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Transfer Function (TF) generation is a fundamental problem in Direct VolumeRendering (DVR). A TF maps voxels to color and opacity values to reveal innerstructures. Existing TF tools are complex and unintuitive for the users who aremore likely to be medical professionals than computer scientists. In thispaper, we propose a novel image-centric method for TF generation where insteadof complex tools, the user directly manipulates volume data to generate DVR.The user's work is further simplified by presenting only the most informativevolume slices for selection. Based on the selected parts, the voxels areclassified using our novel Sparse Nonparametric Support Vector Machineclassifier, which combines both local and near-global distributionalinformation of the training data. The voxel classes are mapped to aestheticallypleasing and distinguishable color and opacity values using harmonic colors.Experimental results on several benchmark datasets and a detailed user surveyshow the effectiveness of the proposed method.

A Novel Image-centric Approach Towards Direct Volume Rendering