Automated Classification of Brain MR Images Using Wavelet-Energy and Support Vector Machines
Authors
Guangshuai Zhang, Qiong Wang, Chunmei Feng, Elizabeth Lee, Genlin Ji, Shuihua Wang, Yudong Zhang, Jie Yan
Corresponding Author
Guangshuai Zhang
Available Online April 2015.
- DOI
- 10.2991/meic-15.2015.155How to use a DOI?
- Keywords
- MR images; MRI; classification
- Abstract
Abstract-It is of great importance to early detect abnormal brains, in order to save social resources. However, potential of wavelet decomposition is not fully explored and widely used. The wavelet-energy was a successful feature descriptor that achieved excellent performance in various applications; hence, we propose a wavelet-energy based new approach for automated classification of MR human brain images. The approach consisted of a three-stage system, including wavelet decomposition, energy extraction, and support vector machines. The results of proposed approach showed its performance was comparable with state-of-the-art algorithms. In addition, it provided a new means to detect features indicative of abnormal brains.
- Copyright
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Guangshuai Zhang AU - Qiong Wang AU - Chunmei Feng AU - Elizabeth Lee AU - Genlin Ji AU - Shuihua Wang AU - Yudong Zhang AU - Jie Yan PY - 2015/04 DA - 2015/04 TI - Automated Classification of Brain MR Images Using Wavelet-Energy and Support Vector Machines BT - Proceedings of the 2015 International Conference on Mechatronics, Electronic, Industrial and Control Engineering PB - Atlantis Press SP - 683 EP - 686 SN - 2352-5401 UR - https://doi.org/10.2991/meic-15.2015.155 DO - 10.2991/meic-15.2015.155 ID - Zhang2015/04 ER -