• DocumentCode
    2109373
  • Title

    Supporting reconstruction of the blood vessel network using graph theory: An abstraction method

  • Author

    Bossard, Antoine ; Kato, Toshihiko ; Masuda, Kohji

  • Author_Institution
    Grad. Sch. of Bio-Applic. & Syst. Eng., Tokyo Univ. of Agric. & Technol., Koganei, Japan
  • fYear
    2012
  • fDate
    Aug. 28 2012-Sept. 1 2012
  • Firstpage
    5470
  • Lastpage
    5473
  • Abstract
    The blood vessel network (BVN) has a complex structure. As this structure is unique for each individual, it is not possible to establish a general model for the BVN. However, many medical applications do rely on this structure. For example, a drug delivery system would be greatly improved if it could control the drug flow towards destination. To address this BVN structure issue, several reconstruction methods have been introduced. In this paper, we describe an abstraction method supporting BVN reconstruction by using graph theory. Starting from an original BVN reconstruction, we define the so-called induced graph of that reconstruction, allowing for an efficient analysis. By applying this method, we were able to improve an original BVN reconstruction of a human kidney by pointing out probable errors inside that original reconstruction.
  • Keywords
    biomedical optical imaging; blood flow measurement; blood vessels; drug delivery systems; drugs; graph theory; image reconstruction; kidney; medical image processing; optical tomography; abstraction method; blood vessel network reconstruction; drug delivery system; drug flow; graph theory; human kidney; induced graph; medical applications; optical tomography; probable errors; Bifurcation; Biomedical imaging; Blood; Doppler effect; Image edge detection; Image reconstruction; Blood Vessels; Computer Graphics; Humans; Kidney;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE
  • Conference_Location
    San Diego, CA
  • ISSN
    1557-170X
  • Print_ISBN
    978-1-4244-4119-8
  • Electronic_ISBN
    1557-170X
  • Type

    conf

  • DOI
    10.1109/EMBC.2012.6347232
  • Filename
    6347232