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3D Statistical Shape Modeling of Long Bones

  • Yuhui Yang
  • Anthony Bull
  • Daniel Rueckert
  • Adam Hill
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4057)

Abstract

The aims of this paper are to devise robust methods for the description of the variability in shapes of long bones using 3D statistical shape models (SSMs), and to test these on a dataset of humeri that demonstrate significant variability in shape. 30 primate humeri were CT scanned and manually segmented. SSMs were constructed from a training set of landmarks. The landmarks of the 3D shapes are extracted automatically using marching cubes and point correspondences are automatically obtained via a volumetric non-rigid registration technique using multiresolution B-Spline deformations. The surface registration resulted in no discernible differences between bone shapes, demonstrating the high accuracy of the registration method. An analysis of variations is applied on the shapes based on the model we built. The first mode of variation accounted for 42% of the variation in bone shape. This single component discriminated directly between great apes (including humans) and monkeys.

Keywords

Active Appearance Model Active Shape Model Statistical Shape Model Reference Shape Bone Shape 
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

  • Yuhui Yang
    • 1
    • 2
  • Anthony Bull
    • 1
  • Daniel Rueckert
    • 2
  • Adam Hill
    • 1
  1. 1.Department of BioengineeringImperial College LondonUnited Kingdom
  2. 2.Department of ComputingImperial College LondonUnited Kingdom

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