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Dissimilarity criteria and their comparison for quantitative evaluation of image segmentation: application to human retina vessels

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Abstract

The quantitative evaluation of image segmentation is an important and difficult task that is required for making a decision on the choice of a segmentation method and for the optimal tuning of its parameter values. To perform this quantitative evaluation, dissimilarity criteria are relevant with respect to the human visual perception, contrary to metrics that have been shown to be visually not adapted. This article proposes to compare eleven dissimilarity criteria together. The field of retina vessels image segmentation is taken as an application issue to emphasize the comparison of five specific image segmentation methods, with regard to their degrees of consistency and discriminancy. The DRIVE and STARE databases of retina images are employed and the manual/visual segmentations are used as a reference and as a control method. The so-called \(\epsilon \) criterion gives results in agreement with perceptually based criterions for achieving the quantitative comparison.

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References

  1. Abdou, I., Pratt, W.: Qualitative design and evaluation of enhancement/thresholding edge detector. Proc. IEEE. 67(5), 753–763 (1979)

    Article  Google Scholar 

  2. Cardoso, J., Corte-Real, L.: Toward a generic evaluation of image segmentation. IEEE Trans. Image Process. 14(11), 1773–1782 (2005). doi:10.1109/TIP.2005.854491

    Article  Google Scholar 

  3. Chalana, V., Kim, Y.: A methodology for evaluation of boundary detection algorithms on medical images. IEEE Trans. Med. Imaging 16(5), 642–652 (1997)

    Article  Google Scholar 

  4. Chanwimaluang, T., Fan, G., Fransen, S.R.: Hybrid retinal image registration. IEEE Trans. Inf. Technol. Biomed. 10(1), 129–142 (2006)

    Article  Google Scholar 

  5. Chaudhuri, S., Chatterjee, S., Katz, N., Nelson, M., Goldbaum, M.: Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. Med. Imaging 8(3), 263–269 (1989). doi:10.1109/42.34715

    Article  Google Scholar 

  6. Deza, M.M., Deza, E.: Dictionary of Distances. Elsevier, Amsterdam (2006)

    Google Scholar 

  7. Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)

    Article  Google Scholar 

  8. Fix, E., Hodges, J.: Discriminatory analysis. nonparametric discrimination: consistency properties. International Statistical Review/Revue Internationale de Statistique 57(3), 238–247 (1989). http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA800276

  9. García, S., Fernández, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Inf. Sci. 180(10), 2044–2064 (2010)

    Article  Google Scholar 

  10. Gavet, Y.: Perception visuelle humaine, complétion des mosaïques et application à la reconstruction d’images de l’endothélium cornéen humain en microscopie optique spéculaire. Ph.D. thesis, École Nationale Supérieure des Mines de Saint-Etienne (2008)

  11. Gavet, Y., Pinoli, J.C.: A geometric dissimilarity criterion between jordan spatial mosaics. Theoretical aspects and application to segmentation evaluation. J. Math. Imaging Vis. 42, 25–49 (2012). doi:10.1007/s10851-011-0272-4

    Article  MathSciNet  MATH  Google Scholar 

  12. Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19(3), 203–210 (2000)

    Article  Google Scholar 

  13. Huang, J., Ling, C.: Using auc and accuracy in evaluating learning algorithms. IEEE Trans. Knowl. Data Eng. 17(3), 299–310 (2005)

  14. Jaccard, P.: Étude comparative de la distribution florale dans une portion des alpes et des jura. Bulletin de la Société Vaudoise des Sciences Naturelles 37, 547–579 (1901)

    Google Scholar 

  15. Jiang, X., Marti, C., Irniger, C., Bunke, H.: Distance measures for image segmentation evaluation. EURASIP J. Appl. Signal Process. 2006, 1–10 (2006). doi:10.1155/ASP/2006/35909

    Article  MATH  Google Scholar 

  16. Marin, D., Aquino, A., Gegundez-Arias, M., Bravo, J.: A new supervised method for blood vessel segmentation in retinal images by using gray-level and moment invariants-based features. IEEE Trans. Med. Imaging 30(1), 146–158 (2011)

    Article  Google Scholar 

  17. Marr, D.: Vision: A Computational Investigation into the Human Representation and Processing of Visual Information. W. H. Freeman and Company, New York (1982)

  18. McGuinness, K., O’Connor, N.E.: A comparative evaluation of interactive segmentation algorithms. Pattern Recognit. 43(2), 434–444 (2010). doi:10.1016/j.patcog.2009.03.008. URL http://www.sciencedirect.com/science/article/B6V14-4VTVPT9-1/2/863e9be0e8f651f41146ef73f2898e0c. Interactive Imaging and Vision

  19. Mendonça, A., Campilho, A.: Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans. Med. Imaging 25(9), 1200–1213 (2006). doi:10.1109/TMI.2006.879955

    Article  Google Scholar 

  20. Minkowski, H.: Volumen und Oberfläche. Mathematische Annalen 57, 447–495 (1903)

    Article  MathSciNet  MATH  Google Scholar 

  21. Philipp-Foliguet, S., Guigues, L.: Évaluation de la segmentation d’images: état de l’art, nouveaux indices et comparaison. Traitement du signal 23(2), 109–124 (2006)

    MATH  Google Scholar 

  22. Pont-Tuset, J., Marques, F.: Measures and meta-measures for the supervised evaluation of image segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2131–2138 (2013)

  23. Soares, J., Leandro, J., Cesar, R., Jelinek, H., Cree, M.: Retinal vessel segmentation using the 2-d gabor wavelet and supervised classification. IEEE Trans. Med. Imaging 25(9), 1214–1222 (2006). doi:10.1109/TMI.2006.879967

    Article  Google Scholar 

  24. Staal, J., Abramoff, M., Niemeijer, M., Viergever, M., van Ginneken, B.: Ridge-based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23(4), 501–509 (2004). doi:10.1109/TMI.2004.825627

    Article  Google Scholar 

  25. Strasters, K.C., Gerbrands, J.J.: Three-dimensional image segmentation using a split, merge and group approach. Pattern Recognit. Lett. 12(5), 307–325 (1991)

    Article  Google Scholar 

  26. Tversky, A.: Features of similarity. Psychol. Rev. 84(4), 327–352 (1977)

  27. Tversky, A., Gati, I.: Similarity, separability and the triangle inequality. Psychol. Rev. 89, 123–154 (1982)

    Article  Google Scholar 

  28. Unnikrishnan, R., Pantofaru, C., Hebert, M.: Toward objective evaluation of image segmentation algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 929–944 (2007)

    Article  Google Scholar 

  29. Villegas, P., Marichal, X.: Perceptually-weighted evaluation criteria for segmentation masks in video sequences. IEEE Trans. Image Process. 13(8), 1092–1103 (2004). doi:10.1109/TIP.2004.828433

  30. Zhang, Y.J.: A survey on evaluation methods for image segmentation. Pattern Recognit. 29(8), 1335–1346 (1996)

    Article  Google Scholar 

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Authors and Affiliations

  1. École Nationale Supérieure des Mines de Saint-Etienne, Saint–Étienne cedex, France

    Yann Gavet, Johan Debayle & Jean-Charles Pinoli

  2. LGF UMR CNRS 5307, 158 Cours Fauriel, 42023 , Saint–Étienne cedex, France

    Yann Gavet, Mathieu Fernandes, Johan Debayle & Jean-Charles Pinoli

  3. CYTOO SA, Grenoble, France

    Mathieu Fernandes

Authors
  1. Yann Gavet
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  2. Mathieu Fernandes
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  3. Johan Debayle
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  4. Jean-Charles Pinoli
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Correspondence to Yann Gavet.

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Gavet, Y., Fernandes, M., Debayle, J. et al. Dissimilarity criteria and their comparison for quantitative evaluation of image segmentation: application to human retina vessels. Machine Vision and Applications 25, 1953–1966 (2014). https://doi.org/10.1007/s00138-014-0625-2

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  • DOI: https://doi.org/10.1007/s00138-014-0625-2

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