Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting.
Machine Learning Based Single-Frame Super-Resolution Processing for Lensless Blood Cell Counting.
复制标题
基于机器学习的单帧超分辨率处理，用于无透镜血细胞计数
DOI：
10.3390/s16111836
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发表时间：
2016-11-02
期刊：
影响因子：
--
通讯作者：
Yu H
中科院分区：
文献类型：
--
作者：
Huang X;Jiang Y;Liu X;Xu H;Han Z;Rong H;Yang H;Yan M;Yu H
集成微流体通道和互补金属氧化物半导体 (CMOS) 图像传感器的无透镜血细胞计数系统是一项很有前景的技术，可用于小型化基于传统光学透镜的即时检测 (POCT) 成像系统。然而，这样的系统分辨率有限，因此必须使用超分辨率（SR）处理从系统级提高分辨率。然而，如何在不降低系统吞吐量的情况下提高分辨率，以实现更好的细胞检测和识别，同时降低处理资源成本，仍然是一个挑战。在本文中，提出并比较了两种基于机器学习的单帧 SR 处理类型，用于无透镜血细胞计数，即基于极限学习机的 SR (ELMSR) 和基于卷积神经网络的 SR (CNNSR)。此外，还通过 ELMSR 和 CNNSR 演示了使用商用 CMOS 图像传感器和定制设计的背照式 CMOS 图像传感器的无透镜血细胞计数原型。当输入一幅捕获的低分辨率无透镜细胞图像时，将输出改进的高分辨率细胞图像。实验结果表明，细胞分辨率提高了4倍，CNNSR在分辨率增强性能上比ELMSR提高了9.5%。细胞计数结果也与商业流式细胞仪吻合良好。因此，此类 ELMSR 和 CNNSR 具有在 POCT 应用中有效提高无透镜血细胞计数系统分辨率的潜力。
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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6.1
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