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Project SummaryWhile medical image databases are now prevalent in clinical and educational settings, and traditional means for interacting with and querying such collections can provide some level of useful functionality, there are few examples of systems that attempt to bridge the “semantic gap”. Through the focus on an archive of 60,000 cervigram images assembled by the National Library of Medicine and National Cancer Institute, in this research, we will follow an information hierarchy that proceeds from raw image data to low-level image features, recognition of objects and tissue types, knowledge-based reasoning about disease processes, and, finally, tools and visualizations to support diagnosis decisions by clinicians. It is anticipated that this work will have a positive impact in areas relating to medical image analysis, including information extraction, organization, representation, and querying, as well as in training. The major activities at Lehigh University have centered around organizing visual information in the large medical image archive (Figure 1(a)) for the purpose of search and query (Figure 1(b)). The following research topics are covered:
 
(a) (b)Figure 1. (a) Organizing visual information through automated segmentation and recognition, interaction, and knowledge representation, (b) User query and returning matched images based on information in the knowledge database.What's new
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Room 114, Packard Laboratory, 19 Memorial Drive West, Lehigh University, |