A novel method for breast mass segmentation: from superpixel to subpixel segmentation

  • Shenghua Gu
  • Yi Chen
  • Fangqing Sheng
  • Tianming ZhanEmail author
  • Yunjie Chen
Special Issue Paper
Part of the following topical collections:
  1. Special Issue on Deep Learning Methods for Biomedical Information Analysis


In this paper, an effective method is proposed for breast mass segmentation using a superpixel generation and curve evolution method. The simple linear iterative clustering method and density-based spatial clustering of applications with noise method are applied to generate superpixels in mammograms at first. Thereafter, a region of interesting (ROI) that contains the breast mass is built on the superpixel generation results. Finally, the image patch and the position of the manual labeled seed are used to build the prior knowledge for the level set method driven by the local Gaussian distribution fitting energy and evolve the curve to capture the edge of breast mass in ROI. Experimental results on mammogram data set demonstrate that the proposed method shows superior performance in contrast to some well-known methods in breast mass segmentation.


Breast mass segmentation Superpixel Level set method Local Gaussian distribution fitting 



This work was financially supported by the Natural Science Foundation of High Education Institutions of Jiangsu Province, China (Nos. 18KJB50030, 18KJB520042 and 17KJB520033), the National Natural Science Foundation of China under grants (Nos. 61502206, 61772277, and 61672291), the Nature Science Foundation of Jiangsu Province under grants (Nos. BK20150523 and BK20171494). The authors would like to thank the anonymous reviewers for their helpful comments and suggestions.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Shenghua Gu
    • 1
  • Yi Chen
    • 2
    • 3
  • Fangqing Sheng
    • 4
    • 5
  • Tianming Zhan
    • 6
    Email author
  • Yunjie Chen
    • 7
  1. 1.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina
  2. 2.School of Computer Science and TechnologyNanjing Normal UniversityNanjingChina
  3. 3.Jiangsu Laboratory of Lake Environment Remote Sensing TechnologiesHuaiyin Institute of TechnologyHuaiyinChina
  4. 4.Faculty of Hospitality and Tourism ManagementMacau University of Science and TechnologyMacauChina
  5. 5.School of Humanity and ArtJiangsu Maritime InstituteNanjingChina
  6. 6.School of Information EngineeringNanjing Audit UniversityNanjingChina
  7. 7.School of Math and StatisticsNanjing University of Information Science and TechnologyNanjingChina

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