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VIČAR, T. CHMELÍK, J. KOLÁŘ, R.
Originální název
Cell Segmentation in Quantitative Phase Images with Improved Iterative Thresholding Method
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
Quantitative Phase Imaging (QPI) is a label-free microscopic technique, which provides images with high contrast, moreover, it provides quantitative cell mass measurements for each pixel. Segmentation of particular cells is an important step in the analysis of QPI image data. This paper presents a method for automatic cell segmentation in QPI images. The proposed method improves iterative thresholding, which is a very promising method, however, it is not able to segment densely clustered cells. Our improved iterative thresholding includes two additional steps -- Laplacian of Gaussian image enhancement and distance transform-based splitting. The method was compared with original iterative thresholding and another method on two cell lines, where the proposed method successfully deals with a densely clustered type of cells and achieves significantly better results on both datasets
Klíčová slova
Cell segmentation, Quantitative Phase Imaging, Iterative thresholding, Laplacian of Gaussian
Autoři
VIČAR, T.; CHMELÍK, J.; KOLÁŘ, R.
Vydáno
30. 11. 2020
Nakladatel
Springer Nature Switzerland AG 2021
Místo
Switzerland
ISBN
978-3-030-64609-7
Kniha
EMBEC 2020, IFMBE Proceedings vol. 80
ISSN
1680-0737
Periodikum
IFMBE PROCEEDINGS
Ročník
80
Stát
Švédské království
Strany od
233
Strany do
239
Strany počet
7
URL
https://link.springer.com/chapter/10.1007/978-3-030-64610-3_27
BibTex
@inproceedings{BUT166123, author="Tomáš {Vičar} and Jiří {Chmelík} and Radim {Kolář}", title="Cell Segmentation in Quantitative Phase Images with Improved Iterative Thresholding Method", booktitle="EMBEC 2020, IFMBE Proceedings vol. 80", year="2020", journal="IFMBE PROCEEDINGS", volume="80", pages="233--239", publisher="Springer Nature Switzerland AG 2021", address="Switzerland", doi="10.1007/978-3-030-64610-3\{_}27", isbn="978-3-030-64609-7", issn="1680-0737", url="https://link.springer.com/chapter/10.1007/978-3-030-64610-3_27" }