Description

Detecting edges in images from a finite sampling of Fourier data is important in a variety of applications. For example, internal edge information can be used to identify tissue boundaries

Detecting edges in images from a finite sampling of Fourier data is important in a variety of applications. For example, internal edge information can be used to identify tissue boundaries of the brain in a magnetic resonance imaging (MRI) scan, which is an essential part of clinical diagnosis. Likewise, it can also be used to identify targets from synthetic aperture radar data. Edge information is also critical in determining regions of smoothness so that high resolution reconstruction algorithms, i.e. those that do not “smear over” the internal boundaries of an image, can be applied.

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Date Created
  • 2014-12-01
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  • Text
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    Identifier
    • Digital object identifier: 10.1007/s10915-014-9836-y
    • Identifier Type
      International standard serial number
      Identifier Value
      0885-7474
    • Identifier Type
      International standard serial number
      Identifier Value
      1573-7691
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    This is a suggested citation. Consult the appropriate style guide for specific citation guidelines.

    Martinez, Adam, Gelb, Anne, & Gutierrez, Alexander (2014). Edge Detection from Non-Uniform Fourier Data Using the Convolutional Gridding Algorithm. JOURNAL OF SCIENTIFIC COMPUTING, 61(3), 490-512. http://dx.doi.org/10.1007/s10915-014-9836-y

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