Automatic Adjustment of Transfer Functions for 3D Volume Visualization

In most volume rendering scenarios implicit classification is performed manually by specification of a transfer function, that maps abstract data values to visual attributes. An appropriate classification requires both specialized knowledge of the interesting structures within the data set as well as the technical knowhow of the computer scientist. Recent automatic data-driven techniques are verywell capable of separating different regions in the data set. However, their applicability in practice is limited, since they do not contain any information about the critical structures which are of interest. In this scenario we propose an efficient and reproducible way to automatically assign transfer function templates, which include individual knowledge as well as personal taste. The presented approach is based on dynamic programming and was successfully applied in medical environment.