ACM SIGGRAPH Computer Graphics - Building Bridges - Science, the Arts & Technology
Gradient magnitude vs. feature size: comparing 2D histograms for transfer function specification
Proceedings of the 2009 Computer Graphics International Conference
Dimensionality reduction on multi-dimensional transfer functions for multi-channel volume data sets
Information Visualization - Special issue on selected papers from visualization and data analysis 2010
Segmentation and visualization of multivariate features using feature-local distributions
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Automating Transfer Function Design with Valley Cell-Based Clustering of 2D Density Plots
Computer Graphics Forum
Technical Section: Transfer function combinations
Computers and Graphics
Anatomical volume visualization with weighted distance fields
EG VCBM'10 Proceedings of the 2nd Eurographics conference on Visual Computing for Biology and Medicine
Fast volumetric data exploration with importance-based accumulated transparency modulation
VG'10 Proceedings of the 8th IEEE/EG international conference on Volume Graphics
A survey of transfer functions suitable for volume rendering
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
Instant volume visualization using maximum intensity difference accumulation
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
Visibility-driven PET-CT visualisation with region of interest (ROI) segmentation
The Visual Computer: International Journal of Computer Graphics
EuroVis '13 Proceedings of the 15th Eurographics Conference on Visualization
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The visualization of complex 3D images remains a challenge, a fact that is magnified by the difficulty to classify or segment volume data. In this paper, we introduce size-based transfer functions, which map the local scale of features to color and opacity. Features in a data set with similar or identical scalar values can be classified based on their relative size. We achieve this with the use of scale fields, which are 3D fields that represent the relative size of the local feature at each voxel. We present a mechanism for obtaining these scale fields at interactive rates, through a continuous scale-space analysis and a set of detection filters. Through a number of examples, we show that size-based transfer functions can improve classification and enhance volume rendering techniques, such as maximum intensity projection. The ability to classify objects based on local size at interactive rates proves to be a powerful method for complex data exploration.