Datar Manasi, Lyu Ilwoo, Kim SunHyung, Cates Joshua, Styner Martin A, Whitaker Ross
Scientific Computing and Imaging Institute, University of Utah, USA.
Department of Computer Science, University of North Carolina at Chapel Hill, USA.
Med Image Comput Comput Assist Interv. 2013;16(Pt 2):19-26. doi: 10.1007/978-3-642-40763-5_3.
Establishing correspondence points across a set of biomedical shapes is an important technology for a variety of applications that rely on statistical analysis of individual subjects and populations. The inherent complexity (e.g. cortical surface shapes) and variability (e.g. cardiac chambers) evident in many biomedical shapes introduce significant challenges in finding a useful set of dense correspondences. Application specific strategies, such as registration of simplified (e.g. inflated or smoothed) surfaces or relying on manually placed landmarks, provide some improvement but suffer from limitations including increased computational complexity and ambiguity in landmark placement. This paper proposes a method for dense point correspondence on shape ensembles using geodesic distances to a priori landmarks as features. A novel set of numerical techniques for fast computation of geodesic distances to point sets is used to extract these features. The proposed method minimizes the ensemble entropy based on these features, resulting in isometry invariant correspondences in a very general, flexible framework.
在一组生物医学形状之间建立对应点,对于依赖个体受试者和群体统计分析的各种应用来说是一项重要技术。许多生物医学形状中明显存在的内在复杂性(如皮质表面形状)和变异性(如心腔),给寻找一组有用的密集对应关系带来了重大挑战。特定应用策略,如简化(如膨胀或平滑)表面的配准或依赖手动放置的地标,虽有一定改进,但存在局限性,包括计算复杂性增加和地标放置的模糊性。本文提出一种方法,利用到先验地标的测地距离作为特征,在形状集合上进行密集点对应。一套用于快速计算到点集的测地距离的新颖数值技术被用于提取这些特征。所提出的方法基于这些特征最小化集合熵,从而在一个非常通用、灵活的框架中产生等距不变对应关系。