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2D/3D Wound Segmentation and Measurement Based on a Robot-Driven Reconstruction System

Chronic wounds, are a worldwide health problem affecting populations and economies as a whole. With the increase in age-related diseases, obesity, and diabetes, the costs of chronic wound healing will further increase. Wound assessment should be fast and accurate in order to reduce possible complica...

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Detalles Bibliográficos
Autores principales: Filko, Damir, Nyarko, Emmanuel Karlo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10058897/
https://www.ncbi.nlm.nih.gov/pubmed/36992009
http://dx.doi.org/10.3390/s23063298
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author Filko, Damir
Nyarko, Emmanuel Karlo
author_facet Filko, Damir
Nyarko, Emmanuel Karlo
author_sort Filko, Damir
collection PubMed
description Chronic wounds, are a worldwide health problem affecting populations and economies as a whole. With the increase in age-related diseases, obesity, and diabetes, the costs of chronic wound healing will further increase. Wound assessment should be fast and accurate in order to reduce possible complications and thus shorten the wound healing process. This paper describes an automatic wound segmentation based on a wound recording system built upon a 7-DoF robot arm with an attached RGB-D camera and high-precision 3D scanner. The developed system represents a novel combination of 2D and 3D segmentation, where the 2D segmentation is based on the MobileNetV2 classifier and the 3D component is based on the active contour model, which works on the 3D mesh to further refine the wound contour. The end output is the 3D model of only the wound surface without the surrounding healthy skin and geometric parameters in the form of perimeter, area, and volume.
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spelling pubmed-100588972023-03-30 2D/3D Wound Segmentation and Measurement Based on a Robot-Driven Reconstruction System Filko, Damir Nyarko, Emmanuel Karlo Sensors (Basel) Article Chronic wounds, are a worldwide health problem affecting populations and economies as a whole. With the increase in age-related diseases, obesity, and diabetes, the costs of chronic wound healing will further increase. Wound assessment should be fast and accurate in order to reduce possible complications and thus shorten the wound healing process. This paper describes an automatic wound segmentation based on a wound recording system built upon a 7-DoF robot arm with an attached RGB-D camera and high-precision 3D scanner. The developed system represents a novel combination of 2D and 3D segmentation, where the 2D segmentation is based on the MobileNetV2 classifier and the 3D component is based on the active contour model, which works on the 3D mesh to further refine the wound contour. The end output is the 3D model of only the wound surface without the surrounding healthy skin and geometric parameters in the form of perimeter, area, and volume. MDPI 2023-03-21 /pmc/articles/PMC10058897/ /pubmed/36992009 http://dx.doi.org/10.3390/s23063298 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Filko, Damir
Nyarko, Emmanuel Karlo
2D/3D Wound Segmentation and Measurement Based on a Robot-Driven Reconstruction System
title 2D/3D Wound Segmentation and Measurement Based on a Robot-Driven Reconstruction System
title_full 2D/3D Wound Segmentation and Measurement Based on a Robot-Driven Reconstruction System
title_fullStr 2D/3D Wound Segmentation and Measurement Based on a Robot-Driven Reconstruction System
title_full_unstemmed 2D/3D Wound Segmentation and Measurement Based on a Robot-Driven Reconstruction System
title_short 2D/3D Wound Segmentation and Measurement Based on a Robot-Driven Reconstruction System
title_sort 2d/3d wound segmentation and measurement based on a robot-driven reconstruction system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10058897/
https://www.ncbi.nlm.nih.gov/pubmed/36992009
http://dx.doi.org/10.3390/s23063298
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