pathological brain detection by a novel image feature—fractional fourier entropy

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ID: 179759
2015
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Ranked #53 of 406 articles by views in European journal of medicinal chemistry

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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.
Reference Key
wang2015entropypathological Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Shuihua Wang;Yudong Zhang;Xiaojun Yang;Ping Sun;Zhengchao Dong;Aijun Liu;Ti-Fei Yuan
Journal European journal of medicinal chemistry
Year 2015
DOI
10.3390/e17127877
URL
http://www.mdpi.com/1099-4300/17/12/7877
Keywords
Machine learning support vector machine Magnetic resonance imaging shannon entropy

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