Multi-scale Heat Kernel based Volumetric Morphology Signature.
Multi-scale Heat Kernel based Volumetric Morphology Signature.
复制标题
基于多尺度热核的体积形态特征。
DOI：
10.1007/978-3-319-24574-4_90
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Wang,Yalin
中科院分区：
文献类型：
--
作者：
Wang,Gang;Wang,Yalin
在这里，我们介绍了一种新的基于多尺度热核的区域形状统计方法，可以提高结构分析的统计能力。这种分析的机制是由图谱和热核理论驱动的，以捕获所构建的四面体网格中的体积几何信息。为了捕捉深刻的体积变化，我们首先使用体积Laplace-Beltrami算子，通过计算四面体网格中的流线来确定两个边界表面之间的点对对应关系。其次，我们提出了一种多尺度的体积形态签名来描述点对之间的随机游走的转移概率，这反映了固有的几何特性。第三，应用点分布模型来降低体积形态特征的维数并生成内部结构特征。多尺度和基于物理的内部结构特征可以带来比其他传统的体积形态分析方法更强的统计能力。为了验证我们的方法，我们应用支持向量机分类合成数据和脑MR图像。在我们的实验中，所提出的工作优于FreeSurfer厚度功能在阿尔茨海默病患者和正常对照主题分类分析。
Here we introduce a novel multi-scale heat kernel based regional shape statistical approach that may improve statistical power on the structural analysis. The mechanism of this analysis is driven by the graph spectrum and the heat kernel theory, to capture the volumetric geometry information in the constructed tetrahedral mesh. In order to capture profound volumetric changes, we first use the volumetric Laplace-Beltrami operator to determine the point pair correspondence between two boundary surfaces by computing the streamline in the tetrahedral mesh. Secondly, we propose a multi-scale volumetric morphology signature to describe the transition probability by random walk between the point pairs, which reflects the inherent geometric characteristics. Thirdly, a point distribution model is applied to reduce the dimensionality of the volumetric morphology signatures and generate the internal structure features. The multi-scale and physics based internal structure features may bring stronger statistical power than other traditional methods for volumetric morphology analysis. To validate our method, we apply support vector machine to classify synthetic data and brain MR images. In our experiments, the proposed work outperformed FreeSurfer thickness features in Alzheimer’s disease patient and normal control subject classification analysis.