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A Model for Semantic Medical Image Retrieval Applied in a Medical Social Network

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Information Technology in Bio- and Medical Informatics (ITBAM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9832))

Abstract

We present in this article a multimodal research model for the retrieval of medical images based on the extracted multimedia information from a radiological collaborative social network. However, opinions shared on a medical image in a medical social network constitute a textual description that requires in most of the time cleaning using a medical thesaurus. In addition, we describe the textual description and medical image in a TF-IDF weight vector using an approach of « bag-of-words ». We use latent semantic analysis to establish relationships between textual and visual terms from the shared opinions on the medical image. Multimodal modeling will search for medical information through multimodal queries. Our model is evaluated on the basis ImageCLEFmed’2015 for which we have the ground-truth. We have carried many experiments with different descriptors and many combinations of modalities. Analysis of the results shows that the model is based on two methods can increase the performance of a research system based on only one modality, either visual or textual.

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Notes

  1. 1.

    https://www.patientslikeme.com/.

  2. 2.

    http://www.dailystrength.org/.

  3. 3.

    https://www.medpics.fr/.

  4. 4.

    https://www.carenity.com/.

  5. 5.

    http://www.lemurproject.org/.

  6. 6.

    Singular Value Decomposition (SVD).

References

  1. Priyatharshini, R., Chitrakala, S.: Association based image retrieval: a survey. In: Das, V.V. (ed.) AIM/CCPE 2012. CCIS, vol. 296, pp. 17–26. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  2. Duan, L., Yuan, B., Wu, C., Li, J., Guo, Q.: Text-image separation and indexing in historic patent document image based on extreme learning machine. In: Cao, J., Mao, K., Cambria, E., Man, Z., Toh, K.-A. (eds.) Proceedings of ELM-2014 Volume 2, PALO, vol. 4, pp. 299–308. Springer, Heidelberg (2014)

    Google Scholar 

  3. Clinchant, S., Csurka, G., Ah-Pine, J.: Semantic combination of textual and visual information in multimedia retrieval. In: Proceedings of 1st ACM International Conference Multimedia Retrieval, New York, NY, USA (2011)

    Google Scholar 

  4. Wang, S., Pan, P., Lu, Y., Xie, L.: Improving cross-modal and multi-modal retrieval combining content and semantics similarities with probabilistic model. J. Multimedia Tools Appl. 74(6), 2009–2032 (2013)

    Article  Google Scholar 

  5. Bouslimi, R., Akaichi, J.: Automatic medical image annotation on social network of physician collaboration. J. Netw. Model. Anal. Health Inform. Bioinform. 4(10), 219–228 (2015)

    Google Scholar 

  6. Bouslimi, R., Akaichi, J., Ayadi, M.G., Hedhli, H.: A medical collaboration network for medical image analysis. J. Netw. Model. Anal. Health Inform. Bioinform. 5(10), 145–165 (2016)

    Google Scholar 

  7. Salton, G., Wong, A., Yang, C.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)

    Article  MATH  Google Scholar 

  8. Csurka, G., Dance, C., Fan, L., Willamowski, J., Bray, C.: Visual categorization with bags of keypoints. In: ECCV 2004 Workshop on Statistical Learning in Computer Vision, pp. 59–74 (2004)

    Google Scholar 

  9. Robertson, S., Walker, S., Hancock-Beaulieu, M., Gull, A., Lau, M.: Okapi at trec-3. In: Text REtrieval Conference, pp. 21–30 (1994)

    Google Scholar 

  10. Zhai, C.: Notes on the lemur TFIDF model. Technical report, Carnegie Mellon University (2001)

    Google Scholar 

  11. Matas, J., Chum, O., Martin, U., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: Proceedings of the British Machine Vision Conference, pp. 384–393. BMVA, September 2002

    Google Scholar 

  12. Abd Rahman, N., Mabni, Z., Omar, N., Fairuz, H., Hanum, M., Nur Amirah, N., Rahim, T.M.: A parallel latent semantic indexing (LSI) algorithm for malay hadith translated document retrieval. In: First International Conference, SCDS 2015, Putrajaya, Malaysia, pp. 154–163 (2015)

    Google Scholar 

  13. de Herrera, A.G.S., Muller, H., Bromuri, S.: Overview of the ImageCLEF 2015 medical classification task. In: Working Notes of CLEF 2015 (Cross Language Evaluation Forum) (2015)

    Google Scholar 

  14. Larlus, D., Dorkó, G., Jurie, F.: Création de vocabulaires visuels efficaces pour la catégorisation d’images. In: Reconnaissance des Formes et Intelligence Artificielle (2006)

    Google Scholar 

  15. Jurie, F., Triggs, W.: Creating efficient codebooks for visual recognition. In: ICCV 2005 (2005)

    Google Scholar 

  16. Vidal-Naquet, M., Ullman, S.: Object recognition with informative features and linear classification. In: ICCV, pp. 281–288 (2003)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Higher Institute of Technological Studies, Jendouba, Tunisia

    Riadh Bouslimi & Mouhamed Gaith Ayadi

  2. Computer Science Department, BESTMOD Lab, ISG-University of Tunis, Le Bardo, Tunisia

    Riadh Bouslimi, Mouhamed Gaith Ayadi & Jalel Akaichi

Authors
  1. Riadh Bouslimi
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  2. Mouhamed Gaith Ayadi
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  3. Jalel Akaichi
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Corresponding author

Correspondence to Riadh Bouslimi .

Editor information

Editors and Affiliations

  1. Institute of Informatics and Telematics , Pisa, Italy

    M. Elena Renda

  2. Czech Technical University in Prague , Prague 6, Czech Republic

    Miroslav Bursa

  3. Medical University Graz , Graz, Austria

    Andreas Holzinger

  4. San José State University , San Jose, California, USA

    Sami Khuri

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© 2016 Springer International Publishing Switzerland

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Bouslimi, R., Ayadi, M.G., Akaichi, J. (2016). A Model for Semantic Medical Image Retrieval Applied in a Medical Social Network. In: Renda, M., Bursa, M., Holzinger, A., Khuri, S. (eds) Information Technology in Bio- and Medical Informatics. ITBAM 2016. Lecture Notes in Computer Science(), vol 9832. Springer, Cham. https://doi.org/10.1007/978-3-319-43949-5_9

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  • DOI: https://doi.org/10.1007/978-3-319-43949-5_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-43948-8

  • Online ISBN: 978-3-319-43949-5

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