• DocumentCode
    249523
  • Title

    On the Way to Big Data Applications in Industrial Computed Tomography

  • Author

    Ditter, Alexander ; Fey, D. ; Schon, Tobias ; Oeckl, Steven

  • Author_Institution
    Dept. of Comput. Sci., Univ. of Erlangen-Nuremberg, Erlangen, Germany
  • fYear
    2014
  • fDate
    June 27 2014-July 2 2014
  • Firstpage
    792
  • Lastpage
    793
  • Abstract
    Computed Tomography (CT) has been around, especially in the medical field, for more than 20 years. Although, the mathematical foundations for CT were known more than a century ago, technical limitations delayed its practical application for more than 70 years. Today, we can build CT systems large enough to scan an entire car, yet, for the processing of the resulting data we are facing a "Big (sensor) Data Problem". We currently do not have suitable methods and tools and cannot handle the large amount of data with conventional state-of-the-art techniques. As industrial CT became more and more prevalent over the last few years, especially due its unique features in the field of non destructive testing, we are proposing and evaluating the use of new methods, work flows and technologies, such as Cloud Computing, in order to provide suitable solutions for handling the steadily growing amount of data and its efficient processing.
  • Keywords
    cloud computing; computerised tomography; image reconstruction; nondestructive testing; 3D reconstruction; CT systems; acquisition time; big data applications; cloud computing; industrial CT; industrial computed tomography; medical field; nondestructive testing; scanned object; Big data; Computed tomography; Linear accelerators; Streaming media; Three-dimensional displays; Wide area networks; X-ray imaging; compression; industrial computed tomography; nondestructive testing; sensor data; streaming;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data (BigData Congress), 2014 IEEE International Congress on
  • Conference_Location
    Anchorage, AK
  • Print_ISBN
    978-1-4799-5056-0
  • Type

    conf

  • DOI
    10.1109/BigData.Congress.2014.125
  • Filename
    6906869