Volume visualization using sparse nonparametric support vector machines and harmoniccolors

In Direct Volume Rendering (DVR), the Transfer Function (TF) to map voxel values to color and opacity values is difficult to obtain. Existing TF design tools are complex and non-intuitive for the end user, who is more likely to be a medical professional than an expert in image processing. In this paper, we propose a volume visualization method where the user directly works on the volume data to simply select the parts he/she would like to visualize. The user´s work is further simplified by presenting only the most informative volume slices for selection. Based on the selected parts, all the voxels are classified using our Sparse Nonparametric Support Vector Machine (SN-SVM) classifier, which combines both local and near-global distributional information of the training data to obtain accurate results. The voxel classes are then mapped to color and opacity values using the concept of harmonic colors, which provides easily distinguishable and aesthetically pleasing results. Experimental results on several benchmark datasets show the effectiveness of the proposed method.