Skip to main content
SpringerLink
Log in

A supervised approach to the evaluation of image segmentation methods

  • Posters
  • Conference paper
  • First Online:
Computer Analysis of Images and Patterns (CAIP 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 970))

Included in the following conference series:

Abstract

Evaluation is an important step in developing a segmentation algorithm for an image analysis system. We first give a review of segmentation evaluation methods, and then demonstrate how a supervised evaluation method based on shape features is used in the development of a segmentation algorithm for fluorescence images of white blood cells.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Albregtsen, F.: Non-parametric histogram thresholding methods — error versus relative object area. Proc. 8th Scadinavian Conf. Image Analysis (1993) 273–280

    Google Scholar 

  2. Eikvil, L., Taxt, T., Moen, K.: A fast adaptive method for binarization of documents images. Proc. 1st Int. Conf. Document Analysis and Recognition (1991)

    Google Scholar 

  3. Haralick, R. M., Shapiro, L. G.: Image segmentation techniques. Comput. Vision Graph. Image Process. 29 (1985) 100–132

    Google Scholar 

  4. Kulpa, Z.: Area and perimeter measurement of blobs in discrete binary pictures. Omput. Graph. Image Process. 6 (1977) 434–451

    Google Scholar 

  5. Lee, S. U., Chung, S. Y., Park, R. H.: A comparative performance study of several global thresholding techniques for segmentation. Comput. Vision Graph. Image Process. 52 (1992) 171–190

    Google Scholar 

  6. Levine, M. D., Nazif, A. M.: An experimental rule-based system for testing low level segmentation strategies. Multicomputers and Image Processing Algorithms and Programs. Academic Press (1982) 149–160

    Google Scholar 

  7. Levine, M. D., Nazif, A. M.: Dynamic measurement of computer generated image segmentations. IEEE Trans. Pattern Anal. Machine Intell. 7 (1985) 155–164

    Google Scholar 

  8. Lim, Y. W., Lee, S. U.: On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques. Patt. Recogn. 23 (1990) 935–952

    Google Scholar 

  9. Prokop, R. J., Reeves, A. P.: A survey of moment-based techniques for unoccluded object representation and recognition. CVGIP: Graphical Models and Image Processing 54 (1992) 438–460

    Google Scholar 

  10. Sahoo, P. K., Soltani, S., Wong, A. K. C., Chen, Y. C.: A survey of thresholding techniques. Comput. Vision Graph. Image Process. 41 (1988) 233–260

    Google Scholar 

  11. Trier, Ø. D., Jain, A. K.: Goal-directed evaluation of binarization methods. Proc. NSF/ARPA Workshop on Performance vs. Methodology in Computer Vision (1994)

    Google Scholar 

  12. Weszka, J. S., Rosenfeld, A.: Threshold evaluation techniques. IEEE Trans. Sys. Man Cyb. 8 (1978) 622–629

    Google Scholar 

  13. Yang, L., Albregtsen, F.: Fast computation of invariant geometric moments: a new method giving correct results. Proc. 12th Int. Conf. Pattern Recognition, Vol. I (1994) 201–204

    Google Scholar 

  14. Yang, L., Albregtsen, F., Lønnestad, T., Grøttum, P.: Methods to estimate areas and perimeters of blob-like objects: a comparison. Proc. IAPR Workshop on Machine Vision Applications (1994) 272–276

    Google Scholar 

  15. Yang, L., Albregtsen, F., Lønnestad, T., Grøttum, P., Iversen, J.-G., Røtnes, J. S., Røttingen, J.-A.: Measuring shape and motion of white blood cells from sequences of fluorescence microscopy images, Proc. 9th Scandinavian Conf. Image Analysis, Vol. I (1995) 219–227

    Google Scholar 

  16. Yanowitz, S. D., Bruckstein, A. M.: A new method for image segmentation. Comput. Vision Graph. Image Process. 46 (1989) 82–95

    Google Scholar 

  17. Yasnoff, W. A., Mui, J. K., Bacus, J. W.: Error measures for scence segmentation. Pattern Recognition 9 (1977) 217–231

    Google Scholar 

  18. Zhang, Y. J., Gerbrands, J. J.: Segmentation evaluation using ultimate measurement accuracy. Proc. SPIE Vol. 1657, Image Processing Algorithms and Techniques III (1992) 449–460

    Google Scholar 

  19. Zhang, Y. J.: Segmentation evaluation and comparison: a study of various algorithms. Proc. SPIE Vol. 2094, Visual Communications and Image Processing (1993) 801–812

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Informatics, University of Oslo, Blindern, P.O. Box 1080, N-0316, Oslo, Norway

    Luren Yang, Fritz Albregtsen, Tor Lønnestad & Per Grøttum

Authors
  1. Luren Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fritz Albregtsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tor Lønnestad
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Per Grøttum
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Václav Hlaváč Radim Šára

Rights and permissions

Reprints and permissions

Copyright information

© 1995 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yang, L., Albregtsen, F., Lønnestad, T., Grøttum, P. (1995). A supervised approach to the evaluation of image segmentation methods. In: Hlaváč, V., Šára, R. (eds) Computer Analysis of Images and Patterns. CAIP 1995. Lecture Notes in Computer Science, vol 970. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-60268-2_377

Download citation

  • DOI: https://doi.org/10.1007/3-540-60268-2_377

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-60268-2

  • Online ISBN: 978-3-540-44781-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation