Pathological brain detection based on wavelet entropy and Hu moment invariants

Yudong Zhang*, Shuihua Wang, Ping Sun, Preetha Phillips

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

137 Citations (Scopus)


With the aim of developing an accurate pathological brain detection system, we proposed a novel automatic computer-aided diagnosis (CAD) to detect pathological brains from normal brains obtained by magnetic resonance imaging (MRI) scanning. The problem still remained a challenge for technicians and clinicians, since MR imaging generated an exceptionally large information dataset. A new two-step approach was proposed in this study. We used wavelet entropy (WE) and Hu moment invariants (HMI) for feature extraction, and the generalized eigenvalue proximal support vector machine (GEPSVM) for classification. To further enhance classification accuracy, the popular radial basis function (RBF) kernel was employed. The 10 runs of k-fold stratified cross validation result showed that the proposed "WE + HMI + GEPSVM + RBF" method was superior to existing methods w.r.t. classification accuracy. It obtained the average classification accuracies of 100%, 100%, and 99.45% over Dataset-66, Dataset-160, and Dataset-255, respectively. The proposed method is effective and can be applied to realistic use.

Original languageEnglish
Pages (from-to)S1283-S1290
JournalBio-Medical Materials and Engineering
Publication statusPublished - 2015
Externally publishedYes


  • Hu's moment invariant
  • Wavelet entropy
  • computer-aided diagnosis
  • magnetic resonance imaging
  • radial basis function
  • support vector machine


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