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Application of stationary wavelet entropy in pathological brain detection

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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.

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Acknowledgments

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

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Authors and Affiliations

  1. School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu, 210046, China

    Shuihua Wang & Sidan Du

  2. University of the Free State, Bloemfontein, 9300, South Africa

    Abdon Atangana

  3. W. P. Carey School of Business, Arizona State University, P.O. Box 873406, Tempe, AZ, 85287, USA

    Aijun Liu

  4. Center of Medical Physics and Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China

    Zeyuan Lu

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  1. Shuihua Wang
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  2. Sidan Du
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  3. Abdon Atangana
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Correspondence to Sidan Du.

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Wang, S., Du, S., Atangana, A. et al. Application of stationary wavelet entropy in pathological brain detection. Multimed Tools Appl 77, 3701–3714 (2018). https://doi.org/10.1007/s11042-016-3401-7

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