Application of stationary wavelet entropy in pathological brain detection

Shuihua Wang, Sidan Du*, Abdon Atangana, Aijun Liu, Zeyuan Lu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)3701-3714
Number of pages14
JournalMultimedia Tools and Applications
Volume77
Issue number3
DOIs
Publication statusPublished - 1 Feb 2018
Externally publishedYes

Keywords

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

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