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Analysis of Similarity Measurements in CBIR Using Clustered Tamura Features for Biomedical Images

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Intelligent Interactive Multimedia Systems and Services 2016

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 55))

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Abstract

Content based image retrieval (CBIR) is an important research topic in many applications, in particular in the biomedical field. In this domain, the CBIR has the aim of helping to improve the diagnosis, retrieving images of patients for which a diagnosis has already been made, similar to the current image. The main issue of CBIR is the selection of the visual contents (feature descriptors) of the images to be extracted for a correct image retrieval. The second issue is the choice of the similarity measurement to use to compare the feature descriptors of the query image to ones of the other images of the database. This paper focuses on a comparison among different similarity measurements in CBIR, with particular interest to a biomedical images database. The adopted technique for CBIR is based on clustered Tamura features. The selected similarity measurements are used both to evaluate the adopted technique for CBIR and to estimate the stability of the results. A comparison with some methods in literature has been carried out, showing the best results for the proposed technique.

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Authors and Affiliations

  1. Institute for High Performance Computing and Networking, National Research Council of Italy (ICAR-CNR), Naples, Italy

    Nadia Brancati

  2. Department of Science and Technology, University of Naples Parthenope, Naples, Italy

    Francesco Camastra

Authors
  1. Nadia Brancati
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  2. Francesco Camastra
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Correspondence to Nadia Brancati .

Editor information

Editors and Affiliations

  1. Institute for High-Perfor Computing Netw, National Research Council of Italy, Naples, Italy

    Giuseppe De Pietro

  2. Institute for High-Perfo Computing Netwo, National Research Council of Italy, Naples, Italy

    Luigi Gallo

  3. KES International, Shoreham-by-sea, United Kingdom

    Robert J. Howlett

  4. University of Canberra, Canberra, South Australia, Australia

    Lakhmi C. Jain

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© 2016 Springer International Publishing Switzerland

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Brancati, N., Camastra, F. (2016). Analysis of Similarity Measurements in CBIR Using Clustered Tamura Features for Biomedical Images. In: Pietro, G., Gallo, L., Howlett, R., Jain, L. (eds) Intelligent Interactive Multimedia Systems and Services 2016. Smart Innovation, Systems and Technologies, vol 55. Springer, Cham. https://doi.org/10.1007/978-3-319-39345-2_1

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  • DOI: https://doi.org/10.1007/978-3-319-39345-2_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39344-5

  • Online ISBN: 978-3-319-39345-2

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