Advertisement

Combining Mathematical Morphology and the Hilbert Transform for Fully Automatic Nuclei Detection in Fluorescence Microscopy

  • Carl J. Nelson
  • Philip T. G. Jackson
  • Boguslaw ObaraEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11564)

Abstract

Accurate and reliable nuclei identification is an essential part of quantification in microscopy. A range of mathematical and machine learning approaches are used but all methods have limitations. Such limitations include sensitivity to user parameters or a need for pre-processing in classical approaches or the requirement for relatively large amounts of training data in deep learning approaches. Here we demonstrate a new approach for nuclei detection that combines mathematical morphology with the Hilbert transform to detect the centres, sizes and orientations of elliptical objects. We evaluate this approach on datasets from the Broad Bioimage Benchmark Collection and compare it to established algorithms and previously published results. We show this new approach to outperform established classical approaches and be comparable in performance to deep-learning approaches. We believe this approach to be a competitive algorithm for nuclei detection in microscopy.

Keywords

Nuclei detection Hilbert transform Mathematical morphology Nuclei counting 

Notes

Acknowledgements

During this work, CJN was supported by an EPSRC (UK) Doctoral Scholarship (EP/K502832/1). PTGJ is supported by an EPSRC (UK) Doctoral Scholarship (EP/M507854/1). The work in this paper was supported by an academic grant from The Royal Society (UK; RF080232).

References

  1. 1.
    Bray, M.A., Fraser, A.N., Hasaka, T.P., Carpenter, A.E.: Workflow and metrics for image quality control in large-scale high-content screens. J. Biomol. Screen. 17(2), 266–274 (2012)CrossRefGoogle Scholar
  2. 2.
    Caicedo, J.C., et al.: Evaluation of deep learning strategies for nucleus segmentation in fluorescence images. bioRxiv (2018)Google Scholar
  3. 3.
    Carpenter, A.E., et al.: CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol. 7(10), 1–11 (2006)CrossRefGoogle Scholar
  4. 4.
    Fornaciari, M., Prati, A., Cucchiara, R.: A fast and effective ellipse detector for embedded vision applications. Pattern Recogn. 47(11), 3693–3708 (2014)CrossRefGoogle Scholar
  5. 5.
    Gurcan, M.N., Pan, T., Shimada, H., Saltz, J.: Image analysis for neuroblastoma classification: segmentation of cell nuclei. In: International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4844–4847 (2006)Google Scholar
  6. 6.
    Jaccard, P.: The distribution of flora in the Alpine Zone. New Phytol. 11(2), 37–50 (1912)CrossRefGoogle Scholar
  7. 7.
    Jackson, P.T.G., Obara, B.: Avoiding over-detection: towards combined object detection and counting. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2017. LNCS (LNAI), vol. 10245, pp. 75–85. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-59063-9_7CrossRefGoogle Scholar
  8. 8.
    Jia, Q., Fan, X., Luo, Z., Song, L., Qiu, T.: A fast ellipse detector using projective invariant pruning. IEEE Trans. Image Process. 26(8), 3665–3679 (2017)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Jung, C., Kim, C.: Segmenting clustered nuclei using H-minima transform-based marker extraction and contour parameterization. IEEE Trans. Biomed. Eng. 57(10), 2600–2604 (2010)CrossRefGoogle Scholar
  10. 10.
    Kong, J., et al.: Automated cell segmentation with 3D fluorescence microscopy images. In: IEEE International Symposium on Biomedical Imaging, pp. 1212–1215 (2015)Google Scholar
  11. 11.
    Li, G., et al.: 3D cell nuclei segmentation based on gradient flow tracking. BMC Cell Biol. 8(1), 1–10 (2007)CrossRefGoogle Scholar
  12. 12.
    Ljosa, V., Sokolnicki, K.L., Carpenter, A.E.: Annotated high-throughput microscopy image sets for validation. Nat. Methods 9(7), 637–637 (2012)CrossRefGoogle Scholar
  13. 13.
    Nandy, K., Chellappa, R., Kumar, A., Lockett, S.J.: Segmentation of nuclei from 3D microscopy images of tissue via graphcut optimization. IEEE J. Sel. Topics Signal Process. 10(1), 140–150 (2016)CrossRefGoogle Scholar
  14. 14.
    Prasad, D.K., Leung, M.K.H., Quek, C.: ElliFit: an unconstrained, non-iterative, least squares based geometric ellipse fitting method. Pattern Recogn. 46(5), 1449–1465 (2013)CrossRefGoogle Scholar
  15. 15.
    Prasad, D.K., Leung, M.K., Cho, S.Y.: Edge curvature and convexity based ellipse detection method. Pattern Recogn. 45(9), 3204–3221 (2012)CrossRefGoogle Scholar
  16. 16.
    Ruusuvuori, P., Lehmussola, A., Selinummi, J., Rajala, T., Huttunen, H., Yli-Harja, O.: Benchmark set of synthetic images for validating cell image analysis algorithms. In: European Signal Processing Conference, pp. 1–5 (2008)Google Scholar
  17. 17.
    Xie, Y., Ji, Q.: A new efficient ellipse detection method. In: Proceedings of the 16th International Conference on Pattern Recognition, vol. 2, pp. 957–960 (2002)Google Scholar
  18. 18.
    Xu, H., Lu, C., Berendt, R., Jha, N., Mandal, M.: Automatic nuclei detection based on generalized Laplacian of Gaussian filters. IEEE J. Biomed. Health Inform. 21(3), 826–837 (2016)CrossRefGoogle Scholar
  19. 19.
    Xu, H., Lu, C., Mandal, M.: An efficient technique for nuclei segmentation based on ellipse descriptor analysis and improved seed detection algorithm. IEEE J. Biomed. Health Inform. 18(5), 1729–1741 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of GlasgowGlasgowUK
  2. 2.Durham UniversityDurhamUK

Personalised recommendations