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Hyperspectral Imaging Combined with Machine Learning Classifiers for Diabetic Leg Ulcer Assessment – A Case Study

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Medical Image Understanding and Analysis (MIUA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1065))

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Abstract

Diabetic ulcers of the foot are a serious complication of diabetes with huge impact on the patient’s life. Assessing which ulcer will heal spontaneously is of paramount importance. Hyperspectral imaging has been used lately to complete this task, since it has the ability to extract data about the wound itself and surrounding tissues. The classification of hyperspectral data remains, however, one of the most popular subjects in the hyperspectral imaging field. In the last decades, a large number of unsupervised and supervised methods have been proposed in response to this hyperspectral data classification problem. The aim of this study was to identify a suitable classification method that could differentiate as accurately as possible between normal and pathological biological tissues in a hyperspectral image with applications to the diabetic foot. The performance of four different machine learning approaches including minimum distance technique (MD), spectral angle mapper (SAM), spectral information divergence (SID) and support vector machine (SVM) were investigated and compared by analyzing their confusion matrices. The classifications outcome analysis revealed that the SVM approach has outperformed the MD, SAM, and SID approaches. The overall accuracy and Kappa coefficient for SVM were 95.54% and 0.9404, whereas for the other three approaches (MD, SAM and SID) these statistical parameters were 69.43%/0.6031, 79.77%/0.7349 and 72.41%/0.6464, respectively. In conclusion, the SVM could effectively classify and improve the characterization of diabetic foot ulcer using hyperspectral image by generating the most reliable ratio among various types of tissue depicted in the final maps with possible prognostic value.

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Acknowledgments

This work was financed by the Romanian Ministry of Research and Innovation by means of the Program No. 19PFE/17.10.2018.

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Authors and Affiliations

  1. National Institute of Research and Development for Optoelectronics – INOE 2000, Magurele, Romania

    Mihaela A. Calin, Dragos Manea & Roxana Savastru

  2. Carol Davila University of Medicine and Pharmacy Bucharest, Bucharest, Romania

    Sorin V. Parasca

  3. Emergency Clinical Hospital for Plastic, Reconstructive Surgery and Burns, Bucharest, Romania

    Sorin V. Parasca

Authors
  1. Mihaela A. Calin
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  2. Sorin V. Parasca
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  3. Dragos Manea
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  4. Roxana Savastru
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Correspondence to Mihaela A. Calin .

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Editors and Affiliations

  1. University of Liverpool, Liverpool, UK

    Yalin Zheng

  2. University of Liverpool, Liverpool, UK

    Bryan M. Williams

  3. University of Liverpool, Liverpool, UK

    Ke Chen

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Calin, M.A., Parasca, S.V., Manea, D., Savastru, R. (2020). Hyperspectral Imaging Combined with Machine Learning Classifiers for Diabetic Leg Ulcer Assessment – A Case Study. In: Zheng, Y., Williams, B., Chen, K. (eds) Medical Image Understanding and Analysis. MIUA 2019. Communications in Computer and Information Science, vol 1065. Springer, Cham. https://doi.org/10.1007/978-3-030-39343-4_7

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  • DOI: https://doi.org/10.1007/978-3-030-39343-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39342-7

  • Online ISBN: 978-3-030-39343-4

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