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Content-Based Access to Medical Image Collections
Abstract
Medical images are a very important resource for the clinical practice and operation. Thousands of them are daily acquired in hospitals to diagnose the health state of patients. However, once they have been archived as part of a large image database, it is very difficult to retrieve the same image again since it requires remembering dates or names. Furthermore, in such cases in which the same image is not required, but a physician is looking for images with particular contents, current technologies are not able to offer such functionality. The ability to find the right visual information in the right place, at the right time, can have great impact in the medical decision making process. This chapter presents two computational strategies for accessing a large collection of medical images: retrieving relevant images given an explicit query and visualizing the structure of the whole collection. Both strategies take advantage of image contents, allowing users to find or identify images that are related by their visual composition. In addition, these strategies are based on machine learning methods to handle complex image patterns, semantic medical concepts, image collection visualizations and summarizations.
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