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.
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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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Fleute, M., Lavallée, S. (1998). Building a complete surface model from sparse data using statistical shape models: Application to computer assisted knee surgery. In: Wells, W.M., Colchester, A., Delp, S. (eds) Medical Image Computing and Computer-Assisted Intervention — MICCAI’98. MICCAI 1998. Lecture Notes in Computer Science, vol 1496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0056276
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DOI: https://doi.org/10.1007/BFb0056276
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