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Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting

A lensless blood cell counting system integrating microfluidic channel and a complementary metal oxide semiconductor (CMOS) image sensor is a promising technique to miniaturize the conventional optical lens based imaging system for point-of-care testing (POCT). However, such a system has limited res...

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Autores principales: Huang, Xiwei, Jiang, Yu, Liu, Xu, Xu, Hang, Han, Zhi, Rong, Hailong, Yang, Haiping, Yan, Mei, Yu, Hao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5134495/
https://www.ncbi.nlm.nih.gov/pubmed/27827837
http://dx.doi.org/10.3390/s16111836
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author Huang, Xiwei
Jiang, Yu
Liu, Xu
Xu, Hang
Han, Zhi
Rong, Hailong
Yang, Haiping
Yan, Mei
Yu, Hao
author_facet Huang, Xiwei
Jiang, Yu
Liu, Xu
Xu, Hang
Han, Zhi
Rong, Hailong
Yang, Haiping
Yan, Mei
Yu, Hao
author_sort Huang, Xiwei
collection PubMed
description A lensless blood cell counting system integrating microfluidic channel and a complementary metal oxide semiconductor (CMOS) image sensor is a promising technique to miniaturize the conventional optical lens based imaging system for point-of-care testing (POCT). However, such a system has limited resolution, making it imperative to improve resolution from the system-level using super-resolution (SR) processing. Yet, how to improve resolution towards better cell detection and recognition with low cost of processing resources and without degrading system throughput is still a challenge. In this article, two machine learning based single-frame SR processing types are proposed and compared for lensless blood cell counting, namely the Extreme Learning Machine based SR (ELMSR) and Convolutional Neural Network based SR (CNNSR). Moreover, lensless blood cell counting prototypes using commercial CMOS image sensors and custom designed backside-illuminated CMOS image sensors are demonstrated with ELMSR and CNNSR. When one captured low-resolution lensless cell image is input, an improved high-resolution cell image will be output. The experimental results show that the cell resolution is improved by 4×, and CNNSR has 9.5% improvement over the ELMSR on resolution enhancing performance. The cell counting results also match well with a commercial flow cytometer. Such ELMSR and CNNSR therefore have the potential for efficient resolution improvement in lensless blood cell counting systems towards POCT applications.
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spelling pubmed-51344952017-01-03 Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting Huang, Xiwei Jiang, Yu Liu, Xu Xu, Hang Han, Zhi Rong, Hailong Yang, Haiping Yan, Mei Yu, Hao Sensors (Basel) Article A lensless blood cell counting system integrating microfluidic channel and a complementary metal oxide semiconductor (CMOS) image sensor is a promising technique to miniaturize the conventional optical lens based imaging system for point-of-care testing (POCT). However, such a system has limited resolution, making it imperative to improve resolution from the system-level using super-resolution (SR) processing. Yet, how to improve resolution towards better cell detection and recognition with low cost of processing resources and without degrading system throughput is still a challenge. In this article, two machine learning based single-frame SR processing types are proposed and compared for lensless blood cell counting, namely the Extreme Learning Machine based SR (ELMSR) and Convolutional Neural Network based SR (CNNSR). Moreover, lensless blood cell counting prototypes using commercial CMOS image sensors and custom designed backside-illuminated CMOS image sensors are demonstrated with ELMSR and CNNSR. When one captured low-resolution lensless cell image is input, an improved high-resolution cell image will be output. The experimental results show that the cell resolution is improved by 4×, and CNNSR has 9.5% improvement over the ELMSR on resolution enhancing performance. The cell counting results also match well with a commercial flow cytometer. Such ELMSR and CNNSR therefore have the potential for efficient resolution improvement in lensless blood cell counting systems towards POCT applications. MDPI 2016-11-02 /pmc/articles/PMC5134495/ /pubmed/27827837 http://dx.doi.org/10.3390/s16111836 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Huang, Xiwei
Jiang, Yu
Liu, Xu
Xu, Hang
Han, Zhi
Rong, Hailong
Yang, Haiping
Yan, Mei
Yu, Hao
Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting
title Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting
title_full Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting
title_fullStr Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting
title_full_unstemmed Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting
title_short Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting
title_sort machine learning based single-frame super-resolution processing for lensless blood cell counting
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5134495/
https://www.ncbi.nlm.nih.gov/pubmed/27827837
http://dx.doi.org/10.3390/s16111836
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