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    • New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty

    Johannes Stegmaier

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    Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced data sets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present work introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images.

    Umfang: XII, 243 S.

    Preis: €54.00 | £50.00 | $95.00

    Fächer:

    Maschinenbau und Verfahrenstechnik 

    Schlagwörter:

    3D Bildanalyse Entwicklungsbiologie Algorithmen Data Mining Software 3D Image Analysis Developmental Biology Algorithms

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    Empfohlene Zitierweise
    Stegmaier, J. 2017. New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000060221
    Stegmaier, J., 2017. New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000060221
    Stegmaier, J. New Methods to Improve Large-scale Microscopy Image Analysis with Prior Knowledge and Uncertainty. KIT Scientific Publishing, 2017. DOI: https://doi.org/10.5445/KSP/1000060221
    Stegmaier, J. (2017). New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000060221
    Stegmaier, Johannes. 2017. New Methods to Improve Large-scale Microscopy Image Analysis with Prior Knowledge and Uncertainty. Karlsruhe: KIT Scientific Publishing. DOI: https://doi.org/10.5445/KSP/1000060221



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    Weitere Informationen

    Veröffentlicht am 8. Februar 2017

    Sprache

    Englisch

    Seitenanzahl:

    266

    ISBN
    Paperback 978-3-7315-0590-7

    DOI
    https://doi.org/10.5445/KSP/1000060221