A Novel Image-centric Approach Towards Direct Volume Rendering

TL;DR

This paper introduces an intuitive image-centric method for transfer function generation in direct volume rendering, simplifying user interaction and improving visualization quality through a novel classifier and selective slice presentation.

Contribution

It presents a new user-friendly approach for transfer function creation using direct volume data manipulation and a novel classifier, enhancing usability for medical professionals.

Findings

Effective transfer function generation demonstrated on benchmark datasets.

User survey indicates improved usability and visualization quality.

The classifier effectively combines local and global data distribution information.

Abstract

Transfer Function (TF) generation is a fundamental problem in Direct Volume Rendering (DVR). A TF maps voxels to color and opacity values to reveal inner structures. Existing TF tools are complex and unintuitive for the users who are more likely to be medical professionals than computer scientists. In this paper, we propose a novel image-centric method for TF generation where instead of complex tools, the user directly manipulates volume data to generate DVR. The user's work is further simplified by presenting only the most informative volume slices for selection. Based on the selected parts, the voxels are classified using our novel Sparse Nonparametric Support Vector Machine classifier, which combines both local and near-global distributional information of the training data. The voxel classes are mapped to aesthetically pleasing and distinguishable color and opacity values using harmonic colors. Experimental results on several benchmark datasets and a detailed user survey show the effectiveness of the proposed method.