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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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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