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Motion Representation of Ciliated Cell Images with Contour-Alignment for Automated CBF Estimation

  • Fan Zhang
  • Yang Song
  • Siqi Liu
  • Paul Young
  • Daniela Traini
  • Lucy Morgan
  • Hui-Xin Ong
  • Lachlan Buddle
  • Sidong Liu
  • Dagan Feng
  • Weidong Cai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9351)

Abstract

Ciliary beating frequency (CBF) estimation is of high interest for the diagnosis and therapeutic assessment of defective mucociliary clearance diseases. Image-based methods have recently become the focus of accurate CBF measurement. The influence from the moving ciliated cell however makes the processing a challenging problem. In this work, we present a registration method for cell movement alignment, based on cell contour segmentation. We also propose a filter feature-based ciliary motion representation, which can better characterize the periodical changes of beating cilia. Experimental results on microscopic time sequence human primary ciliated cell images show the accuracy of our method for CBF computation.

Keywords

Ciliated Cell Primary Ciliary Dyskinesia Ciliary Beat Frequency Motion Representation Region Division 
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.

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References

  1. 1.
    Wanner, A., Salathé, M., et al.: Mucociliary clearance in the airways. American Journal of Respiratory and Critical Care Medicine 154(6), 1868–1902 (1996)CrossRefGoogle Scholar
  2. 2.
    Stannard, W.A., Chilvers, M.A., et al.: Diagnostic testing of patients suspected of primary ciliary dyskinesia. American Journal of Respiratory and Critical Care Medicine 181(4), 307–314 (2010)CrossRefGoogle Scholar
  3. 3.
    Salathe, M.: Effects of β-agonists on airway epithelial cells. Journal of Allergy and Clinical Immunology 110(6), S275–S281 (2002)CrossRefGoogle Scholar
  4. 4.
    Chilvers, M.A., O’Callaghan, C.: Analysis of ciliary beat pattern and beat frequency using digital high speed imaging: comparison with the photomultiplier and photodiode methods. Thorax 55(4), 314–317 (2000)CrossRefGoogle Scholar
  5. 5.
    Sisson, J.H., Stoner, J.A., et al.: All-digital image capture and whole-field analysis of ciliary beat frequency. Journal of Microscopy 211(2), 103–111 (2003)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Smith, C.M., Djakow, J., et al.: ciliaFA: a research tool for automated, high-throughput measurement of ciliary beat frequency using freely available software. Cilia 1(14), 1–7 (2012)Google Scholar
  7. 7.
    Kim, W., Han, T.H., et al.: An automated measurement of ciliary beating frequency using a combined optical flow and peak detection. Healthcare Informatics Research 17(2), 111–119 (2011)CrossRefGoogle Scholar
  8. 8.
    Parrilla, E., Armengot, M., et al.: Optical flow method in phase-contrast microscopy images for the diagnosis of primary ciliary dyskinesia through measurement of ciliary beat frequency. Preliminary results. In: ISBI, pp. 1655–1658 (2012)Google Scholar
  9. 9.
    Zhang, F., et al.: Image-based ciliary beating frequency estimation for therapeutic assessment on defective mucociliary clearance diseases. In: ISBI, pp. 193–196 (2014)Google Scholar
  10. 10.
    Li, C., Xu, C., et al.: Distance regularized level set evolution and its application to image segmentation. IEEE Trans. Imag. Process. 19(12), 3243–3254 (2010)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Rueckert, D., Sonoda, et al.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imag. 18(8), 712–721 (1999)CrossRefGoogle Scholar
  12. 12.
    Myronenko, A., Song, X., Sahn, D.J.: LV motion tracking from 3D echocardiography using textural and structural information. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 428–435. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Varma, M., Zisserman, A.: Classifying images of materials: achieving viewpoint and illumination independence. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part III. LNCS, vol. 2352, pp. 255–271. Springer, Heidelberg (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Fan Zhang
    • 1
  • Yang Song
    • 1
  • Siqi Liu
    • 1
  • Paul Young
    • 2
    • 3
  • Daniela Traini
    • 2
    • 3
  • Lucy Morgan
    • 4
    • 5
  • Hui-Xin Ong
    • 2
    • 3
  • Lachlan Buddle
    • 4
  • Sidong Liu
    • 1
  • Dagan Feng
    • 1
  • Weidong Cai
    • 1
  1. 1.BMIT Research Group, School of ITUniversity of SydneySydneyAustralia
  2. 2.Woolcock Institute of Medical ResearchSydneyAustralia
  3. 3.Discipline of Pharmacology, Sydney Medical SchoolUniversity of SydneySydneyAustralia
  4. 4.Department of Respiratory MedicineConcord Repatriation General HospitalSydneyAustralia
  5. 5.School of MedicineUniversity of SydneySydneyAustralia

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