Multi‐resolution multi‐object statistical shape models based on the locality assumption
Multi‐resolution multi‐object statistical shape models based on the locality assumption
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DOI：
10.1016/j.media.2017.02.003
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发表时间：
2017-05
影响因子：
10.9
通讯作者：
M. Wilms;H. Handels;J. Ehrhardt
中科院分区：
文献类型：
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作者：
M. Wilms;H. Handels;J. Ehrhardt
从先前观察到的训练形状的群体中学习的统计形状模型如今广泛用于医学图像分析以辅助分割或分类。然而，提供优选地手动分割的适当且有代表性的训练群体通常是非常劳动密集型的或者甚至是不可能的。因此，统计形状模型在实践中经常遭受高维低样本量（HDLSS）的问题，导致模型的表达能力不足，在本文中，提出了一种新的方法，用于学习有代表性的多分辨率多目标统计形状模型，从少量的训练样本，充分模拟每个单独的对象的变异性，以及它们之间的相互关系。该方法基于局部性假设，这意味着局部形状变化在遥远区域的影响有限，因此可以独立建模。通过操纵样本协方差矩阵（远距离地标之间的非零协方差被设置为零），该局部性假设被集成到标准统计形状建模框架中。为了允许多对象建模，提出了一种用于计算位于不同对象形状上的点之间的距离的方法。此外，不同级别的局部性被引入通过推导出多分辨率方案，该方案配备有将在不同级别建模的联合收割机可变性信息组合成单个形状模型的方法。在一个单一的形状模型的全局和局部的变化，这种组合表示允许使用经典的主动形状模型策略为基础的模型的图像segmentation.A广泛的评估基础上的公共数据库的247胸部X光片进行展示的建模和分割能力的方法在单和多对象HDLSS的情况下。新的方法不仅相比，经典的形状建模方法，但也有三个国家的最先进的形状建模方法专门设计，以科普HDLSS问题。实验结果表明，该方法在泛化能力和基于模型的分割精度方面明显优于其他方法。
Statistical shape models learned from a population of previously observed training shapes are nowadays widely used in medical image analysis to aid segmentation or classification. However, providing an appropriate and representative training population of preferably manual segmentations is typically either very labor-intensive or even impossible. Therefore, statistical shape models in practice frequently suffer from the high-dimension-low-sample-size (HDLSS) problem resulting in models with insufficient expressiveness.In this paper, a novel approach for learning representative multi-resolution multi-object statistical shape models from a small number of training samples that adequately model the variability of each individual object as well as their interrelations is presented. The method is based on the assumption of locality, which means that local shape variations have limited effects in distant areas and, therefore, can be modeled independently. This locality assumption is integrated into the standard statistical shape modeling framework by manipulating the sample covariance matrix (non-zero covariances between distant landmarks are set to zero). To allow for multi-object modeling, a method for computing distances between points located on different object shapes is proposed. Furthermore, different levels of locality are introduced by deriving a multi-resolution scheme, which is equipped with a method to combine variability information modeled at different levels into a single shape model. This combined representation of global and local variability in a single shape model allows the use of the classical active shape model strategy for model-based image segmentation.An extensive evaluation based on a public data base of 247 chest radiographs is performed to show the modeling and segmentation capabilities of the proposed approach in single- and multi-object HDLSS scenarios. The new approach is not only compared to the classical shape modeling method but also to three state-of-the-art shape modeling approaches specifically designed to cope with the HDLSS problem. The results show that the new approach significantly outperforms all other approaches in terms of generalization ability and model-based segmentation accuracy.