Semantically steered visual analysis of highly detailed morphometric shape spaces
Semantically steered visual analysis of highly detailed morphometric shape spaces
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DOI：
10.1109/biovis.2011.6094060
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发表时间：
2011-10
期刊：
影响因子：
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通讯作者：
M. Hermann;A. C. Schunke;R. Klein
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文献类型：
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作者：
M. Hermann;A. C. Schunke;R. Klein
三维形状分析的常用技术是使用统计变形模型（SDM）来描述形状变化。与对体数据使用稀疏地标数据相比，该SDM基于输入形状的密集配准。为了在生物形态测量学的背景下对形状空间进行有价值的探索，我们确定了视觉调查的两个突出目标。第一个目标是检测在解剖学上不同的个体群体之间可能存在的形状差异。二是整合和开发形状相关领域的专家知识。为了实现第一个目标，我们提倡将降维方法与在用户指定分类上定义的参数化相结合。这个想法已经成功地应用于数据驱动的反射模型中，并且在生物形态计量学的背景下也被证明是有价值的，因为它允许直观地探索形状变化。第二个目标可以通过适当的加权线性分析来实现，该分析可以更好地近似用户定义的感兴趣区域的局部邻域的形状变化。将这些方法应用于啮齿类动物下颌骨的真实生物数据集，并与MPI进化生物学合作验证。为此，我们提供了基于自定义GPU光线投射器的形状空间的交互式动态可视化。我们实现的一个特殊功能是，它直接在卷的密集配准上构建SDM，因此不依赖于特定的非刚性配准方法。
A common technique in 3D shape analysis is to describe shape variability using a statistical deformation model (SDM). In contrast to the use of sparse landmark data for volume data this SDM is based on dense registrations of the input shapes. For a valuable exploration of the shape space in the setting of biological morphometrics we identified two prominent objectives for visual investigation. The first objective is to detect possible shape variations between anatomically different groups of individuals. The second is to integrate and exploit expert knowledge about relevant regions on the shapes. To meet the first objective, we advocate the use of dimensionality reduction methods combined with a parameterization defined on user specified classifications. This idea was already successfully applied in data-driven reflectance models and also turns out to be valuable in the context of biological morphometry, as it allows for intuitive exploration of shape variations. The second objective can be achieved by an appropriate weighted linear analysis which delivers a better approximation of shape variations in local neighbourhoods of a user defined region of interest. The methods were applied to real-world biological datasets of rodent mandibles and validated in cooperation with the MPI for Evolutionary Biology. For this purpose, we provide an interactive dynamic visualization of the shape space based on a custom GPU raycaster. A special feature of our implementation is that it builds the SDM directly on dense registrations of the volumes and does thereby not rely on a specific non-rigid registration method.