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Cell Nuclei Detection Using Globally Optimal Active Contours with Shape Prior

  • Jonas De Vylder
  • Jan Aelterman
  • Mado Vandewoestyne
  • Trees Lepez
  • Dieter Deforce
  • Wilfried Philips
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7432)

Abstract

Cell nuclei detection in fluorescent microscopic images is an important and time consuming task for a wide range of biological applications. Blur, clutter, bleed through and partial occlusion of nuclei make this a challenging task for automated detection of individual nuclei using image analysis. This paper proposes a novel and robust detection method based on the active contour framework. The method exploits prior knowledge of the nucleus shape in order to better detect individual nuclei. The method is formulated as the optimization of a convex energy function. The proposed method shows accurate detection results even for clusters of nuclei where state of the art methods fail.

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References

  1. 1.
    Keller, P.J., Schmidt, A.D., Wittbrodt, J., Stelzer, E.H.K.: Reconstruction of zebrafish early embryonic development by scanned light sheet microscopy. Science 322, 1065–1069 (2008)CrossRefGoogle Scholar
  2. 2.
    Chen, X.W., Zhou, X.B., Wong, S.T.C.: Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy. IEEE Transactions on Biomedical Engineering 53, 762–766 (2006)CrossRefGoogle Scholar
  3. 3.
    Gladilin, E., Goetze, S., Mateos-Langerak, J., Van Driel, R., Eils, R., Rohr, K.: Shape normalization of 3d cell nuclei using elastic spherical mapping. Journal of Microscopy-Oxford 231, 105–114 (2008)CrossRefGoogle Scholar
  4. 4.
    Hukkanen, J., Hategan, A., Sabo, E., Tabus, I.: Segmentation of cell nuclei from histological images by ellipse fitting. In: The 2010 European Signal Processing Conference (2010)Google Scholar
  5. 5.
    Li, G., Liu, T., Nie, J., Guo, L., Chen, J., Zhu, J., Xia, W., Mara, A., Holley, S., Wong, S.T.C.: Segmentation of touching cell nuclei using gradient flow tracking. Journal of Microscopy-Oxford 231, 47–58 (2008)MathSciNetCrossRefGoogle Scholar
  6. 6.
    De Vylder, J., Philips, W.: Computational efficient segmentation of cell nuclei in 2d and 3d fluorescent micrographs. In: Proceedings of SPIE Photonics West: Conference on Imaging, Manipulation and Analysis of Biomolecules, Cells, and Tissues (2011)Google Scholar
  7. 7.
    Gudla, P.R., Nandy, K., Collins, J., Meaburn, K.J., Misteli, T., Lockett, S.J.: A high-throughput system for segmenting nuclei using multiscale techniques. Cytometry Part A 73A, 451–466 (2008)CrossRefGoogle Scholar
  8. 8.
    Sezgin, M., Sankur, B.: Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging 13, 146–168 (2004)CrossRefGoogle Scholar
  9. 9.
    Kamentsky, L., Jones, T.R., Fraser, A., Bray, M.A., Logan, D.J., Madden, K.L., Ljosa, V., Rueden, C., Eliceiri, K.W., Carpenter, A.E.: Improved structure, function and compatibility for cellprofiler: modular high-throughput image analysis software. Bioinformatics 27, 1179–1180 (2011)CrossRefGoogle Scholar
  10. 10.
    Selinummi, J., Seppala, J., Yli-Harja, O., Puhakka, J.A.: Software for quantification of labeled bacteria from digital microscope images by automated image analysis. Biotechniques 39, 859–863 (2005)CrossRefGoogle Scholar
  11. 11.
    Cloppet, F., Boucher, A.: Segmentation of overlapping/aggregating nuclei cells in biological images. In: 19th International Conference on Pattern Recognition, vols. 1-6, pp. 789–792 (2008)Google Scholar
  12. 12.
    Chan, T.F., Esedoglu, S., Nikolova, M.: Algorithms for finding global minimizers of image segmentation and denoising models. Siam Journal on Applied Mathematics 66, 1632–1648 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Bresson, X., Chan, T.F.: Active contours based on chambolle’s mean curvature motion. In: IEEE International Conference on Image Processing, vols. 1-7 , pp. 33–36 (2007)Google Scholar
  14. 14.
    Chan, T., Vese, L.: An active contour model without edges. In: Scale-Space Theories in Computer Vision, vol. 1682, pp. 141–151 (1999)Google Scholar
  15. 15.
    Baraniuk, R.G.: Compressive sensing. IEEE Signal Processing Magazine 24, 118–121 (2007)CrossRefGoogle Scholar
  16. 16.
    Kim, S.J., Koh, K., Lustig, M., Boyd, S., Gorinevsky, D.: An interior-point method for large-scale l1-regularized least squares. IEEE Journal of Selected Topics in Signal Processing 1, 606–617 (2007)CrossRefGoogle Scholar
  17. 17.
    Yang, A.Y., Sastry, S.S., Ganesh, A., Yi, M.: Fast l1-minimization algorithms and an application in robust face recognition: A review. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 1849–1852 (2010)Google Scholar
  18. 18.
    Zibulevsky, M., Elad, M.: L1-l2 optimization in signal and image processing. IEEE Signal Processing Magazine 27, 76–88 (2010)CrossRefGoogle Scholar
  19. 19.
    Ruusuvuori, P., Lehmussola, A., Selinummi, J., Rajala, T., Huttunen, H., Yli-Harja, O.: Set of synthetic images for validating cell image analysis. In: Proc. of the 16th European Signal Processing Conference, EUSIPCO 2008 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jonas De Vylder
    • 1
  • Jan Aelterman
    • 1
  • Mado Vandewoestyne
    • 2
  • Trees Lepez
    • 2
  • Dieter Deforce
    • 2
  • Wilfried Philips
    • 1
  1. 1.Department of Telecommunications and Information Processing, IBBT - Image Processing and InterpretationGhent UniversityGhentBelgium
  2. 2.Laboratory of Pharmaceutical Biotechnology, Faculty of Pharmaceutical SciencesGhent UniversityGhentBelgium

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