Comparison of Fundamental Mesh Smoothing Algorithms for Medical Surface Models

For diagnosis, intervention planning, and medical education it is common to extract surface models from clinical tomography data. Surface extraction suffers from various effects of medical imaging, segmentation, and surface extraction techniques which cause visual disturbing surface artifacts (e.g. stairs, plateaus). Basic mesh smoothing algorithms for such surface models are a common feature of visualization toolkits. However, there exists no study concerning efficiency, accuracy, and appropriateness of different smoothing strategies for surface models derived from medical volume data. To enable an elaborated and target oriented smoothing of medical surface models, we analyze and compare fundamental mesh smoothing algorithms applied to medical surface models. Based on the analysis of artifact sources and the classification of frequent object types that may make different demands on smoothing, we present smoothing recommendations for medical surface models.