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A Statistical Framework for Model-based Image Retrieval in Medical Applications

By Daniel Keysers, Jörg Dahmen, Hermann Ney, Berthold B. Wein and Thomas M. Lehmann

Abstract

Recently, research in the field of content-based image retrieval has attracted a lot of attention. Nevertheless, most existing methods cannot be easily applied to medical image databases, as global image descriptions based on color, texture or shape do not supply sufficient semantics for medical applications. The concept for content-based image retrieval in medical applications (IRMA) is therefore based on the separation of the following processing steps: categorization of the entire image, registration with respect to prototypes, extraction and query-dependent selection of local features, hierarchical blob representation including object identification and finally, image retrieval

Topics: Medical Imaging, Statistical Pattern Recognition, Invariance Modeling, Image Classication
Year: 2003
OAI identifier: oai:CiteSeerX.psu:10.1.1.18.9672
Provided by: CiteSeerX
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