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.
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References
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.
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.
Byng JW, Critten JP, Yaffe MJ. 1997. Thickness-equalization processing for mammographic images. Radiology 203(2):564-568.
Chandrasekhar R, Attikiouzel Y. 1997. A simple method for automatically locating the nipple on mammograms. IEEE Trans Medical Imaging 16(5):483-494.
Lau TK, Bischof WF. 1991. Automated detection of breast tumors using the asymmetry approach. Comput Biomed Res 24:273-295.
Miller P, Astley S. Automated detection of mammographic asymmetry using anatomical features. 1993. Int J Pattern Recognit Artif Intell 7(6):1461-1476.
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.
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.
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.
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.
Kass M, Witkin A, Terzopoulos D. 1988. Snakes: active contour models. Int J Comput Vision 1(4):321-331.
Gonzalez RC, Woods RE. 1992. Digital image processing. Reading, MA:: Addison-Wesley.
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.
Lloyd S. 1982. Least squares quantization in PCM. IEEE Trans Inf Theory 28:129-137.
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.
Lobregt S, Viergever MA. 1995. A discrete dynamic contour model. IEEE Trans Med Imaging 14(1):12-24.
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.
Mattis P, Kimball S. 2005. GIMP-GNU image manipulation program. http://www.gimp.org.
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.
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.
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Ferrari, R.J., Rangayyan, R.M., Desautels, J.E.L., Frère, A.F., Borges, R.A. (2007). Detection Of The Breast Contour In Mammograms By Using Active Contour Models. In: Deformable Models. Topics in Biomedical Engineering. International Book Series. Springer, New York, NY. https://doi.org/10.1007/978-0-387-68413-0_5
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DOI: https://doi.org/10.1007/978-0-387-68413-0_5
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