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Non-Invasive 3D Wound Measurement with RGB-D Imaging

Lena Harkämper, Leo Lebrat, David Ahmedt-Aristizabal, Olivier Salvado, Mattias Heinrich, Rodrigo Santa Cruz

TL;DR

The paper tackles non-invasive, accurate wound assessment by leveraging RGB-D imaging to reconstruct 3D wound beds. It combines RGB-D odometry with a B-spline surface reconstruction to produce detailed wound meshes and automatically extract clinically relevant metrics such as perimeter, surface area, and dimensions. The approach achieves sub-millimeter reconstruction accuracy and greater measurement repeatability than manual tracing, while running in real time enough for clinical deployment, and it outperforms ArUco-based registration and state-of-the-art BundleSDF in both accuracy and speed. The work advances telehealth and remote monitoring by enabling robust, automated wound assessment using common depth cameras in real-world clinical settings.

Abstract

Chronic wound monitoring and management require accurate and efficient wound measurement methods. This paper presents a fast, non-invasive 3D wound measurement algorithm based on RGB-D imaging. The method combines RGB-D odometry with B-spline surface reconstruction to generate detailed 3D wound meshes, enabling automatic computation of clinically relevant wound measurements such as perimeter, surface area, and dimensions. We evaluated our system on realistic silicone wound phantoms and measured sub-millimetre 3D reconstruction accuracy compared with high-resolution ground-truth scans. The extracted measurements demonstrated low variability across repeated captures and strong agreement with manual assessments. The proposed pipeline also outperformed a state-of-the-art object-centric RGB-D reconstruction method while maintaining runtimes suitable for real-time clinical deployment. Our approach offers a promising tool for automated wound assessment in both clinical and remote healthcare settings.

Non-Invasive 3D Wound Measurement with RGB-D Imaging

TL;DR

The paper tackles non-invasive, accurate wound assessment by leveraging RGB-D imaging to reconstruct 3D wound beds. It combines RGB-D odometry with a B-spline surface reconstruction to produce detailed wound meshes and automatically extract clinically relevant metrics such as perimeter, surface area, and dimensions. The approach achieves sub-millimeter reconstruction accuracy and greater measurement repeatability than manual tracing, while running in real time enough for clinical deployment, and it outperforms ArUco-based registration and state-of-the-art BundleSDF in both accuracy and speed. The work advances telehealth and remote monitoring by enabling robust, automated wound assessment using common depth cameras in real-world clinical settings.

Abstract

Chronic wound monitoring and management require accurate and efficient wound measurement methods. This paper presents a fast, non-invasive 3D wound measurement algorithm based on RGB-D imaging. The method combines RGB-D odometry with B-spline surface reconstruction to generate detailed 3D wound meshes, enabling automatic computation of clinically relevant wound measurements such as perimeter, surface area, and dimensions. We evaluated our system on realistic silicone wound phantoms and measured sub-millimetre 3D reconstruction accuracy compared with high-resolution ground-truth scans. The extracted measurements demonstrated low variability across repeated captures and strong agreement with manual assessments. The proposed pipeline also outperformed a state-of-the-art object-centric RGB-D reconstruction method while maintaining runtimes suitable for real-time clinical deployment. Our approach offers a promising tool for automated wound assessment in both clinical and remote healthcare settings.
Paper Structure (12 sections, 3 equations, 3 figures, 3 tables)

This paper contains 12 sections, 3 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Methods and materials
  3. Point cloud generation
  4. Point cloud registration
  5. Meshing algorithms
  6. 3D Wound Measurements
  7. Experiments
  8. Dataset
  9. 3D reconstruction accuracy
  10. Wound measurements reliability
  11. Conclusion
  12. Compliance with Ethical Standards

Figures (3)

  • Figure 1: Manual wound tracing and surface area measurement Elizabeth2015.
  • Figure 2: Segmented mesh (left) and automatic 3D wound bed measurements (right).
  • Figure 4: Visual comparison of silicone wound phantoms and their 3D reconstructions for three wound types (PIS3, PIS4, SD).