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.
Highlights
Pathological brain detection (PBD) is of essential importance
To solve the above two problems, we propose two improvements: on the one hand, we propose a novel image feature—Fractional Fourier Entropy (FRFE)—which is based on two steps: (1) the use of a Fraction Fourier Transform (FRFT) to replace the traditional Fourier transform; and (2) Shannon entropy to extract features from the Fractional Fourier Transform (FRFT) spectrums
We introduce for this purpose two non-parallel SVMs, the generalized eigenvalue proximal SVM (GEPSVM) and twin support vector machine (TSVM)
Summary
Pathological brain detection (PBD) is of essential importance. It can help physicians make decisions, and to avoid wrong judgements on subjects’ condition. Magnetic resonance imaging (MRI) features high-resolution on soft tissues in the subjects’ brains, generating a mass dataset [1]. There are numerous works on the use of brain magnetic resonance (MR) images for solving PBD problems [2,3]. Due to the enormous volume of the imaging dataset from the human brain, traditional manual techniques are either tedious, or time-consuming, or costly. It is necessary to develop a novel computer-aided diagnosis (CAD) system [4] to help patients have enough time to receive treatment
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