Breast Lesion Segmentation Method Using Ultrasound Images

  • Agata WijataEmail author
  • Bartłomiej Pyciński
  • Marta Galińska
  • Dominik Spinczyk
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 925)


Breast cancer is one of the leading causes of death among women. A non-invasive ultrasound examination enables the location and type of lesion. The radiologist, based on the 2D ultrasound image, estimates the volume of the lesion and performs a core needle biopsy procedure. The lesion segmentation may support the diagnostic and therapeutic process. The purpose of this study is to develop a method of breast tumor segmentation using two-dimensional ultrasound images. The lesion mask is based on the fusion of several methods. The proposed method employs active contour models, gradient vector flow, region growing, thresholding, image gradient, watershed transform and morphological operations. The effectiveness of the method was checked with Dice, Jaccard and specificity coefficients. Median values were 0.915, 0.862 and 0.996, respectively.


Breast ultrasound Breast lesion Ultrasound image segmentation Image processing 



This research was partially supported by the Polish National Centre for Research and Development (NCBR), grant no. STRATEGMED2 /267398/3/NCBR/2015.

The authors would also like to thank Andre Woloshuk for his English language corrections.


  1. 1.
    Ferlay, J., Hery, C., Autier, P., Sankaranarayanan, R.: Global burden of breast cancer. In: Breast Cancer Epidemiology, pp. 1–19 (2010)Google Scholar
  2. 2.
    Pflanzer, R., Hofmann, M., Shelke, A., Habib, A., Derwich, W., Schmitz-Rixen, T., Bernd, A., Kaufmann, R., Bereiter-Hahn, J.: Advanced 3D-sonographic imaging as a precise technique to evaluate tumor volume. Transl. Oncol. 7(6), 681–686 (2014)CrossRefGoogle Scholar
  3. 3.
    Czajkowska, J., Pyciński, B., Juszczyk, J., Pietka, E.: Biopsy needle tracking technique in US images. Comput. Med. Imaging Graph. 65, 93–101 (2018)CrossRefGoogle Scholar
  4. 4.
    Lee, L.K., Liew, S.C.: Breast ultrasound automated ROI segmentation with region growing. In: 2015 4th International Conference on Software Engineering and Computer Systems (ICSECS), pp. 177–182 (2015)Google Scholar
  5. 5.
    Galinska, M., Ogieglo, W., Wijata, A., Juszczyk, J., Czajkowska, J.: Breast cancer segmentation method in ultrasound images. In: Gzik, M., Tkacz, E., Paszenda, Z., Piętka, E. (eds.) Innovations in Biomedical Engineering, IBE 2017, Advances in Intelligent Systems and Computing, vol. 623, pp. 23–31 (2017)Google Scholar
  6. 6.
    Wieclawek, W., Rudzki, M., Wijata, A., Galinska, M.: Preliminary development of an automatic breast tumour segmentation algorithm from ultrasound volumetric images. In: Proceedings 6th International Conference Information Technology in Biomedicine, pp. 77–88 (2018)Google Scholar
  7. 7.
    Kollorz, E., Angelopoulou, E., Beck, M., Schmidt, D., Kuwert, T.: Using power watersheds to segment benign thyroid nodules in ultrasound image data. In: Bildverarbeitung für die Medizin, pp. 124–128 (2011)CrossRefGoogle Scholar
  8. 8.
    Badura, P.: Virtual bacterium colony in 3D image segmentation. Comput. Med. Imaging Graph. 65, 152–166 (2018)CrossRefGoogle Scholar
  9. 9.
    Gu, P., Lee, W.M., Roubidoux, M.A., Yuan, J., Wang, X., Carson, P.L.: Automated 3D ultrasound image segmentation to aid breast cancer image interpretation. Ultrasonics 65, 51–58 (2016)CrossRefGoogle Scholar
  10. 10.
    Chang, R.F., Wu, W.J., Moon, W.K., Chen, D.R.: Automatic ultrasound segmentation and morphology based diagnosis of solid breast tumors. Breast Cancer Res. Treat. 89(2), 179–185 (2005)CrossRefGoogle Scholar
  11. 11.
    Minavathi, Murali, S., Dinesh, M.S.: Classification of mass in breast ultrasound images using image processing techniques. Int. J. Comput. Appl. 42(10), 29–36 (2012)Google Scholar
  12. 12.
    Huang, Q., Yang, F., Liu, L., Li, X.: Automatic segmentation of breast lesions for interaction in ultrasonic computer-aided diagnosis. Inf. Sci. 314, 293–310 (2015)CrossRefGoogle Scholar
  13. 13.
    Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: Computer Vision and Pattern Recognition, pp. 60–65 (2005)Google Scholar
  14. 14.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: active contour models. Int. J. Comput. Vis. 1(4), 321–333 (1988)CrossRefGoogle Scholar
  15. 15.
    Xu, C., Prince, J.: Gradient vector flow: a new external force for snakes. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 66–71 (1997)Google Scholar
  16. 16.
    Baraiya, N., Modi, H.: Comparative study of different methods for brain tumor extraction from MRI images using image processing. Indian J. Sci. Technol. 9(4), 1–5 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Agata Wijata
    • 1
    Email author
  • Bartłomiej Pyciński
    • 1
  • Marta Galińska
    • 1
  • Dominik Spinczyk
    • 1
  1. 1.Faculty of Biomedical EngineeringSilesian University of TechnologyZabrzePoland

Personalised recommendations