Pathological brain detection based on wavelet entropy and Hu moment invariants

• Published in: Bio-medical materials and engineering Vol. 26; no. 1_suppl; pp. S1283 - S1290
• Main Authors: Zhang, Yudong, Wang, Shuihua, Sun, Ping, Phillips, Preetha
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.01.2015
• Subjects: Algorithms; Brain - pathology; Brain Diseases - pathology; Entropy; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Pattern Recognition, Automated - methods; Reproducibility of Results; Sensitivity and Specificity; Support Vector Machine; Wavelet Analysis; magnetic resonance imaging; radial basis function; support vector machine; computer-aided diagnosis; Wavelet entropy; Hu’s moment invariant
• ISSN: 0959-2989; 1878-3619
• DOI: 10.3233/BME-151426

Abstract 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.