Abstract
We present an efficient and accurate image categorization system, applied to medical image databases within the ImageCLEF medical annotation task. The methodology is based on local representation of the image content, using a bag–of–visual–words approach. We explore the effect of different parameters on system performance, and show best results using dense sampling of simple features with spatial content in multiple scales, combined with a nonlinear kernel based Support Vector Machine classifier. The system was ranked first in the ImageCLEF 2009 medical annotation challenge, with a total error score of 852.8.
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
Avni U, Goldberger J, Greenspan H (2009) Dense simple features for fast and accurate medical x–ray annotation. In: Working notes of CLEF 2009, Corfu, Greece
Barla A, Odone F, Verri A (2003) Histogram intersection kernel for image classification. In: International conference on image processing, vol 3
Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. Journal of Machine Learning Research 3:993–1022
Bosch A, Muñoz X, Oliver A, Martí J (2006) Modeling and classifying breast tissue density in mammograms. In: Computer Vision and Pattern Recognition, pp 1552–1558
Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
Deselaers T, Deserno TM (2009) Medical image annotation in ImageCLEF 2008. In: CLEF 2008 Proceedings. Lecture Notes in Computer Science (LNCS), vol 5706. Springer, pp 523–530
Deselaers T, Hegerath A, Keysers D, Ney H (2006) Sparse patch–histograms for object classification in cluttered images. In: DAGM Symposium, pp 202–211
Deselaers T, Kalpathy-Cramer J, Müller H, Deserno TM (2008) Hierarchical classification for ImageCLEF 2008 medical image annotation
Fei-Fei L, Perona P (2005) A Bayesian hierarchical model for learning natural scene categories. In: Computer Vision and Pattern Recognition, vol 2, pp 524–531
Lazebnik S, Schmid C, Ponce J (2006) Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In: Computer vision and pattern recognition, vol 2, pp 2169–2178
Lehmann T, Güld M, Thies C, Fischer B, Spitzer K, Keysers D, H Ney MK, Schubert H, Wein B (2004) Content–based image retrieval in medical applications. Methods of Information in Medicine 43(4):354–361
Lehmann TM, Schubert H, Keysers D, Kohnen M, Wein BB (2003) The IRMA code for unique classification of medical images. In: Proceedings SPIE, pp 109–117
Lowe D (1999) Object recognition from local scale–invariant features. International conference on computer vision 2:1150–1157
Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University Press
Müller H, Lovis C, Geissbuhler A (2005) The medGIFT project on medical image retrieval. In: Gao X, Tully C, Lin C, Thom S, Müller H (eds) Medical Imaging and Telemedicine, Wuyishan, Fujian, China, pp 2–7. European Union AsiaICT Program
Nowak E, Jurie F, Triggs B (2006) Sampling strategies for bag–of–features image classification. In: European conference on computer vision. Springer, pp 490–503
Sivic J, Zisserman A (2008) Video google: a text retrieval approach to object matching in videos. In: International conference on computer vision, vol 2, pp 1470–1477
Tommasi T, Orabona F, Caputo B (2008) Discriminative cue integration for medical image annotation. Pattern Recognition Letters 29(15):1996–2002
Tommasi T, Caputo B, Welter P, Güld MO, Deserno TM (2009) Overview of the CLEF 2009 medical image annotation track. In: Working notes of CLEF 2009
Varma M, Zisserman A (2003) Texture classification: are filter banks necessary? In: Computer Vision and Pattern Recognition, vol 2, pp 691–698
Zhang J, Marszalek M, Lazebnik S, Schmid C (2007) Local features and kernels for classification of texture and object categories: A comprehensive study. International Journal of Computer Vision 73(2):213–238
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Avni, U., Goldberger, J., Greenspan, H. (2010). Medical Image Classification at Tel Aviv and Bar Ilan Universities. In: Müller, H., Clough, P., Deselaers, T., Caputo, B. (eds) ImageCLEF. The Information Retrieval Series, vol 32. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15181-1_23
Download citation
DOI: https://doi.org/10.1007/978-3-642-15181-1_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15180-4
Online ISBN: 978-3-642-15181-1
eBook Packages: Computer ScienceComputer Science (R0)