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.
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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)
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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
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DOI: https://doi.org/10.1007/978-3-642-39094-4_54
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