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Dicoogle, a Pacs Featuring Profiled Content Based Image Retrieval

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  • Frederico Valente
  • Carlos Costa
  • Augusto Silva

Abstract

Content-based image retrieval (CBIR) has been heralded as a mechanism to cope with the increasingly larger volumes of information present in medical imaging repositories. However, generic, extensible CBIR frameworks that work natively with Picture Archive and Communication Systems (PACS) are scarce. In this article we propose a methodology for parametric CBIR based on similarity profiles. The architecture and implementation of a profiled CBIR system, based on query by example, atop Dicoogle, an open-source, full-fletched PACS is also presented and discussed. In this solution, CBIR profiles allow the specification of both a distance function to be applied and the feature set that must be present for that function to operate. The presented framework provides the basis for a CBIR expansion mechanism and the solution developed integrates with DICOM based PACS networks where it provides CBIR functionality in a seamless manner.

Suggested Citation

  • Frederico Valente & Carlos Costa & Augusto Silva, 2013. "Dicoogle, a Pacs Featuring Profiled Content Based Image Retrieval," PLOS ONE, Public Library of Science, vol. 8(5), pages 1-12, May.
    • Handle: RePEc:plo:pone00:0061888
    DOI: 10.1371/journal.pone.0061888
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    Cited by:

    1. Hualei Shen & Dacheng Tao & Dianfu Ma, 2013. "Multiview Locally Linear Embedding for Effective Medical Image Retrieval," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-21, December.

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