Abstract
Detecting and classifying curvilinear structure is important in many image interpretation tasks. We focus on the challenging problem of detecting such structure in mammograms and deciding whether it is normal or abnormal. We adopt a discriminative learning approach based on a Dual-Tree Complex Wavelet representation and random forest classification. We present results of a quantitative comparison of our approach with three leading methods from the literature and with learning-based variants of those methods. We show that our new approach gives significantly better results than any of the other methods, achieving an area under the ROC curve Az = 0.923 for curvilinear structure detection, and Az = 0.761 for distinguishing between normal and abnormal structure (spicules). A detailed analysis suggests that some of the improvement is due to discriminative learning, and some due to the DT-CWT representation, which provides local phase information and good angular resolution.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Kingsbury, N.: Complex wavelets for shift invariant analysis and filtering of signals. Applied and Computational Harmonic Analysis 10(3), 234–253 (2001)
Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
Burrell, H.C., Evans, A.J., Robin, A., Wilson, M., et al.: False-negative breast screening assessment: what lessons can we learn? Clin. Radiol. 56(5), 385–388 (2001)
Baker, J.A., Rosen, E.L., Lo, J.Y., Gimenez, E.I., et al.: Computer-Aided Detection (CAD) in Screening Mammography: Sensitivity of Commercial CAD Systems for Detecting Architectural Distortion. Am. J. Roentgenol. 181(4), 1083–1088 (2003)
Karssemeijer, N., te Brake, G.M.: Detection of stellate distortions in mammograms. IEEE Trans. Med. Im. 15(5), 611–619 (1996)
Parr, T.C., Taylor, C.J., Astley, S.M., Boggis, C.R.M.: Statistical modeling of oriented line patterns in mammograms. In: Proc. SPIE Medical Imaging, vol. 3034, pp. 44–55 (1997)
Rangayyan, R., Ayres, F.: Gabor filters and phase portraits for the detection of architectural distortion in mammograms. Medical and Biological Engineering and Computing 44(10), 883–894 (2006)
Dixon, R.N., Taylor, C.J.: Automated asbestos fibre counting. In: Institute of Physics Conference Series, vol. 44, pp. 178–185 (1979)
Cerneaz, N., Brady, M.: Finding curvilinear structures in mammograms. In: Computer Vision, Virtual Reality and Robotics in Medicine, pp. 372–382 (1995)
Zwiggelaar, R., Astley, S.M., Boggis, C.R.M., Taylor, C.J.: Linear structures in mammo-graphic images: detection and classification. IEEE Trans. Med. Im. 23(9), 1077–1086 (2004)
Schenk, V., Brady, M.: Finding CLS Using Multiresolution Orientated Local Energy Feature Detection. In: Proc. 6th International Workshop on Digital Mammography, Bremen, Germany (2002)
McLoughlin, K.J., Bones, P.J., Kovesi, P.: Connective tissue representation for detection of microcalcifications in digital mammograms. In: Proc. SPIE Medical Imaging, vol. 4684, pp. 1246–1256 (2002)
Freeman, W.T., Adelson, E.H.: The Design and Use of Steerable Filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 13, 891–906 (1991)
Wai, L.C.C., Mellor, M., Brady, J.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)
Chen, Z., Berks, M., Astley, S.M., Taylor, C.J.: Classification of Linear Structures in Mammograms Using Random Forests. In: Martí, J., Oliver, A., Freixenet, J., Martí, R. (eds.) IWDM 2010. LNCS, vol. 6136, pp. 153–160. Springer, Heidelberg (2010)
Anderson, R., Kingsbury, N., Fauqueur, J.: Coarse-level object recognition using interlevel products of complex wavelets. In: Proc. IEEE International Conference on Image Processing, pp. 745–748 (2005)
Kingsbury, N.: Rotation-Invariant Local Feature Matching with Complex Wavelets. In: Proc. European Conference on Signal Processing, EUSIPCO (2006)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International Journal of Computer Vision 1(4), 321–331 (1988)
Muralidhar, G.S., Bovik, A.C., Giese, J.D., Sampat, M.P., et al.: Snakules: A Model-Based Active Contour Algorithm for the Annotation of Spicules on Mammography. IEEE Trans. Med. Im. 29(10), 1768–1780 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Berks, M., Chen, Z., Astley, S., Taylor, C. (2011). Detecting and Classifying Linear Structures in Mammograms Using Random Forests. In: Székely, G., Hahn, H.K. (eds) Information Processing in Medical Imaging. IPMI 2011. Lecture Notes in Computer Science, vol 6801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22092-0_42
Download citation
DOI: https://doi.org/10.1007/978-3-642-22092-0_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22091-3
Online ISBN: 978-3-642-22092-0
eBook Packages: Computer ScienceComputer Science (R0)