Background: Compared with the traditional point-based registration in the image-guided neurosurgery system, surface-based registration is preferable because it does not use fiducial markers before image scanning and does not require image acquisition dedicated for navigation purposes. However, most existing surface-based registration methods must include a manual step for coarse registration, which increases the registration time and elicits some inconvenience and uncertainty.

Methods: A new automatic surface-based registration method is proposed, which applies 3D surface feature description and matching algorithm to obtain point correspondences for coarse registration and uses the iterative closest point (ICP) algorithm in the last step to obtain an image-to-patient registration.

Results: Both phantom and clinical data were used to execute automatic registrations and target registration error (TRE) calculated to verify the practicality and robustness of the proposed method. In phantom experiments, the registration accuracy was stable across different downsampling resolutions (18–26 mm) and different support radii (2–6 mm). In clinical experiments, the mean TREs of two patients by registering full head surfaces were 1.30 mm and 1.85 mm.

Conclusion: This study introduced a new robust automatic surface-based registration method based on 3D feature matching. The method achieved sufficient registration accuracy with different real-world surface regions in phantom and clinical experiments.

背景：与基于图像的神经外科手术系统中传统的基于点的配准相比，基于表面的配准是更可取的，因为它在图像扫描之前不使用基准标记，并且不需要用于导航目的的图像获取。但是，大多数现有的基于表面的套准方法必须包括手动进行粗套准的步骤，这会增加套准时间并带来一些不便和不确定性。

方法：提出了一种新的基于表面的自动配准方法，该方法应用3D表面特征描述和匹配算法来获取粗配准的点对应关系，并在最后一步中使用迭代最近点（ICP）算法来获得图像到目标的配准。病人登记。

结果：幻像和临床数据均用于执行自动配准，并计算了目标配准误差（TRE），以验证该方法的实用性和鲁棒性。在幻像实验中，套准精度在不同的下采样分辨率（18–26 mm）和不同的支持半径（2–6 mm）下均稳定。在临床实验中，通过记录全头表面，两名患者的平均TRE为1.30 mm和1.85 mm。

结论：本研究介绍了一种新的基于3D特征匹配的鲁棒自动基于表面的配准方法。在幻像和临床实验中，该方法在不同的实际表面区域上都获得了足够的配准精度。