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Pathological brain detection based on wavelet entropy and Hu moment invariants

Issue title: Frontiers in Biomedical Engineering and Biotechnology – Proceedings of the 4th International Conference on Biomedical Engineering and Biotechnology, 18–21 August 2015, Shanghai, China

Guest editors: Feng Liu, Dong-Hoon Lee, Ricardo Lagoa and Sandeep Kumar

Article type: Research Article

Authors: Zhang, Yudonga; b; * | Wang, Shuihuaa; b; * | Sun, Pingc | Phillips, Preethad

Affiliations: [a] School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China | [b] Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing, Jiangsu 210042, China | [c] Department of Electrical Engineering, The City College of New York, CUNY, New York, NY 10031, USA | [d] School of Natural Sciences and Mathematics, Shepherd University, Shepherdstown, WV 25443, USA

Correspondence: [*] Address for correspondence: Yudong Zhang, School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China. Tel.: +86-15905183664; E-mail: [email protected]. Shuihua Wang, School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, China. Tel.: +86-15905183664; E-mail: [email protected].

Keywords: Wavelet entropy, Hu’s moment invariant, magnetic resonance imaging, support vector machine, computer-aided diagnosis, radial basis function

DOI: 10.3233/BME-151426

Journal: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S1283-S1290, 2015

Published: 2015
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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.

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