TY - JOUR
T1 - Magnetic resonance brain image classification via stationary wavelet transform and generalized eigenvalue proximal support vector machine
AU - Zhang, Yudong
AU - Dong, Zhengchao
AU - Liu, Aijun
AU - Wang, Shuihua
AU - Ji, Genlin
AU - Zhang, Zheng
AU - Yang, Jiquan
N1 - Publisher Copyright:
Copyright © 2015 American Scientific Publishers.
PY - 2015/11/1
Y1 - 2015/11/1
N2 - Background: Automated and accurate classification of MR brain images is of crucially importance for medical analysis and interpretation. We proposed a novel automatic classification system to distinguish abnormal brains from normal brains in MRI scanning. Methods: Our proposed method used stationary wavelet transform (SWT) to extract features from MR brain images. Next, principal component analysis (PCA) was harnessed to reduce the SWT coefficients. Finally, we proposed to use two classifiers, viz., the generalized eigenvalue proximal support vector machine (GEPSVM), and GEPSVM with RBF kernel. We tested our methods on three benchmark datasets. Results: The 10 runs of K-fold cross validation result showed the proposed SWT+PCA+GEPSVM+ RBF method excelled thirteen state-of-the-art methods in terms of classification accuracy. In addition, the SWT+PCA+GEPSVM+RBF method achieved accuracy of 100%, 100%, and 99.41% on Dataset-66, Dataset- 160, and Dataset-255, respectively. Conclusion: We proved the effectiveness of both SWT and GEPSVM. The proposed method may be applied to clinical use.
AB - Background: Automated and accurate classification of MR brain images is of crucially importance for medical analysis and interpretation. We proposed a novel automatic classification system to distinguish abnormal brains from normal brains in MRI scanning. Methods: Our proposed method used stationary wavelet transform (SWT) to extract features from MR brain images. Next, principal component analysis (PCA) was harnessed to reduce the SWT coefficients. Finally, we proposed to use two classifiers, viz., the generalized eigenvalue proximal support vector machine (GEPSVM), and GEPSVM with RBF kernel. We tested our methods on three benchmark datasets. Results: The 10 runs of K-fold cross validation result showed the proposed SWT+PCA+GEPSVM+ RBF method excelled thirteen state-of-the-art methods in terms of classification accuracy. In addition, the SWT+PCA+GEPSVM+RBF method achieved accuracy of 100%, 100%, and 99.41% on Dataset-66, Dataset- 160, and Dataset-255, respectively. Conclusion: We proved the effectiveness of both SWT and GEPSVM. The proposed method may be applied to clinical use.
KW - Classification
KW - Magnetic Resonance Imaging
KW - Pattern Recognition
KW - Principle Component Analysis
KW - Radial Basis Function
KW - Stationary Wavelet Transform
KW - Support Vector Machine
UR - http://www.scopus.com/inward/record.url?scp=85000692374&partnerID=8YFLogxK
U2 - 10.1166/jmihi.2015.1542
DO - 10.1166/jmihi.2015.1542
M3 - Article
AN - SCOPUS:85000692374
SN - 2156-7018
VL - 5
SP - 1395
EP - 1403
JO - Journal of Medical Imaging and Health Informatics
JF - Journal of Medical Imaging and Health Informatics
IS - 7
ER -