Advertisement

A supervised approach to the evaluation of image segmentation methods

  • Luren Yang
  • Fritz Albregtsen
  • Tor Lønnestad
  • Per Grøttum
Posters
Part of the Lecture Notes in Computer Science book series (LNCS, volume 970)

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.

Keywords

Segmentation Algorithm Manual Segmentation Thresholding Method Vision Graph Image Segmentation Method 
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.
    Albregtsen, F.: Non-parametric histogram thresholding methods — error versus relative object area. Proc. 8th Scadinavian Conf. Image Analysis (1993) 273–280Google Scholar
  2. 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. 3.
    Haralick, R. M., Shapiro, L. G.: Image segmentation techniques. Comput. Vision Graph. Image Process. 29 (1985) 100–132Google Scholar
  4. 4.
    Kulpa, Z.: Area and perimeter measurement of blobs in discrete binary pictures. Omput. Graph. Image Process. 6 (1977) 434–451Google Scholar
  5. 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) 171–190Google Scholar
  6. 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) 149–160Google Scholar
  7. 7.
    Levine, M. D., Nazif, A. M.: Dynamic measurement of computer generated image segmentations. IEEE Trans. Pattern Anal. Machine Intell. 7 (1985) 155–164Google Scholar
  8. 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) 935–952Google Scholar
  9. 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) 438–460Google Scholar
  10. 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) 233–260Google Scholar
  11. 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. 12.
    Weszka, J. S., Rosenfeld, A.: Threshold evaluation techniques. IEEE Trans. Sys. Man Cyb. 8 (1978) 622–629Google Scholar
  13. 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) 201–204Google Scholar
  14. 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) 272–276Google Scholar
  15. 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) 219–227Google Scholar
  16. 16.
    Yanowitz, S. D., Bruckstein, A. M.: A new method for image segmentation. Comput. Vision Graph. Image Process. 46 (1989) 82–95Google Scholar
  17. 17.
    Yasnoff, W. A., Mui, J. K., Bacus, J. W.: Error measures for scence segmentation. Pattern Recognition 9 (1977) 217–231Google Scholar
  18. 18.
    Zhang, Y. J., Gerbrands, J. J.: Segmentation evaluation using ultimate measurement accuracy. Proc. SPIE Vol. 1657, Image Processing Algorithms and Techniques III (1992) 449–460Google Scholar
  19. 19.
    Zhang, Y. J.: Segmentation evaluation and comparison: a study of various algorithms. Proc. SPIE Vol. 2094, Visual Communications and Image Processing (1993) 801–812Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Luren Yang
    • 1
  • Fritz Albregtsen
    • 1
  • Tor Lønnestad
    • 1
  • Per Grøttum
    • 1
  1. 1.Department of InformaticsUniversity of OsloOsloNorway

Personalised recommendations