Designing effective transfer functions for volume rendering from photographic volumes

Abstract

ÐPhotographic volumes present a unique, interesting challenge for volume rendering. In photographic volumes, voxel color is predetermined, making color selection through transfer functions unnecessary. However, photographic data does not contain a clear mapping from the multivalued color values to a scalar density or opacity, making projection and compositing much more difficult than with traditional volumes. Moreover, because of the nonlinear nature of color spaces, there is no meaningful norm for the multivalued voxels. Thus, the individual color channels of photographic data must be treated as incomparable data tuples rather than as vector values. Traditional differential geometric tools, such as intensity gradients, density, and Laplacians, are distorted by the nonlinear nonorthonormal color spaces that are the domain of the voxel values. We have developed different techniques for managing these issues while directly rendering volumes from photographic data. We present and justify the normalization of color values by mapping RGB values to the CIE L à u à v à color space. We explore and compare different opacity transfer functions that map three channel color values to opacity. We apply these many-to-one mappings to the original RGB values as well as to the voxels after conversion to L à u à v à space. Direct rendering using transfer functions allows us to explore photographic volumes without having to commit to an a priori segmentation that might mask fine variations of interest. We empirically compare the combined effects of each of the two color spaces with our opacity transfer functions using source data from the Visible Human Project. Index TermsÐVolume rendering, transfer functions, photographic data.

Visualization of color anatomy and molecular fluorescence in whole-mouse cryo-imaging

“…Transfer function design greatly affects the visual outcome of volume rendering [6;8;9]. A transfer function assigns values for optical properties, such as color and opacity, to original values of the data set being visualized.…”

“…An appropriate choice of an opacity transfer function still needs to be made, and this largely depends on the data itself. For example, in routine medical visualizations of CT data it is often possible to use pre-defined 1D opacity transfer functions to highlight certain tissue types, such as bone or liver [6;13]. Further, a combination of data attributes such as color channel values, grayscale value, gradient of channels, and grayscale gradient can be mapped to a suitable opacity value as in the case of multidimensional opacity transfer functions reported in the literature [5;8;9].…”

“…Similar techniques to ours have been previously used on data from the Visible Human Project [15–18]. In particular, opacity transfer functions involving color and color gradient feature detectors [6;8;9] have been employed to derive rendered opacity values. However, we note that the superior resolution and the fluorescence imaging capability of cryo-imaging make our data and techniques distinct from those of Visible Human Project.…”

A User-friendly Tool for Semi-automated Segmentation and Surface Extraction from Color Volume Data Using Geometric Feature-space Operations

“…There are also several approaches for direct volume rendering of photographic volumes [KKH02,EMRY02], where the authors present sophisticated transfer function generations, but extracting surfaces from the color volume data has hardly been addressed. A hardware-assisted volume renderer for the visualization of cryosection data has been presented by Takanashi [TLM + …”

Visualization of multidimensional, multivariate volume data using hardware-accelerated non-photorealistic rendering techniques

“…The multivariate volume data visualization problem has also received a lot of attention [7,28,31,23,4] but with an emphasis more on either color volume visualization or simultaneous visualization of multi-modality data.…”