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A New Combination Method Based on Adaptive Genetic Algorithm for Medical Image Retrieval

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 8870)

Abstract

Medical image retrieval could be based on the text describing the image as the caption or the title. The use of text terms to retrieve images have several disadvantages such as term-disambiguation. Recent studies prove that representing text into semantic units (concepts) can improve the semantic representation of textual information. However, the use of conceptual representation has other problems as the miss or erroneous semantic relation between two concepts. Other studies show that combining textual and conceptual text representations leads to better accuracy. Popularly, a score for textual representation and a score for conceptual representation are computed and then a combination function is used to have one score. Although the existing of many combination methods of two scores, we propose in this paper a new combination method based on adaptive version of the genetic algorithm. Experiments are carried out on Medical Information Retrieval Task of the ImageCLEF 2009 and 2010. The results confirm that the combination of both textual and conceptual scores allows best accuracy. In addition, our approach outperforms the other combination methods.

Keywords

  • medical image retrieval
  • score combination
  • genetic algorithm
  • concept mapping

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Gasmi, K., Torjmen-Khemakhem, M., Tamine, L., Ben Jemaa, M. (2014). A New Combination Method Based on Adaptive Genetic Algorithm for Medical Image Retrieval. In: , et al. Information Retrieval Technology. AIRS 2014. Lecture Notes in Computer Science, vol 8870. Springer, Cham. https://doi.org/10.1007/978-3-319-12844-3_25

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  • DOI: https://doi.org/10.1007/978-3-319-12844-3_25

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-12843-6

  • Online ISBN: 978-3-319-12844-3

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