Multimodal Medical Image Retrieval OHSU at ImageCLEF 2008
We present results from the Oregon Health & Science University’s participation in the medical retrieval task of ImageCLEF 2008. Our web-based retrieval system was built using a Ruby on Rails framework. Ferret, a Ruby port of Lucene was used to create the full-text based index and search engine. In addition to the textual index of annotations, supervised machine learning techniques using visual features were used to classify the images based on image acquisition modality. Our system provides the user with a number of search options including the ability to limit their search by modality, UMLS-based query expansion, and Natural Language Processing-based techniques. Purely textual runs as well as mixed runs using the purported modality were submitted. We also submitted interactive runs using user specified search options. Although the use of the UMLS metathesaurus increased our recall, our system is geared towards early precision. Consequently, many of our multimodal automatic runs using the custom parser as well as interactive runs had high early precision including the highest P10 and P30 among the official runs. Our runs also performed well using the bpref metric, a measure that is more robust in the case of incomplete judgments.
KeywordsImage Retrieval Image Retrieval System Medical Image Retrieval Supervise Machine Learning Technique Image Acquisition Modality
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- 1.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. (June 2006); M2082Google Scholar
- 4.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)CrossRefGoogle Scholar
- 7.Hersh, W.R., 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)CrossRefGoogle Scholar
- 8.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); PMID: 17911931Google Scholar
- 10.Müller, H., Clough, P., Hersh, W., Deselaers, T., Lehmann, T., Geissbuhler, A.: Evaluation axes for medical image retrieval systems: the imageclef experience. In: Proceedings of the 13th annual ACM international conference on Multimedia, Hilton, Singapore, pp. 1014–1022. ACM, New York (2005)CrossRefGoogle Scholar
- 11.Müller, H., Kalpathy-Cramer, J., Kahn Jr., C.E., Hatt, W., Bedrick, S., Hersh, W.: Overview of the ImageCLEFmed 2008 medical image retrieval task. In: Peters, C., et al. (eds.) CLEF 2008. LNCS, vol. 5706, pp. 512–522. Springer, Heidelberg (2009)Google Scholar
- 12.Furnas, G.W., Deerwester, S., Dumais, S.T., Landauer, T.K., Harshman, R.A., Streeter, L.A., Lochbaum, K.E.: Information retrieval using a singular value decomposition model of latent semantic structure. In: Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval, Grenoble, France, pp. 465–480. ACM, New York (1988)Google Scholar