Model-Based Image Interpretation under Uncertainty and Fuzziness

  • Isabelle Bloch
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8256)


Structural models such as ontologies and graphs can encode generic knowledge about a scene observed in an image. Their use in spatial reasoning schemes allows driving segmentation and recognition of objects and structures in images. The developed methods include finding a best segmentation path in a graph, global solving of a constraint satisfaction problem, integrating prior knowledge in deformable models, and exploring images in a progressive fashion. Conversely, these models can be specified based on individual information resulting from the segmentation and recognition process. In particular models relying on spatial relations between structures are relevant and more flexible than shape models to be adapted to potential variations, multiple occurrences, or pathological cases. The problem of semantic gap is addressed by generating spatial representations (in the image space) of relations initially expressed in linguistic or symbolic form, within a fuzzy set formalism. This allows coping with uncertainty and fuzziness, which are inherent both to generic knowledge and to image information. Applications in medical imaging and remote sensing imaging illustrate the proposed paradigm.


Image understanding structural models graphs spatial relations fuzzy modeling model-based segmentation and recognition constraint satisfaction problems 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Anquez, J., Angelini, E., Bloch, I.: Automatic Segmentation of Head Structures on Fetal MRI. In: IEEE International Symposium on Biomedical Imaging (ISBI), Boston, USA, pp. 109–112 (2009)Google Scholar
  2. 2.
    Anquez, J., Bibin, L., Angelini, E.D., Bloch, I.: Segmentation of the fetal envelope on ante-natal MRI. In: IEEE International Symposium on Biomedical Imaging (ISBI), Rotterdam, The Netherlands, pp. 896–899 (2010)Google Scholar
  3. 3.
    Atif, J., Hudelot, C., Fouquier, G., Bloch, I., Angelini, E.: From Generic Knowledge to Specific Reasoning for Medical Image Interpretation using Graph-based Representations. In: International Joint Conference on Artificial Intelligence, IJCAI 2007, Hyderabad, India, pp. 224–229 (2007)Google Scholar
  4. 4.
    Bibin, L., Anquez, J., de la Plata Alcalde, J., Boubekeur, T., Angelini, E.D., Bloch, I.: Whole body pregnant woman modeling by digital geometry processing with detailed utero-fetal unit based on medical images. IEEE Transactions on Biomedical Engineering 57(10), 2346–2358 (2010)CrossRefGoogle Scholar
  5. 5.
    Bloch, I.: Fuzzy Spatial Relationships for Image Processing and Interpretation: A Review. Image and Vision Computing 23(2), 89–110 (2005)CrossRefGoogle Scholar
  6. 6.
    Bloch, I.: Spatial Reasoning under Imprecision using Fuzzy Set Theory, Formal Logics and Mathematical Morphology. International Journal of Approximate Reasoning 41, 77–95 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Bloch, I.: Duality vs. Adjunction for Fuzzy Mathematical Morphology and General Form of Fuzzy Erosions and Dilations. Fuzzy Sets and Systems 160, 1858–1867 (2009)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Bloch, I., Géraud, T., Maître, H.: Representation and Fusion of Heterogeneous Fuzzy Information in the 3D Space for Model-Based Structural Recognition - Application to 3D Brain Imaging. Artificial Intelligence 148, 141–175 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Bloch, I., Maître, H.: Fuzzy Mathematical Morphologies: A Comparative Study. Pattern Recognition 28(9), 1341–1387 (1995)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Cesar, R., Bengoetxea, E., Bloch, I., Larranaga, P.: Inexact Graph Matching for Model-Based Recognition: Evaluation and Comparison of Optimization Algorithms. Pattern Recognition 38, 2099–2113 (2005)CrossRefGoogle Scholar
  11. 11.
    Chein, M., Mugnier, M.L.: Graph-based Knowledge Representation and Reasoning - Computational Foundations of Conceptual Graphs. In: Advanced Information and Knowledge Processing. Springer (2008)Google Scholar
  12. 12.
    Colliot, O., Camara, O., Bloch, I.: Integration of Fuzzy Spatial Relations in Deformable Models - Application to Brain MRI Segmentation. Pattern Recognition 39, 1401–1414 (2006)CrossRefGoogle Scholar
  13. 13.
    Conte, D., Foggia, P., Sansone, C., Vento, M.: Thirty years of graph matching in pattern recognition. International Journal of Pattern Recognition and Artificial Intelligence 18(3), 265–298 (2004)CrossRefGoogle Scholar
  14. 14.
    Cornelius, R., Mejino, J.L.V.: A reference ontology for bioinformatics: the Foundational Model of Anatomy. Journal of Biomedical Informatics 36, 478–500 (2003)CrossRefGoogle Scholar
  15. 15.
    Deruyver, A., Hodé, Y.: Constraint satisfaction problem with bilevel constraint: application to interpretation of over-segmented images. Artificial Intelligence 93(1), 321–335 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Deruyver, A., Hodé, Y.: Qualitative spatial relationships for image interpretation by using a conceptual graph. Image and Vision Computing 27(7), 876–886 (2009)CrossRefGoogle Scholar
  17. 17.
    Dubois, D., Fargier, H., Prade, H.: Possibility theory in constraint satisfaction problems: Handling priority, preference and uncertainty. Applied Intelligence 6(4), 287–309 (1996)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Fouquier, G., Atif, J., Bloch, I.: Sequential model-based segmentation and recognition of image structures driven by visual features and spatial relations. Computer Vision and Image Understanding 116(1), 146–165 (2012)CrossRefGoogle Scholar
  19. 19.
    Ghorbel, I., Rossant, F., Bloch, I., Pâques, M.: Modeling a parallelism constraint in active contours. Application to the segmentation of eye vessels and retinal layers. In: ICIP 2011, Brussels, Belgium, pp. 453–456 (September 2011)Google Scholar
  20. 20.
    Ghorbel, I., Rossant, F., Bloch, I., Tick, S., Pâques, M.: Automated Segmentation of Macular Layers in Images and Quantitative Evaluation of Performances. Pattern Recognition 44(8), 1590–1603 (2011)CrossRefGoogle Scholar
  21. 21.
    Hudelot, C., Atif, J., Bloch, I.: Fuzzy Spatial Relation Ontology for Image Interpretation. Fuzzy Sets and Systems 159, 1929–1951 (2008)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  23. 23.
    Khotanlou, H., Colliot, O., Atif, J., Bloch, I.: 3D Brain Tumor Segmentation in MRI Using Fuzzy Classification, Symmetry Analysis and Spatially Constrained Deformable Models. Fuzzy Sets and Systems 160, 1457–1473 (2009)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Kitchen, L.: Discrete relaxation for matching relational structures. IEEE Transactions on Systems, Man, and Cybernetics 9(12), 869–874 (1978)MathSciNetGoogle Scholar
  25. 25.
    Mohr, R., Henderson, T.C.: Arc and path consistency revisited. Artificial Intelligence 28(2), 225–233 (1986)CrossRefGoogle Scholar
  26. 26.
    Moreno, A., Takemura, C.M., Colliot, O., Camara, O., Bloch, I.: Using Anatomical Knowledge Expressed as Fuzzy Constraints to Segment the Heart in CT images. Pattern Recognition 41, 2525–2540 (2008)CrossRefGoogle Scholar
  27. 27.
    Nempont, O., Atif, J., Bloch, I.: A constraint propagation approach to structural model based image segmentation and recognition. Information Sciences 246, 1–27 (2013)MathSciNetCrossRefGoogle Scholar
  28. 28.
    Perchant, A., Bloch, I.: Fuzzy Morphisms between Graphs. Fuzzy Sets and Systems 128(2), 149–168 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  29. 29.
    Rosenfeld, A., Hummel, R.A., Zucker, S.W.: Scene labeling by relaxation operations. IEEE Transactions on Systems, Man and Cybernetics 6, 420–433 (1976)MathSciNetCrossRefzbMATHGoogle Scholar
  30. 30.
    Rossi, F., Van Beek, P., Walsh, T.: Handbook of constraint programming. Elsevier (2006)Google Scholar
  31. 31.
    Sowa, J.F.: Conceptual Structures: Information Processing in Mind and Machine. Addison-Wesley, Reading (1984)zbMATHGoogle Scholar
  32. 32.
    Srihari, R.K., Zhang, Z.: Show&tell: A semi-automated image annotation system. IEEE Multimedia 7(3), 61–71 (2000)CrossRefGoogle Scholar
  33. 33.
    Vanegas, C.: Spatial relations and spatial reasoning for the interpretation of earth observation images using a structural model. Ph.D. thesis, Telecom ParisTech 2011E003 (January 2011)Google Scholar
  34. 34.
    Vanegas, M.C., Bloch, I., Inglada, J.: Alignment and parallelism for the description of high resolution remote sensing images. IEEE Transactions on Geoscience and Remote Sensing 51(6), 3542–3557 (2013)CrossRefGoogle Scholar
  35. 35.
    Vanegas, M., Bloch, I., Inglada, J.: A fuzzy definition of the spatial relation “surround” - Application to complex shapes. In: EUSFLAT, pp. 844–851 (2011)Google Scholar
  36. 36.
    Waltz, D.: Understanding line drawings of scenes with shadows. In: The Psychology of Computer Vision, pp. 19–91. McGraw-Hill (1975)Google Scholar
  37. 37.
    Wojak, J., Angelini, E.D., Bloch, I.: Introducing shape constraint via Legendre moments in a variational framework for cardiac segmentation on non-contrast CT images. In: VISAPP, Angers, France, pp. 209–214 (2010)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Isabelle Bloch
    • 1
  1. 1.Institut Mines-Telecom, CNRS LTCITelecom ParisTechParisFrance

Personalised recommendations