Pathological brain detection in MRI scanning via Hu moment invariants and machine learning. Issue 2 (4th March 2017) - Zhang, Yudong; Yang, Jianfei; Wang, Shuihua; Dong, Zhengchao; Phillips, Preetha
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).