Quantitative fluorescence microscopy and image deconvolution

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

    38 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

    Keywords

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

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