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The Effect of Image Preprocessing Algorithms on Diabetic Foot Ulcer Classification

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Medical Image Understanding and Analysis (MIUA 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14860))

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Abstract

Diabetic foot ulcers significantly affect patient health and healthcare costs, making an accurate diagnosis crucial. This research examines the impact of image preprocessing algorithms on accurately diagnosing and classifying diabetic foot ulcers using the ResNeXt50 classifier. We introduce new strategies for automatic detection of three conditions, i.e., out-of-focus, poor lighting conditions, and the existence of artifacts. For each condition, we identify suitable image preprocessing algorithms. Comparative analysis against baseline performance metrics revealed notable improvements with various preprocessing techniques. Canny Edge Detection notably enhanced the AUC of out-of-focus conditions, while Adaptive Histogram Equalisation and Gaussian Sharpening also showed positive outcomes for poor lighting conditions. Wavelength-based Denoising showed mixed results for artifacts. Overall, preprocessing algorithms improved diabetic foot ulcer classification performance, suggesting their potential integration into associated classification workflows. Recommendations include ongoing algorithmic evaluation and broader application in medical imaging. This study emphasises the vital role of image preprocessing in enhancing diabetic foot ulcer classification accuracy, with the potential to improve wound care and monitoring in real-world scenarios.

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Authors and Affiliations

  1. Department of Computing and Mathematics, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester, UK

    Njideka Chiamaka Okafor, Bill Cassidy & Moi Hoon Yap

  2. Te Whatu Ora Waikato: Hamilton, Waikato, New Zealand

    Claire O’Shea

  3. Lancashire Teaching Hospitals NHS Foundation Trust, Preston, UK

    Joseph M. Pappachan & Moi Hoon Yap

Authors
  1. Njideka Chiamaka Okafor
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  2. Bill Cassidy
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  3. Claire O’Shea
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  4. Joseph M. Pappachan
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  5. Moi Hoon Yap
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Corresponding author

Correspondence to Bill Cassidy .

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Editors and Affiliations

  1. Manchester Metropolitan University, Manchester, Lancashire, UK

    Moi Hoon Yap

  2. Manchester Metropolitan University, Manchester, UK

    Connah Kendrick

  3. Edge Hill University, Ormskirk, UK

    Ardhendu Behera

  4. Aberystwyth University, Aberystwyth, UK

    Timothy Cootes

  5. Aberystwyth University, Aberystwyth, UK

    Reyer Zwiggelaar

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Chiamaka Okafor, N., Cassidy, B., O’Shea, C., Pappachan, J.M., Yap, M.H. (2024). The Effect of Image Preprocessing Algorithms on Diabetic Foot Ulcer Classification. In: Yap, M.H., Kendrick, C., Behera, A., Cootes, T., Zwiggelaar, R. (eds) Medical Image Understanding and Analysis. MIUA 2024. Lecture Notes in Computer Science, vol 14860. Springer, Cham. https://doi.org/10.1007/978-3-031-66958-3_25

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  • DOI: https://doi.org/10.1007/978-3-031-66958-3_25

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  • Online ISBN: 978-3-031-66958-3

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