Abstract
Recognition and analysis of Diabetic Foot Ulcers (DFU) by computerised methods has been an emerging research area with the evolution of image processing and machine learning algorithms. Precise documentation of wound size over time allows clinicians to gauge responses to treatment, improving healing rates by modifying interventions as required. One of the major issues in the analysis of DFU is non-standardised foot images captured with cameras including factors such as distance of the camera from the foot and orientation of the image. Designing a computerised solution to determine site of DFU and measurements of area for remote assessment and monitoring represents a significant challenge due to the variables involved. In this work, we propose a new computerised solution with the combination of image processing and deep learning algorithms to estimate the site and predict the progress (based on estimated area index) of the DFU irrespective of distance and orientation of the plantar foot. First we segment the foot region and align the foot by fixing the orientation of a series of longitudinal images. Then we localise the region of interest of DFUs and find its relative size to the foot area. We introduce a distribution analysis to determine the site of DFUs. Finally, we introduce an area index (\(Area_t\)) to predict the healing progress of DFU at different time intervals (t). We demonstrate the feasibility of our proposed method on 154 longitudinal DFUs of plantar foot. We achieved 92.3% on site estimation and 84.7% on healing progress prediction.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wild, S., Roglic, G., Green, A., Sicree, R., King, H.: Global prevalence of diabetes estimates for the year 2000 and projections for 2030. Diabet. Care 27(5), 1047–1053 (2004)
Armstrong, D.G., Lavery, L.A., Harkless, L.B.: Validation of a diabetic wound classification system: the contribution of depth, infection, and ischemia to risk of amputation. Diabet. Care 21(5), 855–859 (1998)
Apelqvist, J.: The foot in perspective. Diabet. Metab. Res. Rev. 24(S1), S110–S115 (2008)
Armstrong, D.G., Boulton, A.J.M., Bus, S.A.: Diabetic foot ulcers and their recurrence. N. Engl. J. Med. 376(24), 2367–2375 (2017)
Prompers, L., et al.: Delivery of care to diabetic patients with foot ulcers in daily practice: results of the Eurodiale study, a prospective cohort study. Diabet. Med. 25(6), 700–707 (2008)
Cavanagh, P., Attinger, C., Abbas, Z., Bal, A., Rojas, N., Zhang-Rong, X.: Cost of treating diabetic foot ulcers in five different countries. Diabet. Metab. Res. Rev. 28(S1), 107–111 (2012)
Zimmet, P.Z., Magliano, D.J., Herman, W.H., Shaw, J.E.: Diabetes: a 21st century challenge. Lancet Diabet. Endocrinol. 2(1), 56–64 (2014)
Vinicor, F.: The public health burden of diabetes and the reality of limits. Diabet. Care 21(Supplement 3), C15–C18 (1998)
Brim, C.: A descriptive analysis of the non-urgent use of emergency departments. Nurse Res. 15(3), 72–88 (2008)
Lang, T.A., Hodge, M., Olson, V., Romano, P.S., Kravitz, R.L.: Nurse-patient ratios: a systematic review on the effects of nurse staffing on patient, nurse employee, and hospital outcomes. J. Nurs. Adm. 34(7–8), 326–337 (2004)
Singh, N., Armstrong, D.G., Lipsky, B.A.: Preventing foot ulcers in patients with diabetes. Jama 293(2), 217–228 (2005)
Lazzarini, P.A., et al.: Does the use of store-and-forward telehealth systems improve outcomes for clinicians managing diabetic foot ulcers?: a pilot study. Wound Pract. Res. J. Aust. Wound Manag. Assoc. 18(4), 164 (2010)
Chanussot-Deprez, C., Contreras-Ruiz, J.: Telemedicine in wound care: a review. Adv. Skin Wound Care 26(2), 78–82 (2013)
Goyal, M., Reeves, N.D., Rajbhandari, S., Yap, M.H.: Robust methods for real-time diabetic foot ulcer detection and localization on mobile devices. IEEE J. Biomed. Health Inform. 23(4), 1730–1741 (2018)
Currell, R., Urquhart, C., Wainwright, P., Lewis, R.: Telemedicine versus face to face patient care: effects on professional practice and health care outcomes. Cochrane Database Syst. Rev. 2(2), CD002098 (2000)
Wilbright, W.A., Birke, J.A., Patout, C.A., Varnado, M., Horswell, R.: The use of telemedicine in the management of diabetes-related foot ulceration: a pilot study. Adv. Skin Wound Care 17(5), 232–238 (2004)
van Netten, J.J., Clark, D., Lazzarini, P.A., Janda, M., Reed, L.F.: The validity and reliability of remote diabetic foot ulcer assessment using mobile phone images. Sci. Rep. 7(1), 9480 (2017)
Bowling, F.L., et al.: Remote assessment of diabetic foot ulcers using a novel wound imaging system. Wound Repair Regen. 19(1), 25–30 (2011)
Ince, P., et al.: Use of the SINBAD classification system and score in comparing outcome of foot ulcer management on three continents. Diabet. Care 31(5), 964–967 (2008)
Ince, P., Kendrick, D., Game, F., Jeffcoate, W.: The association between baseline characteristics and the outcome of foot lesions in a UK population with diabetes. Diabet. Med. 24(9), 977–981 (2007)
Goyal, M., Oakley, A., Bansal, P., Dancey, D., Yap, M.H.: Skin lesion segmentation in dermoscopic images with ensemble deep learning methods. IEEE Access 8, 4171–4181 (2019)
Yap, M.H., et al.: Breast ultrasound region of interest detection and lesion localisation. Artif. Intell. Med. 107, 101880 (2020)
Goyal, M., Reeves, N.D., Davison, A.K., Rajbhandari, S., Spragg, J., Yap, M.H.: DFUNet: convolutional neural networks for diabetic foot ulcer classification. IEEE Trans. Emerg. Top. Comput. Intell. 4(5), 728–739 (2018)
Yap, M.H., Cassidy, B., Pappachan, J.M., O’Shea, C., Gillespie, D., Reeves, N.D.: Analysis towards classification of infection and Ischaemia of diabetic foot ulcers. In 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), pp. 1–4. IEEE (2021)
Cassidy, B., Kendrick, C., Brodzicki, A., Jaworek-Korjakowska, J., Yap, M.H.: Analysis of the ISIC image datasets: usage, benchmarks and recommendations. Med. Image Anal. 75, 102305 (2022)
Wang, L., Pedersen, P.C., Agu, E., Strong, D.M., Tulu, B.: Area determination of diabetic foot ulcer images using a cascaded two-stage SVM-based classification. IEEE Trans. Biomed. Eng. 64(9), 2098–2109 (2016)
Goyal, M., Yap, M.H., Reeves, N.D., Rajbhandari, S., Spragg, J.: Fully convolutional networks for diabetic foot ulcer segmentation. In: 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 618–623. IEEE (2017)
Wang, C., et al.: A unified framework for automatic wound segmentation and analysis with deep convolutional neural networks. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2415–2418. IEEE (2015)
Goyal, M., Reeves, N.D., Rajbhandari, S., Ahmad, N., Wang, C., Yap, M.H.: Recognition of Ischaemia and infection in diabetic foot ulcers: dataset and techniques. Comput. Biol. Med. 117, 103616 (2020)
van Netten, J.J., van Baal, J.G., Liu, C., van Der Heijden, F., Bus, S.A.: Infrared thermal imaging for automated detection of diabetic foot complications (2013)
Yap, M.H., et al.: Computer vision algorithms in the detection of diabetic foot ulceration a new paradigm for diabetic foot care? J. Diabet. Sci. Technol. 10(2), 612–613 (2015)
Yap, M.H.: A new mobile application for standardizing diabetic foot images. J. Diabet. Sci. Technol. 12(1), 169–173 (2018)
Brown, R., Ploderer, B., Seng, L.S.D., van Netten, J.J., Lazzarini, P.A.: MyFootCare: a mobile self-tracking tool to promote self-care amongst people with diabetic foot ulcers (2017)
Yap, M.H., Kendrick, C., Reeves, N.D., Goyal, M., Pappachan, J.M., Cassidy, B.: Development of diabetic foot ulcer datasets: an overview. In: Diabetic Foot Ulcers Grand Challenge: Second Challenge, DFUC 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, 27 September 2021, Proceedings, p. 1 (2021)
Shetty, R., Sreekar, H., Lamba, S., Gupta, A.K.: A novel and accurate technique of photographic wound measurement. Indian J. Plastic Surg. 45, 425–429 (2012)
McCardle, J., Smith, M., Brewin, E., Young, M.: Visitrak: wound measurement as an aid to making treatment decisions. Diabet. Foot J. 8(4), 207–211 (2005)
Molik, M., et al.: Comparison of the wound area assessment methods in the diabetic foot syndrome. Biocybern. Biomed. Eng. 30(4), 3–15 (2010)
Rogers, L.C., Bevilacqua, N.J., Armstrong, D.G., Andros, G.: Digital planimetry results in more accurate wound measurements: a comparison to standard ruler measurements, 799–802 (2010)
Cassidy, B., et al.: The DFUC 2020 dataset: analysis towards diabetic foot ulcer detection. touchREVIEWS Endocrinol. 17(1), 5 (2021)
Armstrong, D.G., Boulton, A.J.M., Bus, S.A.: Diabetic foot ulcers and their recurrence. N. Engl. J. Med. 376, 2367–2375 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Goyal, M., Reeves, N.D., Rajbhandari, S., Yap, M.H. (2022). Computerised Methods for Monitoring Diabetic Foot Ulcers on Plantar Foot: A Feasibility Study. In: Yang, G., Aviles-Rivero, A., Roberts, M., Schönlieb, CB. (eds) Medical Image Understanding and Analysis. MIUA 2022. Lecture Notes in Computer Science, vol 13413. Springer, Cham. https://doi.org/10.1007/978-3-031-12053-4_15
Download citation
DOI: https://doi.org/10.1007/978-3-031-12053-4_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-12052-7
Online ISBN: 978-3-031-12053-4
eBook Packages: Computer ScienceComputer Science (R0)