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Computational and Mathematical Methods in Medicine
Volume 2013, Article ID 408595, 19 pages
http://dx.doi.org/10.1155/2013/408595
Research Article

A Probabilistic Approach for Breast Boundary Extraction in Mammograms

Department of Computer Engineering and Mathematics, Rovira i Virgili University, 43007 Tarragona, Spain

Received 31 May 2013; Revised 21 August 2013; Accepted 16 September 2013

Academic Editor: Reinoud Maex

Copyright © 2013 Hamed Habibi Aghdam et al. 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.

Linked References

  1. J. Raundahl, Mammographic pattern recognition [Ph.D. dissertation], The Image Group, Department of Computer Science, Faculty of Science, University of Copenhagen, 2007.
  2. http://www.cancerresearchuk.org/cancer-help/type/breast- cancer/about/screening/mammograms-in-breast-screening.
  3. N. Karssemeijer, “Automated classification of parenchymal patterns in mammograms,” Physics in Medicine and Biology, vol. 43, no. 2, pp. 365–378, 1998. View at Publisher · View at Google Scholar · View at Scopus
  4. V. A. McCormack and I. Dos Santos Silva, “Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis,” Cancer Epidemiology Biomarkers and Prevention, vol. 15, no. 6, pp. 1159–1169, 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Dehghani and M. A. Dezfooli, “A method for improving segmentation of the breast region from background in digitized mammograms,” in Proceedings of the IEEE 3rd International Conference on Communication Software and Networks (ICCSN '11), pp. 572–576, May 2011. View at Publisher · View at Google Scholar · View at Scopus
  6. J. Nagi, S. Abdul Kareem, F. Nagi, and S. Khaleel Ahmed, “Automated breast profile segmentation for ROI detection using digital mammograms,” in Proceedings of the IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES '10), pp. 87–92, December 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. K. Wang and H. Qin, “Automatic registration of mammograms using texture-based anisotropic features,” in Proceedings of the 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 864–867, April 2006. View at Scopus
  8. D. Raba, A. Oliver, J. Martí, M. Peracaula, and J. Espunya, “Breast segmentation with pectoral muscle suppression on digital mammograms,” in Proceedings of the 2nd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA '05), vol. 3523 of Lecture Notes in Computer Science, pp. 471–478, June 2005. View at Scopus
  9. S. Tzikopoulos, H. Georgiou, M. Mavroforakis, N. Dimitropoulos, and S. Theodoridis, “A fully automated complete segmentation scheme for mammograms,” in Proceedings of the 16th International Conference on Digital Signal Processing (DSP '09), pp. 1–6, July 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. S. D. Tzikopoulos, M. E. Mavroforakis, H. V. Georgiou, N. Dimitropoulos, and S. Theodoridis, “A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry,” Computer Methods and Programs in Biomedicine, vol. 102, no. 1, pp. 47–63, 2011. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Masek, Hierarchical segmentation of mammograms based on pixel intensity [Ph.D. dissertation], Centre for Intelligent Information Processing Systems, School of Electrical, Electronic and Computer Engineering, The University of Western Australia, 2004.
  12. I. K. Maitra, S. Nag, and S. K. Bandyopadhyay, “Accurate breast contour detection algorithms in digital mammogram,” International Journal of Computer Applications, vol. 25, no. 5, pp. 1–13, 2011. View at Google Scholar
  13. P. Kuş and I. Karagöz, “Accurate segmentation of the breast region with texture filter in mammograms for CAD applications,” in Proceedings of the 15th National Biomedical Engineering Meeting (BIYOMUT '10), pp. 1–4, Antalya, Turkey, April 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. C. A. Mello and T. G. Tenorio, “A new algorithm for breast segmentation in dig- ital mammographic images,” in IEEE International Conference on Systems, Man, and Cybernetics (SMC '12), pp. 553–558, Seoul, Korea, 2012.
  15. B. K. Maysam Shahedi, R. Amirfattahi, F. T. Azar, and S. Sadri, “Accurate breast region detection in digital mammograms using a local adaptive thresholding method,” in Proceedings of the 8th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS '07), p. 26, Santorini, Greece, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. Z. Zhang, J. Lu, and Y. J. Yip, “Automatic segmentation for breast skin-line,” in Proceedings of the 10th IEEE International Conference on Computer and Information Technology (CIT '10), pp. 1599–1604, July 2010. View at Publisher · View at Google Scholar · View at Scopus
  17. P. K. Saha, J. K. Udupa, E. F. Conant, D. P. Chakraborty, and D. Sullivan, “Breast tissue density quantification via digitized mammograms,” IEEE Transactions on Medical Imaging, vol. 20, no. 8, pp. 792–803, 2001. View at Publisher · View at Google Scholar · View at Scopus
  18. W. Xu, H. Li, and P. Xu, “Estimation of breast skin-line using least square estimation and watershed segmentation,” in Proceedings of the 1st International Conference on Bioinformatics and Biomedical Engineering (ICBBE '07), pp. 913–915, Wuhan, China, July 2007. View at Publisher · View at Google Scholar · View at Scopus
  19. C. A. Silva, C. G. Lima, and J. H. Correia, “Breast skin-line detection using dynamic programming,” in Proceedings of the 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '11), pp. 7775–7778, September 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. H. Mirzaalian, M. R. Ahmadzadeh, and F. Kolahdoozan, “Breast contour detection on digital mammogram, information and communication technologies,” in Proceedings of the Information and Communication Technologies (ICTTA '06), vol. 1, pp. 1804–1808, Damascus, Syria. View at Publisher · View at Google Scholar
  21. M. Tayel and A. Mohsen, “Breast boarder boundaries extraction using statistical properties of mammogram,” in Proceedings of the IEEE 10th International Conference on Signal Processing (ICSP '10), pp. 2468–2471, October 2010. View at Publisher · View at Google Scholar · View at Scopus
  22. C. Pei, W. Chunmei, and S. Xu, “Segmentation of the breast region in mammograms using marker-controlled watershed transform,” in Proceedings of the 2nd International Conference on Information Science and Engineering (ICISE '10), pp. 2371–2374, Hangzhou, China, December 2010. View at Publisher · View at Google Scholar · View at Scopus
  23. W. Kang, G. Wang, and H. Ding, “Segmentation of the breast region in mammograms using watershed transformation,” in Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society (IEEE-EMBS '05), pp. 6500–6503, September 2005. View at Scopus
  24. L. Liu, J. Wang, and T. Wang, “Breast and pectoral muscle contours detection based on goodness of fit measure,” in Proceedings of the 5th International Conference on Bioinformatics and Biomedical Engineering, Breast and Pectoral Muscle Contours Detection Based on Goodness of Fit Measure (iCBBE '11), pp. 1–4, Wuhan, China, May 2011. View at Publisher · View at Google Scholar · View at Scopus
  25. R. J. Ferrari, R. M. Rangayyan, J. E. L. Desautels, R. A. Borges, and A. F. Frère, “Identification of the breast boundary in mammograms using active contour models,” Medical and Biological Engineering and Computing, vol. 42, no. 2, pp. 201–208, 2004. View at Publisher · View at Google Scholar · View at Scopus
  26. M. A. Wirth and A. Stapinski, “Segmentation of the breast region in mammograms using snakes,” in Proceedings of the 1st Canadian Conference on Computer and Robot Vision, pp. 385–392, May 2004. View at Publisher · View at Google Scholar · View at Scopus
  27. S. Thiruvenkadam, M. Acharyya, N. V. Neeba, P. Jhunjhunwala, and S. Ranjan, “A region-based active contour method for extraction of breast skin-line in mammograms,” in Proceedings of the 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI '10), pp. 189–192, April 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. S.-S. Yu, C.-Y. Tsai, and C.-C. Liu, “A breast region extraction scheme for digital mammograms using gradient vector flow snake,” in Proceedings of the 4th International Conference on New Trends in Information Science and Service Science (NISS '10), pp. 715–720, May 2010. View at Scopus
  29. C. Xu and J. L. Prince, “Snakes, shapes, and gradient vector flow,” IEEE Transactions on Image Processing, vol. 7, no. 3, pp. 359–369, 1998. View at Publisher · View at Google Scholar · View at Scopus
  30. R. Marti, A. Oliver, D. Raba, and J. Freixenet, “Breast skin-line segmentation using contour growing,” in Pattern Recognition and Image Analysis, vol. 4478 of Lecture Notes in Computer Science, pp. 564–571, 2007.
  31. M. Mustra, J. Bozek, and M. Grgic, “Breast border extraction and pectoral muscle detection using wavelet decomposition,” in Proceedings of the IEEE EUROCON, pp. 1426–1433, Saint-Petersburg, Russia, May 2009. View at Publisher · View at Google Scholar · View at Scopus
  32. S. M. Kwok, R. Chandrasekhar, Y. Attikiouzel, and M. T. Rickard, “Automatic pectoral muscle segmentation on mediolateral oblique view mammograms,” IEEE Transactions on Medical Imaging, vol. 23, no. 9, pp. 1129–1140, 2004. View at Publisher · View at Google Scholar · View at Scopus
  33. S. Selvarajah and S. R. Kodituwakku, “Analysis and comparison of texture fea- tures for content based image retrieval,” International Journal of Latest Trends in Computing, vol. 2, no. 1, pp. 108–113, 2011. View at Google Scholar
  34. P. Howarth and S. Ruger, “Evaluation of texture features for content-based image retrieval,” in Proceedings of the International Conference on Image and Video Retrieval, vol. 3115 of Lecture Notes in Computer Science, pp. 326–334, 2004.
  35. T. Ojala, M. Pietikäinen, and D. Harwood, “A comparative study of texture measures with classification based on feature distributions,” Pattern Recognition, vol. 29, no. 1, pp. 51–59, 1996. View at Publisher · View at Google Scholar · View at Scopus
  36. T. Ojala, M. Pietikäinen, and T. Mäenpää, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971–987, 2002. View at Publisher · View at Google Scholar · View at Scopus
  37. T. Ahonen, A. Hadid, and M. Pietikäinen, “Face description with local binary patterns: application to face recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037–2041, 2006. View at Publisher · View at Google Scholar · View at Scopus
  38. M. Heikkila and M. Pietikainen, “A texture-based method for modeling the back- ground and detecting moving objects,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 4, pp. 657–662, 2006. View at Google Scholar
  39. G. Zhao and M. Pietikäinen, “Dynamic texture recognition using local binary patterns with an application to facial expressions,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 915–928, 2007. View at Publisher · View at Google Scholar · View at Scopus
  40. T. Wu, C. J. Lin, and R. C. Weng, “Probability estimates for multi class classi- fication by pairwise coupling,” Journal of Machine Learning Research, vol. 5, pp. 975–1005, 2003. View at Google Scholar
  41. C.-C. Chang and C.-J. Lin, “LIBSVM: a Library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, no. 3, article 27, 2011. View at Publisher · View at Google Scholar · View at Scopus
  42. J. Suckling, J. Parker, D. R. Dance et al., “The mammographic image analysis society digital mammogram database,” in Proceedings of the 2nd International Workshop on Digital Mammography, pp. 375–378, 1994.