Shi Yonghong, Shen Dinggang
Digital Medical Research Center, Fudan University, Shanghai, 200032, China.
Med Image Comput Comput Assist Interv. 2008;11(Pt 1):417-24. doi: 10.1007/978-3-540-85988-8_50.
The standard Active Shape Model (ASM) generally uses a whole population to train a single PCA-based shape model for segmentation of all testing samples. Since some testing samples can be similar to only sub-population of training samples, it will be more effective if particular shape statistics extracted from the respective sub-population can be used for guiding image segmentation. Accordingly, we design a set of hierarchical shape statistical models, including a whole-population shape model and a series of sub-population models. The whole-population shape model is used to guide the initial segmentation of the testing sample, and the initial segmentation result is then used to select a suitable sub-population shape model according to the shape similarity between the testing sample and each sub-population. By using the selected subpopulation shape model, the segmentation result can be further refined. To achieve this segmentation process, several particular steps are designed next. First, all linearly aligned samples in the whole population are used to generate a whole-population shape model. Second, an affinity propagation method is used to cluster all linearly aligned samples into several clusters, to determine the samples belonging to the same sub-populations. Third, the original samples of each sub-population are linearly aligned to their own mean shape, and the respective sub-population shape model is built using the newly aligned samples in this sub-population. By using all these three steps, we can generate hierarchical shape statistical models to guide image segmentation. Experimental results show that the proposed method can significantly improve the segmentation performance, compared to conventional ASM.
标准主动形状模型（ASM）通常使用整个样本群体来训练单个基于主成分分析（PCA）的形状模型，以对所有测试样本进行分割。由于一些测试样本可能仅与训练样本的子群体相似，因此如果能使用从各个子群体中提取的特定形状统计量来指导图像分割，将会更有效。因此，我们设计了一组分层形状统计模型，包括一个整体群体形状模型和一系列子群体模型。整体群体形状模型用于指导测试样本的初始分割，然后根据测试样本与每个子群体之间的形状相似性，使用初始分割结果来选择合适的子群体形状模型。通过使用所选的子群体形状模型，可以进一步细化分割结果。为了实现这一分割过程，接下来设计了几个特定步骤。首先，使用整个群体中所有线性对齐的样本生成一个整体群体形状模型。其次，使用亲和传播方法将所有线性对齐的样本聚类为几个簇，以确定属于同一子群体的样本。第三，将每个子群体的原始样本线性对齐到其自身的平均形状，并使用该子群体中重新对齐的样本构建各自的子群体形状模型。通过使用这三个步骤，我们可以生成分层形状统计模型来指导图像分割。实验结果表明，与传统的ASM相比，该方法可以显著提高分割性能。
