Robust generation of high-quality unstructured meshes on realistic biomedical geometry

In this paper, we propose efficient and robust unstructured mesh generation methods based on computed tomography (CT) and magnetic resonance imaging (MRI) data, in order to obtain a patient-specific geometry for high-fidelity numerical simulations. Surface extraction from medical images is carried out mainly using open source libraries, including the Insight Segmentation and Registration Toolkit and the Visualization Toolkit, into the form of facet surface representation. To create high-quality surface meshes, we propose two approaches. One is a direct advancing front method, and the other is a modified decimation method. The former emphasizes the controllability of local mesh density, and the latter enables semi-automated mesh generation from low-quality discrete surfaces. An advancingfront- based volume meshing method is employed. Our approaches are demonstrated with high-fidelity tetrahedral meshes around medical geometries extracted from CT/MRI data