Abstract
With the huge amount of cell images produced in bio-imaging, automatic methods for segmentation are needed in order to evaluate the content of the images with respect to types of cells and their sizes. Traditional PDE-based methods using level-sets can perform automatic segmentation, but do not perform well on images with clustered cells containing sub-structures. We present two modifications for popular methods and show the improved results.
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Acknowledgements
The work was partially supported by the mYeasty pilot-project by the Austrian GEN_AU research program (www.gen-au.at). It was carried out when A. Kuijper, Y. Zhou, and L. He were with the Johann Radon Institute for Computational and Applied Mathematics (RICAM), Linz, Austria.
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Kuijper, A., Heise, B., Zhou, Y., He, L., Wolinski, H., Kohlwein, S. (2015). Segmentation of Clustered Cells in Microscopy Images by Geometric PDEs and Level Sets. In: Paragios, N., Duncan, J., Ayache, N. (eds) Handbook of Biomedical Imaging. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09749-7_26
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DOI: https://doi.org/10.1007/978-0-387-09749-7_26
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