基于改进分水岭分割算法的致密荧光微滴识别

刘聪; 董文飞; 蒋克明; 周武平; 张涛; 黎海文

doi:10.3788/CO.20191204.0783

Volume 12 Issue 4

Aug. 2019

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Chinese Optics > 2019 > 12(4): 783-790.

LIU Cong, DONG Wen-fei, JIANG Ke-ming, ZHOU Wu-ping, ZHANG Tao, LI Hai-wen. Recognition of dense fluorescent droplets using an improved watershed segmentation algorithm[J]. Chinese Optics, 2019, 12(4): 783-790. doi: 10.3788/CO.20191204.0783

Citation:

LIU Cong, DONG Wen-fei, JIANG Ke-ming, ZHOU Wu-ping, ZHANG Tao, LI Hai-wen. Recognition of dense fluorescent droplets using an improved watershed segmentation algorithm[J]. Chinese Optics, 2019, 12(4): 783-790. doi: 10.3788/CO.20191204.0783

Citation:

LIU Cong, DONG Wen-fei, JIANG Ke-ming, ZHOU Wu-ping, ZHANG Tao, LI Hai-wen. Recognition of dense fluorescent droplets using an improved watershed segmentation algorithm[J]. Chinese Optics, 2019, 12(4): 783-790. doi: 10.3788/CO.20191204.0783

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Recognition of dense fluorescent droplets using an improved watershed segmentation algorithm

doi: 10.3788/CO.20191204.0783

1.
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
2.
University of Chinese Academy of Sciences, Beijing 100039, China

Funds:

the National Key Scientific Instrument and Equipment Development Project 2017YFF0108604

More Information

Corresponding author: LI Hai-wen, E-mail:lihw@sibet.ac.cn
Received Date: 28 Sep 2018
Rev Recd Date: 16 Nov 2018
Publish Date: 01 Aug 2019

Abstract

Abstract

Fluorescent droplet images acquired during droplet digital Polymerase Chain Reaction(PCR) detection have dense distribution, low brightness and low contrast, resulting in poor recognition accuracy. In order to correctly identify densely distributed fluorescent droplets, a fluorescent droplet recognition method based on an improved watershed segmentation algorithm is proposed. First, the image is preprocessed using histogram equalization and Gauss filtering, then the local adaptive threshold segmentation method is used to extract the targets from the background, thereby reducing the dependence on image gray level information. Finally, the algorithm combines the prior knowledge of the droplets with a circular and uniform size to define the droplet adhesion function, which reduces the error rate in the watershed segmentation. The experiment results show that compared with the traditional distance-based watershed segmentation method, the accuracy of the proposed algorithm is 97.34%, which is higher than the 85.9% accuracy of its counterpart.
- micro droplets,
- image recognition,
- local adaptive threshold segmentation,
- watershed segmentation

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References(15)

References

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