• DocumentCode
    2476461
  • Title

    Automatic Attribute Threshold Selection for Blood Vessel Enhancement

  • Author

    Kiwanuka, Fred N. ; Wilkinson, Michael H F

  • Author_Institution
    Johann Bernoulli Inst. for Math. & Comput. Sci., Univ. of Groningen, Groningen, Netherlands
  • fYear
    2010
  • fDate
    23-26 Aug. 2010
  • Firstpage
    2314
  • Lastpage
    2317
  • Abstract
    Attribute filters allow enhancement and extraction of features without distorting their borders, and never introduce new image features. These are highly desirable properties in biomedical imaging, where accurate shape analysis is paramount. However, setting the attribute-threshold parameters has to date only been done manually. This paper explores simple, fast and automated methods of computing attribute threshold parameters based on image segmentation, thresholding and data clustering techniques. Though several techniques perform well on blood-vessel filtering, the choice of technique appears to depend on the imaging mode.
  • Keywords
    blood vessels; feature extraction; filtering theory; image enhancement; image segmentation; medical image processing; pattern clustering; attribute filters; automatic attribute threshold selection; biomedical imaging; blood vessel enhancement; blood-vessel filtering; data clustering techniques; feature extraction; image segmentation; Biomedical imaging; Blood vessels; Entropy; Histograms; Manuals; Rats; Shape; Connected filters; attribute filters; automatic thresholding; blood-vessel enhancement; clustering; mathematical morphology;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition (ICPR), 2010 20th International Conference on
  • Conference_Location
    Istanbul
  • ISSN
    1051-4651
  • Print_ISBN
    978-1-4244-7542-1
  • Type

    conf

  • DOI
    10.1109/ICPR.2010.566
  • Filename
    5595741