Three-dimensional alpha shapes
VVS '92 Proceedings of the 1992 workshop on Volume visualization
Mixing translucent polygons with volumes
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
Vicinity Shading for Enhanced Perception of Volumetric Data
Proceedings of the 14th IEEE Visualization 2003 (VIS'03)
High-Quality Multimodal Volume Rendering for Preoperative Planning of Neurosurgical Interventions
IEEE Transactions on Visualization and Computer Graphics
The Watershed Transform: Definitions, Algorithms and Parallelization Strategies
Fundamenta Informaticae
Combining silhouettes, surface, and volume rendering for surgery education and planning
EUROVIS'05 Proceedings of the Seventh Joint Eurographics / IEEE VGTC conference on Visualization
fMRI analysis on the GPU-Possibilities and challenges
Computer Methods and Programs in Biomedicine
Concurrent volume visualization of real-time fMRI
VG'10 Proceedings of the 8th IEEE/EG international conference on Volume Graphics
DTI in context: illustrating brain fiber tracts in situ
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
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Common practice in brain research and brain surgery involves the multi-modal acquisition of brain anatomy and brain activation data. These highly complex three-dimensional data have to be displayed simultaneously in order to convey spatial relationships. Unique challenges in information and interaction design have to be solved in order to keep the visualization sufficiently complete and uncluttered at the same time. The visualization method presented in this paper addresses these issues by using a hybrid combination of polygonal rendering of brain structures and direct volume rendering of activation data. Advanced rendering techniques including illustrative display styles and ambient occlusion calculations enhance the clarity of the visual output. The presented rendering pipeline produces real-time frame rates and offers a high degree of configurability. Newly designed interaction and measurement tools are provided, which enable the user to explore the data at large, but also to inspect specific features closely. We demonstrate the system in the context of a cognitive neurosciences dataset. An initial informal evaluation shows that our visualization method is deemed useful for clinical research.