Occlusion-free feature exploration for volume visualization

Abstract

Direct volume rendering is one of the most effective ways to visualize volume data sets, which employs intuitive 2D images to display internal structures in 3D space. However, opaque features always occlude other parts of the volume, and make some features of interest invisible in the final rendered images. Although a class of highly transparent transfer functions are capable of revealing all features at once, it is still laborious and time-consuming to specify appropriate transfer functions to reduce occlusions on less important structures and highlight features of interest, even for experts. Thus, the research on simpler volume visualization techniques which do not rely on complex transfer functions has been a hotspot in many practical applications. In this paper, an occlusion-free feature exploration approach that consists of modifying the traditional volume rendering integral is proposed, which can achieve better visibility of all internal features of interest with simple linear transfer functions. During the ray casting, a modulation parameter is derived to reduce the contributions of previous samples along the viewing ray, whenever the accumulated opacity value is close to overflow. In addition, several relevant functions are introduced to refine the modulation parameter and highlight the features of interest identified according to the attributes such as scalar, gradient module, occurrence and depth. Thereby, the proposed approach is capable of generating informative rendered images and enhancing the visual perception of features of interest without resorting to complex transfer functions.