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Open AccessArticle

Pathological Brain Detection by a Novel Image Feature—Fractional Fourier Entropy

by Shuihua Wang 1,2,3,†, Yudong Zhang 1,2,3,4,*,†, Xiaojun Yang 5,*, Ping Sun 6, Zhengchao Dong 7, Aijun Liu 8 and Ti-Fei Yuan 1,2,*
1
School of Computer Science and Technology, Nanjing Normal University, Nanjing 210023, China
2
School of Psychology, Nanjing Normal University, Nanjing 210023, China
3
Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing, Nanjing 210042, China
4
Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology, College of Mechanical Engineering, Guangxi University, Nanning 530021, China
5
Department of Mathematics and Mechanics, China University of Mining and Technology, Xuzhou 221008, China
6
Department of Electrical Engineering, The City College of New York, City University of New York, New York, NY 10031, USA
7
Translational Imaging Division & MRI Unit, Columbia University and New York State Psychiatric Institute, New York, NY 10032, USA
8
W. P. Carey School of Business, Arizona State University, P.O. Box 873406, Tempe, AZ 85287, USA
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Carlo Cattani
Entropy 2015, 17(12), 8278-8296; https://doi.org/10.3390/e17127877
Received: 3 October 2015 / Revised: 14 November 2015 / Accepted: 9 December 2015 / Published: 17 December 2015
(This article belongs to the Special Issue Wavelets, Fractals and Information Theory I)
Aim: To detect pathological brain conditions early is a core procedure for patients so as to have enough time for treatment. Traditional manual detection is either cumbersome, or expensive, or time-consuming. We aim to offer a system that can automatically identify pathological brain images in this paper. Method: We propose a novel image feature, viz., Fractional Fourier Entropy (FRFE), which is based on the combination of Fractional Fourier Transform (FRFT) and Shannon entropy. Afterwards, the Welch’s t-test (WTT) and Mahalanobis distance (MD) were harnessed to select distinguishing features. Finally, we introduced an advanced classifier: twin support vector machine (TSVM). Results: A 10 × K-fold stratified cross validation test showed that this proposed “FRFE + WTT + TSVM” yielded an accuracy of 100.00%, 100.00%, and 99.57% on datasets that contained 66, 160, and 255 brain images, respectively. Conclusions: The proposed “FRFE + WTT + TSVM” method is superior to 20 state-of-the-art methods. View Full-Text
Keywords: support vector machine; twin support vector machine; machine learning; magnetic resonance imaging; Shannon entropy; fractional Fourier transform; fractional Fourier entropy support vector machine; twin support vector machine; machine learning; magnetic resonance imaging; Shannon entropy; fractional Fourier transform; fractional Fourier entropy
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MDPI and ACS Style

Wang, S.; Zhang, Y.; Yang, X.; Sun, P.; Dong, Z.; Liu, A.; Yuan, T.-F. Pathological Brain Detection by a Novel Image Feature—Fractional Fourier Entropy. Entropy 2015, 17, 8278-8296.

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