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Wound 3D Geometrical Feature Estimation Using Poisson Reconstruction

delete2021-01-01
delete9
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OA
AI
J
Jan Juszczyk *
A
Agata M. Wijata
J
Joanna Czajkowska
M
Michał Kręcichwost
M
Marcin Rudzki
M
Marta Biesok
B
Bartłomiej Pyciński
J
Jakub Majewski
J
Jacek Kostecki
E
Ewa Piętka
DOI:10.1109/ACCESS.2020.3035125delete
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摘要

摘要

En 中文
With the aging population of the developed countries, the diseases of affluence become common. One of the consequences of their complications are chronic wounds that affect more and more people. Because the healing of chronic wounds is a lengthy process, its monitoring should be as simple as possible, yet providing accurate and reliable documentation. In this article, we present an image acquisition system and wound surface reconstruction method using several imaging modalities: color photography, thermal imaging, and depth perception. The proposed method is dedicated mainly to wounds located on the limbs where body curvature is large, and its influence on 2D results cannot be neglected. Our approach minimizes the extra effort taken by the medical staff to prepare the wound outline as it is still performed using 2D color photo and then mapped into the 3D space. The method was validated on 29 data sets containing two 3D point clouds from two depth imaging devices (depth camera and stereo camera) as well as 2D color photos and thermal maps. The approach was compared with expert delineations as well as other contemporary methods for wound surface reconstruction presented in the literature. Performed experiments and the obtained results show that the proposed method is statistically concordant with expert delineations performed in 3D.
Keyword:
3D model
3D surface reconstruction
chronic wounds
image registration
multimodal image processing
photography
point clouds
stereovision
thermography
wound assessment
wound geometrical parameters
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期刊

IEEE Access 封面图
IEEE Access
IF:
3.6
论文数:
9.8W
被引数:
29.4W

机构

S
Silesian University of Technology
学者数:
6.2K
论文数: 6.2K
被引数: 5.9K
M
Medical University Silesia
学者数:
6.9K
论文数: 5.1K
被引数: 18
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