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Diagnostics 2016, 6(2), 17;

Automated Micro-Object Detection for Mobile Diagnostics Using Lens-Free Imaging Technology

Department of Electronics and Information Engineering, Korea University, Sejong 30019, Korea
Department of Physics, Rajiv Gandhi University, Arunachal Pradesh, Doimukh 791112, India
Maritime Safety Research Division, Korea Research Institute of Ships and Ocean Engineering, Daejeon 34103, Korea
Department of Big Data Science, University of Science and Technology, Daejeon 305350, Korea
Department of Laboratory Medicine, Korea University Ansan Hospital, Ansan 15355, Korea
Author to whom correspondence should be addressed.
Academic Editor: Aydogan Ozcan
Received: 29 December 2015 / Revised: 4 March 2016 / Accepted: 27 April 2016 / Published: 5 May 2016
(This article belongs to the Special Issue Mobile Diagnosis)
Full-Text   |   PDF [5640 KB, uploaded 9 May 2016]   |  


Lens-free imaging technology has been extensively used recently for microparticle and biological cell analysis because of its high throughput, low cost, and simple and compact arrangement. However, this technology still lacks a dedicated and automated detection system. In this paper, we describe a custom-developed automated micro-object detection method for a lens-free imaging system. In our previous work (Roy et al.), we developed a lens-free imaging system using low-cost components. This system was used to generate and capture the diffraction patterns of micro-objects and a global threshold was used to locate the diffraction patterns. In this work we used the same setup to develop an improved automated detection and analysis algorithm based on adaptive threshold and clustering of signals. For this purpose images from the lens-free system were then used to understand the features and characteristics of the diffraction patterns of several types of samples. On the basis of this information, we custom-developed an automated algorithm for the lens-free imaging system. Next, all the lens-free images were processed using this custom-developed automated algorithm. The performance of this approach was evaluated by comparing the counting results with standard optical microscope results. We evaluated the counting results for polystyrene microbeads, red blood cells, and HepG2, HeLa, and MCF7 cells. The comparison shows good agreement between the systems, with a correlation coefficient of 0.91 and linearity slope of 0.877. We also evaluated the automated size profiles of the microparticle samples. This Wi-Fi-enabled lens-free imaging system, along with the dedicated software, possesses great potential for telemedicine applications in resource-limited settings. View Full-Text
Keywords: lens-free; algorithm; telemedicine; cytometer; RBC lens-free; algorithm; telemedicine; cytometer; RBC

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Roy, M.; Seo, D.; Oh, S.; Chae, Y.; Nam, M.-H.; Seo, S. Automated Micro-Object Detection for Mobile Diagnostics Using Lens-Free Imaging Technology. Diagnostics 2016, 6, 17.

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