Abstract
Image segmentation is one of the most vital and significant step in medical applications. The conventional fuzzy c-means (FCM) clustering is the most widely used unsupervised clustering method for brain tumor segmentation on magnetic resonance (MR) images. However, the major limitation of the conventional FCM is its huge computational time and it is sensitive to initial cluster centers. In this paper, we present a novel efficient FCM algorithm to eliminate the drawback of conventional FCM. The proposed algorithm is formulated by incorporating distribution of the gray level information in the image and a new objective function which ensures better stability and compactness of clusters. Experiments are conducted on brain MR images to investigate the effectiveness of the proposed method in segmenting brain tumor. The conventional FCM and the proposed method are compared to explore the efficiency and accuracy of the proposed method.
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References
World health organization cancer fact sheets (2010), http://www.who.int/mediacenter/factsheets/fs297/en/index.html
Zulaikha Beevi, S., Mohamed Sathik, M.: An effective approach for segmentation of MRI images: combining spatial information with fuzzy c-means clustering. European Journal of Scientic Research 41(3), 437–451 (2010)
Hou, Z., Qian, W., Huang, S., Hu, Q., Nowinski, W.L.: Regularized fuzzy c-means method for brain tissue clustering. Pattern Recognition Letters 28, 1788–1794 (2007)
Kannan, S.R., Ramathilagam, S., Pandiyarajan, R., Sathya, A.: Fuzzy clustering approach in segmentation of T1-T2 brain MRI. International Journalof Recent Trends in Engineering 2(1), 157–160 (2005)
Murugavalli, S., Rajamani, V.: A high speed parallel fuzzy c-mean algorithm for brain tumor segmentation. BIME Journal 6(1) (2006)
Cai, W., Chen, S., Zhang, D.: Fast and Robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognition 40, 825–838 (2007)
Belal, M., Hudaib, A., Al-Shboul, B.: A Fast Fuzzy Clustering Algorithm. In: WSEAS International Conference on Artificial Intelligence. Knowledge Engineering and Databases, pp. 28–32 (2007)
de Vargas, R.R., Bedregal, B.R.C.: A comparative study between fuzzy c-means and ckmeans algorithms. In: Fuzzy Information Processing Society, IEEE North American Annual Meeting, pp. 1–6 (2010)
Li, B., Chen, W., Wang, D.: An improved FCM algorithm incorporating spatial information for image segmentation. In: IEEE International Symposium on Computer Science and Computational Technology, pp. 493–495 (2008)
Ray, S., Agarwal, H., Carass, A., Bai, Y.: Fuzzy c-means with variable compactness. In: IEEE International Symposium on Biomedical Imaging:From Nano to Macro, pp. 452–455 (2008)
Fujita, H., Zhang, X., Kido, S., Hara, T.: An introduction and survey of computer aided detection/diagnosis. In: IEEE International Conference on Future Computer, Control and Communication, pp.200–205 (2010)
Uchiyama, Y., Yokoyama, R., Fujitha, H.: Computer aided diagnosis scheme for detection of lacunar infarcts in MR images. Academic Radiology 14, 1554–1561 (2007)
Bouchaffra, D., Tan, J.: Structural hidden markov models for biometrics:fusion of face and fingerprint. Pattern Recognition 41, 852–867 (2008)
Kocionek, M., Materka, A., Strzelecki, M., Szczypinski, P.: Discrete wavelet transform-derived features for digital image texture analysis. In: International Conference on Signals and Electrical Systems, Poland, pp. 163–168 (2001)
Shasidhar, M., Sudheer Raja, V., Vijay Kumar, B.: MRI brain image segmentation using modified fuzzy c-means clustering algorithm. In: IEEE International Conference on Communication Systems and Network Technologies, pp. 473–478 (2011)
Srivastava, A., Asati, A., Bhattacharya, M.: A fast and noise adaptive rough fuzzy hybrid algorithm for medical image segmentation. In: IEEE International Conference on Bioinformatics and Biomedicine, pp. 416–421 (2010)
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Arakeri, M.P., Ram Mohana Reddy, G. (2011). Efficient Fuzzy Clustering Based Approach to Brain Tumor Segmentation on MR Images. In: Das, V.V., Thankachan, N. (eds) Computational Intelligence and Information Technology. CIIT 2011. Communications in Computer and Information Science, vol 250. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25734-6_141
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DOI: https://doi.org/10.1007/978-3-642-25734-6_141
Publisher Name: Springer, Berlin, Heidelberg
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