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Automated classification of brain images using wavelet-energy and biogeography-based optimization

ESI高被引SCI-EEI
WOS被引频次:65
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成果类型:
期刊论文
作者:
Yang, Gelan;Zhang, Yudong;Yang, Jiquan;Ji, Genlin;Dong, Zhengchao;Wang, Shuihua;Feng, Chunmei;Wang, Qiong
通讯作者:
Zhang, YD
作者机构:
[Zhang, Yudong; Wang, Shuihua; Ji, Genlin] Nanjing Normal Univ, Sch Comp Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China.
[Zhang, Yudong; Feng, Chunmei; Wang, Qiong; Yang, Jiquan] Jiangsu Key Lab 3D Printing Equipment & Mfg, Nanjing 210042, Jiangsu, Peoples R China.
[Dong, Zhengchao] Columbia Univ, Translat Imaging Div, New York, NY 10032 USA.
[Dong, Zhengchao] Columbia Univ, MRI Unit, New York, NY 10032 USA.
[Dong, Zhengchao] New York State Psychiat Inst & Hosp, New York, NY 10032 USA.
通讯机构:
[Zhang, Yudong] Nanjing Normal Univ, Sch Comp Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China.
[Zhang, Yudong] Jiangsu Key Lab 3D Printing Equipment & Mfg, Nanjing 210042, Jiangsu, Peoples R China.
语种:
英文
关键词:
Automated classification - Biogeography-based optimizations - Biogeographybased optimizations (BBO) - Brain images - Cross validation - Feature descriptors - Wavelet energy
期刊:
Multimedia Tools and Applications
ISSN:
1380-7501
年:
2016
卷:
75
期:
23
页码:
15601-15617
文献类别:
WOS:Article;EI:Journal article (JA)
所属学科:
ESI学科类别:计算机科学;WOS学科类别:Computer Science, Information Systems;Computer Science, Software Engineering;Computer Science, Theory & Methods;Engineering, Electrical & Electronic
入藏号:
WOS:000388121700030;EI:20151800812924
基金类别:
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]; Science Research Foundation of Hunan Provincial Education Department [12B023]
机构署名:
本校为第一机构
院系归属:
信息与电子工程学院
摘要:
It is very important to early detect abnormal brains, in order to save social and hospital resources. The wavelet-energy was a successful feature descriptor that achieved excellent performances in various applications;hence, we proposed a novel wavelet-energy based approach for automated classification of MR brain images as normal or abnormal. SVM was used as the classifier, and biogeography-based optimization (BBO) was introduced to optimize the weights of the SVM. The results based on a 5 ×5-fold cross validation showed the performance of the proposed BBO-KSVM was superior to BP-NN, KSVM, and PSO-KSVM in terms of sensitivity and accuracy. The study offered a new means to detect abnormal brains with excellent performance. ©2015, Springer Science+Business Media New York.
参考文献:
Bafroui HH, Ohadi A (2014) Application of wavelet energy and Shannon entropy for feature extraction in gearbox fault detection under varying speed conditions. Neurocomputing 133:437–445
Cai ML et al (2014) Motion recognition for 3D human motion capture data using support vector machines with rejection determination. Multimedia Tools Appl 70(2):1333–1362
Chaplot S, Patnaik LM, Jagannathan NR (2006) Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. Biomed Signal Process Control 1(1):86–92
Choudhary R et al (2009) Identification of wheat classes using wavelet features from near infrared hyperspectral images of bulk samples. Biosyst Eng 102(2):115–127
Christy AA, Raj P (2014) Adaptive biogeography based predator–prey optimization technique for optimal power flow. Int J Electr Power Energy Syst 62:344–352

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