title: Pathological brain detection based on wavelet entropy and Hu moment invariants.
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.
authors.affiliation: School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China.|Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu 210042, China.
authors.forename: Yudong
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China.|Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu 210042, China.
authors.forename: Shuihua
authors.initials: S
authors.lastname: Wang
authors.affiliation: Department of Electrical Engineering, The City College of New York, CUNY, New York, NY 10031, USA.
authors.forename: Ping
authors.initials: P
authors.lastname: Sun
authors.affiliation: School of Natural Sciences and Mathematics, Shepherd University, Shepherdstown, WV 25443, USA.
authors.forename: Preetha
authors.initials: P
authors.lastname: Phillips
mesh_terms: D000465:Algorithms; D001921:Brain / Q000473:pathology*; D001927:Brain Diseases / Q000473:pathology*; D019277:Entropy; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D058067:Wavelet Analysis
keywords: Hu’s moment invariant; Wavelet entropy; computer-aided diagnosis; magnetic resonance imaging; radial basis function; support vector machine