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

Mohendra Roy,Yeonghun Chae,Dongmin Seo,Sangwoo Oh,Myung-Hyun Nam,Sungkyu Seo

doi:10.3390/diagnostics6020017

Abstract

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, HepG2, HeLa, and MCF7 cells lines. 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.

Highlights

Analysis of micro-objects, e.g., cells and micro-particles, is among the major tasks in pathology, biological research, and material science research
We develop a mechanism to locally obtain the signal pixels and a clustering procedure to acquire the central positions of the diffraction patterns
Imaging for cell cytometry and particle analysis can be provided by alternative vital information required arrangementsCell such as a islens-free shadow imagingused system

Summary

Introduction

Analysis of micro-objects, e.g., cells and micro-particles, is among the major tasks in pathology, biological research, and material science research. Diagnostics 2016, 6, 17 settings, this type of study is generally conducted using a conventional optical microscope and hemocytometer. In this conventional method, an expert manually inspects the samples, which is tedious and prone to subjective error. Resource-rich laboratories often use sophisticated automated alternatives, such as a Coulter counter and flow cytometer, to handle large sample numbers [2]. These automated systems are bulky and expensive, which limits their application and makes them impractical for resource-limited settings

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Conclusion