Content-based Retrieval of Medical Images

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.




References:
[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