An Image-Guided Tool to Prevent Hospital Acquired Infections

Hospital Acquired Infections (HAI) represent the fourth leading cause of death in the United States, and claims hundreds of thousands of lives annually in the rest of the world. This paper presents a novel low-cost mobile device—called Stery—Hand—that helps to avoid HAI by improving hand hygiene control through providing an objective evaluation of the quality of hand washing. The use of the system is intuitive: having performed hand washing with a soap mixed with UV reflective powder, the skin appears brighter in UV illumination on the disinfected surfaces. Washed hands are inserted into the Stery-Hand box, where a digital image is taken under UV lighting. Automated image processing algorithms are employed in three steps to evaluate the quality of hand washing. First, the contour of the hand is extracted in order to distinguish the hand from the background. Next, a semi-supervised clustering algorithm classifies the pixels of the hand into three groups, corresponding to clean, partially clean and dirty areas. The clustering algorithm is derived from the histogram-based quick fuzzy c-means approach, using a priori information extracted from reference images, evaluated by experts. Finally, the identified areas are adjusted to suppress shading effects, and quantified in order to give a verdict on hand disinfection quality. The proposed methodology was validated through tests using hundreds of images recorded in our laboratory. The proposed system was found robust and accurate, producing correct estimation for over 98% of the test cases. Stery-Hand may be employed in general practice, and it may also serve educational purposes.