Segmentation Approaches for Diabetic Foot Disorders
1
IACTEC Medical Technology Group, Instituto de Astrofísica de Canarias (IAC), 38205 San Cristóbal de La Laguna, Spain
2
Research Institute of Biomedical and Health Sciences (IUIBS), Universidad de Las Palmas de Gran Canaria, 35016 Las Palmas de Gran Canaria, Spain
3
Department of Industrial Engineering, Universidad de La Laguna, 38200 San Cristóbal de La Laguna, Spain
4
Department of Signals and Communications, Universidad de Las Palmas de Gran Canaria, 35016 Las Palmas de Gran Canaria, Spain
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Academic Editor: Bijan Najafi
Sensors 2021, 21(3), 934; https://doi.org/10.3390/s21030934
Received: 31 December 2020
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Revised: 20 January 2021
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Accepted: 26 January 2021
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Published: 30 January 2021
(This article belongs to the Special Issue Artificial Intelligence and Sensing Technologies Based Astrophysics and Applications)
Thermography enables non-invasive, accessible, and easily repeated foot temperature measurements for diabetic patients, promoting early detection and regular monitoring protocols, that limit the incidence of disabling conditions associated with diabetic foot disorders. The establishment of this application into standard diabetic care protocols requires to overcome technical issues, particularly the foot sole segmentation. In this work we implemented and evaluated several segmentation approaches which include conventional and Deep Learning methods. Multimodal images, constituted by registered visual-light, infrared and depth images, were acquired for 37 healthy subjects. The segmentation methods explored were based on both visual-light as well as infrared images, and optimization was achieved using the spatial information provided by the depth images. Furthermore, a ground truth was established from the manual segmentation performed by two independent researchers. Overall, the performance level of all the implemented approaches was satisfactory. Although the best performance, in terms of spatial overlap, accuracy, and precision, was found for the Skin and U-Net approaches optimized by the spatial information. However, the robustness of the U-Net approach is preferred.
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Keywords:
segmentation; thermography (D013817); diabetic foot (D017719); diabetic neuropathy (D003929); supervised and unsupervised algorithms
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MDPI and ACS Style
Arteaga-Marrero, N.; Hernández, A.; Villa, E.; González-Pérez, S.; Luque, C.; Ruiz-Alzola, J. Segmentation Approaches for Diabetic Foot Disorders. Sensors 2021, 21, 934. https://doi.org/10.3390/s21030934
AMA Style
Arteaga-Marrero N, Hernández A, Villa E, González-Pérez S, Luque C, Ruiz-Alzola J. Segmentation Approaches for Diabetic Foot Disorders. Sensors. 2021; 21(3):934. https://doi.org/10.3390/s21030934
Chicago/Turabian StyleArteaga-Marrero, Natalia, Abián Hernández, Enrique Villa, Sara González-Pérez, Carlos Luque, and Juan Ruiz-Alzola. 2021. "Segmentation Approaches for Diabetic Foot Disorders" Sensors 21, no. 3: 934. https://doi.org/10.3390/s21030934
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