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首页> 外文期刊>Visualization and Computer Graphics, IEEE Transactions on >Volume-Preserving Mapping and Registration for Collective Data Visualization
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Volume-Preserving Mapping and Registration for Collective Data Visualization

机译：批量保存映射和注册以实现集体数据可视化

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摘要

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.
机译：为了可视化和分析复杂的集体数据，需要将每个数据的复杂几何结构映射到规范域上，以实现基于地图的视觉探索。本文提出了一种新颖的体积保存映射和配准方法，该方法有助于有效地进行集体数据可视化。给定两个具有相同拓扑的3个流形，它们之间存在一个映射以保留每个本地卷元素。从初始映射开始，基于歧管处的体积形式，将体积恢复微分流构造为可压缩流。这样的流动使原始歧管和目标在其最终状态之间的每个局部体积元素相等。此外，显着特征可用于通过歧管内无散度矢量场引导的不可压缩流将歧管对准参考模板。在将歧管同时注册到模板时，该过程可以保留局部体积元素的相等性。还提出了一种高效实用的算法，以在离散3D体积上生成体积保留映射和显着特征配准，这些3D体积用嵌入3D空间中的四面体网格表示。该方法可以应用于对象之间的体积医学成像数据的比较分析和可视化。我们演示了在多模式神经影像数据分析和集体数据可视化中的示例应用。

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