High-speed cell recognition algorithm for ultra-fast flow cytometer imaging system

Zhao, Wanyue and Wang, Chao and Chen, Hongwei and Chen, Minghua and Yang, Sigang (2018) High-speed cell recognition algorithm for ultra-fast flow cytometer imaging system. Journal of Biomedical Optics, 23 (4). ISSN 1083-3668. (doi:https://doi.org/10.1117/1.JBO.23.4.046001) (Full text available)

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https://doi.org/10.1117/1.JBO.23.4.046001

Abstract

An optical time-stretch flow imaging system enables high-throughput examination of cells/particles with unprecedented high speed and resolution. A significant amount of raw image data is produced. A high-speed cell recognition algorithm is, therefore, highly demanded to analyze large amounts of data efficiently. A high-speed cell recognition algorithm consisting of two-stage cascaded detection and Gaussian mixture model (GMM) classification is proposed. The first stage of detection extracts cell regions. The second stage integrates distance transform and the watershed algorithm to separate clustered cells. Finally, the cells detected are classified by GMM. We compared the performance of our algorithm with support vector machine. Results show that our algorithm increases the running speed by over 150% without sacrificing the recognition accuracy. This algorithm provides a promising solution for high-throughput and automated cell imaging and classification in the ultrafast flow cytometer imaging platform.

Item Type: Article
Uncontrolled keywords: ultrafast technology; cytometry; image recognition algorithm
Divisions: Faculties > Sciences > School of Engineering and Digital Arts
Faculties > Sciences > School of Engineering and Digital Arts > Broadband & Wireless Communications
Depositing User: Chao Wang
Date Deposited: 19 Mar 2018 10:02 UTC
Last Modified: 17 May 2018 11:15 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/66446 (The current URI for this page, for reference purposes)
Wang, Chao: https://orcid.org/0000-0002-0454-8079
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