Abstract
This chapter proposes a unique method for unsupervised segmentation of medical images using genetic algorithm (GA)-based spatial fuzzy C-means (SFCM) clustering. The aim of the algorithm is to segment the medical image into an appropriate number of clusters, whereby the required number of clusters is computed automatically. SFCM takes into account the effect of neighborhood pixels on a central pixel, and thus the set of clusters obtained by SFCM forms the basis for a genetic algorithm where different genetic operators are used to further calibrate the centroids. A validity index is used to obtain the optimal number of clusters. The experimental results of the proposed method are compared with existing methods for further validation.
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References
Pham, D.L., C. Xu, and J.L. Prince. 2000. Current methods in medical image segmentation. Annual Review of Biomedical Engineering 2 (1): 315–337.
Na, S., L. Xumin, and G. Yong. 2010. Research on k-means clustering algorithm: An improved k-means clustering algorithm. In Third International Symposium on Intelligent Information Technology and Security Informatics, 63–67.
Wagstaff, K., C. Cardie, S. Rogers and S. Schrdl. 2001. Constrained k-means clustering with background knowledge. In ICML, vol. 1, 577–584.
Cai, W., S. Chen, and D. Zhang. 2007. Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognition 40 (3): 825–838.
Bezdek, J.C., R. Ehrlich, and W. Full. 1984. FCM: The fuzzy c-means clustering algorithm. Computers and Geosciences 10 (2): 191–203.
Gamarra, Daniel Fernando Tello. 2015. Fuzzy image segmentation using validity indexes correlation. International Journal of Computer Science and Information Technology 7: 15–26.
Kaur, P., A.K. Soni, and A. Gosain. 2013. A robust kernelized intuitionistic fuzzy c-means clustering algorithm in segmentation of noisy medical images. Pattern Recognition Letters 34 (2): 163–175.
Ahmed, M.N., S.M. Yamany, N. Mohamed, A.A. Farag, and T. Moriarty. 2002. A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Transactions on Medical Imaging 21 (3): 193–199.
Chuang, K., H. Tzeng, S. Chen, J. Wu, and T. Chen. 2006. Fuzzy c-means clustering with spatial information for image segmentation. Computerized Medical Imaging and Graphics 30 (1): 9–15.
Kole, D.K., and Amiya Halder. 2010. An efficient image segmentation algorithm using dynamic GA based clustering. International Journal of Logistics and Supply Chain Management 2 (1): 17–20.
Halder, A., S. Pramanik, and A. Kar. 2011. Dynamic image segmentation using fuzzy c-means based genetic algorithm. International Journal of Computer Applications 28 (6): 15–20.
Halder, A., and N. Pathak. 2011. An evolutionary dynamic clustering based colour image segmentation. International Journal of Image Processing 4 (6): 549–556.
Maji, P., and S.K. Pal. 2007. Rough set based generalized fuzzy C-means algorithm and quantitative indices. IEEE Transactions on Systems, Man, and Cybernetics Part B (Cybernetics) 37 (6): 1529–1540.
Halder, A., and S. Guha. 2017. Rough kernelized fuzzy c-means based medical image segmentation. International Conference on Computational Intelligence, Communications, and Business Analytics, 466–474.
Bhattacharya, R.K. 2012. Introduction to genetic algorithms, IIT Guwahati 12.
Ghose. T. 2002. Optimization technique and an introduction to genetic algorithms and simulated annealing. In Proceedings of International workshop on Soft Computing and Systems, 1–19.
Turi. R.H. 2001. Clustering-based colour image segmentation. PhD thesis, Monash University.
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Halder, A., Maity, A., Das, A. (2019). Medical Image Segmentation Using GA-Based Modified Spatial FCM Clustering . In: Krishna, A., Srikantaiah, K., Naveena, C. (eds) Integrated Intelligent Computing, Communication and Security. Studies in Computational Intelligence, vol 771. Springer, Singapore. https://doi.org/10.1007/978-981-10-8797-4_60
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DOI: https://doi.org/10.1007/978-981-10-8797-4_60
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