Multimedia Tools and Applications

, Volume 77, Issue 3, pp 3813–3832 | Cite as

Wavelet energy entropy and linear regression classifier for detecting abnormal breasts

  • Yi Chen
  • Yin Zhang
  • Hui-Min Lu
  • Xian-Qing Chen
  • Jian-Wu Li
  • Shui-Hua WangEmail author


Breast abnormalities are the early symptoms of breast cancers. They may also bring in psychoemotional stresses to women. In this study, we developed a new automatic program based on wavelet energy entropy (WEE) and linear regression classifier (LRC): First, we segment region of interest from mammogram images. Second, we calculate WEE from the segmented images. Third, LRC was used as the classifier. We named our method as “WEE + LRC”. The experiment used 10-fold stratified cross validation that was repeated 10 times. The statistical results showed the classification result was the best when the decomposition level was 4, with a sensitivity of 92.00 ± 3.20%, a specificity of 91.70 ± 3.27%, and an accuracy of 91.85 ± 2.21%. The proposed method was superior to other five state-of-the-art methods. In all, our method is effective in detecting abnormal breasts.


Breast abnormality Digital mammography Wavelet energy entropy Linear regression classifier Least-squares estimation 



This paper was supported by NSFC (61602250, 61503188, 61562041, 61271374), Natural Science Foundation of Jiangsu Province (BK20150983, BK20150982), Program of Natural Science Research of Jiangsu Higher Education Institutions (14KJB520021), Open Research Fund of Hunan Provincial Key Laboratory of Network Investigational Technology (2016WLZC013), Open Fund of Fujian Provincial Key Laboratory of Data Intensive Computing (BD201607), Jiangsu Key Laboratory of Image and Video Understanding for Social Safety, Nanjing University of Science and Technology (30916014107).


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Yi Chen
    • 1
    • 2
    • 3
  • Yin Zhang
    • 4
  • Hui-Min Lu
    • 5
  • Xian-Qing Chen
    • 6
    • 7
  • Jian-Wu Li
    • 8
  • Shui-Hua Wang
    • 1
    • 9
    Email author
  1. 1.School of Computer Science and TechnologyNanjing Normal UniversityNanjingChina
  2. 2.Hunan Provincial Key Laboratory of Network Investigational TechnologyHunan Policy AcademyChangshaChina
  3. 3.Key Laboratory of Image and Video Understanding for Social SafetyNanjing University of Science and TechnologyNanjingChina
  4. 4.School of Information and Safety EngineeringZhongnan University of Economics and LawWuhanChina
  5. 5.Department of Mechanical and Control EngineeringKyushu Institute of TechnologyFukuoka PrefectureJapan
  6. 6.Department of electrical engineering, College of engineeringZhejiang Normal UniversityJinhuaChina
  7. 7.Department of Electrical EngineeringColumbia UniversityNew YorkUSA
  8. 8.School of Computer Science and Technology, Beijing Institute of TechnologyBeijingChina
  9. 9.Department of Electrical EngineeringThe City College of New York, CUNYNew YorkUSA

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