for Volume Classification and Visualization
Carlos D. Correa, Kwan‐Liu Ma · 2009
Fig. 1. Left: MRI of a meningionma. Transfer functions (TF) based on boundaries (insets on the left: top, 1D TF with gradient modulation, bottom, 2D TF of intensity vs. gradient magnitude) cannot separate the tumor from the vessels (where gradients are also strong). A transfer function based on occlusion separates the tumor from the vessels and the ventricular structures from skull and skin. Right: CT dataset with contrast agent. Classification is difficult due to overlap between bone and vessel structures (see red color in ribcage in the insets). An occlusion-based TF properly classifies bone and also highlights internal structures (blue). Abstract—Despite the ever-growing improvements on graphics processing units and computational power, classifying 3D volume data remains a challenge. In this paper, we present a new method for classifying volume data based on the ambient occlusion of voxels. This information stems from the observation that most volumes of a certain type, e.g., CT, MRI or flow simulation, contain occlusion patterns that reveal the spatial structure of their materials or features. Furthermore, these patterns appear to emerge consistently for different data sets of the same type. We call this collection of patterns the occlusion spectrum of a dataset. We show that using this occlusion spectrum leads to better two-dimensional transfer functions that can help classify complex data sets in terms of the spatial relationships among features. In general, the ambient occlusion of a voxel can be interpreted as a weighted average of the intensities in a spherical neighborhood around the voxel. Different weighting schemes determine the ability to separate structures of interest in the occlusion spectrum. We present a general methodology for finding such a weighting. We show results of our approach in 3D imaging for different applications, including brain and breast tumor detection and the visualization of turbulent flow.