Minimally invasive medical procedures have become increasingly common in today's healthcare practice. Images taken during such procedures largely show tissues of human organs, such as the mucosa of the gastrointestinal tract. These surfaces usually have a glossy appearance showing specular highlights. For many visual analysis algorithms, these distinct and bright visual features can become a significant source of error. In this article, we propose two methods to address this problem: (a) a segmentation method based on nonlinear filtering and colour image thresholding and (b) an efficient inpainting method. The inpainting algorithm eliminates the negative effect of specular highlights on other image analysis algorithms and also gives a visually pleasing result. The methods compare favourably to the existing approaches reported for endoscopic imaging. Furthermore, in contrast to the existing approaches, the proposed segmentation method is applicable to the widely used sequential RGB image acquisition systems.

Abstract. Colonoscopy is one of the best methods for screening colon cancer. A variety of research groups have proposed methods for automatic detection of polyps in colonoscopic images to support the doctors during examination. However, the problem can still not be assumed as solved. The major drawback of many approaches is the amount and quality of images used for classifier training and evaluation. Our database consists of more than four hours of high resolution video from colonoscopies which were examined and labeled by medical experts. We applied four methods of texture feature extraction based on Grey-LevelCo-occurence and Local-Binary-Patterns. Using this data, we achieved classification results with an area under the ROC-curve of up to 0.96.

Hand washing is a critical activity in preventing the spread of infection in healthcare environments and contamination in food processing industries. The World Health Organization (WHO) guidelines recommend a hand washing protocol consisting of different hand washing poses to ensure that all parts of the hands are thoroughly cleaned. The assessment and training of workers in proper hand hygiene technique is performed by human trainers and can be very tedious and time consuming. In this paper we present an automated computer vision system which measures the user's hand washing technique to ensure that the WHO guidelines are correctly followed. The main contribution of this work is a system which performs robust hand segmentation and hand washing pose classification. The performance of the system is analysed based on data collected in a clinical trial performed in a hospital ward. The performance of the system was compared with that of human auditors. The agreement between two human auditors and between the system and the human auditors were found to be of the same order, which emphasizes the accuracy and validity of the proposed automated system.

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