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Pregled bibliografske jedinice broj: 1262387

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


Filko, Damir; Emmanuel, Karlo, Nyarko;
2D/3D Wound Segmentation and Measurement Based on a Robot-Driven Reconstruction System // Sensors, 23 (2023), 6; 3298, 23 doi:10.3390/s23063298 (međunarodna recenzija, članak, znanstveni)


CROSBI ID: 1262387 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

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

Autori
Filko, Damir ; Emmanuel, Karlo, Nyarko ;

Izvornik
Sensors (1424-8220) 23 (2023), 6; 3298, 23

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
chronic wound ; segmentation ; measurement ; 2D ; 3D ; active contour model ; convolutional neural network ; robot

Sažetak
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.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Interdisciplinarne tehničke znanosti, Kliničke medicinske znanosti



POVEZANOST RADA


Projekti:
UIP-2019-04-4889 - Metode za 3D rekonstrukciju i analizu kroničnih rana (Vision4Wounds) (Filko, Damir, HRZZ - 2019-04) ( CroRIS)

Ustanove:
Fakultet elektrotehnike, računarstva i informacijskih tehnologija Osijek

Profili:

Avatar Url Damir Filko (autor)

Avatar Url Emmanuel Karlo Nyarko (autor)

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada doi www.mdpi.com

Citiraj ovu publikaciju:

Filko, Damir; Emmanuel, Karlo, Nyarko;
2D/3D Wound Segmentation and Measurement Based on a Robot-Driven Reconstruction System // Sensors, 23 (2023), 6; 3298, 23 doi:10.3390/s23063298 (međunarodna recenzija, članak, znanstveni)
Filko, D., Emmanuel, Karlo, Nyarko & (2023) 2D/3D Wound Segmentation and Measurement Based on a Robot-Driven Reconstruction System. Sensors, 23 (6), 3298, 23 doi:10.3390/s23063298.
@article{article, author = {Filko, Damir}, year = {2023}, pages = {23}, DOI = {10.3390/s23063298}, chapter = {3298}, keywords = {chronic wound, segmentation, measurement, 2D, 3D, active contour model, convolutional neural network, robot}, journal = {Sensors}, doi = {10.3390/s23063298}, volume = {23}, number = {6}, issn = {1424-8220}, title = {2D/3D Wound Segmentation and Measurement Based on a Robot-Driven Reconstruction System}, keyword = {chronic wound, segmentation, measurement, 2D, 3D, active contour model, convolutional neural network, robot}, chapternumber = {3298} }
@article{article, author = {Filko, Damir}, year = {2023}, pages = {23}, DOI = {10.3390/s23063298}, chapter = {3298}, keywords = {chronic wound, segmentation, measurement, 2D, 3D, active contour model, convolutional neural network, robot}, journal = {Sensors}, doi = {10.3390/s23063298}, volume = {23}, number = {6}, issn = {1424-8220}, title = {2D/3D Wound Segmentation and Measurement Based on a Robot-Driven Reconstruction System}, keyword = {chronic wound, segmentation, measurement, 2D, 3D, active contour model, convolutional neural network, robot}, chapternumber = {3298} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus
  • MEDLINE


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