Open Access

Statistical Segmentation of Regions of Interest on a Mammographic Image

  • Mouloud Adel1Email author,
  • Monique Rasigni1,
  • Salah Bourennane1 and
  • Valerie Juhan2
EURASIP Journal on Advances in Signal Processing20072007:049482

https://doi.org/10.1155/2007/49482

Received: 16 November 2006

Accepted: 13 May 2007

Published: 26 July 2007

Abstract

This paper deals with segmentation of breast anatomical regions, pectoral muscle, fatty and fibroglandular regions, using a Bayesian approach. This work is a part of a computer aided diagnosis project aiming at evaluating breast cancer risk and its association with characteristics (density, texture, etc.) of regions of interest on digitized mammograms. Novelty in this paper consists in applying and adapting Markov random field for detecting breast anatomical regions on digitized mammograms whereas most of previous works were focused on masses and microcalcifications. The developed method was tested on 50 digitized mammograms of the mini-MIAS database. Computer segmentation is compared to manual one made by a radiologist. A good agreement is obtained on 68% of the mini-MIAS mammographic image database used in this study. Given obtained segmentation results, the proposed method could be considered as a satisfying first approach for segmenting regions of interest in a breast.

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Authors’ Affiliations

(1)
Institut Fresnel, UMR-CNRS 6133, Equipe GSM, Domaine Universitaire de Saint-Jérôme
(2)
Service de Radiologie, Hôpital de la Timone

References

  1. Wolfe JN: Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer 1976,37(5):2486-2492. 10.1002/1097-0142(197605)37:5<2486::AID-CNCR2820370542>3.0.CO;2-8View ArticleGoogle Scholar
  2. Boyd NF, Byng JW, Jong RA, et al.: Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening study. Journal of the National Cancer Institute 1995,87(9):670-675. 10.1093/jnci/87.9.670View ArticleGoogle Scholar
  3. ACR : Breast Imaging Reporting and Data System (BI-RADS). 2nd edition. American College of Radiology, Reston, Va, USA; 1995.Google Scholar
  4. Tabár L, Tot T, Dean PB: Breast Cancer: The Art and Science of Early Detection with Mammography. Georg Thieme, Stuttgart, Germany; 2005.Google Scholar
  5. Suckling J, Parker J, Dance DR, et al.: The mammographic image analysis society digital mammogram database. Proceedings of the 2nd International Workshop on Digital Mammography, July 1994, York, England, Exerpta Medica, International Congress Series 1069: 375-378.Google Scholar
  6. Muhimmah I, Oliver A, Denton ERE, Pont J, Pérez E, Zwiggelaar R: Comparison between Wolfe, Boyd, BI-RADS and Tabár based mammographic risk assessment. Proceedings of the 8th International Workshop on Digital Mammography (IWDM '06), June 2006, Manchester, UK, Lecture Notes in Computer Science 4046: 407-415.View ArticleGoogle Scholar
  7. Rangayyan RM: Biomedical Image Analysis. CRC Press, Boca Raton, Fla, USA; 2005.Google Scholar
  8. Aylward SR, Hemminger BM, Pisano ED: Mixture modeling for digital mammogram display and analysis. Proceedings of the 4th International Workshop on Digital Mammography (IWDM '98), June 1998, Nijmegen, The Netherlands 305-312.View ArticleGoogle Scholar
  9. Ferrari RJ, Ragayyan RM, Desautels JEL, Frere AF: Segmentation of mammograms: identification of the skin-air boundary, pectoral muscle, and fibro-glandular disc. Proceedings of the 5th International Workshop on Digital Mammography (IWDM '00), June 2000, Toronto, Canada 573-579.Google Scholar
  10. Matsubara T, Yamazaki D, Hara H, Iwase T, Endo T: An automated classification method for mammograms based on evaluation of fibroglandular breast tissue density. Proceedings of the 5th International Workshop on Digital Mammography (IWDM '00), June 2000, Toronto, Canada 737-741.Google Scholar
  11. Zhou C, Chan HP, Petrick N, et al.: Computerized image analysis: estimation of breast density on mammograms. Medical Physics 2001,28(6):1056-1069. 10.1118/1.1376640View ArticleGoogle Scholar
  12. Ferrari RJ, Rangayyan RM, Borges RA, Frère AF: Segmentation of the fibro-glandular disc in mammograms using Gaussian mixture modelling. Medical and Biological Engineering and Computing 2004,42(3):378-387. 10.1007/BF02344714View ArticleGoogle Scholar
  13. Bick U, Giger ML, Schmidt RA, Nishikawa RM, Doi K: Density correction of peripheral breast tissue on digital mammograms. Radio Graphics 1996,16(6):1403-1411.Google Scholar
  14. Byng JW, Critten JP, Yaffe MJ: Thickness-equalization processing for mammographic images. Radiology 1997,203(2):564-568.View ArticleGoogle Scholar
  15. Chandrasekhar R, Attikiouzel Y: A simple method for automatically locating the nipple on mammograms. IEEE Transactions on Medical Imaging 1997,16(5):483-494. 10.1109/42.640738View ArticleGoogle Scholar
  16. Saha PK, Udupa JK, Conant EF, Chakraborty DP, Sullivan D: Breast tissue density quantification via digitized mammograms. IEEE Transactions on Medical Imaging 2001,20(8):792-803. 10.1109/42.938247View ArticleGoogle Scholar
  17. Byng JW, Boydt NF, Fishell E, Jong RA, Yaffe MJ: The quantitative analysis of mammographic densities. Physics in Medicine and Biology 1994,39(10):1629-1638. 10.1088/0031-9155/39/10/008View ArticleGoogle Scholar
  18. Byng JW, Boyd NF, Fishell E, Jong RA, Yaffe MJ: Automated analysis of mammographic densities. Physics in Medicine and Biology 1996,41(5):909-923. 10.1088/0031-9155/41/5/007View ArticleGoogle Scholar
  19. Tahoces PG, Correa J, Souto M, Gomez L, Vidal JJ: Computer-assisted diagnosis: the classification of mammographic breast parenchymal patterns. Physics in Medicine and Biology 1995,40(1):103-117. 10.1088/0031-9155/40/1/010View ArticleGoogle Scholar
  20. Karssemeijer N: Automated classification of parenchymal patterns in mammograms. Physics in Medicine and Biology 1998,43(2):365-378. 10.1088/0031-9155/43/2/011View ArticleGoogle Scholar
  21. Huo Z, Giger ML, Zhong W, Olopade OI: Analysis of relative contributions of mammographic features and age to breast cancer risk prediction. Proceedings of the 5th International Workshop on Digital Mammography (IWDM '00), June 2000, Toronto, Canada 732-736.Google Scholar
  22. Sivaramakrishna R, Obuchowski NA, Chilcote WA, Powell KA: Automatic segmentation of mammographic density. Academic Radiology 2001,8(3):250-256. 10.1016/S1076-6332(03)80534-2View ArticleGoogle Scholar
  23. Masek M, Kwok SM, deSilva CJS, Attikiouzel Y: Classification of mammographic density using histogram distance measures. Proceedings of the World Congress on Medical Physics and Biomedical Engineering, August 2003, Sydney, Australia 1. CD-ROMGoogle Scholar
  24. Zwiggelaar R, Muhimmah I, Denton ERE: Mammographic density classification based on statistical grey-level histogram modeling. Proceedings of the Medical Image Understanding and Analysis (MIUA '05), July 2005, Bristol, UK 183-186.Google Scholar
  25. Muhimmah I, Zwiggelaar R: Mammographic density classification using multiresolution histogram information. Proceedings of the International Special Topic Conference on Information Technology in Biomedicine (ITAB '06), October 2006, Ioannina, GreeceGoogle Scholar
  26. Besag J: Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society. Series B 1974,36(2):192-236.MathSciNetMATHGoogle Scholar
  27. Dubes RC, Jain AK, Nadabar SG, Chen CC: MRF model-based algorithms for image segmentation. Proceedings of International Conference on Computer Applications in Shipbuilding (ICCAS '90), 1990 808-814.Google Scholar
  28. Lakshmanan S, Derin H: Simultaneous parameter estimation and segmentation of Gibbs random fields using simulated annealing. IEEE Transactions on Pattern Analysis and Machine Intelligence 1989,11(8):799-813. 10.1109/34.31443View ArticleGoogle Scholar
  29. Karssemeijer N: Stochastic model for automated detection of calcifications in digital mammograms. Image and Vision Computing 1992,10(6):369-375. 10.1016/0262-8856(92)90023-VView ArticleGoogle Scholar
  30. Haralick RM, Shanmugam K, Dinstein I: Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics 1973,3(6):610-621.View ArticleGoogle Scholar
  31. Berthod M, Kato Z, Yu S, Zerubia J: Bayesian image classification using Markov random fields. Image and Vision Computing 1996,14(4):285-295. 10.1016/0262-8856(95)01072-6View ArticleMATHGoogle Scholar
  32. Geman S, Geman D: Stochastic relaxation. Gibbs distributions and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence 1984,6(6):721-741.View ArticleMATHGoogle Scholar
  33. Besag J: On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society. Series B 1986,48(3):259-302.MathSciNetMATHGoogle Scholar
  34. Caulkin S, Astley S, Asquith J, Boggis C: Sites of occurrence of malignancies in mammograms. Proceedings of the 4th International Workshop on Digital Mammography (IWDM '98), June 1998, Nijmegen, The Netherlands 279-282.View ArticleGoogle Scholar

Copyright

© Mouloud Adel et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.