Open AccessA Contact-Imaging Based Microfluidic Cytometer with Machine-Learning for Single-Frame Super-Resolution Processing
Author(s) -
Xiwei Huang,
Jinhong Guo,
Xiaolong Wang,
Yan Mei,
Yuejun Kang,
Hao Yu
Publication year - 2014
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0104539
Subject(s) - subpixel rendering , microfluidics , frame rate , resolution (logic) , image processing , throughput , computer science , frame (networking) , point (geometry) , artificial intelligence , biomedical engineering , computer vision , materials science , pixel , nanotechnology , image (mathematics) , mathematics , engineering , telecommunications , wireless , geometry
Lensless microfluidic imaging with super-resolution processing has become a promising solution to miniaturize the conventional flow cytometer for point-of-care applications. The previous multi-frame super-resolution processing system can improve resolution but has limited cell flow rate and hence low throughput when capturing multiple subpixel-shifted cell images. This paper introduces a single-frame super-resolution processing with on-line machine-learning for contact images of cells. A corresponding contact-imaging based microfluidic cytometer prototype is demonstrated for cell recognition and counting. Compared with commercial flow cytometer, less than 8% error is observed for absolute number of microbeads; and 0.10 coefficient of variation is observed for cell-ratio of mixed RBC and HepG2 cells in solution.
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