Combining Mathematical Morphology and the Hilbert Transform for Fully Automatic Nuclei Detection in Fluorescence Microscopy
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.
KeywordsNuclei detection Hilbert transform Mathematical morphology Nuclei counting
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).
- 2.Caicedo, J.C., et al.: Evaluation of deep learning strategies for nucleus segmentation in fluorescence images. bioRxiv (2018)Google Scholar
- 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
- 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
- 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
- 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.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