Advertisement

Multimedia Tools and Applications

, Volume 77, Issue 3, pp 3701–3714 | Cite as

Application of stationary wavelet entropy in pathological brain detection

  • Shuihua Wang
  • Sidan Du
  • Abdon Atangana
  • Aijun Liu
  • Zeyuan Lu
Article

Abstract

Labeling brain images as healthy or pathological cases is an important procedure for medical diagnosis. Therefore, we proposed a novel image feature, stationary wavelet entropy (SWE), to extract brain image features. Meanwhile, we replaced the feature extraction procedure in state-of-the-art approaches with the proposed SWE. We found the classification performance improved after replacing wavelet entropy (WE), wavelet energy (WN), and discrete wavelet transform (DWT) with the proposed SWE. This proposed SWE is superior to WE, WN, and DWT.

Keywords

Magnetic resonance imaging Stationary wavelet entropy Pathological brain detection Wavelet entropy Wavelet energy Discrete wavelet transform 

Notes

Acknowledgments

This paper was supported by the National Nature Science of China (No.61271231).

Compliance with ethical standards

Conflict of interest

We have no conflicts of interest to disclose with regard to the subject matter of this paper.

References

  1. 1.
    Babic-Stojic B, Jokanovic V, Milivojevic D et al (2016) Gd2O3 nanoparticles stabilized by hydrothermally modified dextrose for positive contrast magnetic resonance imaging. J Magn Magn Mater 403:118–126CrossRefGoogle Scholar
  2. 2.
    Chen Y, Yang J, Cao Q et al (2016) Curve-like structure extraction using minimal path propagation with back-tracing. IEEE Trans Image Process 25(2):988–1003MathSciNetCrossRefGoogle Scholar
  3. 3.
    Cherif LH, Debbal SM, Bereksi-Reguig F (2010) Choice of the wavelet analyzing in the phonocardiogram signal analysis using the discrete and the packet wavelet transform. Expert Syst Appl 37(2):913–918CrossRefGoogle Scholar
  4. 4.
    Cierpiol S, Schafer S, Gossner J (2015) Compression of the medulla oblongata due to an elongated vertebral artery is a common incidental finding on MRI of the brain. Acta Neurol Belg 115(4):841–842CrossRefGoogle Scholar
  5. 5.
    D’Angelino RHR, Pituco EM, Villalobos EMC et al (2013) Detection of bovine leukemia virus in brains of cattle with a neurological syndrome: pathological and molecular studies. Biomed Res Int 2013:6Google Scholar
  6. 6.
    Dickie DA, Mikhael S, Job DE et al (2015) Permutation and parametric tests for effect sizes in voxel-based morphometry of gray matter volume in brain structural MRI. Magn Reson Imaging 33(10):1299–1305CrossRefGoogle Scholar
  7. 7.
    Dong Z, Phillips P, Wang S et al (2015) Exponential wavelet iterative shrinkage thresholding algorithm for compressed sensing magnetic resonance imaging. Inf Sci 322:115–132MathSciNetCrossRefGoogle Scholar
  8. 8.
    El-Dahshan ESA, Hosny T, Salem ABM (2010) Hybrid intelligent techniques for MRI brain images classification. Digit Signal Proc 20(2):433–441CrossRefGoogle Scholar
  9. 9.
    El-Dahshan ESA, Mohsen HM, Revett K et al (2014) Computer-aided diagnosis of human brain tumor through MRI: a survey and a new algorithm. Expert Syste Appl 41(11):5526–5545CrossRefGoogle Scholar
  10. 10.
    Ella A, Keller M (2015) Construction of an MRI 3D high resolution sheep brain template. Magn Reson Imaging 33(10):1329–1337CrossRefGoogle Scholar
  11. 11.
    Fan YH, Lan LF, Zheng L et al (2015) Spontaneous white matter lesion in brain of stroke-prone renovascular hypertensive rats: a study from MRI, pathology and behavior. Metab Brain Dis 30(6):1479–1486CrossRefGoogle Scholar
  12. 12.
    Fang L, Wu L, Zhang Y (2015) A novel demodulation system based on continuous wavelet transform. Math Probl Eng 2015:9Google Scholar
  13. 13.
    Farzan A, Mashohor S, Ramli AR et al (2015) Boosting diagnosis accuracy of Alzheimer’s disease using high dimensional recognition of longitudinal brain atrophy patterns. Behav Brain Res 290:124–130CrossRefGoogle Scholar
  14. 14.
    Fathabadi H (2015) Two novel proposed discrete wavelet transform and filter based approaches for short-circuit faults detection in power transmission lines. Appl Soft Comput 36:375–382CrossRefGoogle Scholar
  15. 15.
    Goh S, Dong Z, Zhang Y et al (2014) Mitochondrial dysfunction as a neurobiological subtype of autism spectrum disorder: evidence from brain imaging. JAMA Psychiatry 71(6):665–671CrossRefGoogle Scholar
  16. 16.
    Gomez-Pilar J, Poza J, Bachiller A et al (2015) Neural network reorganization analysis during an auditory oddball task in schizophrenia using wavelet entropy. Entropy 17(8):5241–5256CrossRefGoogle Scholar
  17. 17.
    Gorji HT, Haddadnia J (2015) A novel method for early diagnosis of Alzheimer’s disease based on pseudo Zernike moment from structural MRI. Neuroscience 305:361–371CrossRefGoogle Scholar
  18. 18.
    Gu P, Lee WM, Roubidoux MA et al (2016) Automated 3D ultrasound image segmentation to aid breast cancer image interpretation. Ultrasonics 65:51–58CrossRefGoogle Scholar
  19. 19.
    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):33–40CrossRefGoogle Scholar
  20. 20.
    Hayes BC, Ryan S, McGarvey C et al (2016) Brain magnetic resonance imaging and outcome after hypoxic ischaemic encephalopathy. J Matern Fetal Neonatal Med 29(5):777–782CrossRefGoogle Scholar
  21. 21.
    Ji G (2015) A comprehensive survey on particle swarm optimization algorithm and its applications. Math Probl Eng 2015:38MathSciNetGoogle Scholar
  22. 22.
    Ji G, Yang J, Wu J et al (2015) Fruit classification by wavelet-entropy and feedforward neural network trained by fitness-scaled chaotic ABC and biogeography-based optimization. Entropy 17(8):5711–5728Google Scholar
  23. 23.
    Kayvanrad MH, McLeod AJ, Baxter JSH et al (2014) Stationary wavelet transform for under-sampled MRI reconstruction. Magn Reson Imaging 32(10):1353–1364CrossRefGoogle Scholar
  24. 24.
    Lu Z, Wei L, Ji G et al (2015) Fitness-scaling adaptive genetic algorithm with local search for solving the multiple depot vehicle routing problem. Simul Trans Soc Model Simul Int 91(11):1–16Google Scholar
  25. 25.
    Mehra I, Nishchal NK (2015) Optical asymmetric image encryption using gyrator wavelet transform. Opt Commun 354:344–352CrossRefGoogle Scholar
  26. 26.
    Merah M, Abdelmalik TA, Larbi BH (2015) R-peaks detection based on stationary wavelet transform. Comput Methods Prog Biomed 121(3):149–160CrossRefGoogle Scholar
  27. 27.
    Munteanu CR, Fernandez-Lozano C, Abad VM et al (2015) Classification of mild cognitive impairment and Alzheimer’s disease with machine-learning techniques using H-1 magnetic resonance spectroscopy data. Expert Syst Appl 42(15–16):6205–6214CrossRefGoogle Scholar
  28. 28.
    Nascimento MZ, Neves L, Duarte SC et al (2015) Classification of histological images based on the stationary wavelet transform, in 3rd International Conference on Mathematical Modeling in Physical Sciences, E. C. Vagenas, et al., Editors. 2015, Iop Publishing Ltd: BristolGoogle Scholar
  29. 29.
    Nazir M, Wahid F, Khan SA (2015) A simple and intelligent approach for brain MRI classification. J Intell Fuzzy Syst 28(3):1127–1135MathSciNetGoogle Scholar
  30. 30.
    Nguyen N, Vo A, Choi I et al (2015) A stationary wavelet entropy-based clustering approach accurately predicts gene expression. J Comput Biol 22(3):236–249MathSciNetCrossRefGoogle Scholar
  31. 31.
    Nicolis O, Mateu J (2015) 2D anisotropic wavelet entropy with an application to earthquakes in Chile. Entropy 17(6):4155–4172CrossRefGoogle Scholar
  32. 32.
    Nourani V, Alami MT, Vousoughi FD (2015) Wavelet-entropy data pre-processing approach for ANN-based groundwater level modeling. J Hydrol 524:255–269CrossRefGoogle Scholar
  33. 33.
    Padma A, Sukanesh R (2014) Segmentation and classification of brain CT images using combined wavelet statistical texture features. Arab J Sci Eng 39(2):767–776CrossRefGoogle Scholar
  34. 34.
    Patnaik LM, Chaplot S, Jagannathan NR (2006) Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed Signal Process Control 1(1):86–92CrossRefGoogle Scholar
  35. 35.
    Peng B, Liang Y-X, Yang J et al (2016) Image processing methods to elucidate spatial characteristics of retinal microglia after optic nerve transection. Sci Report 6:21816CrossRefGoogle Scholar
  36. 36.
    Prinz V, Hetzer AM, Muller S et al (2015) MRI heralds secondary nigral lesion after brain ischemia in mice: a secondary time window for neuroprotection. J Cereb Blood Flow Metab 35(12):1903–1909CrossRefGoogle Scholar
  37. 37.
    Ramirez-Pacheco JC, Rizo-Dominguez L, Cortez-Gonzalez J (2015) Wavelet-Tsallis entropy detection and location of mean level-shifts in long-memory fGn signals. Entropy 17(12):7979–7995CrossRefGoogle Scholar
  38. 38.
    Saritha M, Paul Joseph K, Mathew AT (2013) Classification of MRI brain images using combined wavelet entropy based spider web plots and probabilistic neural network. Pattern Recogn Lett 34(16):2151–2156CrossRefGoogle Scholar
  39. 39.
    Sun P, Wang S, Phillips P et al (2015) Pathological brain detection based on wavelet entropy and Hu moment invariants. Biomed Mater Eng 26(s1):1283–1290Google Scholar
  40. 40.
    Thorsen F, Fite B, Mahakian LM et al (2013) Multimodal imaging enables early detection and characterization of changes in tumor permeability of brain metastases. J Control Release 172(3):812–822CrossRefGoogle Scholar
  41. 41.
    VanMeerten NJ, Dubke RE, Stanwyck JJ et al (2016) Abnormal early brain responses during visual search are evident in schizophrenia but not bipolar affective disorder. Schizophr Res 170(1):102–108CrossRefGoogle Scholar
  42. 42.
    Wals K, Anthony DC, Davis BG (2015) Multiplexed synchrotron X-Ray fluorescence imaging of brain inflammation using targeted heavy metal nanoparticles. Glia 63:E397Google Scholar
  43. 43.
    Wang S, Chen M, Li Y et al (2015) Detection of dendritic spines using wavelet-based conditional symmetric analysis and regularized morphological shared-weight neural networks. Comput Math Methods Med 2015:12CrossRefMATHGoogle Scholar
  44. 44.
    Wang S, Dong Z, Du S et al (2015) Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection. Int J Imaging Syst Technol 25(2):153–164CrossRefGoogle Scholar
  45. 45.
    Wang S, Pan H, Zhang C et al (2014) RGB-D image-based detection of stairs, pedestrian crosswalks and traffic signs. J Vis Commun Image Represent 25(2):263–272CrossRefGoogle Scholar
  46. 46.
    Wang S, Yang X, Zhang Y et al (2015) Identification of green, Oolong and black teas in China via wavelet packet entropy and fuzzy support vector machine. Entropy 17(10):6663–6682CrossRefGoogle Scholar
  47. 47.
    Wang S, Zhang Y, Liu G et al (2016) Detection of Alzheimer’s disease by three-dimensional displacement field estimation in structural magnetic resonance imaging. J Alzheimers Dis 50(1):233–248CrossRefGoogle Scholar
  48. 48.
    Wang S, Zhang Y, Yang X et al (2015) Pathological brain detection by a novel image feature—fractional Fourier entropy. Entropy 17(12):7877Google Scholar
  49. 49.
    Wibmer A, Hricak H, Gondo T et al (2015) Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores. Eur Radiol 25(10):2840–2850CrossRefGoogle Scholar
  50. 50.
    Wu L (2012) An MR brain images classifier via principal component analysis and kernel support vector machine. Prog Electromagn Res 130:369–388CrossRefGoogle Scholar
  51. 51.
    Yang G, Zhang Y, Yang J et al (2015) Automated classification of brain images using wavelet-energy and biogeography-based optimization. Multimed Tools Appl 1–17Google Scholar
  52. 52.
    Yu D, Shui H, Gen L et al (2015) Exponential wavelet iterative shrinkage thresholding algorithm with random shift for compressed sensing magnetic resonance imaging. IEEJ Trans Electr Electron Eng 10(1):116–117CrossRefGoogle Scholar
  53. 53.
    Zhang Y-D, Chen S, Wang S-H et al (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):317–327CrossRefGoogle Scholar
  54. 54.
    Zhang Y, Dong Z, Ji G et al (2015) Effect of spider-web-plot in MR brain image classification. Pattern Recogn Lett 62:14–16CrossRefGoogle Scholar
  55. 55.
    Zhang Y, Dong Z, Wang S et al (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):1795–1813CrossRefGoogle Scholar
  56. 56.
    Zhang Y, Wang S (2015) Detection of Alzheimer’s disease by displacement field and machine learning. Peer J 3:e1251CrossRefGoogle Scholar
  57. 57.
    Zhang Y, Wang S, Dong Z et al (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 152:41–58CrossRefGoogle Scholar
  58. 58.
    Zhang Y, Wang S, Ji G et al (2013) An MR brain images classifier system via particle swarm optimization and kernel support vector machine. Sci World J 2013:9Google Scholar
  59. 59.
    Zhang Y-D, Wang S-H, Yang X-J et al (2015) Pathological brain detection in MRI scanning by wavelet packet Tsallis entropy and fuzzy support vector machine. Springer Plus 4(1):716CrossRefGoogle Scholar
  60. 60.
    Zhou X, Ji G, Yang J et al (2016) Detection of abnormal MR brains based on wavelet-entropy and feature selection. IEEJ Trans Electr Electron Eng 11(3):1–10CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Shuihua Wang
    • 1
  • Sidan Du
    • 1
  • Abdon Atangana
    • 2
  • Aijun Liu
    • 3
  • Zeyuan Lu
    • 4
  1. 1.School of Electronic Science and EngineeringNanjing UniversityNanjingChina
  2. 2.University of the Free StateBloemfonteinSouth Africa
  3. 3.W. P. Carey School of BusinessArizona State UniversityTempeUSA
  4. 4.Center of Medical Physics and Technology, Hefei Institutes of Physical ScienceChinese Academy of SciencesHefeiChina

Personalised recommendations