Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31584
Medical Image Segmentation and Detection of MR Images Based on Spatial Multiple-Kernel Fuzzy C-Means Algorithm

Authors: J. Mehena, M. C. Adhikary

Abstract:

In this paper, a spatial multiple-kernel fuzzy C-means (SMKFCM) algorithm is introduced for segmentation problem. A linear combination of multiples kernels with spatial information is used in the kernel FCM (KFCM) and the updating rules for the linear coefficients of the composite kernels are derived as well. Fuzzy cmeans (FCM) based techniques have been widely used in medical image segmentation problem due to their simplicity and fast convergence. The proposed SMKFCM algorithm provides us a new flexible vehicle to fuse different pixel information in medical image segmentation and detection of MR images. To evaluate the robustness of the proposed segmentation algorithm in noisy environment, we add noise in medical brain tumor MR images and calculated the success rate and segmentation accuracy. From the experimental results it is clear that the proposed algorithm has better performance than those of other FCM based techniques for noisy medical MR images.

Keywords: Clustering, fuzzy C-means, image segmentation, MR images, multiple kernels.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1109073

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1792

References:


[1] A. Nyma, M. Kang, Y. Kwon, C. Kim and J. M. Kim, “A Hybrid Technique for Medical Image Segmentation”, Journal of Biomedicine and Biotechnology, Vol. 12, pp. 1-7, 2012.
[2] M. S. Atkins and B. T. Mackiewich, “Fully Automatic Segmentation of the Brain in MRI”, IEEE Tran. On medical imaging, Vol. 17 (1), pp.98- 107, 1998.
[3] Omid Jamshidi and Abdol Hamid Pilevar, “Automatic Segmentation of Medical Images using Fuzzy c-Mean and Genetic Algorithm”, Journal of Computational Medicine, Vol. 2(13), pp.91-96, 2013.
[4] M. Brummer, R. Mersereau, R. Eisner, and R. Lewine, “Automatic detection of brain contours in MRI data sets”, IEEE Trans. Medical Imaging., Vol.1(4),pp.153–166, 1993.
[5] A. Hammers, “Automatic anatomical brain MRI segmentation combining label propagation and decision fusion”, NeuroImage, Vol. 33(1), pp.115–126, 2006.
[6] J. Mehena and M. C. Adhikary, “Brain Tumor Segmentation and Extraction of MR Images Based on Improved Watershed Transform”, IOSR Journal of Computer Engineering, Vol.17(1), pp.01-05, 2015.
[7] A. Mustaqeem, A. Javed and T. Fatima, “An Efficient Brain Tumor Detection Algorithm Using Watershed &Thresholding Based Segmentation”, International Journal of Image, Graphics and Signal Processing, Vol.10 (3), pp. 34-39, 2012.
[8] V. Grau, A. U. J. Mewes and M. Alcañiz, “Improved Watershed Transform for Medical Image Segmentation Using Prior Information”, IEEE Transactions on Medical Imaging, Vol. 23(4), pp.447-458, 2004.
[9] R. C. Gonzalez, R.E. Woods and S.L.Eddins, “Digital Image Processing Using MATLAB”, 2nd Edn., McGraw Hill, New Delhi, 2010.
[10] M.C. Clark, L.O. Hall, D.B. Goldgof, “MRI Segmentation using Fuzzy Clustering Techniques”, IEEE Engineering in Medicine and Biology, Vol. 3(5), pp.730-742, 1994.
[11] S. Chaabane Ben, M. Sayadi, F. Fnaiech, and E. Brassart, “Color image segmentation using automatic thresholding and the fuzzy c-means techniques”, Proceedings of the IEEE Mediterranean Electrotechnical Conference, pp. 857–861, 2008.
[12] S. Chen and D. Zhang, “Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure”, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 34,pp. 1907–1916, 2004.
[13] L. Szilagyi, Z. Benyo, S. Szilagyii, and H. Adam, “MR brain image segmentation using an enhanced fuzzy C-means algorithm”, Proceedings of the Annual International Conference of the IEEE EMBS, pp.17–21, 2003.
[14] M. N. Ahmed, S. M. Yamany, N. Mohamed, A. Farag “A Modified Fuzzy C-Means Algorithm for Bias Field Estimation and Segmentation of MRI Data”, IEEE Transactions On Medical Imaging, Vol. 21( 3) , pp.193-199, 2002 .
[15] N. Venu and B. Anuradha, “PSNR Based Fuzzy Clustering Algorithms for MRI Medical Image Segmentation”, International Journal of Image Processing and Visual Communication, Vol.2 (2), pp.01-07, 2013.
[16] L. Chen, C. L. P. Chen and M. Lu, “A multiple-Kernel Fuzzy C-means algorithm for image segmentation”, IEEE Transactionson Systems, Man, and Cybernetics, Part B, Vol. 41(5),pp.1263–1274, 2011.
[17] J. Mehena, “Medical Image edge detection based on mathematical morphology”, International Journal of Computer and communication technology, Vol.-2(6), pp.45-48, 2011.
[18] Senthilkumaran N and Kirubakaran C, “A Case Study on Mathematical Morphology Segmentation for MRI Brain Image”, International Journal of Computer Science and Information Technologies, Vol. 5 (4),pp. 5336- 5340, 2014.