Moghari Mehdi Hedjazi, Abolmaesumi Purang
Department of Electrical and Computer Engineering, Queen's University, Canada.
Med Image Comput Comput Assist Interv. 2005;8(Pt 2):197-204. doi: 10.1007/11566489_25.
We propose a novel incremental surface-based registration technique that employs the Unscented Kalman Filter (UKF) to register two different data sets. The method not only reports the variance of the registration parameters but also has significantly more accurate results in comparison to the Iterative Closest Points (ICP) algorithm. Furthermore, it is shown that the proposed incremental registration algorithm is less sensitive to the initial alignment of the data sets than the ICP algorithm. We have validated the method by registering bone surfaces extracted from a set of 3D ultrasound images to the corresponding surface points gathered from the Computed Tomography (CT) data.
我们提出了一种新颖的基于表面的增量配准技术，该技术采用无迹卡尔曼滤波器（UKF）来配准两个不同的数据集。该方法不仅报告配准参数的方差，而且与迭代最近点（ICP）算法相比，具有显著更准确的结果。此外，结果表明，所提出的增量配准算法比ICP算法对数据集的初始对齐不太敏感。我们通过将从一组3D超声图像中提取的骨表面与从计算机断层扫描（CT）数据中收集的相应表面点进行配准，验证了该方法。
