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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Singular Value Decomposition (SVD).
References
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)
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)
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)
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)
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)
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)
Salton, G., Wong, A., Yang, C.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)
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)
Robertson, S., Walker, S., Hancock-Beaulieu, M., Gull, A., Lau, M.: Okapi at trec-3. In: Text REtrieval Conference, pp. 21–30 (1994)
Zhai, C.: Notes on the lemur TFIDF model. Technical report, Carnegie Mellon University (2001)
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
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)
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)
Larlus, D., Dorkó, G., Jurie, F.: Création de vocabulaires visuels efficaces pour la catégorisation d’images. In: Reconnaissance des Formes et Intelligence Artificielle (2006)
Jurie, F., Triggs, W.: Creating efficient codebooks for visual recognition. In: ICCV 2005 (2005)
Vidal-Naquet, M., Ullman, S.: Object recognition with informative features and linear classification. In: ICCV, pp. 281–288 (2003)
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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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