Information-Guided Transfer Function Refinement

Abstract

This paper examines the methods for exploring volume data by optimization of visualization parameters. The size and complexity of the parameter space controlling the rendering process makes it challenging to generate an informative rendering. In particular, the specification of the transfer function (which is a mapping from data values to visual properties) is frequently a time-consuming and unintuitive task. We propose an information theory based approach to optimize the transfer function based on the intensity distribution of the volume data set and the ability for users to specify priority areas of importance in the resulting image in a simple and intuitive way. This optimization approach reduces the occlusion in the resulting images, and thus improves the perception of structures.