Segmented Volume Based Tetrahedralization Algorithm
基于分类体数据的四面体网格剖分算法

Virtual endoscopy is a new method of diagnosis using computer processing of 3D image datasets(such as CT or MRI scans) to provide simulated visualization. In order to obtain a physically realistic surgery simulation, it is needed to generate the accurate 3D human organ meshes for finite element analysis(FEA) to simulate serials of actions in the surgery. In this paper, a new algorithm is proposed to create the tetrahedral mesh directly from the segmented volume. Because Delaunay triangulation guarantees the well-shape of the final mesh. We follow the idea and classify our method as an incremental insertion algorithm in Delaunay triangulation category. It is composed of three phases: placements of mesh vertices, Delaunay triangulation and restore of tissue boundary. The tissue boundary contained in the original dataset is kept accurately by the featured point selection. An automatic self-adaptive method is presented to vary the density of mesh nodes according to local features of the segmented volume. The adaptive model generated has the attributes of accurate, small scale and well-shaped which is very suitable for complete 3D finite element solvers.