Understanding vasculatures is important for endovascular intervention simulation (EIS). A recent trend attempts to represent vasculatures with three-dimensional (3D) surface meshes by multiview image and graphics-based techniques used in optical and laser scanners. Conversion from image volume data to 3D surface meshes, however, suffers from staircases and noise. Most existing methods in geometric processing often consider mesh smoothing in their independent settings. However, this approach either is ineffective in removing the artifacts or introduces additional types of artifacts (e.g., volume shrinkage and shape distortion). We propose an effective yet vascular-oriented mesh optimization framework. We first employ a weight-adaptive vertex resampling to eliminate staircases; then, we subdivide vascular meshes to denser ones; and finally, we exploit the constrained biquadratic Bezier surface fitting to produce optimized meshes. In EIS, clinicians are interested in the centerline of surface meshes as well as surface meshes. However, centerline extraction from vascular meshes is still challenging. We adopt an existing centerline extraction approach for vascular meshes based on the observation that human blood vessels are generally composed of piecewise cylindrical shapes. Tests on vascular data illustrate that our optimization approach achieves higher quality results regarding surface smoothness compared to previous approaches. In addition, our extraction approach effectively obtains complete centerlines from real data.