Abstract
The infected tumor area from a magnetic resonance image can be segmented, detected, and extracted accurately by the radiologist experts only through the experience. The complexities and limitations involved in this process are investigated/overcome through distributed rough fuzzy C-means (DRFCM). The support vector machine (SVM)-based classifier improves the accuracy and quality of the segmented tissue. Typically, the best clustering process makes the index values of XB, DB, and RAND as minimum as possible. The performance and quality analysis of the proposed method have been evaluated based on the accuracy, specificity, sensitivity, and also the similarity index of dice coefficient.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Smith, T.F., Waterman, M.S.: Identification of common molecular subsequences. J. Mol. Biol. 147, 195–197 (1981)
May, P., Ehrlich, H.C., Steinke, T.: ZIB structure prediction pipeline: composing a complex biological workflow through web services. In: Nagel, W.E., Walter, W.V., Lehner, W. (eds.) Euro-Par 2006.LNCS, vol. 4128, pp. 1148–1158. Springer, Heidelberg (2006)
Fahad, A., Al Shatri, N., Tar, Z., Al Mari, A.: A survey of clustering algorithms for bigdata: taxonomy and empirical analysis. IEEE Trans. Emerg. Top. Comput. 2(3), 267–279 (2014)
Guha, S., Rastogi, R., Shim, K.: Cure: an efficient clustering algorithm for large databases. In: Proceedings of ACMSIGMOD Record, vol. 27, no. 2, pp. 73–84 (2008)
Chen, M.S., Han, J., Yu, P.S.: Data mining: an overview from a database perspective. IEEE Trans. Knowl. Data Eng. 8(6) (2007)
Zait, M., Messatfa, H.: A comparative study of clustering methods. Futur. Gener. Comput. Syst. 13, 149–159 (2005)
Chen, M.S., Han, J., Yu, P.S.: Data mining: an overview from a database perspective. IEEE Trans. Knowl. Data Eng. 8(6) (2005)
Venkateswara Reddy, E., Reddy, E.S.: Image segmentation using rough set based fuzzy KMeans clustering algorithm. Global Journals Inc, USA, GJCST, Vol. 13, Issue 6, Version 1.0, pp. 23–28 (2013)
Venkateswara Reddy, E., Reddy, E.S.: Image segmentation using rough set based fuzzy c means clustering algorithm. In: International Journal of Computer Applications, USA, (IJCA), Vol. 74. No. 14, pp. 23–28 (2014)
Venkateswara Reddy, E., Reddy, E.S.: A comparative study of color image segmentation using hard, fuzzy, rough set based clustering techniques. Counc. Innov. Res. IJCT, 11(8), 2873–2878 (2013)
Emam, W.M., Saad, A.E.H.A., Riad, R., et.al.: Morphometric study and length- weight relationship on the squid Loligo forbesi (Cephalopoda: Loliginidae) from the Egyptian Mediterranean waters. Int. J. Env. Sci. Eng. (ijese) 5, 1–13 (2014)
Li, B., Lai, Y.-K., Rosin, P.L.: Example-based image colorization via automatic feature selection and fusion, Neuro Comput. 266, 687–698 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Eluri, V.R., Ramesh, C., Dhipti, S.N., Sujatha, D. (2019). Analysis of MRI-Based Brain Tumor Detection Using RFCM Clustering and SVM Classifier. In: Wang, J., Reddy, G., Prasad, V., Reddy, V. (eds) Soft Computing and Signal Processing . Advances in Intelligent Systems and Computing, vol 898. Springer, Singapore. https://doi.org/10.1007/978-981-13-3393-4_33
Download citation
DOI: https://doi.org/10.1007/978-981-13-3393-4_33
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3392-7
Online ISBN: 978-981-13-3393-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)