Abstract
This work explores feature selection to improve the performance in the vascular anomaly detection domain. Starting from a previously defined classification framework based on Support Vector Machines (SVM), we attempt to determine features that improve classification performance and to define guidelines for feature selection. Three different strategies were used in the feature selection stage, while a Density Level Detection-SVM (DLD-SVM) was used to validate the performance of the selected features over testing data. Results show that a careful feature selection results in a good classification performance. DLD-SVM shows a poor performance when using all the features together, owing to the curse of dimensionality.
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
Breiman, L.: Random forests. Mach. Learning 45(1), 5–32 (2001)
Florin, C., Paragios, N., Williams, J.: Particle Filters, a Quasi-Monte Carlo solution for segmentation of coronaries. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 246–253. Springer, Heidelberg (2005)
Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A.: Multiscale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)
Fridman, Y., Pizer, S.M., Aylward, S., Bullitt, E.: Segmenting 3D Branching Tubular Structures Using Cores. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 570–577. Springer, Heidelberg (2003)
Guyon, I., Weston, J., Barnhil, S., Vapnik, V.: Gene Selection for Cancer Classification using Support Vector Machines. Mach. Learning 46(1-3), 389–422 (2002)
Hernández Hoyos, M., Orkisz, M., Douek, P.C., Magnin, I.E.: Assessment of carotid artery stenoses in 3D contrast-enhanced magnetic resonance angiography, based on improved generation of the centerline. Mach. Graphics and Vision 14(4), 349–378 (2005)
Hernández Hoyos, M., Serfaty, J.M., Maghiar, A., Mansard, C., Orkisz, M., Magnin, I.E., Douek, P.C.: Evaluation of semi-automatic arterial stenosis quantification. Int. J. of Computer Assisted Radiol. and Surg. 1(3), 167–175 (2006)
Išgum, I., Rutten, A., Prokop, M., van Ginneken, B.: Detection of coronary calcifications from computed tomography scans for automated risk assessment of coronary artery disease. Med. Phys. 34(4), 1450–1461 (2007)
Nain, D., Yezzi, A., Turk, G.: Vessel Segmentation Using a Shape Driven Flow. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 51–59. Springer, Heidelberg (2004)
World Health Organization: The top ten causes of death - Fact sheet N310 (October 2008)
Peng, H., Long, F., Ding, C.: Feature Selection Based on Mutual information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy. IEEE Trans. Pattern Anal. and Mach. Intell. 27(8), 1226–1238 (2005)
Schaap, M., Metz, C., van Walsum, T., van der Giessen, A.G., Weustink, A.C., Mollet, N.R., Bauer, C., Bogunovic, H., Castro, C., Deng, X., Dikici, E., O’Donnell, T., Frenay, M., Friman, O., Hernández Hoyos, M., Kitslaar, P.H., Krissian, K., Kühnel, C., Luengo-Oroz, M.A., Orkisz, M., Smedby, Ö., Styner, M., Szymczak, A., Tek, H., Wang, C., Warfield, S.K., Zambal, S., Zhang, Y., Krestin, G.P., Niessen, W.J.: Standardized Evaluation Methodology and Reference Database for Evaluating Coronary Artery Centerline Extraction Algorithms. Med. Image Analysis 13(5), 701–714 (2009)
Steinwart, I., Hush, D., Scovel, C.: A Classification Framework for Anomaly Detection. J. Mach. Learning Research 6, 211–232 (2005)
Wróblewska, A., Boniński, P., Przelaskowski, A., Kazubek, M.: Segmentation and feature extraction for reliable classification of microclacifications in digital mammograms. Opto-electronics Review 11(3), 227–235 (2003)
Yang, X., Jia, L., Qingmao, H., Zhijun, C., Xiaohua, D, Pheng Ann, H.: F-score Feature Selection Method May Improve Texture-based Liver Segmentation Strategies. In: Proceedings of 2008 IEEE Int. Symp. on IT in Med. and Education, pp. 697–702 (2008)
Zheng, Y., Barbu, A., Georgescu, B., Scheuering, M., Comaniciu, D.: Fast automatic heart chamber segmentation from 3D CT data using marginal space learning and steerable features. In: Int. Conf on Computer Vision, pp. 1–8 (2007)
Zuluaga, M.A., Magnin, I.E., Hernández Hoyos, M., Delgado Leyton, E.J.F., Lozano, F., Orkisz, M.: Automatic detection of abnormal vascular cross-sections based on Density Level Detection and Support Vector Machines. Int. J. Comput. Assisted Radiol. Surg. (in-press, 2010), doi:10.1007/s11548-010-0494-8
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
Zuluaga, M.A., Delgado Leyton, E.J.F., Hernández Hoyos, M., Orkisz, M. (2011). Feature Selection for SVM-Based Vascular Anomaly Detection. In: Menze, B., Langs, G., Tu, Z., Criminisi, A. (eds) Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging. MCV 2010. Lecture Notes in Computer Science, vol 6533. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18421-5_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-18421-5_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-18420-8
Online ISBN: 978-3-642-18421-5
eBook Packages: Computer ScienceComputer Science (R0)