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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Breast Cancer in Men. A complete patient’s guide, http://www.breastdoctor.com/breast/men/cancer.htm
Breast Cancer in Men. Male breast cancer information center, http://interact.withus.com/interact/mbc/
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)
Nadler, M., Smith, E.P.: Pattern Recognition Engineering. Wiley, New York (1993)
Gulsrud, T.O.: Texture analysis of Digital Mammograms, Ph.D. Thesis, Aalborg University, Stavanger, USA, pp. 30–32 (2000)
Haralick, R.M., Shanmugan, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973)
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)
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)
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm intelligence: from nature to artificial systems. Oxford Press (1999)
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)
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)
Karaboga, D., Basturk, B.: On the performance of Artificial Bee Colony (ABC) algorithm. Applied Soft Computing 8(1), 687–697 (2008)
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)
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)
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)
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)
Bezdek, J.: Numerical taxonomy with fuzzy sets. J. Math. Biol. 1, 57–71 (1974)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights 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)