Progress In Electromagnetics Research
ISSN: 1070-4698, E-ISSN: 1559-8985
Home | Search | Notification | Authors | Submission | PIERS Home | EM Academy
Home > Vol. 116 > pp. 65-79


By Y.-D. Zhang, L. Wu, and S. Wang

Full Article PDF (264 KB)

Automated and accurate classification of magnetic resonance (MR) brain images is a hot topic in the field of neuroimaging. Recently many different and innovative methods have been proposed to improve upon this technology. In this study, we presented a hybrid method based on forward neural network (FNN) to classify an MR brain image as normal or abnormal. The method first employed a discrete wavelet transform to extract features from images, and then applied the technique of principle component analysis (PCA) to reduce the size of the features. The reduced features were sent to an FNN, of which the parameters were optimized via an improved artificial bee colony (ABC) algorithm based on both fitness scaling and chaotic theory. We referred to the improved algorithm as scaled chaotic artificial bee colony (SCABC). Moreover, the K-fold stratified cross validation was employed to avoid overfitting. In the experiment, we applied the proposed method on the data set of T2-weighted MRI images consisting of 66 brain images (18 normal and 48 abnormal). The proposed SCABC was compared with traditional training methods such as BP, momentum BP, genetic algorithm, elite genetic algorithm with migration, simulated annealing, and ABC. Each algorithm was run 20 times to reduce randomness. The results show that our SCABC can obtain the least mean MSE and 100% classification accuracy.

Y.-D. Zhang, L. Wu, and S. Wang, "Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm," Progress In Electromagnetics Research, Vol. 116, 65-79, 2011.

1. Mohsin, S. A., N. M. Sheikh, and U. Saeed, "MRI induced heating of deep brain stimulation leads: Effect of the air-tissue interface," Progress In Electromagnetics Research, Vol. 83, 81-91, 2008.

2. Hynynen, K., "MRI-guided focused ultrasound treatments," Ultrasonics, Vol. 50, No. 2, 221-229, 2010.

3. Ravaud, R. and G. Lemarquand, "Magnetic field in MRI yokeless devices: Analytical approach," Progress In Electromagnetics Research, Vol. 94, 327-341, 2009.

4. Cobos Sanchez, C., S. G. Garcia, L. D. Angulo, C. M. De Jong Van Coevorden, and A. Rubio Bretones, "A divergence-free BEM method to model quasi-static currents: Application to MRI coil design," Progress In Electromagnetics Research B, Vol. 20, 187-203, 2010.

5. Mishra, M. and N. Gupta, "Application of quasi monte carlo integration technique in EM scattering from finite cylinders," Progress In Electromagnetics Research Letters, Vol. 9, 109-118, 2009.

6. Valsan, S. P. and K. S. Swarup, "Wavelet transform based digital protection for transmission lines," International Journal of Electrical Power & Energy Systems, Vol. 31, No. 7-8, 379-388, 2009.

7. Danesfahani, R., S. Hatamzadeh-Varmazyar, E. Babolian, and Z. Masouri, "Applying shannon wavelet basis functions to the method of moments for evaluating the radar cross section of the conducting and resistive surfaces," Progress In Electromagnetics Research B, Vol. 8, 257-292, 2008.

8. Huang, C.-W. and K.-C. Lee, "Application of ica technique to PCA based radar target recognition," Progress In Electromagnet ics Research, Vol. 105, 157-170, 2010.

9. Camacho, J., J. Pico, and A. Ferrer, "Corrigendum to `The best approaches in the on-line monitoring of batch processes based on PCA: Does the modelling structure matter?' [Anal. Chim. Acta, Vol. 642, 59-68, 2009],", Vol. 658, No. 1, 106, Analytica Chimica Acta, 2010.

10. Bermani, E., A. Boni, A. Kerhet, and A. Massa, "Kernels evaluation of Svm-based estimators for inverse scattering problems," Progress In Electromagnetics Research, Vol. 53, 167-188, 2005.

11. 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.

12. Yeh, J.-Y. and J. C. Fu, "A hierarchical genetic algorithm for segmentation of multi-spectral human-brain MRI," Expert Systems with Applications, Vol. 34, No. 2, 1285-1295, 2008.

13. Zhang, Y.-D. and L.Wu, "Weights optimization of neural network via improved BCO approach," Progress In Electromagnetics Research, Vol. 83, 185-198, 2008.

14. Coulibaly, P. and N. D. Evora, "Comparison of neural network methods for infilling missing daily weather records," Journal of Hydrology, Vol. 341, No. 1-2, 27-41, 2007.

15. Robotham, H., et al., "Acoustic identification of small pelagic fish species in Chile using support vector machines and neural networks ," Fisheries Research, Vol. 102, No. 1-2, 115-122, 2010.

16. Kellegöz, T., B. Toklu, and J. Wilson, "Elite guided steady-state genetic algorithm for minimizing total tardiness in flowshops," Computers & Industrial Engineering, Vol. 58, No. 2, 300-306, 2010.

17. Kiranyaz, S., et al., "Evolutionary artificial neural networks by multi-dimensional particle swarm optimization," Neural Networks, Vol. 22, No. 10, 1448-1462, 2009.

18. Karaboga, N., A. Kalinli, and D. Karaboga, "Designing digital IIR filters using ant colony optimisation algorithm," Engineering Applications of Artificial Intelligence, Vol. 17, No. 3, 301-309, 2004.

19. Karaboga, D. and B. Basturk, "On the performance of artificial bee colony (ABC) algorithm," Applied Soft Computing, Vol. 8, No. 1, 687-697, 2008.

20. Zhang, S., S.-X. Gong, Y. Guan, P.-F. Zhang, and Q. Gong, "A novel IGA-EDSPSO hybrid algorithm for the synthesis of sparse arrays," Progress In Electromagnetics Research, Vol. 89, 121-134, 2009.

21. Wang, W.-T., S.-X. Gong, Y.-J. Zhang, F.-T. Zha, J. Ling, and T. Wan, "Low RCS dipole array synthesis based on MoM-PSO hybrid algorithm," Progress In Electromagnetics Research, Vol. 94, 119-132, 2009.

22. Chaplot, S., L. M. Patnaik, and N. R. Jagannathan, "Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network," Biomedical Signal Processing and Control, Vol. 1, No. 1, 86-92, 2006.

23. El-Dahshan, E.-S. A., T. Hosny, and A.-B. M. Salem, "Hybrid intelligent techniques for MRI brain images classification," Digital Signal Processing, Vol. 20, No. 2, 433-441, 2010.

24. Zhang, Y., S. Wang, and L. Wu, "A novel method for magnetic resonance brain image classification based on adaptive chaotic PSO," Progress In Electromagnetics Research, Vol. 109, 325-343, 2010.

25. Zhang, Y., et al., "Chaotic artificial bee colony used for cluster analysis," Communications in Computer and Information Science, Vol. 134, No. 1, 205-211, 2011.

26. Korsunsky, A. M. and A. Constantinescu, "Work of indentation approach to the analysis of hardness and modulus of thin coatings ," Materials Science and Engineering: A, Vol. 423, No. 1-2, 28-35, 2006.

27. Wang, Y., B. Li, and T. Weise, "Estimation of distribution and di®erential evolution cooperation for large scale economic load dispatch optimization of power systems," Information Sciences, Vol. 180, No. 12, 2405-2420, 2010.

28. Qiao, S., Z.-G. Shi, T. Jiang, and L.-X. Ran, "A new architecture of UWB radar utilizing microwave chaotic signals and chaos synchronization," Progress In Electromagnetics Research, Vol. 75, 225-237, 2007.

29. Singh, N. and A. Sinha, "Chaos-based secure communication system using logistic map," Optics and Lasers in Engineering, Vol. 48, No. 3, 398-404, 2010.

30. Ludwig, Jr., O., et al., "Applications of information theory, genetic algorithms, and neural models to predict oil flow," Communications in Nonlinear Science and Numerical Simulation, Vol. 14, No. 7, 2870-2885, 2009.

© Copyright 2014 EMW Publishing. All Rights Reserved