Density based breast segmentation for mammograms using graph cut techniques

IEEE Region 10 Annual International Conference, Proceedings/TENCON

Volumn , Issue , 2009, Pages

  Saidin, Nafiza ^a   Ngah, Umi Kalthum ^a   Sakim, Harsa Amylia Mat ^a   Siong, Ding Nik ^a   Hoe, Mok Kim ^a  

^a SCHOOL OF PHYSICS   (Malaysia)

Author keywords

Image processing; Image segmentation; Medical imaging

Indexed keywords

BREAST DENSITY; BREAST SEGMENTATION; BREAST TISSUES; DENSITY CHANGE; DENSITY-BASED; GRAPH CUT; GRAPH-CUT TECHNIQUES; HARD CONSTRAINTS; IMAGING MODALITY; MAMMOGRAPHIC DENSITY; MULTISELECTION; QUANTITATIVE EVALUATION; SEGMENTATION ALGORITHMS;

DIGITAL IMAGE STORAGE; IMAGING SYSTEMS; LABELS; MAMMOGRAPHY; MEDICAL IMAGING; RISK ASSESSMENT; RISK MANAGEMENT; X RAY SCREENS;

IMAGE SEGMENTATION;

EID: 77951096786     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/TENCON.2009.5396042     Document Type: Conference Paper

References (33)

1. Eurostat, "Health statistics: Atlas on mortality in the European Union," Office for Official Publications of the European Union, 2002.
2. F. Bray, P. McCarron, and D. M. Parkin, "The changing global patterns of female breast cancer incidence and mortality," Breast Cancer Res., vol.6, pp. 229-239, 2004. (Pubitemid 39440251)
3. C. H. Yip, N. A. M. Taib and I. Mohamed, "Epidemiology of breast cancer in Malaysia," Asian Pacific Journal of Cancer Prevention, vol.7, pp. 3690-4374, 2006.
4. R. L. Birdwell, D. M. Ikeda, K. D. O'Shaughnessy, and E. A. Sickles, "Mammographic characteristics of 115 missed cancers later detected with screening mammography and the potential utility of computer-aided detection," Radiology, vol.219, pp. 192-202, 2001.
5. F. M. Hall, J. M. Storella, D. Z. Siverstond, and G. Wyshak, "Nonpalpable breast lesions: Recommendations for biopsy based on suspicion of carcinoma at mammography," Radiology, vol.167, pp. 353-358, 1988.
6. T. W. Freer and M. J. Ulissey, "Screening mammography with computeraided detection: Prospective study of 12860 patients in a community breast center," Radiology, vol.220, pp. 781-786, 2001.
7. R2 ImageChecker. (Jan. 2007) [Online]. Available: http://www.r2tech.com.
8. iCAD Second Look. (Jan. 2007) [Online]. Available: http://www.icadmed. com.
9. L. Tabar, T. Tot, and P. B. Dean, "Breast Cancer-The art and science of early detection with mammography," Stuttgart, Germany: Georg Thieme Verlag, 2005.
10. American College of Radiology, "Illustrated Breast Imaging Reporting and Data System BIRADS," 3rd ed. Philadelphia, PA: Amer. College of Radiol, 1998.
11. J. N. Wolfe, "Risk for breast cancer development determined by mammographic parenchymal pattern," Cancer, vol.37, pp. 2486-2492, 1976.
12. N. F. Boyd, J.W. Byng, R. A. Jong, E. K. Fishell, L. E. Little, A. B. Miller, G. A. Lockwood, D. L. Tritchler, and M. J. Yaffe, "Quantitative classification of mammographic densities and breast cancer risk: Results from the Canadian national breast screening study," J. Nat. Cancer Inst., vol.87, pp. 670-675, 1995.
13. V. A. McCormack and I. dos Santos Silva, "Breast density and parenchymal patterns as markers of breast cancer risk: A meta-analysis," Cancer Epidemiol. Biomarkers Prev., vol.15, pp. 1159-1169, 2006.
14. P. Miller and S. M. Astley, "Classification of breast tissue by texture analysis," Image Vision Comput., vol.10, pp. 277-282, 1992.
15. J.W. Byng, N. F. Boyd, E. Fishell, R. A. Jong, and M. J.Yaffe, "Automated analysis of mammographic densities," Phys. Med. Biol., vol.41, pp. 909-923, 1996.
16. K. Bovis and S. Singh, "Classification of mammographic breast density using a combined classifier paradigm," in Proc. Med. Image Understanding Anal. Conf., pp. 177-180, 2002.
17. S. Petroudi, T. Kadir, and M. Brady, "Automatic classification of mammographic parenchymal patterns: A statistical approach," in Proc. IEEE Conf. Eng. Med. Biol. Soc., vol.1, pp. 798-801, 2003.
18. S. Petroudi and M. Brady, "Breast density segmentation using texture," Lect. Notes Comput. Sci., vol.4046, pp. 609-615, 2006. (Pubitemid 44113165)
19. A. Torrent, A. Bardera, A. Oliver, J. Freixenet, I. Boada, M. Feixes, R. Martí, X. Lladó, J. Pont, E. Pérez, S. Pedraza, and J. Martí, "Breast density segmentation: A comparison of clustering and region based techniques," (eds.) IWDM 2008. LNCS, vol.5116, pp. 9-16, Springer, Heidelberg, 2008.
20. A. Bosch, X. Mũnoz, A. Oliver and J. Mart́, "Modeling and classifying breast tissue density in mammograms," in Proc. IEEE Conf. on Computer Vision and Pattern Recognition, 2006.
21. S. Chatzistergos, J. Stoitsis, K.S. Nikita, A. Papaevangelou, "Development of an integrated breast tissue density classification software system," IEEE International Workshop on Imaging Systems and Techniques, pp. 243-245, 2008.
22. A. Oliver, J. Freixenet, R. Marti, J. Pont, E. Perez, E. R. E. Denton, and R. Zwiggelaar, "A novel breast tissue density classification methodology," IEEE Transactions on Information Technology in Biomedicine, Vol.12, pp. 55-65, 2008. (Pubitemid 351266643)
23. J. Suckling, J. Parker, D. Dance, S. Astley, I. Hutt, C. Boggis, I. Ricketts, E. Stamatakis, N. Cerneaz, S. Kok, P. Taylor, D. Betal and J. Savage, "The mammographic images analysis society digital mammogram database," Exerpta Medica. International Congress Series, vol.1069, pp. 375-378, 1994. Available: http://www.wiau.man.ac.uk/services/MIAS/MIAScom.html.
24. L. Ford and D. Fulkerson, Flows in networks. Princeton University Press. 1962.
25. A. Goldberg and R. Tarjan, "A new approach to the maximum flow problem," Journal of the Association for Computing Machinary, 35(4): 921-940, October, 1988.
26. Y. Boykov and M.P. Jolly, "Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images," International Conference on Computer Vision, vol.I, pp. 105-112, July 2001.
27. Z. Wu, and R. Leahy, "An optimal graph theoretic approach to data clustering: theory and its application to image segmentation,: IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11):1101-1113, November, 1993.
28. J. Shi, and J. Malik, "Normalized cuts and image segmentation," IEEE Conference on Computer Vision and Pattern Recognition, pages 731-737. 1997.
29. Y. Boykov, and V. Kolmogorov, "An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision," IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), September, 2004.
30. O. Veksler, "Efficient Graph-based Energy Minimization Methods in Computer Vision," PhD thesis, Cornell University, July 1999.
31. Code. (December, 2008) [Online]. Available: http://vision.csd.uwo.ca/ code/.
32. Y. Boykov, O. Veksler, and R.Zabih, "Fast approximate energy minimization via graph cuts," IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11):1222-1239, November 2001.
33. Y. Boykov, and G. Funka Lea, "Graph cuts and efficient N-D image segmentation," International Journal of Computer Vision, 69(2) 109-131, September, 2006.