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Comparing the Similarity of Statistical Shape Models Using the Bhattacharya Metric

  • K. O. Babalola
  • T. F. Cootes
  • B. Patenaude
  • A. Rao
  • M. Jenkinson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)

Abstract

A variety of different methods of finding correspondences across sets of images to build statistical shape models have been proposed, each of which is likely to result in a different model. When dealing with large datasets (particularly in 3D), it is difficult to evaluate the quality of the resulting models. However, if the different methods are successfully modelling the true underlying shape variation, the resulting models should be similar. If two different techniques lead to similar models, it suggests that they are indeed approximating the true shape change. In this paper we explore a method of comparing statistical shape models by evaluating the Bhattacharya overlap between their implied shape distributions. We apply the technique to investigate the similarity of three models of the same 3D dataset constructed using different methods.

Keywords

Singular Value Decomposition Mesh Model Statistical Shape Model Connected Vertex Free Form Deformation 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • K. O. Babalola
    • 1
  • T. F. Cootes
    • 1
  • B. Patenaude
    • 2
  • A. Rao
    • 3
  • M. Jenkinson
    • 2
  1. 1.Division of Imaging Science and Biomedical EngineeringUniversity of ManchesterUK
  2. 2.FMRIB Centre, John Radcliffe HospitalUniversity of OxfordUK
  3. 3.Visual Information Processing, Department of ComputingImperial College LondonUK

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