A Method for Assessment of Segmentation Success Considering Uncertainty in the Edge Positions

  • Rubén UsamentiagaEmail author
  • Daniel F García
  • Carlos López
  • Diego González
Open Access
Research Article
Part of the following topical collections:
  1. Performance Evaluation in Image Processing


A method for segmentation assessment is proposed. The technique is based on a comparison of the segmentation produced by an algorithm with an ideal segmentation. The procedure to obtain the ideal segmentation is described in detail. Uncertainty regarding the edge positions is accounted for in the discrepancy calculation of each edge using fuzzy reasoning. The uncertainty measurement consists of a generalization, using fuzzy membership functions, of the similarity metrics used by well-known assessment methods. Several alternatives for the fuzzy membership functions, based on statistical properties of the possible positions of each edge, are defined. The proposed uncertainty measurement can be easily applied to other well-known methods. Finally, the segmentation assessment method is used to determine the best segmentation algorithm for thermographic images, and also to tune the optimum parameters of each algorithm.


Information Technology Optimum Parameter Quantum Information Assessment Method Uncertainty Measurement 


  1. 1.
    Haralick RM, Shapiro LG: Image segmentation techniques. Computer Vision, Graphics, and Image Processing 1985, 29(1):100–132. 10.1016/S0734-189X(85)90153-7CrossRefGoogle Scholar
  2. 2.
    Zhang YJ: A survey on evaluation methods for image segmentation. Pattern Recognition 1996, 29(8):1335–1346. 10.1016/0031-3203(95)00169-7CrossRefGoogle Scholar
  3. 3.
    Yang L, Albregtsen F, Lønnestad T, Grøttum P: A supervised approach to the evaluation of image segmentation methods. Proc. 6th International Conference on Computer Analysis of Images and Patterns (CAIP '95), September 1995, Prague, Czech Republic 759–765.CrossRefGoogle Scholar
  4. 4.
    Levine MD, Nazif AM: Dynamic measurement of computer generated image segmentations. IEEE Trans. Pattern Anal. Machine Intell. 1985, 7(2):155–164.CrossRefGoogle Scholar
  5. 5.
    Zhang YJ, Gerbrands JJ: Segmentation evaluation using ultimate measurement accuracy. Image Processing Algorithms and Techniques III, May 1992, San Jose, Calif, USA, Proceedings of SPIE 1657: 449–460.CrossRefGoogle Scholar
  6. 6.
    Román-Roldán R, Gómez Lopera JF, Atae-Allah C, Martínez-Aroza J, Luque-Escamilla PL: A measure of quality for evaluating methods of segmentation and edge detection. Pattern Recognition 2001, 34(5):969–980. 10.1016/S0031-3203(00)00052-2CrossRefGoogle Scholar
  7. 7.
    Yitzhaky Y, Peli E: A method for objective edge detection evaluation and detector parameter selection. IEEE Trans. Pattern Anal. Machine Intell. 2003, 25(8):1027–1033. 10.1109/TPAMI.2003.1217608CrossRefGoogle Scholar
  8. 8.
    Lee SU, Chung SY, Park RH: A comparative performance study of several global thresholding techniques for segmentation. Computer Vision, Graphics, and Image Processing 1990, 52(2):171–190. 10.1016/0734-189X(90)90053-XCrossRefGoogle Scholar
  9. 9.
    Yasnoff WA, Mui WA, Bacus JW: Error measures in scene segmentation. Pattern Recognition 1977, 9(4):217–231. 10.1016/0031-3203(77)90006-1CrossRefGoogle Scholar
  10. 10.
    Pratt WK: Digital Image Processing. Wiley-Interscience, New York, NY, USA; 1977.zbMATHGoogle Scholar
  11. 11.
    van der Heyden F: Evaluation of edge detection algorithms. Proc. 3rd International Conference on Image Processing and its Applications, July 1989, Warwick, UK 618–622.Google Scholar
  12. 12.
    Strasters KC, Gerbrands JJ: Three-dimensional image segmentation using a split, merge and group approach. Pattern Recognition Letters 1991, 12(5):307–325. 10.1016/0167-8655(91)90414-HCrossRefGoogle Scholar
  13. 13.
    Yager RR: On a general class of fuzzy connectives. Fuzzy Sets and Systems 1980, 4(3):235–242. 10.1016/0165-0114(80)90013-5MathSciNetCrossRefGoogle Scholar
  14. 14.
    Dubois D, Prade H: Fuzzy Sets and Systems: Theory and Applications. Academic Press, New York, NY, USA; 1980.zbMATHGoogle Scholar
  15. 15.
    Zadeh LA: Fuzzy sets and systems. Information and Control 1965, 8(3):338–353. 10.1016/S0019-9958(65)90241-XMathSciNetCrossRefGoogle Scholar
  16. 16.
    Usamentiaga R, García DF, López C, González JA: Algorithms for real-time acquisition and segmentation of a stream of thermographic line scans in industrial environments. Journal of Imaging Science and Technology 2005, 49(2):138–153.Google Scholar

Copyright information

© Rubén Usamentiaga et al. 2006

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Authors and Affiliations

  • Rubén Usamentiaga
    • 1
    Email author
  • Daniel F García
    • 1
  • Carlos López
    • 1
  • Diego González
    • 1
  1. 1.Department of Computer ScienceUniversity of OviedoGijónSpain

Personalised recommendations