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Comparison of 3D Texture-Based Image Descriptors in Fluorescence Microscopy

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Combinatorial Image Analysis (IWCIA 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8466))

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Abstract

In recent years, research groups pay even more attention on 3D images, especially in the field of biomedical image processing. Adding another dimension enables to capture the entire object. On the other hand, handling 3D images also requires new algorithms, since not all of them can be modified for higher dimensions intuitively. In this article, we introduce a comparison of various implementations of 3D texture descriptors presented in the literature in recent years. We prepared an unified environment to test all of them under the same conditions. From the results of our tests we came to conclusion, that 3D variants of LBP in the combination with k-NN classifier are a very strong approach with the classification accuracy more than 99% on selected group of 3D biomedical images.

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Author information

Authors and Affiliations

  1. Centre for Biomedical Image Analysis, Masaryk University, Brno, Czech Republic

    Tomáš Majtner & David Svoboda

Authors
  1. Tomáš Majtner
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  2. David Svoboda
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Editor information

Editors and Affiliations

  1. Department of Computer and Information Sciences, State University of New York at Fredonia, 280 Central Ave, 14063, Fredonia, NY, USA

    Reneta P. Barneva

  2. Mathematics Department, SUNY Buffalo State College, 14222, Buffalo, NY, USA

    Valentin E. Brimkov

  3. Department of Mathematics, Brno University of Technology, 616 69, Brno, Czech Republic

    Josef Šlapal

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Majtner, T., Svoboda, D. (2014). Comparison of 3D Texture-Based Image Descriptors in Fluorescence Microscopy. In: Barneva, R.P., Brimkov, V.E., Šlapal, J. (eds) Combinatorial Image Analysis. IWCIA 2014. Lecture Notes in Computer Science, vol 8466. Springer, Cham. https://doi.org/10.1007/978-3-319-07148-0_17

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  • DOI: https://doi.org/10.1007/978-3-319-07148-0_17

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07147-3

  • Online ISBN: 978-3-319-07148-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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