A New Method in Facial Registration in Clinics Based on Structure Light Images
This addresses the need for efficient and accurate image fusion in neurosurgery, though it appears incremental as it builds on existing techniques like dlib and ICP.
The paper tackled the problem of invalid facial depth image registration in neurosurgery by developing a method using dlib for key point recognition and ICP for coarse and fine registration, achieving an RMSE as low as 0.995913 mm and reduced time compared to traditional methods.
Background and Objective: In neurosurgery, fusing clinical images and depth images that can improve the information and details is beneficial to surgery. We found that the registration of face depth images was invalid frequently using existing methods. To abundant traditional image methods with depth information, a method in registering with depth images and traditional clinical images was investigated. Methods: We used the dlib library, a C++ library that could be used in face recognition, and recognized the key points on faces from the structure light camera and CT image. The two key point clouds were registered for coarse registration by the ICP method. Fine registration was finished after coarse registration by the ICP method. Results: RMSE after coarse and fine registration is as low as 0.995913 mm. Compared with traditional methods, it also takes less time. Conclusions: The new method successfully registered the facial depth image from structure light images and CT with a low error, and that would be promising and efficient in clinical application of neurosurgery.