A Novel Image-centric Approach Towards Direct Volume Rendering
This addresses the usability issue for medical professionals in DVR, though it appears incremental as it builds on existing TF generation methods with user-centric improvements.
The paper tackled the problem of complex and unintuitive Transfer Function (TF) generation in Direct Volume Rendering (DVR) by proposing an image-centric method where users directly manipulate volume data, simplifying the process with informative slices and a novel classifier, resulting in effective outcomes as shown in experiments and user surveys.
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.