Parameterization-invariant shape statistics and probabilistic classification of anatomical surfaces.
Parameterization-invariant shape statistics and probabilistic classification of anatomical surfaces.
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参数化不变的形状统计和解剖表面的概率分类。
DOI：
10.1007/978-3-642-22092-0_13
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
Srivastava,Anuj
中科院分区：
文献类型：
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作者：
Kurtek,Sebastian;Klassen,Eric;Ding,Zhaohua;Avison,MalcolmJ;Srivastava,Anuj
我们认为，计算形状统计和分类的三维解剖结构（连续的，参数化的表面）的任务。这需要一个黎曼度量，允许重新参数化的表面等距，和计算测地线。这允许计算曲面的Karcher均值和协方差，这涉及曲面的最佳重新参数化，并导致曲面上几何特征的上级对齐。由此产生的均值和协方差更好地代表了原始数据，并导致简约的形状模型。这两个矩指定形状类别的正态概率模型，用于将测试形状分类为控制组和疾病组。我们通过改进的随机抽样和更高的分类性能证明了该模型的成功。我们研究大脑结构和目前的分类结果注意缺陷多动障碍。使用数据的均值和协方差结构，我们能够达到88%的分类率。
We consider the task of computing shape statistics and classification of 3D anatomical structures (as continuous, parameterized surfaces). This requires a Riemannian metric that allows re-parameterizations of surfaces by isometries, and computations of geodesics. This allows computing Karcher means and covariances of surfaces, which involves optimal re-parameterizations of surfaces and results in a superior alignment of geometric features across surfaces. The resulting means and covariances are better representatives of the original data and lead to parsimonious shape models. These two moments specify a normal probability model on shape classes, which are used for classifying test shapes into control and disease groups. We demonstrate the success of this model through improved random sampling and a higher classification performance. We study brain structures and present classification results for Attention Deficit Hyperactivity Disorder. Using the mean and covariance structure of the data, we are able to attain an 88% classification rate.