Parallel mean shift for interactive volume segmentation
MLMI'10 Proceedings of the First international conference on Machine learning in medical imaging
Automating Transfer Function Design with Valley Cell-Based Clustering of 2D Density Plots
Computer Graphics Forum
Importance Driven Automatic Color Design for Direct Volume Rendering
Computer Graphics Forum
Technical Section: Transfer function combinations
Computers and Graphics
Interactive volume illustration using intensity filtering
Computational Aesthetics'10 Proceedings of the Sixth international conference on Computational Aesthetics in Graphics, Visualization and Imaging
Feature-driven ambient occlusion for direct volume rendering
VG'10 Proceedings of the 8th IEEE/EG international conference on Volume Graphics
Efficient acquisition and clustering of local histograms for representing voxel neighborhoods
VG'10 Proceedings of the 8th IEEE/EG international conference on Volume Graphics
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
Prostate cancer visualization from MR imagery and MR spectroscopy
EuroVis'11 Proceedings of the 13th Eurographics / IEEE - VGTC conference on Visualization
Drilling into complex 3D models with gimlenses
Proceedings of the 19th ACM Symposium on Virtual Reality Software and Technology
Visibility-driven PET-CT visualisation with region of interest (ROI) segmentation
The Visual Computer: International Journal of Computer Graphics
A fractal-based 2D expansion method for multi-scale volume data visualization
Journal of Visualization
The Visual Computer: International Journal of Computer Graphics
Hi-index | 0.00 |
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 \emph{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.