Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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)
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)
Adams, R., Bischof, L.: Seeded Region Growing. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 641–647 (1994)
Hojjatoleslami, S.A., Kittler, J.: Region growing: a new approach. IEEE Transactions on Image Processing 7, 1079–1084 (1998)
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)
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)
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)
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)
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)
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)
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)
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)
Kupinski, M.A., Giger, M.L.: Automated Seeded Lesion Segmentation on Digital Mammograms. IEEE Transactions on Medical Imaging 17, 510–517 (1998)
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)
Cao, Y., Hao, X., Xia, S.: An improved region-growing algorithm for mammographic mass segmentation. In: Proceedings of SPIE 2009, vol. 7497 (2009), doi:10.1117/12.833044
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Melouah, A. (2013). A Novel Region Growing Segmentation Algorithm for Mass Extraction in Mammograms. In: Amine, A., Otmane, A., Bellatreche, L. (eds) Modeling Approaches and Algorithms for Advanced Computer Applications. Studies in Computational Intelligence, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-319-00560-7_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-00560-7_14
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-00559-1
Online ISBN: 978-3-319-00560-7
eBook Packages: EngineeringEngineering (R0)