Measuring the Effects of Scalar and Spherical Colormaps on Ensembles of DMRI Tubes

TL;DR

This study empirically evaluates how different scalar and spherical colormaps affect the visualization accuracy of DMRI ensembles, revealing that certain colormaps improve ensemble mean estimation and orientation-tracing accuracy.

Contribution

It provides a systematic comparison of colormaps for DMRI ensemble visualization, identifying the most effective color encodings for scalar and directional data.

Findings

Extended-blackbody, coolwarm, and blackbody are most effective for ensemble average identification.

Hue influences ensemble mean estimates more than luminance.

Boy's surface embedding and absolute color schemes improve orientation-tracing accuracy.

Abstract

We report empirical study results on the color encoding of ensemble scalar and orientation to visualize diffusion magnetic resonance imaging (DMRI) tubes. The experiment tested six scalar colormaps for average fractional anisotropy (FA) tasks (grayscale, blackbody, diverging, isoluminant-rainbow, extended-blackbody, and coolwarm) and four three-dimensional (3D) directional encodings for tract tracing tasks (uniform gray, absolute, eigenmap, and Boy's surface embedding). We found that extended-blackbody, coolwarm, and blackbody remain the best three approaches for identifying ensemble average in 3D. Isoluminant-rainbow coloring led to the same ensemble mean accuracy as other colormaps. However, more than 50% of the answers consistently had higher estimates of the ensemble average, independent of the mean values. Hue, not luminance, influences ensemble estimates of mean values. For ensemble orientation-tracing tasks, we found that the Boy's surface embedding (greatest spatial resolution and contrast) and absolute color (lowest spatial resolution and contrast) schemes led to more accurate answers than the eigenmaps scheme (medium resolution and contrast), acting as the uncanny-valley phenomenon of visualization design in terms of accuracy.