,” in ACM SIG-GRAPH, 1995.
S. C. Zhu, Y. Wu, and D. Mumford, “Filters, random fields, and maximum entropy (Frame) — Towards a unified theory for texture modeling,” Int. J. Comput. Vision, To appear.
K. Popat and R. W. Picard, “Cluster-based probability model and its application to image and texture processing,” IEEE Trans. Image Process., 6, (2), pp. 268–284, 1997.
J. DeBonet and P. Viola, “A non-parametric multi-scale statistical model for natural images,” in Adv. in Neural Info. Processing, To appear, 1997.
E. P. Simoncelli, “Statistical models for images: compression, restoration and synthesis,” in 31st Asilomar Conf. Signals, Systems, and Computers, IEEE, New York, 1997.
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Gee, J.C., Le Briquer, L. (1998). An Empirical Model of Brain Shape. In: Erickson, G.J., Rychert, J.T., Smith, C.R. (eds) Maximum Entropy and Bayesian Methods. Fundamental Theories of Physics, vol 98. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5028-6_16
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DOI: https://doi.org/10.1007/978-94-011-5028-6_16
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