Multimedia Tools and Applications

, Volume 77, Issue 17, pp 22589–22604 | Cite as

Exploring a smart pathological brain detection method on pseudo Zernike moment

  • Yu-Dong Zhang
  • Yongyan Jiang
  • Weiguo Zhu
  • Siyuan LuEmail author
  • Guihu ZhaoEmail author


Pathological brain detection by computer vision is now attracting intense attentions from academic fields. Nevertheless, most of recent methods suffer from low-accuracy. This study combined two successful techniques: pseudo Zernike moment and kernel support vector machine. Three open datasets were downloaded and used. The 10 times of K-fold stratified cross validation showed our method using 19-order pseudo Zernike moments achieved perfect classification on the first dataset. It achieved a sensitivity of 99.93 ± 0.23%, a specificity of 98.50 ± 2.42%, and an accuracy of 99.75 ± 0.32% on the second dataset. It achieved a sensitivity of 99.64 ± 0.42%, a specificity of 98.29 ± 2.76%, and an accuracy of 99.45 ± 0.38% on the third dataset. This approach performs better than eleven state-of-the-art smart pathological brain detection methods.


Pathological brain detection Pseudo Zernike moment 



This work was supported by National Natural Science Foundation of China (61602250), Open fund for Jiangsu Key Laboratory of Advanced Manufacturing Technology (HGAMTL1601), Open fund of Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence (2016CSCI01), Leading Initiative for Excellent Young Researcher (LEADER) of Ministry of Education, Culture, Sports, Science and Technology-Japan (16809746), Natural Science Foundation of Jiangsu Province (BK20150983), Open fund of Key Laboratory of Guangxi High Schools Complex System and Computational Intelligence (2016CSCI01), Opening Project of State Key Laboratory of Digital Publishing Technology.


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Jiangsu Key Laboratory of Advanced Manufacturing TechnologyHuaiyinChina
  2. 2.Hunan Provincial Key Laboratory of Network Investigational TechnologyHunan Policy AcademyChangshaChina
  3. 3.School of Computer Science and TechnologyNanjing Normal UniversityNanjingChina
  4. 4.College of ScienceZhongyuan University of TechnologyZhengzhouChina
  5. 5.School of Information Science and EngineeringCentral South UniversityChangshaChina

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