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Proceedings Paper

Complete facial point cloud obtainment of patients with port wine stains based on point cloud fusion technology
Author(s): Zihao Guo; Ya Zhou; Tianlong Guo; Yingyi Gui; Xiaoming Hu
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Paper Abstract

Objective and accurate lesion assessment is one of the key factors for the optimal photodynamic therapy (PDT) of Port Wine Stains (PWS). The evaluation method based on 3D point cloud can effectively solve the problems. Comparing to the traditional point cloud registration algorithms such as ICP which tend to global registration, the feature description method is more suitable for facial point clouds fusion since each clouds is obtained from different angle and just overlap in certain area. In this paper, the method of fusion of multiple point clouds is applied, in which point cloud fusion is achieved by characterizing randomly selected sampling points. The point cloud fusion result is obtained by matching the public area between the two point clouds. Several fusion experiments are designed and conducted using the facial point cloud acquired by Artec Eva three-dimensional scanner. The experimental results show that it can accomplish the point cloud fusion of patient facial point cloud (including all the information on the skin lesions of PWS), which could be an efficient support for effective 3D lesion assessment.

Paper Details

Date Published: 12 March 2020
PDF: 10 pages
Proc. SPIE 11434, 2019 International Conference on Optical Instruments and Technology: Optical Systems and Modern Optoelectronic Instruments, 1143403 (12 March 2020); doi: 10.1117/12.2540413
Show Author Affiliations
Zihao Guo, Beijing Institute of Technology (China)
Ya Zhou, Beijing Institute of Technology (China)
Tianlong Guo, Beijing Institute of Technology (China)
Yingyi Gui, Beijing Institute of Technology (China)
Xiaoming Hu, Beijing Institute of Technology (China)

Published in SPIE Proceedings Vol. 11434:
2019 International Conference on Optical Instruments and Technology: Optical Systems and Modern Optoelectronic Instruments
Juan Liu; Baohua Jia; Xincheng Yao; Yongtian Wang; Takanori Nomura, Editor(s)

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