基于分类体数据的四面体网格剖分算法

虚拟内窥手术是以真实病人的CT或者MRI扫描数据为基础，首先通过组织分割，在计算机内部建立起三维模型，然后通过虚拟现实技术来模拟窥镜手术全过程的一项技术。其中，人体器官的三维网格建模是该技术中一个十分重要的部分，为了准确地进行了人体器官三维网格建模，在对三维体数据进行组织分割的基础上，提出了一种由分类体数据直接建立三维四面体网格的方法，由于Delaunay三角剖分所产生的网格质量比较高，所以该方法沿用逐点插入算法的思想，以特征点的提取和Steiner布点为基础来生成四面体网格，并通过组织边界的判定准则和利用flip操作来恢复组织边界，实践证明，该方法所生成的网格具有自适应的网格密度。

Segmented Volume Based Tetrahedralization Algorithm

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.