Advertisement

Biomedical Image Processing Integration Through INBIOMED: A Web Services-Based Platform

  • David Pérez del Rey
  • José Crespo
  • Alberto Anguita
  • Juan Luis Pérez Ordóñez
  • Julián Dorado
  • Gloria Bueno
  • Vicente Feliú
  • Antonio Estruch
  • José Antonio Heredia
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3745)

Abstract

New biomedical technologies need to be integrated for research on complex diseases. It is necessary to combine and analyze information coming from different sources: genetic-molecular, clinical data and environmental risks. This paper presents the work carried on by the INBIOMED research network within the field of biomedical image analysis. The overall objective is to respond to the growing demand of advanced information processing methods for: developing analysis tools, creating knowledge structure and validating them in pharmacogenetics, epidemiology, molecular and image based diagnosis research environments. All the image processing tools and data are integrated and work within a web services-based application, the so called INBIOMED platform. Finally, several biomedical research labs offered real data and validate the network tools and methods in the most prevalent pathologies: cancer, cardiovascular and neurological. This work provides a unique biomedical information processing platform, open to the incorporation of data coming from other feature disease networks.

Keywords

Mobile Agent Active Contour Model Client Application Cancer Epidemiology Common Object Request Broker Architecture 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Lopez-Alonso, V., Sanchez, J.P., Liebana, I., Hermosilla, I., Martin-Sanchez, F.: INBIOMED: a platform for the integration and sharing of genetic, clinical and epidemiological data oriented to biomedical research. In: Proceedings of Bioinformatics and Bioengineering, BIBE 2004, pp. 222–226 (2004)Google Scholar
  2. 2.
    Erl, T.: Service-Oriented Architecture: A Field Guide to Integrating XML and Web Services. Prentice Hall, New Jersey (2004)Google Scholar
  3. 3.
    Grosso, W.: Java RMI. O’Reilly and Associates, Inc., Sebastopol, CA, USA (2001) Google Scholar
  4. 4.
    Waldo, J.: Remote procedure calls and Java Remote Method Invocation. IEEE Concurrency, 5–7 (July 1998)Google Scholar
  5. 5.
    Vinoski, S.: Where is Middleware? IEEE Internet Computing 6(2), 83–85 (2002)CrossRefGoogle Scholar
  6. 6.
    Felber, P., Garbinato, B., Guerraoui, R.: The Design of a CORBA Group Communication Service. In: Proceedings of 15th Symposium on Reliable Distributed Systems (1996)Google Scholar
  7. 7.
    Vinoski, S.: CORBA: Integrating Diverse Applications Within Distributed Heterogeneous Environments. IEEE Communications Magazine 14(2) (February 1997)Google Scholar
  8. 8.
    Chung, Y.H., Yajnik, S., Liang, D., Shin, J., Wang, C.Y., Wang, Y.M.: DCOM and CORBA: Side by Side, Step by Step, and Layer by Layer. C++ Report, 18-29 (1998)Google Scholar
  9. 9.
    Adamopoulos, D.X., Pavlou, G., Papandreou, C.A., Manolessos, E.: Distributed Object Platforms in Telecommunications: A Comparison Between DCOM and CORBA. British Telecommunications Engineering 18, 43–49 (1999)Google Scholar
  10. 10.
    Liang, K.C., Chu, W.C., Yuan, S.M., Lo, W.: From Legacy RPC Services to Distributed Objects. In: Proceedings of the Sixth IEEE Computer Society Workshop on Future Trends of Distributed Computing Systems (1997)Google Scholar
  11. 11.
    Snell, J., Tidwell, D., Kulchenko, P.: Programming Web Services with Soap. O’Reilly and Associates, Inc., Sebastopol (2001)Google Scholar
  12. 12.
    Kotz, D., Gray, R.S.: Mobile agents and the future of the Internet. ACM Operating Systems Review 33(3), 7–13 (1999)CrossRefGoogle Scholar
  13. 13.
    Bote-Lorenzo, M., Dimitriadis, A., Gómez-Sánchez, E.: Grid Characteristics and Uses: a Grid Definition. In: Fernández Rivera, F., Bubak, M., Gómez Tato, A., Doallo, R. (eds.) Across Grids 2003. LNCS, vol. 2970, pp. 291–298. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  14. 14.
    Ernemann, C., Hamscher, V., Schwiegelshohn, U., Yahyapour, R., Streit, A.: On Advantages of Grid Computing for Parallel Job Scheduling. In: Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid, CCGRID 2002 (2002)Google Scholar
  15. 15.
    ImageProcess0.4 – Image Processing Tool, developed by sougeforge.net (2004), Available in http://imageprocess.sourceforge.net/index.shtml
  16. 16.
    Smith, K., Paranjape, R.: Mobile Agents for Web-Based Medical Image Retrieval. In: Proceedings of the 1999 IEEE Canadian Conference on Electrical and Computer Engineering, Shaw Conference Center, Edmonton, Alberta, Canada, May 9-12 (1999)Google Scholar
  17. 17.
    Estruch, A., Heredia, J.A.: Technological platform to aid the exchange of information and applications using web services. In: Barreiro, J.M., Martín-Sánchez, F., Maojo, V., Sanz, F. (eds.) ISBMDA 2004. LNCS, vol. 3337, pp. 458–468. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  18. 18.
    Serra, J.: Mathematical Morphology: theoretical advances, vol. II. Academic Press, London (1988)Google Scholar
  19. 19.
    Soille, P.: Morphological Image Analysis, Principles and Applications. Springer, Heidelberg (2003)zbMATHGoogle Scholar
  20. 20.
    Salembier, P., Serra, J.: Flat zones filtering, connected operators, and filters by reconstruction. IEEE Transactions on Image Processing 4(8), 1153–1160 (1995)CrossRefGoogle Scholar
  21. 21.
    Crespo, J., Maojo, V.: New Results on the Theory of Morphological Filters by Reconstruction. Pattern Recognition 31(4), 419–429 (1998)CrossRefGoogle Scholar
  22. 22.
    Meyer, F., Beucher, S.: Morphological segmentation. J. Visual Commun. Image Repres. 1(1), 21–45 (1990)CrossRefGoogle Scholar
  23. 23.
    Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. Int.J. Comput. Vis. 14(26), 321–331 (1988)CrossRefGoogle Scholar
  24. 24.
    Malladi, R., Sethian, J.A., Vemuri, B.C.: Shape Modeling with Front Propagation: A Level Set Approach. IEEE Trans. on PAMI 17, 158–175 (1995)Google Scholar
  25. 25.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic Active Contours. Int. J. Comput. Vis. 22(1), 61–79 (1997)zbMATHCrossRefGoogle Scholar
  26. 26.
    Bueno, G., Martínez, A., Adán, A.: Fuzzy-Snake Segmentation of Anatomical Structures. In: Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. LNCS, vol. 3212, pp. 33–42. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  27. 27.
    Maojo, V., Kulikowski, C.A.: Bioinformatics and medical informatics: collaborations on the road to genomic medicine. Journal of the American Medical Informatics Association 10(6), 515–522 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • David Pérez del Rey
    • 1
  • José Crespo
    • 1
  • Alberto Anguita
    • 1
  • Juan Luis Pérez Ordóñez
    • 2
  • Julián Dorado
    • 2
  • Gloria Bueno
    • 3
  • Vicente Feliú
    • 3
  • Antonio Estruch
    • 4
  • José Antonio Heredia
    • 4
  1. 1.Biomedical Informatics Group, Artificial Intelligence Department, School of Computer ScienceUniversidad Politécnica de MadridBoadilla del Monte, Madrid
  2. 2.RNASA-IMEDIR (Lab. Redes de Neuronas Artificiales y Sistemas Adaptativos, Centro de Informática Médica y Diagnóstico Radiológico)Universidade da Coruña 
  3. 3.UCLM-ISA, E.T.S.I. Industriales, Ingeniería de Sistema y AutomáticaUniversidad de Castilla-La ManchaCiudad Real
  4. 4.Departamento de TecnologíaUniversitat Jaume ICastellón

Personalised recommendations