Visually guided mesh smoothing for medical applications

Surface models derived from medical image data often exhibit artifacts, such as noise and staircases, which can be reduced by applying mesh smoothing filters. Usually, an iterative adaption of smoothing parameters to the specific data and continuous re-evaluation of accuracy and curvature is required. Depending on the number of vertices and the filter algorithm, computation time may vary strongly and interfere with an interactive mesh generation procedure. In this paper, we present an approach to improve the handling of mesh smoothing filters. Based on a GPU mesh smoothing implementation, model quality is evaluated in real-time and provided to the user as quality graphs to support the mental optimization of input parameters. Moreover, this framework is used to find optimal smoothing parameters automatically and to provide data-specific parameter suggestions.