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Mining Knowledge in Computer Tomography Image Databases

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Multimedia Data Mining and Knowledge Discovery

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Abstract

This chapter presents our research results obtained for texture extraction, classification, segmentation, and retrieval of normal soft tissues in Computed Tomography (CT) studies of the chest and abdomen. The texture extraction step consists of various texture methods applied to the collection of tissue data in order to derive a set of features characterizing the best the visual perception of texture. The classification step involves different data mining learning models used to automatically map similar texture features to the same type of tissues, and produce a set of rules that can be used for automatic classification and annotation of unlabelled image data.

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Authors
  1. Daniela Stan Raicu
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Editors and Affiliations

  1. Accenture Technology Labs, Accenture Ltd, 161 N. Clark St., 60601, Chicago, IL, USA

    Valery A. Petrushin MS, PhD

  2. School of Forest Resources and Conservation, University of Florida, EC 31 2601 N. Floyd Rd., 75080-1407, Richardson, TX, USA

    Latifur Khan BS, MS, PhD

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© 2007 Springer

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Raicu, D.S. (2007). Mining Knowledge in Computer Tomography Image Databases. In: Petrushin, V.A., Khan, L. (eds) Multimedia Data Mining and Knowledge Discovery. Springer, London. https://doi.org/10.1007/978-1-84628-799-2_25

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  • DOI: https://doi.org/10.1007/978-1-84628-799-2_25

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84628-436-6

  • Online ISBN: 978-1-84628-799-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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