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Statistical Multi-Object Shape Models

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Abstract

The shape of a population of geometric entities is characterized by both the common geometry of the population and the variability among instances. In the deformable model approach, it is described by a probabilistic model on the deformations that transform a common template into various instances. To capture shape features at various scale levels, we have been developing an object-based multi-scale framework, in which a geometric entity is represented by a series of deformations with different locations and degrees of locality. Each deformation describes a residue from the information provided by previous steps. In this paper, we present how to build statistical shape models of multi-object complexes with such properties based on medial representations and explain how this may lead to more effective shape descriptions as well as more efficient statistical training procedures. We illustrate these ideas with a statistical shape model for a pair of pubic bones and show some preliminary results on using it as a prior in medical image segmentation.

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Authors and Affiliations

  1. Medical Image Display & Analysis Group, University of North Carolina, Chapel Hill, USA

    Conglin Lu, Stephen M. Pizer, Sarang Joshi & Ja-Yeon Jeong

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  1. Conglin Lu
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  2. Stephen M. Pizer
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  3. Sarang Joshi
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  4. Ja-Yeon Jeong
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Correspondence to Conglin Lu.

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Lu, C., Pizer, S.M., Joshi, S. et al. Statistical Multi-Object Shape Models. Int J Comput Vis 75, 387–404 (2007). https://doi.org/10.1007/s11263-007-0045-0

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