Manual Query Modification and Data Fusion for Medical Image Retrieval

  • Jeffery R. Jensen
  • William R. Hersh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4022)


Image retrieval has great potential for a variety of tasks in medicine but is currently underdeveloped. For the ImageCLEF 2005 medical task, we used a text retrieval system as the foundation of our experiments to assess retrieval of images from the test collection. We conducted experiments using automatic queries, manual queries, and manual queries augmented with results from visual queries. The best performance was obtained from manual modification of queries. The combination of manual and visual retrieval results resulted in lower performance based on mean average precision but higher precision within the top 30 results. Further research is needed not only to sort out the relative benefit of textual and visual methods in image retrieval but also to determine which performance measures are most relevant to the operational setting.


Image Retrieval Data Fusion Average Precision Query Term Mean Average Precision 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jeffery R. Jensen
    • 1
  • William R. Hersh
    • 1
  1. 1.Department of Medical Informatics & Clinical EpidemiologyOregon Health & Science UniversityPortlandUSA

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