Name
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33463-4_5 | MeshMed |
Keywords | Field | DocType |
clinical ct image,femur cortical surface,absolute error,outer cortical surface,cortical surface,active shape model,outer cortical mesh,automatic meshing,mean mesh thickness,segment cortical surface,piecewise parametric mesh,cortical geometry,cortical morphology | Computer vision,Active shape model,Polygon mesh,Statistical shape analysis,Computer science,Femur,Root mean square,Artificial intelligence,Standard deviation,Piecewise,Approximation error | Conference |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ju Zhang | 1 | 5 | 1.99 |
| Duane Malcolm | 2 | 3 | 0.91 |
| Jacqui Hislop-Jambrich | 3 | 3 | 0.91 |
| C. David L. Thomas | 4 | 5 | 1.31 |
| Poul M. F. Nielsen | 5 | 328 | 29.20 |