This research paper proposes a simple image processing technique for automatic lameness detection in dairy cows under farm conditions. Seventy-five cows were selected from a dairy farm and visually assessed for a reference/real lameness score (RLS) as they left the milking parlor, while simultaneously being video-captured. The method employed a designated walking path and video recordings processed through image analysis to derive a new computerized automatic lameness score (ALDS) based on calculated factors from back arch posture. The proposed automatic lameness detection system was calibrated using 12 cows, and the remaining 63 were used to evaluate the diagnostic characteristics of the ALDS. The agreement and correlation between ALDS and RLS were investigated. ALDS demonstrated high diagnostic accuracy with 100% sensitivity and specificity and was found to be 100% accurate with a perfect agreement (ρc = 1) and strong correlation (r = 1, P < 0.001) for lameness detection in binary scores (lame/non-lame). Moreover, the ALDS had a strong agreement (ρc = 0.885) and was highly correlated (r = 0.840; 0.796–1.000 95% confidence interval, P < 0.001) with RLS in ordinal scores (lameness severity; LS1 to LS5). Our findings suggest that the proposed method has the potential to compete with vision-based lameness detection methods in dairy cows in farm conditions.

Bees play a significant role in the health of terrestrial ecosystems. The decline of bee populations due to colony collapse disorder around the world constitutes a severe ecological danger. Maintaining high yield of honey and understanding of bee behaviour necessitate constant attention to the hives. Research initiatives have been taken to establish monitoring programs to study the behaviour of bees in accessing their habitat. Monitoring the sanitation and development of bee brood allows for preventative measures to be taken against mite infections and an overall improvement in the brood's health. This study proposed a precision beekeeping method that aims to reduce bee colony mortality and improve conventional apiculture through the use of technological tools to gather, analyse, and understand bee colony characteristics. This research presents the application of advanced digital image processing with computer vision techniques for the visual identification and analysis of bee brood at various developing stages. The beehive images are first preprocessed to enhance the important features of object. Further, object is segmented and classified using computer vision techniques. The research is carried out with the images containing variety of immature brood stages. The suggested method and existing methods are tested and compared to evaluate efficiency of proposed methodology.

In vivo fluorescence microscopy is a powerful tool to image the beating heart in its early development stages. A high acquisition frame rate is necessary to study its fast contractions, but the limited fluorescence intensity requires sensitive cameras that are often too slow. Moreover, the problem is even more complex when imaging distinct tissues in the same sample using different fluorophores. We present Paired Alternating AcQuisitions, a method to image cyclic processes in multiple channels, which requires only a single (possibly slow) camera. We generate variable temporal illumination patterns in each frame, alternating between channel-specific illuminations (fluorescence) in odd frames and a motion-encoding brightfield pattern as a common reference in even frames. Starting from the image pairs, we find the position of each reference frame in the cardiac cycle through a combination of image-based sorting and regularized curve fitting. Thanks to these estimated reference positions, we assemble multichannel videos whose frame rate is virtually increased. We characterize our method on synthetic and experimental images collected in zebrafish embryos, showing quantitative and visual improvements in the reconstructed videos over existing nongated sorting-based alternatives. Using a 15 Hz camera, we showcase a reconstructed video containing two fluorescence channels at 100 fps.

The Australian SKA Pathfinder (ASKAP) is being used to undertake a campaign to rapidly survey the sky in three frequency bands across its operational spectral range. The first pass of the Rapid ASKAP Continuum Survey (RACS) at 887.5 MHz in the low band has already been completed, with images, visibility datasets, and catalogues made available to the wider astronomical community through the CSIRO ASKAP Science Data Archive (CASDA). This work presents details of the second observing pass in the mid band at 1367.5 MHz, RACS-mid, and associated data release comprising images and visibility datasets covering the whole sky south of $\delta_{\text{J2000}}=+49^\circ$. This data release incorporates selective peeling to reduce artefacts around bright sources, as well as accurately modelled primary beam responses. The Stokes I images reach a median noise of 198 $\mu$Jy PSF$^{-1}$ with a declination-dependent angular resolution of 8.1–47.5 arcsec that fills a niche in the existing ecosystem of large-area astronomical surveys. We also supply Stokes V images after application of a widefield leakage correction, with a median noise of 165 $\mu$Jy PSF$^{-1}$. We find the residual leakage of Stokes I into V to be $\lesssim 0.9$–$2.4$% over the survey. This initial RACS-mid data release will be complemented by a future release comprising catalogues of the survey region. As with other RACS data releases, data products from this release will be made available through CASDA.

Manufacturing process (MP) selection systems require a large amount of labelled data, typically not provided as design outputs. This issue is made more severe with the continuous development of Additive Manufacturing systems, which can be increasingly used to substitute traditional manufacturing technologies. The objective of this paper is to investigate the application of image processing for classifying MPs in an unsupervised approach. To this scope, k-means and hierarchical clustering algorithms are applied to an unlabelled image dataset. The input dataset is constructed from freely accessible web databases and consists of twenty randomly selected CAD models and corresponding images of machine elements: 35% additively manufactured parts and 65% manufactured with traditional manufacturing technologies. The input images are pre-processed to have the same colour and size. The k-means and hierarchical clustering algorithms reported 65% and 60% accuracy, respectively. The algorithms show comparable performance, however, the k-means algorithm failed to predict the correct subdivisions. The research shows promising potential for MP classification and image processing applications.

This paper presents a low-cost, accurate indoor positioning system that integrates image acquisition and processing and data-driven modeling algorithms for robotics research and education. Multiple overhead cameras are used to obtain normalized image coordinates of ArUco markers, and a new procedure is developed to convert them to the camera coordinate frame. Various data-driven models are proposed to establish a mapping relationship between the camera and the world coordinates. One hundred fifty data pairs in the camera and world coordinates are generated by measuring the ArUco marker at different locations and then used to train and test the data-driven models. With the model, the world coordinate values of the ArUco marker and its robot carrier can be determined in real time. Through comparison, it is found that a straightforward polynomial regression outperforms the other methods and achieves a positioning accuracy of about 1.5 cm. Experiments are also carried out to evaluate its feasibility for use in robot control. The developed system (both hardware and algorithms) is shared as an open source and is anticipated to contribute to robotic studies and education in resource-limited environments and underdeveloped regions.

Electron cryo-tomography is an imaging technique for probing 3D structures with at the nanometer scale. This technique has been used extensively in the biomedical field to study the complex structures of proteins and other macromolecules. With the advancement in technology, microscopes are currently capable of producing images amounting to terabytes of data per day, posing great challenges for scientists as the speed of processing of the images cannot keep up with the ever-higher throughput of the microscopes. Therefore, automation is an essential and natural pathway on which image processing—from individual micrographs to full tomograms—is developing. In this paper, we present Ot2Rec, an open-source pipelining tool which aims to enable scientists to build their own processing workflows in a flexible and automatic manner. The basic building blocks of Ot2Rec are plugins which follow a unified application programming interface structure, making it simple for scientists to contribute to Ot2Rec by adding features which are not already available. In this paper, we also present three case studies of image processing using Ot2Rec, through which we demonstrate the speedup of using a semi-automatic workflow over a manual one, the possibility of writing and using custom (prototype) plugins, and the flexibility of Ot2Rec which enables the mix-and-match of plugins. We also demonstrate, in the Supplementary Material, a built-in reporting feature in Ot2Rec which aggregates the metadata from all process being run, and output them in the Jupyter Notebook and/or HTML formats for quick review of image processing quality. Ot2Rec can be found at https://github.com/rosalindfranklininstitute/ot2rec.

An emergent volume electron microscopy technique called cryogenic serial plasma focused ion beam milling scanning electron microscopy (pFIB/SEM) can decipher complex biological structures by building a three-dimensional picture of biological samples at mesoscale resolution. This is achieved by collecting consecutive SEM images after successive rounds of FIB milling that expose a new surface after each milling step. Due to instrumental limitations, some image processing is necessary before 3D visualization and analysis of the data is possible. SEM images are affected by noise, drift, and charging effects, that can make precise 3D reconstruction of biological features difficult. This article presents Okapi-EM, an open-source napari plugin developed to process and analyze cryogenic serial pFIB/SEM images. Okapi-EM enables automated image registration of slices, evaluation of image quality metrics specific to pFIB-SEM imaging, and mitigation of charging artifacts. Implementation of Okapi-EM within the napari framework ensures that the tools are both user- and developer-friendly, through provision of a graphical user interface and access to Python programming.

Galaxy-galaxy strong lensing in galaxy clusters is a unique tool for studying the subhalo mass distribution, as well as for testing predictions from cosmological simulations. We describe a novel method that simulates realistic lensed features embedded inside the complexity of observed data by exploiting high-precision cluster lens models. Such methodology is used to build a large dataset with which Convolutional Neural Networks have been trained to identify strong lensing events in galaxy clusters. In particular, we inject lensed sources around cluster members using the images acquired by the Hubble Space Telescope. The resulting simulated mock data preserve the complexity of observation by taking into account all the physical components that could affect the morphology and the luminosity of the lensing events. The trained networks achieve a purity-completeness level of ∼ 91% in detecting such events. The methodology presented can be extended to other data-intensive surveys carried out with the next-generation facilities.

Upcoming large-scale surveys like LSST are expected to uncover approximately 105 strong gravitational lenses within massive datasets. Traditional manual techniques are too time-consuming and impractical for such volumes of data. Consequently, machine learning methods have emerged as an alternative. In our prior work (Thuruthipilly et al. 2022), we introduced a self-attention-based machine learning model (transformers) for detecting strong gravitational lenses in simulated data from the Bologna Lens Challenge. These models offer advantages over simpler convolutional neural networks (CNNs) and competitive performance compared to state-of-the-art CNN models. We applied this model to the datasets from Bologna Lens Challenge 1 and 2 and simulated data on Euclid.

The three-dimensional characterization of internal features, via metrics such as orientation, porosity, and connectivity, is important to a wide variety of scientific questions. Many spatial and morphological metrics only can be measured accurately through direct in situ three-dimensional observations of large (i.e., big enough to be statistically representative) volumes. For samples that lack material contrast between phases, serial grinding and imaging—which relies solely on color and textural characteristics to differentiate features—is a viable option for extracting such information. Here, we present the Grinding, Imaging, Reconstruction Instrument (GIRI), which automatically serially grinds and photographs centimeter-scale samples at micron resolution. Although the technique is destructive, GIRI produces an archival digital image stack. This digital image stack is run through a supervised machine-learning-based image processing technique that quickly and accurately segments data into predefined classes. These classified data then can be loaded into three-dimensional visualization software for measurement. We share three case studies to illustrate how GIRI can address questions with a significant morphological component for which two-dimensional or small-volume three-dimensional measurements are inadequate. The analyzed metrics include: the morphologies of objects and pores in a granular material, the bulk mineralogy of polyminerallic solids, and measurements of the internal angles and symmetry of crystals.

The method of equivariant moving frames is employed to construct and completely classify the differential invariants for the action of the projective group on functions defined on the two-dimensional projective plane. While there are four independent differential invariants of order $\leq 3$, it is proved that the algebra of differential invariants is generated by just two of them through invariant differentiation. The projective differential invariants are, in particular, of importance in image processing applications.

Identifying embryos with a high potential for implementation remains a challenge in in vitro fertilization (IVF) cycles. Despite progress in IVF treatment, only a minority of generated embryos has the ability to implant. Another drawback of this practice is the high frequency of multiple pregnancies. This problem leads to economic and health problems. Therefore, the transfer of a single embryo with high implantation potential is the ideal strategy. Morphometric evaluation of two-pronucleus zygote images is a helpful technique when aiming to transfer a single embryo with a high implantation potential. In this study, an automated zygote morphometric evaluation algorithm, called the zygote morphology evaluation (ZME) algorithm, was created to analyze the zygote and provide morphological measurements. The first and most crucial step of the ZME algorithm is the noise reduction step, which was first applied to zygote images. After that, the proposed algorithm detects different parts of the zygote that are indicators of embryo viability and normality, that is the oolemma, perivitelline space, zona pellucida, and nucleolar precursor bodies (NPBs). In addition, a novel dataset was prepared for this task. This dataset consisted of 703 human zygote images, and called the human zygote morphometric evaluation dataset (HZME-DS). Our experimental results in the HZME-DS showed that the ZME algorithm was able to achieve 79.58% average accuracy in identifying the oolemma region, 79.40% average accuracy in determining the perivitelline space, and 79.72% accuracy in identifying the zona pellucida. To calculate the accuracy of identifying NPBs, the proposed algorithm uses Recall and Precision measures, and their harmonic average (F1 measure) reached values of 81.14% and 79.53%, respectively. These encouraging results for our proposed method, which is an automatic and very fast method, showed that the ZME algorithm could help embryologists to evaluate the best zygotes in real time and the best embryos subsequently.

Weathering of silicate-rich industrial wastes such as slag can reduce emissions from the steelmaking industry. During slag weathering, different minerals spontaneously react with atmospheric CO2 to produce calcite. Here, we evaluate the CO2 uptake during slag weathering using image-based analysis. The analysis was applied to an X-ray computed tomography (XCT) dataset of a slag sample associated with the former Ravenscraig steelworks in Lanarkshire, Scotland. The element distribution of the sample was studied using scanning electron microscopy (SEM), coupled with energy-dispersive spectroscopy (EDS). Two advanced image segmentation methods, namely trainable WEKA segmentation in the Fiji distribution of ImageJ and watershed segmentation in Avizo ® 9.3.0, were used to segment the XCT images into matrix, pore space, calcite, and other precipitates. Both methods yielded similar volume fractions of the segmented classes. However, WEKA segmentation performed better in segmenting smaller pores, while watershed segmentation was superior in overcoming the partial volume effect presented in the XCT data. We estimate that CO2 has been captured in the studied sample with an uptake between 20 and 17 kg CO2/1,000 kg slag for TWS and WS, respectively, through calcite precipitation.

Integrated pest management (IPM) seeks to minimize the environmental impact of pesticide application, and reduce risks to human and animal health. IPM is based on two important aspects – prevention and monitoring of diseases and insect pests – which today are being assisted by sensing and artificial-intelligence (AI) techniques. In this paper, we surveyed the detection and diagnosis, with AI, of diseases and insect pests, in cotton, which have been published between 2014 and 2021. This research is a systematic literature review. The results show that AI techniques were employed – mainly – in the context of (i) classification, (ii) image segmentation and (iii) feature extraction. The most used algorithms, in classification, were support vector machines, fuzzy inference, back-propagation neural-networks and recently, convolutional neural networks; in image segmentation, k-means was the most used; and, in feature extraction, histogram of oriented gradients, partial least-square regression, discrete wavelet transform and enhanced particle-swarm optimization were equally used. The most used sensing techniques were cameras, and field sensors such as temperature and humidity sensors. The most investigated insect pest was the whitefly, and the disease was root rot. Finally, this paper presents future works related to the use of AI and sensing techniques, to manage diseases and insect pests, in cotton; for instance, implement diagnostic, predictive and prescriptive models to know when and where the diseases and insect pests will attack and make strategies to control them.

This article presents a fast and highly efficient algorithm developed to reconstruct a three-dimensional (3D) volume with a high spatial precision from a set of field ion microscopy (FIM) images, and specific tools developed to characterize crystallographic lattice and defects. A set of FIM digital images and image processing algorithms allow the construction of a 3D reconstruction of the sample at the atomic scale. The capability of the 3D FIM to resolve the crystallographic lattice and the finest defects in metals opens a new way to analyze materials. This spatial precision was quantified on tungsten, analyzed at different analyzing conditions. A specific data mining tool, based on Fourier transforms, was also developed to characterize lattice distortions in the reconstructed volumes. This tool has been used in simulated and experimental volumes to successfully locate and characterize defects such as dislocations and grain boundaries.

A method is presented to determine the feature resolution of physically relevant metrics of data obtained from segmented image sets. The presented method determines the best-fit distribution curve of a dataset by analyzing a truncated portion of the data. An effective resolvable size for the metric of interest is established when including parts of the truncated dataset results in exceeding a specified error tolerance. As such, this method allows for the determination of the feature resolution regardless of the processing parameters or imaging instrumentation. Additionally, the number of missing objects that exist below the resolution of the instrumentation may be estimated. The application of the developed method was demonstrated on data obtained via 2D scanning electron microscopy of a pressed explosive material and from 3D micro X-ray computed tomography of a polymer-bonded explosive material. It was shown that the minimum number of pixels/voxels required for the accurate determination of a physically relevant metric is dependent on the metric of interest. This proposed method, utilizing the prior knowledge of the distribution of metrics of interest, was found to be well suited to determine the feature resolution in applications where large datasets can be achieved.

Providing high-quality electron images and hyperspectral X-ray maps is a focus of many modern electron microscopy laboratories. Nevertheless, further image processing and annotations are often needed to prepare them for publications and reports. For multi-user facilities, accessibility to processing software can be a limitation either through license costs or availability of processing stations. Open-source software running on multiple platforms allows for post-acquisition data processing in-lab or on user-owned devices. We developed Probelab ReImager to supersede our vendor-supplied acquisition software's exportation by being efficient and highly customizable. This article describes its main features and capabilities.

Diaphorina is a species-rich genus, native to the tropics and subtropics of the Old World, particularly of more arid regions. One of the species, Diaphorina citri, is the economically most important pest of citrus. Diaphorina species are morphologically similar which makes their identification difficult. In this study, the accuracy of DNA barcoding, using mitochondrial cytochrome c oxidase subunit 1 (COI), geometric morphometrics of the forewing and digital image processing methods were tested for identification of the three Diaphorina species: D. chobauti, D. citri and D. zygophylli. Moreover, the published COI sequences of D. citri, D. communis and D. lycii obtained from Genbank were used for cluster analyses. DNA barcodes for D. chobauti and D. zygophylli are deposited in Genbank for the first time. The results of the molecular and geometric morphometric analyses are congruent and place D. chobauti as the sister taxon of the other Diaphorina species. The geometric morphometric analysis shows that in D. zygophylli the fore margin is slightly curved proximally and sharply bent distally, while in D. chobauti and D. citri it is straight proximally and weakly bent distally. The results of digital image processing show that the distribution of the dark pattern differs consistently in the three studied species.

This research communication presents an automatic method for the counting of somatic cells in buffalo milk, which includes the application of a fuzzy clustering method and image processing techniques (somatic cell count with fuzzy clustering and image processing|, SCCFCI). Somatic cell count (SCC) in milk is the main biomarker for assessing milk quality and it is traditionally performed by exhaustive methods consisting of the visual observation of cells in milk smears through a microscope, which generates uncertainties associated with human interpretation. Unlike other similar works, the proposed method applies the Fuzzy C-Means (FCM) method as a preprocessing step in order to separate the images (objects) of the cells into clusters according to the color intensity. This contributes signficantly to the performance of the subsequent processing steps (thresholding, segmentation and recognition/identification). Two methods of thresholding were evaluated and the Watershed Transform was used for the identification and separation of nearby cells. A detailed statistical analysis of the results showed that the SCCFCI method is able to provide results which are consistent with those obtained by conventional counting. This method therefore represents a viable alternative for quality control in buffalo milk production.