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    Focus classification in digital holographic microscopy using deep convolutional neural networks

    Pitkäaho, Tomi and Manninen, Aki and Naughton, Thomas J. (2017) Focus classification in digital holographic microscopy using deep convolutional neural networks. In: Advances in Microscopic Imaging. Proceedings of SPIE-OSA (10414). SPIE, Bellingham, WA, 104140K. ISBN 9781510612860

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    Digital holographic microscopy (DHM) is a label-free, single-shot technique that is well suited for imaging three dimensional objects [1]. DHM overcomes a problem present in conventional optical microscopes of a shallow depth-of-field, permitting one to reconstruct at different in-focus planes of a volume. Nevertheless, an object of interest is usually in-focus only in one or few depths as a single reconstruction layer still has a shallow depth-of-field. To solve the problem of finding a focus plane of an object different methods have been proposed. These methods can be based on self-entropy [2], amplitude analysis [3], power spectra [4] or other metrics [5]. Common to all of these single wavelength methods is that they reconstruct a stack of images that the focus metric is applied to. A minimum or a maximum value, depending on the used focus metric, is used to determine the in-focus plane. This procedure is applied to each of the holograms. We propose to use deep learning as an autofocusing method. The greatest benefit of the proposed method is that after the training is completed, the in-focus plane can be obtained by using only the single in the hologram plane and without any reconstruction.

    Item Type: Book Section
    Additional Information: This paper has been presented at European Conferences on Biomedical Optics, 2017, Munich, Germany
    Keywords: Digital holography; Three-dimensional image processing;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 12025
    Identification Number:
    Depositing User: Thomas Naughton
    Date Deposited: 17 Dec 2019 11:34
    Publisher: SPIE
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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