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

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 c...

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Main Authors: Mohendra Roy, Dongmin Seo, Sangwoo Oh, Yeonghun Chae, Myung-Hyun Nam, Sungkyu Seo
Format: Article
Language:English
Published: MDPI AG 2016-05-01
Series:Diagnostics
Subjects:
RBC
Online Access:http://www.mdpi.com/2075-4418/6/2/17
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spelling doaj-34aef18346604547a6ce7521aca50b792020-11-24T21:43:30ZengMDPI AGDiagnostics2075-44182016-05-01621710.3390/diagnostics6020017diagnostics6020017Automated Micro-Object Detection for Mobile Diagnostics Using Lens-Free Imaging TechnologyMohendra Roy0Dongmin Seo1Sangwoo Oh2Yeonghun Chae3Myung-Hyun Nam4Sungkyu Seo5Department of Electronics and Information Engineering, Korea University, Sejong 30019, KoreaDepartment of Electronics and Information Engineering, Korea University, Sejong 30019, KoreaDepartment of Electronics and Information Engineering, Korea University, Sejong 30019, KoreaDepartment of Big Data Science, University of Science and Technology, Daejeon 305350, KoreaDepartment of Laboratory Medicine, Korea University Ansan Hospital, Ansan 15355, KoreaDepartment of Electronics and Information Engineering, Korea University, Sejong 30019, KoreaLens-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.http://www.mdpi.com/2075-4418/6/2/17lens-freealgorithmtelemedicinecytometerRBC
collection DOAJ
language English
format Article
sources DOAJ
author Mohendra Roy
Dongmin Seo
Sangwoo Oh
Yeonghun Chae
Myung-Hyun Nam
Sungkyu Seo
spellingShingle Mohendra Roy
Dongmin Seo
Sangwoo Oh
Yeonghun Chae
Myung-Hyun Nam
Sungkyu Seo
Automated Micro-Object Detection for Mobile Diagnostics Using Lens-Free Imaging Technology
Diagnostics
lens-free
algorithm
telemedicine
cytometer
RBC
author_facet Mohendra Roy
Dongmin Seo
Sangwoo Oh
Yeonghun Chae
Myung-Hyun Nam
Sungkyu Seo
author_sort Mohendra Roy
title Automated Micro-Object Detection for Mobile Diagnostics Using Lens-Free Imaging Technology
title_short Automated Micro-Object Detection for Mobile Diagnostics Using Lens-Free Imaging Technology
title_full Automated Micro-Object Detection for Mobile Diagnostics Using Lens-Free Imaging Technology
title_fullStr Automated Micro-Object Detection for Mobile Diagnostics Using Lens-Free Imaging Technology
title_full_unstemmed Automated Micro-Object Detection for Mobile Diagnostics Using Lens-Free Imaging Technology
title_sort automated micro-object detection for mobile diagnostics using lens-free imaging technology
publisher MDPI AG
series Diagnostics
issn 2075-4418
publishDate 2016-05-01
description 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.
topic lens-free
algorithm
telemedicine
cytometer
RBC
url http://www.mdpi.com/2075-4418/6/2/17
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