Optical Image Acquisition Research Articles

Retraction Note: Simulation of 3D digital design of clothing fabrics based on optical imaging detection and image acquisition

Retraction Note: Simulation of 3D digital design of clothing fabrics based on optical imaging detection and image acquisition

Droplet size, spray structure and droplet velocity mapping in hollow cone sprays using SLIPI-based techniques

The Structured Laser Illumination Planar Imaging (SLIPI) technique received significant attention in recent years due to notable improvements in the measurement of droplet size and the visualization of internal structure in both dilute and dense sprays. However, despite the improvements, the technique remains limited to these microscopic characteristics of the spray, primarily due to the complexity of the optical setup and image acquisition process. In this article, the capabilities of the SLIPI application are exploited, specifically employing the 3p-SLIPI-LIF/Mie (SLSD), and combined application of 1p-SLIPI-LIF & PIV (1p-SLIPI-PIV) for mapping of both Sauter Mean Diameter (SMD) and mean droplet velocity in combination with detailed spray structure analysis. The measurements were carried out in hollow cone sprays operated at the range of injection pressures from 25 to 107 bar. The results obtained using the SLIPI techniques were compared with the Phase Doppler Anemometry (PDA) measurements showing good agreement for both the SMD and mean droplet velocity. The significant improvements in the important microscopic characteristics (droplet size & velocity) and visualization of the internal structure in a wider flow field of hollow cone sprays allowed detailed analysis and understanding of the dispersed phase flow owing to the suppression of diffused light resulting from the multiple scattering effects.

RETRACTED ARTICLE: Simulation of 3D digital design of clothing fabrics based on optical imaging detection and image acquisition

RETRACTED ARTICLE: Simulation of 3D digital design of clothing fabrics based on optical imaging detection and image acquisition

Equations for smartphone prediction of adiposity and appendicular lean mass in youth soccer players

Digital anthropometry by three-dimensional optical imaging systems and smartphones has recently been shown to provide non-invasive, precise, and accurate anthropometric and body composition measurements. To our knowledge, no previous study performed smartphone-based digital anthropometric assessments in young athletes. The aim of this study was to investigate the reproducibly and validity of smartphone-based estimation of anthropometric and body composition parameters in youth soccer players. A convenience sample of 124 male players and 69 female players (median ages of 16.2 and 15.5 years, respectively) was recruited. Measurements of body weight and height, one whole-body Dual-Energy X-ray Absorptiometry (DXA) scan, and acquisition of optical images (performed in duplicate by the Mobile Fit app to obtain two avatars for each player) were performed. The reproducibility analysis showed percent standard error of measurement values < 10% for all anthropometric and body composition measurements, thus indicating high agreement between the measurements obtained for the two avatars. Mobile Fit app overestimated the body fat percentage with respect to DXA (average overestimation of + 3.7% in males and + 4.6% in females), while it underestimated the total lean mass (− 2.6 kg in males and − 2.5 kg in females) and the appendicular lean mass (− 10.5 kg in males and − 5.5 kg in females). Using data of the soccer players, we reparameterized the equations previously proposed to estimate the body fat percentage and the appendicular lean mass and we obtained new equations that can be used in youth athletes for body composition assessment through conventional anthropometrics-based prediction models.

Experimental study on dynamic characteristics of charged particles in vacuum arc

The vacuum arc is essentially a process of evaporation, collision and ionization of metal materials to keep burning. Electron temperature and density are important parameters to describe vacuum arc plasma transport. This paper was based on the theory of continuous spectral radiation, combined with the principle of two-colorimetric temperature measurement, and realized 2D calculation and observation of electron temperature and density. The experimental platform for vacuum arc breaking and an optical image acquisition system were established to achieve synchronous acquisition of two wavelengths of arc by imaging separation method. On the basis of partial local thermodynamic equilibrium hypothesis, and obtained the 2D dynamic distribution of the electron temperature and density. The results showed that electron density decayed along the radial direction and the density near anode was lower in the axial direction. The 2D distribution trend of electron temperature was opposite to the density. Electron temperature had a little change and the electron density expands outward in different arcing times. With the increase of current amplitude, the peak value of electron density increased, and the electron density in the arc gap also increased. The measured results could reflect the formation and movement of microscopic particles at the initial diffusion stage.

Preface

A peer-reviewed International Conference on Engineering of Optical System and Precision Instruments (ICEOP 2023) was held on August 26-27 at the academic exchange center of Huazhong University of Science and Technology, in Wuhan, China.The conference was made stimulating and informative by three plenary keynote speeches by the pioneers of their fields. Prof. Haiming Luo, Professor of Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology (HUST), China; Prof. Jinbo Pang, Professor of Chemistry and Materials Science, Institute for Advanced Interdisciplinary Research (iAIR), University of Jinan (UJN), China; Prof. Martin Becker, Professor of Leibniz Institute of Photonic Technology, Germany, delivered their wonderful talks. There are also 10 oral presentations and 5 posters are presented at the ICEOP2023 conference. At the end of each presentation, there will be 5 minutes for a question-and-answer to facilitate communication among participants.This volume is a compilation of 18 selected papers from ICEOP 2023 and focuses on recent advances experiences, and emerging trends that have positively impacted our world in optical engineering and precision instruments, which has provided such a platform to researchers, engineers, industrial professionals, and practitioners of the optical engineering and precision instruments field from all over the globe. Four sections are divided: Modern Optical Engineering, Image Acquisition and Processing, Laser Technology, and Optoelectronic Engineering and Precision Control. It has kindled validated discussions on the technological advancements, recent trends, various issues of sustainability, and the solutions adopted to address these issues in the amalgamating field of engineering, technology and science by embodying innovative ideas and pivoting advanced technologies.The organizing committee addresses the thanks to esteemed associates of the conference, eminent speakers, the technical committee members, reviewers, participants, and all the members who have supported the conference. We are grateful for your attendance and contribution to a successful ICEOP 2023.Organizing Committee of ICEOP 2023Valery Tuchin (Conference Chair)Head of Optics and Biophotonics Chair, Director of Science Medical CenterSaratov State University, RussiaList of Editorial Board Members, Scientific Committee are available in this Pdf.

Fundus Imaging-Based Healthcare: Present and Future

A fundus image is a two-dimensional pictorial representation of the membrane at the rear of the eye that consists of blood vessels, the optical disc, optical cup, macula, and fovea. Ophthalmologists use it during eye examinations to screen, diagnose, and monitor the progress of retinal diseases or conditions such as diabetes, age-marked degeneration (AMD), glaucoma, retinopathy of prematurity (ROP), and many more ocular ailments. Developments in ocular optical systems, image acquisition, processing, and management techniques over the past few years have contributed to the use of fundus images to monitor eye conditions and other related health complications. This review summarizes the various state-of-the-art technologies related to the fundus imaging device, analysis techniques, and their potential applications for ocular diseases such as diabetic retinopathy, glaucoma, AMD, cataracts, and ROP. We also present potential opportunities for fundus imaging–based affordable, noninvasive devices for scanning, monitoring, and predicting ocular health conditions and providing other physiological information, for example, heart rate (HR), blood components, pulse rate, heart rate variability (HRV), retinal blood perfusion, and more. In addition, we present different types of technological, economical, and sociological factors that impact the growth of the fundus imaging–based technologies for health monitoring.

Resolution enhancement with a task-assisted GAN to guide optical nanoscopy image analysis and acquisition

Super-resolution fluorescence microscopy methods enable the characterization of nanostructures in living and fixed biological tissues. However, they require the adjustment of multiple imaging parameters while attempting to satisfy conflicting objectives, such as maximizing spatial and temporal resolution while minimizing light exposure. To overcome the limitations imposed by these trade-offs, post-acquisition algorithmic approaches have been proposed for resolution enhancement and image-quality improvement. Here we introduce the task-assisted generative adversarial network (TA-GAN), which incorporates an auxiliary task (for example, segmentation, localization) closely related to the observed biological nanostructure characterization. We evaluate how the TA-GAN improves generative accuracy over unassisted methods, using images acquired with different modalities such as confocal, bright-field, stimulated emission depletion and structured illumination microscopy. The TA-GAN is incorporated directly into the acquisition pipeline of the microscope to predict the nanometric content of the field of view without requiring the acquisition of a super-resolved image. This information is used to automatically select the imaging modality and regions of interest, optimizing the acquisition sequence by reducing light exposure. Data-driven microscopy methods like the TA-GAN will enable the observation of dynamic molecular processes with spatial and temporal resolutions that surpass the limits currently imposed by the trade-offs constraining super-resolution microscopy.

Fast optical coherence tomography angiography image acquisition and reconstruction pipeline for skin application.

Traditional high-quality OCTA images require multi-repeated scans (e.g., 4-8 repeats) in the same position, which may cause the patient to be uncomfortable. We propose a deep-learning-based pipeline that can extract high-quality OCTA images from only two-repeat OCT scans. The performance of the proposed image reconstruction U-Net (IRU-Net) outperforms the state-of-the-art UNet vision transformer and UNet in OCTA image reconstruction from a two-repeat OCT signal. The results demonstrated a mean peak-signal-to-noise ratio increased from 15.7 to 24.2; the mean structural similarity index measure improved from 0.28 to 0.59, while the OCT data acquisition time was reduced from 21 seconds to 3.5 seconds (reduced by 83%).

Synthetic Aperture Radar Remote Sensing for Crop Classification

This Study proposes the approach for crop classification using the Grey Level Co-occurrence Matrix feature of Synthetic Aperture Radar (SAR) images. The method utilizes the SAR Images acquired by Sentinel 1A SAR Data and extract textural features using GLCM. In this study, we investigate the potential of Grey Level Co-occurrence Matrix (GLCM)-based texture information for horticulture crop classification with SAR images in Kharif and cloud weather condition. A study on Synthetic Aperture Radar (SAR) satellite imagery was conducted in Chhattisgarh with the objective to evaluate the potential of different texture parameters among crop. The SAR data were pre-processed for textural analysis having entire angle and equal distance quantization. The results were categorized among different parameters showing significant variation for horticulture crops for Contrast, Dissimilarity, Homogeneity, ASM, Energy, Entropy and GLCM Mean. The statistical analysis was done for fruit crop along with major kharif crop of study area. The results shows that mean backscatter value was lowest for banana (99.12 dB) and highest for Mango (198.26 dB) regarding contrast textural property in VH Channel whereas mean backscatter value in VH Channel w.r.t to energy was maximum for banana (0.60 dB) followed by papaya (0.49 dB) and guava (0.45 dB) and least for mango (0.44 dB). The mean backscatter value for GLCM mean textural property in VH channel was shown maximum by banana (51.24 dB) followed by papaya (41.96 dB) and mango (32.98 dB). These results indicate the usefulness of texture information for classification of SAR images, particularly when acquisition of optical images is difficult in Kharif and cloud weather condition for crop classification. Thus GLCM feature of SAR Data proven to be significant for the classification of horticulture crops.

Acoustic plane wave detection of intracranial cavitation in a polyacrylamide human head model under blunt impact

Traumatic brain injuries (TBIs) occur in everyday life in the form of blunt impacts, while military personnel experience a combination of blast and blunt impacts from explosives. Damage mechanisms in TBIs, including cavitation, are poorly understood due to the inability to optically monitor brain deformation inside the skull during collisions. To overcome these limitations, high frame rate acoustic plane wave imaging in conjunction with high-speed optical imaging were combined to visualize intracranial cavitation during impact of a transparent brain phantom. The full-scale phantom was composed of 7% and 10% (w/v) polyacrylamide (PAA) andenclosed in a 3D printed skull with acrylic windows for optical image acquisition. This surrogate utilizes simplified geometry to represent key anatomical features such as the gyri, sulci, and ventricles while possessing rheological properties similar to the human brain. Optical and acoustic data correlate cavitation to high-contrast regions. Acoustic spectra show expected harmonic and broadband behavior during cavitation bubble growth and collapse. Further processing with superresolution techniques may enhance cavitation localization providing a stand-alone acoustic tool applicable in various TBI scenarios allowing observations at depth, where optical techniques are limited.

Public Data Acquisition of Optical Coherence Tomography Images of Fundus and Its Analysis Algorithms

Public Data Acquisition of Optical Coherence Tomography Images of Fundus and Its Analysis Algorithms

Method for Frequent High Resolution of Optical Sensor Image Acquisition using Satellite-Based SAR Image for Disaster Mitigation

Method for frequent high resolution of optical sensor imagery data acquisition from satellite-based SAR (Synthetic Aperture Radar) image for disaster mitigation is proposed. The proposed method is based on Generative Adversarial Network: GAN-based super resolution and conversion method from a SAR imagery data to the corresponding optical sensor imagery data in order to increase observation frequency. Through experiments, it is found that it is possible to convert SAR imagery data to the corresponding optical sensor imagery data and also found that the spatial resolution of SAR imagery data is improved remarkably. Thus, initial stage of disaster (small scale of disaster) can be detected with resolution enhanced optical sensor imagery data derived from the corresponding SAR imagery data which results in prevention of secondary occurrence of relatively large scale of disaster. It is also found that 2.5 m of spatial resolution of optical sensor imagery data can be acquired every 2.5 days in the case that only Sentinel-1/SAR and Sentinel-2/MSI (Multi Spectral Imager) are used, for instance.

Upright patient positioning for pelvic radiotherapy treatments.

Radiotherapy is typically delivered in supine position. However, upright positioning may affect organ volume, positioning, and movement, compared to supine/prone positioning which might have beneficial impacts. In this study, we report patient positioning data in an upright positioning system designed by Leo Cancer Care®. Sixteen patients with pelvic tumors were included in this study. They had 3 setups in an upright position: an initial setup with acquisition of reference optical images, and 2 repositioning setups. The intra-fraction motion was assessed during two 20-minute chair rotation sessions. The patient comfort in supine and upright position was assessed with a 5-point Likert scale questionnaire. Eight women and 8 men treated on regular linacs between October 2021 and June 2022 were included. Their median age and weight were 62.5years (35 to 81years) and 75.1kg (41 to 107kg). The inter-fraction shift means were -0.5mm (SD=2.5), -0.4mm (SD=1.3) and -0.9mm (SD=2.7) in left-right (LR), antero-posterior (AP), and cranio-caudal (CC) directions, respectively. The intrafraction shifts after 20min were 0.0mm (SD=1.5), 0.2mm (SD=1.1) and 0.0mm (SD=0.3) in LR, CC, and AP directions, respectively. Average global comfort was 4.1 (3 to 5) for the upright position and 3.9 (2 to 5) for the supine position. In conclusion, the first study on pelvic cancer patients positioned in upright position on a chair is promising, and it opens a potential new direction for the treatment of cancer patients. Evaluation of thoracic and head and neck tumors is ongoing, and imaging with vertical CT is expected to start soon.

Automatic Detection of Marine Litter: A General Framework to Leverage Synthetic Data

The spatial and temporal coverage of spaceborne optical imaging systems are well suited for automated marine litter monitoring. However, developing machine learning-based detection and identification algorithms requires large amounts of data. Indeed, when it comes to marine debris, ground validated data is scarce. In this study, we propose a general methodology that leverages synthetic data in order to avoid overfitting and generalizes well. The idea is to utilize realistic models of spaceborne optical image acquisition and marine litter to generate large amounts of data to train the machine learning algorithms. These can then be used to detect marine pollution automatically on real satellite images. The main contribution of our study is showing that algorithms trained on simulated data can be successfully transferred to real-life situations. We present the general components of our framework, our modeling of satellites and marine debris and a proof of concept implementation for macro-plastic detection with Sentinel-2 images. In this case study, we generated a large dataset (more than 16,000 pixels of marine debris) composed of seawater, plastic, and wood and trained a Random Forest classifier on it. This classifier, when tested on real satellite images, successfully discriminates marine litter from seawater, thus proving the effectiveness of our approach and paving the way for machine learning-based marine litter detection with even more representative simulation models.

A Novel Operational Rice Mapping Method Based on Multi-Source Satellite Images and Object-Oriented Classification

Combining optical and synthetic aperture radar (SAR) data for crop mapping has become a crucial way to improve classification accuracy, especially in cloudy and rainy areas. However, the acquisition of optical images is significantly unstable due to the influence of cloudy and rainy weather, which seriously restricts the application of this method in practice. To solve this problem, this study proposed an optical-SAR imagery-based rice mapping method which has the advantages of less dependence on optical images, easy operation and high classification accuracy. To account for the trait of sparse availability of optical images, this method only needs one clear sky optical image in the rice growth period and combined it with multi-temporal SAR images to achieve a high accuracy rice mapping result. Meanwhile, this paper also proposed a comprehensively multi-scale segmentation parameter optimization algorithm, which considers the area consistency, shape error and location difference between the segmented object and reference object, and adopts an orthogonal experiment approach. Based on the optical image, the boundaries of the parcel objects can be segmented, which were subsequently used to perform the object-oriented classification. The results show that the overall accuracy of the proposed method in Yangzhou City is 94.64%. Moreover, according to a random pick test, it is encouraging that the proposed method has strong robustness in response to the instability of the acquisition time of SAR images. A relatively high overall accuracy of 90.09% suggested that the proposed method can provide a reliable rice mapping result in cloudy and rainy areas.

Advanced Fluorescence Microscopy Methods to Study Dynamics of Fluorescent Proteins In Vivo.

Fluorescent proteins are standard tools for addressing biological questions in a cell biology laboratory. The genetic tagging of protein of interest with fluorescent proteins opens the opportunity to follow them in vivo and to understand their interactions and dynamics. In addition, the latest advances in optical microscopy image acquisition and processing allow us to study many cellular processes in vivo. Techniques such as fluorescence lifetime microscopy and hyperspectral imaging provide valuable tools for understanding fluorescent protein interactions and their photophysics. Finally, fluorescence fluctuation analysis opens the possibility to address questions of molecular diffusion, protein-protein interactions, and oligomerization, among others, yielding quantitative information on the subject of study. This chapter will cover some of the more important advances in cutting-edge technologies and methods that, combined with fluorescent proteins, open new frontiers for biological studies.

A photosensor employing data-driven binning for ultrafast image recognition

Pixel binning is a technique, widely used in optical image acquisition and spectroscopy, in which adjacent detector elements of an image sensor are combined into larger pixels. This reduces the amount of data to be processed as well as the impact of noise, but comes at the cost of a loss of information. Here, we push the concept of binning to its limit by combining a large fraction of the sensor elements into a single “superpixel” that extends over the whole face of the chip. For a given pattern recognition task, its optimal shape is determined from training data using a machine learning algorithm. We demonstrate the classification of optically projected images from the MNIST dataset on a nanosecond timescale, with enhanced dynamic range and without loss of classification accuracy. Our concept is not limited to imaging alone but can also be applied in optical spectroscopy or other sensing applications.

Extraction and Analysis of Foot Bone Shape Features Based on Deep Learning.

With the rapid development of artificial intelligence, more and more researchers and research institutions begin to pay attention to the bone feature recognition field. Human bone movement is very complex, and human bone shape recognition technology can be widely used in medical treatment, sports, and other fields. At present, there are mainly two kinds of methods for extracting the shape features of human foot bone based on optical image acquisition technology and sensor information perception technology. However, due to the interference factors such as target posture change, camera shake, and individual behavior differences, it is still a very challenging task to design a robust algorithm for extraction and analysis of foot bone shape features. In recent years, convolutional neural network- (CNN-) based foot contour feature recognition methods emerge one after another and have made breakthrough progress. How to use and how to fully explore the potential relationship of various characteristics contained in the foot bone data and how to enhance the robustness of view changes and other aspects need to be further studied. In this context, this paper proposed an improved CNN model, which not only has the capability of deep feature extraction of the CNN model but also can obtain the optimal model parameters with the combination of particle swarm optimization algorithm. The effectiveness of the proposed method in the extraction and analysis of foot bone shape features is verified in the simulation experiment.

Design and characterization of a new microscopy probe using COMSOL and ANSYS

This paper presents the design, simulation, and analysis of different 3D-shapes of scanning near-field optical microscopy probes, allowing the acquisition of both topographic and optical images. The study is conducted using COMSOL and ANSYS simulation tools to study the probe sensitivity and deflection. The simulation calculations of deflection and von Mises stress are denoted, analyzed, and compared based on the change in the probe shapes for the same applied force. The results obtained from the two finite element tools were similar, converged to the same ideal design, and showed the impact of the probe structure on the probe performance.