Transferring transfer functions (TTF): A guided approach to transfer function optimization in volume visualization

Abstract

In volume visualization, a transfer function tailored for one volume usually does not work for other similar volumes without careful tuning. This process can be tedious and time-consuming for a large set of volumes. In this work, we present a novel approach to transfer function optimization based on the differentiable volume rendering of a reference volume and its corresponding transfer function. Using two fully connected neural networks, our approach learns a continuous 2D separable transfer function that visualizes the features of interest with consistent visual properties between the volumes. Because many volume visualization software packages support separable transfer functions, users can export the optimized transfer function into a domain-specific application for further interactions. In tandem with domain experts’ input and assessments, we present two use cases to demonstrate the effectiveness of our approach. The first use case tracks the effect of an asteroid blast near the ocean surface. In this application, a volume and its corresponding transfer function seed our method, cascading transfer function optimization for the proceeding time steps. The second use case focuses on the visualization of white matter, gray matter, and cerebrospinal fluid in magnetic resonance imaging (MRI) volumes. We optimize an intensity-gradient transfer function for one volume from its segmentation. Then we use these results to visualize other brain volumes with different intensity ranges acquired on different MRI machines.