International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Tissue Recognition Approach to Pressure Ulcer Area Estimation with Neural Networks
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Computer-aided tumor detection in endoscopic video using color wavelet features
IEEE Transactions on Information Technology in Biomedicine
| Hi-index | 0.00 |
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.