Volume-Preserving Mapping and Registration for Collective Data Visualization

In order to visualize and analyze complex collective data, complicated geometric structure of each data is desired to be mapped onto a canonical domain to enable map-based visual exploration. This paper proposes a novel volume-preserving mapping and registration method which facilitates effective co...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2014-12, Vol.20 (12), p.2664-2673
Main Authors: Jiaxi Hu, Zou, Guangyu Jeff, Jing Hua
Format: Article
Language:English
Subjects:
Algorithms
Brain - anatomy & histology
Brain - physiology
Complexity theory
Computer Graphics
Data visualization
Humans
Imaging, Three-Dimensional - methods
Information analysis
Magnetic Resonance Imaging
Multimodal Imaging - methods
Positron-Emission Tomography
Shape analysis
Three-dimensional displays
Volume measurement
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Summary:In order to visualize and analyze complex collective data, complicated geometric structure of each data is desired to be mapped onto a canonical domain to enable map-based visual exploration. This paper proposes a novel volume-preserving mapping and registration method which facilitates effective collective data visualization. Given two 3-manifolds with the same topology, there exists a mapping between them to preserve each local volume element. Starting from an initial mapping, a volume restoring diffeomorphic flow is constructed as a compressible flow based on the volume forms at the manifold. Such a flow yields equality of each local volume element between the original manifold and the target at its final state. Furthermore, the salient features can be used to register the manifold to a reference template by an incompressible flow guided by a divergence-free vector field within the manifold. The process can retain the equality of local volume elements while registering the manifold to a template at the same time. An efficient and practical algorithm is also presented to generate a volume-preserving mapping and a salient feature registration on discrete 3D volumes which are represented with tetrahedral meshes embedded in 3D space. This method can be applied to comparative analysis and visualization of volumetric medical imaging data across subjects. We demonstrate an example application in multimodal neuroimaging data analysis and collective data visualization.
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2014.2346457