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.
Similar content being viewed by others
References
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. 1–9
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. 1403–1411
Byng, J. W., Critten, J. P., andYaffe, M. J. (1997): ‘Thickness-equalization processing for mammographic images’,Radiology,203, pp. 564–568
Chandrasekhar, R., andAttikiouzel, Y. (1997): ‘A simple method for automatically locating the nipple on mammograms’,IEEE Trans. Med. Imag.,16, pp. 483–494
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. 573–579
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. 953–964
Gonzalez, R. C., andWoods, R. E. (1992): ‘Digital image processing’, (Addison-Wesley, Reading, MA, 1992)
Kass, M., Witkin, A., andTerzopoulos, D., (1988): ‘Snakes: active contour models,Int. J. Comput. Vis.,1, pp. 321–331
Lau, T. K., andBischof, W. F. (1991): ‘Automated detection of breast tumors using the asymmetry approach’,Comput. Biomed. Res.,24, pp. 273–295
Lloyd, S. (1982): ‘Least squares quantization in PCM’,IEEE Trans. Inf. Theory,28, pp. 129–137
Lobregt, S., andViergever, M. A. (1995): ‘A discrete dynamic contour model’,IEEE Trans. Med. Imag.,14, pp. 12–24
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. 205–220
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 1995
Mattis, P., andKimball, S. ‘GIMP: GNU Image Manipulation Program’, version 1.1.17. http://www.gimp.org, GNU General Public License, GPL
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. 253–262
Miller, P., andAstley, S. (1993): ‘Automated detection of mammographic asymmetry using anatomical features’,Int. J. Pattern Recognit. Artif. Intell.,7, pp. 1461–1476
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. 375–378
Williams, D. J., andShah, M. (1992): ‘A fast algorithm for active contours and curvature estimation’,Comput. Vis. Graph. Image Process.: Image Underst.,55, pp. 14–26
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Ferrari, R.J., Frère, A.F., Rangayyan, R.M. et al. Identification of the breast boundary in mammograms using active contour models. Med. Biol. Eng. Comput. 42, 201–208 (2004). https://doi.org/10.1007/BF02344632
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF02344632