Transfer Function Refinement for Exploring Volume Data

Volume visualization is a powerful technique for depicting layered structures in 3D volume data sets. However, it is a major challenge to obtain clear visualizations of a volume with layers clearly revealed. In particular, the specification of the transfer function is frequently a time-consuming and unintuitive task in volume rendering. In this paper, we describe a global optimization and two user-driven refinement methods for modulating transfer functions in order to assist the exploration of volume data. This optimization is dependent on the distribution of scalar values of the volume data set and is designed to reduce general occlusion and improve the clarity of layers of structures in the resulting images. The user can explore a volume by interactively specifying different priority intensity ranges and observe which layers of structures are revealed. In addition we show how the technique can be applied for time-varying volume data sets by adaptively refining the transfer function based on the histogram of each time-step. Experimental results on various data sets are presented to demonstrate the effectiveness of our method.