Multimedia Tools and Applications

, Volume 75, Issue 23, pp 15601–15617 | Cite as

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

  • Gelan Yang
  • Yudong ZhangEmail author
  • Jiquan Yang
  • Genlin Ji
  • Zhengchao Dong
  • Shuihua Wang
  • Chunmei Feng
  • Qiong Wang


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.


Classification Pattern recognition Support vector machine Magnetic resonance imaging Biogeography-based optimization 



This paper was supported by 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), and Science Research Foundation of Hunan Provincial Education Department (12B023).

Conflict of interest

We have no conflicts of interest to disclose with regard to the subject matter of this paper.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Gelan Yang
    • 1
  • Yudong Zhang
    • 2
    • 3
    Email author
  • Jiquan Yang
    • 3
  • Genlin Ji
    • 2
  • Zhengchao Dong
    • 4
  • Shuihua Wang
    • 2
  • Chunmei Feng
    • 3
  • Qiong Wang
    • 3
  1. 1.School of Information Science and EngineeringHunan City UniversityYiyangChina
  2. 2.School of Computer Science and TechnologyNanjing Normal UniversityNanjingChina
  3. 3.Jiangsu Key Laboratory of 3D Printing Equipment and ManufacturingNanjingChina
  4. 4.Translational Imaging Division & MRI UnitColumbia University and New York State Psychiatric InstituteNew YorkUSA

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