You are in:Home/Publications/Facial landmark-guided surface matching for image-to-patient registration with an RGB-D camera.

Ass. Lect. Mohamed Hosny Ali Mohamed Elnogomy :: Publications:

Title:
Facial landmark-guided surface matching for image-to-patient registration with an RGB-D camera.
Authors: Yixian Su; Yu Sun; Mohamed Hosny; Wenpeng Gao; Yili Fu
Year: 2022
Keywords: deep learning; facial landmark; image‐to‐patient registration; RGB‐D camera; surface matching
Journal: International Journal of Medical Robotics and Computer Assisted Surgery
Volume: Not Available
Issue: Not Available
Pages: e2373
Publisher: Wiley Online Library
Local/International: International
Paper Link:
Full paper Mohamed Hosny _Robotics Computer Surgery - 2022 - Su - Facial landmark‐guided surface matching for image‐to‐patient registration with an.pdf
Supplementary materials Not Available
Abstract:

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.

Google ScholarAcdemia.eduResearch GateLinkedinFacebookTwitterGoogle PlusYoutubeWordpressInstagramMendeleyZoteroEvernoteORCIDScopus