Babalola K O, Cootes T F, Patenaude B, Rao A, Jenkinson M
Division of Imaging Science and Biomedical Engineering, University of Manchester, M13 9PT, UK.
Med Image Comput Comput Assist Interv. 2006;9(Pt 1):142-50. doi: 10.1007/11866565_18.
A variety of different methods of finding correspondences across sets of images to build statistical shape models have been proposed, each of which is likely to result in a different model. When dealing with large datasets (particularly in 3D), it is difficult to evaluate the quality of the resulting models. However, if the different methods are successfully modelling the true underlying shape variation, the resulting models should be similar. If two different techniques lead to similar models, it suggests that they are indeed approximating the true shape change. In this paper we explore a method of comparing statistical shape models by evaluating the Bhattacharya overlap between their implied shape distributions. We apply the technique to investigate the similarity of three models of the same 3D dataset constructed using different methods.
已经提出了多种不同的方法来在图像集之间寻找对应关系以构建统计形状模型，每种方法都可能产生不同的模型。在处理大型数据集时（尤其是在3D中），很难评估所得模型的质量。然而，如果不同的方法成功地对真实的潜在形状变化进行建模，那么所得模型应该是相似的。如果两种不同的技术导致相似的模型，这表明它们确实在逼近真实的形状变化。在本文中，我们探索了一种通过评估其隐含形状分布之间的 Bhattacharya 重叠来比较统计形状模型的方法。我们应用该技术来研究使用不同方法构建的同一3D数据集的三个模型的相似性。
