298
Views
6
CrossRef citations to date
0
Altmetric
Research Article

Diabetic foot thermal image segmentation using Double Encoder-ResUnet (DE-ResUnet)

ORCID Icon, ORCID Icon &
Pages 378-392 | Received 03 Sep 2021, Accepted 10 May 2022, Published online: 31 May 2022

References

  • Deshpande AD, Harris-Hayes M, Schootman M. Epidemiology of diabetes and diabetes-related complications. Phys Ther. 2008;88(11):1254–1264.
  • Harding JL, Pavkov ME, Magliano DJ, et al. Global trends in diabetes complications: a review of current evidence. Diabetologia. 2019;62(1):3–16.
  • van Netten JJ, Bus AS, Apelqvist J, et al. Definitions and criteria for diabetic foot disease. Diabetes Metab Res Rev. 2020;36(S1):e3268.
  • Mishra SC, Chhatbar KC, Kashikar A, et al. Diabetic foot. BMJ. 2017;359:j5064.
  • Armstrong DG, Holtz-Neiderer K, Wendel C, et al. Skin temperature monitoring reduces the risk for diabetic foot ulceration in high-risk patients. Am J Med. 2007;120(12):1042–6.
  • Adam M, Ng EYK, Tan JH, et al. Computer aided diagnosis of diabetic foot using infrared thermography: a review. Comput Biol Med. 2017;91:326–336.
  • Muller AC, Narayanan S. Cognitively-engineered multisensor image fusion for military applications. Inf Fusion. 2009;10(2):137–149.
  • Okada T. Thermography of asteroid and future applications in space missions. Appl Sci. 2020;10(6):2158.
  • Younsi M, Diaf M, Siarry P. Automatic multiple moving humans detection and tracking in image sequences taken from a stationary thermal infrared camera. Expert Syst Appl. 2020;146:113171.
  • Jiang LJ, Ng EYK, Yeo ACB, et al. A perspective on medical infrared imaging. J Med Eng Technol. 2005;29(6):257–267.
  • Cajacuri LAV. Early diagnostic of diabetic foot using thermal images. 2013. p. 140.
  • Saxena A, Ng EYK, Lim ST. Infrared (IR) thermography as a potential screening modality for carotid artery stenosis. Comput Biol Med. 2019;113:103419.
  • Casas-Alvarado A, Mota-Rojas D, Hernández-Ávalos I, et al. Advances in infrared thermography: surgical aspects, vascular changes, and pain monitoring in veterinary medicine. J Therm Biol. 2020;92:102664.
  • Singh D, Singh AK. Role of image thermography in early breast cancer detection- past, present and future. Comput Methods Progr Biomed. 2020;183:105074.
  • Roback K. An overview of temperature monitoring devices for early detection of diabetic foot disorders. Expert Rev Med Dev. 2010;7(5):711–8.
  • Vilcahuaman L, et al. Automatic analysis of plantar foot thermal images in at-risk type II diabetes by using an infrared camera. In: D. A. Jaffray, Éd. World Congress on Medical Physics and Biomedical Engineering, June 7-12, 2015, Toronto, Canada, Cham: Springer International Publishing, 2015. p. 228–231.
  • Fraiwan L, AlKhodari M, Ninan J, et al. Diabetic foot ulcer mobile detection system using smart phone thermal camera: a feasibility study. Biomed Eng OnLine. 2017;16(1):117.
  • Liu C, van der Heijden F, Klein ME, et al. Infrared dermal thermography on diabetic feet soles to predict ulcerations: a case study, San Francisco, California, USA, mars 2013. p. 85720N.
  • Kaabouch N, Chen Y, Hu W-C, et al. Early detection of foot ulcers through asymmetry analysis, Lake Buena Vista, FL, févr2009. p. 72621L.
  • Bougrine A, Harba R, Canals R, et al. On the segmentation of plantar foot thermal images with deep learning. In: 2019 27th European Signal Processing Conference (EUSIPCO), A Coruna, Spain, 2019. p. 1–5.
  • Bougrine A, Harba R, Canals R, et al. A joint snake and atlas-based segmentation of plantar foot thermal images. In: 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), Montreal, QC, 2017. p. 1–6.
  • Bouallal D, et al. Segmentation of plantar foot thermal images: application to diabetic foot diagnosis. In: 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), Niterói, Brazil, 2020, p. 116–121.
  • Sun Y, Zuo W, Liu M. RTFNet: RGB-thermal fusion network for semantic segmentation of urban scenes. IEEE Robot Autom Lett. 2019;4(3):2576–2583.
  • Ha Q, Watanabe K, Karasawa T, et al. MFNet: towards real-time semantic segmentation for autonomous vehicles with multi-spectral scenes. In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, 2017. p. 5108–5115.
  • Hazirbas C, Ma L, Domokos C, et al. FuseNet: incorporating depth into semantic segmentation via fusion-based CNN architecture. In S.-H. Lai, V. Lepetit, K. Nishino, et Y. Sato, Éd. Computer vision – ACCV, Cham: Springer International Publishing, 2017. p. 213–228.
  • Zhang Z, Liu Q, Wang Y. Road extraction by deep residual U-Net. IEEE Geosci Remote Sensing Lett. 2018;15(5):749–753.
  • Diakogiannis FI, Waldner F, Caccetta P, et al. ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed data. ISPRS J Photogramm Remote Sens. 2020;162:94–114.
  • FLIR ONE Pro Thermal Imaging Camera for Smartphones | FLIR Systems. https://www.flir.com/products/flir-one-pro/. (consulté le mars 29, 2021.
  • What is MSX®? https://www.flir.com/discover/professional-tools/what-is-msx/. (consulté le mars 29, 2021.
  • Chan AW, MacFarlane IA, Bowsher DR. Contact thermography of painful diabetic neuropathic foot. Diabetes Care. 1991;14(10):918–922.
  • Nagase T, et al. Variations of plantar thermographic patterns in normal controls and non-ulcer diabetic patients: novel classification using angiosome concept. J Plast Reconstr Aesthet Surg. 2011;64(7):860–6.
  • Bastyr EJ, Price KL, Bril V. Development and validity testing of the neuropathy total symptom score-6: questionnaire for the study of sensory symptoms of diabetic peripheral neuropathy. Clin Ther. 2005;27(8):1278–1294.
  • Label images for computer vision applications - MATLAB. https://www.mathworks.com/help/vision/ref/imagelabeler-app.html. (consulté le avr. 04, 2021.
  • Tan C, Sun F, Kong T, et al. A survey on deep transfer learning. In V. Kůrková, Y. Manolopoulos, B. Hammer, L. Iliadis, et I. Maglogiannis, Éd. Artificial neural networks and machine learning – ICANN. Cham: Springer International Publishing, 2018. p. 270–279.
  • Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation ArXiv150504597 Cs, mai 2015. Consulté le: janv. 10, 2020. [En ligne]. Disponible sur: http://arxiv.org/abs/1505.04597.
  • Shelhamer E, Long J, Darrell T. Fully Convolutional Networks for Semantic Segmentation ArXiv160506211 Cs, mai 2016. Consulté le: nov. 20, 2020. [En ligne]. Disponible sur: http://arxiv.org/abs/1605.06211.
  • Badrinarayanan V, Kendall A, Cipolla R. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation ArXiv151100561 Cs, oct2016. Consulté le: janv. 28, 2020. [En ligne]. Disponible sur: http://arxiv.org/abs/1511.00561.
  • He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition ArXiv151203385 Cs, déc2015. Consulté le: janv. 28, 2020. [En ligne]. Disponible sur: http://arxiv.org/abs/1512.03385.
  • Garcia-Garcia A, Orts-Escolano S, Oprea S, et al. A Review on Deep Learning Techniques Applied to Semantic Segmentation ArXiv170406857 Cs, avr2017. Consulté le: mars 29, 2021. [En ligne]. Disponible sur: http://arxiv.org/abs/1704.06857.
  • Chen L-C, Papandreou G, Schroff F, et al. Rethinking Atrous Convolution for Semantic Image Segmentation ArXiv170605587 Cs, déc2017. Consulté le: juin 16, 2021. [En ligne]. Disponible sur: http://arxiv.org/abs/1706.05587.
  • Hernandez-Contreras D, Peregrina-Barreto H, Rangel-Magdaleno J, et al. Narrative review: diabetic foot and infrared thermography. Infrared Phys Technol. 2016;78:105–117.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.