Active Contour Segmentation based on Approximate Entropy - Application to Cell Membrane Segmentation in Confocal Microscopy

Aymeric Histace, Elizabeth Bonnefoye, Luis Garrido, Bogdan J. Matuszewski, Mark Murphy

Abstract

Segmentation of cellular structures is of primary interest in cell imaging for cell shape reconstruction and to provide crucial information about possible cell morphology changes during radiotherapy for instance. From the particular perspective of predictive oncology, this paper reports on a novel method for membrane segmentation from single channel actin tagged fluorescence confocal microscopy images, which remains a challenging task. Proposed method is based on the use of the Approximate Entropy formerly introduced by Pincus embedded within a Geodesic Active Contour approach. Approximate Entropy can be seen as an estimator of the regularity of a particular sequence of values and, consequently, can be used as an edge detector. In this prospective study, a preliminary study on Approximate Entropy as an edge detector function is first proposed with a particular focus on the robustness to noise, and some promising membrane segmentation results obtained on confocal microscopy images are also shown.

References

  1. (2011). Image filtering using anisotropic structure tensor for cell membrane enhancement in 3d microscopy. In Proceedings of International Conference on Image Processing ICIP 2011, pages 2085-2088.
  2. Goldenberg, R., Kimmel, R., Rivlin, E., and Rudzsky, M. (2001). Fast Geodesic Active Contours. IEEE TRANSACTIONS ON IMAGE PROCESSING, 10(10):1467-75.
  3. Hall, A. (2009). The cytoskeleton and cancer, volume 28. Springer Netherlands, Philadelphia, PA, USA.
  4. Histace, A., Meziou, L., Matuszewski, B., Precioso, F., Murphy, M., and Carreiras, F. (2013). Statistical region based active contour using a fractional entropy descriptor: Application to nuclei cell segmentation in confocal microscopy images. Annals of British Machine Vision Association, 2013(5):1-15.
  5. Matuszewski, B., Murphy, M., Burton, D., Marchant, T., Moore, C., Histace, A., and Precioso, F. (2011). Segmentation of Cellular Structures in Actin Tagged Fluorescence Confocal Microscopy Images. In IEEE ICIP 2011, pages pp. 3081-3084, Bruxelles, Belgium.
  6. McKinley, R. A., McIntire, L. K., Schmidt, R., Repperger, D. W., and Caldwell, J. A. (2011). Evaluation of eye metrics as a detector of fatigue. Human Factors: The Journal of the Human Factors and Ergonomics Society, 53(4):403-414.
  7. Meziou, L., Histace, A., Precioso, F., Matuszewski, B., and Carreiras, F. (2012). 3D Confocal Microscopy data analysis using level-set segmentation with alphadivergence similarity measure. In International Conference on Computer Vision Theory and Applications, pages 861-864, Rome, Italy.
  8. Meziou, L., Histace, A., Precioso, F., Matuszewski, B., and Murphy, M. (2011). Confocal Microscopy Segmentation Using Active Contour Based on AlphaDivergence. In Proceedings of ICIP 2011, pages 3138-3141.
  9. Mosaliganti, K., Gelas, A., Gouaillard, A., Noche, R., Obholzer, N., and Megason, S. (2009). Detection of spatially correlated objects in 3d images using appearance models and coupled active contours. In Proceedings of MICCAI'09, pages 641-648, Berlin, Heidelberg. Springer-Verlag.
  10. Ortiz De Solorzano, C., Garcia Rodriguez, E., Jones, A., Pinkel, D., Gray, J. W., Sudar, D., and Lockett, S. J. (1999). Segmentation of confocal microscope images of cell nuclei in thick tissue sections. Journal of Microscopy, 193(3):212-226.
  11. Osher, S. and Sethian, J. A. (1988). Fronts propagating with curvature dependent speed: Algorithms based on hamilton-jacobi formulations. Journal of Comp. Phy., 79:12-49.
  12. Parker, G. J., Schnabel, J. A., and Barker, G. J. (1999). Nonlinear smoothing of MR images using approximate entropy - A local measure of signal intensity irregularity. In Kuba, A., Sámal, M., and Todd-Pokropek, A., editors, Information Processing in Medical Imaging, volume 1613 of Lecture Notes in Computer Science, pages 484-489. Springer Berlin Heidelberg.
  13. Perona, P. and Malik, J. (1990). Scale-space and edge detection using anistropic diffusion. IEEE Transcations on Pattern Analysis and Machine Intelligence, 12(7):629-639.
  14. Pincus, S. and Kalman, R. E. (2004). Irregularity, volatility, risk, and financial market time series. Proceedings of the National Academy of Sciences of the United States of America, 101(38):13709-13714.
  15. Pincus, S. M. (1991). Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences, 88(6):2297-2301.
  16. Pincus, S. M., Gladstone, I. M., and Ehrenkranz, R. A. (1991). A regularity statistic for medical data analysis. Journal of Clinical Monitoring, 7(4):335-345.
  17. Pincus, S. M. and Goldberger, A. L. (1994). Physiological time-series analysis: what does regularity quantify? AJP - Heart and Circulatory Physiology, 266(4):H1643-1656.
  18. Sarti, A., Malladi, R., and Sethian, J. A. (2002). Subjective surfaces: A geometric model for boundary completion. Int. J. Comput. Vision, 46(3):201-221.
  19. Yan, P., Zhou, X., Shah, M., and Wong, S. T. C. (2008). Automatic segmentation of high throughput rnai fluorescent cellular images. IEEE Transactions on Information Technology in Biomedicine, 12(1):109-117.
  20. Zanella, C., Campana, M., Rizzi, B., Melani, C., Sanguinetti, G., Bourgine, P., Mikula, K., Peyriéras, N., and Sarti, A. (2010). Cells segmentation from 3d confocal images of early zebrafish embryogenesis. IEEE trans. on IP, 19(3):770-781.
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Paper Citation


in Harvard Style

Histace A., Bonnefoye E., Garrido L., Matuszewski B. and Murphy M. (2014). Active Contour Segmentation based on Approximate Entropy - Application to Cell Membrane Segmentation in Confocal Microscopy . In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014) ISBN 978-989-758-011-6, pages 270-277. DOI: 10.5220/0004903002700277


in Bibtex Style

@conference{biosignals14,
author={Aymeric Histace and Elizabeth Bonnefoye and Luis Garrido and Bogdan J. Matuszewski and Mark Murphy},
title={Active Contour Segmentation based on Approximate Entropy - Application to Cell Membrane Segmentation in Confocal Microscopy},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014)},
year={2014},
pages={270-277},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004903002700277},
isbn={978-989-758-011-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2014)
TI - Active Contour Segmentation based on Approximate Entropy - Application to Cell Membrane Segmentation in Confocal Microscopy
SN - 978-989-758-011-6
AU - Histace A.
AU - Bonnefoye E.
AU - Garrido L.
AU - Matuszewski B.
AU - Murphy M.
PY - 2014
SP - 270
EP - 277
DO - 10.5220/0004903002700277