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Content-Based Image Retrieval for Digital Mammography

Content-Based Image Retrieval for Digital Mammography
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Author(s): Issam El Naqa (Washington University School of Medicine, USA), Liyang Wei (Hologic, Inc, USA) and Yongyi Yang (Illinois Institute of Technology, USA)
Copyright: 2010
Pages: 24
Source title: Ubiquitous Health and Medical Informatics: The Ubiquity 2.0 Trend and Beyond
Source Author(s)/Editor(s): Sabah Mohammed (Lakehead University, Canada) and Jinan Fiaidhi (Lakehead University, Canada)
DOI: 10.4018/978-1-61520-777-0.ch023

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Abstract

Content-based image retrieval (CBIR) is an emerging field for computerized detection and diagnosis of breast cancer lesions. The underlying principle of CBIR in mammography is to query mammogram databases for diagnostic information based on the content or extracted features of the images instead of their textual annotation. Potentially, this would provide the radiologist with archived examples that are similar to his/her current case. This chapter reviews recent advances in CBIR technology, discuss its expanding role in medical imaging and its particular application to mammography, provides working examples based on the authors’ experience for developing machine-learning methods for CBIR in mammography, and highlights the potential opportunities in this field for computer vision research and clinical decision-making.

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