Improvement Categorizing MRI Images Using the Neural Network

The IJNCPS's Authors that presented the article:

  • Ghazal Davoudi Department of Computer Engineering, Ahvaz Branch, Islamic Azad University, Ahwaz, Iran
  • Seyyed Enayatollah Alavi Assistant Professor, Department of Computer Engineering, Faculty of Engineering, Shahid Chamran University of Ahvaz, Ahwaz, Iran
Keywords: neural networks, algorithm, MRI, grouping, pattern recognition technics, feature extraction


The present study was aimed to identify the sick from the healthy persons based on a set of MRI images through using image categorization algorithms.  Furthermore, this research tries to categorize images through the application of neural networks' pattern recognition technics and, to propose a model with adequate precision and efficiency for categorization of images that could be used for determining both the normality and abnormality of MRI images of the brain and, the cause of illness. Pre-processing on data using software rapidminerand in the process of extracting the featuresfrom histogram, wavelet, coincidence matrix, fourire transform…. Feature selection is performed by the interact algorithm. Finally, the image processing process is performed using the neural network.  The software of Clemente and MATLAB were used for simulation purposes.


[1] Z. Zhao and H. Liu, “Searching for Interacting Features,” in Proceedings of the 20th International Joint Conference on Artifical Intelligence, 2007, pp. 1156–1161.
[2] J. W. H. Li, J. Sun, “Predicting business failure using classification and regression tree:An empirical comparison with popular classical statistical methods and top classification mining methods,” Expert Syst. Appl., vol. 29, no. 1, pp. 65–74, 2010.
[3] Dimitar Filev and Ronald R. Yager, “Learning OWA Operator Weights from Data,” 1994.
[4] N. Alajlan, Y. Bazi, S. Member, H. S. Alhichri, F. Melgani, R. R. Yager, and L. Fellow, “Using OWA Fusion Operators for the Classification of Hyperspectral Images,” IEEE J. Sel. Top. Appl. EARTH Obs. Remote Sens., pp. 1–13, 2013.
[5] Mehdi Jafari, Shohreh Kasaei, Automatic Brain Tissue Detection in MRIImages Using Seeded Region Growing Segmentation and Neural NetworkClassification, Australian Journal of Basic and Applied Sciences, 5(8): 1066-1079, 2011, ISSN 1991-8178.
[6] İnan Güler, Ayşe Demirhan, Combining m and self-organizing maps for brain MR image segmentation, Engineering Applications of Artificial Intelligence,March 2011
[7]Amir Ehsan Lashkari, A Neural Network based Method for Brain AbnormalityDetection in MR Images Using Gabor Wavelets, International Journal ofComputer Applications,Volume 4 – No.7, July 2010.
[8]El-Sayed Ahmed El-Dahshan,Tamer Hosny, Abdel-Badeeh M. Salem, Hybridintelligent techniques for MRI brain images classification, Elsevier, Digital Signal Processing, 2010, pp. 433-441.
[9]Mamata S.Kalas, An Artificial Neural Network for Detection of Biological Early Brain Cancer, International Journal of Computer Applications (0975 –8887),2010, Volume 1 – No. 6.
[10]Xiao Xuan, Qingmin Liao, Statistical Structure Analysis in MRI Brain TumorSegmentation, Fourth International Conference on Image and Graphics, 2008.
[11]D.Jude hemanthl, D.Selvathi, J.Anitha, Effective Fuzzy Clustering Algorithmfor Abnormal MR Brain Image Segmentation, IEEE International AdvanceComputing Conference, IACC 2009.
[12]Song-yun Xie,Rang Guo,Ning-fei Li,Ge Wang,Hai-tao Zhao, Brain MRIProcessing and Classification Based on Combination of PCA and SVM,Proceedings of International Joint Conference on Neural Networks, Atlanta,Georgia, USA, June 14-19, 2009.
[13] azar, A T. and EL-said,s a.2013.performance analysis of support vector machines classifiers in breast cancer mammography recognition neural computing and application,24:1-15.
[14] budka,m. and Gabrys, B.2013. density –presrvng sampling: Robust and efficient alternative to cross –validation for error estimation neural networks and learning systems,IEEE transactions on 24:22-34.
[15] Goswami,s. and Bhaiya,l.k.p.2013,brain tumour detection using unsupervised learning based neural network communication systems and network technologiec (CSNT),2013,international conference on Xiamen 6-8 april,pp:573-577.
[16] Kalbkhani,h.shayesteh, M.G. and zali-vargahan,B.2013.robust algorithm for brain magnetic resonance image (MRI) classification based on GARCH variances series biomedical signal processing and control.8:909-919.
[17] jude hemanth,d.,vijila,c.,selvakumar, A.I. and anitha, j .2013. performance improved iteration –free artificial neural networks for abnormal magnetic resonance brain image classification neurocomputing .13:98-107.
[18] Mohsen,h.,EL-Dahshan.E.S. and salem ,A.2012.A machince learning technique for MRI brain image informatics (INFOS),2012 ,8th international cinference
[19] Hui, l.,Mei, G.D. and xiang , l.2013. cirrhosis classification based on MRI with duplicative –feature support vector machine (DFSVM)biomedical signal processing and control 8:346-353.
[20] Mahanand,B,S.,suresh ,s,.sundararajan,N. and Kumar, M.A.2013.ICGA-ELM classifier for alzheimers disease detection medical informatics and telemedicine (ICMIT)2013 indian conference on kharagpur , 28-30 march ,pp:48-52.
[21] Mishra ,R.2010.MRI based brain tumor detection using wavelet packet feature and artificical neural networks proceedings of the international conference and workshop on emerging trends in technology Mumbai,pp:656-659.
[22] El-dahshan,E-S.A,hosny,T. and salem ,A-B.M.2012. hybrid intelligent techniques for MRI brain image classification digital signal processing ,20:433-441.
[23] Lashkari,A.2010.A neural network based method for brain abnormality detection in MRI images using gabor wavelets .international journal of computer application . 4:9-15.
[24] Ain,Q-u.,mehmood,i.,naqi,SM. And jaffar ,M.A.2010. BAYESIAN classification using DCTfeatures for brain tumor detection knowledge –based and intelligent information and engineering systems .627:340-349.
[25] Kharrat,A.,Gasmi,K.,Messaoud ,M.B.,Benamrane,N. and abid ,m.2012.A hybrid approach for automatic classification of brain MRI using genetic algorithm and support vector machine Leonardo j.sci.17:71-82.
[26] farias.G.,santos,M.. AND lopez ,v.2008.brain tumour diagnosis with wavelets and support vector machines intelligent syste and knowledge engineering ,2008.ISKE2008 .3rd international conference on Xiamen ,17-19 nov .,pp:1453-1459.
[27] Hackmack,k., paul,f., weyhandt,M.,allefeld ,c.and Haynes,h.d.2012.multi system classification of disease using struction MRI and wavelet transform neuroImagc.62:48-58.
[28] Abdullah,N.,Ngah,U.K., and aziz,S.A.,2011.image classification of brain MRI using support vector machine imaging systems and techniques (IST).2011.IEEE international conference on penang,17-18 may ,pp.242-247.