Automatic Medical Image Multilingual Indexation Through a Medical Social Network

  • Mouhamed Gaith Ayadi
  • Riadh Bouslimi
  • Jalel Akaichi
  • Hana Hedhli
Part of the Lecture Notes in Social Networks book series (LNSN)


Medical social networking sites enabled multimedia content sharing in large volumes, by allowing physicians and patients to upload their medical images. These images are diagnosed and commented, in different languages, by several specialists instantly. Moreover, it is necessary to employ new techniques, in order to automatically extract information and analyze knowledge from the huge number of comments expressing specialist’s analyzes and recommendations. For this reason, we propose a terms-based method in order to extract the relevant terms and words which can describe the medical image. Furthermore, significant extracted terms and keywords will be used later to index medical images, in order to facilitate their search through the social network site. In fact, we need to take account, in our work, that existing comments are expressed in different languages. So, it is essential to implement a multilingual indexation method to eliminate the ambiguity which will be the cause of the effectiveness’s reduction of the search function. In order to palliate this situation, we propose a multilingual mixed approach which concentrates on algorithms based on statistical methods and external multilingual semantic resources, in order to handle and to cover different languages. The use of external resources, such as semantic multilingual thesaurus, can improve the efficiency of the indexing process. Our proposed method can be applied in different languages. It is also essential to implement an auto-correction of the medical terms by using a medical dictionary. The correction of terms helps to eliminate the ambiguity which will be the cause of the reduction in the frequency of appearance of such terms. The correction of terms has taken into consideration that terms are presented in different languages. Our study is validated by a set of experiments and a comparison study with some existing approaches in literature. Experimental results have indicated that the proposed system has a superior performance compared to other systems, which is satisfactory.


Social Network Medical Image Social Network Site Average Precision Unify Medical Language 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.


  1. 1.
    Zhi W, Wenwu Z, Peng C, Lifeng S, Shiqiang Y (2013) Social media recommendation. In: Social media retrieval. Computer communications and networks. Springer, Berlin. doi:10.1007/978-1-4471-4555-4 3Google Scholar
  2. 2.
    Doganay S (2014) Healthcare social networks: new choices for doctors, Patients. Available from
  3. 3.
    Franklin V, Greene S (2007) Sweet talk: a text messaging support system. J Diabetes Nurs 11(1):22–26Google Scholar
  4. 4.
    Grenier C (2003) The role of intermediate subject to understand the structuring of an organizational network of actors and technology case of a care network. In: Proceedings of the 9th conference of the association information and management, GrenobleGoogle Scholar
  5. 5.
    Messaoudi A, Bouslimi R, Akaichi J (2013) Indexing medical images based on collaborative experts reports. Int J Comput Appl (0975-887) 70(5):1–9Google Scholar
  6. 6.
    Daniel RG, Liza SR, Jennifer LK (2013) Dangers and opportunities for social media in medicine. Clin Obstet Gynecol 56(3). doi:10.1097/GRF.0b013e318297dc38Google Scholar
  7. 7.
    Feldman DL (2012) Medical social media networks: communicating across the virtual highway. Q J Health Care Practice Risk Manag Infocus 18(1):2–5Google Scholar
  8. 8.
    Maisonnasse L, Gaussier E, Chevallet J-P (2009) Combination of semantic analysis to search for medical information. In: RISE (Research Information semantics) within the INFORSID’ conference, ToulouseGoogle Scholar
  9. 9.
    Gaussier E, Maissonnasse L, Chevallet JP (2008) Multiplying concept sources for graph modeling. In: CLEF 2007. LNCS 5152 proceedings, pp 585–592Google Scholar
  10. 10.
    Lacoste C, Chevallet JP, Lim j-h, Wei X, Raccoceanu D, Hoang D, Vuillenemot F (2006) Ipal knowledge-based medical image retrieval in imageCLEFmed 2006. In: Working notes for the CLEF 2006 workshop, AlicanteGoogle Scholar
  11. 11.
    Neil S, Velte T, Jie H, Wei Z, Clement Y (2007) Knowledge intensive conceptual retrieval and passage extraction of biomedical literature. In: 30th annual 66 international ACM SIGIR conference on research and development in information retrievalGoogle Scholar
  12. 12.
    Li L-J, Fei-Fei L (2009) Optimol: automatic online picture collection via incremental model learning. Int J Comput Vis 88(2):147–168CrossRefGoogle Scholar
  13. 13.
    Collins B, Deng J, Li K, Fei-Fei L (2008) Towards scalable dataset construction: an active learning approach. In: Proceedings of the European conference on computer visionGoogle Scholar
  14. 14.
    Mihalcea R, Leong C-W (2009) Towards communicating simple sentences using pictorial representations. Mach Transl 22:153–173CrossRefGoogle Scholar
  15. 15.
    Von Ahn L, Dabbish L (2004) Labeling images with a computer game. In: Proceedings of the SIGCHI conference on human factors in computing systems, Vienna. ACM, New York, pp 319–326Google Scholar
  16. 16.
    Truran M, Goulding J, Ashman H (2005) Co-active intelligence for image retrieval. In Proc. of the 13th annual ACM international conference on multimedia, Hilton. ACM, New York, pp 547–550Google Scholar
  17. 17.
    Li Q, Lu SCY (2008) Collaborative tagging applications and approaches. IEEE Multimed 15(3):14–21CrossRefGoogle Scholar
  18. 18.
    Shevade B, Sundaram H, Xie L (2007) Modeling personal and social network context for event annotation in images. In: Proceedings of the 7th ACM/IEEE-CS joint conference on digital libraries, Vancouver, BC. ACM, New York, pp 127–134Google Scholar
  19. 19.
    Stone Z, Zickler T, Darrell T (2008) Autotagging facebook: social network context improves photo annotation. In: Proceedings of the 1st IEEE workshop on internet vision (CVPR 2008), p 8Google Scholar
  20. 20.
    Bouslimi R, Messaoudi A, Akaichi J (2013) Using a bag of words for automatic medical image annotation with a latent semantic. Int J Artif Intell Appl 4(3):51Google Scholar
  21. 21.
    Barnard K, Forsyth D (2007) Learning the semantics of words and pictures. In: Proceedings of international conference on computer visionGoogle Scholar
  22. 22.
    Jeon J, Lavrenko V, Manmatha R (2007) Automatic image annotation and retrieval using cross-media relevance models. In: Proceedings of the ACM SIGIR conference on research and development in information retrievalGoogle Scholar
  23. 23.
    Makadia A, Pavlovic V, Kumar S (2008) A new baseline for image annotation. In: Proceedings of the European conference on computer visionCrossRefGoogle Scholar
  24. 24.
    Wang C, Blei David, Fei-Fei Li (2009) Simultaneous image classification and annotation. In: Proceedings of the IEEE conference on computer vision and pattern recognitionGoogle Scholar
  25. 25.
    Zunjarwad A, Sundaram H, Xie L (2007) Contextual wisdom: social relations and correlations for multimedia event annotation. In: Proceedings of the 15th international conference on multimedia, Augsburg. ACM, New York, pp 615–624CrossRefGoogle Scholar
  26. 26.
    Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University Press, New YorkCrossRefzbMATHGoogle Scholar
  27. 27.
    Fuming S, Yong G, Dongxia W, Xueming W (2010). A collaborative approach for image annotation. In: PSIVT, 2010, image and video technology, Pacific-Rim symposium on, image and video technology, Pacific-Rim symposium on 2010, pp 192–196. doi:10.1109/PSIVT.2010.39Google Scholar
  28. 28.
    Sun F, Ge Y, Wang D, Wang X (2010) A collaborative approach for image annotation. In: Proceedings of the PSIVT’10. IEEE Computer Society 2010, Singapore, pp 192–196. ISBN:978-0-7695-4285-0Google Scholar
  29. 29.
    Kanishcheva O, Angelova G (2015) A pipeline approach to image auto-tagging refinement. In: BCI ’15 proceedings of the 7th Balkan conference on informatics conference, New York, NY. doi:10.1145/2801081.2801108Google Scholar
  30. 30.
    Bouslimi R, Akaichi J (2015) Automatic medical image annotation on social network of physician collaboration. Netw Model Anal Health Inform Bioinforma 4:10. doi:10.1007/s13721-015-0082-5CrossRefGoogle Scholar
  31. 31.
    Harrathi F (2010) Extraction de concepts et de relations entre concepts à partir des documents multilingues: approche statistique et ontologique. PhD Thesis, INSA LyonGoogle Scholar
  32. 32.
    Gong J, Sun S (2011) Individual doctor recommendation model on medical social network. In: Proceedings of the 7th international conference on advanced data mining and applications (ADMA’11)Google Scholar
  33. 33.
    Almansoori W, Zarour O, Jarada TN, Karampales P, Rokne J, Alhajj R (2011) Applications of social network construction and analysis in the medical referral process. In: Proceedings of the 2011 IEEE ninth international conference on dependable, autonomic and secure computing (DASC’11)Google Scholar
  34. 34.
    Xie Y, Chen Z, Cheng Y, Zhang K, Agrawal A, Liao WK, Choudhary A (2013) Detecting and tracking disease outbreaks by mining social media data. In: Proceedings of the twenty-third international joint conference on artificial intelligence (IJCAI’13)Google Scholar
  35. 35.
    Li J (2014) Data protection in healthcare social networks. J IEEE Softw 31(1):46–53MathSciNetCrossRefGoogle Scholar
  36. 36.
    AMA Policy (2012) Professionalism in the use of social media. American Medical Association, 2012 Annual meeting.
  37. 37.
    Levenshtein VI (1966) Binary codes capable of correcting deletions, insertions and reversals. Sov Phys Dokl 10:707–710MathSciNetzbMATHGoogle Scholar
  38. 38.
    Jaccard P (1901) Distribution de la flore alpine dans le Bassin des Drouces et dans quelques regions voisines. Bull Soc Vaud Sci Nat 37(140):241–272Google Scholar
  39. 39.
    Heasoo H, Lauw Hady W, Getoor L, Ntoulas A (2012) Organizing user search histories. IEEE Trans J Mag Knowl Data Eng 24:912–925CrossRefGoogle Scholar
  40. 40.
    Navarro G (2001) A guided tour to approximate string matching. ACM Comput Surv 33(1):31–88CrossRefGoogle Scholar
  41. 41.
    Frakes WB, Fox CJ (2003) Strength and similarity of affix removal stemming algorithms. In: Newsletter of ACM SIGIR forum homepage archive, vol 37(1), New York, pp 26–30Google Scholar
  42. 42.
    Jones K (1972) A statistical interpretation of term specificity and its application in retrieval. J Doc 28(1):11–21CrossRefGoogle Scholar
  43. 43.
    Paukkeri M, Honkela T (2010) Likey: unsupervised language-independent keyphrase extraction. In: Proceedings of the 5th international workshop on semantic evaluation, Uppsala, Sweden, pp 162–165Google Scholar
  44. 44.
    NLM (2009) NLM unified medical language system fact sheet. Available from:

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mouhamed Gaith Ayadi
    • 1
  • Riadh Bouslimi
    • 1
  • Jalel Akaichi
    • 1
  • Hana Hedhli
    • 2
  1. 1.Department of Computer SciencesISG, BESTMODTunisTunisia
  2. 2.Emergency Department, Charles Nicolle HospitalTunis El Manar UniversityTunisTunisia

Personalised recommendations