• DocumentCode
    1804040
  • Title

    Robust biological image sequence analysis using graph based approaches

  • Author

    Delibaltov, Diana ; Karthikeyan, S. ; Jagadeesh, Vignesh ; Manjunath, B.S.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Univ. of California, Santa Barbara, Santa Barbara, CA, USA
  • fYear
    2012
  • fDate
    4-7 Nov. 2012
  • Firstpage
    1588
  • Lastpage
    1592
  • Abstract
    Robust methods for segmentation and tracking are critical for quantitative biology. We give an overview of our recent work on graph based methods for various microscopy image analysis, including tracing over 3-D electron microscopy image stacks, tracking in time-lapse confocal image sequences, and 3D segmentation. We present results on a variety of datasets such as 3-D confocal membrane volumes of the ascidian Ciona, electron micrograph stacks from the rabbit retina, and bright field microscopy time sequence data from mouse retina.
  • Keywords
    biological techniques; biology computing; electron microscopy; graph theory; image segmentation; image sequences; object tracking; 3D confocal membrane volumes; 3D electron microscopy image stacks; 3D segmentation; ascidian Ciona; bright field microscopy time sequence data; electron micrograph stacks; graph based approaches; image segmentation; image tracking; microscopy image analysis; mouse retina; quantitative biology; rabbit retina; robust biological image sequence analysis; time-lapse confocal image sequences;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signals, Systems and Computers (ASILOMAR), 2012 Conference Record of the Forty Sixth Asilomar Conference on
  • Conference_Location
    Pacific Grove, CA
  • ISSN
    1058-6393
  • Print_ISBN
    978-1-4673-5050-1
  • Type

    conf

  • DOI
    10.1109/ACSSC.2012.6489297
  • Filename
    6489297