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Automatic Spectral Unmixing of Leishmania Infection Macrophage Cell Cultures Image

  • Luís Ferro
  • Marco Marques
  • Pedro Leal
  • Susana Romão
  • Tânia Cruz
  • Ana M. Tomás
  • Helena Castro
  • Pedro Quelhas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7950)

Abstract

Evaluation of parasite infection indexes on in vitro cell cultures is a practice commonly employed by biomedical researchers to address biological questions or to test the efficacy of novel anti-parasitic compounds. In the case of Leishmania infantum, infection indexes are usually determined either by visual inspection of cells directly under the microscope or by counting digital images using appropriate software. In either case assessment of infection indexes is time consuming, thus motivating the creation of automatic image analysis approaches.

One problem in developing a fully automatic methodology for infection indexes evaluation is the low image quality that occur due to problem with the fluorescence of cells. In our previous work we approach cell and parasite segmentation using a Difference of Gaussians filter with a self tuning parametrization, but did not correct existing fluorescence problems. We propose an automatic linear spectral unmixing step that is integrated into our automatic segmentation approach loop to promote image quality improvements for higher analysis performance.

Results show that our approach can improve image quality and the final detection results when the image being processed presents overlapping spectral profiles.

Keywords

Linear spectral unmixing cell nuclei detection microscopy image segmentation 

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References

  1. 1.
    Castro, H., Sousa, C., Santos, M., Cordeiro-da-Silva, A., Flohé, L., Tomás, A.M.: Complementary antioxidant defence by cytoplasmic and mitochondrial peroxiredoxins in Leishmania infantum. Free Radical Biology and Medicine 33, 1552–1562 (2002)CrossRefGoogle Scholar
  2. 2.
    Chen, X., Zhou, X., Wong, S.: Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy. IEEE Trans. on Biomedical Engineering 53(4), 762–766 (2006)CrossRefGoogle Scholar
  3. 3.
    Marcuzzo, M., Quelhas, P., Campilho, A., Maria Mendonça, A., Campilho, A.: Automated arabidopsis plant root cell segmentation based on svm classification and region merging. Computers in Biology and Medicine 39(9), 785–793 (2009)CrossRefGoogle Scholar
  4. 4.
    Yan, P., Zhou, X., Shah, M., Wong, S.T.C.: Automatic segmentation of high-throughput RNAi fluorescent cellular images. IEEE Trans. Inf. Technol. Biomed. 12(1), 109–117 (2008)CrossRefGoogle Scholar
  5. 5.
    Schmitt, O., Hasse, M.: Morphologic multiscale decomposition of connected regions with emphasis on cell clusters. CVIUl 113(2), 188–201 (2009)Google Scholar
  6. 6.
    Usaj, M., Torkar, D., Kanduser, M., Miklavcic, D.: Cell counting tool parameters optimization approach for electroporation efficiency determination of attached cells in phase contrast image. Journal of Microscopy 241(3), 303–314 (2010)CrossRefGoogle Scholar
  7. 7.
    Esteves, T., Quelhas, P., Mendonça, A.M., Campilho, A.: Gradient convergence filters and a phase congruency approach for in vivo cell nuclei detection. Machine Vision and Applications 23, 623–638 (2012)CrossRefGoogle Scholar
  8. 8.
    Leal, P., Ferro, L., Marques, M., Romão, S., Cruz, T., Tomá, A.M., Castro, H., Quelhas, P.: Automatic Assessment of Leishmania Infection Indexes on In Vitro Macrophage Cell Cultures. In: Campilho, A., Kamel, M. (eds.) ICIAR 2012, Part II. LNCS, vol. 7325, pp. 432–439. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Gammon, S.T., Leevy, W.M., Gross, S., Gokel, G.W., Piwnica-Worms, D.: Spectral unmixing of multicolored bioluminescence emitted from heterogeneous biological sources. Anal. Chem. 78(5), 1520–1527 (2006)CrossRefGoogle Scholar
  10. 10.
    Al-Kofahi, Y., Lassoued, W., Lee, W., Roysam, B.: Improved Automatic Detection and Segmentation of Cell Nuclei in Histopathology Images. IEEE Trans. Biomedical Engineering 57(4), 841–852 (2010)CrossRefGoogle Scholar
  11. 11.
    Lowe, D.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  12. 12.
    Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging 13(1), 146–165 (2003)Google Scholar
  13. 13.
    Zimmermann, T., Rietdorf, J., Pepperkok, R.: Spectral imaging and its applications in live cell microscopy. FEBS Lett. 546(1), 87–92 (2003)CrossRefGoogle Scholar
  14. 14.
    Neher, R., Mitkovski, M., Kirchhoff, F., Neher, E., Theis, F., Zeug, A.: Blind Source Separation Techniques for the Decomposition of Multiply Labeled Fluorescence Images. Biophysical J. 96(9), 3791–3800 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Luís Ferro
    • 1
  • Marco Marques
    • 1
  • Pedro Leal
    • 1
  • Susana Romão
    • 2
  • Tânia Cruz
    • 2
  • Ana M. Tomás
    • 2
  • Helena Castro
    • 2
  • Pedro Quelhas
    • 1
    • 3
  1. 1.INEB - Instituto de Engenharia BiomédicaPortoPortugal
  2. 2.IBMC - Instituto de Biologia Molecular e CelularUniversidade do PortoPortugal
  3. 3.Faculdade de Engenharia, Departamento de Engenharia Electrotécnica e ComputadoresUniversidade do PortoPortugal

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