Low-cost Imaging Research Articles

Morphology-Based In-Ovo Sexing of Chick Embryos Utilizing a Low-Cost Imaging Apparatus and Machine Learning

The routine culling of male chicks in the laying hen industry raises significant ethical, animal welfare, and sustainability concerns. Current methods to determine chick embryo sex before hatching are costly, time-consuming, and invasive. This study aimed to develop a low-cost, non-invasive solution to predict chick embryo sex before hatching using the morphological features of eggs. A custom imaging apparatus was created using a smartphone and light box, enabling consistent image capture of chicken eggs. Egg length, width, area, eccentricity, and extent were measured, and machine learning models were trained to predict chick embryo sex. The wide neural network model achieved the highest accuracy of 88.9% with a mean accuracy of 81.5%. Comparison of the imaging apparatus to a high-cost industrial 3D scanner demonstrated comparable accuracy in capturing egg morphology. The findings suggest that this method can contribute to the prevention of up to 6.2 billion male chicks from being culled annually by destroying male embryos before they develop the capacity to feel pain. This approach offers a feasible, ethical, and scalable alternative to current practices, with potential for further improvements in accuracy and adaptability to different industry settings.

NeOPT: neural optical projection tomography with low-cost imaging setup

Optical projection tomography (OPT) is an imaging technology that employs light to reconstruct 3D models of objects. Recent advancements in neural radiance field (NeRF) have improved 3D biomedical imaging, yet they perform suboptimally with low signal-to-noise ratio (SNR) data in low-cost setups and are not directly applicable to OPT systems. Here, we introduce neural optical projection tomography (NeOPT), leveraging a low-cost OPT imaging setup combined with a pose-robust neural-based 3D reconstruction method to achieve high-quality OPT results. NeOPT incorporates discrete wavelet transform (DWT) to reduce imaging noise under low-SNR conditions. Experiments demonstrate that NeOPT significantly outperforms traditional OPT techniques.

Attention-driven low-cost image contrast enhancement for low-power OLED display

Attention-driven low-cost image contrast enhancement for low-power OLED display

Pericoronary adipose tissue feature analysis in computed tomography calcium score images in comparison to coronary computed tomography angiography.

We investigated the feasibility and advantages of using non-contrast CT calcium score (CTCS) images to assess pericoronary adipose tissue (PCAT) and its association with major adverse cardiovascular events (MACE). PCAT features from coronary computed tomography angiography (CCTA) have been shown to be associated with cardiovascular risk but are potentially confounded by iodine. If PCAT in CTCS images can be similarly analyzed, it would avoid this issue and enable its inclusion in formal risk assessment from readily available, low-cost CTCS images. To identify coronaries in CTCS images that have subtle visual evidence of vessels, we registered CTCS with paired CCTA images having coronary labels. We developed an "axial-disk" method giving regions for analyzing PCAT features in three main coronary arteries. We analyzed hand-crafted and radiomic features using univariate and multivariate logistic regression prediction of MACE and compared results against those from CCTA. Registration accuracy was sufficient to enable the identification of PCAT regions in CTCS images. Motion or beam hardening artifacts were often prevalent in "high-contrast" CCTA but not CTCS. Mean HU and volume were increased in both CTCS and CCTA for the MACE group. There were significant positive correlations between some CTCS and CCTA features, suggesting that similar characteristics were obtained. Using hand-crafted/radiomics from CTCS and CCTA, AUCs were 0.83/0.79 and 0.83/0.77, respectively, whereas Agatston gave AUC = 0.73. Preliminarily, PCAT features can be assessed from three main coronary arteries in non-contrast CTCS images with performance characteristics that are at the very least comparable to CCTA.

Periodic Light Modulations for Low-Cost Wide-Field Imaging of Luminescence Kinetics Under Ambient Light.

Imaging luminescence kinetics is invaluable in many fields, including biology and chemistry. However, the luminescence lifetime of most photo-activated states is in the low ns-µs range and its measurement requires adding costly image intensifiers to cameras to access the fast phenomena present. Here, the Rectified Imaging under Optical Modulation (RIOM) and Heterodyne Imaging under Optical Modulation (HIOM) protocols make this possible with standard low-cost cameras only, even under ambient light. RIOM and HIOM originate from a thorough theoretical analysis, which showed that modulated illumination of any reversibly photoactivable luminophore probed at high frequency generates components of the luminescence response that are detectable at low frequency: RIOM harnesses the mean luminescence response to modulated light at a single frequency, whereas HIOM exploits the luminescence response to modulated light at two close frequencies. When the luminophore behavior obeys a two-state model, the frequency dependence of the RIOM and HIOM observables can be used to extract a photoactivation time. In this work, their ability to retrieve maps of the luminescence lifetime of a reversibly photoswitchable protein, and phosphorescent microsensors are demonstrated. Where the luminophore does not obey a two-state model, RIOM and HIOM can still retrieve rich information, as here exemplified by the recovery of kinetic fingerprints of the physiological state of a plant.

Assembly and application of a low-cost high-resolution imaging device for hyphae in soil.

Soil imaging in the field and laboratory has greatly advanced our understanding of plant root systems. Soil fungi function as important plant symbionts and decomposers of complex organic material in soil environments. For fungal hyphae, however, the application of soil imaging remains scarce, limiting our understanding of hyphal systems in soil. This scarce application is partly due to the challenging development of a soil imaging device for hyphae: technical requirements to resolve fine hyphae (2-5 μm in diameter) are high, while the device cost must be low to facilitate sufficient deployment that can capture the high spatial heterogeneity of hyphal dynamics in soil. This protocol describes the do-it-yourself assembly and application of a low-cost high-resolution imaging device for observing hyphae in soil. The assembly of the open-source imaging device relies on many 3D-printed parts, reducing material costs to ca. 930 USD. The application of the imaging device yields soil profile images with a resolution of up to 0.52 μm px-1 (49000 dpi) within an observable volume of 70 × 210 × 1.5 mm. By repeatedly imaging a soil profile using the presented techniques, changes in the amount, distribution, and morphology of hyphae in soil can be observed and quantified.

Can a Cochlear Implant Be Used as an Electrical Impedance Tomography Device?

The imaging of the live cochlea is a challenging task. Regardless of the quality of images obtained from modern clinical imaging techniques, the internal structures of the cochlea mainly remain obscured. Electrical impedance tomography (EIT) is a safe, low-cost alternative medical imaging technique with applications in various clinical scenarios. In this article, EIT is investigated as an alternative method to image and extract the centre of gravity of the modiolus invivo. This information can be used to augment present postoperative medical imaging techniques to investigate the cochlea. The cochlear implant EIT system was simulated by modelling user-specific electrode array trajectories within a simple conductive medium containing an inhomogeneity representing the modiolus. The method included an adapted adjacent stimulation protocol for data collection. For the image reconstruction, NOSER and Tikhonov priors were considered. A parameter analysis was conducted to find the most robust combination of image priors and hyperparameters for this application. The cochlear implant EIT methodology was validated at different noise levels for four electrode array trajectories. Comparing the NOSER and Tikhonov priors, it was observed that the NOSER prior exhibits superior centre of gravity localisation performance in cochlear implant EIT image reconstruction for different noise levels and user-dependent variability in electrode array trajectories. Image reconstruction, using a NOSER prior at a hyperparameter value of approximately 0.001, resulted in an average centre of gravity localisation error of less than 4% for all electrode array trajectories using difference imaging and less than 5.5% using absolute imaging.

Deep learning framework for velocity field reconstruction from low-cost particle image velocimetry measurements

Deep learning framework for velocity field reconstruction from low-cost particle image velocimetry measurements

Compact modular open platform for low-cost ultrasound imaging: Ralisation and measurements in biological settings

Compact modular open platform for low-cost ultrasound imaging: Ralisation and measurements in biological settings

A Low-Cost Modulated Laser-Based Imaging System Using Square Ring Laser Illumination for Depressing Underwater Backscatter

Underwater vision data facilitate a variety of underwater operations, including underwater ecosystem monitoring, topographical mapping, mariculture, and marine resource exploration. Conventional laser-based underwater imaging systems with complex system architecture rely on high-cost laser systems with high power, and software-based methods can not enrich the physical information captured by cameras. In this manuscript, a low-cost modulated laser-based imaging system is proposed with a spot in the shape of a square ring to eliminate the overlap between the illumination light path and the imaging path, which could reduce the negative effect of backscatter on the imaging process and enhance imaging quality. The imaging system is able to achieve underwater imaging at long distance (e.g., 10 m) with turbidity in the range of 2.49 to 7.82 NTUs, and the adjustable divergence angle of the laser tubes enables the flexibility of the proposed system to image on the basis of application requirements, such as the overall view or partial detail information of targets. Compared with a conventional underwater imaging camera (DS-2XC6244F, Hikvision, Hangzhou, China), the developed system could provide better imaging performance regarding visual effects and quantitative evaluation (e.g., UCIQUE and IE). Through integration with the CycleGAN-based method, the imaging results can be further improved, with the UCIQUE increased by 0.4. The proposed low-cost imaging system with a compact system structure and low consumption of energy could be equipped with platforms, such as underwater robots and AUVs, to facilitate real-world underwater applications.

Research on real-time 3D imaging technology based on CMOS sensors

Abstract. With the continuous progress of science and technology, 3D imaging technology has been widely used in many fields, such as medical imaging, virtual reality and protection of cultural heritage. It can provide more information than traditional 2D images, greatly improving the accuracy and reliability of various applications. CMOS (Complementary Metal Oxide Semiconductor) sensors, as efficient and low-cost image sensors, have been widely used in image imaging and processing technologies in recent years. However, how to realize real-time and high-precision 3D imaging with CMOS sensors is still a technical challenge to be solved. Based on this background, this paper will focus on real-time 3D imaging techniques based on CMOS sensors through three topics. This paper will first introduce how CMOS sensors work, followed by the introduction of real-time 3D imaging technology, and finally the real-time 3D imaging technology based on CMOS sensors. The research in this paper will be valuable for the research and application of real-time 3D imaging technology based on CMOS sensors.

Short-term forecast of solar irradiance components using an alternative mathematical approach for the identification of cloud features

Solar energy technologies require precise solar forecasting to reduce power generation losses and protect equipment from irradiance fluctuations. This study introduces an alternative methodology for short-term forecasting of direct normal irradiance (DNI) and global horizontal irradiance (GHI) utilizing ground-based sky images captured by a single device. A low-cost all-sky imager (ASI) was developed, which implements an angular transformation and an optical flow technique to extract cloud features such as shape and velocity. A mathematical model calculates cloud transmittance based on pixel intensity, eliminating complex training steps. Results from a 30-day experimental campaign, incorporating diverse meteorological conditions, were compared against a secondary standard solarimetric station, a smart persistence model, and state-of-the-art approaches. The DNI forecast achieved an RMSE (relative error) of 46.79 W/m2 (11.99%) for 1-min intervals and 90.21 W/m2 (17.54%) for 10-min intervals, while GHI ranged from 31.73 W/m2 (4.68%) to 75.02 W/m2 (13.63%). Pearson correlation coefficients exceeded 0.9 overall, reaching 0.98 and 0.99 for the 1-min DNI and GHI forecasts, and 0.91 and 0.96 for the 10-min DNI and GHI forecasts, respectively, underscoring the system’s accuracy and robustness in complex meteorological scenarios.

Continuous Growth Monitoring and Prediction with 1D Convolutional Neural Network Using Generated Data with Vision Transformer.

Crop growth information is collected through destructive investigation, which inevitably causes discontinuity of the target. Real-time monitoring and estimation of the same target crops can lead to dynamic feedback control, considering immediate crop growth. Images are high-dimensional data containing crop growth and developmental stages and image collection is non-destructive. We propose a non-destructive growth prediction method that uses low-cost RGB images and computer vision. In this study, two methodologies were selected and verified: an image-to-growth model with crop images and a growth simulation model with estimated crop growth. The best models for each case were the vision transformer (ViT) and one-dimensional convolutional neural network (1D ConvNet). For shoot fresh weight, shoot dry weight, and leaf area of lettuce, ViT showed R2 values of 0.89, 0.93, and 0.78, respectively, whereas 1D ConvNet showed 0.96, 0.94, and 0.95, respectively. These accuracies indicated that RGB images and deep neural networks can non-destructively interpret the interaction between crops and the environment. Ultimately, growers can enhance resource use efficiency by adapting real-time monitoring and prediction to feedback environmental controls to yield high-quality crops.

Comprehensive calibration of omnidirectional mirror and catadioptric optical system dedicated to high-speed video registration of lightning

The paper presents calibration setup and method dedicated to almost all types of omnidirectional mirrors such as hyperbolic and elliptic as well as including those with analytically or even numerically defined curvature. At its initial stage the calibration procedure use a pinhole and fisheye camera calibration algorithms to estimate and remove optical distortions introduced to catadioptric system by the image recorder and lens. The next stage allows to calculate the projection characteristics of the mirror and complex matrix of image deformation which can be further used for a low-cost image correction. Calibration procedure was verified using mirror prototypes manufactured with application of CNC milling machines and Physical Vapor Deposition technique. Three mirrors with 15°, 30° and 45° top view angles were tested. Projection characteristics were evaluated for a set of wide range cloud-to-ground lightning video registrations. Proposed method can be implemented to improve quality of omnidirectional observation and photogrammetry.

Extent of Resection for Supratentorial Gliomas Using the B-mode Ultrasound as an Intraoperative Aid.

The cornerstone of glioma treatment is the surgical resection of the visible tumor, knowing fully that the disease extends beyond what magnetic resonance imaging (MRI) is able to show and the efficacy of the surgery is highly dependent on the surgeon's expertise. Different intraoperative technologies have emerged to aid in the goal of optimizing the extent of resection for glial tumors. Intraoperative ultrasound (iUS) is an attractive option due to its low cost and real-time imaging. This paper aims to illustrate the utility of this technology as an intraoperative aid for the resection of supratentorial gliomas. This is an observational and retrospective study that included patients who received surgical resection for supratentorial gliomas using iUS as the only imaging intraoperative aid. Adult patients with supratentorial gliomas who were taken into surgery for tumor resection were included in the analysis. Demographic and clinical variables of the patients at the time of diagnosis were collected, including sex and age. Tumor morphological variables were collected, including the affected lobes, tumor volume, histological and molecular diagnosis, and extent of resection. The IBM SPSS Statistics for Windows, Version 27.0 (Released 2020; IBM Corp., Armonk, New York, United States) was used for the statistical analysis. A total of 44 patients were included in the analysis. Thirty-six patients (81.8%) had a high-grade glioma as the final diagnosis. The extentof resection achieved was over 90% in 70.5% of the cases. Conclusion: iUS by itself has been proven to be a valuable tool to improve the localization and resection of supratentorial gliomas.

Imaging of permeability defect distribution by electromagnetic tomography with hybrid L1 normand nuclear normpenalty terms.

Electromagnetic tomography (EMT), with the advantages of being non-contact, non-invasiveness, low cost, simple structure, and fast imaging speed, is a multi-functional tomography technique based on boundary measurement voltages to image the conductivity distribution within the sensing field. EMT is widely used in industrial and biomedical fields. Currently, there are few studies on the application of EMT in magnetic permeability materials, which makes it difficult to obtain high-quality reconstructed images due to its own properties that lead to obvious attenuation of electromagnetic waves during propagation, as well as the ill-posed and ill-conditioned characteristics of EMT. In this paper, a multi-feature objective function integrating L2 normregularization, L1 normregularization, and low-rank normregularization is proposed to solve the challenge of magnetic permeability material imaging. This approach emphasizes the smoothness and sparsity. The split Bregman algorithm is introduced to efficiently solve the proposed objective function by decomposing the complex optimization problem into several simple sub-task iterative schemes. In addition, a nine-coil planar array electromagnetic sensor was developed and a flexible modular EMT system was constructed. We use correlation coefficient and error coefficient as indicators to evaluate the performance of the proposed image reconstruction algorithm. The effectiveness of the proposed method in improving the reconstruction accuracy and robustness is verified through numerical simulations and experiments.

Rapid simulation of knitted fabrics based on a ribbon yarn model

In the study, an innovative three-dimensional ribbon-based yarn model simulation technique is introduced, aimed at significantly enhancing the realism and speed of knitted fabric simulation through efficient model construction and rendering processes. The method successfully overcomes the limitations of traditional two-dimensional loop models and tubular yarn models, accurately capturing the textural details of knitted fabrics with a vivid three-dimensional representation. Experimental results demonstrate the outstanding performance of this simulation system in simulating the fuzz effects of yarn and in processing speed. This technology offers significant potential in textile design and production, notably in boosting design efficiency and accelerating new product development. By employing low-cost image capture methods, the study effectively reduces technical and financial barriers in knitted fabric simulation, enhancing its practical and industrial applicability.

Towards transforming malaria vector surveillance using VectorBrain: a novel convolutional neural network for mosquito species, sex, and abdomen status identifications

Malaria is a major public health concern, causing significant morbidity and mortality globally. Monitoring the local population density and diversity of the vectors transmitting malaria is critical to implementing targeted control strategies. However, the current manual identification of mosquitoes is a time-consuming and intensive task, posing challenges in low-resource areas like sub-Saharan Africa; in addition, existing automated identification methods lack scalability, mobile deployability, and field-test validity. To address these bottlenecks, a mosquito image database with fresh wild-caught specimens using basic smartphones is introduced, and we present a novel CNN-based architecture, VectorBrain, designed for identifying the species, sex, and abdomen status of a mosquito concurrently while being efficient and lightweight in computation and size. Overall, our proposed approach achieves 94.44±2% accuracy with a macro-averaged F1 score of 94.10±2% for the species classification, 97.66±1% accuracy with a macro-averaged F1 score of 96.17±1% for the sex classification, and 82.20±3.1% accuracy with a macro-averaged F1 score of 81.17±3% for the abdominal status classification. VectorBrain running on local mobile devices, paired with a low-cost handheld imaging tool, is promising in transforming the mosquito vector surveillance programs by reducing the burden of expertise required and facilitating timely response based on accurate monitoring.

Implementation of Artificial Intelligence in Retinopathy of Prematurity Care: Challenges and Opportunities.

The diagnosis of retinopathy of prematurity (ROP) is primarily image-based and suitable for implementation of artificial intelligence (AI) systems. Increasing incidence of ROP, especially in low and middle-income countries, has also put tremendous stress on health care systems. Barriers to the implementation of AI include infrastructure, regulatory, legal, cost, sustainability, and scalability. This review describes currently available AI and imaging systems, how a stable telemedicine infrastructure is crucial to AI implementation, and how successful ROP programs have been run in both low and middle-income countries and high-income countries. More work is needed in terms of validating AI systems with different populations with various low-cost imaging devices that have recently been developed. A sustainable and cost-effective ROP screening program is crucial in the prevention of childhood blindness.

Tactile Simultaneous Localization and Mapping Using Low-Cost, Wearable LiDAR

Tactile maps are widely recognized as useful tools for mobility training and the rehabilitation of visually impaired individuals. However, current tactile maps lack real-time versatility and are limited because of high manufacturing and design costs. In this study, we introduce a device (i.e., ClaySight) that enhances the creation of automatic tactile map generation, as well as a model for wearable devices that use low-cost laser imaging, detection, and ranging (LiDAR,) used to improve the immediate spatial knowledge of visually impaired individuals. Our system uses LiDAR sensors to (1) produce affordable, low-latency tactile maps, (2) function as a day-to-day wayfinding aid, and (3) provide interactivity using a wearable device. The system comprises a dynamic mapping and scanning algorithm and an interactive handheld 3D-printed device that houses the hardware. Our algorithm accommodates user specifications to dynamically interact with objects in the surrounding area and create map models that can be represented with haptic feedback or alternative tactile systems. Using economical components and open-source software, the ClaySight system has significant potential to enhance independence and quality of life for the visually impaired.