Feature of Interest-Based Direct Volume Rendering Using Contextual Saliency-Driven Ray Profile Analysis

Abstract

Direct volume rendering (DVR) visualization helps interpretation because it allows users to focus attention on the subset of volumetric data that is of most interest to them. The ideal visualization of the features of interest (FOIs) in a volume, however, is still a major challenge. The clear depiction of FOIs depends on accurate identification of the FOIs and appropriate specification of the optical parameters via transfer function (TF) design and it is typically a repetitive trial-and-error process. We address this challenge by introducing a new method that uses contextual saliency information to group the voxels along a viewing ray into distinct FOIs where contextual saliency' is a biologically inspired attribute that aids the identification of features that the human visual system considers important. The saliency information is also used to automatically define the optical parameters that emphasize the visual depiction of the FOIs in DVR. We demonstrate the capabilities of our method by its application to a variety of volumetric data sets and highlight its advantages by comparison to current state-of-the-art ray profile analysis methods.