Abstract
The large amount of data generated in biological experiments that rely on advanced microscopy can be handled only with automated image analysis. Most analyses require a reliable cell image segmentation eventually capable of detecting subcellular structures.
We present an automatic segmentation method to detect Polycomb group (PcG) proteins areas isolated from nuclei regions in high-resolution fluorescent cell image stacks. It combines two segmentation algorithms that use an active contour model and a classification technique serving as a tool to better understand the subcellular three-dimensional distribution of PcG proteins in live cell image sequences. We obtained accurate results throughout several cell image datasets, coming from different cell types and corresponding to different fluorescent labels, without requiring elaborate adjustments to each dataset.
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Notes
- 1.
The inner loop is an iterative algorithm solving the scheme in (Fig. 2).
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Acknowledgments
Work partially supported by FIRB 2010 Project n.~RBFR106S1Z002.
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Gregoretti, F., Cesarini, E., Lanzuolo, C., Oliva, G., Antonelli, L. (2016). An Automatic Segmentation Method Combining an Active Contour Model and a Classification Technique for Detecting Polycomb-group Proteinsin High-Throughput Microscopy Images. In: Lanzuolo, C., Bodega, B. (eds) Polycomb Group Proteins. Methods in Molecular Biology, vol 1480. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-6380-5_16
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DOI: https://doi.org/10.1007/978-1-4939-6380-5_16
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