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Identification of the breast boundary in mammograms using active contour models

  • R. J. Ferrari
  • A. F. Frère
  • R. M. Rangayyan
  • J. E. L. Desautels
  • R. A. Borges
Article

Abstract

A method for the identification of the breast boundary in mammograms is presented. The method can be used in the preprocessing stage of a system for computeraided diagnosis (CAD) of breast cancer and also in the reduction of image file size in picture archiving and communication system applications. The method started with modification of the contrast of the original image. A binarisation procedure was then applied to the image, and the chain-code algorithm was used to find an approximate breast contour. Finally, the identification of the true breast boundary was performed by using the approximate contour as the input to an active contour model algorithm specially tailored for this purpose. After demarcation of the breast boundary, all artifacts outside the breast region were eliminated. The method was applied to 84 medio-lateral oblique mammograms from the Mini-MIAS database. Evaluation of the detected breast boundary was performed based upon the percentage of false-positive and false-negative pixels determined by a quantitative comparison between the contours identified by a radiologist and those identified by the proposed method. The average false positive and false negative rates were 0.41% and 0.58%, respectively. The two radiologists who evaluated the results considered the segmentation results to be acceptable for CAD purposes.

Keywords

Mammogram segmentation Skin-air boundary Breast boundary Mammography Active contour model 

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References

  1. Bick, U., Giger, M. L., Schmidt, R. A., Nishikawa, R. M., Wolverton, D. E., andDoi, K. (1995): ‘Automated segmentation of digitized mammograms’,Acad. Radiol.,2, pp. 1–9CrossRefGoogle Scholar
  2. Bick, U., Giger, M. L., Schmidt, R. A., Nishikawa, R. M., andDoi, K. (1996): ‘Density correction of peripheral breast tissue on digital mammograms’,Radio Graphics,16, pp. 1403–1411Google Scholar
  3. Byng, J. W., Critten, J. P., andYaffe, M. J. (1997): ‘Thickness-equalization processing for mammographic images’,Radiology,203, pp. 564–568Google Scholar
  4. Chandrasekhar, R., andAttikiouzel, Y. (1997): ‘A simple method for automatically locating the nipple on mammograms’,IEEE Trans. Med. Imag.,16, pp. 483–494Google Scholar
  5. Ferrari, R. J., Rangayyan, R. M., Desautels, J. E. L., andFrère, A. F. (2000): ‘Segmentation of mammograms: identification of the skin-air boundary, pectoral muscle, and fibro-glandular disc’, inYaffe, M. J. (Ed.): ‘Proc. 5th Int. workshop on digital mammography’ Toronto, ON, Canada, pp. 573–579Google Scholar
  6. Ferrari, R. J., Rangayyan, R. M., Desautels, J. E. L., andFrère, A. F. (2001): ‘Analysis of asymmetry in mammograms via directional filtering with Gabor wavelets’,IEEE Trans. Med. Imag.,20, pp. 953–964Google Scholar
  7. Gonzalez, R. C., andWoods, R. E. (1992): ‘Digital image processing’, (Addison-Wesley, Reading, MA, 1992)Google Scholar
  8. Kass, M., Witkin, A., andTerzopoulos, D., (1988): ‘Snakes: active contour models,Int. J. Comput. Vis.,1, pp. 321–331Google Scholar
  9. Lau, T. K., andBischof, W. F. (1991): ‘Automated detection of breast tumors using the asymmetry approach’,Comput. Biomed. Res.,24, pp. 273–295CrossRefGoogle Scholar
  10. Lloyd, S. (1982): ‘Least squares quantization in PCM’,IEEE Trans. Inf. Theory,28, pp. 129–137CrossRefzbMATHMathSciNetGoogle Scholar
  11. Lobregt, S., andViergever, M. A. (1995): ‘A discrete dynamic contour model’,IEEE Trans. Med. Imag.,14, pp. 12–24Google Scholar
  12. Lou, S. L., Lin, H. D., Lin, K. P., andHoogstrate, D. (2000): ‘Automatic breast region extraction from digital mammograms for PACS and telemammography applications’,Comput. Med. Imag. Graph.,24, pp. 205–220CrossRefGoogle Scholar
  13. Mackiewich, B. (1995): ‘Intracranial boundary detection and radio frequency correction in magnetic resonance images’. Master’s thesis, School of Computing Science, Simon Fraser University, Burnaby, B.C., Canada, August 1995Google Scholar
  14. Mattis, P., andKimball, S. ‘GIMP: GNU Image Manipulation Program’, version 1.1.17. http://www.gimp.org, GNU General Public License, GPLGoogle Scholar
  15. Méndez, A. J., Tahoces, P. G., Lado, M. J., Souto, M., Correa, J. L., andVidal, J. J. (1996): ‘Automatic detection of breast border and nipple in digital mammograms’,Comput. Methods Progr. Biomed.,49, pp. 253–262Google Scholar
  16. Miller, P., andAstley, S. (1993): ‘Automated detection of mammographic asymmetry using anatomical features’,Int. J. Pattern Recognit. Artif. Intell.,7, pp. 1461–1476Google Scholar
  17. Suckling, J., Parker, J., Dance, D. R., Astley, S., Hutt, I., Boggis, C. R. M., Ricketts, I., Stamatakis, E., Cerneaz, N., Kok, S. L., Taylor, P., Betal, D., andSavage, J. (1994): ‘The Mammographic Image Analysis Society digital mammogram database’, inGale, A. G., Astley, S. M., Dance, D. R., andCairns, A. Y. (Eds.): ‘Proc. 2nd Int. Workshop on Digital Mammography, vol. 1069 of Excerpta Medica International Congress Series’, York, England, pp. 375–378Google Scholar
  18. Williams, D. J., andShah, M. (1992): ‘A fast algorithm for active contours and curvature estimation’,Comput. Vis. Graph. Image Process.: Image Underst.,55, pp. 14–26Google Scholar

Copyright information

© IFMBE 2004

Authors and Affiliations

  • R. J. Ferrari
    • 1
    • 2
  • A. F. Frère
    • 2
    • 5
  • R. M. Rangayyan
    • 1
    • 3
  • J. E. L. Desautels
    • 1
    • 4
  • R. A. Borges
    • 5
  1. 1.Department of Electrical & Computer EngineeringUniversity of CalgaryCalgaryCanada
  2. 2.Department of Electrical EngineeringUniversity of São PauloSão CarlosBrazil
  3. 3.Department of RadiologyUniversity of CalgaryCalgaryCanada
  4. 4.Screen Test AlbertaCalgaryCanada
  5. 5.Nucleus of Science & TechnologyUniversity of Mogi das CruzesBrazil

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