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.

Visual-auditory Volume Rendering of Scalar Fields

“…The examples of such situations are the simultaneous analysis of several scalar fields with different field features and underlying processes; scalar fields after postprocessing with image processing applied to 3D textures; application of various optical models in the visualisation pipeline. The main aim of those procedures is to highlight features of interest [1], enhance visual analysis quality [2] and handle image quality issues, arising due to limitations of scanning devices and human perception. Without these techniques, we may get a wrong insight on data, which fails the entire analysis process [3].…”