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AN IMPROVED TECHNIQUE FOR IDENTIFICATION AND CLASSIFICATION OF BRAIN DISORDER FROM MRI BRAIN IMAGE

Finitha Joseph, S. Raja

Abstract


Medical image processing is developing recently due to its wide applications. An efficient MRI image segmentation is needed at present. In this paper, MRI brain segmentation is done by Semi supervised learning which does not require pathology modelling and, thus, allows high degree of automation. In abnormality detection, a vector is characterized as anomalous if it does not comply with the probability distribution obtained from normal data. The estimation of the probability density function, however, is usually not feasible due to large data dimensionality. In order to overcome this challenge, we treat every image as a network of locally coherent image partitions (overlapping blocks). We formulate and maximize a strictly concave likelihood function estimating abnormality for each partition and fuse the local estimates into a globally optimal estimate that satisfies the consistency constraints, based on a distributed estimation algorithm. After this features are extracted by Gray-Level Co-occurrence Matrices (GLCM) algorithm and those features are given to Particle Spam Optimization (PSO) and finally classification is done by using Library Support Vector Machine (LIBSVM).Thus results are evaluated and proved its efficiency using accuracy.

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L. G. Qurat-Ul-Ain, S. B. Kazmi, M. A. Jaffar, and A. M Mirza, “Classification and Segmentation of Brain Tumor using Texture Analysis”, Recent Advances in Artificial Intelligence, Knowledge Engineering and Data Bases, ISBN: 978-960-474-154-0, pp. 147- 155, 2010.

L. M. DeAngelis, “Brain Tumors”, The New England Journal of Medicine, Vol. 344, No. 2, pp 114-123, 2001.

A. M. Riad, A. Atwan, H. M. El-Bakry, R. R. Mostafa, H. K. Elminir, and N. Mastorakis, “A New Approach for Segmentation of Brain MR Image”, Proceedings of the WSEAS International Conference on Environment, Medicine and Health Sciences, ISSN: 1790- 5125, ISBN: 978-960-474-170-0, pp. 74-83, 2010.

H. B. Kekre, and S. Gharge, “Direct Variance on MRI Images for Tumor Detection”, Journal of Science, Engineering and Technology Management, Vol. 2, No. 1, January 2010, pp. 3-11.

P. Vasuda, and S. Satheesh, “Improved Fuzzy C-Means Algorithm for MR Brain Image Segmentation”, International Journal on Computer Science and Engineering (IJCSE), Vol. 02, No. 05, pp. 1713-1715, 2010.

P. Anbeek, K. L. Vincken, and M. A.Viergever, “Automated MS-Lesion Segmentation by K-Nearest Neighbor Classification”, 2008.

R. Ganesan, and S. Radhakrisham, “Segmentation of Computed Tomography Brains Images Using Genetic Algorithm”, International Journal of Soft Computing 4(4): pp. 157-161, 2009.

J. Roerdink, and A. Meijster, “The Watershed Transform: Definitions, Algorithms and Parallelization Strategies”, Fundamenta Informaticae, pp. 187-228, IOS Press, 2001.

S. Geman and D. Geman, “Stochastic relaxation, Gibbs distribution and the Bayesian restoration of images,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 6, no. 6, pp. 721–741, Jun. 1984.

J. Besag, “On the statistical analysis of dirty pictures,” J. Roy. Statist. Soc. B, vol. 48, pp. 259–302, 1986.

D. Geiger and F. Girosi, “Parallel and deterministic algorithm from MRF‟s: Surface reconstruction,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 13, no. 5, pp. 401–412, May 1991.

F. Jeng and J. Woods, “Compound Gauss-Markov random fields for image estimation,” IEEE Trans. Signal Process., vol. 39, no. 3, pp. 683–697, Mar. 1991.

R. Molina, J. Mateos, A. Katsaggelos, and M. Vega, “Bayesian multichannel image restoration using compound Gauss-Markov random fields,” IEEE Trans. Image Process., vol. 12, no. 12, pp. 1642–1654, Dec. 2003.

J. Sun, N. Zheng, and H. Shum, “Stereo matching using belief propagation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 7, pp. 787–800, Jul. 2003.

F. Arduini, C. Dambra, and C. S. Regazzoni, “A coupled MRF model for sar image restoration and edge-extraction,” in Proc. Int. Geosci. Remote Sensing Symp., 1992, vol. 2, pp. 1120–1122.

K. Held, E. Kops, J. Krause, W. Wells, R. Kikinis, and H. Muller- Gartner, “Markov random field segmentation of brain MR images,” IEEE Trans. Med. Imag., vol. 16, no. 6, pp. 878–886, Jun. 1997.

G. Erus, E. I. Zacharaki, R. N. Bryan, and C. Davatzikos, “Learning high-dimensional image statistics for abnormality detection on medical images,” in Proc. IEEE Comput. Soc. Workshop Math. Methods Biomed. Image Anal., 2010, pp. 139–145.

S. Samar, S. Boyd, and D. Gorinevsky, “Distributed estimation via dual decomposition,” in Proc. Eur. Control Conf., 2007, pp. 1511–1516.

P.K. Sahoo, S. Soltani, and A.K.C.Wong. A survey of thresholding techniques. Comput. Vis. Graph. Im. Proc., 41:233–260, 1988.

G. Evelin Sujji, Y.V.S. Lakshmi, G. Wiselin Jiji,” MRI Brain Image Segmentation based on Thresholding “,International Journal of Advanced Computer Research (ISSN (print): 2249-7277 ISSN (online): 2277-7970) Volume-3 Number-1 Issue-8, March-2013, pp.97-101.

Dzung L. Phamy, Chenyang Xu, Jerry L. Prince,” A Survey Of Current Methods In Medical Image Segmentation” Annual Review of Biomedical Engineering January 19, 1998.

Xin-Bo Zhang And Li Jiang “An image Segmentation algorithm Based On Fuzzy C-Means Clustering”, International Conference On Digital Image Processing,March 2009 ,pp 22-26.

G.B. Coleman and H.C. Andrews. Image segmentation by clustering. Proc. IEEE, 5:773–785, 1979

P. Comon, “Independent component analysis - a new concept”, Signal Processing, 36, 1994, pp: 287-314

C. Jutten, J. Herault, “Blind separation of sources”, Signal Processing, Part I: An adaptive algorithm based on neuromimetic architecture. 24, 1991, pp: 1-10.

Y. Shi, and R. Eberhart, "A modified particle swarm optimizer," in the Proceedings of the IEEE International Conference on Evolutionary Computation, Piscataway, NJ, pp. 69-73, 1998.

J. Kennedy, and R. Eberhart, Swarm Intelligence, San Francisco: Morgan Kaufmann Publishers, 2001.

Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin,” A Practical Guide to Support Vector Classification”, April 15, 2010.




DOI: http://dx.doi.org/10.6084/ijact.v3i4.274

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