摘要
There is major interest within the bio-engineering community in developing accurate and non-invasive means for visualizing, modeling and analyzing bone micro-structures. Bones are composed of hierarchical bio-composite materials characterized by complex multi-scale structural geometry. The process of reconstructing a volumetric bone model is usually based upon CT/MRI scanned images. Meshes generated by current commercial CAD systems cannot be used for further modeling or analysis. Moreover, recently developed methods are only capable of capturing the micro-structure for small volumes （biopsy samples）. This paper examines the problem of re-meshing a 3D computerized model of bone micro- structure. The proposed method is based on the following phases： defining sub-meshes of the original model in a grid-based structure, remeshing each sub-mesh using the neural network （NN） method, and merging the sub-meshes into a global mesh. Applying the NN method to micro-structures proved to be quite time consuming. Therefore, a parallel, grid-based approach was applied, yielding a simpler structure in each grid cell. The performance of this method is analyzed, and the method is demonstrated on real bone micro-structures. Furthermore, the method may be used as the basis for generating a multi-resolution bone geometric model.
There is major interest within the bio-engineering community in developing accurate and non-invasive means for visualizing, modeling and analyzing bone micro-structures. Bones are composed of hierarchical bio-composite materials characterized by complex multi-scale structural geometry. The process of reconstructing a volumetric bone model is usually based upon CT/MRI scanned images. Meshes generated by current commercial CAD systems cannot be used for further modeling or analysis. Moreover, recently developed methods are only capable of capturing the micro-structure for small volumes （biopsy samples）. This paper examines the problem of re-meshing a 3D computerized model of bone micro- structure. The proposed method is based on the following phases： defining sub-meshes of the original model in a grid-based structure, remeshing each sub-mesh using the neural network （NN） method, and merging the sub-meshes into a global mesh. Applying the NN method to micro-structures proved to be quite time consuming. Therefore, a parallel, grid-based approach was applied, yielding a simpler structure in each grid cell. The performance of this method is analyzed, and the method is demonstrated on real bone micro-structures. Furthermore, the method may be used as the basis for generating a multi-resolution bone geometric model.
