Abstract
Mammographic registration is a challenging problem due in part to the intrinsic complexity of mammographic images, and partly because of the substantial differences that exist between two mammograms that are to be matched. In this paper, we propose a registration algorithm for mammograms which incorporates junctions of Curvilinear structures (CLS) as internal landmarks. CLS depict connective tissue, blood vessels, and milk ducts. These are detected by an algorithm based on the monogenic signal and afforced by a CLS physical model. The junctions are extracted using a local energy (LE)-based method, which utilises the orientation information provided by the monogenic signal. Results using such junctions as internal landmarks in registration are presented and compared with conventional approaches using boundary landmarks, in order to highlight the potential of anatomical based feature extraction in medical image analysis. We demonstrate how computer vision techniques such as phase congruency (PC), local energy (LE) and multi-resolution can be applied in linear (1-D) and junction (2-D) detection as well as their application to medical image registration problems.
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
Sallam, M.Y., Bowyer, K.W.: Registration and difference analysis of corresponding mammogram images. Medical Image Analysis 3(2), 103–118 (1999)
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)
Marias, K., Behrenbruch, C., Parbhoo, S., Seifalian, A., Brady, M.: A Registration Framework for the Comparison of Mammogram Sequences. IEEE Transactions on Medical Imaging 24(6), 782–790 (2005)
van Engeland, S., Snoeren, P., Hendriks, J., Karssemeijer, N.: A Comparison of Methods for Mammogram Registration. IEEE Transactions on Medical Imaging 22(11), 1436–1444 (2003)
Vujovic, N.: Establishing the correspondence between control points in pairs of mammographic images. IEEE Transactions on Image Processing 6(10), 1388–1399 (1997)
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)
Wai, L.C.C., Mellor, M., Brady, M.J.: A multi-resolution CLS detection algorithm for mammographic image analysis. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3217, pp. 865–872. Springer, Heidelberg (2004)
Cerneaz, N.: Model-based analysis of mammograms., DPhil Thesis, University of Oxford (see also Chapter 11 of Highnam and Brady q.v.) (1994)
Weidner, N., Semple, J.P., Welch, W.R., Folkman, J.: Tumor angiogenesis and metastasis-correlation in invasive breast carcinoma. New England Journal of Medicine 324(1), 1–8 (1991)
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., 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)
Schenk, V.U.B.: Visual identification of fine surface incisions., DPhil Thesis, University of Oxford (2001)
Tromans, C.E., Brady, M.J., Warren, R.: Breast Boundary Segmentation and its use in Breast Density Estimation. In: Pisano, E. (ed.) Proceedings of the International Workshop on Digital Mammography, IWDM 2004 (2004) (to appear)
Mokhtarian, F., Mackworth, A.K.: A Theory of Multi-Scale, Curvature-Based Shape Representation for Planar Curves. IEEE Trans. Pattern Analysis and Machine Intelligence 14(8), 789–805 (1992)
Bookstein, F.L.: Principal Warps: Thin-Plate Splines and the Decomposition of Deformations. IEEE Trans. Pattern Analysis and Machine Intelligence 11(6) (1989)
Highnam, R., Brady, M.: Mammographic Image Analysis. Kluwer Academic, Dordrecht (1999)
Kadir, T., Brady, M.: Scale Saliency and Image Description. International Journal of Computer Vision 45(2), 82–105 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wai, L.C.C., Brady, M. (2005). Curvilinear Structure Based Mammographic Registration. In: Liu, Y., Jiang, T., Zhang, C. (eds) Computer Vision for Biomedical Image Applications. CVBIA 2005. Lecture Notes in Computer Science, vol 3765. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11569541_27
Download citation
DOI: https://doi.org/10.1007/11569541_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29411-5
Online ISBN: 978-3-540-32125-5
eBook Packages: Computer ScienceComputer Science (R0)