A novel image-centric approach toward direct volume rendering
ACM Transactions on Intelligent Systems and Technology (TIST), 2018•dl.acm.org
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 article, 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 …
(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 article, 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 …
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 article, 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.
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