Robust tissue classification for reproducible wound assessment in telemedicine environments

Hazem Wannous (1) , Sylvie Treuillet (1) , Yves Lucas (1)

1 IRAUS - Département Images, Robotique, Automatique et Signal [Orléans]

In telemedicine environments, a standardized and reproducible assessment of wounds, using a simple free-handled digital camera, is an essential requirement. However, to ensure robust tissue classification, particular attention must be paid to the complete design of the color processing chain. We introduce the key steps including color correction, merging of expert labeling, and segmentation-driven classification based on support vector machines. The tool thus developed ensures stability under lighting condition, viewpoint, and camera changes, to achieve accurate and robust classification of skin tissues. Clinical tests demonstrate that such an advanced tool, which forms part of a complete 3-D and color wound assessment system, significantly improves the monitoring of the healing process. It achieves an overlap score of 79.3 against 69.1% for a single expert, after mapping on the medical reference developed from the image labeling by a college of experts.

Mots clés: color classification; wounds

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Hazem Wannous, Sylvie Treuillet, Yves Lucas. Robust tissue classification for reproducible wound assessment in telemedicine environments. Journal of Electronic Imaging, 2010, pp.023002-1-9. ⟨10.1117/1.3378149⟩. ⟨hal-00648504⟩