Knowledge Based and Statistical Based Approaches in Biomedical Image Analysis

Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 120)

Abstract

Biomedical Imaging has grown significantly for the past twenty years, as it is considered as a unique method for visualizing biological processes within living organisms in a non-invasive manner. Although works in biomedical image analysis rely on underlying biological problems, scientists are just beginning to embrace the idea that these works will benefit from multidisciplinary interactions. Moreover, within the computer vision community, time has come for a more holistic and integrated approach in order to articulate statistical/machine learning and knowledge-based approaches. In this paper we present studies based on these two classic approaches and show how their complementarity may benefit biomedical imaging.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Beucher, S., Meyer, F.: In: Dougherty, E. (ed.) Mathematical Morphology in Image Processing., Marcel Dekker, New York (1992)Google Scholar
  2. 2.
    Boucher, A., Cloppet, F., Vincent, N., Jouve, P.: Visual Perception Driven Registration of Mammograms. In: Proceedings of International Conference on Pattern Recognition (ICPR), pp. 2374–2377 (2010)Google Scholar
  3. 3.
    Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. International Journal of Computer Vision 22(1), 61–69 (1997)MATHCrossRefGoogle Scholar
  4. 4.
    Cloppet, F., Boucher, A.: Segmentation of complex nucleus configurations in biological images. Pattern Recognition Letters 31, 755–761 (2010)CrossRefGoogle Scholar
  5. 5.
    Cloppet, F., Oliva, J.M., Stamon, G.: Angular Bisector Network, a simplified generalized Voronoi diagram: application to processing complex intersections in biomedical images. IEEE Transactions on Pattern Analysis Machine Intelligence 22(1), 120–128 (2000)CrossRefGoogle Scholar
  6. 6.
    Descombes, X., Kruggel, F., Willny, G., Gertz, H.J.: An object-based approach for detecting small brain lesions: application to Virchow-Robin spaces. IEEE Trans. on Pattern Analysis and Machine Intelligence 23(2), 246–255 (2004)Google Scholar
  7. 7.
    Frapart, Y.: In Vivo Electron Paramagnetic Resonance and Imaging in Biomedical Science, p. 7Google Scholar
  8. 8.
    Grau, V., Mewes, A., Alcañiz, M., Kkinis, R., Warfield, S.: Improved watershed transform for medical image segmentation using prior information. IEEE Transactions on Medical Imaging 23(4), 447–458 (2004)CrossRefGoogle Scholar
  9. 9.
    Guo, C.E., Zhu, S.C., Wu, Y.N.: Modeling visual patterns by integrating descriptive and generative methods. Int. Journal of Computer Vision 53(1), 5–29 (2003)CrossRefGoogle Scholar
  10. 10.
    Hurtut, T., Landes, P.-E., Thollot, J., Gousseau, Y., Drouilhet, R., Coeurjolly, J.-F.: Appearance-guided Synthesis of Element Arrangements by Example. In: NPAR: Proc. of the 7th International Symposium on Non-photorealistic Animation and Rendering (2009)Google Scholar
  11. 11.
    Illian, J., Penttinen, A., Stoyan, H.: Statistical analysis and modelling of spatial point patterns. Wiley Interscience (2008)Google Scholar
  12. 12.
    Kass, M., Witkins, A., Terzopoulos, D.: Snakes: active contours models. International Journal of Computer Vision 4(1), 321–331 (1997)Google Scholar
  13. 13.
    Lafarge, F., Gimel’farb, G., Descombes, X.: Geometric Feature Extraction by a Multi-Marked Point Process. IEEE Trans. on Pattern Analysis and Machine Intelligence 32(9), 1597–1609 (2010)CrossRefGoogle Scholar
  14. 14.
    Lefèvre, S.: Knowledge from markers in watershed segmentation. In: Kropatsch, W.G., Kampel, M., Hanbury, A. (eds.) CAIP 2007. LNCS, vol. 4673, pp. 579–586. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  15. 15.
    Luminata, A., Vese, L.A., Chan, T.F.: A multiphase level set framework for image segmentation using the Mumford and Shah model. International Journal of Computer Vision 50(3), 271–293 (2002)CrossRefGoogle Scholar
  16. 16.
    Mattfeldt, T., Eckel, S., Fleischer, F., Schmidt, V.: Statistical modelling of the geometry of planar sections of prostatic capillaries on the basis of stationary Strauss Hard-core processes. Journal of Microscopy, 272–281 (2007)Google Scholar
  17. 17.
    Mattfeldt, T., Eckel, S., Fleischer, F., Schmidt, V.: Statistical analysis of labelling patterns of mammary carcinoma cell nuclei on histological sections. Journal of Microscopy, 106–118 (2009)Google Scholar
  18. 18.
    Vincent, L., Soille, P.: Watershed in digital spaces, an efficient algorithm based on immersion simulation. IEEE Transactions on Pattern Analysis Machine Intelligence 13(6), 583–598 (1991)CrossRefGoogle Scholar
  19. 19.
    Wirjadi, O., Kim, Y.-J., Breuel, T.: Spatial Statistics for Tumor Cell Counting and Classification. In: Denzler, J., Notni, G., Süße, H. (eds.) Pattern Recognition. LNCS, vol. 5748, pp. 492–501. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  20. 20.
    Zhang, B., Zimmer, C., Olivo-Marin, J.C.: Tracking fluorescent cells with coupled geometric active contours. In: Proceedings of IEEE International Symposium on Biomedical Imaging, vol. 1, pp. 476–479 (2004)Google Scholar
  21. 21.
    Zhu, S.C., Yuille, A.: Region competition: unifying snakes, region growing and Bayes/MDL for multiband image segmentation. IEEE Transactions on Pattern Analysis Machine Intelligence 18(9), 884–900 (1996)CrossRefGoogle Scholar
  22. 22.
    Zimmer, C., Olivo-Marin, J.C.: Coupled Parametric Active Contours. IEEE Transactions on Pattern Analysis Machine Intelligence 27(11), 838–1842 (2005)CrossRefGoogle Scholar

Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Laboratoire d’Informatique de Paris Descartes (LIPADE)Paris Descartes UniversityParisFrance

Personalised recommendations