• DocumentCode
    791414
  • Title

    Simple Linear Models of Scanning Impedance Imaging for Fast Reconstruction of Relative Conductivity of Biological Samples

  • Author

    Oliphant, T.E. ; Hongze Liu ; Hawkins, A.R. ; Schultz, S.M.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Brigham Young Univ., Provo, UT
  • Volume
    53
  • Issue
    11
  • fYear
    2006
  • Firstpage
    2323
  • Lastpage
    2332
  • Abstract
    Scanning impedance imaging (SII) uses a noncontacting electrical probe held at a known voltage and scanned over a thin sample on a ground plane in a conductive medium to obtain images of current. The current image is related in a nonlinear way to the conductivity of the sample. This paper develops the theory behind SII showing how the measured current relates to the desired conductivity. Also included is the development of a simplified, linear model that is effective in explaining many of the experimental results. Good agreement of the linear model with step-response data over an insulator is shown. The linear model shows that the current is a blurred version of the conductivity. Simple deblurring methods can, therefore, be applied to obtain relative conductivity images from the raw current data. Raw SII data from a flower-petal and a leaf sample are shown as well as relative conductivity images deblurred using the linear model
  • Keywords
    electric impedance imaging; electrical conductivity; image restoration; medical image processing; biological samples; deblurring methods; fast reconstruction; flower-petal; leaf sample; noncontacting electrical probe; relative conductivity; scanning impedance imaging; simple linear models; Biological system modeling; Biomedical imaging; Conductivity; Frequency; Image reconstruction; Impedance; Optical imaging; Optical sensors; Probes; Voltage; Biomedical electrodes; biomedical impedance imaging; biomedical microscopy; impedance imaging; Algorithms; Computer Simulation; Electric Impedance; Image Enhancement; Image Interpretation, Computer-Assisted; Linear Models; Models, Biological; Reproducibility of Results; Sensitivity and Specificity; Tomography;
  • fLanguage
    English
  • Journal_Title
    Biomedical Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9294
  • Type

    jour

  • DOI
    10.1109/TBME.2006.883601
  • Filename
    1710174