Multidimensional transfer functions for interactive volume rendering

Journal ArticleAbstract-Most direct volume renderings produced today employ one-dimensional transfer functions which assign color and opacity to the volume based solely on the single scalar quantity which comprises the data set. Though they have not received widespread attention, multidimensional transfer functions are a very effective way to extract materials and their boundaries for both scalar and multivariate data. However, identifying good transfer functions is difficult enough in one dimension, let alone two or three dimensions. This paper demonstrates an important class of three-dimensional transfer functions for scalar data, and describes the application of multidimensional transfer functions to multivariate data. We present a set of direct manipulation widgets that make specifying such transfer functions intuitive and convenient. We also describe how to use modern graphics hardware to both interactively render with multidimensional transfer functions and to provide interactive shadows for volumes. The transfer functions, widgets, and hardware combine to form a powerful system for interactive volume exploration

Volume rendering with multidimensional peak finding

Journal ArticlePeak finding provides more accurate classification for direct volume rendering by sampling directly at local maxima in a transfer function, allowing for better reproduction of high-frequency features. However, the 1D peak finding technique does not extend to higherdimensional classification. In this work, we develop a new method for peak finding with multidimensional transfer functions, which looks for peaks along the image of the ray. We use piecewise approximations to dynamically sample in transfer function space between world-space samples. As with unidimensional peak finding, this approach is useful for specifying transfer functions with greater precision, and for accurately rendering noisy volume data at lower sampling rates. Multidimensional peak finding produces comparable image quality with order-of-magnitude better performance, and can reproduce features omitted entirely by standard classification. With no precomputation or storage requirements, it is an attractive alternative to preintegration for multidimensional transfer functions

Gaussian transfer functions for multi-field volume visualization

Journal ArticleVolume rendering is a flexible technique for visualizing dense 3D volumetric datasets. A central element of volume rendering is the conversion between data values and observable quantities such as color and opacity. This process is usually realized through the use of transfer functions that are precomputed and stored in lookup tables. For multidimensional transfer functions applied to multivariate data, these lookup tables become prohibitively large. We propose the direct evaluation of a particular type of transfer functions based on a sum of Gaussians. Because of their simple form (in terms of number of parameters), these functions and their analytic integrals along line segments can be evaluated efficiently on current graphics hardware, obviating the need for precomputed lookup tables. We have adopted these transfer functions because they are well suited for classification based on a unique combination of multiple data values that localize features in the transfer function domain. We apply this technique to the visualization of several multivariate datasets (CT, cryosection) that are difficult to classify and render accurately at interactive rates using traditional approaches

Multidimensional Transfer Functions in Volume Rendering of Medical Datasets

In volume rendering, transfer functions are used to map voxel property into color and opacity. The most common voxel property used in transfer functions is the voxel intensity. Multiple voxel properties can also be used, and we then get a multidimensional transfer function. In this thesis, we want to test how well a two-dimensional transfer function performs, compared to a transfer function of just one dimension. To test this, we have implemented a fast volume rendering application using GPU shader programming. We got a radiologist, a surgeon and a computer engineer to evaluate our application using both one and two-dimensional transfer functions on different datasets. The test shows that a transfer function of both voxel intensity and gradient magnitude is better than a transfer function of just intensity for reducing noise in the rendering. The test also shows how difficult manual transfer function manipulation can be

Morse-Smale decomposition of multivariate transfer function space for separably-sampled volume rendering

We present a topology-guided technique for improving performance of multifield volume rendering with peak finding and preintegration with 2D transfer functions. We apply Morse-Smale decomposition to segment the multidimensional transfer function domain. This segmentation helps to reduce the number of cases where sampling in transfer function space should be performed, effectively reducing the rendering cost for equivalent sampling quality. We show that the overall performance is increased depending on the topology of a transfer function

Interactive visualization tool for multi-channel confocal microscopy data in neurobiology research

Journal ArticleConfocal microscopy is widely used in neurobiology for studying the three-dimensional structure of the nervous system. Confocal image data are often multi-channel, with each channel resulting from a different fluorescent dye or fluorescent protein; one channel may have dense data, while another has sparse; and there are often structures at several spatial scales: subneuronal domains, neurons, and large groups of neurons (brain regions). Even qualitative analysis can therefore require visualization using techniques and parameters fine-tuned to a particular dataset. Despite the plethora of volume rendering techniques that have been available for many years, the techniques standardly used in neurobiological research are somewhat rudimentary, such as looking at image slices or maximal intensity projections. Thus there is a real demand from neurobiologists, and biologists in general, for a flexible visualization tool that allows interactive visualization of multi-channel confocal data, with rapid fine-tuning of parameters to reveal the three dimensional relationships of structures of interest. Together with neurobiologists, we have designed such a tool, choosing visualization methods to suit the characteristics of confocal data and a typical biologist's workflow. We use interactive volume rendering with intuitive settings for multidimensional transfer functions, multiple render modes and multi-views for multi-channel volume data, and embedding of polygon data into volume data for rendering and editing. As an example, we apply this tool to visualize confocal microscopy datasets of the developing zebrafish visual system