Abstract
We present a new method for segmentation of phase-contrast microscopic images of cells. The algorithm is based on the variational formulation of the level set method, i.e. minimizing of a functional, which describes the level set function. The functional is minimized by a gradient flow described by an evolutionary partial differential equation. The most significant new ideas are initialization using thresholding and the introduction of a new term based on local variance that speeds up convergence and achieves more accurate results. The proposed algorithm is applied on real data and compared with another algorithm. Our method yields an average gain in accuracy of 2 %.
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Acknowledgements
The study was supported by the GAUK grant No. 914813/2013, the grant GAČR No. 13-29225S, the grant SVV-2015-260223 and the grant SVV-2016-260332. The authors would also like to thank the staff of the Working Place of Tissue Culture - Certified Laboratory at Nové Hrady for their assistance with the manual segmentation of the cells.
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Bílková, Z., Soukup, J., Kučera, V. (2016). Cell Segmentation Using Level Set Methods with a New Variance Term. In: Campilho, A., Karray, F. (eds) Image Analysis and Recognition. ICIAR 2016. Lecture Notes in Computer Science(), vol 9730. Springer, Cham. https://doi.org/10.1007/978-3-319-41501-7_21
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DOI: https://doi.org/10.1007/978-3-319-41501-7_21
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