Computer-Aided Diagnosis in Wound Images with Neural Networks
Pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear or friction. Diagnosis, care and treatment of pressure ulcers can result in extremely expensive costs for health systems. A reliable diagnosis supported by precise wound evaluation is crucial in order to success on the treatment decision and, in some cases, to save the patient’s life. However, current evaluation procedures, focused mainly on visual inspection, do not seem to be accurate enough to accomplish this important task. This paper presents a computer-vision approach based on image processing algorithms and supervised learning techniques to help detecting and classifying wound tissue types which play an important role in wound diagnosis. The system proposed involves the use of the k-means clustering algorithm for image segmentation and a standard multilayer perceptron neural network to classify effectively each segmented region as the appropriate tissue type. Results obtained show a high performance rate which enables to support ulcer diagnosis by a reliable computational system.
KeywordsSupport Vector Machine Colour Space Pressure Ulcer Healing Tissue Tissue Class
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- 1.European Pressure Ulcer Advisory Panel (EPUAP): Guidelines on treatment of pressure ulcers. EPUAP Review 1, 31–33 (1999)Google Scholar
- 4.Stratton, R., Green, C., Elia, M.: Disease-related Malnutrition: An evidence-based approach to treatment. CABI Publishing, Wallingford, United Kingdom (2003)Google Scholar
- 7.Edsberg, L.E.: Pressure ulcer tissue histology: An appraisal of current knowledge. Ostomy/Wound Management 53, 40–49 (2007)Google Scholar
- 8.Cula, O., Dana, K., Murphy, F., Rao, B.: Skin texture modeling. International Journal of Computer Vision 62, 97–119 (2005)Google Scholar
- 12.Wannous, H., Treuillet, S., Lucas, Y.: Supervised tissue classification from color images for a complete wound assessment tool. In: Proceedings of the 29th Annual International Conference of the IEEE EMBS, Cité Internationale, Lyon, France, pp. 6031–6034 (2007)Google Scholar