{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:48:34Z","timestamp":1775328514565,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T00:00:00Z","timestamp":1611964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100019170","name":"Cabildo de Tenerife","doi-asserted-by":"publisher","award":["TF INNOVA 2016-2021"],"award-info":[{"award-number":["TF INNOVA 2016-2021"]}],"id":[{"id":"10.13039\/501100019170","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004895","name":"European Social Fund","doi-asserted-by":"publisher","award":["POC 2014-2020"],"award-info":[{"award-number":["POC 2014-2020"]}],"id":[{"id":"10.13039\/501100004895","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s21030934","type":"journal-article","created":{"date-parts":[[2021,1,30]],"date-time":"2021-01-30T08:56:01Z","timestamp":1611996961000},"page":"934","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Segmentation Approaches for Diabetic Foot Disorders"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1645-0810","authenticated-orcid":false,"given":"Natalia","family":"Arteaga-Marrero","sequence":"first","affiliation":[{"name":"IACTEC Medical Technology Group, Instituto de Astrof\u00edsica de Canarias (IAC), 38205 San Crist\u00f3bal de La Laguna, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2508-2845","authenticated-orcid":false,"given":"Abi\u00e1n","family":"Hern\u00e1ndez","sequence":"additional","affiliation":[{"name":"Research Institute of Biomedical and Health Sciences (IUIBS), Universidad de Las Palmas de Gran Canaria, 35016 Las Palmas de Gran Canaria, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1028-6957","authenticated-orcid":false,"given":"Enrique","family":"Villa","sequence":"additional","affiliation":[{"name":"IACTEC Medical Technology Group, Instituto de Astrof\u00edsica de Canarias (IAC), 38205 San Crist\u00f3bal de La Laguna, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7830-4248","authenticated-orcid":false,"given":"Sara","family":"Gonz\u00e1lez-P\u00e9rez","sequence":"additional","affiliation":[{"name":"IACTEC Medical Technology Group, Instituto de Astrof\u00edsica de Canarias (IAC), 38205 San Crist\u00f3bal de La Laguna, Spain"},{"name":"Department of Industrial Engineering, Universidad de La Laguna, 38200 San Crist\u00f3bal de La Laguna, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0442-0785","authenticated-orcid":false,"given":"Carlos","family":"Luque","sequence":"additional","affiliation":[{"name":"IACTEC Medical Technology Group, Instituto de Astrof\u00edsica de Canarias (IAC), 38205 San Crist\u00f3bal de La Laguna, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3545-2328","authenticated-orcid":false,"given":"Juan","family":"Ruiz-Alzola","sequence":"additional","affiliation":[{"name":"IACTEC Medical Technology Group, Instituto de Astrof\u00edsica de Canarias (IAC), 38205 San Crist\u00f3bal de La Laguna, Spain"},{"name":"Research Institute of Biomedical and Health Sciences (IUIBS), Universidad de Las Palmas de Gran Canaria, 35016 Las Palmas de Gran Canaria, Spain"},{"name":"Department of Signals and Communications, Universidad de Las Palmas de Gran Canaria, 35016 Las Palmas de Gran Canaria, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1586\/erd.10.35","article-title":"An overview of temperature monitoring devices for early detection of diabetic foot disorders","volume":"7","author":"Roback","year":"2010","journal-title":"Expert Rev. Med. Devices"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"026003","DOI":"10.1117\/1.JBO.20.2.026003","article-title":"Automatic detection of diabetic foot complications with infrared thermography by asymmetric analysis","volume":"20","author":"Liu","year":"2015","journal-title":"J. Biomed. Opt."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1796","DOI":"10.1053\/apmr.2002.35661","article-title":"Plantar tissue stiffness in patients with diabetes mellitus and peripheral neuropathy","volume":"83","author":"Klaesner","year":"2002","journal-title":"Arch. Phys. Med. Rehabil."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/S1056-8727(98)00022-1","article-title":"Hardness of plantar skin in diabetic neuropathic feet","volume":"13","author":"Piaggesi","year":"1999","journal-title":"J. Diabetes Complicat."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1007\/s12555-019-0242-y","article-title":"Monitoring System for Diabetic Foot Ulceration Patients Using Robotic Palpation","volume":"18","author":"Choi","year":"2020","journal-title":"Int. J. Control Autom. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.infrared.2017.08.022","article-title":"Automatic segmentation of thermal images of diabetic-at-risk feet using the snakes algorithm","volume":"86","author":"Etehadtavakol","year":"2017","journal-title":"Infrared Phys. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"108074","DOI":"10.1016\/j.diabres.2020.108074","article-title":"Accuracy of a foot temperature monitoring mat for predicting diabetic foot ulcers in patients with recent wounds or partial foot amputation","volume":"161","author":"Gordon","year":"2020","journal-title":"Diabetes Res. Clin. Pract."},{"key":"ref_8","first-page":"74810L","article-title":"Flexible 640 \u00d7 480 pixel infrared camera module for fast prototyping","volume":"Volume 7481","author":"Bergeron","year":"2009","journal-title":"Electro-Optical and Infrared Systems: Technology and Applications VI"},{"key":"ref_9","first-page":"91432A","article-title":"Microbolometer Characterization with the Electronics Prototype of the IRCAM for the JEM-EUSO Mission","volume":"Volume 9143","author":"Joven","year":"2014","journal-title":"Space Telescopes and Instrumentation 2014: Optical, Infrared, and Millimeter Wave"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"973","DOI":"10.2337\/dc16-2294","article-title":"Feasibility and efficacy of a smart mat technology to predict development of diabetic plantar ulcers","volume":"40","author":"Frykberg","year":"2017","journal-title":"Diabetes Care"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"960","DOI":"10.3171\/jns.2004.101.6.0960","article-title":"Intraoperative infrared imaging of brain tumors","volume":"101","author":"Gorbach","year":"2004","journal-title":"J. Neurosurg."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.jtherbio.2017.10.014","article-title":"3D brain tumor localization and parameter estimation using thermographic approach on GPU","volume":"71","author":"Bousselham","year":"2018","journal-title":"J. Therm. Biol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Hoffmann, N., Koch, E., Steiner, G., Petersohn, U., and Kirsch, M. (2016). Learning thermal process representations for intraoperative analysis of cortical perfusion during ischemic strokes. Deep Learning and Data Labeling for Medical Applications, Springer.","DOI":"10.1007\/978-3-319-46976-8_16"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1002\/ana.10646","article-title":"Intraoperative infrared functional imaging of human brain","volume":"54","author":"Gorbach","year":"2003","journal-title":"Ann. Neurol."},{"key":"ref_15","first-page":"217","article-title":"SEP-induced activity and its thermographic cortical representation in a murine model","volume":"58","author":"Hoffmann","year":"2013","journal-title":"Biomed. Eng. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Benito-de Pedro, M., Becerro-de Bengoa-Vallejo, R., Losa-Iglesias, M.E., Rodr\u00edguez-Sanz, D., L\u00f3pez-L\u00f3pez, D., Cos\u00edn-Matamoros, J., Mart\u00ednez-Jim\u00e9nez, E.M., and Calvo-Lobo, C. (2019). Effectiveness between dry needling and ischemic compression in the triceps surae latent myofascial trigger points of triathletes on pressure pain threshold and thermography: A single blinded randomized clinical trial. J. Clin. Med., 8.","DOI":"10.3390\/jcm8101632"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"102639","DOI":"10.1016\/j.jtherbio.2020.102639","article-title":"Validation of ThermoHuman automatic thermographic software for assessing foot temperature before and after running","volume":"92","year":"2020","journal-title":"J. Therm. Biol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Villa, E., Arteaga-Marrero, N., and Ruiz-Alzola, J. (2020). Performance Assessment of Low-Cost Thermal Cameras for Medical Applications. Sensors, 20.","DOI":"10.3390\/s20051321"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bougrine, A., Harba, R., Canals, R., Ledee, R., and Jabloun, M. (December, January 28). A joint snake and atlas-based segmentation of plantar foot thermal images. Proceedings of the IEEE 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), Montreal, QC, Canada.","DOI":"10.1109\/IPTA.2017.8310081"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Hern\u00e1ndez, A., Arteaga-Marrero, N., Villa, E., Fabelo, H., Callic\u00f3, G.M., and Ruiz-Alzola, J. (2019, January 9\u201313). Automatic Segmentation Based on Deep Learning Techniques for Diabetic Foot Monitoring Through Multimodal Images. Proceedings of the International Conference on Image Analysis and Processing, Trento, Italy.","DOI":"10.1007\/978-3-030-30645-8_38"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1313","DOI":"10.1109\/TBCAS.2018.2856407","article-title":"Registration and fusion of thermographic and visual-light images in neurosurgery","volume":"12","author":"Chen","year":"2018","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"478","DOI":"10.1016\/j.infrared.2017.07.010","article-title":"A survey of infrared and visual image fusion methods","volume":"85","author":"Jin","year":"2017","journal-title":"Infrared Phys. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, R., Zhang, Y., Xing, L., and Li, W. (2018, January 14\u201316). An Adaptive Foot-image Segmentation Algorithm Based on Morphological Partition. Proceedings of the 2018 IEEE International Conference on Progress in Informatics and Computing (PIC), Suzhou, China.","DOI":"10.1109\/PIC.2018.8706305"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bougrine, A., Harba, R., Canals, R., Ledee, R., and Jabloun, M. (2019, January 2\u20136). On the segmentation of plantar foot thermal images with Deep Learning. Proceedings of the IEEE 2019 27th European Signal Processing Conference (EUSIPCO), A Coru\u00f1a, Spain.","DOI":"10.23919\/EUSIPCO.2019.8902691"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1016\/j.neuroimage.2006.01.015","article-title":"User-Guided 3D Active Contour Segmentation of Anatomical Structures: Significantly Improved Efficiency and Reliability","volume":"31","author":"Yushkevich","year":"2006","journal-title":"Neuroimage"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1109\/TMI.2004.828354","article-title":"Simultaneous truth and performance level estimation (STAPLE): An algorithm for the validation of image segmentation","volume":"23","author":"Warfield","year":"2004","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_28","unstructured":"Iglovikov, V., and Shvets, A. (2018). Ternausnet: U-net with vgg11 encoder pre-trained on imagenet for image segmentation. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kolkur, S., Kalbande, D., Shimpi, P., Bapat, C., and Jatakia, J. (2017). Human skin detection using RGB, HSV and YCbCr color models. arXiv.","DOI":"10.2991\/iccasp-16.2017.51"},{"key":"ref_31","unstructured":"Badrinarayanan, V., Handa, A., and Cipolla, R. (2015). Segnet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4522","DOI":"10.1093\/bioinformatics\/btz259","article-title":"Biomedical image augmentation using Augmentor","volume":"35","author":"Bloice","year":"2019","journal-title":"Bioinformatics"},{"key":"ref_33","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"297","DOI":"10.2307\/1932409","article-title":"Measures of the amount of ecologic association between species","volume":"26","author":"Dice","year":"1945","journal-title":"Ecology"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1111\/j.1469-8137.1912.tb05611.x","article-title":"The distribution of the flora in the alpine zone. 1","volume":"11","author":"Jaccard","year":"1912","journal-title":"New Phytol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1432","DOI":"10.2307\/4586294","article-title":"Statistical problems in assessing methods of medical diagnosis, with special reference to X-ray techniques","volume":"62","author":"Yerushalmy","year":"1947","journal-title":"Public Health Rep."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1552","DOI":"10.1136\/bmj.308.6943.1552","article-title":"Diagnostic tests. 1: Sensitivity and specificity","volume":"308","author":"Altman","year":"1994","journal-title":"Br. Med. J."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1136\/bmj.309.6947.102","article-title":"Statistics Notes: Diagnostic tests. 2: Predictive values","volume":"309","author":"Altman","year":"1994","journal-title":"Br. Med. J."},{"key":"ref_40","unstructured":"RStudio Team (2020). RStudio: Integrated Development Environment for R, RStudio."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1080\/01621459.1972.10481279","article-title":"Constructing confidence sets using rank statistics","volume":"67","author":"Bauer","year":"1972","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_42","unstructured":"Toutenburg, H., Hollander, M., and Wolfe, D.A. (1973). Nonparametric Statistical Methods, John Wiley & Sons."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"800","DOI":"10.1093\/biomet\/75.4.800","article-title":"A sharper Bonferroni procedure for multiple tests of significance","volume":"75","author":"Hochberg","year":"1988","journal-title":"Biometrika"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.procs.2015.07.362","article-title":"Comparative study of skin color detection and segmentation in HSV and YCbCr color space","volume":"57","author":"Shaik","year":"2015","journal-title":"Procedia Comput. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/934\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:17:40Z","timestamp":1760159860000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/3\/934"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,30]]},"references-count":44,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21030934"],"URL":"https:\/\/doi.org\/10.3390\/s21030934","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,30]]}}}