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Building a complete surface model from sparse data using statistical shape models: Application to computer assisted knee surgery

  • Markus Fleute
  • Stéphane Lavallée
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1496)

Abstract

This paper addresses the problem of extrapolating very few range data to obtain a complete surface representation of an antomical structure. A new method that uses statistical shape models is proposed and its application to modeling a few points manually digitized on the femoral surface is detailed, in order to improve visualization of a system developped by TIMC laboratory for computer assisted anterior cruciate ligament (ACL) reconstruction. The model is built from a population of 11 femur specimen digitized manually. Data sets are registered together using an elastic registration method of Szeliski and Lavallée based on octree-splines. Principal Components Analysis (PCA) is performed on a field of surface deformation vectors. Fitting this statistical model to a few points is performed by non-linear optimisation. Results are presented for both simulated and real data. The method is very flexible and can be applied to any structures for which the shape is stable.

Keywords

Anterior Cruciate Ligament Root Mean Square Iterative Close Point Iterative Close Point Rigid Registration 
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 1998

Authors and Affiliations

  • Markus Fleute
    • 1
  • Stéphane Lavallée
    • 1
  1. 1.Faculté de Médecine de GrenobleTIMC - IABLa Tronche CedexFrance

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