title: A new method of detecting micro-calcification clusters in mammograms using contourlet transform and non-linking simplified PCNN.
abstract: OBJECTIVE
  Mammography analysis is an effective technology for early detection of breast cancer. Micro-calcification clusters (MCs) are a vital indicator of breast cancer, so detection of MCs plays an important role in computer aided detection (CAD) system, this paper proposes a new hybrid method to improve MCs detection rate in mammograms.
  
  
  METHODS
  The proposed method comprises three main steps: firstly, remove label and pectoral muscle adopting the largest connected region marking and region growing method, and enhance MCs using the combination of double top-hat transform and grayscale-adjustment function; secondly, remove noise and other interference information, and retain the significant information by modifying the contourlet coefficients using nonlinear function; thirdly, we use the non-linking simplified pulse-coupled neural network to detect MCs.
  
  
  RESULTS
  In our work, we choose 118 mammograms including 38 mammograms with micro-calcification clusters and 80 mammograms without micro-calcification to demonstrate our algorithm separately from two open and common database including the MIAS and JSMIT; and we achieve the higher specificity of 94.7%, sensitivity of 96.3%, AUC of 97.0%, accuracy of 95.8%, MCC of 90.4%, MCC-PS of 61.3% and CEI of 53.5%, these promising results clearly demonstrate that the proposed approach outperforms the current state-of-the-art algorithms. In addition, this method is verified on the 20 mammograms from the People's Hospital of Gansu Province, the detection results reveal that our method can accurately detect the calcifications in clinical application.
  
  
  CONCLUSIONS
  This proposed method is simple and fast, furthermore it can achieve high detection rate, it could be considered used in CAD systems to assist the physicians for breast cancer diagnosis in the future.
authors.affiliation: School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China. Electronic address: guoyn10@lzu.edu.cn.
authors.forename: Ya'nan
authors.initials: Y
authors.lastname: Guo
authors.affiliation: School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
authors.forename: Min
authors.initials: M
authors.lastname: Dong
authors.affiliation: School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Yang
authors.affiliation: School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
authors.forename: Xiaoli
authors.initials: X
authors.lastname: Gao
authors.affiliation: School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
authors.forename: Keju
authors.initials: K
authors.lastname: Wang
authors.affiliation: School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
authors.forename: Chongfan
authors.initials: C
authors.lastname: Luo
authors.affiliation: School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
authors.forename: Yide
authors.initials: Y
authors.lastname: Ma
authors.affiliation: School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China.
authors.forename: Jiuwen
authors.initials: J
authors.lastname: Zhang
mesh_terms: D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D002114:Calcinosis / Q000175:diagnosis*; D005260:Female; D006801:Humans; D008327:Mammography; D012680:Sensitivity and Specificity
keywords: Contourlet transform; Mammography; Micro-calcification clusters (MCs) detection; Simplified pulse-coupled neural network (SPCNN)