Pathological brain detection in MRI scanning via Hu moment invariants and machine learning. Issue 2 (4th March 2017)

• Record Type: Journal Article
• Authors: Zhang, Yudong; Yang, Jianfei; Wang, Shuihua; Dong, Zhengchao; Phillips, Preetha
• Is Part Of: Journal of experimental & theoretical artificial intelligence. Volume 29:Issue 2(2017)
• Page Start: 299
• Page End: 312
• Publication Date: 2017-03-04
• Subjects: Pathological brain detection -- magnetic resonance imaging -- computer-aided diagnosis -- support vector machine (SVM) -- generalised eigenvalue proximal SVM -- twin SVM -- Hu moment invariant; Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/0952813X.2015.1132274
• Languages: English
• ISSNs: 0952-813X

Abstract

Abstract: Background : We proposed a new automatic and rapid computer-aided diagnosis system to detect pathological brain images obtained in the scans of magnetic resonance imaging (MRI). Methods : For simplification, we transformed the problem to a binary classification task (pathological or normal). It consisted of two steps: first, Hu moment invariants (HMI) were extracted from a specific MR brain image; then, seven HMI features were fed into two classifiers: twin support vector machine (TSVM) and generalised eigenvalue proximal SVM (GEPSVM). Results : Then, a 5 × 5-fold cross validation on a data set containing 90 MR brain images, demonstrated that the proposed methods "HMI + GEPSVM" and "HMI + TSVM" achieved classification accuracy of 98.89%, higher than eight state-of-the-art methods: "DWT + PCA + BP-NN", "DWT + PCA + RBF-NN", "DWT + PCA + PSO-KSVM", "WE + BP-NN", "WE + KSVM", "DWT + PCA + GA-KSVM", "WE + PSO-KSVM" and "WE + BBO-KSVM". Conclusion : The proposed methods are superior to other methods on pathological brain detection ( p < 0.05).