Abstract
Advances in the medical imaging technology has lead to an exponential growth in the number of digital images that needs to be acquired, analyzed, classified, stored and retrieved in medical centers. As a result, medical image classification and retrieval has recently gained high interest in the scientific community. Despite several attempts, such as the yearly-held ImageCLEF Medical Image Annotation Challenge, the proposed solutions are still far from being sufficiently accurate for real-life implementations.
In this paper we summarize the technical details of our experiments for the ImageCLEF 2009 medical image annotation challenge. We use a direct and two ensemble classification schemes that employ local binary patterns as image descriptors. The direct scheme employs a single SVM to automatically annotate X-ray images. The two proposed ensemble schemes divide the classification task into sub-problems. The first ensemble scheme exploits ensemble SVMs trained on IRMA sub-codes. The second learns from subgroups of data defined by frequency of classes. Our experiments show that ensemble annotation by training individual SVMs over each IRMA sub-code dominates its rivals in annotation accuracy with increased process time relative to the direct scheme.
This work was supported in part by the Marie Curie Programme of the European Commission under FP6 IRonDB project MTK-CT-2006-047217.
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Long, L.R., Pillemer, S.R., Lawrence, R.C., Goh, G.H., Neve, L., Thoma, G.R.: WebMIRS: web-based medical information retrieval system. In: Sethi, I.K., Jain, R.C. (eds.) Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 3312, pp. 392–403 (December 1997)
Shyu, C.R., Brodley, C.E., Kak, A.C., Kosaka, A., Aisen, A.M., Broderick, L.S.: Assert: a physician-in-the-loop content-based retrieval system for hrct image databases. Comput. Vis. Image Underst. 75(1-2), 111–132 (1999)
Rahman, M.M., Desai, B.C., Bhattacharya, P.: Medical image retrieval with probabilistic multi-class support vector machine classifiers and adaptive similarity fusion. Computerized Medical Imaging and Graphics 32(2), 95–108 (2008)
Mueen, A., Sapian Baba, M., Zainuddin, R.: Multilevel feature extraction and x-ray image classification. J. Applied Sciences 7(8), 1224–1229 (2007)
Jacquet, V., Jeanne, V., Unay, D.: Automatic detection of body parts in x-ray images. In: IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis, MMBIA (2009)
Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Unay, D., Soldea, O., Ekin, A., Cetin, M., Ercil, A.: Automatic Annotation of X-ray Images: A Study on Attribute Selection. In: Medical Content-based Retrieval for Clinical Decision Support (MCBR-CDS) Workshop in Conjunction with MICCAI 2009 (2009)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Müller, H., Deselaers, T., Deserno, T., Clough, P., Kim, E., Hersh, W.: Overview of the imageCLEFmed, medical retrieval and medical annotation tasks. In: Peters, C., Clough, P., Gey, F.C., Karlgren, J., Magnini, B., Oard, D.W., de Rijke, M., Stempfhuber, M. (eds.) CLEF 2006. LNCS, vol. 4730, pp. 595–608. Springer, Heidelberg (2007)
Tommasi, T., Caputo, B., Welter, P., Güld, M.O., Deserno, T.M.: Overview of the CLEF2009 medical image annotation task. In: Peters, C., et al. (eds.) CLEF 2009 Workshop, Part II. LNCS, vol. 6242, pp. 85–93. Springer, Heidelberg (2010)
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Unay, D., Soldea, O., Ozogur-Akyuz, S., Cetin, M., Ercil, A. (2010). Automated X-Ray Image Annotation. In: Peters, C., et al. Multilingual Information Access Evaluation II. Multimedia Experiments. CLEF 2009. Lecture Notes in Computer Science, vol 6242. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15751-6_30
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DOI: https://doi.org/10.1007/978-3-642-15751-6_30
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