Abstract
In this paper, we focus on improving retrieval performance, especially early precision, in the task of solving medical multimodal queries. The queries we deal with consist of a visual component, given as a set of image-examples, and textual annotation, provided as a set of words. The queries’ semantic content can be classified along three domain dimensions: anatomy, pathology, and modality. To solve these queries, we interpret their semantic content using both textual and visual data. Medical images often are accompanied by textual annotations, which in turn typically include explicit mention of their image’s anatomy or pathology. Annotations rarely include explicit mention of image modality, however. To address this, we use an image’s visual features to identify its modality. Our system thereby performs image retrieval by combining purely visual information about an image with information derived from its textual annotations. In order to experimentally evaluate our approach, we performed a set of experiments using the 2009 ImageCLEFmed collection using our integrated system as well as a purely textual retrieval system. Our integrated approach consistently outperformed our text-only system by 43% in MAP and by 71% in precision within the top 5 retrieved documents. We conclude that this improved performance is due to our method of combining visual and textual features.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Hersh, W.R., Müller, H., Jensen, J.R., Yang, J., Gorman, P.N., Ruch, P.: Advancing biomedical image retrieval: Development and analysis of a test collection. J. Am. Med. Inform. Assoc., M2082 (June 2006)
Smeulders, A., Worring, M., Santini, S., Gupta, A., Jain, R.: Content-based image retrieval at the end of the early years. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)
Tagare, H.D., Jaffe, C.C., Duncan, J.: Medical image databases: A content-based retrieval approach. J. Am. Med. Inform. Assoc. 4(3), 184–198 (1997)
Aisen, A.M., Broderick, L.S., Winer-Muram, H., Brodley, C.E., Kak, A.C., Pavlopoulou, C., Dy, J., Shyu, C.R., Marchiori, A.: Automated storage and retrieval of thin-section ct images to assist diagnosis: System description and preliminary assessment. Radiology 228(1), 265–270 (2003)
Schmid-Saugeona, P., Guillodb, J., Thirana, J.P.: Towards a computer-aided diagnosis system for pigmented skin lesions. Computerized Medical Imaging and Graphics: The Official Journal of the Computerized Medical Imaging Society 27(1), 65–78 (2003)
Müller, H., Michoux, N., Bandon, D., Geissbuhler, A.: A review of content-based image retrieval systems in medical applications–clinical benefits and future directions. International Journal of Medical Informatics 73(1), 1–23 (2004)
Jansen, B., Spink, A.: How are we searching the world wide web? a comparison of nine search engines. Information Processing and Management 42(1), 248–263 (2006)
Kalpathy-Cramer, J., Hersh, W.: Automatic image modality based classification and annotation to improve medical image retrieval. Studies in Health Technology and Informatics 129(Pt 2), 1334–1338 (2007)
Hersh, W., Kalpathy-Cramer, J., Jensen, J.: Medical image retrieval and automated annotation: Ohsu at imageclef 2006. 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. 660–669. Springer, Heidelberg (2007)
Radhouani, S., HweeLim, J.: pierre Chevallet, J., Falquet, G.: Combining textual and visual ontologies to solve medical multimodal queries. In: IEEE International Conference on Multimedia and Expo., pp. 1853–1856 (2006)
Radhouani, S.: Un modèle de recherche d’information orienté précision fondé sur les dimensions de domaine. PhD thesis, University of Geneva, Switzerland, and University of Grenoble, France (2008)
Müller, H., Kalpathy-Cramer, J., Eggel, I., Bedrick, S., Radhouani, S., Bakke, B., Kann, J.C.E.: Overview of the medical retrieval task at imageclef 2009. In: Peters, C., et al. (eds.) CLEF 2009 Workshop, Part II. LNCS, vol. 6242, pp. 72–84. Springer, Heidelberg (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Radhouani, S., Kalpathy-Cramer, J., Bedrick, S., Bakke, B., Hersh, W. (2010). Using Media Fusion and Domain Dimensions to Improve Precision in Medical Image Retrieval. 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_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-15751-6_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-15750-9
Online ISBN: 978-3-642-15751-6
eBook Packages: Computer ScienceComputer Science (R0)