You are viewing a javascript disabled version of the site. Please enable Javascript for this site to function properly.
Go to headerGo to navigationGo to searchGo to contentsGo to footer
In content section. Select this link to jump to navigation

Preliminary research on abnormal brain detection by wavelet-energy and quantum- behaved PSO

Abstract

It is important to detect abnormal brains accurately and early. The wavelet-energy (WE) was a successful feature descriptor that achieved excellent performance in various applications; hence, we proposed a WE based new approach for automated abnormal detection, and reported its preliminary results in this study. The kernel support vector machine (KSVM) was used as the classifier, and quantum-behaved particle swarm optimization (QPSO) was introduced to optimize the weights of the SVM. The results based on a 5 × 5-fold cross validation showed the performance of the proposed WE + QPSO-KSVM was superior to ``DWT + PCA + BP-NN'', ``DWT + PCA + RBF-NN'', ``DWT + PCA + PSO-KSVM'', ``WE + BPNN'', ``WE +$ KSVM'', and ``DWT $+$ PCA $+$ GA-KSVM'' w.r.t. sensitivity, specificity, and accuracy. The work provides a novel means to detect abnormal brains with excellent performance.

References

[1] 

Zhang Y.D., , Dong Z.C., , Ji G.L., and Wang S.H., An improved reconstruction method for CS-MRI based on exponential wavelet transform and iterative shrinkage/thresholding algorithm. Journal of Electromagnetic Waves and Applications 28, 2327 (2014).

[2] 

Goh S., , Dong Z., , Zhang Y., , DiMauro S., and Peterson B.S., Mitochondrial dysfunction as a neurobiological subtype of autism spectrum disorder: Evidence from brain imaging. JAMA Psychiatry 71, 665 (2014).

[3] 

Zhang Y., , Wang S., , Ji G., and Dong Z., An improved quality guided phase unwrapping method and its applications to MRI. Progress in Electromagnetics Research 145, 273 (2014).

[4] 

Thorsen F., , Fite B., , Mahakian L.M., , Seo J.W., , Qin S.P., , Harrison V., , Johnson S., , Ingham E., , Caskey C., , Sundstrom T., , Meade T.J., , Harter P.N., , Skaftnesmo K.O., and Ferrara K.W., Multimodal imaging enables early detection and characterization of changes in tumor permeability of brain metastases. Journal of Controlled Release 172, 812 (2013).

[5] 

Maji P., , Kundu M.K., and Chanda B., Second order fuzzy measure and weighted co-occurrence matrix for segmentation of brain MR images. Fundam Inform 88, 161 (2008).

[6] 

El-Dahshan E.S.A., , Hosny T., and Salem A.B.M., Hybrid intelligent techniques for MRI brain images classification. Digital Signal Processing 20, 433 (2010).

[7] 

Zhang Y., , Wu L., and Wang S., Magnetic resonance brain image classification by an improved artificial bee colony algorithm. Progress in Electromagnetics Research 116, 65 (2011).

[8] 

Zhang Y., , Dong Z., , Wu L., and Wang S., A hybrid method for MRI brain image classification. Expert Systems with Applications 38, 10049 (2011).

[9] 

Ramasamy R., , \reffpageand Anandhakumar P., Brain tissue classification of MR images using fast Fourier transform based expectation-maximization Gaussian mixture model, Advances in Computing and Information Technology, Springer (2011), pp. 387-398.

[10] 

Zhang Y., and Wu L., An MR brain images classifier via principal component analysis and kernel support vector machine. Progress in Electromagnetics Research 130, 369 (2012).

[11] 

Saritha M., , Paul Joseph K., and Mathew A.T., Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network. Pattern Recognition Letters 34, 2151 (2013).

[12] 

Zhang Y., , Dong Z., , Ji G., and Wang S., Effect of spider-web-plot in MR brain image classification. Pattern Recognition Letters 62, 14 (2015).

[13] 

Das S., , Chowdhury M., and Kundu M.K., Brain MR image classification using multiscale geometric analysis of ripplet. Progress in Electromagnetics Research-Pier 137, 1 (2013).

[14] 

Kalbkhani H., , Shayesteh M.G., and Zali-Vargahan B., Robust algorithm for brain magnetic resonance image (MRI) classification based on GARCH variances series. Biomedical Signal Processing and Control 8, 909 (2013).

[15] 

Zhang Y., , Wang S., , Ji G., and Dong Z., An MR brain images classifier system via particle swarm optimization and kernel support vector machine. The Scientific World Journal 2013, 9 (2013).

[16] 

Padma A., and Sukanesh R., Segmentation and classification of brain CT images using combined wavelet statistical texture features. Arab J Sci Eng 39, 767 (2014).

[17] 

El-Dahshan E.S.A., , Mohsen H.M., , Revett K., and Salem A.B.M., Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm. Expert Systems with Applications 41, 5526 (2014).

[18] 

Zhou X., , Wang S., , Xu W., , Ji G., , Phillips P., , Sun P., and Zhang Y., , Detection of pathological brain in MRI scanning based on wavelet-entropy and naive Bayes classifier, Bioinformatics and Biomedical Engineering, edited by Ortuño F., and Rojas I., Springer International Publishing (2015), pp. 201-209.

[19] 

Zhang Y., , Dong Z., , Wang S., , Ji G., and Yang J., 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 17, 1795 (2015).

[20] 

Wang S., , Ji G., , Phillips P., and Dong Z., Application of genetic algorithm and kernel support vector machine to pathological brain detection in MRI Scanning. The 2nd National Conference on Information Technology and Computer Science (2015), pp. 450-456.

[21] 

Yang G., , Zhang Y., , Yang J., , Ji G., , Dong Z., , Wang S., , Feng C., and Wang Q., Automated classification of brain images using wavelet-energy and biogeography-based optimization. Multimedia Tools and Applications 1, (2015). doi: 10.1007/s11042-015-2649-7.

[22] 

Wang S., , Zhang Y., , Dong Z., , Du S., , Ji G., , Yan J., , Yang J., , Wang Q., , Feng C., and Phillips P., Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection. International Journal of Imaging Systems and Technology 25, 153 (2015).

[23] 

Zhang Y., , Dong Z., , Phillips P., , Wang S., , Ji G., , Yang J., and Yuan T.-F., 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 66, 1 (2015).

[24] 

Lee S.H., , Lee C.K., , Park J.B., and Choi Y.H., Diagnostic method for insulated power cables based on wavelet energy. IEICE Electronics Express 10, (2013). pp. 335-335.

[25] 

Yaroshenko T.Y., , Krysko D.V., , Dobriyan V., , Zhigalov M.V., , Vos H., , Vandenabeele P., and Krysko V.A., Wavelet modeling and prediction of the stability of states: the Roman Empire and the European Union. Communications in Nonlinear Science and Numerical Simulation 26, 265 (2015).

[26] 

Wu Y.L., , Yeh C.T., , Hung W.C., and Tang C.Y., Gaze direction estimation using support vector machine with active appearance model. Multimedia Tools and Applications 70, 2037 (2014).

[27] 

Nieto P.J.G., , Garcia-Gonzalo E., , Lasheras F.S., and Juez F.J.D., Hybrid PSO-SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability. Reliability Engineering & System, Safety 138, 219 (2015).

[28] 

Zhang Y., , Balochian S., , Agarwal P., , Bhatnagar V., and Housheya O.J., Artificial intelligence and its applications. Mathematical Problems in Engineering 2014, 10 (2014).

[29] 

Shahzad F., , Masood S., and Khan N.K., Probabilistic opposition-based particle swarm optimization with velocity clamping. Knowledge and, Information Systems 39, 703 (2014).

[30] 

Lin L., , Guo F., , Xie X.L., and Luo B., Novel adaptive hybrid rule network based on TS fuzzy rules using an improved quantum-behaved particle swarm optimization. Neurocomputing 149, 1003 (2015).

[31] 

van den Bergh F., and Engelbrecht A.P., A study of particle swarm optimization particle trajectories. Information Sciences 176, 937 (2006).

[32] 

Davoodi E., , Hagh M.T., and Zadeh S.G., A hybrid improved quantum-behaved particle swarm optimization-simplex method (IQPSOS) to solve power system load flow problems. Applied Soft Computing 21, 171 (2014).

[33] 

Fu X., , Liu W.S., , Zhang B., and Deng H., Quantum behaved particle swarm optimization with neighborhood search for numerical optimization. Mathematical Problems in Engineering (2013), vol. 2013, doi: 10.1155/2013/469723.

[34] 

Messina A., Refinements of damage detection methods based on wavelet analysis of dynamical shapes. International Journal of Solids and Structures 45, 4068 (2008).

[35] 

Choudhary R., , Mahesh S., , Paliwal J., and Jayas D.S., Identification of wheat classes using wavelet features from near infrared hyperspectral images of bulk samples. Biosystems Engineering 102, 115 (2009).

[36] 

Mookiah M.R.K., , Acharya U.R., , Lim C.M., , Petznick A., and Suri J.S., Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features. Knowledge-Based Systems 33, 73 (2012).

[37] 

Guo D.L., , Zhang Y.D., , Xiang Q., and Li Z.H., Improved radio frequency identification indoor localization method via radial basis function neural network. Mathematical Problems in Engineering (2014), vol. 2014, doi: 10.1155/2014/420482.