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

Entropy

Shuihua Wang³  Yudong Zhang³  Xiaojun Yang²  Ping Sun¹  Zhengchao Dong⁴  Aijun Liu⁵  Ti-Fei Yuan³ 

[1] Department of Electrical Engineering, The City College of New York, City University of New York, New York, NY 10031, USA;;Department of Mathematics and Mechanics, China University of Mining and Technology, Xuzhou 221008, ChinaSchool of Computer Science and Technology, Nanjing Normal University, Nanjing 210023, China;Translational Imaging Division & MRI Unit, Columbia University and New York State Psychiatric Institute, New York, NY 10032, USA;W. P. Carey School of Business, Arizona State University, P.O. Box 873406, Tempe, AZ 85287, USA;

关键词: support vector machine;    twin support vector machine;    machine learning;    magnetic resonance imaging;    Shannon entropy;    fractional Fourier transform;    fractional Fourier entropy;   

DOI  :  10.3390/e17127877

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.