College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China; Beijing Key Laboratory of Digital Preservation and Virtual Reality for Cultural Heritage, Beijing, 100875, China.
College of Information Science and Technology, Beijing Normal University, Beijing, 100875, China; Beijing Key Laboratory of Digital Preservation and Virtual Reality for Cultural Heritage, Beijing, 100875, China.
Comput Biol Med. 2017 Nov 1;90:33-49. doi: 10.1016/j.compbiomed.2017.08.023. Epub 2017 Sep 7.
Previous studies have used principal component analysis (PCA) to investigate the craniofacial relationship, as well as sex determination using facial factors. However, few studies have investigated the extent to which the choice of principal components (PCs) affects the analysis of craniofacial relationship and sexual dimorphism. In this paper, we propose a PCA-based method for visual and quantitative analysis, using 140 samples of 3D heads (70 male and 70 female), produced from computed tomography (CT) images. There are two parts to the method. First, skull and facial landmarks are manually marked to guide the model's registration so that dense corresponding vertices occupy the same relative position in every sample. Statistical shape spaces of the skull and face in dense corresponding vertices are constructed using PCA. Variations in these vertices, captured in every principal component (PC), are visualized to observe shape variability. The correlations of skull- and face-based PC scores are analysed, and linear regression is used to fit the craniofacial relationship. We compute the PC coefficients of a face based on this craniofacial relationship and the PC scores of a skull, and apply the coefficients to estimate a 3D face for the skull. To evaluate the accuracy of the computed craniofacial relationship, the mean and standard deviation of every vertex between the two models are computed, where these models are reconstructed using real PC scores and coefficients. Second, each PC in facial space is analysed for sex determination, for which support vector machines (SVMs) are used. We examined the correlation between PCs and sex, and explored the extent to which the choice of PCs affects the expression of sexual dimorphism. Our results suggest that skull- and face-based PCs can be used to describe the craniofacial relationship and that the accuracy of the method can be improved by using an increased number of face-based PCs. The results show that the accuracy of the sex classification is related to the choice of PCs. The highest sex classification rate is 91.43% using our method.
先前的研究已经使用主成分分析（PCA）来研究颅面关系，以及使用面部因素进行性别确定。然而，很少有研究探讨选择主成分（PC）对颅面关系和性别二态性分析的影响程度。在本文中，我们提出了一种基于 PCA 的方法，用于视觉和定量分析，使用 140 个 3D 头部样本（70 名男性和 70 名女性），由计算机断层扫描（CT）图像生成。该方法有两个部分。首先，手动标记颅骨和面部标志，以指导模型的配准，从而使密集对应的顶点在每个样本中占据相同的相对位置。使用 PCA 构建颅骨和面部密集对应顶点的统计形状空间。在每个主成分（PC）中捕获的这些顶点的变化被可视化，以观察形状可变性。分析颅骨和面部 PC 得分的相关性，并使用线性回归拟合颅面关系。我们根据该颅面关系和颅骨的 PC 得分计算一个基于面部的 PC 系数，并将该系数应用于估计颅骨的 3D 面部。为了评估计算出的颅面关系的准确性，计算两个模型之间的每个顶点的平均值和标准偏差，其中使用真实的 PC 得分和系数重建这些模型。其次，对面部空间中的每个 PC 进行性别确定分析，为此使用支持向量机（SVM）。我们检查了 PC 与性别的相关性，并探讨了选择 PC 对性别二态性表达的影响程度。我们的结果表明，颅骨和面部 PC 可用于描述颅面关系，并且通过使用更多的基于面部的 PC 可以提高方法的准确性。结果表明，性别分类的准确性与 PC 的选择有关。使用我们的方法，性别分类的最高准确率为 91.43%。
