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Fuzzy Vector Filters for cDNA Microarray Image Processing

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Fuzzy Systems in Bioinformatics and Computational Biology

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 242))

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Summary

A data-adaptive fuzzy filtering framework is designed to remove noise in microarray images without the requirement for fuzzy rules and local statistics estimation, or under unrealistic assumptions that the original signal is available. This is achieved by utilizing the inference engine in the form of transformed distance metrics between the samples within the supporting window. The training of the filter coefficients is thus based on local image features. Proposed fuzzy filters can preserve important structural elements and eliminate degradations introduced during microarray image formation.

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Author information

Authors and Affiliations

  1. Epson Edge, 3771 Victoria Park Avenue, Toronto, Ontario, M1W 3Z5, Canada

    Rastislav Lukac

  2. The Edward S. Rogers Sr. Department of ECE, University of Toronto, 10 King’s College Road, Toronto, Ontario, M5S 3G4, Canada

    Konstantinos N. Plataniotis

Authors
  1. Rastislav Lukac
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  2. Konstantinos N. Plataniotis
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Editor information

Editors and Affiliations

  1. Honda Research Institute Honda Research Institute Europe , Offenbach, Germany

    Yaochu Jin

  2. Nanyang Technological University, Singapore

    Lipo Wang

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Lukac, R., Plataniotis, K.N. (2009). Fuzzy Vector Filters for cDNA Microarray Image Processing. In: Jin, Y., Wang, L. (eds) Fuzzy Systems in Bioinformatics and Computational Biology. Studies in Fuzziness and Soft Computing, vol 242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89968-6_4

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  • DOI: https://doi.org/10.1007/978-3-540-89968-6_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89967-9

  • Online ISBN: 978-3-540-89968-6

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