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Automatic Meshing of Femur Cortical Surfaces from Clinical CT Images

  • Ju Zhang
  • Duane Malcolm
  • Jacqui Hislop-Jambrich
  • C. David L. Thomas
  • Poul Nielsen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7599)

Abstract

We present an automated image-to-mesh workflow that meshes the cortical surfaces of the human femur, from clinical CT images. A piecewise parametric mesh of the femoral surface is customized to the in-image femoral surface by an active shape model. Then, by using this mesh as a first approximation, we segment cortical surfaces via a model of cortical morphology and imaging characteristics. The mesh is then customized further to represent the segmented inner and outer cortical surfaces. We validate the accuracy of the resulting meshes against an established semi-automated method. Root mean square error for the inner and outer cortical meshes were 0.74 mm and 0.89 mm, respectively. Mean mesh thickness absolute error was 0.03 mm with a standard deviation of 0.60 mm. The proposed method produces meshes that are correspondent across subjects, making it suitable for automatic collection of cortical geometry for statistical shape analysis.

Keywords

Femoral Head Root Mean Square Cortical Thickness Material Point Cortical Surface 
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 2012

Authors and Affiliations

  • Ju Zhang
    • 1
  • Duane Malcolm
    • 1
  • Jacqui Hislop-Jambrich
    • 2
  • C. David L. Thomas
    • 3
  • Poul Nielsen
    • 1
    • 4
  1. 1.Auckland Bioengineering InstituteThe University of AucklandNew Zealand
  2. 2.Clinical Applications Research CenterToshiba MedicalSydneyAustralia
  3. 3.The Melbourne Dental SchoolThe University of MelbourneAustralia
  4. 4.Department of Engineering ScienceThe University of AucklandNew Zealand

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