A Novel Region Growing Segmentation Algorithm for Mass Extraction in Mammograms

  • Ahlem Melouah
Part of the Studies in Computational Intelligence book series (SCI, volume 488)


This article presents an automatic mass extraction approach by application of a novel region growing algorithm. The region-growing process is guided by regional features analysis consequently; the result will be a robust algorithm able of respecting various image characteristics. The evaluation of the proposed approach was carried out on all MiniMIAS database mammograms containing circumscribed lesions. All masses from various characters of background tissues are well detected.


region growing algorithm features mass detection mammogram 


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  1. 1.
    Department of Health and Human Services, Centers for disease control and prevention: The National Breast and Cervical Cancer Early Detection Program. At-A-Glance. U.S. Resource ID: 4776 (1998) Google Scholar
  2. 2.
    Mencattini, A., Rabottino, G., Salmeri, M., Lojacono, R., Colini, E.: Breast mass segmentation in mammographic images by an effective region growing algorithm. In: Blanc-Talon, J., Bourennane, S., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2008. LNCS, vol. 5259, pp. 948–957. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Adams, R., Bischof, L.: Seeded Region Growing. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 641–647 (1994)CrossRefGoogle Scholar
  4. 4.
    Hojjatoleslami, S.A., Kittler, J.: Region growing: a new approach. IEEE Transactions on Image Processing 7, 1079–1084 (1998)CrossRefGoogle Scholar
  5. 5.
    Pohlman, S., Powell, K.A., Obuchowski, N.A., Chilcote, W.A., Grundfest-Broniatowski, S.: Quantitative Classification of Breast Tumors in Digitized Mammograms. Medical Physics 23, 1337–1346 (1996)CrossRefGoogle Scholar
  6. 6.
    Sahiner, B., Chan, H.P., Wei, D., Petrick, N., Helvie, M.A., Adler, D.D., Goodsit, M.M.: Image Feature Selection by a Genetic Algorithm: Application to Classification of Mass and Normal Breast Tissue. Medical Physics 23, 1671–1684 (1996)CrossRefGoogle Scholar
  7. 7.
    Petrick, N., Chan, H.P., Sahiner, B., Helvie, M.A.: Combined adaptive enhancement and region-growing segmentation of breast masses on digitized mammograms. Medical Physics 26, 1642–1654 (1999)CrossRefGoogle Scholar
  8. 8.
    Pohle, R., Toennies, K.D.: Segmentation of medical images using adaptive region growing. In: Proceedings of SPIE Medical Imaging, vol. 4322, pp. 1337–1346 (2001)Google Scholar
  9. 9.
    Shan, J., Cheng, H.D., Wang, Y.: A novel automatic seed point selection algorithm for breast ultrasound images. In: Proceedings of International Conference on Pattern Recognition, Tampa, Finland, pp. 1–4 (2008)Google Scholar
  10. 10.
    Hejazi, M.R., Ho, Y.-S.: Automated Detection of Tumors in Mammograms Using Two Segments for Classification. PCM. In: Proceedings of the 6th Pacific-Rim Conference on Advances in Multimedia Information Processing, vol. 1, pp. 910–921 (2005)Google Scholar
  11. 11.
    Senthilkumar, B., Umamaheswari, G., Karthik, J.: A novel region growing segmentation algorithm for the detection of breast cancer. crossref, pp. 1–4 (December 28, 2010) Google Scholar
  12. 12.
    Cao, Y., Hao, X., Zhu, X., Xia, S.: An adaptive region growing algorithm for breast masses in mammograms. Front. Electr. Electron. Eng. 5, 128–136 (2010)CrossRefGoogle Scholar
  13. 13.
    Kupinski, M.A., Giger, M.L.: Automated Seeded Lesion Segmentation on Digital Mammograms. IEEE Transactions on Medical Imaging 17, 510–517 (1998)CrossRefGoogle Scholar
  14. 14.
    Kinnard, L., Lo, S.-C.B., Wang, P., Freedman, M.T., Chouikha, M.A.: Maximum-Likelihood Automated Approach to Breast Mass Segmentation. In: Proceedings of the IEEE Symposium on Biomedical Imaging, pp. 241–244 (July 2002)Google Scholar
  15. 15.
    Cao, Y., Hao, X., Xia, S.: An improved region-growing algorithm for mammographic mass segmentation. In: Proceedings of SPIE 2009, vol. 7497 (2009), doi:10.1117/12.833044Google Scholar
  16. 16.
    Rabottino, G., Mencattini, A., Salmeri, M., Caselli, F., Lojacono, R.: Performance Evaluation of a Region Growing Procedure for Mammographic Breast Lesion Identification. Computer Standard and Interfaces Elsevier 33, 128–135 (2011)CrossRefGoogle Scholar
  17. 17.
    Yang, S.-C., Wang, C.-M., Chung, Y.-N., Hsu, G.-C., Lee, S.-K., Chung, P.-C., Chang, C.-I.: A computer-aided system for mass detection and classification in digitized mammograms. Biomedical Engineering-Applications Basis & Communication 17, 215–228 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Badji-Mokhtar Annaba UniversityAnnabaAlgeria

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