Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
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Nosocomial infections are the undesirable result of a treatment in a hospital, or a health care service unit, not related to the patient's original condition. Despite the evolution of medicine, fundamental problems with hand hygiene remain existent, leading to the spread of nosocomial infections. Our group has been working on a generic solution to provide a method and apparatus to teach and verify proper hand disinfection. The general idea is to mark the skin surfaces that were sufficiently treated with alcoholic hand rub. Digital image processing is employed to determine the location of these areas and overlay it on the segmented hand, visualizing the results in an intuitive form. A non-disruptive ultraviolet marker is mixed to a commercially available hand rub, therefore leaving the original hand washing workflow intact. Digital images are taken in an enclosed device we developed for this purpose. First, robust hand contour segmentation is performed, then a histogram-based formulation of the fuzzy c-means algorithm is employed for the classification of clean versus dirty regions, minimizing the processing time of the images. The method and device have been tested in 3 hospitals in Hungary, Romania and Singapore, on surgeons, residents, medical students and nurses. A health care professional verified the results of the segmentation, since no gold standard is available for the recorded human cases. We were able to identify the hand boundaries correctly in 99.2% of the cases. The device can give objective feedback to medical students and staff to develop and maintain proper hand disinfection practice.