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<p>Diabetic foot ulcer (DFU) is a common and severe complication of diabetes that leads to amputation if not effectively managed. Intelligent offloading or rehabilitation devices or boots are used to manage or observe the ulcer and the process of treatment which requires the segmentation and clustering analysis of 3D scanned foot models. This study examines the effectiveness of two widely used clustering algorithms, K-Means and Gaussian Mixture Models (GMM), in segmenting scanned foot models to use in rehabilitation boots. The performance of K-Means and GMM was compared across 98 foot models. GMM consistently achieved higher silhouette scores (0.58 vs. 0.42 for <i>K</i> = 5), lower Davies-Bouldin scores (0.47 vs. 0.54 for <i>K</i> = 5), and more stable clustering across anatomical sections, despite requiring almost 20% more computation time. These results highlight GMM’s superior ability to capture the complex nonlinear structures of diabetic feet, with implications for more precise and personalized offloading boot design. Precise segmentation of scanned foot models is a crucial step in various anatomical and medical applications, such as the design of custom orthotic devices, rehabilitation offloading boots, and the analysis of foot biomechanics—particularly useful within DFU management.</p>

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Segmentation and Clustering Analysis of Scanned Foot Models for Intelligent Diabetic Foot Ulcer Offloading Boots

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月10日

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  • Yaser Sabzehmeidani,
  • Andrei-Alexandru Popa

摘要

Diabetic foot ulcer (DFU) is a common and severe complication of diabetes that leads to amputation if not effectively managed. Intelligent offloading or rehabilitation devices or boots are used to manage or observe the ulcer and the process of treatment which requires the segmentation and clustering analysis of 3D scanned foot models. This study examines the effectiveness of two widely used clustering algorithms, K-Means and Gaussian Mixture Models (GMM), in segmenting scanned foot models to use in rehabilitation boots. The performance of K-Means and GMM was compared across 98 foot models. GMM consistently achieved higher silhouette scores (0.58 vs. 0.42 for K = 5), lower Davies-Bouldin scores (0.47 vs. 0.54 for K = 5), and more stable clustering across anatomical sections, despite requiring almost 20% more computation time. These results highlight GMM’s superior ability to capture the complex nonlinear structures of diabetic feet, with implications for more precise and personalized offloading boot design. Precise segmentation of scanned foot models is a crucial step in various anatomical and medical applications, such as the design of custom orthotic devices, rehabilitation offloading boots, and the analysis of foot biomechanics—particularly useful within DFU management.