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

  • Karim Gasmi
  • Mouna Torjmen-Khemakhem
  • Lynda Tamine
  • Maher Ben Jemaa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8870)


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.


medical image retrieval score combination genetic algorithm concept mapping 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abderahman, Y.A.: Fusion of similarity measures using genetic algorithm for searching chemical database (2008)Google Scholar
  2. 2.
    Abdulahhad, K., Chevallet, J., Berrut, C.: Solving concept mismatch through bayesian framework by extending umls meta-thesaurus. In: Conference en Recherche d Infomations et Applications - CORIA 2011, pp. 311–326 (2011)Google Scholar
  3. 3.
    Aronson, A.R.: Effective mapping of biomedical text to the umls metathesaurus: the metamap program. In: Annual Symposium AMIA, pp. 17–21 (2001)Google Scholar
  4. 4.
    Aslam, J.A., Montague, M.: Models for metasearch. In: Proceedings of the 24th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 276–284 (2001)Google Scholar
  5. 5.
    Baziz, M.: Indexation conceptuelle guidée par ontologie pour la recherche d’information. In: Thesis, University Paul Sabatier, Toulouse, France (2005)Google Scholar
  6. 6.
    Chevallet, J., Lim, J., Le, D.T.H.: Domain knowledge conceptual inter-media indexing: application to multilingual multimedia medical reports. In: Proceedings of the sixteenth ACM conference on Conference on information and knowledge management, CIKM 2007, pp. 495–504 (2007)Google Scholar
  7. 7.
    Cimino, J.J., Min, H., Perl, Y.: Consistency across the hierarchies of the umls semantic network and metathesaurus. Journal of Biomedical Informatics 36(6), 450–461 (2003)CrossRefGoogle Scholar
  8. 8.
    Crestani, F.: Exploiting the similarity of non-matching terms at retrieval time. Journal of Information Retrieval 2, 25–45 (1999)Google Scholar
  9. 9.
    Dinh, D., Tamine, L.: Combining global and local semantic contexts for improving biomedical information retrieval. In: Clough, P., Foley, C., Gurrin, C., Jones, G.J.F., Kraaij, W., Lee, H., Mudoch, V. (eds.) ECIR 2011. LNCS, vol. 6611, pp. 375–386. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Fang, H., Zhai, C.: An exploration of axiomatic approaches to information retrieval. In: Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2005, pp. 480–487 (2005)Google Scholar
  11. 11.
    Fieschi, M., Aronson, A.R., Morky, J.G., Gay, C.W., Humphrey, S.M., Rogers, W.J.: The nlm indexing initiative’s medical text indexer. In: Proceedings of the 11th World Congress on Medical Informatics Demner-Fushman and Lin Answering Clinical Questions, pp. 268–272 (2004)Google Scholar
  12. 12.
    Gasmi, K., Khemakhem, M., Jemaa, M.B.: A conceptual model for word sense disambiguation in medical image retrieval. In: AIRS, pp. 296–307 (2013)Google Scholar
  13. 13.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc. (1989)Google Scholar
  14. 14.
    Hinrich, J.O.P.: Information retrieval based on word senses (1995)Google Scholar
  15. 15.
    Hull, D.: Using statistical testing in the evaluation of retrieval experiments. In: Proceedings of the 16th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 1993, pp. 329–338 (1993)Google Scholar
  16. 16.
    Jimeno-Yepes, A.J., McInnes, B.T., Aronson, A.R.: Exploiting mesh indexing in medline to generate a data set for word sense disambiguation. BMC Bioinformatics 12, 223 (2011)CrossRefGoogle Scholar
  17. 17.
    Khan, L., McLeod, D., Hovy, E.: Retrieval effectiveness of an ontology-based model for information selection. VLDB Journal, 71–85 (2004)Google Scholar
  18. 18.
    Lopez-Herrera, A., Herrera-Viedma, E., Herrera, F.: Applying multi-objective evolutionary algorithms to the automatic learning of extended boolean queries in fuzzy ordinal linguistic information retrieval systems. vol. 160, p. 2192–2205 (2009)Google Scholar
  19. 19.
    Mihalcea, R., Tarau, P., Figa, E.: Pagerank on semantic networks with application to word sense disambiguation. In: International Conference on Computational Linguistics (COLING), pp. 1126–1132 (2004)Google Scholar
  20. 20.
    Ogilvie, P., Callan, J.: Combining document representations for known-item search. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, SIGIR 2003, pp. 143–150 (2003)Google Scholar
  21. 21.
    Malo, P., Pyry Siitari, A.S.: Automated query learning with wikipedia and genetic programming 194, 86–110 (2013)Google Scholar
  22. 22.
    Salton, G.: The smart retrieval system: Experiments in automatic document processing (1970)Google Scholar
  23. 23.
    Salton, G., McGill, M.: Introduction to modern information retrieval (1983)Google Scholar
  24. 24.
    Shaw, J.A., Fox, E.A., Shaw, J.A., Fox, E.A.: Combination of multiple searches. In: The Second Text REtrieval Conference (TREC-2), pp. 243–252 (1994)Google Scholar
  25. 25.
    Ventresque, A., Cazalens, S., Lamarre, P., Valduriez, P.: Improving interoperability using query interpretation in semantic vector spaces. In: Bechhofer, S., Hauswirth, M., Hoffmann, J., Koubarakis, M. (eds.) ESWC 2008. LNCS, vol. 5021, pp. 539–553. Springer, Heidelberg (2008)Google Scholar
  26. 26.
    Weeber, M., Mork, J., Aronson, A.: Developping a test collection for biomedical word senes disambiguation. In: Annual Symposium. AMIA Symposium, pp. 746–750 (2001)Google Scholar
  27. 27.
    Wilcoxon, F.: Individual Comparisons by Ranking Methods. Biometrics Bulletin pp. 80–83 (1945)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Karim Gasmi
    • 1
  • Mouna Torjmen-Khemakhem
    • 1
  • Lynda Tamine
    • 2
  • Maher Ben Jemaa
    • 1
  1. 1.ReDCAD LaboratoryUniversity of SfaxTunisia
  2. 2.IRIT LaboratoryUniversity of Paul SabatierToulouseFrance

Personalised recommendations