Journal of Digital Imaging

, Volume 24, Issue 4, pp 739–748 | Cite as

Managing Biomedical Image Metadata for Search and Retrieval of Similar Images

  • Daniel KorenblumEmail author
  • Daniel Rubin
  • Sandy Napel
  • Cesar Rodriguez
  • Chris Beaulieu


Radiology images are generally disconnected from the metadata describing their contents, such as imaging observations (“semantic” metadata), which are usually described in text reports that are not directly linked to the images. We developed a system, the Biomedical Image Metadata Manager (BIMM) to (1) address the problem of managing biomedical image metadata and (2) facilitate the retrieval of similar images using semantic feature metadata. Our approach allows radiologists, researchers, and students to take advantage of the vast and growing repositories of medical image data by explicitly linking images to their associated metadata in a relational database that is globally accessible through a Web application. BIMM receives input in the form of standard-based metadata files using Web service and parses and stores the metadata in a relational database allowing efficient data query and maintenance capabilities. Upon querying BIMM for images, 2D regions of interest (ROIs) stored as metadata are automatically rendered onto preview images included in search results. The system’s “match observations” function retrieves images with similar ROIs based on specific semantic features describing imaging observation characteristics (IOCs). We demonstrate that the system, using IOCs alone, can accurately retrieve images with diagnoses matching the query images, and we evaluate its performance on a set of annotated liver lesion images. BIMM has several potential applications, e.g., computer-aided detection and diagnosis, content-based image retrieval, automating medical analysis protocols, and gathering population statistics like disease prevalences. The system provides a framework for decision support systems, potentially improving their diagnostic accuracy and selection of appropriate therapies.

Key words

Imaging informatics data mining databases decision support body imaging cancer detection computed tomography computer-aided diagnosis (CAD) image retrieval PACS ROC curve ROC-based analysis web technology digital imaging and communications in medicine (DICOM) algorithms 



This study is supported in part by NIH CA72023.


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Copyright information

© Society for Imaging Informatics in Medicine 2010

Authors and Affiliations

  • Daniel Korenblum
    • 1
    Email author
  • Daniel Rubin
    • 1
    • 2
  • Sandy Napel
    • 1
  • Cesar Rodriguez
    • 3
  • Chris Beaulieu
    • 1
  1. 1.Department of RadiologyStanford UniversityStanfordUSA
  2. 2.Department of Stanford Medical InformaticsStanford UniversityStanfordUSA
  3. 3.BIOFABUniversity of California at BerkeleyBerkeleyUSA

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