A Multi-resolution Approach for Content-based Image Retrieval on the Grid

Summary

Objectives: Hospitals and medical centers are producing more and more data that need to be processed. Those data are confidential, heterogeneous, and limited to the geographic site where they have been produced. Unless properly anonymized, they cannot be distributed on wide area networks.

Methods: Grid technologies allow the globalization of storage and processing resources, and enable large-scale experimentations on distributed data. They constitute a promising tool to treat the different data and analyze the knowledge they contain, while offering secured access and high-performance computing capacities to the different users. Our aim is to evaluate the possibilities of grid technologies for handling medical data.

Results and Conclusions: In this paper, we focus on a breast cancer diagnosis assistance tool, based on distributed and incremental knowledge construction and a content-based image retrieval system. We analyze the different scenarios of uses of such a tool. We further propose an algorithm that indexes mammographic images for content-based query purposes. This algorithm is tested on images of different resolutions in order to reduce the indexation time and we analyze its performance with experiments on the grid.

Keywords

Medical imaging - grid computing - breast cancer - mammographic indexation - multiresolution

• References

• 1 Foster I, Kesselmann C. The GRID: Blueprint for a new computing infrastructure. USA: Morgan Kaufmann Publishers; 1999
• 2 Foster I, Kesselmann C. The anatomy of the grid: enabling scalable virtual organizations. International Journal of Supercomputer Applications. 2001
• 3 Etiam: multimedia connectivity for healthcare. Available at. http://www.etiam.com.
• 4 MDinteractive: Electronic medical record (emr) and disease management system. Available at. http://www.mdinteractive.com/v2/.
• 5 European datagrid project. Available at. http://eudatagrid.web.cern.ch/eu-datagrid/.
• 6 Medigrid: Imagerie médicale haute performance sur la grille de calcul. Action concertée incitative globalisation des ressources informatiques 2002. Available at. http://creatis-www.insa-lyon.fr/MEDIGRID.
• 7 Ragtime Rhône-Alpes: Grille pour le traitement d’informations médicales. Available at. http://liris.univ-lyon2.fr/~miguet/ragtime/projet/index.html.
• 8 Heath M, Bowyer KW, Kopans D. Current status of the digital database for screening mammography. In Digital Mammography. luwer Academic Publishers; 1998: 457-60. Available at http://marathon.csee.usf.edu/Mammography/Database.html.
• 9 Highnam R, Brady M, Shepstone B. A representation for mammographic image processingq. In. Ayache N. editor, First international conference on computer vision, virtual reality and robotics in medicine. CVRMed’95, Nice, France. Springer-Verlag: Lecture Notes in Computer Science; 1995: 905.
• 10 Tweed T, Miguet S. Automatic detection of regions in interest in mammographies based on a combined analysis of texture and histogram. In ICPR 2002 (International Conference on Pattern Recognition). Québec City, Canada: August 11-15 2002: 448-552.
• 11 Haralick R, Shanmugam K, Dinstein I. Textural features for image classication. IEEE Trans Syst Man Cyber 1973; 3 (06) 610-21.
• 12 Tweed T, Miguet S. Medical image database on the grid: strategies of data distribution. In Proceeding of the First European Health Grid Conference. Lyon, France: January 16-17 2003: 152-62.
• 13 Centre de calcul in2p3. http://webcc.in2p3.fr.