Quantitative fluorescence microscopy and image deconvolution

    Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

    37 Citations (Scopus)

    Abstract

    Quantitative imaging and image de-convolution have become standard techniques for the modern cell biologist because they can form the basis of an increasing number of assays for molecular function in a cellular context. There are two major types of de-convolution approaches—deblurring and restoration algorithms. Deblurring algorithms remove blur but treat a series of optical sections as individual two-dimensional entities and therefore sometimes mishandle blurred light. Restoration algorithms determine an object that, when convolved with the point-spread function of the microscope, could produce the image data. The advantages and disadvantages of these methods are discussed in this chapter. Image de-convolution in fluorescence microscopy has usually been applied to high-resolution imaging to improve contrast and thus detect small, dim objects that might otherwise be obscured. Their proper use demands some consideration of the imaging hardware, the acquisition process, fundamental aspects of photon detection, and image processing. This can prove daunting for some cell biologists, but the power of these techniques has been proven many times in the works cited in the chapter and elsewhere. Their usage is now well defined, so they can be incorporated into the capabilities of most laboratories.
    Original languageEnglish
    Title of host publicationDigital microscopy
    EditorsGreenfield Sluder, David E. Wolf
    Place of PublicationAmsterdam
    PublisherAcademic Press
    Pages447-465
    Number of pages19
    Edition3rd
    ISBN (Print)9780123740250
    DOIs
    Publication statusPublished - 2007

    Publication series

    NameMethods in Cell Biology
    PublisherAcademic Press
    Volume81

    Fingerprint

    Fluorescence Microscopy
    Photons
    Light

    Keywords

    • Evaluation Studies as Topic
    • Image Processing, Computer-Assisted
    • Microscopy, Fluorescence
    • Models, Theoretical

    Cite this

    Swedlow, J. R. (2007). Quantitative fluorescence microscopy and image deconvolution. In G. Sluder, & D. E. Wolf (Eds.), Digital microscopy (3rd ed., pp. 447-465). (Methods in Cell Biology; Vol. 81). Amsterdam: Academic Press. https://doi.org/10.1016/S0091-679X(06)81021-6
    Swedlow, Jason R. / Quantitative fluorescence microscopy and image deconvolution. Digital microscopy. editor / Greenfield Sluder ; David E. Wolf. 3rd. ed. Amsterdam : Academic Press, 2007. pp. 447-465 (Methods in Cell Biology).
    @inbook{42e61e96fa5d4730b3fe5df2add302d9,
    title = "Quantitative fluorescence microscopy and image deconvolution",
    abstract = "Quantitative imaging and image de-convolution have become standard techniques for the modern cell biologist because they can form the basis of an increasing number of assays for molecular function in a cellular context. There are two major types of de-convolution approaches—deblurring and restoration algorithms. Deblurring algorithms remove blur but treat a series of optical sections as individual two-dimensional entities and therefore sometimes mishandle blurred light. Restoration algorithms determine an object that, when convolved with the point-spread function of the microscope, could produce the image data. The advantages and disadvantages of these methods are discussed in this chapter. Image de-convolution in fluorescence microscopy has usually been applied to high-resolution imaging to improve contrast and thus detect small, dim objects that might otherwise be obscured. Their proper use demands some consideration of the imaging hardware, the acquisition process, fundamental aspects of photon detection, and image processing. This can prove daunting for some cell biologists, but the power of these techniques has been proven many times in the works cited in the chapter and elsewhere. Their usage is now well defined, so they can be incorporated into the capabilities of most laboratories.",
    keywords = "Evaluation Studies as Topic, Image Processing, Computer-Assisted, Microscopy, Fluorescence, Models, Theoretical",
    author = "Swedlow, {Jason R.}",
    year = "2007",
    doi = "10.1016/S0091-679X(06)81021-6",
    language = "English",
    isbn = "9780123740250",
    series = "Methods in Cell Biology",
    publisher = "Academic Press",
    pages = "447--465",
    editor = "Sluder, {Greenfield } and Wolf, {David E. }",
    booktitle = "Digital microscopy",
    edition = "3rd",

    }

    Swedlow, JR 2007, Quantitative fluorescence microscopy and image deconvolution. in G Sluder & DE Wolf (eds), Digital microscopy. 3rd edn, Methods in Cell Biology, vol. 81, Academic Press, Amsterdam, pp. 447-465. https://doi.org/10.1016/S0091-679X(06)81021-6

    Quantitative fluorescence microscopy and image deconvolution. / Swedlow, Jason R.

    Digital microscopy. ed. / Greenfield Sluder; David E. Wolf. 3rd. ed. Amsterdam : Academic Press, 2007. p. 447-465 (Methods in Cell Biology; Vol. 81).

    Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

    TY - CHAP

    T1 - Quantitative fluorescence microscopy and image deconvolution

    AU - Swedlow, Jason R.

    PY - 2007

    Y1 - 2007

    N2 - Quantitative imaging and image de-convolution have become standard techniques for the modern cell biologist because they can form the basis of an increasing number of assays for molecular function in a cellular context. There are two major types of de-convolution approaches—deblurring and restoration algorithms. Deblurring algorithms remove blur but treat a series of optical sections as individual two-dimensional entities and therefore sometimes mishandle blurred light. Restoration algorithms determine an object that, when convolved with the point-spread function of the microscope, could produce the image data. The advantages and disadvantages of these methods are discussed in this chapter. Image de-convolution in fluorescence microscopy has usually been applied to high-resolution imaging to improve contrast and thus detect small, dim objects that might otherwise be obscured. Their proper use demands some consideration of the imaging hardware, the acquisition process, fundamental aspects of photon detection, and image processing. This can prove daunting for some cell biologists, but the power of these techniques has been proven many times in the works cited in the chapter and elsewhere. Their usage is now well defined, so they can be incorporated into the capabilities of most laboratories.

    AB - Quantitative imaging and image de-convolution have become standard techniques for the modern cell biologist because they can form the basis of an increasing number of assays for molecular function in a cellular context. There are two major types of de-convolution approaches—deblurring and restoration algorithms. Deblurring algorithms remove blur but treat a series of optical sections as individual two-dimensional entities and therefore sometimes mishandle blurred light. Restoration algorithms determine an object that, when convolved with the point-spread function of the microscope, could produce the image data. The advantages and disadvantages of these methods are discussed in this chapter. Image de-convolution in fluorescence microscopy has usually been applied to high-resolution imaging to improve contrast and thus detect small, dim objects that might otherwise be obscured. Their proper use demands some consideration of the imaging hardware, the acquisition process, fundamental aspects of photon detection, and image processing. This can prove daunting for some cell biologists, but the power of these techniques has been proven many times in the works cited in the chapter and elsewhere. Their usage is now well defined, so they can be incorporated into the capabilities of most laboratories.

    KW - Evaluation Studies as Topic

    KW - Image Processing, Computer-Assisted

    KW - Microscopy, Fluorescence

    KW - Models, Theoretical

    U2 - 10.1016/S0091-679X(06)81021-6

    DO - 10.1016/S0091-679X(06)81021-6

    M3 - Chapter (peer-reviewed)

    SN - 9780123740250

    T3 - Methods in Cell Biology

    SP - 447

    EP - 465

    BT - Digital microscopy

    A2 - Sluder, Greenfield

    A2 - Wolf, David E.

    PB - Academic Press

    CY - Amsterdam

    ER -

    Swedlow JR. Quantitative fluorescence microscopy and image deconvolution. In Sluder G, Wolf DE, editors, Digital microscopy. 3rd ed. Amsterdam: Academic Press. 2007. p. 447-465. (Methods in Cell Biology). https://doi.org/10.1016/S0091-679X(06)81021-6