The skeletal structures of solid objects play an important role in medical and industrial applications. Given a volumetrically sampled solid object, our method extracts a nice-looking skeletal structure represented as a polygon mesh. The purpose is to achieve a noise-robust extraction of the skeletal mesh from a real-world object obtained using a scanning technology such as the CT scan method. We first approximate the input through a set of spherically supported polynomials that provide an adaptively smoothed intensity field, and then perform a polygonization process to find the extremal sheet of the field, which is regarded as a skeletal sheet in this research. In our polygonization, a subset of the weighted Delaunay tetrahedrization defined by a set of spherical supports is used as an adaptively sampled grid. The derivatives for detecting extremality are analytically evaluated at the tetrahedron vertices. We also demonstrate the effectiveness of our method by extracting skeletal meshes from noisy CT images.
['Yukie Nagai', 'Yutaka Ohtake', 'Kiwamu Kase', 'Hiromasa Suzuki']
Polygonizing skeletal sheets of CT-scanned objects by partitioin of unity approximations