Abstract
Curvilinear structures (CLS) are locally one-dimensional, relatively thin objects which complicate analysis of a mammogram. They comprise a number of anatomical features, most especially connective tissue, blood vessels, and milk ducts. The segmentation, identification and removal of such structures potentially facilitate a wide range of mammographic image processing applications, such as mass detection and temporal registration. In this paper, we present a novel CLS detection algorithm which is based on the monogenic signal afforced by a CLS physical model. The strength of the proposed model-based CLS detector is that it is able to identify even low contrast CLS. In addition, a noise suppression approach, based on local energy thresholding, is proposed to further improve the quality of segmentation. A local energy (LE)-based junction detection method which utilises the orientation information provided by the monogenic signal is also presented. Experiments demonstrate that the proposed CLS detection framework is capable of producing well-localized, highly noise-tolerated responses as well as robust performances as compared to classical orientation-sampling approach.
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Vujovic, N.: Establishing the correspondence between control points in pairs of mammographic images. IEEE Transactions on Image Processing 6(10), 1388–1399 (1997)
Marti, R., Zwiggelaar, R., Rubin, C.: Automatic registration of mammograms based on linear structures. In: Insana, M.F., Leahy, R.M. (eds.) IPMI 2001. LNCS, vol. 2082, pp. 162–168. Springer, Heidelberg (2001)
Cerneaz, N.: Model-based analysis of mammograms. DPhil Thesis, University of Oxford (1994)
Zwiggelaar, R., Parr, T., Taylor, C.: Finding orientated line patterns in digital mammographic images. In: Proceedings 7th British Machine Vision Conference, pp. 715–724 (1996)
Schenk, V.U.B., Brady, M.: Finding CLS using multiresolution oriented local energy feature detection. In: Proceedings 6th International Workshop on Digital Mammography (IWDM 2002) (June 2002)
Kovesi, P.: Image features from phase congruency. Videre: A Journal of Computer Vision Research 1(3) (1999)
Felsberg, M., Sommer, G.: A new extension of linear signal processing for estimating local properties and detecting features. In: Proceedings of DAGM Symposium, pp. 195–202. Springer, Heidelberg (2002)
Schenk, V.U.B.: Visual identification of fine surface incisions. DPhil Thesis, University of Oxford (2001)
Campbell, F.W., Robson, J.B.: Application of Fourier analysis to the visibility of gratings. Journal of Physiology 197, 551–566 (1968)
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© 2004 Springer-Verlag Berlin Heidelberg
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Wai, L.C.C., Mellor, M., Brady, M. (2004). A Multi-resolution CLS Detection Algorithm for Mammographic Image Analysis. In: Barillot, C., Haynor, D.R., Hellier, P. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004. MICCAI 2004. Lecture Notes in Computer Science, vol 3217. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30136-3_105
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DOI: https://doi.org/10.1007/978-3-540-30136-3_105
Publisher Name: Springer, Berlin, Heidelberg
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