An MR Brain Images Classifier via Principal Component Analysis and Kernel Support Vector Machine

Abstract

Abstract-Automated and accurate classification of MR brain images is extremely important for medical analysis and interpretation. Over the last decade numerous methods have already been proposed. In this paper, we presented a novel method to classify a given MR brain image as normal or abnormal. The proposed method first employed wavelet transform to extract features from images, followed by applying principle component analysis (PCA) to reduce the dimensions of features. The reduced features were submitted to a kernel support vector machine (KSVM). The strategy of Kfold stratified cross validation was used to enhance generalization of KSVM. We chose seven common brain diseases (glioma, meningioma, Alzheimer's disease, Alzheimer's disease plus visual agnosia, Pick's disease, sarcoma, and Huntington's disease) as abnormal brains, and collected 160 MR brain images (20 normal and 140 abnormal) from Harvard Medical School website. We performed our proposed methods with four different kernels, and found that the GRB kernel achieves the highest classification accuracy as 99.38%. The LIN, HPOL, and IPOL kernel achieves 95%, 96.88%, and 98.12%, respectively. We also compared our method to those from literatures in the last decade, and the results showed our DWT+PCA+KSVM with GRB kernel still achieved the best accurate classification results. The averaged processing time for a 256 × 256 size image on a laptop of P4 IBM with 3 GHz processor and 2 GB RAM is 0.0448 s. From the experimental data, our method was effective and rapid. It could be applied to the field of MR brain image classification and can assist the doctors to diagnose where a patient is normal or abnormal to certain degrees.

Classification of Alzheimer Disease Based on Structural Magnetic Resonance Imaging by Kernel Support Vector Machine Decision Tree

“…It is achieved by transforming the data set to a new set of ordered variables according to their variances or importance. This technique has three effects: it orthogonalizes the components of the input vectors so that they are not correlated with each other, it orders the resulting orthogonal components so that those with the largest variation come first, and it eliminates those components contributing the least to the variation in the data set [23]. It should be noted that the input vectors should be normalized to have zero mean and unity variance before performing PCA.…”

Multi-Feature Based Detection of Landmines Using Ground Penetrating Radar

“…Therefore, it can be used as a unique feature of each landmine. PCA has been used for a wide spectrum of applications such as [49][50][51][52][53]. In this work, it is used for landmine identification.…”

Pathological Brain Detection in Magnetic Resonance Imaging Scanning by Wavelet Entropy and Hybridization of Biogeography-Based Optimization and Particle Swarm Optimization

“…Ramasamy and Anandhakumar [10] used fast-Fourier-transform based expectation-maximization Gaussian mixture model for brain tissue classification of MR images. Zhang and Wu [11] proposed to use kernel SVM (kSVM), and they suggested three new kernels as homogeneous polynomial (HPOL), inhomogeneous polynomial (IPOL), and Gaussian radial basis (GRB). Saritha et al [12] proposed a novel feature of wavelet-entropy (WE), and employed spider-web plots (SWP) to further reduce features.…”

“…We compared the proposed HBP with BP [37], MBP [38], GA [39], SA [40], 2.4.1 [41], BBO [42], PSO [43], and BPSO [50] III. We compared the proposed HBP-FNN with fourteen state-of-the-art classification methods as DWT + PCA + FP-ANN [7], DWT + PCA + KNN [7], DWT + PCA + SCABC-FNN [8], DWT + PCA + SVM + HPOL [11], DWT + PCA + SVM + IPOL [11], DWT + PCA + SVM + GRB [11], WE + SWP + PNN [12], RT + PCA + LS-SVM [14], PCNN + DWT + PCA + BPNN [17], DWPT + SE + GEPSVM [18], DWPT + TE + GEPSVM [18], WE + NBC [19], WEnergy + SVM [22], and SWT + PCA + HPA-FNN [26]. IV.…”