An Architecture for a CBR Image Segmentation System

  • Petra Perner
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1650)

Abstract

Image Segmentation is a crucial step if extracting information from a digital image. It is not easy to set up the segmentation parameter so that it fits best over the entire set of images, which should be segmented. In the paper, we propose a novel architecture for image segmentation method based on CBR, which can adapt to changing image qualities and environmental conditions. We describe the whole architecture, the methods used for the various components of the systems and show how it performs on medical images.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    P. Perner, Different Learning Strategies in a Case-Based Reasoning System for Image Interpretation, Advances in Case-Based Reasoning, B. Smith and P. Cunningham (Eds.), LNAI 1488, Springer Verlag 1998, S. 251–261.Google Scholar
  2. 2.
    P. Perner, Case-Based Reasoning For Image Interpretation in Non-destructive Testing, 1st European Workshop on Case-Based Reasoning, Otzenhausen Nov. 1993, Proc. SFB 314 Univ. Kaiserslautern, Hrsg. M. Richter, vol. II, pp. 403–410Google Scholar
  3. 3.
    Bettin, J. Dietrich, C. Dannenberg, H. Barthel, D. Zedlick, K. Jobst, W.H. Knapp, “Früherkennung von Hirnleistungstörungen-Vergleich linearer und volumetrischer Parameter (CT) mit Ergebnissen der Perfusions-SPET,” 78. Deutscher Röntgenkongreß Wiesbaden 1997Google Scholar
  4. 4.
    S. Zhang, “Evaluation and Comparision of different Segmentation Algorithm,” Pattern Recognition Letters, v. 18, No. 10, pp. 963–968, 1997.CrossRefGoogle Scholar
  5. 5.
    G. Kummer and P. Perner, Motion Analysis, IBaI Report January 1999, ISSN 1431-2360Google Scholar
  6. 6.
    H. Dreyer and W. Sauer, Prozeßanalyse, Verlag Technik Berlin 1982Google Scholar
  7. 7.
    R. Ohlander, K. Price, and D.R. Reddy, “Picture Segmentation using recursive region splitting method,” Comput. Graphics and Image Processing, 8: 313–333, 1978CrossRefGoogle Scholar
  8. 8.
    C.H. Lee, “Recursive region splitting at the hierarchical scope views,” Computer Vision Graphics, and Image Processing, 33, 237–259, 1986CrossRefGoogle Scholar
  9. 9.
    P. Perner, Similarity-Based Image Segmentation, IBaI Report 1996 ISSN 1431-2360Google Scholar
  10. 10.
    A. Tversky, „Feature of Similarity“, Psychological Review, vol. 84, No. 4, pp. 327–350, 1977.CrossRefGoogle Scholar
  11. 11.
    Alzheimer Study “Degenerative Erkrankungen des zentralen und peripheren Nervensystems-Klinik und Grundlage”, BMFT Study, Abschlußbericht der medizinische Fakultüt der Uni Leipzig 1996Google Scholar
  12. 12.
    A. Tschammler et. al, “Computerized tomography volumetry of cerebrospinal fluid by semiautomatic contour recognition and gray value histogram analysis”, Rofo Fortschr. Geb. Roentgenstr. Neue Bildgeb. Verfahren 1996, Jan: 164(1): 13–1Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Petra Perner
    • 1
  1. 1.Institute of Computer Vision and Applied Computer SciencesLeipzigGermany

Personalised recommendations