Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
International Journal of Computer Vision - Special Issue on Texture Analysis and Synthesis
Neural Computation
Computer-aided tumor detection in endoscopic video using color wavelet features
IEEE Transactions on Information Technology in Biomedicine
Pattern Recognition and Image Analysis
Computer-aided diagnosis in wound images with neural networks
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
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Pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue with high prevalence rates in aged people. Diagnosis and treatment of pressure ulcers involve high costs for sanitary systems. Accurate wound-state evaluation is a critical task for optimizing the effectiveness of treatments. Reliable trace of wound-state evolution can be done by precisely registering the wound area. Clinicians estimate the wound area with often subjective and imprecise manual methods. This article presents a computer-vision approach based on machine hybrid-learning techniques to precise automatic estimation of wound dimensions on pressure ulcer real images taken under non-controlled illumination conditions. The system combines neural networks and Bayesian classifiers to effectively recognize and separate skin and healing regions from wound-tissue regions to be measured. This tissue-recognition approach to wound area estimation gives high performance rates and operates better than a widespread clinical method when approximating real wound areas of variable size.