期刊
JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS
卷 5, 期 7, 页码 1395-1403出版社
AMER SCIENTIFIC PUBLISHERS
DOI: 10.1166/jmihi.2015.1542
关键词
Magnetic Resonance Imaging; Support Vector Machine; Pattern Recognition; Stationary Wavelet Transform; Principle Component Analysis; Radial Basis Function; Classification
资金
- NSFC [610011024, 61273243, 51407095]
- Program of Natural Science Research of Jiangsu Higher Education Institutions [13KJB460011, 14KJB520021]
- Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing [BM2013006]
- Key Supporting Science and Technology Program (Industry) of Jiangsu Province [BE2012201, BE2014009-3, BE2013012-2]
- Special Funds for Scientific and Technological Achievement Transformation Project in Jiangsu Province [BA2013058]
- Nanjing Normal University Research Foundation for Talented Scholars [2013119XGQ0061, 2014119XGQ0080]
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.
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