Statistical shape analysis using 3D Poisson equation--A quantitatively validated approach.
Statistical shape analysis using 3D Poisson equation--A quantitatively validated approach.
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DOI：
10.1016/j.media.2015.12.007
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发表时间：
2016-05
影响因子：
10.9
通讯作者：
Bouix S
中科院分区：
文献类型：
--
作者：
Gao Y;Bouix S
统计形状分析一直是一个重要的研究领域与应用在生物学，解剖学，神经科学，农业，古生物学等不幸的是，所提出的方法很少进行定量评估，并在最近的研究表明，当他们进行评估，存在显着的差异，在他们的输出。在这项工作中，我们集中在两个人口的形状之间找到一致的变形位置的问题。我们提出了一个新的形状分析算法沿着的框架，以执行其性能的定量评估。具体地说，该算法通过求解两个泊松方程构造一个符号泊松映射（SPoM），并对SPoM进行统计分析.该方法在合成形状上进行了定量评价，并应用于脑结构中的真实的形状数据集。
Statistical shape analysis has been an important area of research with applications in biology, anatomy, neuroscience, agriculture, paleontology, etc. Unfortunately, the proposed methods are rarely quantitatively evaluated, and as shown in recent studies, when they are evaluated, significant discrepancies exist in their outputs. In this work, we concentrate on the problem of finding the consistent location of deformation between two population of shapes. We propose a new shape analysis algorithm along with a framework to perform a quantitative evaluation of its performance. Specifically, the algorithm constructs a Signed Poisson Map (SPoM) by solving two Poisson equations on the volumetric shapes of arbitrary topology, and statistical analysis is then carried out on the SPoMs. The method is quantitatively evaluated on synthetic shapes and applied on real shape data sets in brain structures.