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 x 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.
Wang, Shuihua; Zhang, Yudong; Yang, Xiaojun; Sun, Ping; Dong, Zhengchao; Lu, Aijun; and Yuan, Ti-Fei, "Pathological Brain Detection by a Novel Image Feature—Fractional Fourier Entropy" (2015). CUNY Academic Works.