PIER B
 
Progress In Electromagnetics Research B
ISSN: 1937-6472
Home | Search | Notification | Authors | Submission | PIERS Home | EM Academy
Home > Vol. 49 > pp. 31-54

BRAIN TUMOR TISSUE CATEGORIZATION IN 3D MAGNETIC RESONANCE IMAGES USING IMPROVED PSO FOR EXTREME LEARNING MACHINE

By B. Arunadevi and S. N. Deepa

Full Article PDF (402 KB)

Abstract:
Magnetic Resonance Imaging (MRI) technique is one of the most useful diagnostic tools for human soft tissue analysis. Moreover, the brain anatomy features and internal tissue architecture of brain tumor are a complex task in case of 3-D anatomy. The additional spatial relationship in transverse, longitudinal planes and the coronal plane information has been proved to be helpful for clinical applications. This study extends the computation of gray level co-occurrence matrix (GLCM) and Run length matrix (RLM) to a three-dimensional form for feature extraction. The sub-selection of rich optimal bank of features to model a classifier is achieved with custom Genetic Algorithm design. An improved Extreme Learning Machine (ELM) classifier algorithm is explored, for training single hidden layer artificial neural network, integrating an enhanced swarm-based method in optimization of the best parameters (input-weights, bias, norm and hidden neurons), enhancing generalization and conditioning of the algorithm. The method is modeled for automatic brain tissue and pathological tumor classification and segmentation of 3D MRI tumor images. The method proposed demonstrates good generalization capability from the best individuals obtained in the learning phase to handle sparse image data on publically available benchmark dataset and real time data sets.

Citation:
B. Arunadevi and S. N. Deepa, "Brain Tumor Tissue Categorization in 3D Magnetic Resonance Images Using Improved PSO for Extreme Learning Machine," Progress In Electromagnetics Research B, Vol. 49, 31-54, 2013.
doi:10.2528/PIERB13010202

References:
1. Materka, A. and M. Strzelecki, "Texture analysis methods --- A review,", Technical University of Lodz, Institute of Electronics, COST B11 Report, Brussels, 1998.

2. Byun, H. and S. Lee, "Applications of support vector machines for pattern recognition: A survey," Proc. Int. Work. Pattern Recognition with Support Vector Machines, 213-236, Canada, 2003.
doi:10.1109/18.661502

3. Barlett, P. L., "The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network," IEEE Trans. Inform. Theory, Vol. 44, 525-536, 1998.
doi:10.1016/0167-8655(90)90112-F

4. Chu, A., C. M. Sehgal, and J. F. Greenleaf, "Use of gray value distribution of run lengths for texture analysis," Pattern Recognition Letters, Vol. 11, No. 6, 415-420, 1990.
doi:10.1109/4235.985692

5. Clerc, M. and J. Kennedy, "The particle swarm --- Explosion stability, and convergence in a multidimensional complex space," IEEE Trans. on Evolutionary Computation, Vol. 6, No. 1, 58-73, 2002.
doi:10.1016/S1361-8415(03)00037-9

6. Cocosco, C. A., A. P. Zijdenbos, and A. C. Evans, "A fully automatic and robust brain MRI tissue classification method," Medical Image Analysis, Vol. 7, No. 4, 513-527, 2003.
doi:10.1016/j.crad.2004.07.008

7. Castellano, G., L. Bonilha, L. M. Li, and F. Cendes, "Texture analysis of medical images," Clinical Radiology, Vol. 59, No. 12, 1061-1069, 2004.

8. Cobzas, D., N. Birkbeck, M. Schmidt, and M. Jagersan, "3D variational brain tumor segmentation using a high dimensional feature set," Proc. Int. Conf. Computer Vision, 1-8, 2007.

9. Deepa, S. N. and D. B. Aruna, "Second order sequential minimal optimization for brain tumour classification," European Journal of Scientific Research, Vol. 64, No. 3, 377-386, 2011.
doi:10.1117/12.812470

10. Padfield, D. and J. Ross, "Validation tools for image segmentation," Proc. of SPIE, Medical Imaging, 72594W, 2009.

11. Dalai, N., B. Triggs, I. Rhone-Alps, and F. Montbonnot, "Histograms of oriented gradients for human detection," Proc. Conference on Computer Vision and Pattern Recognition, 886-893, 2005.

12. Xu, D.-H., A. S. Kurani, J. D. Furst, and D. S. Raicu, "Run length encoding for volumetric texture," Proc. of Visualization, Imaging, and Image Processing, 2004.
doi:10.1002/mrm.22147

13. Zacharaki, E. I., S. Wang, S. Chawla, D. S. Yoo, R. Wolf, E. R. Melhem, C. Davatzikos, "Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme," Magn. Reson. Med., Vol. 62, No. 6, 1609-1618, 2009.

14. Han, F., H.-F. Yao, and Q.-H. Ling, "An improved extreme learning machine based on particle swarm optimization," Proc. of International Conference on Intelligent Computing, 699-704, 2012.
doi:10.1006/nimg.2002.1202

15. Good, C. D., R. I. Scahill, and N. T. Fox, "Automatic differentiation of anatomical patterns in the human brain: Validation with studies of degenerative dementias," Neuroimage, Vol. 17, No. 1, 29-46, 2002.
doi:10.1016/S0146-664X(75)80008-6

16. Galloway, M. M., "Texture analysis using grey level run lengths," Comp. Graph. and Image Proc., Vol. 4, 172-179, 1975.

17. Georgiadis, P., D. Cavouras, I. Kalatzis, D. Glotsos, K. Sifaki, M. Malamas, G. Nikiforidis, and E. Solomou, "Computer aided discrimination between primary and secondary brain tumors on MRI: From 2D to 3D texture analysis," E-Journal of Science & Technology (E-JST), 9-18, 2008.

18. Hu, X., K. K. Tan, and D. N. Levin, "Three-dimensional magnetic resonance images of the brain: Application to neurosurgical planning," J. Neurosurg., Vol. 72, 433-440, 1990.
doi:10.1016/j.neucom.2005.12.126

19. Huang, G. B., Q. Y. Zhu, and C. K. Siew, "Extreme learning machine: Theory and applications," Neurocomputing, Vol. 70, No. 1, 489-501, 2006.

20. Huang, G. B., Q. Y. Zhu, and C. K. Siew, "Extreme learning machine: A new learning scheme of feed forward neural networks," Proc. Int. Conf. Neural Networks, 985-990, Budapest, Hungary, 2004.
doi:10.1109/TNN.2003.809401

21. Huang, G. B., "Learning capability and storage capacity of two-hidden-layer feed forward networks," IEEE Transactions on Neural Networks, Vol. 14, No. 2, 274-281, 2004.

22. Huang, G. B. and H. Babri, "General approximation theorem on feed forward networks," Proc. International Conference on Information, Communications and Signal Processing, 698-702, Singapore, 1997.
doi:10.1109/TSMC.1973.4309314

23. Haralick, R. M., K. Shanmugam, and I. Dinstein, "Textural features for image classification," IEEE Trans. Syst. Man Cybern., Vol. 3, 610-621, 1973.

24. Guyon, I. and A. Elisseeff, "An introduction to variable and feature selection," Journal of Machine Learning Research, Vol. 3, 1157-1182, 2003.
doi:10.1007/s11682-011-9142-3

25. Zhang, J., C. Yu, G. Jiang, W. Liu, and L. Tong, "3D texture analysis on MRI images of Alzheimer's disease," Brain Imaging and Behavior, Vol. 6, 61-69, 2012.

26. Kurani, A. S., D. H. Xu, J. D. Furst, and D. S. Raicu, "Co-occurence matrices for volumetric data," Proc. 7th IASTED International Conf. on Computer Graphics and Imaging, 2004.

27. Khalid, K. M. and I. Nystrom, "A modified particle swarm optimization applied in image registration," Proc. Int. Conf. Pattern Recognition, 2302-2305, 2010.

28. Krohling, R. A. and E. Mendel, "Bare bones particle swarm optimization with Gaussian or Cauchy jumps," Proc. of Congress on Evolutionary Computation, 3285-3291, 2009.
doi:10.1109/42.811270

29. Leemput, K. V., F. Maes, D. Vandermeulen, and P. Suetens, "Automated model-based tissue classification of MR images of the brain," IEEE Trans. Med. Imag., Vol. 18, No. 10, 897-908, 1999.
doi:10.1023/B:VISI.0000029664.99615.94

30. Lowe, D. G., "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, Vol. 60, 91-110, 2004.

31. Kaus, M. R., S. K. Warfield, A. Nabavi, M. Peter, A. Ferenc, and R. Kikinis, "Automated segmentation of MR images of brain tumors," Radiology, Vol. 218, 586-591, 2001.
doi:10.1016/0031-3203(95)00067-4

32. Ojala, T., M. Pietikinen, and D. Harwood, "A comparative study of texture measures with classification based on featured distributions," Pattern Recognition, Vol. 9, No. 1, 51-59, 1996.
doi:10.1007/s11721-007-0002-0

33. Poli, R., J. Kennedy, and T. Blackwell, "Particle swarm optimization: An overview," Swarm Intelligence, Vol. 1, 33-57, 2007.
doi:10.1109/34.761261

34. Randen, T. and J. H. Husoy, "Filtering for texture classification: A comparative study," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 21, No. 4, 291-310, 1999.
doi:10.1016/j.engappai.2010.06.009

35. Suresh, S., S. Saraswathi, and N. Sundararajan, "Performance enhancement of extreme learning machine for multi-category sparse cancer classification problems," Engineering Applications of Artificial Intelligence, Vol. 23, 1149-1157, 2010.
doi:10.1109/TCBB.2010.13

36. Saraswathi, S., S. Suresh, N. Sundararajan, Z. Michael, and N. Marit, "Performance enhancement of extreme learning machine for multi-category sparse cancer classification problems," ACM Transactions on Computational Biology and Bioinformatics, Vol. 8, No. 2, 452-463, 2011.
doi:10.1016/j.asoc.2008.07.005

37. Suresh, S., V. Babu, and H. J. Kim, "No-reference image quality assessment using modified extreme learning machine classifier," Applied Soft Computing, Vol. 9, 541-552, 2009.
doi:10.1016/0167-8655(89)90037-8

38. Siedlecki, W. and J. Sklanky, "A note on genetic algorithms for large-scale feature selection," Pattern Recognition Letters, Vol. 10, No. 5, 335-347, 1989.

39. Xu, Y. and Y. Shu, "Evolutionary extreme learning machine --- Based on particle swarm optimization," Proc. Inter. Symp. Neural Networks, Vol. 3791, 644-652, 2006.
doi:10.1016/j.patcog.2005.03.028

40. Zhu, Q. Y., A. K. Qin, P. N. Suganthan, and G. B. Huang, "Evolutionary extreme learning machine," Pattern Recognition, Vol. 38, No. 10, 1759-1763, 2005.

41. Zhang, Y. and L.Wu, "An MR brain images classifier via principal component analysis and kernel support vector machine," Progress In Electromagnetics Research, Vol. 130, 369-388, 2012.


© Copyright 2010 EMW Publishing. All Rights Reserved