Advertisement

A pathological brain detection system based on kernel based ELM

  • 376 Accesses

  • 20 Citations

Abstract

Magnetic resonance (MR) imaging is widely used in daily medical treatment. It could help in pre-surgical, diagnosis, prognosis, and postsurgical processes. It could be beneficial for diagnosis to classify MR images of brain into healthy or abnormal automatically and accurately, since the information set MRIs generate is too large to interpret with manual methods. We propose a new approach with wavelet-entropy as the features and the kernel based extreme learning machine (K-ELM) to be the classifier. Our method employs 2D-discreet wavelet transform (DWT), and calculates the entropy as features. Then, a K-ELM is trained to classify images as pathological or healthy. A 10 × 10-fold cross validation is conducted to prevent overfitting. The method achieves the sensitivity as 97.48 %, the specificity as 94.44 %, and the overall accuracy as 97.04 % based on 125 MR images. The performance suggests the classifier is robust and effective by comparison with the recently published approaches.

This is a preview of subscription content, log in to check access.

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Fig. 1
Fig. 2

References

  1. 1.

    Aguiar V, Guedes I (2015) Shannon entropy, Fisher information and uncertainty relations for log-periodic oscillators. Phys A: Stat Mech Appl 423:72–79

  2. 2.

    Bamford C, Olsen K, Davison C, Barnett N, Lloyd J, Williams D, Firbank M, Mason H, Donaldson C, O’Brien J (2016) Is there a preference for PET or SPECT brain imaging in diagnosing dementia? The views of people with dementia, carers, and healthy controls. Int Psychogeriatr 28(1):123–131

  3. 3.

    Cao WB, Ma JS, Su P, Liang XT (2016) Binary hologram generation based on discrete wavelet transform. Optik 127(2):558–561

  4. 4.

    Chen Y, Shi L, Feng Q, Yang J, Shu H (2014) Artifact suppressed dictionary learning for low-dose CT image processing. IEEE Trans Med Imaging 33(12):2271–2292

  5. 5.

    Chen Y, Yang J, Cao Q, Yang G, Chen J, Shu H, Luo L, Coatrieux J-L, Feng Q (2016) Curve-like structure extraction using minimal path propagation with back-tracing. IEEE Trans Image Process 25(2):988–1003

  6. 6.

    Chen Y, Yin X, Shi L (2013) Improving abdomen tumor low-dose CT images using a fast dictionary learning based processing. Phys Med Biol 58(16):5803–5820

  7. 7.

    Dong Z, Ji G, Yang J (2015) 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(4):1795–1813

  8. 8.

    Dong Z, Phillips P, Wang S, Ji G, Yang J, Yuan T-f (2015) Detection of subjects and brain regions related to Alzheimer’s disease using 3D MRI scans based on eigenbrain and machine learning. Front Comput Neurosci 66(9):1–15

  9. 9.

    El-Dahshan ESA, Hosny T, Salem ABM (2010) Hybrid intelligent techniques for MRI brain images classification. Digital Signal Process 20(2):433–441

  10. 10.

    Ertugrul OF (2016) Forecasting electricity load by a novel recurrent extreme learning machines approach. Int J Electr Power Energy Syst 78:429–435

  11. 11.

    Fallah M, Modarresi J, Ajami A, Bina MT (2016) Improvement of indirect harmonic compensation method using online discrete wavelet transform. J Circuits Syst Comput 25(4):20

  12. 12.

    Harikumar R, Kumar BV (2015) Performance analysis of neural networks for classification of medical images with wavelets as a feature extractor. Int J Imaging Syst Technol 25(1):33–40

  13. 13.

    He YL, Geng ZQ, Zhu QX (2016) Soft sensor development for the key variables of complex chemical processes using a novel robust bagging nonlinear model integrating improved extreme learning machine with partial least square. Chemom Intell Lab Syst 151:78–88

  14. 14.

    Hu CH, Sepulcre J, Johnson KA, Fakhri GE, Lu YM, Li QZ (2016) Matched signal detection on graphs: theory and application to brain imaging data classification. Neuroimage 125:587–600

  15. 15.

    Huang G-B, Chen L (2007) Convex incremental extreme learning machine. Neurocomputing 70(16–18):3056–3062

  16. 16.

    Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

  17. 17.

    Huo Y, Wu L (2010) Feature extraction of brain MRI by stationary wavelet transform and its applications. J Biol Syst 18(1):115–132

  18. 18.

    Hwang M, Song JS, Lee YS, Li C, Shim EB, Pak HN (2016) Electrophysiological rotor ablation in in-silico modeling of atrial fibrillation: comparisons with dominant frequency, Shannon entropy, and phase singularity. Plos One 11(2):15

  19. 19.

    Kumar A, Singh M (2015) Optimal selection of wavelet function and decomposition level for removal of ECG signal artifacts. J Med Imaging Health Inf 5(1):138–146

  20. 20.

    Kushnirsky M, Nguyen V, Katz JS, Steinklein J, Rosen L, Warshall C, Schulder M, Knisely JPS (2016) Time-delayed contrast-enhanced MRI improves detection of brain metastases and apparent treatment volumes. J Neurosurg 124(2):489–495

  21. 21.

    Li B, Rong XW, Li YB (2014) An improved kernel based extreme learning machine for robot execution failures. Sci World J 7

  22. 22.

    Liu G, Phillips P, Yuan T-F (2016) Detection of Alzheimer’s disease by three-dimensional displacement field estimation in structural magnetic resonance imaging. J Alzheimers Dis 50(1):233–248

  23. 23.

    Ma C, Ouyang JH, Guan, J (2014) Hybrid improved Gravitional search algorithm and kernel based extreme learning machine method for classification problems. 2014 International Conference on Security, Pattern Analysis, and Cybernetics (Spac) 299–304

  24. 24.

    Maitra M, Chatterjee A (2006) A Slantlet transform based intelligent system for magnetic resonance brain image classification. Biomed Signal Process Control 1(4):299–306

  25. 25.

    Mondal U, Sengupta A, Pathak RR (2016) Servomechanism for periodic reference input: Discrete wavelet transform-based repetitive controller. Trans Inst Meas Control 38(1):14–22

  26. 26.

    Mulia IE, Asano T, Nagayama A (2016) Real-time forecasting of near-field tsunami waveforms at coastal areas using a regularized extreme learning machine. Coast Eng 109:1–8

  27. 27.

    Saber A, Emam A, Amer R (2016) Discrete wavelet transform and support vector machine-based parallel transmission line faults classification. IEEJ Trans Electr Electron Eng 11(1):43–48

  28. 28.

    Shamshirband S, Mohammadi K, Tong CW, Petkovic D, Porcu E, Mostafaeipour A, Ch S, Sedaghat A (2016) Application of extreme learning machine for estimation of wind speed distribution. Clim Dyn 46(5–6):1893–1907

  29. 29.

    Sudeb D, Manish C, Malay KK (2013) Brain Mr image classification using multiscale geometric analysis of Ripplet. Prog Electromagn Res 137:1–17

  30. 30.

    Tang J, Deng C, Huang GB (2015) Extreme learning machine for multilayer perceptron. IEEE Trans Neural Netw Learn Syst

  31. 31.

    Wang S, Dong Z, Du S, Ji G, Yan J, Yang J, Wang Q, Feng C, Phillips P (2015) Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection. Int J Imaging Syst Technol 25(2):153–164

  32. 32.

    Wang S, Du S, Atangana A, Liu A, Lu Z (2016) Application of stationary wavelet entropy in pathological brain detection. Multimed Tools Appl 1–14

  33. 33.

    Wang B, Huang S, Qiu J, Liu Y, Wang G (2015) Parallel online sequential extreme learning machine based on MapReduce. Neurocomputing 149:224–232

  34. 34.

    West RJH, Elliott CJH, Wade AR (2015) Classification of Parkinson’s disease genotypes in drosophila using spatiotemporal profiling of vision. Sci Rep 5:13

  35. 35.

    Wong PK, Wong KI, Vong CM, Cheung CS (2015) Modeling and optimization of biodiesel engine performance using kernel-based extreme learning machine and cuckoo search. Renew Energy 74:640–647

  36. 36.

    Yamashita Y, Wakahara T (2016) Affine-transformation and 2D-projection invariant k-NN classification of handwritten characters via a new matching measure. Pattern Recogn 52:459–470

  37. 37.

    Yang G, Zhang Y, Yang J, Ji G, Dong Z, Wang S, Feng C, Wang Q (2015) Automated classification of brain images using wavelet-energy and biogeography-based optimization. Multimed Tools Appl, 1–17

  38. 38.

    Yaroshenko TY, Krysko DV, Dobriyan V, Zhigalov MV, Vos H, Vandenabeele P, Krysko VA (2015) Wavelet modeling and prediction of the stability of states: the Roman Empire and the European Union. Commun Nonlinear Sci Numer Simul 26(1–3):265–275

  39. 39.

    Yuvaraj R, Murugappan M, Acharya UR, Adeli H, Ibrahim NM, Mesquita E (2016) Brain functional connectivity patterns for emotional state classification in Parkinson’s disease patients without dementia. Behav Brain Res 298:248–260

  40. 40.

    Zhang Y, Dong Z, Ji G (2015) Effect of spider-web-plot in MR brain image classification. Pattern Recogn Lett 62:14–16

  41. 41.

    Zhang Y, Wang S (2015) Detection of Alzheimer’s disease by displacement field and machine learning. Peer J 3

  42. 42.

    Zhang Y, Wang S, Dong Z, Phillips P, Ji G, Yang J (2015) Pathological brain detection in magnetic resonance imaging scanning by wavelet entropy and hybridization of biogeography-based optimization and particle swarm optimization. Prog Electromagn Res 152:41–58

  43. 43.

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

  44. 44.

    Zhang Y, Wang S, Ji G, Dong Z (2013) An MR brain images classifier system via particle swarm optimization and kernel support vector machine. TheScientificWorldJOURNAL 2013:130134

  45. 45.

    Zhang Y, Wang S, Phillips P, Dong Z, Ji G, Yang J (2015) Detection of Alzheimer’s disease and mild cognitive impairment based on structural volumetric MR images using 3D-DWT and WTA-KSVM trained by PSOTVAC. Biomed Signal Process Control 21:58–73

  46. 46.

    Zhang Y, Wang S, Phillips P, Yang J, Yuan T-F (2016) Three-dimensional Eigenbrain for the detection of subjects and brain regions related with Alzheimer’s disease. J Alzheimers Dis 50(4):1163–1179

  47. 47.

    Zhang Y, Wu L (2012) An Mr brain images classifier via principal component analysis and kernel support vector machine. Prog Electromagn Res 130:269–388

  48. 48.

    Zhang Y, Yang X, Cattani C, Rao R, Wang S, Phillips P (2016) Tea category identification using a novel fractional Fourier entropy and Jaya algorithm. Entropy 18(3):77

  49. 49.

    Zhou X, Wang S, Xu W, Ji G, Phillips P, Sun P, Zhang Y (2015) Detection of pathological brain in MRI scanning based on wavelet-entropy and naive Bayes classifier. In: Ortuño F, Rojas I (eds) Bioinformatics and biomedical engineering, vol 9043. Springer International Publishing, Granada, pp 201–209

Download references

Acknowledgments

This study is financially supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Natural Science Foundation of Jiangsu Province (BK20150983), Open Project Program of the State Key Lab of CAD&CG, Zhejiang University (A1616), the Fundamental Research Funds for the Central Universities (LGYB201604)

Author information

Correspondence to Shuihua Wang.

Ethics declarations

Conflict of interest

The authors declare no conflict of interest involved in this paper.

Additional information

Siyuan Lu, Zhihai Lu and Jianfei Yang contributed equally to this work.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lu, S., Lu, Z., Yang, J. et al. A pathological brain detection system based on kernel based ELM. Multimed Tools Appl 77, 3715–3728 (2018). https://doi.org/10.1007/s11042-016-3559-z

Download citation

Keywords

  • Wavelet entropy
  • K-ELM
  • Classification
  • Pattern recognition