期刊论文

Zhang, Yudong

英文

Multimedia Tools and Applications

1380-7501

2016

75

23

15601-15617

10.1007/s11042-015-2649-7

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).

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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 ...