Pathological brain detection in MRI scanning by wavelet packet Tsallis entropy and fuzzy support vector machine

Yu Dong Zhang*, Shui Hua Wang, Xiao Jun Yang, Zheng Chao Dong, Ge Liu, Preetha Phillips, Ti Fei Yuan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

77 Citations (Scopus)


An computer-aided diagnosis system of pathological brain detection (PBD) is important for help physicians interpret and analyze medical images. We proposed a novel automatic PBD to distinguish pathological brains from healthy brains in magnetic resonance imaging scanning in this paper. The proposed method simplified the PBD problem to a binary classification task. We extracted the wavelet packet Tsallis entropy (WPTE) from each brain image. The WPTE is the Tsallis entropy of the coefficients of the discrete wavelet packet transform. The, the features were submitted to the fuzzy support vector machine (FSVM). We tested the proposed diagnosis method on 3 benchmark datasets with different sizes. A ten runs of K-fold stratified cross validation was carried out. The results demonstrated that the proposed WPTE + FSVM method excelled 17 state-of-the-art methods w.r.t. classification accuracy. The WPTE is superior to discrete wavelet transform. The Tsallis entropy performs better than Shannon entropy. The FSVM excels standard SVM. In closing, the proposed method “WPTE + FSVM” is effective in PBD.

Original languageEnglish
Article number716
Pages (from-to)1-16
Number of pages16
Issue number1
Publication statusPublished - 1 Dec 2015
Externally publishedYes


  • Computer-aided diagnosis
  • Discrete wavelet packet transform
  • Fuzzy support vector machine
  • Magnetic resonance imaging
  • Pathological brain detection (PBD)
  • Pattern recognition
  • Tsallis entropy


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