Abstract
We propose a novel Statistical Deformable Model (SDM) for bone-related soft tissue prediction, which we called Br-SDM. In Br-SDM, we have integrated Finite Element Method (FEM) and SDM to achieve both accurate and efficient prediction for orthognathic surgery planning. By combining FEM-based sample generation and SDM-Based soft tissue prediction, we are able to capture the prior knowledge of bone-related soft tissue deformation. Then the post-operative appearance can be predicted in a more efficient way from a Br-SDM based optimisation. Our experiments have shown that Br-SDM is able to give comparable soft tissue prediction accuracy with respect to conventional FEM-based prediction while reducing the computation cost from O(n2) to O(n) at the same time.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 217-230 |
| Number of pages | 14 |
| Journal | International Journal of Functional Informatics and Personalised Medicine |
| Volume | 2 |
| Issue number | 2 |
| DOIs | |
| State | Published - 2009 |
Keywords
- FEM
- SDM
- finite element method
- operation prediction
- orthognathic surgery
- statistical deformable model
- surgery planning
ASJC Scopus subject areas
- Clinical Neurology
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