Background: Fiducial marker‐based image‐to‐patient registration is the most common way in image‐guided neurosurgery, which is labour‐intensive, time consuming, invasive and error prone. Methods: We proposed a method of facial landmark‐guided surface matching for image‐to‐patient registration using an RGB‐D camera. Five facial landmarks are localized from preoperative magnetic resonance (MR) images using deep learning and RGB image using Adaboost with multi‐scale block local binary patterns, respectively. The registration of two facial surface point clouds derived from MR images and RGB‐D data is initialized by aligning these five landmarks and further refined by weighted iterative closest point algorithm. Results: Phantom experiment results show the target registration error is less than 3 mm when the distance from the camera to the phantom is less than 1000 mm. The registration takes less than 10 s. Conclusions: The proposed method is comparable to the state‐of‐the‐arts in terms of the accuracy yet more time‐saving and non‐invasive. |
