Abstract
Classification of linear structures, such as blood vessels, milk ducts, spiculations and fibrous tissue can be used to aid the automated detection and diagnosis of mammographic abnormalities. We use a combination of dual-tree complex wavelet coefficients and random forest classification to detect and classify different types of linear structure. Encouraging results are presented for synthetic linear structures added to real mammographic backgrounds, and spicules in real mammograms. For spicule/non-spicule classification in real mammograms we report an area Az = 0.764 under the receiver operating characteristic.
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
Prajna, S., et al.: Detection of Architectural Distortion in Mammograms Acquired Prior to the Detection of Breast Cancer using Texture and Fractal Analysis. In: Giger, M.L., Karssemeijer, N. (eds.) proc. SPIE Medical Imaging, vol. 6915 (2008)
Parr, T., et al.: Statistical Modelling of Lines and Structures in Mammograms. In: Duncan, J.S., Gindi, G. (eds.) IPMI 1997. LNCS, vol. 1230, pp. 405–410. Springer, Heidelberg (1997)
Karssemeijer, N., te Brake, G.M.: Detection of Stellate Distortions in Mammograms. IEEE Transactions on Medical Imaging 15(5), 611–619 (1996)
Bornefalk, H.: Use of Phase and Certainty Information in Automatic Detection of Stellate Patterns in Mammograms. In: Proc. SPIE Medical Imaging, vol. 5370, pp. 97–107 (2004)
Wai, L.C., Mellor, M., Brady, M.: 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)
Schenk, V., Brady, M.: Finding CLS Using Multiresolution Orientated Local Energy Feature Detection. In: Heitgen, H. (ed.) Proc. 6th International Workshop on Digital Mammography, pp. 64–68 (2002)
McLoughlin, et al.: Connective Tissue Representation for Detection of Microcalcifications in Digital Mammograms. In: Sonka, M., Fitzpatrick, J.M. (eds.) Proc. SPIE Medical Imaging, vol. 4684, pp. 1246–1256 (2002)
Zwiggelaar, R., Astley, S.M., Boggis, C.R., et al.: Linear Structures in Mammographic Images: Detection and Classification. IEEE Transactions on Medcal Imaging 23(9), 1077–1086 (2004)
Breiman, L.: Random Forests. Machine Learning 45(1), 5–32 (2001)
Selesnick, I., Baraniuk, R.G., Kingsbury, N.G.: The Dual-Tree Complex Wavelet Transform. IEEE Signal Processing Magazine 22(6), 123–151 (2005)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contour Models. International Journal of Computer Vision 1(4), 321–331 (1988)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, Z., Berks, M., Astley, S., Taylor, C. (2010). Classification of Linear Structures in Mammograms Using Random Forests. In: Martí, J., Oliver, A., Freixenet, J., Martí, R. (eds) Digital Mammography. IWDM 2010. Lecture Notes in Computer Science, vol 6136. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13666-5_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-13666-5_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13665-8
Online ISBN: 978-3-642-13666-5
eBook Packages: Computer ScienceComputer Science (R0)