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Symbolic indexing of cardiological sequences through dynamic curve representations

  • M. Baroni
  • G. Congiu
  • A. Del Bimbo
  • A. Evangelisti
  • E. Vicario
Biomedical Applications II
Part of the Lecture Notes in Computer Science book series (LNCS, volume 974)

Abstract

Digital image analysis supports diagnostic activities by highlighting geometric and temporal features of physiological phenomena that are not perceivable to the human observation. These features can be exploited to build up symbolic representations of visual data in medical reports and to index them within large databases. The comparison of such representations against descriptive queries capturing the properties of significant physiological phenomena supports new diagnostic approaches through the systematic analysis of database reports. A prototype system is presented which supports the construction of symbolic representations and their comparison against descriptive queries capturing geometric and temporal properties of time-varying 2D shapes deriving from dynamic cardiac analyses. The system is embedded within a visual shell allowing physicians to compose content-oriented queries through iconic interaction.

Keywords

Apical Region Symbolic Representation Visual Data Systolic Phase Chain Code 
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.

References

  1. 1.
    J.F. Allen, ”Maintaining Knowledge about Temporal Intervals,” in Comunications of the ACM, Vol.26, n.11, Nov. 1983.Google Scholar
  2. 2.
    M.Baroni,G.Barletta, ”Digital Curvature Estimation for Left Ventricular Shape Analysis,” Image and Vision Computing, 1992.Google Scholar
  3. 3.
    E.Binaghi, I.Gagliardi, R.Schettini, ”Indexing and Fuzzy Logic-Based Retrieval of Color Images,” in IFIP Trans. Visual Database Systems II, Knuth, Wegner (Eds.), Elsevier Pub. 1992.Google Scholar
  4. 4.
    S.K.Chang, Q.Y.Shi, C.W.Yan, ”Iconic Indexing by 2-D Strings”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.9, No.3, July 1987.Google Scholar
  5. 5.
    G.Congiu, ”Rappresentazione e Ricerca Visuale di Sequenze di Immagini Cardiologiche (In Italian),” Doctoral Thesis, Univ. Florence, Italy, July, 1994.Google Scholar
  6. 6.
    A.Del Bimbo, E.Vicario, D.Zingoni, ”Symbolic Description and Visual querying of Image Sequences Using Spatio-Temporal Logic,” accepted for publication on IEEE Transactions on Knowledge and Data Engineering.Google Scholar
  7. 7.
    K.Hirata, T.Kato, ”Query by Visual Example: Content-Based Image Retrieval,” In Advances in Database Technology — EDBT'92, A.Pirotte, C.Delobel, G.Gottlob (Eds.), Lecture Notes on Computer Science, Vol.580, Springer Verlag, Berlin, 1992.Google Scholar
  8. 8.
    M.Leyton, “Shape and Casual History,” in Visual Form, Plenum Press, 1982.Google Scholar
  9. 9.
    S.Marshall, “Review of Shape Coding Techniques,” in Image and Vision Computing, Nov.1989.Google Scholar
  10. 10.
    A.Nagasaka, Y.Tanaka, ”Automatic Video Indexing and Full Video Search for Object Appearances,” in IFIP Trans. Visual Database Sys. II, Knuth, Wegner (Eds.), Elsevier Pub. 1992.Google Scholar
  11. 11.
    S.E.Palmer, ”The Psychology of Perceptual Organization,: a Transformational Approach,” in Human and Machine Vision, Academic Press, New York, 1983.Google Scholar
  12. 12.
    W.Richards, D.D.Hoffman, ”Codon Constraints on Closed 2D Shapes,” in Computer Vision II, Natural Computation Group, M.I.T., Cambridge, 1984.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • M. Baroni
    • 1
  • G. Congiu
    • 2
  • A. Del Bimbo
    • 2
    • 3
  • A. Evangelisti
    • 2
  • E. Vicario
    • 2
  1. 1.Dip. Ingegneria ElettronicaUniversità di FirenzeItaly
  2. 2.Dip. Sistemi e InformaticaUniversità di FirenzeItaly
  3. 3.Dip. Elettronica per l'AutomazioneUniversità di BresciaItaly

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