Bijaya Raj Basnet1, Abeer Alsadoon1, Chandana Withana2, Anand Deva3, Manoranjan Paul1. 1. School of Computing and Mathematics, Charles Sturt University, Sydney Campus, Sydney, Australia. 2. School of Computing and Mathematics, Charles Sturt University, Sydney Campus, Sydney, Australia. cwithana@studygroup.com. 3. Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia.
Abstract
PURPOSE: Augmented reality-based constructive jaw surgery has been facing various limitations such as noise in real-time images, the navigational error of implants and jaw, image overlay error, and occlusion handling which have limited the implementation of augmented reality (AR) in corrective jaw surgery. This research aimed to improve the navigational accuracy, through noise and occlusion removal, during positioning of an implant in relation to the jaw bone to be cut or drilled. METHOD: The proposed system consists of a weighting-based de-noising filter and depth mapping-based occlusion removal for removing any occluded object such as surgical tools, the surgeon's body parts, and blood. RESULTS: The maxillary (upper jaw) and mandibular (lower jaw) jaw bone sample results show that the proposed method can achieve the image overlay error (video accuracy) of 0.23~0.35 mm and processing time of 8-12 frames per second compared to 0.35~0.45 mm and 6-11 frames per second by the existing best system. CONCLUSION: The proposed system concentrates on removing the noise from the real-time video frame and the occlusion. Thus, the acceptable range of accuracy and the processing time are provided by this study for surgeons for carrying out a smooth surgical flow.
PURPOSE: Augmented reality-based constructive jaw surgery has been facing various limitations such as noise in real-time images, the navigational error of implants and jaw, image overlay error, and occlusion handling which have limited the implementation of augmented reality (AR) in corrective jaw surgery. This research aimed to improve the navigational accuracy, through noise and occlusion removal, during positioning of an implant in relation to the jaw bone to be cut or drilled. METHOD: The proposed system consists of a weighting-based de-noising filter and depth mapping-based occlusion removal for removing any occluded object such as surgical tools, the surgeon's body parts, and blood. RESULTS: The maxillary (upper jaw) and mandibular (lower jaw) jaw bone sample results show that the proposed method can achieve the image overlay error (video accuracy) of 0.23~0.35 mm and processing time of 8-12 frames per second compared to 0.35~0.45 mm and 6-11 frames per second by the existing best system. CONCLUSION: The proposed system concentrates on removing the noise from the real-time video frame and the occlusion. Thus, the acceptable range of accuracy and the processing time are provided by this study for surgeons for carrying out a smooth surgical flow.
Authors: Kurt Schicho; Michael Figl; Rudolf Seemann; Markus Donat; Michael L Pretterklieber; Wolfgang Birkfellner; Astrid Reichwein; Felix Wanschitz; Franz Kainberger; Helmar Bergmann; Arne Wagner; Rolf Ewers Journal: J Neurosurg Date: 2007-04 Impact factor: 5.115
Authors: Max J Zinser; Robert A Mischkowski; Timo Dreiseidler; Oliver C Thamm; Daniel Rothamel; Joachim E Zöller Journal: Br J Oral Maxillofac Surg Date: 2013-09-14 Impact factor: 1.651
Authors: Vladimir M Ivanov; Anton M Krivtsov; Sergey V Strelkov; Anton Yu Smirnov; Roman Yu Shipov; Vladimir G Grebenkov; Valery N Rumyantsev; Igor S Gheleznyak; Dmitry A Surov; Michail S Korzhuk; Valery S Koskin Journal: J Imaging Date: 2022-06-30