Advertisement

Novel Approach to Segment the Pectoral Muscle in the Mammograms

  • Vaishali Shinde
  • B. Thirumala Rao
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 768)

Abstract

The X-ray technique is widely used to detect the breast cancer. The X-ray image contains the breast part along with the pectoral muscles. The pectoral muscles are similar to breast tissue in terms of texture and appearance but it is not a part of breast tissue. Hence pectoral muscles removal is an essential task for breast tumor detection. In the first phase of the proposed approach, the three existing pectoral muscles segmentation methods, region growing, thresholding, and k-mean clustering has been implemented. In a later phase, machine learning-based approach to segment out the pectoral muscle has been implemented. The proposed system provides the promising results on the MIAS database.

Keywords

K-means clustering Machine learning Pectoral muscle removal Region growing Thresholding 

References

  1. 1.
  2. 2.
    Sreedevi, S., Sherly, Elizabeth: A novel approach for removal of pectoral muscles in digital mammogram. Int. Conf. Inf. Commun. Technol. (ICICT), Procedia Comput. Sci. 46, 1724–1731 (2015)Google Scholar
  3. 3.
    Sucklin, J.: The mammographic image analysis society digital mammogram database exerpta medica. Int. Congr. Ser. 1069, 375–378 (1994)Google Scholar
  4. 4.
    Lakshmanan, R., Thomas, S.T.P.V., Jacob, S.M., Pratab, T.: Pectoral muscle boundary detection-A preprocessing method for early breast cancer detection. In: World automation congress (WAC), Waikoloa, HI, pp. 258–263 (2014)Google Scholar
  5. 5.
    Vikhe, P.S., Thool, V.R.: Intensity-based automatic boundary identification of pectoral muscle in mammograms. In: The 7th international conference on communication, computing and virtualization, pp. 262–269 (2016)Google Scholar
  6. 6.
    Shrivastava, A., Chaudhary, A., Kulshreshtha, D., Prakash Singh, V., Srivastava, R.: Automated digital mammogram segmentation using dispersed region growing and sliding window algorithm. In: 2nd international conference on image, vision and computing (ICIVC), Chengdu, pp. 366–370 (2017)Google Scholar
  7. 7.
    Alam, N., Islam, M.J.: Pectoral muscle elimination on mammogram using K-means clustering approach. Int. J. Comput. Vision Sig. Process. 4(1), 11–21 (2014)Google Scholar
  8. 8.
    Brzakovic, D., Luo, X., Brzakovic, P.: An approach to automated detection of tumors in mammograms. IEEE Trans. Med. Imag. 9(3), 233–241 (1990)CrossRefGoogle Scholar
  9. 9.
    Kamdi, S., Krishna, R.K.: Image segmentation and region growing algorithm. Int. J. Comput. Technol. Electron. Eng. (IJCTEE) 2(1), 103–107 (2012)Google Scholar
  10. 10.
  11. 11.
    Albregtsen, F.: Statistical texture measures computed from gray-level co-occurrence matrices. In: Image Processing Laboratory Department of Informatics University of Oslo November 5, (2008)Google Scholar
  12. 12.
    Vapnik, V.: The nature of statistical learning theory. Springer, N.Y. (1995). ISBN 0-387-94559-8CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.KL UniversityVijayawadaIndia

Personalised recommendations