Abstract
Mortality rate by the brain tumor was very high some years before. But now this rate is decreased in the recent years due to the earlier diagnosis and proper treatment. Chances of the long survival of the patient can be increased by the accurate brain tumor diagnosis. For this regard we are proposing more accurate and efficient system for brain tumor diagnosis and brain tumor region extraction. Proposed system first diagnosis the tumor from the brain MR images using naïve bayes classification. After diagnosis brain tumor region is extracted using K-means clustering and boundary detection techniques. We are achieving diagnosis accuracy more than 99%. Qualitative results show that accurate tumor region is extracted by the proposed system. The proposed technique is tested against the datasets of different patients received from Holy Family hospital and Abrar MRI&CT Scan center Rawalpindi.
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References
Sonka, M., Tadikonda, S.K., Collins, S.M.: Know1edge-Based Interpretation of MR Brain Images. IEEE Transaction on Medical Imaging 15(4), 443–452 (1996)
Khayam, S.A.: The Discrete Cosine Transform: Theory and Application. WAVES lab technical report (May 2004)
Mitchell, T.M.: Machine Learning, Bayesian Learning, pp. 154–178. McGraw Hill, New York (1997)
MacQueen, J.B.: Some Methods for classification and Analysis of Multivariate Observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297. University of California Press, Berkeley (1967)
El-Dahshan, E.S.A., Salem, A.-B.M., Younis, T.H.: A hybrid technique for automatic MRI brain images classification. Studia Univ, Babes Bolyai, Informatica LIV, 55–67 (2009)
Cocosco, C.A., Zijdenbos, A.P., Evans, A.C.: A fully automatic and robust brain MRI tissue classification method. Medical Image Analysis 7(4), 513–527 (2003)
Wang, P., Wang, H.: A modified FCM algorithm for MRI brain image segmentation. In: International Seminar on Future BioMedical Information Engineering, pp. 26–29. IEEE computer society, Los Alamitos (2008)
Ratan, R., Sharma, S., Sharamac, S.k.: Brain tumor detection based on multi-parameter MRI image analysis. ICGST-GVIP Journal 9(III), 9–17 (2009) ISSN 1687-398X
Dubey, R.B., Hanmandlu, M., Gupta, S.K.: Semi-automatic Segmentation of MRI Brain Tumor. ICGST-GVIP Journal 9(4), 33–40 (2009) ISSN: 1687-398X
Ahmed, M.M., Mohamad, D.B.: Segmentation of Brain MR Images for Tumor Extraction by Combining Kmeans Clustering and Perona-Malik Anisotropic Diffusion Model. International Journal of Image Processing 2(1), 27–34 (2008)
Bala Ganesan, T., Sukanesh, R.: Segmentation of Brain Images Using Fuzzy Clustering Method with Silhouutte Method. Journal of Engineering and Applied Sciences, Medwell Journals, 792–795 (2008)
Chen, P.-F., Grant Steen, R., Yezzi, A., Krim, H.: Brain MRI T1-map and T1 weighted image segmentation in a variational framework. In: Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 417–420 (2009)
Strang, G.: The Discrete Cosine Transform. SIAM Review 41(1), 135–147 (1999)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification, 2nd edn. John Wiley and Sons, New York (2001)
Harvard Medical School, Web, http://med.harvard.edu/AANLIB/
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Ain, Qu., Mehmood, I., Naqi, S.M., Jaffar, M.A. (2010). Bayesian Classification Using DCT Features for Brain Tumor Detection. In: Setchi, R., Jordanov, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based and Intelligent Information and Engineering Systems. KES 2010. Lecture Notes in Computer Science(), vol 6276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15387-7_38
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DOI: https://doi.org/10.1007/978-3-642-15387-7_38
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