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A Clustering Approach for Prediction of Diabetic Foot Using Thermal Images

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Computational Science and Its Applications – ICCSA 2020 (ICCSA 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12251))

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Abstract

Diabetes Mellitus (DM) is one of the most predominant diseases in the world, causing a high number of deaths. Diabetic foot is one of the main complications observed in diabetic patients, which can lead to the development of ulcers. As the risk of ulceration is directly linked to an increase of the temperature in the plantar region, several studies use thermography as a method for automatic identification of problems in diabetic foot. As the distribution of plantar temperature of diabetic patients do not follow a specific pattern, it is difficult to measure temperature changes and, therefore, there is an interest in the development of methods that allow the detection of these abnormal changes.

The objective of this work is to develop a methodology that uses thermograms of the feet of diabetic and healthy individuals and analyzes the thermal changes diversity in the plantar region, classifying each foot as belonging to a DM or a healthy individual. Based on the concept of clustering, a binary classifier to predict diabetic foot is presented; both a quantitative indicator and a classification thresholder (evaluated and validated by several performance metrics) are presented.

To measure the binary classifier performance, experiments were conducted on a public dataset (with 122 images of DM individuals and 45 of healthy ones), being obtained the following metrics: Sensitivity = 0.73, Fmeasure = 0.81 and AUC = 0.84.

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References

  1. Brison, D.W.: Definition, diagnosis, and classification. In: Baumeister, A.A., (ed.) Menial Retardation, pp. 1–19 (2017). Amelior. Ment. Disabil. Quest. Retard

    Google Scholar 

  2. Clark, Jr., C.M., Lee, D.A.: Prevention and treatment of the complications of diabetes mellitus. New Engl. J. Med. 332(18), 1210–1217 (1995)

    Google Scholar 

  3. S. Edition, “IDF Diabetes Atlas,” Int. Diabetes Fed (2015)

    Google Scholar 

  4. Diabetes. https://www.who.int/en/news-room/fact-sheets/detail/diabetes. Accessed 04 May 2020

  5. Apelqvist, J., Larsson, J.: What is the most effective way to reduce incidence of amputation in the diabetic foot? Diab. Metab. Res. Rev. 16(SUPPL. 1), S75–S83 (2000)

    Google Scholar 

  6. Leung, P.: Diabetic foot ulcers - a comprehensive review. Surgeon 5, 219–231 (2007)

    Google Scholar 

  7. Glaudemans, A.W.J.M., Uçkay, I., Lipsky, B.A.: Challenges in diagnosing infection in the diabetic foot. Diab. Med. 32(6), 748–759 (2015)

    Google Scholar 

  8. Frykberg, R.G., et al.: Diabetic foot disorders: a clinical practice guideline (2006 revision). J. Foot Ankle Surg. 45(5 SUPPL), S1–S66 (2006)

    Google Scholar 

  9. Ring, F.: The Herschel heritage to medical thermography. J. Imaging 2(2), 13 (2016)

    Google Scholar 

  10. Hernandez-Contreras, D., Peregrina-Barreto, H., Rangel-Magdaleno, J., Gonzalez-Bernal, J.: Narrative review: Diabetic foot and infrared thermography. Infrared Phys. Technol. 78, 105–117 (2016)

    Google Scholar 

  11. Pereira, C.B., Yu, X., Dahlmanns, S., Blazek, V., Leonhardt, S., Teichmann, D.: InfrCHed thermography. In: Multi-Modality Imaging: Applications and Computational Techniques, pp. 1–30. Springer International Publishing (2018)

    Google Scholar 

  12. Hernandez-Contreras, D., Peregrina-Barreto, H., Rangel-Magdaleno, J., Gonzalez-Bernal, J.A., Altamirano-Robles, L.: A quantitative index for classification of plantar thermal changes in the diabetic foot. Infrared Phys. Technol. 81, 242–249 (2017)

    Google Scholar 

  13. 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. Aesthetic Surg. 64(7), 860–866 (2011)

    Google Scholar 

  14. Mori, T., et al.: Morphological pattern classification system for plantar thermography of patients with diabetes. J. Diab. Sci. Technol. 7(5), 1102–1112 (2013)

    Google Scholar 

  15. Adam, M., Ng, E.Y.K., Tan, J.H., Heng, M.L., Tong, J.W.K., Acharya, U.R.: Computer aided diagnosis of diabetic foot using infrared thermography: a review. Comput. Biol. Med. 91, 326–336 (2017)

    Google Scholar 

  16. Madhava Prabhu, S., Verma, S.: A systematic literature review for early detection of type II diabetes. In: 2019 5th International Conference on Advanced Computing and Communication Systems, ICACCS 2019, pp. 220–224 (2019)

    Google Scholar 

  17. Balbinot, L.F., Robinson, C.C., Achaval, M., Zaro, M.A., Brioschi, M.L.: Repeatability of infrared plantar thermography in diabetes patients: a pilot study. J. Diab. Sci. Technol. 7(5), 1130–1137 (2013)

    Google Scholar 

  18. Agurto, C., Barriga, S., Burge, M., Soliz, P.: Characterization of diabetic peripheral neuropathy in infrared video sequences using independent component analysis. In: 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP), vol. 2015, pp. 1–6 (2015)

    Google Scholar 

  19. Kaabouch, N., Chen, Y., Anderson, J., Ames, F., Paulson, R.: Asymmetry analysis based on genetic algorithms for the prediction of foot ulcers. Vis. Data Anal. 7243, 724304 (2009)

    Google Scholar 

  20. Liu, C., Van Baal, J.G., Bus, S.A.: Automatic detection of diabetic foot complications with infrared thermography by asymmetric analysis with infrared thermography by asymmetric analysis. J. Biomed. Opt. 20(2), 026003 (2015)

    Google Scholar 

  21. Hernandez-Contreras, D.A., Peregrina-Barreto, H., De Jesus Rangel-Magdaleno, J., Orihuela-Espina, F.: Statistical approximation of plantar temperature distribution on diabetic subjects based on beta mixture model. IEEE Access 7, 28383–28391 (2019)

    Google Scholar 

  22. Hernandez-Contreras, D., Peregrina-Barreto, H., Rangel-Magdaleno, J., Ramirez-Cortes, J., Renero-Carrillo, F.: Automatic classification of thermal patterns in diabetic foot based on morphological pattern spectrum. Infrared Phys. Technol. 73, 149–157 (2015)

    Google Scholar 

  23. Hernandez-Contreras, D.A., Peregrina-Barreto, H., Rangel-Magdaleno, J.D.J., Renero-Carrillo, F.J.: Plantar thermogram database for the study of diabetic foot complications. IEEE Access 7, 161296–161307 (2019)

    Google Scholar 

  24. Macdonald, A., et al.: Thermal symmetry of healthy feet: a precursor to a thermal study of diabetic feet prior to skin breakdown. Physiol. Meas. 38(1), 33–44 (2017)

    MathSciNet  Google Scholar 

  25. Macdonald, A., et al.: Between visit variability of thermal imaging of feet in people attending podiatric clinics with diabetic neuropathy at high risk of developing foot ulcers. Physiol. Meas. 40(8), 084004. (2019)

    Google Scholar 

  26. Peregrina-Barreto, H., Morales-Hernandez, L.A., Rangel-Magdaleno, J.J., Avina-Cervantes, J.G., Ramirez-Cortes, J.M., Morales-Caporal, R.: Quantitative estimation of temperature variations in plantar angiosomes: A study case for diabetic foot. In: Computational and Mathematical Methods in Medicine (2014)

    Google Scholar 

  27. Peregrina-Barreto, H., Morales-Hernandez, L.A., Rangel-Magdaleno, J.J., Vazquez-Rodriguez, P.D.: Thermal image processing for quantitative determination of temperature variations in plantar angiosomes. In: 2013 IEEE International Instrumentation and Measurement Technology Conference, pp. 816–820 (2013)

    Google Scholar 

  28. Omran, M.G.H., Engelbrecht, A.P., Salman, A.: An overview of clustering methods. Intell. Data Anal. 11(6), 583–605 (2007)

    Google Scholar 

  29. Dhanachandra, N., Manglem, K., Chanu, Y.J.: Image segmentation using k-means clustering algorithm and subtractive clustering algorithm. Procedia Comput. Sci. 54, 764–771 (2015)

    Google Scholar 

  30. Hajian-Tilaki, K.: Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Casp. J. Int. Med. 4(2), 627–635 (2013)

    Google Scholar 

  31. Taha, A.A., Hanbury, A.: Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med. Imaging 15(1), 29 (2015)

    Google Scholar 

  32. Unal, I.: Defining an optimal cut-point value in ROC analysis: an alternative approach. In: Computational and Mathematical Methods in Medicine, vol. 2017 (2017)

    Google Scholar 

Download references

Acknowledgments

This work is financed by National Funds through the Portuguese funding agency, FCT – Fundação para a Ciência e a Tecnologia within project UIDB/50014/2020.

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Authors and Affiliations

  1. University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5001-801, Vila Real, Portugal

    Vítor Filipe, Pedro Teixeira & Ana Teixeira

  2. INESC TEC - INESC Technology and Science, 4200-465, Porto, Portugal

    Vítor Filipe

  3. Mathematics Centre CMAT, Pole CMAT - UTAD, Vila Real, Portugal

    Ana Teixeira

Authors
  1. Vítor Filipe
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  2. Pedro Teixeira
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  3. Ana Teixeira
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Correspondence to Ana Teixeira .

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Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Chair- Center of ICT/ICE, Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Clayton School of Information Technology, Monash University, Clayton, VIC, Australia

    David Taniar

  7. Department of Information Science, Kyushu Sangyo University, Fukuoka, Japan

    Bernady O. Apduhan

  8. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  9. Polytechnic University of Bari, Bari, Italy

    Eufemia Tarantino

  10. Polytechnic University of Bari, Bari, Italy

    Carmelo Maria Torre

  11. Department of Neurology, University of Massachusetts Medical School, Worcester, MA, USA

    Yeliz Karaca

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Filipe, V., Teixeira, P., Teixeira, A. (2020). A Clustering Approach for Prediction of Diabetic Foot Using Thermal Images. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2020. ICCSA 2020. Lecture Notes in Computer Science(), vol 12251. Springer, Cham. https://doi.org/10.1007/978-3-030-58808-3_45

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  • DOI: https://doi.org/10.1007/978-3-030-58808-3_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58807-6

  • Online ISBN: 978-3-030-58808-3

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