Abstract
The extraction of elongated curvilinear structure in mammographic images is an important objective for the automated detection of breast cancers. We develop an approach which relies on a fixed-grid, localized Radon transform for line segment extraction and a Markov random field model to incorporate local interactions and refine the line structure. The energy of the resulting distribution is minimized stochastically via a Markov chain Monte Carlo iterative procedure. Experimental results demonstrate that the method can accurately extract blurred and low-contrast elongated continuous curvilinear structures, including those radiating from cancerous masses.
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
NHS Breast Screening Programme 2011 Annual Review (2011), http://www.cancerscreening.nhs.uk/breastscreen/publications/2011review.html
Wai, L.C.C., Brady, M.: Curvilinear structure based mammographic registration. In: Liu, Y., Jiang, T., Zhang, C. (eds.) CVBIA 2005. LNCS, vol. 3765, pp. 261–270. Springer, Heidelberg (2005)
Yates, K., Evans, C., Brady, M.: Improving te Brake’s mammographic mass-detection algorithm using phase congruency. In: Proc. of DICTA, pp. 179–183 (2002)
Linguraru, M., Marias, K., English, R., Brady, M.: A biologically inspired algorithm for microcalcification cluster detection. Med. Image Anal. 10(6), 850–862 (2006)
Zwiggelaar, R., Astley, S.M., Boggis, C.R.M., Taylor, C.J.: Linear structures in mammographic images: detection and classification. IEEE Trans. Med. Imaging 23(9), 1077–1086 (2004)
Sampat, M., Whitman, G., Bovik, A., Markey, M.: Comparison of algorithms to enhance spicules of spiculated masses on mammography. J. Digit. Imaging 21(1), 9–17 (2008)
Berks, M., Chen, Z., Astley, S., Taylor, C.: Detecting and classifying linear structures in mammograms using random forests. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 510–524. Springer, Heidelberg (2011)
Gao, R., Bischof, W.F.: Detection of linear structures in remote-sensed images. In: Kamel, M., Campilho, A. (eds.) ICIAR 2009. LNCS, vol. 5627, pp. 896–905. Springer, Heidelberg (2009)
Lacoste, C., Descombes, X., Zerubia, J.: Point processes for unsupervised line network extraction in remote sensing. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1568–1579 (2005)
Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Patt. Anal. Mach. Intell. 6, 721–741 (1984)
Hastings, W.: Monte Carlo sampling method using Markov chains and their applications. Biometrika 57, 97–109 (1970)
Bracewell, R.N.: Two-Dimensional Imaging. Prentice-Hall, Englewood Cliffs (1995)
Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, W.P.: The digital database for screening mammography. In: Yaffe, M. (ed.) Proc. of the Fifth Int. Workshop on Digital Mammography, pp. 212–218. Medical Physics Publ. (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Krylov, V.A., Taylor, S., Nelson, J.D.B. (2013). Stochastic Extraction of Elongated Curvilinear Structures in Mammographic Images. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2013. Lecture Notes in Computer Science, vol 7950. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39094-4_54
Download citation
DOI: https://doi.org/10.1007/978-3-642-39094-4_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39093-7
Online ISBN: 978-3-642-39094-4
eBook Packages: Computer ScienceComputer Science (R0)