Abstract
We propose an automated segmentation method for estimating the fibroglandular (i.e., dense) tissue in breast MRI. The first step of our method is to segment the breast as an organ from other imaged parts through an integrated edge extraction and voting algorithm. Then, we apply the nonparametric non-uniform intensity normalization (N3) algorithm to the segmented breast to correct bias field which is common in breast MRI. After that, fuzzy C-means clustering is performed to categorize the breast tissue into two clusters, i.e., fibroglandular tissue and fat. The automated segmentation results are compared to manual segmentations, verified by an experienced breast imaging radiologist, to assess the accuracy of the algorithm, where the Dice’s Similarity Coefficient (DSC) shows a 0.73 agreement in our experiments. The benefit of the bias correction step is also shown through the comparison with the results obtained by excluding the bias correction step.
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
Lin, M., Chan, S., Chen, J.H., Chang, D., Nie, K., Chen, S.T., Lin, C.J., Shih, T.C., Nalcioglu, O., Su, M.Y.: A new bias field correction method combining N3 and FCM for improved segmentation of breast density on MRI. Medical Physics 38(1) (2011)
Nie, K., Chen, J.H., Chan, S., Chau, M.K., Yu, H.J., Bahri, S., Tseng, T., Nalcioglu, O., Su, M.Y.: Development of a quantitative method for analysis of breast density based on three-dimensional breast MRI. Med. Phys. 35, 5253–5262 (2008)
Thompson, D.J., Leach, M.O., Kwan-Lim, G., Gayther, S.A., Ramus, S.J., Warsi, I., Lennard, F., Khazen, M., Bryant, E., Reed, S., Boggis, C.R., Evans, D.G., Eeles, R.A., Easton, D.F., Warren, R.M.: Assessing the usefulness of a novel MRI-based breast density estimation algorithm in a cohort of women at high genetic risk of breast cancer: the UK MARIBS study. Breast Cancer Res. 11(6) (2009)
Klifa, C., Carballido-Gamio, J., Wilmes, L., Laprie, A., Shepherd, J., Gibbs, J., Fan, B., Noworolski, S., Hylton, N.: Magnetic resonance imaging for secondary assessment of breast density in a high-risk cohort. Magn. Reson. Imag. 28(1), 8–15 (2010)
Wei, J., Chan, H.P., Helvie, M.A., Roubidoux, M.A., Sahiner, B., Hadjiiski, L.M., Zhou, C., Paquerault, S., Chenevert, T., Goodsitt, M.M.: Correlation between mammographic density and volumetric fibroglandular tissue estimated on breast MR images. Med. Phys. 31(4), 933–942 (2004)
Kontos, D., Xing, Y., Bakic, P.R., Conant, E.F., Maidment, A.D.A.: A comparative study of volumetric breast density estimation in digital mammography and magnetic resonance imaging: results from a high-risk population. In: Conf. Proc. Med. Imag.: Comp.-Aid. Diag. SPIE (2010)
Boyd, N.F., Dite, G.S., Stone, J., Gunasekara, A., English, D.R., McCredie, M.R., Glies, G.G., Tritchler, D., Chiarelli, A., Yaffe, M.J., Hopper, J.L.: Heritability of mammographic density, a risk factor for breast cancer. N. Engl. J. Med. 347, 886–894 (2002)
Yaffe, M.J., Boyd, N.F., Byng, J.W., Jong, R.A., Fishell, E., Lockwood, G.A., Little, L.E., Tritchler, D.K.: Breast cancer risk and measured mammographic density. Eur. J. Cancer Prev. 7, S47–S55 (1998)
Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE TMI 17, 87–97 (1998)
Li, C., Xu, C., Anderson, A.W., Gore, J.C.: MRI Tissue Classification and Bias Field Estimation Based on Coherent Local Intensity Clustering: A Unified Energy Minimization Framework. In: Prince, J.L., Pham, D.L., Myers, K.J. (eds.) IPMI 2009. LNCS, vol. 5636, pp. 288–299. Springer, Heidelberg (2009)
Wu, S.D., Weinstein, S.P., Conant, E.F., Localio, A.R., Schnall, M.D., Kontos, D.: Fully automated chest wall line segmentation in breast MRI by using context information. In: SPIE Medical Imaging: Computer-Aided Diagnosis, San Diego, CA (February 2012)
Weinstein, S., Rosen, M.: Breast MR imaging: current indications and advanced imaging techniques. Radiol. Clin. N. Am. 48(5), 1013–1042 (2010)
Weinstein, S.P., Localio, A.R., Conant, E.F., Rosen, M., Thomas, K.M., Schnall, M.D.: Multimodality screening of high-risk women: a prospective cohort study. J. Clin. Onco. 27(36), 6124–6128 (2009)
Gubern-Mérida, A., Kallenberg, M., Martí, R., Karssemeijer, N.: Multi-class Probabilistic Atlas-Based Segmentation Method in Breast MRI. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds.) IbPRIA 2011. LNCS, vol. 6669, pp. 660–667. Springer, Heidelberg (2011)
Molleran, V., Mahoney, M.C.: The BI-RADS breast magnetic resonance imaging lexicon. Magn. Reson. Imag. Clin. N. Am. 18(2), 171–185 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, S., Weinstein, S., Keller, B.M., Conant, E.F., Kontos, D. (2012). Fully-Automated Fibroglandular Tissue Segmentation in Breast MRI. In: Maidment, A.D.A., Bakic, P.R., Gavenonis, S. (eds) Breast Imaging. IWDM 2012. Lecture Notes in Computer Science, vol 7361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31271-7_32
Download citation
DOI: https://doi.org/10.1007/978-3-642-31271-7_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31270-0
Online ISBN: 978-3-642-31271-7
eBook Packages: Computer ScienceComputer Science (R0)