Abstract
Breast MRI to X-ray mammography registration usingpatient-specific biomechanical models is one challenging task in medical imaging. To solve this problem, the accurate knowledge about internal and external factors of the breast, such as internal tissues distribution, is needed for modelling a suitable physical behavior. In this work, we compare four different tissue segmentation algorithms, two intensity-based segmentation algorithms (Fuzzy C-means and Gaussian mixture model) and two improvements that incorporate spatial information (Kernelized Fuzzy C-means and Markov Random Fields, respectively), and analyze their effect to the multi-modal registration. The overall framework consists on using a density estimation software (Volpara\(^{TM}\)) to extract the glandular tissue from full-field digital mammograms, meanwhile, a biomechanical model is used to mimic the mammographic acquisition from the MRI, computing the glandular tissue traversed by the X-ray beam. Results with 40 patients show a high agreement between the amount of glandular tissue computed for each method.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Baluwala, H., Sanghani, P., Malcom, D., Nielsen, P., Nash, M.: Comparison of fibroglandular tissue segmentation algorithms in breast MRI. In: Harz, M. et al. (ed.) Workshop MICCAI Breast Image Analysis, pp. 105–112 (2015)
Bezdek, J., Pal, M., Keller, J.: Fuzzy Models and Algorithms for Pattern Recognition and Image Processiong. Kluwer Academic Publishers, Dordrecht (1999)
Damases, C., Brennan, P., Mello-Thoms, C., McEntee, M.: Mammographic breast density assessment using automated volumetric software and breast imaging reporting and data system (BIRADS) categorization by expert radiologist. Acad. Radiol. 23(1), 70–77 (2015)
Dietzel, M., Hopp, T., Ruiter, N., Zoubi, R., Runnebaum, I.B., Kaiser, W.A., Baltzer, P.A.T.: Fusion of dynamic contrast-enhanced magnetic resonance mammography at 3.0T with X-ray mammograms: pilot study evaluation using dedicated semi-automatic registration software. Eur. J. Radiol. 79(2), 98–102 (2011)
Gubern-Mérida, A., Kallenberg, M., Platel, B., Mann, R., Martí, R., Karssemeijer, N.: Volumetric breast density estimation from full-field digital mammograms: a validation study. PLoS One 9(1), e85952 (2014)
Han, L., Hipwell, J., Tanner, C., Taylor, Z., Mertzanidou, T., Cardoso, J., Ourselin, S., Hawkes, D.: Development of patient-specific biomechanical models for predicting large breast deformation. Phys. Med. Biol. 57(2), 455–472 (2012)
Highnam, R., Brady, S.M., Yaffe, M.J., Karssemeijer, N., Harvey, J.: Robust breast composition measurement - Volpara\({}^{TM}\). In: Martí, J., Oliver, A., Freixenet, J., Martí, R. (eds.) IWDM 2010. LNCS, vol. 6136, pp. 342–349. Springer, Heidelberg (2010)
Hopp, T., Ruiter, N.V.: 2D/3D registration for localization of mammographically depicted lesions in breast MRI. In: Maidment, A.D.A., Bakic, P.R., Gavenonis, S. (eds.) IWDM 2012. LNCS, vol. 7361, pp. 627–634. Springer, Heidelberg (2012)
Hopp, T., Duric, N., Ruiter, N.: Image fusion of ultrasound computer tomography volumes with X-ray mammograms using a biomechanical model based 2D/3D registration. Comput. Med. Imaging Graph 40, 170–181 (2015)
Johnsen, S., Taylor, Z.A., Clarkson, M., Hipwell, J., Modat, M., Eiben, B., Han, L., Hu, Y., Mertzanidou, T., Hawkes, D.J., Ourselin, S.: NiftySim: a GPU-based nonlinear finite element package for simulation of soft tissue biomechanics. J. Comput. Assist. Radiol. Surg. 10(7), 1077–1095 (2014)
Malur, S., Wurdinger, S., Moritz, A., Michels, W., Schneider, A.: Comparison of written reports of mammography, sonography and magnetic resonance mammography for preoperative evaluation of breast lesions, with special emphasis on magnetic resonance mammography. Breast Cancer Res. 3(1), 55–60 (2001)
Roth, S.: Ray casting for modeling solids. Comput. Graph. Image Process. 18(2), 109–144 (1982)
Si, H.: Tetgen, a Delaunay-based quality tetrahedral mesh generator. ACM Trans. Math. Softw. 41(2), 1–36 (2015)
Tanner, C., Degenhard, A., Schnabel, J., Smith, A.C., Hayes, C., Sonoda, L., Lach, M., Hose, D., Hill, D., Hawkes, D.: A method for the comparison of biomechanical breast models. IEEE Workshop MMBIA 2001, 11–18 (2001)
Tustison, N., Avants, B., Cook, P., Zheng, Y., Egan, A., Yushkevich, P., Gee, J.: N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29(6), 1310–1320 (2010)
Wellman, P.: Tactile imaging. Ph.D. thesis, Cambridge, MA, Harvard University’s Division of Engineering and Applied Sciences (1999)
Wu, Z., Xies, W., Yu, J.: Fuzzy C-means clustering algorithm based on kernel method. In: Proceedings Fifth International Conference on Computational Intelligence and Multimedia Applications, ICCIMA 2003, pp. 49–54. IEEE (2003)
Acknowledgement
This research has been partially supported from the Ministry of Economy and Competitiveness of Spain, under project references TIN2012-37171-C02-01 and DPI2015-68442-R, and the FPI grant BES-2013-065314.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
García, E. et al. (2016). Comparison of Four Breast Tissue Segmentation Algorithms for Multi-modal MRI to X-ray Mammography Registration. In: Tingberg, A., Lång, K., Timberg, P. (eds) Breast Imaging. IWDM 2016. Lecture Notes in Computer Science(), vol 9699. Springer, Cham. https://doi.org/10.1007/978-3-319-41546-8_62
Download citation
DOI: https://doi.org/10.1007/978-3-319-41546-8_62
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-41545-1
Online ISBN: 978-3-319-41546-8
eBook Packages: Computer ScienceComputer Science (R0)