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Entropy 2015, 17(12), 8278-8296; doi:10.3390/e17127877

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

1,2,3,†
,
1,2,3,4,†,* , 5,* , 6
,
7
,
8
and
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
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Academic Editor: Carlo Cattani
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)
View Full-Text   |   Download PDF [1777 KB, uploaded 17 December 2015]   |  

Abstract

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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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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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