Lam Ka Chun, Gu Xianfeng, Lui Lok Ming
Med Image Comput Comput Assist Interv. 2014;17(Pt 3):25-32. doi: 10.1007/978-3-319-10443-0_4.
This paper presents a novel algorithm to obtain landmark-based genus-1 surface registration via a special class of quasi-conformal maps called the Teichmüller maps. Registering shapes with important features is an important process in medical imaging. However, it is challenging to obtain a unique and bijective genus-1surface matching that satisfies the prescribed landmark constraints. In addition, as suggested by [11], conformal transformation provides the most natural way to describe the deformation or growth of anatomical structures. This motivates us to look for the unique mapping between genus-1 surfaces that matches the features while minimizing the maximal conformality distortion. Existence and uniqueness of such optimal diffeomorphism is theoretically guaranteed and is called the Teichmüller extremal mapping. In this work, we propose an iterative algorithm, called the Quasi-conformal (QC) iteration, to find the Teichmüller extremal mapping between the covering spaces of genus-1 surfaces. By representing the set of diffeomorphisms using Beltrami coefficients (BCs), we look for an optimal BC which corresponds to our desired diffeomorphism that matches prescribed features and satisfies the periodic boundary condition on the covering space. Numerical experiments show that our proposed algorithm is efficient and stable for registering genus-1 surfaces even with large amount of landmarks. We have also applied the algorithm on registering vertebral bones with prescribed feature curves, which demonstrates the usefulness of the proposed algorithm.
本文提出了一种新颖的算法,通过一类特殊的拟共形映射(称为泰希米勒映射)来实现基于地标的亏格为1的曲面配准。在医学成像中,对具有重要特征的形状进行配准是一个重要过程。然而,要获得满足规定地标约束的唯一且双射的亏格为1的曲面匹配具有挑战性。此外,如文献[11]所指出的,共形变换为描述解剖结构的变形或生长提供了最自然的方式。这促使我们寻找亏格为1的曲面之间的唯一映射,该映射在匹配特征的同时最小化最大共形失真。这种最优微分同胚的存在性和唯一性在理论上是有保证的,并且被称为泰希米勒极值映射。在这项工作中,我们提出了一种迭代算法,称为拟共形(QC)迭代,以找到亏格为1的曲面覆盖空间之间的泰希米勒极值映射。通过使用贝尔特拉米系数(BCs)来表示微分同胚集,我们寻找一个最优的BC,它对应于我们期望的微分同胚,该微分同胚匹配规定的特征并满足覆盖空间上的周期边界条件。数值实验表明,我们提出的算法对于配准亏格为1的曲面即使有大量地标也是高效且稳定的。我们还将该算法应用于对具有规定特征曲线的椎骨进行配准,这证明了所提出算法的实用性。