Interactive feature specification for focus+context visualization of complex simulation data
VISSYM '03 Proceedings of the symposium on Data visualisation 2003
VISUALIZATION '00 Proceedings of the 11th IEEE Visualization 2000 Conference (VIS 2000)
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Extensions of Parallel Coordinates for Interactive Exploration of Large Multi-Timepoint Data Sets
IEEE Transactions on Visualization and Computer Graphics
IEEE Transactions on Visualization and Computer Graphics
Linking Multidimensional Feature Space Cluster Visualization to Multifield Surface Extraction
IEEE Computer Graphics and Applications
A Novel Interface for Interactive Exploration of DTI Fibers
IEEE Transactions on Visualization and Computer Graphics
Exploring 3D DTI Fiber Tracts with Linked 2D Representations
IEEE Transactions on Visualization and Computer Graphics
Two-Phase Mapping for Projecting Massive Data Sets
IEEE Transactions on Visualization and Computer Graphics
Local Affine Multidimensional Projection
IEEE Transactions on Visualization and Computer Graphics
Exploring Brain Connectivity with Two-Dimensional Neural Maps
IEEE Transactions on Visualization and Computer Graphics
A tri-space visualization interface for analyzing time-varying multivariate volume data
EUROVIS'07 Proceedings of the 9th Joint Eurographics / IEEE VGTC conference on Visualization
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Fiber tracts detection is an increasingly common technology for diagnosis and also understanding of brain function. Although tools for tracing and presenting brain fibers are advanced, it is still difficult for physicians or students to explore the dataset in 3D due to their intricate topology. In this work we present a visual exploration approach for fiber tracts data aimed at supporting exploration of such data. The work employs a local, precise and fast 2D multidimensional projection technique that allows a large number of fibers to be handled simultaneously and to select groups of bundled fibers for further exploration. In this approach, a DTI feature dataset, including curvature as well as spatial features, is projected on a 2D or 3D view. By handling groups formed in this view, exploration is linked to corresponding brain fibers in object space. The link exists in both directions and fibers selected in object space are also mapped to feature space. Our approach also allows users to modify the projection, controlling and improving, if necessary, the definition of groups of fibers for small and large datasets, due to the local nature of the projection. Compared to other related work, the method presented here is faster for creating visual representations, making it possible to explore complete sets of fibers tracts up to 250K fibers, which was not possible previously. Additionally, the ability to change configuration of the feature space representation adds a high degree of flexibility to the process. © 2012 Wiley Periodicals, Inc.