Styner Martin A, Rajamani Kumar T, Nolte Lutz-Peter, Zsemlye Gabriel, Székely Gábor, Taylor Chris J, Davies Rhodri H
M.E. Müller Institute for Surgical Technology and Biomechanics, University of Bern, 3001 Bern, Switzerland.
Inf Process Med Imaging. 2003 Jul;18:63-75. doi: 10.1007/978-3-540-45087-0_6.
The correspondence problem is of high relevance in the construction and use of statistical models. Statistical models are used for a variety of medical application, e.g. segmentation, registration and shape analysis. In this paper, we present comparative studies in three anatomical structures of four different correspondence establishing methods. The goal in all of the presented studies is a model-based application. We have analyzed both the direct correspondence via manually selected landmarks as well as the properties of the model implied by the correspondences, in regard to compactness, generalization and specificity. The studied methods include a manually initialized subdivision surface (MSS) method and three automatic methods that optimize the object parameterization: SPHARM, MDL and the covariance determinant (DetCov) method. In all studies, DetCov and MDL showed very similar results. The model properties of DetCov and MDL were better than SPHARM and MSS. The results suggest that for modeling purposes the best of the studied correspondence method are MDL and DetCov.
对应问题在统计模型的构建和使用中具有高度相关性。统计模型用于多种医学应用，例如分割、配准和形状分析。在本文中，我们对四种不同对应建立方法在三种解剖结构上进行了比较研究。所有呈现研究的目标都是基于模型的应用。我们分析了通过手动选择地标进行的直接对应以及对应所隐含的模型在紧凑性、泛化性和特异性方面的属性。所研究的方法包括手动初始化细分曲面（MSS）方法以及三种优化对象参数化的自动方法：SPHARM、MDL和协方差行列式（DetCov）方法。在所有研究中，DetCov和MDL显示出非常相似的结果。DetCov和MDL的模型属性优于SPHARM和MSS。结果表明，出于建模目的，所研究的对应方法中最佳的是MDL和DetCov。
