Lüthi Marcel, Albrecht Thomas, Vetter Thomas
Computer Science Department, University of Basel, Switzerland.
Med Image Comput Comput Assist Interv. 2009;12(Pt 2):1-8. doi: 10.1007/978-3-642-04271-3_1.
Statistical shape models have gained widespread use in medical image analysis. In order for such models to be statistically meaningful, a large number of data sets have to be included. The number of available data sets is usually limited and often the data is corrupted by imaging artifacts or missing information. We propose a method for building a statistical shape model from such "lousy" data sets. The method works by identifying the corrupted parts of a shape as statistical outliers and excluding these parts from the model. Only the parts of a shape that were identified as outliers are discarded, while all the intact parts are included in the model. The model building is then performed using the EM algorithm for probabilistic principal component analysis, which allows for a principled way to handle missing data. Our experiments on 2D synthetic and real 3D medical data sets confirm the feasibility of the approach. We show that it yields superior models compared to approaches using robust statistics, which only downweight the influence of outliers.
统计形状模型在医学图像分析中已得到广泛应用。为使此类模型具有统计意义，必须纳入大量数据集。可用数据集的数量通常有限，而且数据常常因成像伪影或信息缺失而受到破坏。我们提出一种从这类“糟糕”数据集中构建统计形状模型的方法。该方法通过将形状的受损部分识别为统计异常值并将这些部分排除在模型之外来发挥作用。只有被识别为异常值的形状部分会被舍弃，而所有完整部分都会包含在模型中。然后使用用于概率主成分分析的期望最大化（EM）算法来执行模型构建，这为处理缺失数据提供了一种有原则的方法。我们在二维合成和真实三维医学数据集上的实验证实了该方法的可行性。我们表明，与仅降低异常值影响的稳健统计方法相比，该方法能产生更优的模型。
