Skip to main content

Part of the book series: Advances in Intelligent and Soft Computing ((AINSC,volume 131))

  • 2960 Accesses

Abstract

Breast cancer screening programs attempt to detect and eradicate cancer at the earliest possible stage to increase the rate of survival amongst women. The early detection of breast cancer greatly improves the prognosis. One of the earliest signs of cancer is the formation of clusters of microcalcifications. Various efforts have been made to improve the performance of the bio-inspired algorithms such as Genetic Algorithm(GA), Ant Colony Optimization (ACO), Particle Swarm Optimization(PSO) and Bee Colony Optimization(BCO) algorithms for classification in various domains. This paper introduces some novel methods on a biologically inspired adaptive models. Bio-inspired algorithms are more powerful for solving more complex optimization problems. In this paper, the extracted features from mammogram images are selected using ACO, GA and BCO algorithms. Fuzzy-C-Means algorithm has been employed for validation through classification.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Breast Cancer in Men. A complete patient’s guide, http://www.breastdoctor.com/breast/men/cancer.htm

  2. Breast Cancer in Men. Male breast cancer information center, http://interact.withus.com/interact/mbc/

  3. Thangavel, K., Karnan, M.: CAD system for Preprocessing and Enhancement of Digital Mammograms. International Journal on Graphics Vision and Image Processing 5(9), 69–74 (2005)

    Google Scholar 

  4. http://www.wiau.man.ac.uk/services/MIAS/MIASweb.html

  5. Nadler, M., Smith, E.P.: Pattern Recognition Engineering. Wiley, New York (1993)

    MATH  Google Scholar 

  6. Gulsrud, T.O.: Texture analysis of Digital Mammograms, Ph.D. Thesis, Aalborg University, Stavanger, USA, pp. 30–32 (2000)

    Google Scholar 

  7. Haralick, R.M., Shanmugan, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973)

    Article  Google Scholar 

  8. Thangavel, K., Jaganathan, P., Pethalakshmi, A., Karnan, M.: Effective Classifications with improved quick reduct for medical data base. International Journal on Bio Informatics Medical Engineering 5(1), 69–74 (2005)

    Google Scholar 

  9. Velayutham, C., Thangavel, K.: Improved Rough Set Algorithms for Optimal Attribute Reduct. Journal of Electronic Science and Technology (JEST) (International) 9(2), 108–117 (2011)

    Google Scholar 

  10. Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm intelligence: from nature to artificial systems. Oxford Press (1999)

    Google Scholar 

  11. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: Artificial Bee Colony (ABC) algorithm. Journal of Global Optimization 39(3), 459–471 (2007)

    Article  MathSciNet  Google Scholar 

  12. Karaboga, D., Basturk, B.: Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems. In: Melin, P., Castillo, O., Aguilar, L.T., Kacprzyk, J., Pedrycz, W. (eds.) IFSA 2007. LNCS (LNAI), vol. 4529, pp. 789–798. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  13. Karaboga, D., Basturk, B.: On the performance of Artificial Bee Colony (ABC) algorithm. Applied Soft Computing 8(1), 687–697 (2008)

    Article  Google Scholar 

  14. Lucic, P.: Modelling Transportation Problems Using Concepts of Swarm Intelligence and Soft Computing, Ph.D. theses, faculty of the Virginia Polytechnic Instisute and State University, Virginia (2002)

    Google Scholar 

  15. Suguna, N., Thanushkodi, K.: A Novel Rough Set Reduct Algorithm For Medical Domain Based On Bee Colony Optimization. Journal of Computing 2(6), 49–54 (2010)

    Google Scholar 

  16. Wedde, H.F., Farooq, M., Zhang, Y.: BeeHive: An Efficient Fault-Tolerant Routing Algorithm Inspired by Honey Bee Behavior. In: Dorigo, M., Birattari, M., Blum, C., Gambardella, L.M., Mondada, F., Stützle, T. (eds.) ANTS 2004. LNCS, vol. 3172, pp. 83–94. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  17. Wedde, H., Farooq, M., Pannenbaecker, T., Vogel, B., Mueller, C., Meth, J., Jeruschkat, R.: BeeAdHoc: an energy efficient routing algorithm for mobile ad hoc networks inspired by bee behavior. In: GECCO 2005, pp. 153–160 (2005)

    Google Scholar 

  18. Bezdek, J.: Numerical taxonomy with fuzzy sets. J. Math. Biol. 1, 57–71 (1974)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to K. Thangavel .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer India Pvt. Ltd.

About this paper

Cite this paper

Thangavel, K., Velayutham, C. (2012). Mammogram Image Analysis: Bio-inspired Computational Approach. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 131. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0491-6_87

Download citation

  • DOI: https://doi.org/10.1007/978-81-322-0491-6_87

  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-0490-9

  • Online ISBN: 978-81-322-0491-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics