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Digital in-line holographic microscopy (DIHM) is a non-invasive, real-time, label-free technique that captures three-dimensional (3D) positional, orientational, and morphological information from digital holographic images of living biological cells. Unlike conventional microscopies, the DIHM technique enables precise measurements of dynamic behaviors exhibited by living cells within a 3D volume. This review outlines the fundamental principles and comprehensive digital image processing procedures employed in DIHM-based cell tracking methods. In addition, recent applications of DIHM technique for label-free identification and digital tracking of various motile biological cells, including human blood cells, spermatozoa, diseased cells, and unicellular microorganisms, are thoroughly examined. Leveraging artificial intelligence has significantly enhanced both the speed and accuracy of digital image processing for cell tracking and identification. The quantitative data on cell morphology and dynamics captured by DIHM can effectively elucidate the underlying mechanisms governing various microbial behaviors and contribute to the accumulation of diagnostic databases and the development of clinical treatments. [ABSTRACT FROM AUTHOR]

Real-time monitoring of suspended particulate matter (PM) has become essential in daily life due to the adverse effects of long-term exposure to PMs on human health and ecosystems. However, conventional techniques for measuring micro-scale particulates commonly require expensive instruments. In this study, a smartphone-based device is developed for real-time monitoring of suspended PMs by integrating a smartphone-based digital holographic microscopy (S-DHM) and deep learning algorithms. The proposed S-DHM-based PM monitoring device is composed of affordable commercial optical components and a smartphone. Overall procedures including digital image processing, deep learning training, and correction process are optimized to minimize the prediction error and computational cost. The proposed device can rapidly measure the mass concentrations of coarse and fine PMs from holographic speckle patterns of suspended polydisperse PMs in water with measurement errors of 22.8 ± 18.1% and 13.5 ± 9.8%, respectively. With further advances in data acquisition and deep learning training, this study would contribute to the development of hand-held devices for monitoring polydisperse non-spherical pollutants suspended in various media. [ABSTRACT FROM AUTHOR]

Plants are considered as a possible modality to reduce particulate matter (PM) particles from ambient air in an ecofriendly manner. A new precise monitoring technique that can explore interactions between individual PM particles and a leaf surface is necessary to understand the underlying mechanisms of PM removal of plant leaves. In this study, a digital in-line holographic microscopy (DIHM) was employed to experimentally investigate the settling motions of PM particles over the leaf surface. The in-plane positions and sizes of opaque PMs with irregular shapes were obtained from the projection images of numerically reconstructed holographic images. The depth positions of PMs were determined by using proper selection of an autofocusing criterion with automatic segmentation method. The edge of a hairy Perilla frutescens leaf was detected by adopting several digital imaging processing techniques. The DIHM technique was applied in this study to accurately detect 3D settling trajectories of PMs with velocity information of PMs in the midair and near leaf surface, simultaneously. ga1 • DHM based monitoring technique for 3D volumetric analysis of PM is proposed. • Sizes and 3D motions of hundreds of PMs can be extracted at the same time. • Settling behaviors of PMs in midair and near a real plant leaf are investigated. [ABSTRACT FROM AUTHOR]