Commenced in January 2007
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Paper Count: 33122
Content-based Retrieval of Medical Images
Authors: Lilac A. E. Al-Safadi
Abstract:
With the advance of multimedia and diagnostic images technologies, the number of radiographic images is increasing constantly. The medical field demands sophisticated systems for search and retrieval of the produced multimedia document. This paper presents an ongoing research that focuses on the semantic content of radiographic image documents to facilitate semantic-based radiographic image indexing and a retrieval system. The proposed model would divide a radiographic image document, based on its semantic content, and would be converted into a logical structure or a semantic structure. The logical structure represents the overall organization of information. The semantic structure, which is bound to logical structure, is composed of semantic objects with interrelationships in the various spaces in the radiographic image.Keywords: Semantic Indexing, Content-Based Retrieval, Radiographic Images, Data Model
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1082997
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[1] Cheng L., Zheng J., Savova G., Erickson B. (2009). Discerning Tumor Status from Unstructured MRI Reports Completeness of Information in Existing Reports and Utility of Automated Natural Language Processing. J Digit Imaging.
[2] Fujii H., Yamagishi H., Ando Y., Tsukamoto N., Kawaguchi O, Kasamatsu T, et al. (2007). Structuring of free-text diagnostic report. Stud Health Technol Inform;129(pt 1):669-73.
[3] ISO 8613 International Standard. Information Processing - Text and Office Systems - Open Document Architecture (ODA) and Interchange Format (ODIF) (1988).
[4] ISO 8879 International Standard. Information Processing - Text and Office Systems - Standard Generalized Markup Language (SGML), (1986).
[5] Kelly, P. M., Cannon, T. M. and Hush, D. R. (1995). Query by image example: the CANDID approach, Storage and Retrieval for Image and Video Databases III, vol. 2420, pp. 238-248.
[6] Korn, F., Sidiropoulos, N. , Faloutsos, C. , Siegel, E. and Protopapas, Z. (1998). Fast and effective retrieval of medical tumor shapes. IEEE Trans. Knowl. Data Eng., vol. 10, no. 6, pp. 889-904.
[7] Mechouche, A., Golbreich, C., and Gibaud, B. (2007). Towards an hybrid system using an ontology enriched by rules for the semantic annotation of brain MRI images. In Marchiori, M., Pan, J., and de Sainte Marie, C., editors, Lecture Notes in Computer Science, volume 4524, pages 219_228.
[8] Nah, Y. and Sheu, P. C. (2002). Image content modeling for neuroscience databases. in Proc. Int. Software Engineering and Knowledge Engineering Conf., Italy: Ischia, pp. 91-98
[9] Orphanoudakis S., Petrakis, E. and Kofakis, P. (1989). A Medical Image Database System for Tomographic Images", Proceedings of CAR'89, Springer-Verlag, Berlin, pp.618-622.
[10] Papadopoulosa, G. T., Mezaris, V., Dasiopoulou, S., and Kompatsiaris, I. (2006). Semantic image analysis using a learning approach and spatial context. In Proceedings of the 1st international conference on Semantics And digital Media Technologies (SAMT).
[11] Shyu, C. R., Brodley, C. E., Kak, A. C., Kosaka, A. Aisen, A. and Broderick, L. S. (1999). ASSERT: A physician-in-the-loop content based image retrieval system for HRCT image databases, Comput. Vis. and Image Understanding, vol. 75, no. 1/2, pp. 111-132, 1999.
[12] Sistrom C., Dreyer K., Dang P., Weilburg J., Boland G., Rosenthal D., et al. (2009). Recommendations for additional imaging in radiology reports: multifactorial analysis of 5.9 million examinations. Radiology;253(2):453-61.
[13] Sonntag, D., Moller, M. (2010). Prototyping Semantic Dialogue Systems for Radiologists, Sixth International Conference of Intelligent Environments. DOI 10.1109/IE.2010.23
[14] Taira R., Soderland S., Jakobovits R. (2001). Automatic structuring of radiology free-text reports. Radiographics 2001 Jan-Feb;21(1):237-45.
[15] Vompras, J. (2005). Towards adaptive ontology-based image retrieval. In Stefan Brass, C. G., editor, 17th GI-Workshop on the Foundations of Databases, Wörlitz, Germany, pages 148_152. Institute of Computer Science, Martin-Luther-University Halle-Wittenberg.
[16] Wesley W., Victor Z., Liu (2003). A Knowledge-based Approach for Scenario-specific Content Correlation in a Medical Digital Library Cached, in a Medical Digital Library. UCLA Computer Science Technical Report, # 030039.
[17] Pinon, J.-M., Calabretto, S. and Poullet, L. (1997). Document Semantic Model: an experiment with patient medical records. Electronic Publishing '97 - New Models and Opportunities: Proceedings of an ICCC/IFIP conference held at the University of Kent, Kenterbury, UK, April 14-16 1997.
[18] Poullet, L., Calabretto, S. and Pinon, J.-M. (1997). A Semantic Model for Information Retrieval in Documents: an experiment with patient medical records. Electronic Publishing '97 - New Models and Opprtunities: Proceedings of an ICCC/IFIP conference held at the University of Kent, Kenterbury, UK, April 14-16 1997.
[19] Lin C, Ma L, Yin J, Chen J. (2009). A medical image semantic modeling based on hierarchical Bayesian networks. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2009 Apr;26(2):400-4.
[20] Berrut C., Mulhem P., Fourel F. and Mechkour M. (1998). Indexing, Navigation and retrieval of multimedia structured documents: the PRIME information retrieval system. Multimedia Information Analysis and Retrieval, Lecture Notes in Computer Science, 1998, Volume 1464/1998, 224-241, DOI: 10.1007/BFb0016501