Employing 2D Projections for Fast Visual Exploration of Large Fiber Tracking Data

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 t...

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Vydáno v:Computer graphics forum Ročník 31; číslo 3pt2; s. 1075 - 1084
Hlavní autoři: Poco, Jorge, Eler, Danilo M., Paulovich, Fernando V., Minghim, Rosane
Médium: Journal Article
Jazyk:angličtina
Vydáno: Oxford, UK Blackwell Publishing Ltd 01.06.2012
Témata:
Analysis
Brain
Computer graphics
Curvature
Diagnostics
Exploration
Fibers
H.5.0 [Information Interfaces and Presentation]: General
I.3.6 [Computer Graphics]: Methodology and Techniques
I.3.8 [Computing Methodologies]: Computer Graphics-Applications
Projection
Studies
Three dimensional
Two dimensional
Visual
Visualization
ISSN:0167-7055, 1467-8659
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Shrnutí: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.
Bibliografie:istex:04E3F1A861AE3D97DAB7FC9F6A8315E3BBFF76E9
ArticleID:CGF3100
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ISSN:0167-7055
1467-8659
DOI:10.1111/j.1467-8659.2012.03100.x