Elimination of Linear Structures as an Attempt to Improve the Specificity of Cancerous Mass Detection in Mammograms

  • Marcin Bator
  • Leszek J. Chmielewski
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 45)


In the screening mammographic examination of large parts of populations thousands of mammograms are analysed. The Computer Aided Diagnosis methods available still tend to produce too many false positive (FP) detections with respect to the number of true positive (TP) detections, which makes it impractical to use such methods to support the human observer in the analysis of mammograms. In this paper an attempt has been made to decrease the number of FP errors in the hierarchical correlation-based cancerous mass detection method by eliminating the images of linear structures (LSs) from the mammograms. The LSs were detected with an accumulationbased line detector and the image intensity function in the regions of the LSs was interpolated with an anisotropic membrane. Examples of images representing typical detection problems caused by the LSs selected from the MIAS database suggest the feasibility of the proposed approach.


Linear Structure False Positive Detection Digital Mammogram Cancerous Mass Anisotropic Membrane 
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.


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  1. 1.
    Bator M, Nieniewski M (2006) The usage of template matching and multiresolution for detecting cancerous masses in mammograms. In E. Picetka et al (eds), Proc. 11th Int. Conf. Medical Informatics & Technology, Wisla-Malinka, Poland, 25–27 Sept 2006, pp 324–329Google Scholar
  2. 2.
    Blake A, Zisserman A (1987) Visual Reconstruction. MIT Press, Cambridge, MA, LondonGoogle Scholar
  3. 3.
    Chmielewski L (2006) Detection of non-parametric lines by evidence accumulation: Finding blood vessels in mammograms. In K. Wojciechowski et al (eds) Computer Vision and Graphics: Proc. Int. Conf. Computer Vision and Graphics ICCVG 2004, volume 32 of Computational Imaging and Vision, Warsaw, Poland, 22–24 Sept, 2004. Springer, Berlin Heidelberg New York, pp 373–380Google Scholar
  4. 4.
    Chmielewski L (2005) Specification of the evidence accumulation-based line detection algorithm. In Kurzyński M., Woźniak M, Puchala E et al. (eds). Proceedings of international conference on computer recognition systems CORES 2005, Rydzyna, Poland, 22–25 May 2005. Volume of Advances in Soft Computing. Springer, Berlin Heidelberg New York, pp 355–362Google Scholar
  5. 5.
    Chmielewski LJ (2006) Metody akumulacji danych w analizie obrazów cyfrowych. Akademicka Oficyna Wydawnicza EXIT, WarszawaGoogle Scholar
  6. 6.
    Dziukowa E, Wesolowska E (eds) (2006) Mammografia w diagnostyce raka sutka. Medipage, Warszawa, second editionGoogle Scholar
  7. 7.
    Hong B-W, Brady M (2003) Segmentation of mammograms in topographic approach. In Proc. IEE Int. Conf. on Visual Information Engineering VIE’03, Guildford, U.K., July 2003Google Scholar
  8. 8.
    Kopans DB (1998) Breast Imaging. Lippincott-Raven, PhiladelphiaGoogle Scholar
  9. 9.
    Liu S, Babbs CF Delp EJ (2001) Multiresolution detection of spiculated lesions in digital mammograms. IEEE Trans Image Proc 10(6):874–884zbMATHCrossRefGoogle Scholar
  10. 10.
    Rangayyan RM, Ayres FJ (2006) Gabor filters and phase portraits for the detection of architectural distortion in mammograms. Med Bio Eng Comput 44(10):883–894CrossRefGoogle Scholar
  11. 11.
    Sampat MP, Markey MK, Bovik AC (2005) Computer-aided detection and diagnosis in mammography. In Bovik AC (ed) Handbook of Image and Video Processing. Academic Press, pp 1195–1217Google Scholar
  12. 12.
    Sheshadri HS, Kandaswamy A (2005) Detection of breast cancer tumor based on morphological watershed algorithm. ICGST Int J Graph Vision and Image Proc 05(V5):17–21Google Scholar
  13. 13.
    Suckling J, Parker J et al (1994) The Mammographic Images Analysis Society digital mammogram database. In: Gale AG, Astley SM et al (eds) Digital Mammography. Exerpta medica international congress series, vol 1069, pp 375–378Google Scholar
  14. 14.
    te Brake GM, Karssemeijer N (1999) Single and multiscale detection of masses in digital mammograms. IEEE Trans Med Imaging 18(7):628–639CrossRefGoogle Scholar
  15. 15.
    Thangavel K, Karnan M et al (2005) Automatic detection of microcalcifications in mammograms — A review. ICGST Int J Graph, Vision and Image Proc 05(V5):31–61Google Scholar
  16. 16.
    Zwiggelaar R, Astley SM et al (2004) Linear structures in mammographie images: detection and classification. IEEE Trans Med Imaging 23(9):1077–1086CrossRefGoogle Scholar
  17. 17.
    Zwiggelaar R, Parr TC et al (1999) Model-based detection of spiculated lesions in mammograms. Med Image Anal 3(l):39–62CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Marcin Bator
    • 1
  • Leszek J. Chmielewski
    • 1
  1. 1.Institute of Fundamental Technological ResearchPolish Academy of SciencesWarsaw

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