Leukocyte Detection Using Nucleus Contour Propagation

  • Daniela M. Ushizima
  • Rodrigo T. Calado
  • Edgar G. Rizzatti
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4091)


We propose a new technique for medical image segmentation, focused on front propagation in blood smear images to fully automate leukocyte detection. The current approach also incorporates contextual information, which it is especially important in direct general algorithms to the applied problem. A Bayesian classification of pixels is used to estimate cytoplasm color and is embedded in the speed function to accomplish cytoplasm boundary estimation. We report encouraging results, with evaluations considering difficult situations as cell adjacency and filamentous cytoplasmic projections.


Front Propagation Hairy Cell Speed Function Computational Pipeline Medical Image Segmentation 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agosti, S.J., Cornbleet, P.J., Galagan, K., Gewirtz, A.S., Glassy, E.F., Novak, R., Spier, C.: Color Atlas of Hematology: an illustrated field guide based on proficiency testing, 1st edn. (1998)Google Scholar
  2. 2.
    Sabino, D.M.U., da F Costa, L., Calado, R.T., Zago, M.A.: Automatic leukemia diagnosis. Acta Microscopica 12(1), 1–6 (2003)Google Scholar
  3. 3.
    Sethian, J.A.: Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry. Cambridge University Press, Cambridge (1999)zbMATHGoogle Scholar
  4. 4.
    Sabino, D.M.U., da F Costa, L., Rizzatti, E.G., Zago, M.A.: A texture approach to leukocyte recognition. Real-Time Imaging 10(4), 205–216 (2004)CrossRefGoogle Scholar
  5. 5.
    Ushizima, D.M., Lorena, A.C., Carvalho, A.C.P.L.F.: Support vector machines applied to white blood cell recognition. In: V Int. Conf. Hybrid Intel. Systems (2005)Google Scholar
  6. 6.
    Nilsson, B., Heyden, A.: Model-based segmentation of leukocyte clusters. In: 16th International Conference on Pattern Recognition, Quebec, Canada, pp. 727–730. IEEE, Los Alamitos (2002)Google Scholar
  7. 7.
    Gonzalez, R., Woods, R.: Digital Image Processing. Addison-wesley Pub. Co., Reading (1992)Google Scholar
  8. 8.
    Castleman, K.R.: Digital Image Processing, 1st edn. Prentice Hall, Englewood Cliffs (1996)Google Scholar
  9. 9.
    Malladi, R., Sethian, J.A., Vemuri, B.C.: A topology independent shape modeling scheme. In: Proc. of SPIE Conf. on Geometric Methods in Computer Vision II, vol. 2031, pp. 246–258 (1993)Google Scholar
  10. 10.
    Sethian, J., Osher, S.: Fronts propagating with curvature-dependent speed - algorithms based on hamilton-jacobi formulations. Journal of Comp. Physics 79(1), 12–49 (1988)zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Caselles, V., Catte, F., Coll, T., Dibos, F.: A geometric model for active contours. Numerische Mathematik 66 (1993)Google Scholar
  12. 12.
    Kimmel, R.: Numerical Geometry of Images: Theory, Algorithms, and Applications. Springer, Heidelberg (2004)zbMATHGoogle Scholar
  13. 13.
    Sapiro, G.: Geometric Partial Differential Equations and Image Processing. Cambridge University Press, Cambridge (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Daniela M. Ushizima
    • 1
  • Rodrigo T. Calado
    • 2
  • Edgar G. Rizzatti
    • 2
  1. 1.Inteligent System GroupCatholic University of Santos 
  2. 2.College of MedicineUniversity of Sao Paulo 

Personalised recommendations