Content-Based Image Retrieval for Medical Image Analysis

Content-Based Image Retrieval for Medical Image Analysis

Jianhua Yao (National Institutes of Health, USA) and Ronald M. Summers (National Institutes of Health, USA)
Copyright: © 2013 |Pages: 17
DOI: 10.4018/978-1-4666-2455-9.ch056
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The growing repositories of clinical imaging data generate a need for effective image management and access that demands more than simple text-based queries. Content-Based Image Retrieval (CBIR) is an active research field and has drawn attention in recent years. It is a technique to organize and search image archives by their visual content. It is a multi-discipline field that integrates technologies from computer vision, machine learning, information retrieval, human-machine interaction, database systems, and data mining. CBIR consists of four main components: database and indexing, feature extraction, query formation and interface, and similarity measures. The applications of CBIR to the medical field include PACS integration, image annotation/codification, computer-aided diagnosis, case-based reasoning, and teaching tools. This chapter intends to disseminate the CBIR techniques to their applications to medical image management and analysis and to attract greater interest from various research communities to advance research in this field.
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Cbir Techniques

The techniques in CBIR evolve from many other disciplines such as computer vision, computer graphics, artificial intelligence and information retrieval. This section will describe the architecture and its main components. Most descriptions are high level with references for further reading.

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