A Novel Two-Stage Approach For Automatic Detection of Brain Tumor
DOI:
https://doi.org/10.24297/jac.v12i25.4427Keywords:
Brain tumor detection, Hierarchical Centroid Shape Descriptor, Modified K-means, MRI, Dice index.Abstract
Brain tumor is one of the most life-threatening diseases, and it is the most common type of cancer that occurs among those in the age group belonging to 0-19. It is also a major cause of cancer-related deaths in children (males and females) under age 20 hence its detection should be fast and accurate. Manual detection of brain tumors using MRI scan images is effective but time-consuming. Many automation techniques and algorithms for detection of brain tumors are being proposed recently. In this paper, we propose an integrated two-step approach combining modified K-means clustering algorithm and Hierarchical Centroid Shape Descriptor (HCSD). The images are clustered using modified K-means based on pixel intensity, and then HCSD helps to select those having a specific shape thus making this approach more effective and reliable. Simulation of the proposed work is done in MATLAB R2013a. Tests are carried out on T1 weighted MRI scan images.Downloads
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References
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2. S. Aswathy, G. Glan Deva Dhas, and S. Kumar, 2014. “A survey on detection of brain tumor from MRI brain images,†in Control, Instrumentation, Communication and Computational Technologies (ICCICCT), In-ternational Conference on, July 2014, pp. 871–877.
3. S. Ghanavati, J. Li, T. Liu, P. Babyn, W. Doda, and G. Lampropoulos, 2012. “Automatic brain tumor detection in magnetic resonance images,†in Biomedical Imaging (ISBI), 2012 9th IEEE International Symposium on, May 2012, pp. 574–577.
4. S. Charutha and M. Jayashree, 2014. “An efficient brain tumor detection by integrating modified texture based region growing and cellular automata edge detection,†in Control, Instrumentation, Communication and Com-putational Technologies (ICCICCT), 2014 International Conference on, July 2014, pp. 1193–1199.
5. Islam, S. Reza, and K. Iftekharuddin, 2013. “Multifractal texture estimation for detection and segmentation of brain tumors,†Biomedical Engineer-ing, IEEE Transactions on, vol. 60, no. 11, pp. 3204–3215.
6. R. Preetha and G. Suresh, 2014. “Performance analysis of fuzzy c means algorithm in automated detection of brain tumor,†in Computing and Communication Technologies (WCCCT), 2014 World Congress on, Feb 2014, pp. 30–33.
7. Farmaki, K. Mavrigiannakis, K. Marias, M. Zervakis, and V. Sakkalis, 2010. “Assessment of automated brain structures segmentation based on the mean-shift algorithm: Application in brain tumor,†in Information Technology and Applications in Biomedicine (ITAB), 2010 10th IEEE International Conference on, Nov 2010, pp. 1–5.
8. T. E. M. A. Bianchi A, Miller JV, 2013. “Brain tumor segmentation with symmetric texture and symmetric intensity-based decision forests,†in Proceedings / IEEE International Symposium on Biomedical Imaging: from Nano to macro IEEE International Symposium on Biomedical Imaging., 2013, pp. 748 – 751.
9. K. W. . B. K. Dvok, P., 2013 “Automatic brain tumor detection in t2-weighted magnetic resonance images.†Measurement Science Review, vol. 13, no. 5, pp. 223 – 230.
10. Y. Zhang, Z. Dong, L. Wu, and S. Wang, 2011. “A hybrid method for MRI brain image classification,†Expert Systems with Applications, vol. 38, no. 8, pp. 10 049 – 10 053.
11. E. Ulku and A. Camurcu,2013. “Computer aided brain tumor detection with histogram equalization and morphological image processing techniques,†in Electronics, Computer and Computation (ICECCO), 2013 International Conference on, Nov 2013, pp. 48–51.
12. P. Dhage, M. Phegade, and S. Shah, “Watershed segmentation brain tumor detection,†in Pervasive Computing (ICPC), 2015 International Conference on, Jan 2015, pp. 1–5.
13. M. Ozic, Y. Ozbay, and O. Baykan, 2014.“Detection of tumor with otsu-pso method on brain mr image,†in Signal Processing and Communications Applications Conference (SIU), 2014 22nd, April 2014, pp. 1999–2002.
14. S. Satheesh, R. Kumar, K. Prasad, and K. Reddy, 2011. “Skull removal of noisy magnetic resonance brain images using contourlet transform and morphological operations,†in Computer Science and Network Technology (ICCSNT), 2011 International Conference on, vol. 4, Dec 2011, pp. 2627–2631.
15. J. Chiverton, K. Wells, E. Lewis, C. Chen, B. Podda, and D. Johnson, “Statistical morphological skull stripping of adult and infant MRI data,†Computers in Biology and Medicine, vol. 37, no. 3, pp. 342 – 357, 2007.
16. Dogdas, D. W. Shattuck, and R. M. Leahy, “Segmentation of skull and scalp in 3-d human MRI using mathematical morphology data,†Human Brain Mapping, vol. 26, no. 4, p. 273285, 2007.
17. N. Otsu, “A Threshold Selection Method from Gray-level Histograms,†IEEE Transactions on Systems, Man and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979.
18. Elisee´ Ilunga-Mbuyamba, Juan Gabriel Avina-Cervantes, Dirk Lindner,Jesus Guerrero-Turrubiates, Claire Chalopin, 2016. “Automatic Brain Tumor Tissue Detection based on Hierarchical Centroid Shape Descriptor in T1-weighted MR imagesâ€, 2016 International Conference On Electronics, Communications And Computers (Conielecomp).
19. Shailendra Singh Raghuwanshi , PremNarayanArya ,“Comparison of K-means and Modified K-mean algorithms for Large Data-setâ€,International Journal of Computing, Communications and Networking,Volume 1, No.3, November – December 2012.
20. Malay K. Pakhira,â€A Modified k-means Algorithm to Avoid Empty Clustersâ€, International Journal of Recent Trends in Engineering, Vol 1, No. 1, May 2009.
21. Alan Sexton, Alison Todman, and Kevin Woodward , “Font Recognition Using Shape-Based Quad-tree and Kd-tree Decompositionâ€, in Proceedings of The Joint Conference On information sciences , vol.5,no. 2.2000, pp. 212-215.
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Published
2016-12-26
How to Cite
Murthi, A., & Shameer, S. (2016). A Novel Two-Stage Approach For Automatic Detection of Brain Tumor. JOURNAL OF ADVANCES IN CHEMISTRY, 12(25), 5653–5660. https://doi.org/10.24297/jac.v12i25.4427
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