8 February 2017 An evaluation on CT image acquisition method for medical VR applications
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Proceedings Volume 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016); 102250S (2017) https://doi.org/10.1117/12.2266772
Event: Eighth International Conference on Graphic and Image Processing, 2016, Tokyo, Japan
Abstract
Recent medical virtual reality (VR) applications to minimize re-operations are being studied for improvements in surgical efficiency and reduction of operation error. The CT image acquisition method considering three-dimensional (3D) modeling for medical VR applications is important, because the realistic model is required for the actual human organ. However, the research for medical VR applications has focused on 3D modeling techniques and utilized 3D models. In addition, research on a CT image acquisition method considering 3D modeling has never been reported. The conventional CT image acquisition method involves scanning a limited area of the lesion for the diagnosis of doctors once or twice. However, the medical VR application is required to acquire the CT image considering patients’ various postures and a wider area than the lesion. A wider area than the lesion is required because of the necessary process of comparing bilateral sides for dyskinesia diagnosis of the shoulder, pelvis, and leg. Moreover, patients’ various postures are required due to the different effects on the musculoskeletal system. Therefore, in this paper, we perform a comparative experiment on the acquired CT images considering image area (unilateral/bilateral) and patients’ postures (neutral/abducted). CT images are acquired from 10 patients for the experiments, and the acquired CT images are evaluated based on the length per pixel and the morphological deviation. Finally, by comparing the experiment results, we evaluate the CT image acquisition method for medical VR applications.
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Seong-wook Jang, Junho Ko, Yon-sik Yoo, Yoonsang Kim, "An evaluation on CT image acquisition method for medical VR applications", Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 102250S (8 February 2017); doi: 10.1117/12.2266772; https://doi.org/10.1117/12.2266772
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