Automatic transfer function generation and extinction-based approaches in direct volume visualization

Abstract

Direct volume visualization has become an important tool in many domains for visualizing and examining volumetric datasets. The tremendous increase in computing power of the hardware over the past years makes it possible to immediately visualize volumetric datasets obtained from scanning devices at fully interactive frame rates. However, despite this change of paradigm compared to the slow offline methods of the past, direct volume visualization suffers from disadvantages constricting an immediate, reliable analysis of volumetric datasets.This thesis begins with an overview of different methods for direct volume visualization followed by an in-depth review of the theoretical foundation including inherent challenges. Subsequently selected state-of-the-art techniques used in this thesis are explained in detail. One challenge that all techniques have in common is the dependency on good transfer functions. Only good transfer functions allow for the right insight into the dataset permitting a reliable analysis. These transfer functions are often constructed manually in a time consuming and cumbersome trial-and-error process. We propose an automated general purpose approach for generating a set of best transfer functions based on information theory. Our algorithm appraises the information content of the images generated by a particular transfer function when rotating the dataset, as it is the case in interactive sessions. Quantifying the quality of a transfer function in this way enables a directed search for the set of best transfer functions in a feedback loop employing a combination of two different optimization algorithms. This set of best, distinct transfer functions helps the user to gain an immediate overview of each facet of a dataset.When visualizing volumetric datasets, it is of major importance that domain experts are able to recognize small features, to distinguish the relationship and connectivity between them and to get the right perception. For this the applied illumination and shading model plays an important part. Sophisticated models including realistic looking directional shadows, ambient occlusion and color bleeding effects can greatly enhance the perception. Unfortunately common models exhibiting these effects are expensive to compute and not suitable for interactive applications. We present a method showing how these effects can be applied to GPU volume ray-casting while fully maintaining interactivity based on the original, exponential extinction coefficient of the volume rendering integral. Exploiting the fact that the original, exponential extinction coefficient is summable, our framework is built on top of a 3D summed area table that allows for quick lookups of extinction queries.Technically volumetric datasets consist of discrete scalar or sometimes vector data. As the resolution of this data hardly ever fits the resolution of the output device, the data needs to be interpolated or reconstructed. Volume visualization methods based on 3D textures can profit from fast built-in trilinear interpolation of the hardware. However, trilinear interpolation is not the first choice when it comes to image quality. Volume splatting on the other hand is a volume visualization technique that makes it easy to integrate arbitrary interpolation schemes. The performance of volume splatting is directly related to the applied interpolation scheme and the resulting interpolation kernel respectively. In this thesis we introduce an algorithm for volume splatting that greatly enhances the performance by reducing the required amount of splatting operations from interpolation kernel slices. Further, we show how the image quality of volume visualization can be enhanced by using the original, exponential extinction coefficient of the volume rendering integral instead of common alpha-blending simplifications.