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Predicting Complex Patterns of Human Movements Using Bayesian Online Learning in Medical Imaging Applications
Abstract
Medical images, in its tough sense, are fundamental in most clinical procedures and have become part of the medical act. Different acquisition methodologies result in a large variety of challenges or diagnostic tasks. Overall, most applications are dedicated to imaging structures so that complex measurements may be achieved. However, function analysis necessitates imaging structures through the time, either at the level of the image itself or at the interaction strategy between the user and the image. This chapter presents a Bayesian Framework which allows an adequate temporal follow up of very complex human movements, which somehow have been imaged. The Bayesian strategy is implemented through a particle filter, resulting in real time tracking of these complex patterns. Two different imaged patterns illustrate the potential of the procedure: a precise tracking a pathologist in a Virtual Microscopy context and a temporal follow up of gait patterns.
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