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Exploring a smart pathological brain detection method on pseudo Zernike moment

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Abstract

Pathological brain detection by computer vision is now attracting intense attentions from academic fields. Nevertheless, most of recent methods suffer from low-accuracy. This study combined two successful techniques: pseudo Zernike moment and kernel support vector machine. Three open datasets were downloaded and used. The 10 times of K-fold stratified cross validation showed our method using 19-order pseudo Zernike moments achieved perfect classification on the first dataset. It achieved a sensitivity of 99.93 ± 0.23%, a specificity of 98.50 ± 2.42%, and an accuracy of 99.75 ± 0.32% on the second dataset. It achieved a sensitivity of 99.64 ± 0.42%, a specificity of 98.29 ± 2.76%, and an accuracy of 99.45 ± 0.38% on the third dataset. This approach performs better than eleven state-of-the-art smart pathological brain detection methods.

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References

  1. Abdillah AA, Suwarno (2016) Diagnosis of diabetes using support vector machines with radial basis function kernels. Int J Technol 7(5):849–858. doi:10.14716/ijtech.v7i5.1370

    Article  Google Scholar 

  2. Atangana A (2016) Application of stationary wavelet entropy in pathological brain detection. Multimedia Tools and Applications. doi:10.1007/s11042-016-3401-7

    Google Scholar 

  3. Chen X-Q (2016) Fractal dimension estimation for developing pathological brain detection system based on Minkowski-Bouligand method. IEEE Access 4:5937–5947. doi:10.1109/ACCESS.2016.2611530

    Article  Google Scholar 

  4. Chen H (2017) Seven-layer deep neural network based on sparse autoencoder for voxelwise detection of cerebral microbleed. Multimedia Tools and Applications. doi:10.1007/s11042-017-4554-8

    Google Scholar 

  5. Chen S, Yang J-F, Phillips P (2015) Magnetic resonance brain image classification based on weighted-type fractional Fourier transform and nonparallel support vector machine. Int J Imaging Syst Technol 25(4):317–327. doi:10.1002/ima.22144

    Article  Google Scholar 

  6. Chen Y, Zhang Y, Lu H (2016) Wavelet energy entropy and linear regression classifier for detecting abnormal breasts. Multimedia Tools and Applications. doi:10.1007/s11042-016-4161-0

    Google Scholar 

  7. Das S, Chowdhury M, Kundu MK (2013) Brain MR image classification using multiscale geometric analysis of Ripplet. Progress in Electromagnetics Research-Pier 137:1–17. doi:10.2528/pier13010105

    Article  Google Scholar 

  8. Deng AW, Wei CH, Gwo CY (2016) Stable, fast computation of high-order Zernike moments using a recursive method. Pattern Recogn 56:16–25. doi:10.1016/j.patcog.2016.02.014

    Article  Google Scholar 

  9. Dong Z (2014) Classification of Alzheimer disease based on structural magnetic resonance imaging by kernel support vector machine decision tree. Prog Electromagn Res 144:171–184. doi:10.2528/PIER13121310

    Article  Google Scholar 

  10. Douglass S, Hsu SW, Cokus S, Goldberg RB, Harada JJ, Pellegrini M (2016) A naive Bayesian classifier for identifying plant microRNAs. Plant J 86(6):481–492. doi:10.1111/tpj.13180

    Article  Google Scholar 

  11. Feng C (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. doi:10.1002/ima.22132

    Article  Google Scholar 

  12. Gajovic S, Pochet R (2014) Brain extracellular matrix meets COST-Matrix for European research networks. In: Dityatev A, WehrleHaller B, Pitkanen A (eds) Brain Extracellular Matrix In Health And Disease, vol 214. Progress in Brain Research. Elsevier Science Bv, Amsterdam, pp XIX-XXIII

  13. Ghasemi F, Fassihi A, Perez-Sanchez H, Dehnavi AM (2017) The role of different sampling methods in improving biological activity prediction using deep belief network. J Comput Chem 38(4):195–203. doi:10.1002/jcc.24671

    Article  Google Scholar 

  14. Gorji HT, Haddadnia J (2015) A novel method for early diagnosis of Alzheimer's disease based on pseudo Zernike moment from structural MRI. Neuroscience 305:361–371. doi:10.1016/j.neuroscience.2015.08.013

    Article  Google Scholar 

  15. Gorriz JM, Ramírez J (2016) Wavelet entropy and directed acyclic graph support vector machine for detection of patients with unilateral hearing loss in MRI scanning. Front Comput Neurosci 10. doi:10.3389/fncom.2016.00106

  16. Huo Y, Plassard AJ, Carass A, Resnick SM, Pham DL, Prince JL, Landman BA (2016) Consistent cortical reconstruction and multi-atlas brain segmentation. NeuroImage 138:197–210. doi:10.1016/j.neuroimage.2016.05.030

    Article  Google Scholar 

  17. Huo YK, Asman AJ, Plassard AJ, Landman BA (2017) Simultaneous total intracranial volume and posterior fossa volume estimation using multi-atlas label fusion. Hum Brain Mapp 38(2):599–616. doi:10.1002/hbm.23432

    Article  Google Scholar 

  18. Ji G (2014) Fruit classification using computer vision and feedforward neural network. J Food Eng 143:167–177. doi:10.1016/j.jfoodeng.2014.07.001

    Article  Google Scholar 

  19. Johnson KA, Becker JA (2016) The whole brain atlas. http://www.med.harvard.edu/AANLIB/home.html

  20. Kahyaei S, Moin MS (2016) Robust Matching of Fingerprints Using Pseudo-Zernike Moments. In: 4th International Conference on Control, Instrumentation, And Automation (ICCIA), Qazvin, IRAN. IEEE, pp 116–120

  21. Liu A (2015) Magnetic resonance brain image classification via stationary wavelet transform and generalized eigenvalue proximal support vector machine. J Med Imaging Health Inform 5(7):1395–1403. doi:10.1166/jmihi.2015.1542

    Article  Google Scholar 

  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

    Google Scholar 

  23. Lu HM (2016) Facial emotion recognition based on biorthogonal wavelet entropy, fuzzy support vector machine, and stratified cross validation. IEEE Access 4:8375–8385. doi:10.1109/ACCESS.2016.2628407

    Article  Google Scholar 

  24. Marathe AS, Vyas V, Chavhan M (2016) Petrographic image classification using Optimized Radial Basis Function Support Vector Machine & Validation of its asymptotic behavior. In: International Conference on Signal Processing, Communications And Computing (ICSPCC), Hong Kong, P R China. IEEE, pp 6–11

  25. Nayak DR (2017) Detection of unilateral hearing loss by stationary wavelet entropy. CNS Neurol Disord Drug Targets 16(1). doi:10.2174/1871527315666161026115046

  26. Nayak R, Patra D (2016) Super resolution image reconstruction using weighted combined pseudo-Zernike moment invariants. AEU-Int J Electron Commun 70(11):1496–1505. doi:10.1016/j.aeue.2016.09.001

    Article  Google Scholar 

  27. Nayak DR, Dash R, Majhi B (2016) Brain MR image classification using two-dimensional discrete wavelet transform and AdaBoost with random forests. Neurocomputing 177:188–197. doi:10.1016/j.neucom.2015.11.034

    Article  Google Scholar 

  28. Pham BT, Bui DT, Prakash I, Dholakia MB (2017) Hybrid integration of multilayer perceptron neural networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS. Catena 149:52–63. doi:10.1016/j.catena.2016.09.007

    Article  Google Scholar 

  29. Phillips P (2016) Three-dimensional Eigenbrain for the detection of subjects and brain regions related with Alzheimer's disease. J Alzheimers Dis 50(4):1163–1179. doi:10.3233/jad-150988

    Article  Google Scholar 

  30. Phillips P, Dong Z, 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. doi:10.2528/PIER15040602

    Article  Google Scholar 

  31. Scott J, Geoffroy PA, Sportiche S, Brichant-Petit-Jean C, Gard S, Kahn JP, Azorin JM, Henry C, Etain B, Bellivier F (2017) Cross-validation of clinical characteristics and treatment patterns associated with phenotypes for lithium response defined by the Alda scale. J Affect Disord 208:62–67. doi:10.1016/j.jad.2016.08.069

    Article  Google Scholar 

  32. Singh SP, Urooj S (2016) An improved CAD system for breast cancer diagnosis based on generalized pseudo-Zernike moment and Ada-DEWNN classifier. J Med Syst 40(4). doi:10.1007/s10916-016-0454-0

  33. Singh C, Aggarwal A, Ranade SK (2017) A new convolution model for the fast computation of Zernike moments. AEU-Int J Electron Commun 72:104–113. doi:10.1016/j.aeue.2016.11.014

    Article  Google Scholar 

  34. Soman K, Sathiya A, Suganthi N (2014) Classification of Stress of Automobile Drivers using Radial Basis Function Kernel Support Vector Machine. In: International Conference on Information Communication And Embedded Systems (ICICES), Chennai, India. IEEE, pp 5–10

  35. Sun P (2015) Pathological brain detection based on wavelet entropy and Hu moment invariants. Biomed Mater Eng 26(s1):1283–1290. doi:10.2528/PIER13121310

    Google Scholar 

  36. Sun Y (2016) A multilayer perceptron based smart pathological brain detection system by fractional Fourier entropy. J Med Syst 40(7). doi:10.1007/s10916-016-0525-2

  37. Wang S-H (2016) Single slice based detection for Alzheimer’s disease via wavelet entropy and multilayer perceptron trained by biogeography-based optimization. Multimedia Tools and Applications. doi:10.1007/s11042-016-4222-4

    Google Scholar 

  38. Wang H, Lv Y (2016) Smart pathological brain detection system by predator-prey particle swarm optimization and single-hidden layer neural-network. Multimedia Tools and Applications. doi:10.1007/s11042-016-4242-0

    Google Scholar 

  39. Wang S, Lu S, Dong Z, Yang J, Yang M, Zhang Y (2016) Dual-Tree Complex Wavelet Transform and Twin Support Vector Machine for Pathological Brain Detection. Appl. Sci. 2016, 6(6):169. doi:10.3390/app6060169"10.3390/app6060169

  40. Wang S, Chen M, Li Y, Shao Y, Zhang Y, Du S, Wu J (2016) Morphological analysis of dendrites and spines by hybridization of ridge detection with twin support vector machine. PeerJ 4:e2207. doi:10.7717/peerj.2207. eCollection 2016

  41. Wei L (2015) Fruit classification by wavelet-entropy and feedforward neural network trained by fitness-scaled chaotic ABC and biogeography-based optimization. Entropy 17(8):5711–5728. doi:10.3390/e17085711

    Google Scholar 

  42. Wu L (2011) A hybrid method for MRI brain image classification. Expert Syst Appl 38(8):10049–10053

    Article  Google Scholar 

  43. Wu L (2012) An MR brain images classifier via principal component analysis and kernel support vector machine. Prog Electromagn Res 130:369–388

    Article  Google Scholar 

  44. Wu X (2016) Smart detection on abnormal breasts in digital mammography based on contrast-limited adaptive histogram equalization and chaotic adaptive real-coded biogeography-based optimization. SIMULATION 92(9):873–885. doi:10.1177/0037549716667834

    Article  Google Scholar 

  45. Wu J (2016) Fruit classification by biogeography-based optimization and feedforward neural network. Expert Syst 33(3):239–253. doi:10.1111/exsy.12146

    Article  Google Scholar 

  46. 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. doi:10.3390/e17041795

    Article  Google Scholar 

  47. Yang J (2015) Identification of green, oolong and black teas in China via wavelet packet entropy and fuzzy support vector machine. Entropy 17(10):6663–6682. doi:10.3390/e17106663

    Google Scholar 

  48. Yang J (2017) Pathological brain detection in MRI scanning via Hu moment invariants and machine learning. Journal of Experimental & Theoretical Artificial Intelligence 29(2):299–312. doi:10.1080/0952813X.2015.1132274

    Article  Google Scholar 

  49. Yang JF, Sun P (2016) Magnetic resonance brain classification by a novel binary particle swarm optimization with mutation and time-varying acceleration coefficients. Biomed Eng-Biomed Tech 61(4):431–441. doi:10.1515/bmt-2015-0152

    Google Scholar 

  50. Yang X, Sun P, Dong Z, Liu A, Yuan T-F (2015) Pathological brain detection by a novel image feature—fractional Fourier entropy. Entropy 17(12):8278–8296. doi:10.3390/e17127877

    Article  Google Scholar 

  51. Zhan TM, Chen Y (2016) Multiple sclerosis detection based on biorthogonal wavelet transform, RBF kernel principal component analysis, and logistic regression. IEEE Access 4:7567–7576. doi:10.1109/ACCESS.2016.2620996

    Article  MathSciNet  Google Scholar 

  52. Zhang Y (2014) Binary PSO with mutation operator for feature selection using decision tree applied to spam detection. Knowledge-Based Syst 64:22–31

    Article  Google Scholar 

  53. Zhang YD, Wang SH, Yang XJ, Dong ZC, Liu G, Phillips P, Yuan TF (2015) Pathological brain detection in MRI scanning by wavelet packet Tsallis entropy and fuzzy support vector machine. SpringerPlus 4:716. doi:10.1186/s40064-015-1523-4. eCollection 2015.

  54. Zhou X-X (2016) Comparison of machine learning methods for stationary wavelet entropy-based multiple sclerosis detection: decision tree, k-nearest neighbors, and support vector machine. SIMULATION 92(9):861–871. doi:10.1177/0037549716666962

    Article  Google Scholar 

  55. Zhou XX, Zhang GS (2016) Detection of abnormal MR brains based on wavelet entropy and feature selection. IEEJ Trans Electr Electron Eng 11(3):364–373. doi:10.1002/tee.22226

    Article  Google Scholar 

  56. Zhou X-X, Yang J-F, Sheng H, Wei L, Yan J, Sun P (2016) Combination of stationary wavelet transform and kernel support vector machines for pathological brain detection. SIMULATION 92(9):827–837. doi:10.1177/0037549716629227

    Article  Google Scholar 

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Acknowledgments

This work was supported by National Natural Science Foundation of China (61602250), Open fund for Jiangsu Key Laboratory of Advanced Manufacturing Technology (HGAMTL1601), Open fund of Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence (2016CSCI01), Leading Initiative for Excellent Young Researcher (LEADER) of Ministry of Education, Culture, Sports, Science and Technology-Japan (16809746), Natural Science Foundation of Jiangsu Province (BK20150983), Open fund of Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence (2016CSCI01), Opening Project of State Key Laboratory of Digital Publishing Technology.

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  1. Yu-Dong Zhang, Yongyan Jiang and Weiguo Zhu contributed equally to this work.

Authors and Affiliations

  1. Jiangsu Key Laboratory of Advanced Manufacturing Technology, Huaiyin, Jiangsu, 223003, China

    Yu-Dong Zhang & Weiguo Zhu

  2. Hunan Provincial Key Laboratory of Network Investigational Technology, Hunan Policy Academy, Changsha, Hunan, 410138, China

    Yu-Dong Zhang

  3. School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu, 210023, China

    Yu-Dong Zhang & Siyuan Lu

  4. College of Science, Zhongyuan University of Technology, Zhengzhou, 450007, China

    Yongyan Jiang

  5. School of Information Science and Engineering, Central South University, Changsha, 410083, China

    Guihu Zhao

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  1. Yu-Dong Zhang
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  4. Siyuan Lu
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Correspondence to Siyuan Lu or Guihu Zhao.

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Zhang, YD., Jiang, Y., Zhu, W. et al. Exploring a smart pathological brain detection method on pseudo Zernike moment. Multimed Tools Appl 77, 22589–22604 (2018). https://doi.org/10.1007/s11042-017-4703-0

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