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Detection of Pathological Brain in MRI Scanning Based on Wavelet-Entropy and Naive Bayes Classifier

  • Xingxing Zhou
  • Shuihua Wang
  • Wei Xu
  • Genlin Ji
  • Preetha Phillips
  • Ping Sun
  • Yudong Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9043)

Abstract

An accurate diagnosis is important for the medical treatment of patients suffered from brain disease. Nuclear magnetic resonance images are commonly used by technicians to assist the pre-clinical diagnosis, rating them by visual evaluations. The classification of NMR images of normal and pathological brains poses a challenge from technological point of view, since NMR imaging generates a large information set that reflects the conditions of the brain. In this work, we present a computer assisted diagnosis method based on a wavelet-entropy (In this paper 2D-discrete wavelet transform has been used, in that it can extract more information) of the feature space approach and a Naive Bayes classifier classification method for improving the brain diagnosis accuracy by means of NMR images. The most relevant image feature is selected as the wavelet entropy, which is used to train a Naive Bayes classifier. The results over 64 images show that the sensitivity of the classifier is as high as 94.50%, the specificity 91.70%, the overall accuracy 92.60%. It is easily observed from the data that the proposed classifier can detect abnormal brains from normal controls within excellent performance, which is competitive with latest existing methods.

Keywords

Wavelet transform Entropy Naïve Bayes classifier Classification 

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References

  1. 1.
    Goh, S., Dong, Z., Zhang, Y., DiMauro, S., Peterson, B.S.: Mitochondrial dysfunction as a neurobiological subtype of autism spectrum disorder: Evidence from brain imaging. JAMA Psychiatry 71, 665–671 (2014)CrossRefGoogle Scholar
  2. 2.
    Hou, X.S., Han, M., Gong, C., Qian, X.M.: SAR complex image data compression based on quadtree and zerotree Coding in Discrete Wavelet Transform Domain: A Comparative Study. Neurocomputing 148, 561–568 (2015)CrossRefGoogle Scholar
  3. 3.
    Lingala, S.G., Jacob, M.: Blind Compressive Sensing Dynamic MRI. IEEE Transactions on Medical Imaging 32, 1132–1145 (2013)CrossRefGoogle Scholar
  4. 4.
    Dong, Z., Zhang, Y., Liu, F., Duan, Y., Kangarlu, A., Peterson, B.S.: Improving the spectral resolution and spectral fitting of 1H MRSI data from human calf muscle by the SPREAD technique. NMR in Biomedicine 27, 1325–1332 (2014)CrossRefGoogle Scholar
  5. 5.
    Zhang, Y., Wang, S., Ji, G., Dong, Z.: Exponential wavelet iterative shrinkage thresholding algorithm with random shift for compressed sensing magnetic resonance imaging. IEEJ. Transactions on Electrical and Electronic Engineering 10, 116–117 (2015)CrossRefGoogle Scholar
  6. 6.
    Schneider, M.F., Krick, C.M., Retz, W., Hengesch, G., Retz-Junginger, P., Reith, W., Rösler, M.: Impairment of fronto-striatal and parietal cerebral networks correlates with attention deficit hyperactivity disorder (ADHD) psychopathology in adults – A functional magnetic resonance imaging (fMRI) study. Psychiatry Research: Neuroimaging 183, 75–84 (2010)CrossRefGoogle Scholar
  7. 7.
    Arjmandi, M.K., Pooyan, M.: An optimum algorithm in pathological voice quality assessment using wavelet-packet-based features, linear discriminant analysis and support vector machine. Biomedical Signal Processing and Control 7, 3–19 (2012)CrossRefGoogle Scholar
  8. 8.
    Jero, S.E., Ramu, P., Ramakrishnan, S.: Discrete Wavelet Transform and Singular Value Decomposition Based ECG Steganography for Secured Patient Information Transmission. Journal of Medical Systems 38 (2014)Google Scholar
  9. 9.
    Lustig, M., Donoho, D., Pauly, J.M.: Sparse MRI: The application of compressed sensing for rapid MR imaging. Magn. Reson. Med. 58, 1182–1195 (2007)CrossRefGoogle Scholar
  10. 10.
    Chen, J.K., Li, G.Q.: Tsallis Wavelet Entropy and Its Application in Power Signal Analysis. Entropy 16, 3009–3025 (2014)CrossRefGoogle Scholar
  11. 11.
    Frantzidis, C.A., Vivas, A.B., Tsolaki, A., Klados, M.A., Tsolaki, M., Bamidis, P.D.: Functional disorganization of small-world brain networks in mild Alzheimer’s Disease and amnestic Mild Cognitive Impairment: an EEG study using Relative Wavelet Entropy (RWE). Frontiers in aging neuroscience 6 (2014)Google Scholar
  12. 12.
    Anderson, M.P., Dubnicka, S.R.: A sequential naive Bayes classifier for DNA barcodes. Statistical Applications in Genetics and Molecular Biology 13, 423–434 (2014)CrossRefzbMATHMathSciNetGoogle Scholar
  13. 13.
    Zhang, Y., Wang, S., Phillips, P., Ji, G.: Binary PSO with mutation operator for feature selection using decision tree applied to spam detection. Knowledge-Based Systems 64, 22–31 (2014)CrossRefGoogle Scholar
  14. 14.
    Kotsiantis, S.: Integrating Global and Local Application of Naive Bayes Classifier. International Arab Journal of Information Technology 11, 300–307 (2014)Google Scholar
  15. 15.
    Ravasi, M., Tenze, L., Mattavelli, M.: A scalable and programmable architecture for 2-D DWT decoding. IEEE Transactions on Circuits and Systems for Video Technology 12, 671–677 (2002)CrossRefGoogle Scholar
  16. 16.
    Zhang, Y., Wang, S., Dong, Z.: Classification of Alzheimer Disease Based on Structural Magnetic Resonance Imaging by Kernel Support Vector Machine Decision Tree. Progres. Electromagnetics Research 144, 171–184 (2014)CrossRefGoogle Scholar
  17. 17.
    Chavez-Roman, H., Ponomaryov, V.: Super Resolution Image Generation Using Wavelet Domain Interpolation With Edge Extraction via a Sparse Representation. IEEE Geosci. Remote Sens. Lett. 11, 1777–1781 (2014)CrossRefGoogle Scholar
  18. 18.
    Darji, A.D., Kushwah, S.S., Merchant, S.N., Chandorkar, A.N.: High-performance hardware architectures for multi-level lifting-based discrete wavelet transform. Eurasip Journal on Image and Video Processing (2014)Google Scholar
  19. 19.
    Ganesan, K., Acharya, U.R., Chua, C.K., Min, L.C., Abraham, T.K.: Automated Diagnosis of Mammogram Images of Breast Cancer Using Discrete Wavelet Transform and Spherical Wavelet Transform Features: A Comparative Study. Technology in Cancer Research & Treatment 13, 605–615 (2014)Google Scholar
  20. 20.
    Zhang, Y.D., Dong, Z.C., Ji, G.L., 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, 2327–2338 (2014)CrossRefGoogle Scholar
  21. 21.
    Nicolaou, N., Georgiou, J.: Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines. Expert Systems with Applications 39, 202–209 (2012)CrossRefGoogle Scholar
  22. 22.
    Lee, S.G., Yun, G.J., Shang, S.: Reference-free damage detection for truss bridge structures by continuous relative wavelet entropy method. Structural Health Monitoring-an International Journal 13, 307–320 (2014)CrossRefGoogle Scholar
  23. 23.
    Bakhshi, A.D., Bashir, S., Loan, A., Maud, M.A.: Application of continuous-time wavelet entropy for detection of cardiac repolarisation alternans. IET Signal Processing 7, 783–790 (2013)CrossRefGoogle Scholar
  24. 24.
    Sheppard, S., Nathan, D., Zhu, L.J.: Accurate identification of polyadenylation sites from 3 ’ end deep sequencing using a naive Bayes classifier 29, 2564 (2013), Bioinformatics (Oxford, England) 30, 596–596 (2014) Google Scholar
  25. 25.
    Chaplot, S., Patnaik, L.M., Jagannathan, N.R.: Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomedical Signal Processing and Control 1, 86–92 (2006)CrossRefGoogle Scholar
  26. 26.
    El-Dahshan, E.S.A., Hosny, T., Salem, A.B.M.: Hybrid intelligent techniques for MRI brain images classification. Digital Signal Processing 20, 433–441 (2010)CrossRefGoogle Scholar
  27. 27.
    Zhang, Y., Wu, L.: An Mr Brain Images Classifier via Principal Component Analysis and Kernel Support Vector Machine. Progres. Electromagnetics Research 130, 369–388 (2012)CrossRefGoogle Scholar
  28. 28.
    Zhang, Y., Wang, S., Ji, G., Dong, Z.: An MR Brain Images Classifier System via Particle Swarm Optimization and Kernel Support Vector Machine. The Scientific World Journal 2013, 9 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Xingxing Zhou
    • 1
  • Shuihua Wang
    • 2
  • Wei Xu
    • 3
  • Genlin Ji
    • 1
  • Preetha Phillips
    • 4
  • Ping Sun
    • 5
  • Yudong Zhang
    • 1
  1. 1.School of Computer Science and TechnologyNanjing Normal UniversityNanjingChina
  2. 2.School of Electronic Science and EngineeringNanjing UniversityNanjingChina
  3. 3.Student Affairs OfficeNanjing Institute of Industry TechnologyNanjingChina
  4. 4.School of Natural Sciences and MathematicsShepherd UniversityShepherdstownUSA
  5. 5.Department of Electrical EngineeringThe City College of New York, CUNYNew YorkUSA

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