» Articles » PMID: 28350891

An On-chip Instrument for White Blood Cells Classification Based on a Lens-less Shadow Imaging Technique

Overview
Journal PLoS One
Date 2017 Mar 29
PMID 28350891
Citations 4
Authors
Affiliations
Soon will be listed here.
Abstract

Routine blood tests provide important basic information for disease diagnoses. The proportions of three subtypes of white blood cells (WBCs), which are neutrophils, monocytes, lymphocytes, is key information for disease diagnosis. However, current instruments for routine blood tests, such as blood cell analyzers, flow cytometers, and optical microscopes, are cumbersome, time consuming and expensive. To make a smaller, automatic low-cost blood cell analyzer, much research has focused on a technique called lens-less shadow imaging, which can obtain microscopic images of cells in a lens-less system. Nevertheless, the efficiency of this imaging system is not satisfactory because of two problems: low resolution and imaging diffraction phenomena. In this paper, a novel method of classifying cells with the shadow imaging technique was proposed. It could be used for the classification of the three subtypes of WBCs, and the correlation of the results of classification between the proposed system and the reference system (BC-5180, Mindray) was 0.93. However, the instrument was only 10 × 10 × 10 cm, and the cost was less than $100. Depending on the lens-free shadow imaging technology, the main hardware could be integrated on a chip scale and could be called an on-chip instrument.

Citing Articles

Label-Free CD34+ Cell Identification Using Deep Learning and Lens-Free Shadow Imaging Technology.

Baik M, Shin S, Kumar S, Seo D, Lee I, Jun H Biosensors (Basel). 2023; 13(12).

PMID: 38131753 PMC: 10741567. DOI: 10.3390/bios13120993.


Pursuing the Diffraction Limit with Nano-LED Scanning Transmission Optical Microscopy.

Moreno S, Canals J, Moro V, Franch N, Vila A, Romano-Rodriguez A Sensors (Basel). 2021; 21(10).

PMID: 34064543 PMC: 8151575. DOI: 10.3390/s21103305.


A Quantized CNN-Based Microfluidic Lensless-Sensing Mobile Blood-Acquisition and Analysis System.

Liao Y, Yu N, Tian D, Li S, Li Z Sensors (Basel). 2019; 19(23).

PMID: 31766471 PMC: 6928811. DOI: 10.3390/s19235103.


The Use of Motion Analysis as Particle Biomarkers in Lensless Optofluidic Projection Imaging for Point of Care Urine Analysis.

Kun J, Smieja M, Xiong B, Soleymani L, Fang Q Sci Rep. 2019; 9(1):17255.

PMID: 31754152 PMC: 6872526. DOI: 10.1038/s41598-019-53477-8.

References
1.
Lee J, Shin Y . Oligonol supplementation affects leukocyte and immune cell counts after heat loading in humans. Nutrients. 2014; 6(6):2466-77. PMC: 4073162. DOI: 10.3390/nu6062466. View

2.
Yagi M, Shibata T . An image representation algorithm compatible with neural-associative-processor-based hardware recognition systems. IEEE Trans Neural Netw. 2008; 14(5):1144-61. DOI: 10.1109/TNN.2003.819038. View

3.
Huang X, Guo J, Wang X, Yan M, Kang Y, Yu H . A contact-imaging based microfluidic cytometer with machine-learning for single-frame super-resolution processing. PLoS One. 2014; 9(8):e104539. PMC: 4128713. DOI: 10.1371/journal.pone.0104539. View

4.
Ah Lee S, Leitao R, Zheng G, Yang S, Rodriguez A, Yang C . Color capable sub-pixel resolving optofluidic microscope and its application to blood cell imaging for malaria diagnosis. PLoS One. 2011; 6(10):e26127. PMC: 3191177. DOI: 10.1371/journal.pone.0026127. View

5.
Chung J, Ou X, Kulkarni R, Yang C . Counting White Blood Cells from a Blood Smear Using Fourier Ptychographic Microscopy. PLoS One. 2015; 10(7):e0133489. PMC: 4506059. DOI: 10.1371/journal.pone.0133489. View