Iglesias J Eugenio, de Bruijne Marleen, Loog Marco, Lauze François, Nielsen Mads
Department of Computer Science, University of Copenhagen, Denmark.
Med Image Comput Comput Assist Interv. 2007;10(Pt 2):178-85. doi: 10.1007/978-3-540-75759-7_22.
Principal Component Analysis (PCA) has been widely used for dimensionality reduction in shape and appearance modeling. There have been several attempts of making PCA robust against outliers. However, there are cases in which a small subset of samples may appear as outliers and still correspond to plausible data. The example of shapes corresponding to fractures when building a vertebra shape model is addressed in this study. In this case, the modeling of "outliers" is important, and it might be desirable not only not to disregard them, but even to enhance their importance. A variation on PCA that deals naturally with the importance of outliers is presented in this paper. The technique is utilized for building a shape model of a vertebra, aiming at segmenting the spine out of lateral X-ray images. The results show that the algorithm can implement both an outlier-enhancing and a robust PCA. The former improves the segmentation performance in fractured vertebrae, while the latter does so in the unfractured ones.
主成分分析（PCA）已被广泛应用于形状和外观建模中的降维。已经有几种使PCA对异常值具有鲁棒性的尝试。然而，存在这样的情况，即一小部分样本可能表现为异常值，但仍然对应于合理的数据。本研究讨论了在构建椎骨形状模型时与骨折相对应的形状示例。在这种情况下，“异常值”的建模很重要，不仅可能希望不要忽略它们，甚至可能希望增强它们的重要性。本文提出了一种自然处理异常值重要性的PCA变体。该技术用于构建椎骨的形状模型，旨在从侧面X射线图像中分割出脊柱。结果表明，该算法可以实现异常值增强和鲁棒的PCA。前者提高了骨折椎骨的分割性能，而后者提高了未骨折椎骨的分割性能。
