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Inter-media Concept-Based Medical Image Indexing and Retrieval with UMLS at IPAL

  • Caroline Lacoste
  • Jean-Pierre Chevallet
  • Joo-Hwee Lim
  • Diem Thi Hoang Le
  • Wei Xiong
  • Daniel Racoceanu
  • Roxana Teodorescu
  • Nicolas Vuillenemot
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4730)

Abstract

We promote the use of explicit medical knowledge to solve retrieval of information both visual and textual. For text, this knowledge is a set of concepts from a Meta-thesaurus, the Unified Medical Language System (UMLS). For images, this knowledge is a set of semantic features that are learned from examples using SVM within a structured learning framework. Image and text index are represented in the same way: a vector of concepts. The use of concepts allows the expression of a common index form: an inter-media index, offering the opportunity of homogeneous indexing/querying time fusion techniques. Top results obtained with concept based approaches show the potential of conceptual indexing.

Keywords

Noun Phrase Mean Average Precision Global Indexing Vector Space Model CBIR System 
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 2007

Authors and Affiliations

  • Caroline Lacoste
    • 1
  • Jean-Pierre Chevallet
    • 1
  • Joo-Hwee Lim
    • 1
  • Diem Thi Hoang Le
    • 1
  • Wei Xiong
    • 1
  • Daniel Racoceanu
    • 1
  • Roxana Teodorescu
    • 1
  • Nicolas Vuillenemot
    • 1
  1. 1.IPAL International Joint Lab, Institute for Infocomm Research (I2R), Centre National de la Recherche Scientifique (CNRS), 21 Heng Mui Keng Terrace, 119613Singapore

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