Multimedia Tools and Applications

, Volume 77, Issue 18, pp 24333–24352 | Cite as

SCM-motivated enhanced CV model for mass segmentation from coarse-to-fine in digital mammography

  • Ya’nan Guo
  • Xiaoli Gao
  • Zhen Yang
  • Jing Lian
  • Shiqiang Du
  • Huaiqing Zhang
  • Yide MaEmail author


A novel approach for mass segmentation from coarse-to-fine in digital mammography, termed as SCM-motivated enhanced CV algorithm, is presented in this paper. As well known, it is difficult to robustly achieve mammogram mass segmentation due to low contrast between normal and lesion tissues, as well as high density tissue interference in mammograms. Therefore, Spiking Cortical Model with biology background is introduced to achieve mammary-specific and mass edge detection, and this mass candidate is regarded as the initial contour of improved CV model followed by, effectively overcoming the drawback that CV method is sensitive to the initial contour; especially, the enhanced CV model innovatively combines the techniques of physical imaging principle, and local region-scalable force, harvesting the coarse-to-fine mass boundary accurately. The proposed method is tested totally on 400 mammograms from two well-known digitized datasets (digital database for screening mammography and mammography image analysis society database), achieving the average detection rate of 93.25%. By comparing with the region-based model with bias field (Method 1) and typical CV model (Method 2), we can reach the conclusion that proposed method is outperform other methods, yielding the average sensitivity of 95.83%, specificity of 99.13%, dice similarity co-efficient of 92.21% and AUC of 98.02%. In addition, this method is verified on the mammograms from Gansu Provincial Cancer Hospital, the detection results reveal that our method can accurately detect the abnormal in clinical application.


Mammography Mass segmentation Spiking Cortical Model (SCM) Enhanced CV model Local Region-Scalable Force (LRSF) 



Authors would like to thank the retrieval of all the public database for the experiments of this paper. This study was funded by the National Natural Science Foundation of China (nos.61175012 and 61201421) and Natural Science Foundation of Gansu Province (nos. 145RJZA181 and 1208RJZA265).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Ya’nan Guo
    • 1
  • Xiaoli Gao
    • 1
  • Zhen Yang
    • 1
  • Jing Lian
    • 1
  • Shiqiang Du
    • 1
  • Huaiqing Zhang
    • 1
  • Yide Ma
    • 1
    Email author
  1. 1.School of Information Science and EngineeringLanzhou UniversityLanzhouChina

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