Print Email Facebook Twitter Performing patient alignment utilizing point-cloud surface registration techniques in HoloNav Title Performing patient alignment utilizing point-cloud surface registration techniques in HoloNav Author Weyns, Maarten (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Thabit, Abdullah (mentor) Marroquim, Ricardo (mentor) Hanjalic, A. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2022-06-22 Abstract In order to be able to use the Microsoft HoloLens for surgical navigation purposes, performing good patient alignment is of utmost importance. This paper will discuss how this patient alignment can be done using different point cloud registration algorithms.A lot of research is being conducted on point cloud registration algorithms. However, most research assumes that the point clouds to be aligned are almost identical, while patient alignment aims at aligning a very detailed pre-operative scan with a very sparse point cloud obtained by the surgeon using an optical marker.In order to get around this problem, the HoloLens' depth camera is used to obtain a detailed point cloud so that registration algorithms can be used. Then the performance of different point cloud registration algorithms is tested on this depth sensor data to see whether using the HoloLens' depth sensor is a viable option for patient alignment.From the results, it appears that algorithmic approaches for performing patient alignment are feasible, but the performance of these algorithms is very dependent on the quality of the input data. Subject Point Cloud RegistrationPatient AlignmentSurgical Navigation To reference this document use: http://resolver.tudelft.nl/uuid:092a18dd-8d3f-4f68-b8b7-e381e7e02702 Part of collection Student theses Document type bachelor thesis Rights © 2022 Maarten Weyns Files PDF Performing_patient_alignm ... oloNav.pdf 2.64 MB Close viewer /islandora/object/uuid:092a18dd-8d3f-4f68-b8b7-e381e7e02702/datastream/OBJ/view