Abstract
A fully automatic algorithm is developed for breast arterial calcification extraction in mammograms. This algorithm is implemented in two major steps: a random-walk based tracking step and a compiling and linking step. With given seeds from detected calcification points, the tracking algorithm traverses the vesselness map by exploring the uncertainties of three tracking factors, i.e., traversing direction, jumping distance, and vesselness value, to generate all possible sampling paths. The compiling and linking algorithm further organizes and groups all sampling paths into calcified vessel tracts. The experimental results show that the performance of the proposed automatic calcification extraction algorithm is statistically close to that obtained by manual delineations.
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.
Similar content being viewed by others
References
Kemmeren, J.M., et al.: Breast Arterial Calcifications: Associations with Diabetes Mellitus and Cardiovascular Mortality. Radiology 201, 75–78 (1996)
Reddy, J., Bilezikian, J.P., Smith, S.J., Mosca, L.: Reduced Bone Mineral Density Is Associated with Breast Arterial Calcification. J. Clin. Endocrinol. Metab. 93, 208–211 (2008)
Rotter, M.A., et al.: Breast Arterial Calcifications (BACs) Found on Screening Mammography and Their Association with Cardiovascular Disease. Menopause 15, 276–281 (2008)
Dale, P., Mascarhenas, C., Richards, M., Mackie, G.: Mammography as a Screening Tool for Coronary Artery Disease. Journal of Surgical Research 148, 1–6 (2008)
Ge, J., et al.: Automated Detection of Breast Vascular Calcification on Full-Field Digital Mammograms. In: Proc. SPIE Med. Img. 2008, 691517, pp. 1–7 (2008)
Ge, J., et al.: Computer Aided Detection of Clusters of Microcalcifications on Full Field Digital Mammograms. Medical Physics 33, 2975–2988 (2006)
Frangi, A., Niessen, W., Vincken, K., Viergever, M.: Multiscale Vessel Enhancement Filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)
Aylward, S.R., Bullitt, E.: Initialization, Noise, Singularities, and Scale in Height Ridge Traversal for Tubular Object Centerline Extraction. IEEE TMI 21, 61–75 (2002)
Wong, W.C.K., Chung, A.C.S.: Probabilistic Vessel Axis Tracing and Its Applications to Vessel Segmentation with Stream Surfaces and Minimum Cost Paths. MedIA 11, 567–587 (2007)
Schaap, M., et al.: Bayesian Tracking of Elongated Structures in 3D Images. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 74–85. Springer, Heidelberg (2007)
Lazar, M., Alexander, A.L.: Bootstrap White Matter Tractography (BOOT-TRAC). NeuroImage 24, 524–532 (2005)
Jones, D.K.: Tractography Gone Wild: Probabilistic Fibre Tracking Using the Wild Bootstrap with Diffusion Tensor MRI. IEEE TMI 27, 1268–1274 (2008)
Shi, Y., et al.: Joint Sulci Detection Using Graphical Models and Boosted Priors. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 98–109. Springer, Heidelberg (2007)
Doucet, A., Godsill, S., Andrieu, C.: On Sequential Monte Carlo Sampling Methods for Bayesian Filtering. Statistics and Computing 10, 197–208 (2000)
Ren, X., Fowlkes, C.C., Malik, J.: Learning Probabilistic Models for Contour Completion in Natural Images. Int. J. Comput. Vis. 77, 47–63 (2008)
Daniel, W.W.: Applied Nonparametric Statistics. Houghton Mifflin, Boston (1978)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Cheng, JZ., Cole, E.B., Pisano, E.D., Shen, D. (2009). Detection of Arterial Calcification in Mammograms by Random Walks. In: Prince, J.L., Pham, D.L., Myers, K.J. (eds) Information Processing in Medical Imaging. IPMI 2009. Lecture Notes in Computer Science, vol 5636. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02498-6_59
Download citation
DOI: https://doi.org/10.1007/978-3-642-02498-6_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02497-9
Online ISBN: 978-3-642-02498-6
eBook Packages: Computer ScienceComputer Science (R0)