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
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
Multi-dimensional reduction and transfer function design using parallel coordinates
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
Visibility-driven PET-CT visualisation with region of interest (ROI) segmentation
The Visual Computer: International Journal of Computer Graphics
Visualization and analysis of 3D time-varying simulations with time lines
Journal of Visual Languages and Computing
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Visualization of volumetric data faces the difficult task of finding effective parameters for the transfer functions. Those parameters can determine the effectiveness and accuracy of the visualization. Frequently, volumetric data includes multiple structures and features that need to be differentiated. However, if those features have the same intensity and gradient values, existing transferfunctions are limited at effectively illustrating those similar features with different rendering properties. We introduce texture-based transfer functions for direct volume rendering. In our approach, the voxel’s resulting opacity and color are based on local textural properties rather than individual intensity values. For example, if the intensity values of the vessels are similar to those on the boundary of the lungs, our texture-based transfer function will analyze the textural properties in those regions and color them differently even though they have the same intensity values in the volume. The use of texture-based transfer functions has several benefits. First, structures and features with the same intensity and gradient values can be automatically visualized with different rendering properties. Second, segmentation or prior knowledge of the specific features within the volume is not required for classifying these features differently. Third, textural metrics can be combined and/or maximized to capture and better differentiate similar structures. We demonstrate our texture-based transfer function for direct volume rendering with synthetic and real-world medical data to show the strength of our technique.