Abstract
Mammogram images have the ability to assist physicians in detecting breast cancer caused by cells abnormal growth. But due to visual interpretation, false results can be obtained. In this paper, to reduce false results, image segmentation is carried out to find breast cancer mass. Image segmentation using Fuzzy clustering: K means, FCM, and FPCM shows result better than other existing methods but initialization problem and sensitivity to noise do not make them to achieve better accuracy. Various extension of the FCM for segmentation is developed. But most of them modify the objective function which changes the basic FCM algorithm. Hence efforts have been made to develop FCM algorithm without modifying objective function for better segmentation. We have proposed a technique GA-ACO-FCM, which is the hybridization of optimization tools: genetic algorithm and ant colony optimization with fuzzy C means .GA-ACO-FCM is suitable to overcome initialization problem of FCM and shows better results with achieving high accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Dong, A., Wang, B.: Feature selection and analysis on mammogram classification. In: Communications, Computers and Signal Processing, pp. 731–735 (2009)
Thangavel, K., Mohideen, A.K.: Semi-supervised k-means clustering for outlier detection in mammogram classification. In: IEEE International Conference on Trendz in Information Sciences & Computing (TISC), pp. 68–72 (2010)
Cahoon, T.C., Sutton, M.A., Bezdek, J.C.: Breast cancer detection using image processing techniques. In: IEEE 9th International Conference on Fuzzy Systems, vol. 2, pp. 973–976 (2000)
Barrea, A.: Local fuzzy C-means clustering for medical spectroscopy images. Appl. Math. Sci. 5(30), 1449–1458 (2011)
Maitra, I.K., Nag, S., Bandyopadhyay, S.K.: Identification of abnormal masses in digital mammography images. Int. J. Comput. Graph. 2(1), 17–30 (2011)
Singh, N., Mohapatra, A.G., Kanungo, G.K.: Breast cancer mass detection in mammograms using K-means and fuzzy C-means clustering. Int. J. Comput. Appl. 22(2), 15–21 (2011)
Prasad, K.S., Basha, S.S.: Automatic detection of breast cancer mass in mammograms using morphological operators and fuzzy C-means clustering. J. Theor. Appl. Info. Technol. 5, 704–709 (2009)
Das, P., Bhattacharyya, D., Bandyopadhyay, S.K., Kim, T.H.: Analysis and diagnosis of breast cancer. Int. J. u- and e-Serv. Sci. Technol. 2(2), 1–12 (2009)
Peng, Y., Hou, X., Liu, S.: The k-means clustering algorithm based on density and ant colony. In: IEEE International Conference in Neural Networks and Signal Processing, vol. 1, pp. 457–460 (2003)
Azadeh, A., Keramati, A., Panahi, H.: A hybrid GA-ant colony approach for exploring the relationship between IT and firm performance. Int. J. Bus. Info. Syst. Arch. 4(5), 542–563 (2009)
Acknowledgment
The authors thank to Prof. A.K. Tripathy, Dr. B.B. Mishra, and Dept. of Electronics and Telecommunication for their valuable tips and suggestion on this topic and programming.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer India
About this paper
Cite this paper
Kanungo, G.K., Singh, N., Dash, J., Mishra, A. (2015). Mammogram Image Segmentation Using Hybridization of Fuzzy Clustering and Optimization Algorithms. In: Jain, L., Patnaik, S., Ichalkaranje, N. (eds) Intelligent Computing, Communication and Devices. Advances in Intelligent Systems and Computing, vol 309. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2009-1_46
Download citation
DOI: https://doi.org/10.1007/978-81-322-2009-1_46
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2008-4
Online ISBN: 978-81-322-2009-1
eBook Packages: EngineeringEngineering (R0)