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

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Abstract

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.

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Acknowledgements

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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Author notes

  1. Shenghua Gu, Yi Chen and Fangqing Sheng have contributed equally to this work.

Authors and Affiliations

  1. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Shenghua Gu

  2. School of Computer Science and Technology, Nanjing Normal University, Nanjing, 210023, China

    Yi Chen

  3. Jiangsu Laboratory of Lake Environment Remote Sensing Technologies, Huaiyin Institute of Technology, Huaiyin, 223001, China

    Yi Chen

  4. Faculty of Hospitality and Tourism Management, Macau University of Science and Technology, Macau, China

    Fangqing Sheng

  5. School of Humanity and Art, Jiangsu Maritime Institute, Nanjing, 211100, China

    Fangqing Sheng

  6. School of Information Engineering, Nanjing Audit University, Nanjing, 211815, China

    Tianming Zhan

  7. School of Math and Statistics, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Yunjie Chen

Authors
  1. Shenghua Gu
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  2. Yi Chen
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  3. Fangqing Sheng
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  4. Tianming Zhan
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Correspondence to Tianming Zhan.

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Gu, S., Chen, Y., Sheng, F. et al. A novel method for breast mass segmentation: from superpixel to subpixel segmentation. Machine Vision and Applications 30, 1111–1122 (2019). https://doi.org/10.1007/s00138-019-01020-0

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  • DOI: https://doi.org/10.1007/s00138-019-01020-0

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