• DocumentCode
    2632631
  • Title

    Snakules for automatic classification of candidate spiculated mass locations on mammography

  • Author

    Muralidhar, Gautam S. ; Markey, Mia K. ; Bovik, Alan C.

  • Author_Institution
    Dept. of Biomed. Eng., Univ. of Texas, Austin, TX, USA
  • fYear
    2010
  • fDate
    23-25 May 2010
  • Firstpage
    197
  • Lastpage
    200
  • Abstract
    In this paper, we describe a novel approach for the automatic classification of candidate spiculated mass locations on mammography. Our approach is based on “Snakules” — an evidence-based active contour algorithm that we have recently developed for the annotation of spicules on mammography. We use snakules to extract features characteristic of spicules and spiculated masses, and use these features to classify whether a region of a mammogram contains a spiculated mass or not. The results from our initial classification experiment demonstrate the strong potential of snakules as an image analysis technique to extract features specific to spicules and spiculated masses, which can subsequently be used to distinguish true spiculated mass locations from non-lesion locations on a mammogram and improve the specificity of computer-aided detection (CADe) algorithms.
  • Keywords
    Active contours; Biomedical engineering; Breast cancer; Detection algorithms; Ducts; Feature extraction; Image analysis; Lesions; Mammography; Solids; active contours; computer-aided detection; snakes; snakules; spiculated masses;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Image Analysis & Interpretation (SSIAI), 2010 IEEE Southwest Symposium on
  • Conference_Location
    Austin, TX, USA
  • Print_ISBN
    978-1-4244-7801-9
  • Type

    conf

  • DOI
    10.1109/SSIAI.2010.5483885
  • Filename
    5483885