5 April 2018 High-speed cell recognition algorithm for ultrafast flow cytometer imaging system
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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.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Wanyue Zhao, Wanyue Zhao, Chao Wang, Chao Wang, Hongwei Chen, Hongwei Chen, Minghua Chen, Minghua Chen, Sigang Yang, Sigang Yang, } "High-speed cell recognition algorithm for ultrafast flow cytometer imaging system," Journal of Biomedical Optics 23(4), 046001 (5 April 2018). https://doi.org/10.1117/1.JBO.23.4.046001 . Submission: Received: 19 October 2017; Accepted: 16 March 2018
Received: 19 October 2017; Accepted: 16 March 2018; Published: 5 April 2018
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