Abstract
In this paper, the automatic annotation task of the 2005 CLEF cross-language image retrieval campaign (ImageCLEF) is described. This paper focuses on the database used, the task setup, and the plans for further medical image annotation tasks in the context of ImageCLEF. Furthermore, a short summary of the results of 2005 is given. The automatic annotation task was added to ImageCLEF in 2005 and provides the first international evaluation of state-of-the-art methods for completely automatic annotation of medical images based on visual properties.
The aim of this task is to explore and promote the use of automatic annotation techniques to allow for extracting semantic information from little-annotated medical images. A database of 10.000 images was established and annotated by experienced physicians resulting in 57 classes, each with at least 10 images. Detailed analysis is done regarding the (i) image representation, (ii) classification method, and (iii) learning method. Based on the strong participation of the 2005 campain, future benchmarks are planned.
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Ballesteros, L. and Petkova, D. 2005. A clustered retrieval approach for categorizing and annotating images. In Working Notes of the CLEF Workshop 2005, Vienna, Austria, LNCS 4022, pp. 662–672.
Besançon, R. and Millet, C. 2005. Data fusion of retrieval results from different media: Experiments at ImageCLEF 2005. In Working Notes of the CLEF Workshop 2005, Vienna, Austria, LNCS 4022, pp. 622–631.
Chang, Y.C., Lin, W.C., and Chen, H.H. 2005. A corpus-based relevance feedback approach to cross-language image retrieval. In Working Notes of the CLEF Workshop 2005, Vienna, Austria, LNCS 4022, pp. 592–601.
Chen, D.R., Chang, R.F., and Huang, Y.L. 1999. Computer-aided diagnosis applied to use of solid breast nodules by using neural networks. Radiology, 213:407–412.
Cheng, P.C., Chien, B.C., Ke, H.R., and Yang, W.P. 2005. Combining textual and visual features for cross-language medical image retrieval. In Working Notes of the CLEF Workshop 2005, Vienna, Austria, LNCS 4022, pp. 712–723.
Clough, P., Müller, H., Deselaers, T., Grubinger, M., Lehmann, T.M., Jensen, J., et al. 2005. The CLEF 2005 cross-language image retrieval track. In Workshop of the Cross–Language Evaluation Forum (CLEF 2005), Vienna, Austria. LNCS 4022, pp. 535–557.
Deselaers, T., Keysers, D., and Ney, H. 2005. Discriminative training for object recognition using image patches. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR 05), San Diego, CA, Vol. 2, pp. 157–162.
Eidenberger, H. 2003. How good are the visual MPEG-7 features? In Proceedings SPIE Visual Communications and Image Processing Conference 2003; Lugano, Italy, Vol. 5150, pp. 476–488.
Everingham, M., Zisserman, A., Williams, C.K.I., van Gool, L., Allan, M., et al. 2006. The 2005 PASCAL visual object classes challenge. In Selected Proceedings of the First PASCAL Challenges Workshop. Lecture Notes in Artificial Intelligence, Southampton, UK, Springer, LNAI 3944, pp. 117–176.
Gabor, D. 1946. Theory of communication. J. IEE, 93:429–457.
Güld, M.O., Kohnen, M., Keysers, D., Schubert, H., Bredno, J., and Lehmann, T.M. 2002. Quality of DICOM header information for image categorization. In SPIE 2002, Vol. 4685, pp. 280–287.
Keysers, D., Dahmen, J., Ney, H., Wein, B., and Lehmann, T.M. 2003. Statistical framework for model-based image retrieval in medical applications. J. Electron. Imaging, 12(1):59–68.
Keysers, D., Gollan, C., and Ney, H. 2004. Classification of medical images using non-linear distortion models. In Proc. BVM 2004, Bildverarbeitung für die Medizin, Berlin, Germany, pp. 366–370.
Lehmann, T.M., Güld, M.O., Deselaers, T., Keysers, D., Schubert, H., Spitzer, K., Ney, H., and Wein, B.B. 2005. Automatic categorization of medical images for content-based retrieval and data mining. Comput. Med. Imaging and Graphics, 29:143–155.
Lehmann, T.M., Schubert, H., Keysers, D., Kohnen, M., and Wein, B.B. 2003. The IRMA code for unique classification of medical images. In Proc. SPIE 2003, Vol. 5033, pp. 440–451.
Marée, R., Geurts, P., Piater, J., and Wehenkel, L. 2005. Biomedical image classification with random subwindows and decision trees. In Proc. ICCV workshop on Computer Vision for Biomedical Image Applications (CVIBA 2005). Lecture Notes in Computer Science 3765, pp. 220–229.
Martínez-Fernandez, J.L., Villena-Roman, J., Garcia-Serrano, A.M., and Gonzales-Cristobal, J.C. 2005. Combining textual and visual features for image retrieval. In Working Notes of the CLEF Workshop 2005, Vienna, Austria, LNCS 4022, pp. 680–691.
Müller, H., Geissbuhler, A., Marty, J., Lovis, C., and Ruch, P. 2006. The use of medGIFT and easyIR for ImageCLEF 2005. In Proceedings of the Cross Language Evaluation Forum 2005. Lecture Notes in Computer Science, Vienna, Austria, LNCS 4022, pp. 724–731.
Müller, H., Michoux, N., Bandon, D., and Geissbuhler, A. 2004. A review of content-based image retrieval systems in medical applications—clinical benefits and future directions. International Journal of Medical Informatics, 73:1–23.
Müller, H., Squire, D.M., and Pun, T. 2004. Learning from user behavious in image retrieval: applications of the market basket analysis. Int. J. Comput. Vision, 56(1–2) (Special Issue on Content-based Visual Information Retrieval):65–77.
Rahman, M., Desai, B.C., and Bhattacharya, P. 2005. Supervised machine learning based medical image annotation and retrieval. In Working Notes of the CLEF Workshop 2005, Vienna, Austria, LNCS 4022, pp. 692–701.
Tamura, H., Mori, S., and Yamawaki, T. 1978. Textural features corresponding to visual perception. IEEE Trans. Syst., Man, Cybernetics, 8(6):460–472.
Tourassi, G.D. 1999. Journey toward computer-aided diagnosis: role of image texture analysis. Radiology, 213:317–320.
Xiong, W., Qiu, B., Tian, Q., Xu, C., Ong, S.H., and Foong, K. 2005. Combining visual features for medical image retrieval and annotation. In Working Notes of the CLEF Workshop 2005, Vienna, Austria, LNCS 4022, pp. 632–641.
Yamamoto, S., Jiang, H., Matsumoto, M., Tateno, Y., Iinuma, T., and Matsumoto, T. 1996. Image processing for computer-aided diagnosis of lung cancer by CT. In 3rd IEEE Workshop on Applications of Computer Vision, Sarasota, FL, USA, pp. 236–241.
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Deselaers, T., Müller, H., Clough, P. et al. The CLEF 2005 Automatic Medical Image Annotation Task. Int J Comput Vision 74, 51–58 (2007). https://doi.org/10.1007/s11263-006-0007-y
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DOI: https://doi.org/10.1007/s11263-006-0007-y