Computer Engineering Department, Middle East Technical University, Turkey.
Computer & Information Science Department, University of Pennsylvania, USA.
Med Image Anal. 2015 Jul;23(1):15-27. doi: 10.1016/j.media.2015.03.005. Epub 2015 Apr 8.
We present an algorithm for volumetric registration of 3D solid shapes. In comparison to previous work on image based registration, our technique achieves higher efficiency by leveraging a template tetrahedral mesh. In contrast to point- and surface-based registration techniques, our method better captures volumetric nature of the data, such as bone thickness. We apply our algorithm to study pathological skull deformities caused by a particular condition, i.e., craniosynostosis. The input to our system is a pair of volumetric 3D shapes: a tetrahedral mesh and a voxelized object represented by a set of voxel cells segmented from computed tomography (CT) scans. Our general framework first performs a global registration and then launches a novel elastic registration process that uses as much volumetric information as possible while deforming the generic template tetrahedral mesh of a healthy human skull towards the underlying geometry of the voxel cells. Both data are high-resolution and differ by large non-rigid deformations. Our fully-automatic solution is fast and accurate, as compared with the state of the arts from the reconstruction and medical image registration fields. We use the resulting registration to match the ground-truth surfaces extracted from the medical data as well as to quantify the severity of the anatomical deformity.
我们提出了一种用于 3D 实体形状体积配准的算法。与基于图像的配准的先前工作相比，我们的技术通过利用模板四面体网格实现了更高的效率。与基于点和基于表面的配准技术相比，我们的方法更好地捕获了数据的体积性质，例如骨厚度。我们将我们的算法应用于研究由特定条件引起的病理性颅骨畸形，即颅缝早闭。我们系统的输入是一对体积 3D 形状：一个四面体网格和一个由从计算机断层扫描 (CT) 扫描中分割的一组体素细胞表示的体素化对象。我们的总体框架首先执行全局配准，然后启动一个新的弹性配准过程，该过程在变形通用模板健康人类颅骨的四面体网格以适应体素细胞的底层几何形状时，尽可能多地使用体积信息。这两个数据都是高分辨率的，并且具有很大的非刚体变形。与重建和医学图像配准领域的最新技术相比，我们的全自动解决方案快速而准确。我们使用所得的配准来匹配从医学数据中提取的真实表面，并量化解剖畸形的严重程度。
