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PIER PIER B PIER C PIER M PIER Letters
2015-06-26
Pathological Brain Detection in Magnetic Resonance Imaging Scanning by Wavelet Entropy and Hybridization of Biogeography-Based Optimization and Particle Swarm Optimization
By
Yudong Zhang,

    Prof. Yudong Zhang

    Department of Psychiatry

    Columbia University

    USA

    Homepage

Shuihua Wang,

    Dr. Shuihua Wang

    School of Computer Science and Technology

    Nanjing Normal University

    China

    Homepage

Zhengchao Dong,

    Dr. Zhengchao Dong

    Department of Psychiatry

    Columbia University

    USA

    Homepage

Preetha Phillip,

    Dr. Preetha Phillip

    School of Natural Sciences and Mathematics

    Shepherd University

    USA

    Homepage

Genlin Ji,

    Dr. Genlin Ji

    School of Computer Science and Technology

    Nanjing Normal University

    China

    Homepage

Jiquan Yang

    Dr. Jiquan Yang

    Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing

    China

    Homepage

Progress In Electromagnetics Research, Vol. 152, 41-58, 2015
doi:10.2528/PIER15040602
Abstract
(Background) We proposed a novel computer-aided diagnosis (CAD) system based on the hybridization of biogeography-based optimization (BBO) and particle swarm optimization (PSO), with the goal of detecting tumors from normal brains in MRI scanning. (Methods) The proposed method used wavelet entropy (WE) to extract features from MR brain images, followed by feed-forward neural network (FNN) with training method of a Hybridization of BBO and PSO (HBP), which combined the exploration ability of BBO and exploitation ability of PSO. (Results) The 10×K-fold cross validation result showed that the proposed HBP outperformed existing FNN training methods and that the proposed WE + HBP-FNN outperformed state-of-the-art CAD systems of MR brain classification in terms of classification accuracy. Moreover, the proposed method achieved accuracy of 100%, 100%, and 99.49% over Dataset-66, Dataset-160, and Dataset-255, respectively. The offline learning cost 208.2510 s for Dataset-255, and merely 0.053 s for online prediction. (Conclusion) The proposed WE + HBP-FNN method achieved nearly perfect detection on tumors in MRI scanning.
Citation
Yudong Zhang, Shuihua Wang, Zhengchao Dong, Preetha Phillip, Genlin Ji, and Jiquan Yang, "Pathological Brain Detection in Magnetic Resonance Imaging Scanning by Wavelet Entropy and Hybridization of Biogeography-Based Optimization and Particle Swarm Optimization," Progress In Electromagnetics Research, Vol. 152, 41-58, 2015.
doi:10.2528/PIER15040602
References

1. Goh, S., et al. "Mitochondrial dysfunction as a neurobiological subtype of autism spectrum disorder: Evidence from brain imaging," JAMA Psychiatry, Vol. 71, No. 6, 665-671, 2014.
doi:10.1001/jamapsychiatry.2014.179

2. Zhang, Y., et al. "An MR brain images classifier system via particle swarm optimization and kernel support vector machine," The Scientific World Journal, Vol. 2013, No. 9, 2013.

3. Maji, P., M. K. Kundu, and B. Chanda, "Second order fuzzy measure and weighted co-occurrence matrix for segmentation of brain MR images," Fundamenta Informaticae, Vol. 88, No. 1-2, 161-176, 2008.

4. Wu, W., et al. "Brain tumor detection and segmentation in a CRF (conditional random fields) framework with pixel-pairwise affinity and superpixel-level features," International Journal of Computer Assisted Radiology and Surgery, Vol. 9, No. 2, 241-253, Mar. 2014.
doi:10.1007/s11548-013-0922-7

5. 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.
doi:10.1016/j.bspc.2006.05.002

6. Maitra, M. and A. Chatterjee, "A Slantlet transform based intelligent system for magnetic resonance brain image classification," Biomedical Signal Processing and Control, Vol. 1, No. 4, 299-306, Oct. 2006.
doi:10.1016/j.bspc.2006.12.001

7. 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, Mar. 2010.
doi:10.1016/j.dsp.2009.07.002

8. Zhang, Y., 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.
doi:10.2528/PIER11031709

9. Zhang, Y., et al. "A hybrid method for MRI brain image classification," Expert Systems with Applications, Vol. 38, No. 8, 10049-10053, 2011.
doi:10.1016/j.eswa.2011.02.012

10. Ramasamy, R. and P. Anandhakumar, "Brain tissue classification of MR images using fast fourier transform based expectation-maximization Gaussian mixture model," Advances in Computing and Information Technology, 387-398, Springer, 2011.
doi:10.1007/978-3-642-22555-0_40

11. 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.
doi:10.2528/PIER12061410

12. Saritha, M., K. P. Joseph, and A. T. Mathew, "Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network," Pattern Recognition Letters, Vol. 34, No. 16, 2151-2156, Dec. 2013.
doi:10.1016/j.patrec.2013.08.017

13. Zhang, Y., et al. "Effect of spider-web-plot in MR brain image classification," Pattern Recognition Letters, Vol. 62, 14-16, Sep. 1, 2015.

14. Das, S., M. Chowdhury, and M. K. Kundu, "Brain MR image classification using multiscale geometric analysis of ripplet," Progress In Electromagnetics Research, Vol. 137, 1-17, 2013.
doi:10.2528/PIER13010105

15. Kalbkhani, H., M. G. Shayesteh, and B. Zali-Vargahan, "Robust algorithm for brain magnetic resonance image (MRI) classification based on GARCH variances series," Biomedical Signal Processing and Control, Vol. 8, No. 6, 909-919, 2013.
doi:10.1016/j.bspc.2013.09.001

16. Padma, A. and R. Sukanesh, "Segmentation and classification of brain CT images using combined wavelet statistical texture features," Arabian Journal for Science and Engineering, Vol. 39, No. 2, 767-776, Feb. 2014.
doi:10.1007/s13369-013-0649-3

17. El-Dahshan, E. S. A., et al. "Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm," Expert Systems with Applications, Vol. 41, No. 11, 5526-5545, Sep. 2014.
doi:10.1016/j.eswa.2014.01.021

18. Zhang, Y., et al. "Preclinical diagnosis of magnetic resonance (MR) brain images via discrete wavelet packet transform with Tsallis entropy and generalized eigenvalue proximal support vector machine (GEPSVM)," Entropy, Vol. 17, No. 4, 1795-1813, 2015.
doi:10.3390/e17041795

19. Zhou, X., et al. "Detection of pathological brain in MRI scanning based on wavelet-entropy and naive Bayes classifier," Bioinformatics and Biomedical Engineering, Vol. 9043, 201-209, F. Ortu˜no and I. Rojas, Eds., Springer International Publishing, Granada, Spain, 2015.

20. Damodharan, S. and D. Raghavan, "Combining tissue segmentation and neural network for brain tumor detection," International Arab Journal of Information Technology, Vol. 12, No. 1, 42-52, Jan. 2015.

21. Yang, G., et al. "Automated classification of brain images using wavelet-energy and biogeography-based optimization," Multimedia Tools and Applications, 1-17, May 1, 2015.

22. Zhang, G.-S., et al. "Automated classification of brain MR images using wavelet-energy and support vector machines," Proceedings of the 2015 International Conference on Mechatronics, Electronic, Industrial and Control Engineering, C. Liu, et al. (eds.), 683–686, Atlantis Press, USA, 2015.

23. Nazir, M., F. Wahid, and S. A. Khan, "A simple and intelligent approach for brain MRI classification," Journal of Intelligent & Fuzzy Systems, Vol. 28, No. 3, 1127-1135, 2015.

24. Zhang, Y., et al. "Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigenbrain and machine learning," Frontiers in Computational Neuroscience, Vol. 66, No. 9, 1-15, 2015.

25. Harikumar, R and B. V. Kumar, "Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor," International Journal of Imaging Systems and Technology, Vol. 25, No. 1, 33-40, Mar. 2015.
doi:10.1002/ima.22118

26. Wang, S., et al. "Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection," International Journal of Imaging Systems and Technology, Vol. 25, No. 2, 153-164, 2015.
doi:10.1002/ima.22132

27. Zhang, Y., et al. "Detection of Alzheimer’s disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC," Biomedical Signal Processing and Control, Vol. 21, 58-73, Aug. 2015.
doi:10.1016/j.bspc.2015.05.014

28. Yildiz, A., et al. "Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction," Expert Systems with Applications, Vol. 36, No. 4, 7390-7399, May 2009.
doi:10.1016/j.eswa.2008.09.003

29. Zhang, Y.-D. and L. Wu, "Weights optimization of neural network via improved BCO approach," Progress In Electromagnetics Research, Vol. 83, 185-198, 2008.
doi:10.2528/PIER08051403

30. Fang, L., L. Wu, and Y. Zhang, "A novel demodulation system based on continuous wavelet transform," Mathematical Problems in Engineering, Vol. 2015, No. 9, 2015.

31. Yang, Y. H., et al. "Wavelet kernel entropy component analysis with application to industrial process monitoring," Neurocomputing, Vol. 147, 395-402, Jan. 2015.
doi:10.1016/j.neucom.2014.06.045

32. Aswathy, S. U., G. G. Deva Dhas, and S. S. Kumar, "A survey on detection of brain tumor from MRI brain images," 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 871-877, 2014.
doi:10.1109/ICCICCT.2014.6993081

33. Guo, D., et al. "Improved radio frequency identification indoor localization method via radial basis function neural network," Mathematical Problems in Engineering, 2014.

34. Fuangkhon, P., "An incremental learning preprocessor for feed-forward neural network," Artificial Intelligence Review, Vol. 41, No. 2, 183-210, Feb. 2014.
doi:10.1007/s10462-011-9304-0

35. Llave, Y. A., T. Hagiwara, and T. Sakiyama, "Artificial neural network model for prediction of cold spot temperature in retort sterilization of starch-based foods," Journal of Food Engineering, Vol. 109, No. 3, 553-560, 2012.
doi:10.1016/j.jfoodeng.2011.10.024

36. 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.
doi:10.1016/j.cnsns.2008.12.011

37. Shojaee, S. A., et al. "Prediction of the binary density of the ionic liquids plus water using back-propagated feed forward artificial neural network," Chemical Industry & Chemical Engineering Quarterly, Vol. 20, No. 3, 325-338, Jul.-Sep. 2014.
doi:10.2298/CICEQ121128014S

38. Karmakar, S., G. Shrivastava, and M. K. Kowar, "Impact of learning rate and momentum factor in the performance of back-propagation neural network to identify internal dynamics of chaotic motion," Kuwait Journal of Science, Vol. 41, No. 2, 151-174, May 2014.

39. Chandwani, V., V. Agrawal, and R. Nagar, "Modeling slump of ready mix concrete using genetic algorithms assisted training of artificial neural networks," Expert Systems with Applications, Vol. 42, No. 2, 885-893, Feb. 2015.
doi:10.1016/j.eswa.2014.08.048

40. Manoochehri, M. and F. Kolahan, "Integration of artificial neural network and simulated annealing algorithm to optimize deep drawing process," International Journal of Advanced Manufacturing Technology, Vol. 73, No. 1-4, 241-249, Jul. 2014.
doi:10.1007/s00170-014-5788-5

41. Awan, S. M., et al. "An efficient model based on artificial bee colony optimization algorithm with neural networks for electric load forecasting," Neural Computing & Applications, Vol. 25, No. 7-8, 1967-1978, Dec. 2014.
doi:10.1007/s00521-014-1685-y

42. Simon, D., "Biogeography-based optimization," IEEE Transactions on Evolutionary Computation, Vol. 12, No. 6, 702-713, Dec. 2008.
doi:10.1109/TEVC.2008.919004

43. Momeni, E., et al. "Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks," Measurement, Vol. 60, 50-63, Jan. 2015.
doi:10.1016/j.measurement.2014.09.075

44. Christy, A. A. and P. Raj, "Adaptive biogeography based predator-prey optimization technique for optimal power flow," International Journal of Electrical Power & Energy Systems, Vol. 62, 344-352, Nov. 2014.

45. Guo, W. A., et al. "Biogeography-based particle swarm optimization with fuzzy elitism and its applications to constrained engineering problems," Engineering Optimization, Vol. 46, No. 11, 1465-1484, Nov. 2014.
doi:10.1080/0305215X.2013.854349

46. Simon, D., "A probabilistic analysis of a simplified biogeography-based optimization algorithm," Evolutionary Computation, Vol. 19, No. 2, 167-188, Summer 2011.
doi:10.1162/EVCO_a_00018

47. Zhang, Y., S.Wang, and Z. Dong, "Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree," Progress In Electromagnetics Research, Vol. 144, 171-184, 2014.
doi:10.2528/PIER13121310

48. Shahzad, F., S. Masood, and N. K. Khan, "Probabilistic opposition-based particle swarm optimization with velocity clamping ," Knowledge and Information Systems, Vol. 39, No. 3, 703-737, Jun. 2014.
doi:10.1007/s10115-013-0624-z

49. Clerc, M. and J. Kennedy, "The particle swarm — Explosion, stability, and convergence in a multidimensional complex space," IEEE Transactions on Evolutionary Computation, Vol. 6, No. 1, 58-73, Feb. 2002.
doi:10.1109/4235.985692

50. Mo, , H. W. and L. F. Xu, "Research of biogeography particle swarm optimization for robot path planning," Neurocomputing, Vol. 148, 91-99, Jan. 2015.
doi:10.1016/j.neucom.2012.07.060

51. Kim, S. S., et al. "Biogeography-based optimization for optimal job scheduling in cloud computing," Applied Mathematics and Computation, Vol. 247, 266-280, Nov. 2014.

52. Kiran, M. S. and M. Gunduz, "A recombination-based hybridization of particle swarm optimization and artificial bee colony algorithm for continuous optimization problems," Applied Soft Computing, Vol. 13, No. 4, 2188-2203, Apr. 2013.
doi:10.1016/j.asoc.2012.12.007

53. Sait, S. M., A. T. Sheikh, and A. H. El-Maleh, "Cell assignment in hybrid CMOS/nanodevices architecture using a PSO/SA hybrid algorithm," Journal of Applied Research and Technology, Vol. 11, 653-664, Oct. 2013.

54. Zhang, Y., et al. "Remote-sensing image classification based on an improved probabilistic neural network," Sensors, Vol. 9, No. 9, 7516-7539, 2009.
doi:10.3390/s90907516

55. Zhang, Y., et al. "Fruit classification using computer vision and feedforward neural-network," Journal of Food Engineering, Vol. 143, No. 0, 167-177, 2014.
doi:10.1016/j.jfoodeng.2014.07.001

56. Dong, Z., et al. "Improving the spectral resolution and spectral fitting of 1H MRSI data from human calf muscle by the SPREAD technique," NMR in Biomedicine, Vol. 27, No. 11, 1325-1332, 2014.
doi:10.1002/nbm.3193

57. Figlus, T. and M. Stanczyk, "Diagnosis of the wear of gears in the gearbox using the wavelet packet transform," Metalurgija, Vol. 53, No. 4, 673-676, Oct.-Dec. 2014.

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