Abstract
Medical systems increasingly demand methods to deal with the large amount of images that are daily generated. Therefore, the development of fast and scalable applications to store and retrieve images in large repositories becomes an important concern. Moreover, it is necessary to handle textual and content-based queries over such data coupled with DICOM image metadata and their visual patterns. While DBMSs have been extensively used to manage applications’ textual information, content-based processing tasks usually rely on specific solutions. Most of these solutions are targeted to relatively small and controlled datasets, being unfeasible to be employed in real medical environments that deal with voluminous databases. Moreover, since in existing systems the content-based retrieval is detached from the DBMS, queries integrating content- and metadata-based predicates are executed isolated, having their results joined in additional steps. It is easy to realize that this approach prevent from many optimizations that would be employed in an integrated retrieval engine. In this paper we describe the MedFMI-SiR system, which handles medical data joining textual information, such as DICOM tags, and intrinsic image features integrated in the retrieval process. The goal of our approach is to provide a subsystem that can be shared by many complex data applications, such as data analysis and mining tools, providing fast and reliable content-based access over large sets of images. We present experiments that show that MedFMI-SiR is a fast and scalable solution, being able to quickly answer integrated content- and metadata-based queries over a terabyte-sized database with more than 10 million medical images from a large clinical hospital.
This work has been supported by CNPq, FAPESP, Capes and Microsoft Research.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Akgül, C., Rubin, D., Napel, S., Beaulieu, C., Greenspan, H., Acar, B.: Content-based image retrieval in radiology: Current status and future directions. J. Digital Imaging 24, 208–222 (2011)
Alto, H., Rangayyan, R.M., Desautels, J.E.L.: Content-based retrieval and analysis of mammographic masses. Journal of Electronic Imaging 14(2), 1–17 (2005)
Balan, A.G.R., Traina, A.J.M., Ribeiro, M.X., Marques, P.M.D.A., Traina Jr., C.: HEAD: the human encephalon automatic delimiter. In: CBMS 2007, Maribor, Slovenia, pp. 171–176. IEEE, Los Alamitos (2007)
Barioni, M.C.N., Razente, H.L., Traina, A.J.M., Traina, C.J.: SIREN: A similarity retrieval engine for complex data. In: VLDB 2006, Seoul, South Korea, pp. 1155–1158. ACM, New York (2006)
Berchtold, S., Böhm, C., Keim, D.A., Krebs, F., Kriegel, H.P.: On optimizing nearest neighbor queries in high-dimensional data spaces. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973, pp. 435–449. Springer, Heidelberg (2000)
Bueno, J.M., Chino, F.J.T., Traina, A.J.M., Traina Jr., C., Marques, P.M.d.A.: How to add content-based image retrieval capability in a PACS. In: CBMS 2002, Maribor, Slovenia, pp. 321–326. IEEE, Los Alamitos (2002)
Böhm, C., Berchtold, S., Keim, D.A.: Searching in high-dimensional spaces - index structures for improving the performance of multimedia databases. ACM Computing Surveys 33(3), 322–373 (2001)
Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image retrieval: Ideas, influences, and trends of the new age. ACM Computing Surveys 40(2), 1–60 (2008)
Deselaers, T., Keysers, D., Ney, H.: Features for image retrieval: An experimental comparison. Information Retrieval 11(2), 77–107 (2008)
Dimitrovski, I., Guguljanov, P., Loskovska, S.: Implementation of web-based medical image retrieval system in oracle. In: ICAST, pp. 192–197. IEEE, Los Alamitos (2009)
Felipe, J.C., Traina Jr., C., Traina, A.J.M.: A new family of distance functions for perceptual similarity retrieval of medical images. J. Digital Imaging 22(2), 183–201 (2009)
Greenspan, H., Pinhas, A.T.: Medical image categorization and retrieval for PACS using the GMM-KL framework. IEEE Trans. on Inf. Technology in Biomedicine 11(2), 190–202 (2005)
Guliato, D., Melo, E.V., Rangayyan, R.M., Soares, R.C.: PostgreSQL-IE: An image-handling extension for PostgreSQL. J. Digital Imaging 22(2), 149–165 (2008)
Hsu, W., Antani, S., Long, L.R., Neve, L., Thoma, G.R.: SPIRS: A web-based image retrieval system for large biomedical databases. International Journal of Medical Informatics 78(1), 13–24 (2009)
IBM Corp.: Image, audio, and video extenders administration and programming guide, DB2 Universal Database Version 8 (2003)
Kalpathy-Cramer, J., Hersh, W.: Multimodal medical image retrieval: image categorization to improve search precision. In: MIR 2010, Philadelphia, Pennsylvania, USA, pp. 165–174. ACM, New York (2010)
Kaster, D.S., Bugatti, P.H., Traina, A.J.M., Traina Jr., C.: FMI-SiR: A flexible and efficient module for similarity searching on Oracle database. JIDM 1(2), 229–244 (2010)
Lehmann, T.M., Güld, M., Thies, C., Fischer, B., Spitzer, K., Keysers, D., Ney, H., Kohnen, M., Schubert, H., Wein, B.B.: Content-based image retrieval in medical applications. Methods of Informatics in Medicine 43, 354–361 (2004)
Long, L.R., Antani, S., Deserno, T.M., Thoma, G.R.: Content-based image retrieval in medicine: Retrospective assessment, state of the art, and future directions. IJHISI 4(1), 1–16 (2009)
Müller, H., Deselaers, T., Deserno, T.M., Kalpathy–Cramer, J., Kim, E., Hersh, W.: Overview of the imageCLEFmed 2007 medical retrieval and medical annotation tasks. In: Peters, C., Jijkoun, V., Mandl, T., Müller, H., Oard, D.W., Peñas, A., Petras, V., Santos, D. (eds.) CLEF 2007. LNCS, vol. 5152, pp. 472–491. Springer, Heidelberg (2008)
Müller, H., Michoux, N., Bandon, D., Geissbuhler, A.: A review of content-based image retrieval systems in medical applications-clinical benefits and future directions. Int. Journal of Medical Informatics 73(1), 1–23 (2004)
Névéol, A., Deserno, T.M., Darmoni, S.J., Güld, M.O., Aronson, A.R.: Natural language processing versus content-based image analysis for medical document retrieval. J. of the American Society for Information Science and Technology 60(1), 123–134 (2009)
Oracle Corp.: Oracle interMedia User’s Guide, 10g Release 2 (10.2) (2005)
Oracle Corp.: Oracle Multimedia DICOM Developer’s Guide, 11g Release 2 (2009)
Pereira Jr., R.R., de Azevedo-Marques, P.M., Honda, M.O., Kinoshita, S.K., Engelmann, R., Muramatsu, C., Doi, K.: Usefulness of texture analysis for computerized classification of breast lesions on mammograms. Journal of Digital Imaging 20(3), 248–255 (2007)
Rahman, M.M., Antani, S.K., Thoma, G.R.: A classification-driven similarity matching framework for retrieval of biomedical images. In: MIR 2010, Philadelphia, Pennsylvania, USA, pp. 147–154. ACM, New York (2010)
Samet, H.: Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann, San Francisco (2006)
Silva, Y.N., Aref, W.G., Ali, M.H.: The similarity join database operator. In: ICDE 2010, Long Beach, California, USA, pp. 892–903. IEEE, Los Alamitos (2010)
Tan, Y., Zhang, J., Hua, Y., Zhang, G., Huang, H.: Content-based image retrieval in picture archiving and communication systems. In: Medical Imaging 2006: PACS and Imaging Informatics, San Diego, CA, USA, vol. 6145, pp. 282–289. SPIE, San Jose (2006)
Traina Jr., C., Traina, A.J.M., Faloutsos, C., Seeger, B.: Fast indexing and visualization of metric datasets using Slim-trees. IEEE Trans. on Knowl. and Data Eng. 14(2), 244–260 (2002)
Traina Jr., C., Traina, A.J.M., Vieira, M.R., Arantes, A.S., Faloutsos, C.: Efficient processing of complex similarity queries in RDBMS through query rewriting. In: CIKM 2006, Arlington, VA, USA, pp. 4–13. ACM, New York (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kaster, D.S. et al. (2011). MedFMI-SiR: A Powerful DBMS Solution for Large-Scale Medical Image Retrieval. In: Böhm, C., Khuri, S., Lhotská, L., Pisanti, N. (eds) Information Technology in Bio- and Medical Informatics. ITBAM 2011. Lecture Notes in Computer Science, vol 6865. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23208-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-642-23208-4_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23207-7
Online ISBN: 978-3-642-23208-4
eBook Packages: Computer ScienceComputer Science (R0)