Spatialized Transfer Functions

Abstract

Multi-dimensional transfer functions are an efficient way to visualize features in scalar volume data produced by CT or MRI scanners. However, the optimal transfer function is difficult to find in general. We present an automatic yet powerful method for the automatic setup of multi-dimensional transfer functions by adding spatial information to the histogram of a volume. Using this information we can easily classify the histogram and derive a transfer function by assigning unique colors to each class of the histogram. Each feature can be selected interactively by pointing and clicking at the corresponding class in the transfer function. In order to render the classified volume with adequate quality we propose an extension of the wellknown pre-integration technique. Furthermore, we demonstrate the flexibility of our approach by giving examples for the imaging of segmented, diffusion-tensor and multi-modal data.