Journal
JOURNAL OF MEDICAL IMAGING AND HEALTH INFORMATICS
Volume 6, Issue 5, Pages 1218-1222Publisher
AMER SCIENTIFIC PUBLISHERS
DOI: 10.1166/jmihi.2016.1901
Keywords
Wavelet Entropy; RBFNN; Classification; Pattern Recognition
Funding
- Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), NSFC [51407095, 61503188]
- Natural Science Foundation of Jiangsu Province [BK20150983]
- Program of Natural Science Research of Jiangsu Higher Education Institutions [15KJB470010]
- Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing [BM2013006]
- Special Funds for Scientific and Technological Achievement Transformation Project in Jiangsu Province [BA2013058]
- Nanjing Normal University Research Foundation [2013119XGQ0061, 2014119XGQ0080]
- Open Fund of Guangxi Key Laboratory of Manufacturing System and Advanced Manufacturing Technology [15-140-30-008K]
- Open Project Program of the State Key Lab of CAD and CG, Zhejiang University [A1616]
- Fundamental Research Funds for Central Universities [LGYB201604]
Ask authors/readers for more resources
(Aim) It is beneficial to classify brain images as healthy or pathological automatically, since the information set of 3D brain is too large to interpret with manual methods. Among various 3D brain imaging techniques, magnetic resonance (MR) imaging is widely used in daily medical treatment, because it can help in the four ways of diagnosis, prognosis, pre-surgical, and postsurgical procedures. Although there are automatic detection methods, they suffer from low accuracy. (Method) Therefore, we proposed a novel approach, which employed 2D discrete wavelet transform (DWT), and calculated the entropy as features. Then, a radial basis function neural network (RBFNN) was trained to classify images as pathological or healthy. A 10 x 10-fold cross validation was conducted to prevent overfitting. (Result) The method achieved a sensitivity of 95.89%, a specificity of 92.78%, and an overall accuracy of 95.44% over 125 MR brain images. (Conclusion) The performance suggests the proposed classifier is robust and effective in comparison with recently state-of-the-art approaches.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available