Taquet Maxime, Macq Benoît, Warfield Simon K
ICTEAM Institute, Université Catholique de Louvain, Louvain-La-Neuve, Belgium.
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):590-7. doi: 10.1007/978-3-642-23629-7_72.
Log-euclidean polyaffine transforms have recently been introduced to characterize the local affine behavior of the deformation in principal anatomical structures. The elegant mathematical framework makes them a powerful tool for image registration. However, their application is limited to large structures since they require the pre-definition of affine regions. This paper extends the polyaffine registration to adaptively fit a log-euclidean polyaffine transform that captures deformations at smaller scales. The approach is based on the sparse selection of matching points in the images and the formulation of the problem as an expectation maximization iterative closest point problem. The efficiency of the algorithm is shown through experiments on inter-subject registration of brain MRI between a healthy subject and patients with multiple sclerosis.
对数欧几里得多仿射变换最近被引入，用于表征主要解剖结构中变形的局部仿射行为。其优雅的数学框架使其成为图像配准的强大工具。然而，它们的应用仅限于大型结构，因为它们需要预先定义仿射区域。本文扩展了多仿射配准，以自适应地拟合一个对数欧几里得多仿射变换，该变换能够捕捉较小尺度上的变形。该方法基于图像中匹配点的稀疏选择，并将问题表述为期望最大化迭代最近点问题。通过对健康受试者与多发性硬化症患者之间的脑磁共振成像进行受试者间配准的实验，展示了该算法的效率。
