Image Acquisition Method Research Articles

Improving Real-Scene 3D Model Quality of Unmanned Aerial Vehicle Oblique-Photogrammetry with a Ground Camera

In UAV (unmanned aerial vehicle) oblique photogrammetry, the occlusion of ground objects, particularly at their base, often results in low-quality real-scene 3D models. To address this issue, we propose a method to enhance model quality by integrating ground-based camera images. This innovative image acquisition method allows the rephotographing of areas in the 3D model that exhibit poor quality. Three critical parameters for reshooting are the reshooting distance and the front- and side-overlap ratios of reshooting images. The proposed method for improving 3D model quality focuses on point accuracy, dimensional accuracy, texture details, and the triangular mesh structure. Validated by a case study involving a complex building, this method demonstrates that integrating ground camera photos significantly improves the overall quality of the 3D model. The findings show that optimal settings for reshooting include a distance (in meter units) of 1.5–1.6 times the camera’s focal length (in millimeter units), a front overlap ratio of 30%, and a side overlap ratio of 20%. Furthermore, we conclude that an overlap rate of 20–30% in reshooting is a usable value.

GoToCloud optimization of cloud computing environment for accelerating cryo-EM structure-based drug design

Cryogenic electron microscopy (Cryo-EM) is a widely used technique for visualizing the 3D structures of many drug design targets, including membrane proteins, at atomic resolution. However, the necessary throughput for structure-based drug design (SBDD) is not yet achieved. Currently, data analysis is a major bottleneck due to the rapid advancements in detector technology and image acquisition methods. Here we show “GoToCloud”, a cloud-computing-based platform for advanced data analysis and data management in Cryo-EM. With GoToCloud, it is possible to optimize computing resources and reduce costs by selecting the most appropriate parallel processing settings for each processing step. Our benchmark tests on GoToCloud demonstrate that parallel computing settings, including the choice of computational hardware, as well as a required target resolution have significant impacts on the processing time and cost performance. Through this optimization of a cloud computing environment, GoToCloud emerges as a promising platform for the acceleration of Cryo-EM SBDD.

Estimation of Maize Water Requirements Based on the Low-Cost Image Acquisition Methods and the Meteorological Parameters

Estimation of Maize Water Requirements Based on the Low-Cost Image Acquisition Methods and the Meteorological Parameters

A Hybrid Approach for Image Acquisition Methods Based on Feature-Based Image Registration.

This paper presents a novel hybrid approach to feature detection designed specifically for enhancing Feature-Based Image Registration (FBIR). Through an extensive evaluation involving state-of-the-art feature detectors such as BRISK, FAST, ORB, Harris, MinEigen, and MSER, the proposed hybrid detector demonstrates superior performance in terms of keypoint detection accuracy and computational efficiency. Three image acquisition methods (i.e., rotation, scene-to-model, and scaling transformations) are considered in the comparison. Applied across a diverse set of remote-sensing images, the proposed hybrid approach has shown marked improvements in match points and match rates, proving its effectiveness in handling varied and complex imaging conditions typical in satellite and aerial imagery. The experimental results have consistently indicated that the hybrid detector outperforms conventional methods, establishing it as a valuable tool for advanced image registration tasks.

Atomic resolution scanning transmission electron microscopy at liquid helium temperatures for quantum materials

Fundamental quantum phenomena in condensed matter, ranging from correlated electron systems to quantum information processors, manifest their emergent characteristics and behaviors predominantly at low temperatures. This necessitates the use of liquid helium (LHe) cooling for experimental observation. Atomic resolution scanning transmission electron microscopy combined with LHe cooling (cryo-STEM) provides a powerful characterization technique to probe local atomic structural modulations and their coupling with charge, spin and orbital degrees-of-freedom in quantum materials. However, achieving atomic resolution in cryo-STEM is exceptionally challenging, primarily due to sample drifts arising from temperature changes and noises associated with LHe bubbling, turbulent gas flow, etc. In this work, we demonstrate atomic resolution cryo-STEM imaging at LHe temperatures using a commercial side-entry LHe cooling holder. Firstly, we examine STEM imaging performance as a function of He gas flow rate, identifying two primary noise sources: He-gas pulsing and He-gas bubbling. Secondly, we propose two strategies to achieve low noise conditions for atomic resolution STEM imaging: either by temporarily suppressing He gas flow rate using the needle valve or by acquiring images during the natural warming process. Lastly, we show the applications of image acquisition methods and image processing techniques in investigating structural phase transitions in Cr2Ge2Te6, CuIr2S4, and CrCl3. Our findings represent an advance in the field of atomic resolution electron microscopy imaging for quantum materials and devices at LHe temperatures, which can be applied to other commercial side-entry LHe cooling TEM holders.

Two-Dimensional Mammography Imaging Techniques for Screening Women with Silicone Breast Implants: A Pilot Phantom Study.

This study aimed to evaluate the impact of three two-dimensional (2D) mammographic acquisition techniques on image quality and radiation dose in the presence of silicone breast implants (BIs). Then, we propose and validate a new International Atomic Energy Agency (IAEA) phantom to reproduce these techniques. Images were acquired on a single Hologic Selenia Dimensions® unit. The mammography of the left breast of a single clinical case was included. Three methods of image acquisition were identified. They were based on misused, recommended, and reference settings. In the clinical case, image criteria scoring and the signal-to-noise ratio on breast tissue (SNRBT) were determined for two 2D projections and compared between the three techniques. The phantom study first compared the reference and misused settings by varying the AEC sensor position and, second, the recommended settings with a reduced current-time product (mAs) setting that was 13% lower. The signal-difference-to-noise ratio (SDNR) and detectability indexes at 0.1 mm (d' 0.1 mm) and 0.25 mm (d' 0.25 mm) were automatically quantified using ATIA software. Average glandular dose (AGD) values were collected for each acquisition. A statistical analysis was performed using Kruskal-Wallis and corrected Dunn tests (p < 0.05). The SNRBT was 2.6 times lower and the AGD was -18% lower with the reference settings compared to the recommended settings. The SNRBT values increased by +98% with the misused compared to the recommended settings. The AGD increased by +79% with the misused settings versus the recommended settings. The median values of the reference settings were 5.8 (IQR 5.7-5.9), 1.2 (IQR 0.0), 7.0 (IQR 6.8-7.2) and 1.2 (IQR 0.0) mGy and were significantly lower than those of the misused settings (p < 0.03): 7.9 (IQR 6.1-9.7), 1.6 (IQR 1.3-1.9), 9.2 (IQR 7.5-10.9) and 2.2 (IQR 1.4-3.0) mGy for the SDNR, d' 0.1 mm, d' 0.25 mm and the AGD, respectively. A comparison of the recommended and reduced settings showed a reduction of -6.1 ± 0.6% (p = 0.83), -7.7 ± 0.0% (p = 0.18), -6.4 ± 0.6% (p = 0.19) and -13.3 ± 1.1% (p = 0.53) for the SDNR, d' 0.1 mm, d' 0.25 mm and the AGD, respectively. This study showed that the IAEA phantom could be used to reproduce the three techniques for acquiring 2D mammography images in the presence of breast implants for raising awareness and for educational purposes. It could also be used to evaluate and optimize the manufacturer's recommended settings.

Quality in beans: tracking and tracing coffee through automation and machine learning

PurposeCoffee is one of the most consumed beverages in the world and the global coffee industry is worth over $100bn. However, the industry faces significant sustainability challenges. Developing a quality traceability system to select the coffee beans and to ensure their authentication would result in economic advantages, because it allows for fraud to be avoided and increases consumer confidence.Design/methodology/approachTraceability is one of the key elements of sustainability in the coffee sector. The literature reveals that near-infrared (NIR) approaches have a huge potential for gaining rapid information about the origin and properties of coffee beans, without invasive procedures. This study demonstrates the scalability potential of automated methods of manipulation and image acquisition of coffee beans, from experimental scale to industrial lines.FindingsA solution based on the interaction of a manipulation system, a NIR spectrometer acquisition station integrated with a machine learning infrastructure and a compressed air classifier allows for the automatic separation of coffee beans into different classes of origin.Originality/valueApart from traceability, the wide industrialization of this system offers further advantages, including reduced workforce, decreased subjectivity in the evaluation and the acquisition of real-time data for labeling.

Accelerating breast MRI acquisition with generative AI models.

To investigate the use of the score-based diffusion model to accelerate breast MRI reconstruction. We trained a score-based model on 9549 MRI examinations of the female breast and employed it to reconstruct undersampled MRI images with undersampling factors of 2, 5, and 20. Images were evaluated by two experienced radiologists who rated the images based on their overall quality and diagnostic value on an independent test set of 100 additional MRI examinations. The score-based model produces MRI images of high quality and diagnostic value. Both T1- and T2-weighted MRI images could be reconstructed to a high degree of accuracy. Two radiologists rated the images as almost indistinguishable from the original images (rating 4 or 5 on a scale of 5) in 100% (radiologist 1) and 99% (radiologist 2) of cases when the acceleration factor was 2. This fraction dropped to 88% and 70% for an acceleration factor of 5 and to 5% and 21% with an extreme acceleration factor of 20. Score-based models can reconstruct MRI images at high fidelity, even at comparatively high acceleration factors, but further work on a larger scale of images is needed to ensure that diagnostic quality holds. The number of MRI examinations of the breast is expected to rise with MRI screening recommended for women with dense breasts. Accelerated image acquisition methods can help in making this examination more accessible. Accelerating breast MRI reconstruction remains a significant challenge in clinical settings. Score-based diffusion models can achieve near-perfect reconstruction for moderate undersampling factors. Faster breast MRI scans with maintained image quality could revolutionize clinic workflows and patient experience.

Enlarge the dynamic range of Shack-Hartmann wavefront sensor based on biplanar image acquisition and segmentation method

In present paper, a simple and robust method to expand the SHWS dynamic range is proposed. The intensity patterns are obtained with biplanar image acquisition and segmentation. The process involves calculating the spot centroids of two images and then matching the centroid of the spots of light with the center of a micro-lens array. This also resolves the issue of mandating the determination of centroids exclusively within the corresponding detection area. It is performed a comparative analysis between our proposed method and the conventional method for measuring large wavefronts, utilizing both numerical simulations and experiments. Additionally, a statistical analysis of the dynamic range of the method for wavefront measurement was conducted. This study demonstrates that the biplanar image acquisition and segmentation method provided a larger dynamic range while ensuring accuracy relative to the single image acquisition of conventional algorithm

Towards multicenter β-amyloid PET imaging in mouse models: A triple scanner head-to-head comparison

Aimβ-amyloid (Aβ) small animal PET facilitates quantification of fibrillar amyloidosis in Alzheimer's disease (AD) mouse models. Thus, the methodology is receiving growing interest as a monitoring tool in preclinical drug trials. In this regard, harmonization of data from different scanners at multiple sites would allow the establishment large collaborative cohorts and may facilitate efficacy comparison of different treatments. Therefore, we objected to determine the level of agreement of Aβ-PET quantification by a head-to-head comparison of three different state-of-the-art small animal PET scanners, which could help pave the way for future multicenter studies. MethodsWithin a timeframe of 5 ± 2 weeks, transgenic APPPS1 (n = 9) and wild-type (WT) (n = 8) mice (age range: 13–16 months) were examined three times by Aβ-PET ([18F]florbetaben) using a Siemens Inveon DPET, a MedisonanoScan PET/MR, and a MedisonanoScan PET/CT with harmonized reconstruction protocols. Cortex-to-white-matter 30–60 min p.i. standardized uptake value ratios (SUVRCTX/WM) were calculated to compare binding differences, effect sizes (Cohen's d) and z-score values of APPPS1 relative to WT mice. Correlation coefficients (Pearson's r) were calculated for the agreement of individual SUVR between different scanners. Voxel-wise analysis was used to determine the agreement of spatial pathology patterns. For validation of PET imaging against the histological gold standard, individual SUVR values were subject to a correlation analysis with area occupancy of methoxy‑X04 staining. ResultsAll three small animal PET scanners yielded comparable group differences between APPPS1 and WT mice (∆PET=20.4 % ± 2.9 %, ∆PET/MR=18.4 % ± 4.5 %, ∆PET/CT=18.1 % ± 3.3 %). Voxel-wise analysis confirmed a high degree of congruency of the spatial pattern (Dice coefficient (DC)PETvs.PET/MR=83.0 %, DCPETvs.PET/CT=69.3 %, DCPET/MRvs.PET/CT=81.9 %). Differences in the group level variance of the three scanners resulted in divergent z-scores (zPET=11.5 ± 1.6; zPET/MR=5.3 ± 1.3; zPET/CT=3.4 ± 0.6) and effect sizes (dPET=8.5, dPET/MR=4.5, dPET/CT=4.1). However, correlations at the individual mouse level were still strong between scanners (rPETvs.PET/MR=0.96, rPETvs.PET/CT=0.91, rPET/MRvs.PET/CT=0.87; all p ≤ 0.0001). Methoxy-X04 staining exhibited a significant correlation across all three PET machines combined (r = 0.76, p < 0.0001) but also at individual level (PET: r = 0.81, p = 0.026; PET/MR: r = 0.89, p = 0.0074; PET/CT: r = 0.93, p = 0.0028). ConclusionsOur comparison of standardized small animal Aβ-PET acquired by three different scanners substantiates the possibility of moving towards a multicentric approach in preclinical AD research. The alignment of image acquisition and analysis methods achieved good overall comparability between data sets. Nevertheless, differences in variance of sensitivity and specificity of different scanners may limit data interpretation at the individual mouse level and deserves methodological optimization.

High-resolution STEM Image Acquisition Method for Tilted Specimen Using a New Type of Aberration Corrector

High-resolution STEM Image Acquisition Method for Tilted Specimen Using a New Type of Aberration Corrector

White matter tract crossing and bottleneck regions in the fetal brain.

There is a growing interest in using diffusion MRI to study the white matter tracts and structural connectivity of the fetal brain. Recent progress in data acquisition and processing suggests that this imaging modality has a unique role in elucidating the normal and abnormal patterns of neurodevelopment in utero. However, there have been no efforts to quantify the prevalence of crossing tracts and bottleneck regions, important issues that have been extensively researched for adult brains. In this work, we determined the brain regions with crossing tracts and bottlenecks between 23 and 36 gestational weeks. We performed probabilistic tractography on 59 fetal brain scans and extracted a set of 51 distinct white tracts, which we grouped into 10 major tract bundle groups. We analyzed the results to determine the patterns of tract crossings and bottlenecks. Our results showed that 20-25% of the white matter voxels included two or three crossing tracts. Bottlenecks were more prevalent. Between 75-80% of the voxels were characterized as bottlenecks, with more than 40% of the voxels involving four or more tracts. The results of this study highlight the challenge of fetal brain tractography and structural connectivity assessment and call for innovative image acquisition and analysis methods to mitigate these problems.

Quantitative and qualitative evaluation of high-quality hepatobiliary phase imaging with shortened timing and utility in patients with compromised liver function.

To compare high flip angle (FA) hepatobiliary-phase (hHBP) imaging with variable time intervals to conventional HBP (cHBP) to assess the impact of increased FA on image quality in shortened HBP imaging. Data from 218 patients, divided into normal liver group (n = 184) and decompensated liver group (n = 34), who underwent liver magnetic resonance imaging (MRI) including 10-min, 15-min, 20-min hHBP, and cHBP were analyzed. Signal-to-noise ratio (SNR), contrast-ratio (CR), contrast-to-noise ratio (CNR), signal intensity ratios (SIRs), and relative enhancement (RE) of the liver were calculated for quantitative analysis. Sharpness, noise, and artifacts of the image, contrast media visibility, overall image quality, and lesion conspicuity were evaluated by two abdominal radiologists. Quantitative analysis showed that SNR, RE, SIR for liver/muscle, liver/spleen, and CR of all hHBP images demonstrated a significantly higher value compared to cHBP images in the normal liver group (p < 0.001). These values were also superior in the normal liver group compared to the decompensated liver group (p < 0.01). In qualitative analysis, both normal and decompensated liver groups exhibited significantly superior image sharpness in all hHBP images compared to cHBP images and the overall image quality of the 15-min and 20-min hHBP did not show significant difference compared to cHBP. All values tended to be better in the normal liver group than the decompensated liver group with statistical significance except for lesion conspicuity (p < 0.01). High-FA HBP has proven to be a valuable image acquisition method, potentially shortening liver MR imaging time while maintaining acceptable image quality.

Doppler Sonography Evaluation of Median Nerve Intraneural Blood Flow: A Systematic Review

Objective: This systematic review aims to determine the extent, scope, and nature of research using the sonographic measurement of intraneural blood flow within the median nerve and to identify, characterize, and compare image acquisition and analysis protocols that have been reported as potential candidate techniques for standardizing research and clinical applications. Materials and Methods: This systematic review summarizes image acquisition protocols and analysis methodologies from 52 current research studies using Doppler ultrasound to examine median nerve intraneural blood flow. Results: Four types of Doppler technologies were identified: power Doppler, color Doppler, spectral Doppler, and Superb Microvascular Imaging, but there were inconsistencies in how images were acquired and analyzed. Intraneural blood flow measurements were categorized into four types based on a combination of measurement level (e.g., binary, ordinal, continuous) and type of flow indicator (e.g., pixel count, intensity, velocity). Conclusion: Standardized imaging protocols and reporting guidelines are needed to improve consistency. Future studies should evaluate the accuracy and reliability of different image acquisition methods and analysis measurements.

Practical methods for aerial image acquisition and reflectance conversion using consumer-grade cameras on manned and unmanned aircraft

Practical methods for aerial image acquisition and reflectance conversion using consumer-grade cameras on manned and unmanned aircraft

Analysis of swimming trajectories in Daphnia similis as an environmental impact assessment tool

Analysis of swimming trajectories in <i>Daphnia similis</i> as an environmental impact assessment tool

AraDQ: an automated digital phenotyping software for quantifying disease symptoms of flood-inoculated Arabidopsis seedlings

BackgroundPlant scientists have largely relied on pathogen growth assays and/or transcript analysis of stress-responsive genes for quantification of disease severity and susceptibility. These methods are destructive to plants, labor-intensive, and time-consuming, thereby limiting their application in real-time, large-scale studies. Image-based plant phenotyping is an alternative approach that enables automated measurement of various symptoms. However, most of the currently available plant image analysis tools require specific hardware platform and vendor specific software packages, and thus, are not suited for researchers who are not primarily focused on plant phenotyping. In this study, we aimed to develop a digital phenotyping tool to enhance the speed, accuracy, and reliability of disease quantification in Arabidopsis.ResultsHere, we present the Arabidopsis Disease Quantification (AraDQ) image analysis tool for examination of flood-inoculated Arabidopsis seedlings grown on plates containing plant growth media. It is a cross-platform application program with a user-friendly graphical interface that contains highly accurate deep neural networks for object detection and segmentation. The only prerequisite is that the input image should contain a fixed-sized 24-color balance card placed next to the objects of interest on a white background to ensure reliable and reproducible results, regardless of the image acquisition method. The image processing pipeline automatically calculates 10 different colors and morphological parameters for individual seedlings in the given image, and disease-associated phenotypic changes can be easily assessed by comparing plant images captured before and after infection. We conducted two case studies involving bacterial and plant mutants with reduced virulence and disease resistance capabilities, respectively, and thereby demonstrated that AraDQ can capture subtle changes in plant color and morphology with a high level of sensitivity.ConclusionsAraDQ offers a simple, fast, and accurate approach for image-based quantification of plant disease symptoms using various parameters. Its fully automated pipeline neither requires prior image processing nor costly hardware setups, allowing easy implementation of the software by researchers interested in digital phenotyping of diseased plants.

3D Reverse-Time Migration Imaging for Multiple Cross-Hole Research and Multiple Sensor Settings of Cross-Hole Seismic Exploration

The two-dimensional (2D) cross-hole seismic computed tomography (CT) imaging acquisition method has the potential to characterize the target zone optimally compared to surface seismic surveys. It has wide applications in oil and gas exploration, engineering geology, etc. Limited to 2D hole velocity profiling, this method cannot acquire three-dimensional (3D) information on lateral geological structures outside the profile. Additionally, the sensor data received by cross-hole seismic exploration constitute responses from geological bodies in 3D space and are potentially affected by objects outside the well profiles, distorting the imaging results and geological interpretation. This paper proposes a 3D cross-hole acoustic wave reverse-time migration imaging method to capture 3D cross-hole geological structures using sensor settings in multi-cross-hole seismic research. Based on the analysis of resulting 3D cross-hole images under varying sensor settings, optimizing the observation system can aid in the cost-efficient obtainment of the 3D underground structure distribution. To verify this method’s effectiveness on 3D cross-hole structure imaging, numerical simulations were conducted on four typical geological models regarding layers, local high-velocity zones, large dip angles, and faults. The results verify the model’s superiority in providing more reliable and accurate 3D geological information for cross-hole seismic exploration, presenting a theoretical basis for processing and interpreting cross-hole data.

Swin-Net: A Swin-Transformer-Based Network Combing with Multi-Scale Features for Segmentation of Breast Tumor Ultrasound Images

Breast cancer is one of the most common cancers in the world, especially among women. Breast tumor segmentation is a key step in the identification and localization of the breast tumor region, which has important clinical significance. Inspired by the swin-transformer model with powerful global modeling ability, we propose a semantic segmentation framework named Swin-Net for breast ultrasound images, which combines Transformer and Convolutional Neural Networks (CNNs) to effectively improve the accuracy of breast ultrasound segmentation. Firstly, our model utilizes a swin-transformer encoder with stronger learning ability, which can extract features of images more precisely. In addition, two new modules are introduced in our method, including the feature refinement and enhancement module (RLM) and the hierarchical multi-scale feature fusion module (HFM), given that the influence of ultrasonic image acquisition methods and the characteristics of tumor lesions is difficult to capture. Among them, the RLM module is used to further refine and enhance the feature map learned by the transformer encoder. The HFM module is used to process multi-scale high-level semantic features and low-level details, so as to achieve effective cross-layer feature fusion, suppress noise, and improve model segmentation performance. Experimental results show that Swin-Net performs significantly better than the most advanced methods on the two public benchmark datasets. In particular, it achieves an absolute improvement of 1.4–1.8% on Dice. Additionally, we provide a new dataset of breast ultrasound images on which we test the effect of our model, further demonstrating the validity of our method. In summary, the proposed Swin-Net framework makes significant advancements in breast ultrasound image segmentation, providing valuable exploration for research and applications in this domain.

Building typology classification using convolutional neural networks utilizing multiple ground-level image process for city-scale rapid seismic vulnerability assessment

Several studies have focused on generating seismic vulnerability maps for earthquake-prone areas, particularly in Indonesia. Building typologies are a key factor in determining vulnerability to earthquakes. However, conducting large-scale field surveys to determine the spatial distribution of building typologies in a city is uneconomical. This paper explores the use of a convolutional neural network (CNN) to automatically detect building typologies from diverse regions in Indonesia, utilizing both conventional and automated building image acquisition processes. In this study, datasets from three distinct image acquisition methods are trained with four unique CNN architectures to identify the best-performing model to classify building typologies. The sample size effect on CNN performance is also investigated. The results showed that randomly sampled Google Street View (GSV) images are the most effective dataset for the CNN model, achieving an f1-score of 84.33%. Among the network architectures tested, MobileNet demonstrated superior performance on the majority of evaluated datasets. As the sample size increases by about 350% in the dataset, there is a positive correlation with up to 2.3% f1-score improvement. Using the best-performing CNN model, two building vulnerability models were employed to assess the spatial distribution of building damage in the urban area of Bandung, considering a hypothetical scenario of an M7 earthquake. Incorporating local construction data, one of the generated maps estimated that approximately 55% of buildings in Bandung would experience moderate to severe structural damage. This study showcases the potential of CNN models in automating regional seismic assessments and providing valuable insights for comprehensive seismic mitigation strategies.