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Measuring Plantar Temperature Changes in Thermal Images Using Basic Statistical Descriptors

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Computational Science and Its Applications – ICCSA 2021 (ICCSA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12953))

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Abstract

One of the principal complications of patients that suffer from Diabetes Mellitus (DM) and that can lead to ulceration is the Diabetic foot. As tissue inflammation causes temperature variation, several studies show that thermography can be used to detect complications in diabetic foot and help predicting the risk of ulceration. It is known that, although healthy individuals present characteristic plantar temperature variation patterns, the same does not happen with diabetic patients, for which a particular pattern can not be found; thus, making the measurement of the temperature variation more difficult. Given that, it is important to research in this field in order to obtain methods that can detect atypical variations of the temperature in the sole of the foot. With this in mind, the objective of this work is to present a methodology to analyze the distribution of temperature in thermograms of the foot’s plant and classify it as belonging to a DM individual with risk of ulceration or a healthy individual. After foot partitioning with a clustering algorithm, basic statistical descriptors are computed for each cluster. A binary classifier to predict the risk of ulceration in the diabetic foot was evaluated with the different descriptors; both a quantitative temperature index and a classification threshold are calculated for each descriptor. To evaluate the performance of the classifier, experiments were conducted using a public dataset (containing 45 thermograms of healthy individuals and 122 images of DM ones); the following metrics were obtained: Accuracy = 78%, AUC = 86% and F-measure = 84%, with the best descriptor.

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Acknowledgments

This work was supported by the project “WalkingPAD - Patient education on a quantified supervised home-based exercise therapy to improve walking ability in patients with peripheral arterial disease and intermittent claudication” (PTDC/MEC-VAS/31161/2017) funded by Fundação para a Ciência e a Tecnologia (FCT), Portugal.

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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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Corresponding author

Correspondence to Vítor Filipe .

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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. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. University of Cagliari, Cagliari, Italy

    Chiara Garau

  5. University of Cagliari, Cagliari, Italy

    Ivan Blečić

  6. Monash University, Clayton, VIC, Australia

    David Taniar

  7. 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

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Filipe, V., Teixeira, P., Teixeira, A. (2021). Measuring Plantar Temperature Changes in Thermal Images Using Basic Statistical Descriptors. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12953. Springer, Cham. https://doi.org/10.1007/978-3-030-86976-2_30

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  • DOI: https://doi.org/10.1007/978-3-030-86976-2_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86975-5

  • Online ISBN: 978-3-030-86976-2

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