Advertisement

Detection Of The Breast Contour In Mammograms By Using Active Contour Models

  • Ricardo J. Ferrari
  • Rangaraj M. Rangayyan
  • J. E. Leo Desautels
  • Annie F. Frère
  • Rejane A. Borges
Part of the Topics in Biomedical Engineering. International Book Series book series (ITBE)

We present a method for identification of the breast boundary in mammograms that is intended to be used in the preprocessing stage of a system for computer-aided diagnosis (CAD) of breast cancer and also in the reduction of image file size in Picture Archiving and Communication System (PACS) applications. The method starts by modifying the contrast of the original image. A binarization procedure is then applied to the image, and the chaincode algorithm is used to find an approximate breast contour. Finally, identification of the true breast boundary is performed by using the approximate contour as the input to an active contour model algorithm specially tailored for this purpose. After demarcating the breast boundary, all artifacts outside the breast region are eliminated. The method was applied to 84 medio-lateral oblique mammograms from the Mini-MIAS (Mammographic Image Analysis Society, London, UK) database. Evaluation of the breast boundary detected was performed based upon the percentage of false-positive (FP) and false-negative (FN) pixels determined by a quantitative comparison between the contours identified by a radiologist and by the proposed method. The average FP and FN rates are 0.41 and 0.58%, respectively. According to two radiologists who evaluated the results, the segmentation results were considered acceptable for CAD purposes.

Keywords

Original Image Active Contour Active Contour Model Initial Contour Digital Mammogram 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lou SL, Lin HD, Lin KP, Hoogstrate D. 2000. Automatic breast region extraction from digital mammograms for PACS and telemammography applications. Comput Med Imaging Graphics 24:205-220.CrossRefGoogle Scholar
  2. 2.
    Bick U, Giger ML, Schmidt RA, Nishikawa RM, Doi K. 1996. Density correction of peripheral breast tissue on digital mammograms. RadioGraphics 16(6):1403-1411.Google Scholar
  3. 3.
    Byng JW, Critten JP, Yaffe MJ. 1997. Thickness-equalization processing for mammographic images. Radiology 203(2):564-568.Google Scholar
  4. 4.
    Chandrasekhar R, Attikiouzel Y. 1997. A simple method for automatically locating the nipple on mammograms. IEEE Trans Medical Imaging 16(5):483-494.CrossRefGoogle Scholar
  5. 5.
    Lau TK, Bischof WF. 1991. Automated detection of breast tumors using the asymmetry approach. Comput Biomed Res 24:273-295.CrossRefGoogle Scholar
  6. 6.
    Miller P, Astley S. Automated detection of mammographic asymmetry using anatomical features. 1993. Int J Pattern Recognit Artif Intell 7(6):1461-1476.CrossRefGoogle Scholar
  7. 7.
    M éndez AJ, Tahoces PG, Lado MJ, Souto M, Correa JL, Vidal JJ. 1996. Automatic detection of breast border and nipple in digital mammograms. Comput Methods Programs Biomed 49:253-262.CrossRefGoogle Scholar
  8. 8.
    Bick U, Giger ML, Schmidt RA, Nishikawa RM, Wolverton DE, Doi K. 1995. Automated seg-mentation of digitized mammograms. Acad Radiol 2(1):1-9.CrossRefGoogle Scholar
  9. 9.
    Masek M, AttikiouzelY, deSilva CJS. 2000. Combining data from different algorithms to segment the skin-air interface in mammograms. In Proceedings of the 22nd annual EMBS international conference, Vol. 2, pp. 1195-1198. Washington, DC: IEEE.Google Scholar
  10. 10.
    Ferrari RJ, Rangayyan RM, Desautels JEL, Fr ère AF. 2000. Segmentation of mammograms: identification of the skin-air boundary, pectoral muscle, and fibro-glandular disc. In Proceedings of the 5th international workshop on digital Mammography, pp. 573-579. Ed MJYaffe. Madison, WI: Medical Physics Publishing.Google Scholar
  11. 11.
    Kass M, Witkin A, Terzopoulos D. 1988. Snakes: active contour models. Int J Comput Vision 1(4):321-331.CrossRefGoogle Scholar
  12. 12.
    Gonzalez RC, Woods RE. 1992. Digital image processing. Reading, MA:: Addison-Wesley.Google Scholar
  13. 13.
    Suckling J, Parker J, Dance DR, Astley S, Hutt I, Boggis CRM, Ricketts I, Stamatakis E, Cerneaz N, Kok SL, Taylor P, Betal D, Savage J. 1994. The mammographic image analysis society digital mammogram database. In Proceedings of the 2nd international workshop on digital mammogra-phy, pp. 375-378. Ed AG Gale, SM Astley, DR Dance, AY Cairns. Excerpta Medica International Congress Series, Vol. 1069. Amsterdam: Elsevier.Google Scholar
  14. 14.
    Lloyd S. 1982. Least squares quantization in PCM. IEEE Trans Inf Theory 28:129-137.MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Mackiewich B. 1995. Intracranial boundary detection and radio frequency correction in mag-netic resonance images. Master’s thesis, School of Computing Science, Simon Fraser University, Burnaby, BC, Canada.Google Scholar
  16. 16.
    Lobregt S, Viergever MA. 1995. A discrete dynamic contour model. IEEE Trans Med Imaging 14(1):12-24.CrossRefGoogle Scholar
  17. 17.
    Williams DJ, Shah M. 1992.A fast algorithm for active contours and curvature estimation. Comput Vision Graphics Image Proces: Image Understand 55(1):14-26.MATHGoogle Scholar
  18. 18.
    Mattis P, Kimball S. 2005. GIMP-GNU image manipulation program. http://www.gimp.org.
  19. 19.
    Ferrari RJ, Rangayyan RM, Desaultels JEL, Frère AF. 2001. Analysis of asymmetry in mammo-grams via directional filtering with Gabor wavelets. IEEE Trans Med Imaging 20(9):953-964.CrossRefGoogle Scholar
  20. 20.
    Mackiewich B, Desautels JEL, Borges RA, Frère AF. 2004. Intracranial boundary detection and radio frequency correction in magnetic resonance images. Med Biol Eng Comput 42(2):201-208.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Ricardo J. Ferrari
    • 1
  • Rangaraj M. Rangayyan
    • 1
  • J. E. Leo Desautels
    • 1
  • Annie F. Frère
    • 1
  • Rejane A. Borges
    • 2
  1. 1.Department of Electrical and Computer EngineeringUniversity of CalgaryCalgaryCanada
  2. 2.Nucleus of Science and TechnologyUniversity of Mogi das CruzesSáo PauloBrazil

Personalised recommendations