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Journal of Biomedicine and Biotechnology
Volume 2008, Article ID 526343, 11 pages
http://dx.doi.org/10.1155/2008/526343
Research Article

Textural Classification of Mammographic Parenchymal Patterns with the SONNET Selforganizing Neural Network

1QinetiQ, St Andrews Road, Malvern, Worcestershire WR14 3PS, UK
2Department of Computer Science and Information Systems, College of Informatics and Electronics, University of Limerick, Ireland
3Faculty of Electronics and Telecommunications, “Gh.Asach” Technical University of Iasi, 700050 Iasi IS, Romania

Received 12 September 2007; Accepted 16 January 2008

Academic Editor: Halima Bensmail

Copyright © 2008 Daniel Howard 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. S. W. Duffy, R. A. Smith, R. Gabe, L. Tabár, A. M. Yen, and T. H. Chen, “Screening for breast cancer,” Surgical Oncology Clinics of North America, vol. 14, no. 4, pp. 671–697, 2005. View at Publisher · View at Google Scholar · View at PubMed
  2. G. Carpenter and S. Grossberg, “ART2: self-organization of stable category recognition codes for analog input patterns,” Applied Optics, vol. 26, no. 23, pp. 4919–4930, 1987. View at Google Scholar
  3. L. Tabár, Teaching Course of Mammography: Diagnosis and In-depth Differential Diagnosis of Breast Diseases, Mammography Education, Cave Creek, Ariz, USA, 2006.
  4. D. Howard, S. C. Roberts, and L. Tabár, “Mammography taxonomy for improvement of lesion detection rates,” in Proceedings of the 6th International Workshop on Digital Mammography (IWDM '02), pp. 27–32, Bremen, Germany, June 2002.
  5. N. Jamal, K.-H. Ng, L.-M. Looi et al., “Quantitative assessment of breast density from digitized mammograms into Tabar's patterns,” Physics in Medicine and Biology, vol. 51, no. 22, pp. 5843–5857, 2006. View at Publisher · View at Google Scholar · View at PubMed
  6. S. Beer, Brain of the Firm, John Wiley & Sons, New York, NY, USA, 2nd edition, 1981.
  7. N. F. Boyd, C. Wolfson, M. Moskowitz et al., “Observer variation in the classification of mammographic parenchymal patterns,” Journal of Chronic Diseases, vol. 39, no. 6, pp. 465–472, 1986. View at Publisher · View at Google Scholar
  8. A. Nigrin, Neural Networks for Pattern Recognition, Bradford Books, MIT Press, Cambridge, Mass, USA, 1993.
  9. D. O. Hebb, The Organization of Behavior, John Wiley & Sons, New York, NY, USA, 1949.
  10. O. Paulsen and T. J. Sejnowski, “Natural patterns of activity and long-term synaptic plasticity,” Current Opinion in Neurobiology, vol. 10, no. 2, pp. 172–179, 2000. View at Publisher · View at Google Scholar
  11. S. Harford, “Automatic segmentation, learning and retrieval of melodies using a self-organizing neural network,” in Proceedings of the 4th International Conference on Music Information Retrieval (ISMIR '03), Baltimore, Md, USA, October 2003.
  12. J. A. Marshall and V. S. Gupta, “Generalization and exclusive allocation of creditin unsupervised category learning,” Network: Computation in Neural Systems, vol. 9, no. 2, pp. 279–302, 1998. View at Publisher · View at Google Scholar
  13. M. W. Spratling and M. H. Johnson, “Neural coding strategies and mechanisms of competition,” Cognitive Systems Research, vol. 5, no. 2, pp. 93–117, 2004. View at Publisher · View at Google Scholar
  14. D. Scutt, G. A. Lancaster, and J. T. Manning, “Breast asymmetry and predisposition to breast cancer,” Breast Cancer Research, vol. 8, no. 2, article R14, 2006. View at Publisher · View at Google Scholar · View at PubMed
  15. D. Howard, S. C. Roberts, and C. Ryan, “Machine vision: exploring context with genetic programming,” in Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '02), pp. 756–763, Morgan Kaufmann, New York, NY, USA, July 2002.
  16. D. Howard, S. C. Roberts, and C. Ryan, “Pragmatic genetic programming strategy for the problem of vehicle detection in airborne reconnaissance,” Pattern Recognition Letters, vol. 27, no. 11, pp. 1275–1288, 2006. View at Publisher · View at Google Scholar