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


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.


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



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.


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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

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