Specification of the Evidence Accumulation-Based Line Detection Algorithm

Towards Finding Blood Vessels in Mammograms
  • Leszek J. Chmielewski
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 30)


The recently proposed algorithm, using the evidence accumulation principle, for finding lines (ridges) having shape which can be neither parameterized nor tabularized is described in detail. This fuzzy, multi-scale algorithm stores the evidence in the accumulator congruent with the image domain. The primary application was finding blood vessels in mammograms.


Central Pixel Hough Transform Mammographic Image Elementary Accumulation Evidence Accumulation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Hough PVC (1959) In: Proc Int Conf High Energy Accelerators and Instrumentation. CERN.Google Scholar
  2. 2.
    Maître H (1985) Un panorama de la transformation de Hough. Traitement du Signal, 2(4):305–317MathSciNetGoogle Scholar
  3. 3.
    Leavers VF (1993) Which Hough transform? CVGIP-IU 58:250–264CrossRefGoogle Scholar
  4. 4.
    Lam WCY, Lam MTS et al. (1994) A general evidence accumulation technique for Hough transformation. In: Proc IEEE Int Conf SMC, vol 3, 2414–2419Google Scholar
  5. 5.
    Aguado AS, Nixon MS, Montiel EM (1998) Parameterizing arbitrary shapes via Fourier descriptors for evidence-gathering extraction. CVIU 69(2):202–211Google Scholar
  6. 6.
    Merlin PM, Farber DJ (1975) A parallel mechanism for detecting curves in pictures. IEEE Trans Comp 24:96–98zbMATHCrossRefGoogle Scholar
  7. 7.
    Ballard DH (1981) Generalizing the Hough transform to detect arbitrary shapes. Pat Rec 13:111–122zbMATHCrossRefGoogle Scholar
  8. 8.
    Chmielewski L (2004) Detection of non-parametric lines by evidence accumulation: Finding blood vessels in mammograms. In: Proc. ICCVG 2004, vol of Computational Imaging and Vision. Springer. In print.Google Scholar
  9. 9.
    Strauss O (1999) Use the Fuzzy Hough Transform towards reduction of the precision-uncertainty duality. Pat Rec 32:1911–1922CrossRefGoogle Scholar
  10. 10.
    Reisfeld D, Wolfson H, Yeshurun Y (1995) Context-free attentional operators: the Generalised Symmetry Transform. Int J Comput Vis 14:119–130CrossRefGoogle Scholar
  11. 11.
    Zwiggelaar R, Astley SM et al. (2004) Linear structures in mammographic images: detection and classification. IEEE Trans Med Imag 23(9):1077–1086CrossRefGoogle Scholar
  12. 12.
    Zwiggelaar R, Parr TC, Taylor CJ (1996) Finding orientated line patterns in digital mammographic images. In: Proc 7th BMVC 96 715–724Google Scholar
  13. 13.
    Dixon RN, Taylor CJ (1979) Automated asbestos fibre counting. In: Proc Inst Phys Conf Series vol 44, 178–185Google Scholar
  14. 14.
    Lindeberg T (1998) Edge detection and ridge detection with automatic scale selection. Int J Comput Vis 30(2):117–156CrossRefGoogle Scholar
  15. 15.
    Zwiggelaar R, Boggis CRM (2001) Classification of linear structures in mammographic images. In: Proc Conf Med Image Underst Analysis 2001 Google Scholar
  16. 16.
    Chmielewski L (2005) Scale and direction invariance of the evidence accumulation-based line detection algorithm. In: Proc. CORES 2005, vol of Advances in Soft Computing. Springer. (In the same volume.)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Leszek J. Chmielewski
    • 1
  1. 1.Institute of Fundamental Technological ResearchPASWarsawPoland

Personalised recommendations