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Visibility observations and accurate forecasts are essential in meteorology, requiring a dense network of observation stations. This paper investigates image processing techniques for object detection and visibility determination using static cameras. It proposes a comprehensive method that includes image preprocessing, landmark identification, and visibility estimation, mirroring the observation process of professional meteorological observers. This study validates the visibility observation procedure using the k-nearest neighbors machine learning method across six locations, including four in the Czech Republic, one in the USA, and one in Germany. By comparing our results with professional observations, the paper demonstrates the suitability of the proposed method for operational application, particularly in foggy and low visibility conditions. This versatile method holds potential for adoption by meteorological services worldwide. [ABSTRACT FROM AUTHOR]

The purpose of this study was to determine if micrographs produced with a colour charged couple device (CCD) camera were comparable in quality to micrographs obtained with a 35-mm colour film camera. The Axiocam (Zeiss, Jena, Germany) CCD camera produced images of 3900 × 3090 pixels.Each picture in a series of 50 pairs of film and digital micrographs was rated independently by each of three observers on the categories of colour, detail and overall impression. A total of 28 of the 50 pairs of micrographs were discarded due to colour differences.Digital micrographs were rated lower on colour and higher on detail compared to film micrographs. The film and digital micrographs were rated equally on overall impression. The difference in colour between film and digital micrographs can be compensated for using image processing software on the digital image. However, such manipulation was not carried out as part of the present study. The study originally included 50 digital micrographs and 50 film micrographs. The micrographs were paired and 22 of the 50 pairs of micrographs were assessed. Thus 28 pairs were not evaluated due to colour differences in the non-manipulated pictures. The interobserver difference on colour in the remaining 22 pairs given as mean square ± standard error of the mean was− 0.27 ± 0.10, 0.62 ± 0.10 and 0.90 ± 0.11. The interobserver difference on detail was− 0.48 ± 0.10, 0.95 ± 0.10 and 1.43 ± 0.10. The interobserver difference on overall impression was− 0.70 ± 0.10, 0.57 ± 0.10 and 1.27 ± 0.10. All the results differed significantly (p < 0.001).Digital micrographs are sufficient in detail, colour and overall impression to replace the traditional 35-mm film recordings for use in histopathology. [ABSTRACT FROM AUTHOR]

Conventional ultrasound (US) is the first-line imaging method for abdominal pathologies, but its diagnostic accuracy is operator-dependent, and data storage is usually limited to two-dimensional images. A novel tomographic US system (Curefab CS, Munich, Germany) processes imaging data combined with three-dimensional spatial information using a magnetic field tracking. This enables standardized image presentation in axial planes and a review of the entire examination. The applicability and diagnostic performance of this tomographic US approach was analyzed in an abdominal setting using conventional US as reference. Tomographic US data were successfully compiled in all subjects of a training cohort (20 healthy volunteers) and in 50 patients with abdominal lesions. Image quality (35% and 79% for training and patient cohort respectively) and completeness of organ visualization (45% and 44%) were frequently impaired in tomographic US compared to conventional US. Conventional and tomographic US showed good agreement for measurement of organ sizes in the training cohort (right liver lobe and both kidneys with a median deviation of 5%). In the patient cohort, tomographic US identified 57 of 74 hepatic or renal lesions detected by conventional ultrasound (sensitivity 77%). In conclusion, this study illustrates the diagnostic potential of abdominal tomographic US, but current significant limitations of the tomographic ultrasound device demand further technical improvements before this and comparable approaches can be implemented in clinical practice. [ABSTRACT FROM AUTHOR]

Many technologies in precision agriculture (PA) require image analysis and image- processing with weed and background differentiations. The detection of weeds on mulched cropland is one important image-processing task for sensor based precision herbicide applications. The article introduces a special vegetation index, the Difference Index with Red Threshold (DIRT), for the weed detection on mulched croplands. Experimental investigations in weed detection on mulched areas point out that the DIRT performs better than the Normalized Difference Vegetation Index (NDVI). The result of the evaluation with four different decision criteria indicate, that the new DIRT gives the highest reliability in weed/background differentiation on mulched areas. While using the same spectral bands (infrared and red) as the NDVI, the new DIRT is more suitable for weed detection than the other vegetation indices and requires only a small amount of additional calculation power. The new vegetation index DIRT was tested on mulched areas during automatic ratings with a special weed camera system. The test results compare the new DIRT and three other decision criteria: the difference between infrared and red intensity (Diff), the soil-adjusted quotient between infrared and red intensity (Quotient) and the NDVI. The decision criteria were compared with the definition of a worse case decision quality parameter Q, suitable for mulched croplands. Although this new index DIRT needs further testing, the index seems to be a good decision criterion for the weed detection on mulched areas and should also be useful for other image processing applications in precision agriculture. The weed detection hardware and the PC program for the weed image processing were developed with funds from the German Federal Ministry of Education and Research (BMBF). [ABSTRACT FROM AUTHOR]

Presents an approach that combines the advantages of classification with linear spectral unmixing. Objectives of hyperspectral image analysis in the urban environment; Iterative linear spectral unmixing approach based on pixel-oriented endmember selection; Analysis of airborne hyperspectral data of the DAIS 7915 instrument for automated area-wide differentiation of urban surface cover types in Dresden, Germany.

Traditional approaches to modeling and processing discrete pixels are mainly based on image features or model optimization. These methods often result in excessive shrinkage or expansion of the restored pixel region, inhibiting accurate recovery of the target pixel region shape. This paper proposes a simultaneous source and mask-images optimization model based on skeleton divergence that overcomes these problems. In the proposed model, first, the edge of the entire discrete pixel region is extracted through bilateral filtering. Then, edge information and Delaunay triangulation are used to optimize the entire discrete pixel region. The skeleton is optimized with the skeleton as the local optimization center and the source and mask images are simultaneously optimized through edge guidance. The technique for order of preference by similarity to ideal solution (TOPSIS) and point-cloud regularization verification are subsequently employed to provide the optimal merging strategy and reduce cumulative error. In the regularization verification stage, the model is iteratively simplified via incremental and hierarchical clustering, so that point-cloud sampling is concentrated in the high-curvature region. The results of experiments conducted using the moving-target region in the RGB-depth (RGB-D) data (Technical University of Munich, Germany) indicate that the proposed algorithm is more accurate and suitable for image processing than existing high-performance algorithms. [ABSTRACT FROM AUTHOR]

Objective. To monitor size and shape changes of bone erosions and changes in BMD in the vicinity of the erosion and in the periarticular trabecular compartment of patients with RA using high-resolution peripheral quantitative CT (HR-pQCT) imaging and to compare an automated three-dimensional (3D) image processing technique with manual measurements of erosion width and depth. Methods. The shape of 40 bone erosions and composition of bone around the erosions were analysed in the MCP joints of 22 RA patients both manually and by semi-automated 3D image processing at two different time points. Periosteal segmentation was performed using volume growing and morphological operations. Image registration was applied for transfer of baseline segmentations to follow-up datasets. Results. Eight erosions decreased in size, 6 increased and 28 remained stable. Increasing erosions were more spherical and smaller at baseline compared with decreasing or stable erosions. BMD in the vicinity of shrinking erosions increased, while it decreased next to expanding erosions. There was moderate agreement in the determination of erosion volume between semi-automated and manual measurements, but agreement was poor when assessing changes in volume over time. Conclusion. Longitudinal changes in erosion size and shape and of BMD in the vicinity of an erosion can be measured. BMD changes are associated with progression and regression of erosions. However, the semi-automated and manual approaches did not classify longitudinal changes of erosion volume in the same way. Further research is necessary to define the nature of these differences. [ABSTRACT FROM AUTHOR]

SUMMARY Passive seismic interferometry is a new promising methodology for seismic exploration. Interferometry allows information about the subsurface structure to be extracted from ambient seismic noise. In this study, we apply the cross-correlation technique to approximately 25 hr of recordings of ambient seismic noise at the Ketzin experimental CO2 storage site, Germany. Common source gathers were generated from the ambient noise for all available receivers along two seismic lines by cross-correlation of noise records. This methodology isolates the interstation Green's functions that can be directly compared to active source gathers. We show that the retrieved response includes surface waves, refracted waves and reflected waves. We use the dispersive behaviour of the retrieved surface waves to infer geological properties in the shallow subsurface and perform passive seismic imaging of the subsurface structure by processing the retrieved reflected waves. [ABSTRACT FROM AUTHOR]

A new semi-automated method is proposed in this study that detects joint traces from digital images and calculates the length distribution through three-dimensional data structuring. The method comprises detecting the pixels corresponding to the joint trace in digital images and calculating the length distribution through three-dimensional data structuring of the pixels detected as the joint trace. Semantic segmentation based on deep learning techniques is applied to the detection of joint trace pixels to overcome the limitations of rule-based image processing techniques. For training a deep learning network, various rock joint images were sampled and labeled, following which the data sets for training, validation, and testing were prepared through classification and augmentation. Using the prepared datasets, the performance of the classifiers based on widely used semantic segmentation networks, namely U-Net (University of Freiburg, Germany) network and DeepLabV3+ (Google, USA) network, was compared. The classifier based on the DeepLabV3+ network was ultimately selected and applied. The trained classifier successfully detected pixels corresponding to the joint trace in images of both flat and uneven rock surfaces, indicating that the deep learning technique could be applied effectively to joint trace detection from digital images. Further, a data structuring technique is proposed to calculate the joint trace length using pixelwise data detected through the trained classifier. This technique uses point cloud data obtained by photogrammetry, and comprises two-dimensional thinning and segmentation, three-dimensional projection, segmentation, and segment linking. The entire proposed method, from detecting the joint trace pixels to three-dimensional data structuring, was verified by applying it to both simple flat and uneven rock surface models. • New technique for joint trace detection using deep learning network is proposed. • New data structuring technique for pixelwise data is proposed. • Semi-automatic calculation of the trace length is possible using the two techniques. • Proposed techniques is verified by applying it to both flat and uneven rock surfaces. [ABSTRACT FROM AUTHOR]

Since 1980, severe thunderstorms have caused nearly USD 3 billion in damages annually inEurope. Hailstorms account for a significant portion of thunderstorm-related losses, yet theclimatological aspects of large hail are not well understood. While the European SevereWeather Database (ESWD) contains the most comprehensive archive of hail observations inEurope, spatial and temporal inhomogeneities and discontinuities preclude the database frombeing used to create a representative long-term hail climatology. We therefore propose amethod to generate a hail event set for Central Europe by: 1) objectively identifying recenthail swaths from high-resolution radar data, and 2) combining the identified hail swathproperties with a probabilistic convective hazard model and ESWD hail observations tosimulate individual hailstorms. Such a hail event set will facilitate the evaluation of theclimatological aspects of large hail and time-dependent changes in the frequency ofhailstorms. In addition, the hail event set will be combined with exposure and vulnerabilitydata to produce detailed loss estimates and better understand the risk of large hail inEurope. Hail swaths occurring in Germany over a 4-year period (2015–2018) were identifiedusing the vertically integrated ice (VII) product from the Deutscher Wetterdienst (DWD).Specifically, we isolated continuous regions where 6-hour accumulated VII satisfied aminimum intensity (25 kg m−3) criterion, and then used Python image processing to generatehail swath ellipses and analyze their spatial characteristics. Next, for each 6-hour intervalduring the 1979–2018 period, we applied the convective hazard model to ERA-Interimreanalysis data to simulate the occurrence/non-occurrence of hail in each reanalysis grid cell.If hail was simulated in a given grid cell, we also predicted the number of hail swaths in thegrid cell during the 6-hour period. For each hail swath, we simulated the length,area, and orientation based on the spatial properties of the previously identified hailswaths. In addition, we simulated the maximum hail diameter based on the statisticaldistributions of observed hail diameter under various instability and deep-layer shearscenarios. Results from the VII hail detection algorithm suggest that large hail in Germany occursprimarily after 12 UTC, with a maximum frequency during the afternoon and early evening(12–18 UTC). While the majority of identified hail swaths affected areas less than 100km2, hail swaths exceeding 500 km2 were not especially uncommon. Hail swathsgenerally followed trajectories between 225˚ (southwest-to-northeast) and 315˚ (northwest-to-southeast) and were typically associated with right-moving thunderstorm cellswhen the synoptic-scale environment was moderately-to-strongly sheared. The simulated hailswaths exhibit a meridional gradient in the predicted frequency of large hail, with thelowest frequencies near the North and Baltic Seas, and the highest frequencies overthe eastern Alps. The number of simulated hail cases peaks in July and August,whereas large hail as reported in the ESWD is most prevalent between May and July. [ABSTRACT FROM AUTHOR]

Objective. To develop a precise three-dimensional (3D) segmentation technique for bone erosions in high-resolution peripheral quantitative CT (HR-pQCT) datasets to measure their volume, surface area and shape parameters. Assessment of bone erosions in patients with RA is important for diagnosis and evaluation of treatment efficacy. HR-pQCT allows quantifying periarticular bone loss in arthritis.Methods. HR-pQCT scans with a spatial resolution of about 120 µm of the second to fourth metacarpophalangeal joints were acquired in patients with RA. Erosions were identified by placing a seed point in each of them. After applying 3D segmentation, the volume, surface area and sphericity of erosions were calculated. Results were compared with an approximation method using manual measurements. Intra- and interoperator precision analysis was performed for both the 3D segmentation and the manual technique.Results. Forty-three erosions were assessed in 18 datasets. Intra- and interoperator precisions (RMSCV/RMSSD) for erosion volume were 5.66%/0.49 mm3 and 7.76%/0.76 mm3, respectively. The correlation between manual measurements and their simulation using segmentation volumes was r = 0.87. Precision errors for the manual method were 15.39% and 0.36 mm3, respectively.Conclusion. We developed a new precise 3D segmentation technique for quantification of bone erosions in HR-pQCT datasets that correlates to the volume, shape and surface area of the erosion. The technique allows fast and effective measurement of the erosion size and could therefore be helpful for rapid and quantitative assessment of erosion size. [ABSTRACT FROM PUBLISHER]

Especially for investigator-initiated research at universities and academic institutions, Internet-based rare disease registries (RDR) are required that integrate electronic data capture (EDC) with automatic image analysis or manual image annotation. We propose a modular framework merging alpha-numerical and binary data capture. In concordance with the Office of Rare Diseases Research recommendations, a requirement analysis was performed based on several RDR databases currently hosted at Uniklinik RWTH Aachen, Germany. With respect to the study management tool that is already successfully operating at the Clinical Trial Center Aachen, the Google Web Toolkit was chosen with Hibernate and Gilead connecting a MySQL database management system. Image and signal data integration and processing is supported by Apache Commons FileUpload-Library and ImageJ-based Java code, respectively. As a proof of concept, the framework is instantiated to the German Calciphylaxis Registry. The framework is composed of five mandatory core modules: (1) Data Core, (2) EDC, (3) Access Control, (4) Audit Trail, and (5) Terminology as well as six optional modules: (6) Binary Large Object (BLOB), (7) BLOB Analysis, (8) Standard Operation Procedure, (9) Communication, (10) Pseudonymization, and (11) Biorepository. Modules 1-7 are implemented in the German Calciphylaxis Registry. The proposed RDR framework is easily instantiated and directly integrates image management and analysis. As open source software, it may assist improved data collection and analysis of rare diseases in near future. [ABSTRACT FROM AUTHOR]