Segmentation of Muscle Fibres in Fluorescence Microscopy Images

  • Aurora Sáez
  • Adoración Montero-Sánchez
  • Luis M. Escudero
  • Begoña Acha
  • Carmen Serrano
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7325)

Abstract

The morphological analysis of muscle biopsy helps in diagnosis of neuromuscular disease. The presence, extent, size, shape, and other morphological appearance of the muscle fibres are important indicators for presence or severity of disease. However, estimation of these parameters by simple visual inspection is inaccurate and subjective and manual delineation of individual muscle fibres from muscle biopsy images is time-consuming and tedious. In this study, two automatic segmentation methods are proposed. Both methods operate on fluorescence microscopy images. The first uses a level set framework and the second one a marker-driven watershed transform. In a first stage, mathematical morphology is used to detect the presence of muscle fibres. The result of this step provides requirements for both segmentation methods (initial contour and markers). Experimental results demonstrate that segmentation of watershed detects fibres contours more accurately and with a lower computational cost.

Keywords

Muscle Biopsy Segmentation Method Active Contour Neuromuscular Disease Initial Contour 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Gurcan, M.N., Boucheron, L.E., Can, A., Madabhushi, A., Rajpoot, N.M., Yener, B.: Histopathological Image Analysis: A Review. IEEE Rev. Biomed. Eng. 2, 147–171 (2009)CrossRefGoogle Scholar
  2. 2.
    Dubowitz, V., Sewry, C.A.: Muscle biopsy: a practical approach, 3rd edn. Eselvier (2007)Google Scholar
  3. 3.
    Castleman, K.R., Chui, L.A., Martin, T.P., Edgerton, V.R.: Quatitative muscle biopsy analysis. Monogr. Clin. Cytol. 9, 101–116 (1984)Google Scholar
  4. 4.
    Plissiti, M.E., Nikou, C., Charchanti, A.: Watershed-based segmentation of cell nuclei boundaries in Pap smear images. ITAB, art. no. 5687745 (2010)Google Scholar
  5. 5.
    Li, S., Wu, L., Sun, Y.: Cell image segmentation based on an improved watershed transformation. In: CASoN 2010 , art. no. 5636803, pp. 93–96 (2010)Google Scholar
  6. 6.
    Harandi, N.M., Sadri, S., Moghaddam, N.A., Amirfattahi, R.: An automated method for segmentation of epithelial cervical cells in images of ThinPrep. J. Med. Syst. Journal 34(6), 1043–1058 (2010)CrossRefGoogle Scholar
  7. 7.
    Bergmeir, C., Garcá Silvente, M., Esquivias López-Cuervo, J., Bentez, J.M.: Segmentation of cervical cell images using mean-shift filtering and morphological operators. In: Proceedings of SPIE, vol. 7623, art. no. 76234C (2010)Google Scholar
  8. 8.
    Todman, A.G., Claridge, E.: Low-level grouping mechanisms for contour completion. Information Sciences 125(1-4), 19–35 (2000)MATHCrossRefGoogle Scholar
  9. 9.
    Sertel, O., Dogdas, B., Chiu, C.S., Gurcan, M.N.: Microscopic image analysis for quantitative characterization of muscle fiber type composition. Comput. Med. Imag. Grap. 35(7-8), 616–628 (2011)CrossRefGoogle Scholar
  10. 10.
    Kim, Y.J., Brox, T., Feiden, W., Weickert, J.: Fully automated segmentation and morphometrical analysis of muscle fiber images. Cytometry 71(1), 8–15 (2007)CrossRefGoogle Scholar
  11. 11.
    Karen, P., Števanec, M., Smerdu, V., Cvetko, E., Kubnov, L., Eren, I.: Software for muscle fibre type classification and analysis. Eur. J. Histochem. 53(2), 87–95 (2009)Google Scholar
  12. 12.
    Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, Berlin (1999)MATHGoogle Scholar
  13. 13.
    Osher, S., Sethian, J.A.: Fronts propagating with curvaturedependent speed algorithms based on hamilton-jacobi formulations. J. Comput. Phys. 79, 12–49 (1998)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Li, C., Xu, C., Gui, C., Fox, M.D.: Level set evolution without re-initialization: a new variational formulation. In: Proc. CVPR IEEE (2005)Google Scholar
  15. 15.
    Meyer, F., Beucher, S.: Morphological segmentation. JVCIR 1(1), 21–46 (1990)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Aurora Sáez
    • 1
  • Adoración Montero-Sánchez
    • 2
  • Luis M. Escudero
    • 2
  • Begoña Acha
    • 1
  • Carmen Serrano
    • 1
  1. 1.Dpto. Teoría de la Señal y ComunicacionesUniversidad de SevillaSpain
  2. 2.Instituto de Biomedicina de SevillaHospital Universitario Virgen del Rocío/ CSIC/Universidad de SevillaSpain

Personalised recommendations