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Image processing is widely growing as a useful tool in biosensing applications. It can be used to convert any camera/microscope into an optical sensor with wide range of capabilities such as monitoring completion of colorimetric reactions, differentiating and counting cells, and tracking motile cells/organisms. However, implementation of image processing in Lab-on-Chip devices is still challenging for researchers with little expertise in this field. Here, we present a multi-purpose real-time machine vision platform for tracking and analyzing objects inside lab-on-chip devices and for automating many microfluidic applications. Our LabVIEW-based machine vision platform, which is freely available on our webpage ( http://www.assiutmicrofluidics.com/research ), enables non-experts in image processing and machine vision to easily assemble their image processing pipeline based on the intended application. The program was designed for plug and play interfacing with a wide range of imaging devices including USB microscopes, high-speed cameras, and smartphone cameras. Moreover, to achieve portability, the program can be loaded on myRIO, a portable pocket size fully functional LabVIEW platform, to perform all program capabilities outside the lab without the need for a PC. To prove functionality of our program, we used it in real-time closed loop control of hydrodynamic focusing and control of flow velocity of cells inside a microchannel. We also demonstrated the program abilities in different lab-on-chip applications such as tracking, differentiation, and counting of blood cells.
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http://dx.doi.org/10.1007/s10544-019-0401-1 | DOI Listing |
Int J Cosmet Sci
September 2025
Department of Materials, School of Natural Sciences, The University of Manchester, Manchester, UK.
Objectives: Machine-based cyclic combing of hair tresses under dry conditions is a proven method for evaluating hair strength and the impact of treatments. Recent advancements in image analysis allow for a detailed review of hair fragment lengths and quantities produced after specific combing cycles. Our aim is to provide an in-depth analysis of the kinetics of hair fragment formation.
View Article and Find Full Text PDFBehav Res Methods
September 2025
Czech Technical University in Prague, Faculty of Electrical Engineering, Department of Cybernetics, Prague, Czech Republic.
Automatic markerless estimation of infant posture and motion from ordinary videos carries great potential for movement studies "in the wild", facilitating understanding of motor development and massively increasing the chances of early diagnosis of disorders. There has been a rapid development of human pose estimation methods in computer vision, thanks to advances in deep learning and machine learning. However, these methods are trained on datasets that feature adults in different contexts.
View Article and Find Full Text PDFJ Safety Res
September 2025
Department of Civil & Environmental Engineering, The University of Tennessee, Knoxville, United States. Electronic address:
Introduction: Pedestrian safety is a growing concern in the United States transportation sector, with around 7,500 pedestrian crash fatalities reported in the United States in recent years. Pedestrians are at an even higher risk of crashes at night due to limited visibility and alcohol impairment of the drivers or pedestrians. The U.
View Article and Find Full Text PDFPLoS One
September 2025
School of Computer Science, CHART Laboratory, University of Nottingham, Nottingham, United Kingdom.
Background And Objective: Male fertility assessment through sperm morphology analysis remains a critical component of reproductive health evaluation, as abnormal sperm morphology is strongly correlated with reduced fertility rates and poor assisted reproductive technology outcomes. Traditional manual analysis performed by embryologists is time-intensive, subjective, and prone to significant inter-observer variability, with studies reporting up to 40% disagreement between expert evaluators. This research presents a novel deep learning framework combining Convolutional Block Attention Module (CBAM) with ResNet50 architecture and advanced deep feature engineering (DFE) techniques for automated, objective sperm morphology classification.
View Article and Find Full Text PDFExp Brain Res
September 2025
School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China.
This study explores how differences in colors presented separately to each eye (binocular color differences) can be identified through EEG signals, a method of recording electrical activity from the brain. Four distinct levels of green-red color differences, defined in the CIELAB color space with constant luminance and chroma, are investigated in this study. Analysis of Event-Related Potentials (ERPs) revealed a significant decrease in the amplitude of the P300 component as binocular color differences increased, suggesting a measurable brain response to these differences.
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