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Determining microplastics in environmental samples quickly and reliably is a challenging task. With a largely automated combination of optical particle analysis, Fourier transform infrared (FT-IR), and Raman microscopy along with spectral database search, particle sizes, particle size distributions, and the type of polymer including particle color can be determined. We present a self-developed, open-source software package for realizing a particle analysis approach with both Raman and FT-IR microspectroscopy. Our software GEPARD (Gepard Enabled PARticle Detection) allows for acquiring an optical image, then detects particles and uses this information to steer the spectroscopic measurement. This ultimately results in a multitude of possibilities for efficiently reviewing, correcting, and reporting all obtained results.

The rapidly growing use of imaging infrastructure in the energy materials domain drives significant data accumulation in terms of their amount and complexity. The applications of routine techniques for image processing in materials research are often \textit{ad hoc}, indiscriminate, and empirical, which renders the crucial task of obtaining reliable metrics for quantifications obscure. Moreover, these techniques are expensive, slow, and often involve several preprocessing steps. This paper presents a novel deep learning-based approach for the high-throughput analysis of the particle size distributions from transmission electron microscopy (TEM) images of carbon-supported catalysts for polymer electrolyte fuel cells. Our approach employs training an instance segmentation model, called StarDist [Schmidt et al. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2018, Lecture Notes in Computer Science, vol 11071. Springer, Cham], which resolves the main challenge in the pixel-wise localization of nanoparticles in TEM images: the overlapping particles. The segmentation maps outperform models reported in the literature, and the results on particle size analyses agree well with manual particle size measurements, albeit at a significantly lower cost.

This paper investigates the spreadability of the spherical CoCrWMo powder for Laser- Powder Bed Fusion (PBF-LB) by using image processing algorithms coded in MATLAB. Besides, it also aims to examine the spreadability correlation with the other characteristics such as flow rate, apparent density, angle of repose. Powder blends in four different particle size distributions are prepared, characterized, and spreadability tests are performed with the PBF-LB. The results demonstrate that an increase in fine particle ratio by volume (below 10 µm) enhances the agglomeration and decreases the flowability, causing poor spreadability. These irregularities on the spread layers are quantified with simple illumination invariant analysis.

Soot is an important material with impacts that depend on particle morphology. Transmission electron microscopy (TEM) represents one of the most direct routes to qualitatively assess particle characteristics. However, producing quantitative information requires robust image processing tools, which is complicated by the low image contrast and complex aggregated morphologies characteristic of soot. The current work presents a new convolutional neural network explicitly trained to characterize soot, using pre-classified images of particles from a natural gas engine; a laboratory gas flare; and a marine engine. The results are compared against other existing classifiers before considering the effect that the classifiers have on automated primary particle size methods. Estimates of the overall uncertainties between fully automated approaches of aggregate characterization range from 25% in d_{p,100} to 85% in D_{TEM}. A consistent correlation is observed between projected-area equivalent diameter and primary particle size across all of the techniques., Comment: 23 pages, 8 figures, 5 tables, for associated code see https://github.com/tsipkens/atems and https://github.com/maxfrei750/CarbonBlackSegmentation

Particle size and shape significantly affect powder processing and their end-product quality in a variety of industries. Imaging methods can successfully characterize populations of needle-like particles. Prior to off-line imaging, adjusting the particle density can reduce particle overlaps but increase measurement/processing times. Discarding data of overlapping particles, as most image processing algorithms do, biases particle size and shape distributions. Building on previous efforts, we here provide an image processing technique that accurately separates and sizes overlapping needle-like particles. Our algorithm combines edge detection, layer-stripping watershed segmentation and length approximation. To test the algorithm, a large number of real particle projections were randomly overlapped with various overlap intensities. Approximately 92–72% of the particles were detected and the particles’ dimensions were characterized with an accuracy of 87–75%, with these ranges corresponding to low and high overlap intensities. Overall, the algorithm removes biases to considerably improve characterization accuracy of powders containing needle-like particles.

In the measurement of overlapping particle size, it is necessary to perform image processing on the stacked particle image to obtain accurate measurement results. In this paper morphological filtering is used to remove the isolated small area and fill the holes. The distance image combined with h-minima transform is used to get the seed points. Then the seed points and background are marked on the distance image. Finally, the distance image is segmented by watershed. Due to the partial missing after segmentation of conglutinated particles, according to the prior knowledge that the shape of particles is similar to ellipse, this paper reconstructs the contour of the incomplete particles by ellipse fitting technology. Finally, the measurement algorithm of particle shape and particle size characteristics is determined. Two groups experiments are carried out for particle size measurement, and the error of particle size measurement is analyzed. It is proved that the measurement is accurate.

A method is presented for simultaneously inferring both the refractive index and the size of a particle with extended interferometric particle imaging technique. The optical system of extended IPI with opposite two-sheet illumination at dual scattering angles is laid out for the experiment. The size of a particle is evaluated by the interference fringe recorded at the scattering angle of 90°, which is from the two reflected lights with two counter-propagating sheet illuminations. And then the refractive index is calculated by the fringe pattern recorded in the side scattering angle region with one of two-sheet illumination when combined with droplet size determined. Experiments on the polystyrene microsphere and water droplet suggest that the method presented herein is promising for many relevant applications, such as fuel combustion and environmental monitoring, in accurately measuring both the particle size and its refractive index.

In this study, U-Net based deep convolutional networks are used to achieve the segmentation of particle regions in a microscopic image of colorants. The material appearance of products is greatly affected by the distribution of the particle size. From that fact, it is important to obtain the distribution of the particle size to design the material appearance of products. To obtain the particle size distribution, it is necessary to segment particle regions in the microscopic image of colorants. Conventionally, this segmentation is performed manually using simple image processing. However, this manual processing leads to low reproducibility. Therefore, in this paper, to extract the particle region with high reproducibility, segmentation is performed using U-Net based deep convolutional networks. We improved deep convolutional U-Net type networks based on the feature maps trained for a microscopic image of colorants. As a result, we obtained more accurate segmentation results using the improved network than conventional U-Net.

Nanoparticle properties are correlated to their size, size distribution, and shape, it is essential to accurately measure these features in the field of nanoscience. In this study, silver nanoparticles (AgNPs) were synthesized with the ultrasonic-spray-pyrolysis (USP) method from a water solution of silver nitrate. The synthesized AgNPs were characterized by Dynamic Light Scattering (DLS) analysis and Scanning Electron Microscopy (SEM) to reveal their size and size distribution. A search algorithm based on an image-processing technique to obtain particle size and particle-size distribution from SEM micrographs is proposed. In order to obtain more quantitative information and data with respect to the morphology of particles synthesized under different process parameters, SEM micrographs with a nonhomogeneous background contrast were examined via image-processing techniques in MATLAB. Due to the inhomogeneous contrast of SEM micrographs, defining an overall threshold value was insufficient in the detection of whole nanoparticles. Thus, subimages were directly created according to the maximum and minimum particle size specified by the user to determine local threshold values. The obtained results were automatically combined to represent both particle dimension and location in the SEM micrographs. We confirmed that the results of our DLS analysis, theoretical calculation, and image-processing technique were correlated with our expected results.

With the difficulties to measure inter-particle distance in a suspension, the theoretical value under the several assumptions has been used. In this study, an image processing method for directly calculating inter-particle distances in a suspension was developed. This method enables inter-particle distances to be calculated regardless of the particle size, distribution, and morphology. A surface of suspension composed of particles and wax-polymeric binder was polished with acetone to expose the particles from the binder. The particles measured by scanning electron microscope were distinguished from the binder through the developed image processing method based on the intensity difference between the particles and binder. The inter-particle distances between the randomly selected particles and neighboring particles were calculated using the centroids and boundaries obtained by image processing. To determine the inter-particle distance as a representative value, a Monte Carlo method was employed. The inter-particle variation was defined to determine the iteration number for the Monte Carlo method. Finally, the inter-particle distances for the particle volume fraction of 40 and 50 vol% were obtained by the developed method. In addition, this developed method is available to describe the physical phenomenon such as a powder-binder separation and critical solid loading.

Gas-solid two phase flow measurements have a wide range of applications in industrial production and scientific research. Based on optical principle and image processing, a measurement method of solid phase particle size and concentration of gas-solid two phase flow was presented, and an experimental system was built. By theoretical analysis and image processing algorithms, solid phase particle sizes and concentrations in gas-solid two phase flow were achieved.

The efficacy of Dynamic Image Analysis (DIA) for evaluating particle size and shape parameters was explored using three natural sands, having varying particle morphologies. Two-dimensional (2D) captures binary images of the particles as they free fall in the imaging frame. Although 2D DIA is practical for statistical size and shape analysis, there is a prevailing perception that it fails to fully quantify particle granulometry. In the past few years, 3D DIA has been introduced and has gained acceptance in the pharmaceutical industry. In 3D DIA the system tracks a particle as it falls through the imaging frame and captures gray-scale images from 8 to 12 perspectives of the same particle, and the results are analyzed using average values of these 2D images, which are believed to verge on true particle morphology. Although 2D and 3D devices employ similar methodology they differ in resolution, frame rate, lighting systems, and algorithms. In this work we compare the performance of 2D and 3D DIA. Particle size distributions were expressed using EQPC and a variety of Feret diameters, while particle shape descriptors including Aspect Ratio, Sphericity, Convexity and Roundness were compared for both systems. It is shown that 3D DIA requires a smaller number of sand particles to achieve mean particle shape values. Particle size characterization is generally independent of the machines and algorithms used in this study; however, 3D DIA provides maximum and minimum particle axes which are closer to the real sand particle sizes. Image-based particle shape characterization is more sensitive to the technology employed; it largely depends on image quality, particle angularity, and a hierarchy of shape descriptors; thus, at this time shape analysis for engineering applications must be carried out with similar machines and algorithms. In particular, the image resolutions captured by the available 2D and 3D DIA apparatus are 4 μm and 15 μm per pixel, respectively. At this time, the higher resolution and shorter exposure time of 2D DIA permits particle shape analysis down to particle sizes having D50 on the order of 40 μm, while 3D DIA is limited to D50 larger than 150 μm. These thresholds will certainly change as technology allows finer resolutions and shorter exposure times. Finally, the importance of image processing cannot be overlooked, and manufacturers and researchers are encouraged to open-source their algorithms in order to establish confidence in them.

Although 3D X-ray tomographic analysis of mineral particles in packed particle beds has been developed in past years and applied successfully for irregularly shaped particles, it has been a challenge to accurately analyze multisize particles due to segmentation limitations. An improved image segmentation technique for multisize particles has been developed for accurate analysis with reduced scanning and image processing time. In this paper, tomographic images of coarse particles (76.2 × 6.35 mm) produced with a high speed milliscanner and fine particles (5 × 0.35 mm) produced with a high resolution microscanner are segmented by means of a 3D morphological algorithm, based on a multiscale extraction of markers for watershed segmentation. In this way, multisize particles in a packed particle bed which range in size by an order of magnitude can be imaged with good accuracy.

Particle overlapping is a serious but largely ignored problem in size distribution estimation using image processing technology. A new correction method for particle overlapping error in coal size distribution estimation is proposed in this paper. Particles of four different size fractions were each sprayed with different colors to easily identify the size fractions. A semi-automatic local-segmentation algorithm was proposed to segment coal particle regions in this investigation. Moreover, an interval statistical method was used to calculate the probability of overlapping particles that belong to the four size fractions, and the probability distribution curves of the four size fractions were fitted by the Least Square Method. Similar function models were selected according to the characteristics and shapes of the probability distribution curves. Root Mean Square Error (RMSE) and R-squared parameters were used to measure the fitting effects. It was found that using overlap correction models reduced the maximum and minimum absolute error of particle size estimation from 7.5% and 4.3% with no overlap correction to 4.5% and 0.5%, respectively, when the developed overlap models were used.

Image segmentation is one of the essential tools to determine the foreground from background and at the same time enhance visual perception for better understanding through image manipulation. It is very useful for pattern recognition and image processing. This enables users to determine the high quality and high resolutions of the final result of the analysis. In this present study, PKS-Powder has been characterized with the use of Scanning Electron Microscopy (SEM), and Energy Dispersive X-ray (EDX) analyses. Digital Vibratory milling machine was employed for the mechanical milling at the time interval of 0, 10, 15 and 20 min. ImageJ software was employed for image processing by sectioning an image into various regions using thresholding segmentation method. It was revealed that at 0 min (i.e. 300 µm sieved), it has the highest mean area value of 127.169 µm2 and area standard deviation of 4,091.487 µm2 with the least value of a number of particle size distribution of 458 µm. In contrast, 20 min milled has the lowest values for mean area and area standard deviation of 52.913 µm2 and 795.413 µm2 respectively with the highest number of particle size distribution of 1,315 µm. It was observed that milling time increases the number of particle sizes distributions and reduces the area of particle size. EDX analysis revealed that Ca, Al, Si, Fe, C, K, and O are the main elemental constituents of PKS-powder.

Summary Three-dimensional particle morphology is a significant problem in the discrete element modeling of granular sand. The major technical challenge is generating a realistic 3D sand assembly that is composed of a large number of random-shaped particles containing essential morphological features of natural sands. Based on X-ray micro-computed tomography data collected from a series of image processing techniques, we used the spherical harmonics (SH) analysis to represent and reconstruct the multi-scale features of real 3D particle morphologies. The SH analysis was extended to some highly complex particles with sharp corners and surface cavities. We then proposed a statistical approach for the generation of realistic particle assembly of a given type of sand based on the principle component analysis (PCA). The PCA aims to identify the major pattern of the coefficient matrix, which is made up of the SH coefficients of all the particles involved in the analysis. This approach takes into account the particle size effect on the variation of particle morphology, which is observed from the available results of micro-computed tomography and QICPIC analyses of sand particle morphology. Using the aforementioned approach, two virtual sand samples were generated, whose statistics of morphological parameters were compared with those measured from real sand particles. The comparison shows that the proposed approach is capable of generating a realistic sand assembly that retains the major morphological features of the mother sand. Copyright © 2016 John Wiley & Sons, Ltd.

Image-based soil particle size and shape characterization relies on computer methods to process and analyze the images. For contacting particles spread on a flat surface this requires delineation of particle boundaries through shape-based image segmentation. The traditional method using watershed analysis fails for particles that have constrictions (are peanut-shaped). The oversegmentation interprets such particles as being two, thereby underestimating the long particle dimension by about 50% and overestimating particle sphericity by about a factor of two. This paper presents a solution to the problem of oversegmentation through morphologic reconstruction. The key to this improvement is distinguishing the necks in peanut shaped particles from actual contacts between particles. A parameter α is defined to quantify the necks and contacts. Approximately 220,000 particles in a range of 2.0–35.0 mm having various shapes and angularities were studied to find typical α values for necks and contacts. An algorithm is proposed to correct the oversegmentation based on α . The results show that this improved watershed analysis accurately segments sand particles at contacts while preserving the continuity of peanut shaped particles. Example lab tests demonstrate the significance of the problem and its solution.

Reclaimed Asphalt Pavement (RAP) particles are created from the impact removal and/or reprocessing of existing asphalt layers. RAP particles contain a combination of asphalt and aggregates with varying degrees of coating and morphology. Particle size and shape properties, amount of asphalt coating the RAP particles, and the binder content of the RAP are among the important engineering properties that control the performance of this material. This paper introduces an innovative machine vision-based inspection system to quantify the percentage of asphalt coating in different RAP aggregate sources. The Enhanced-University of Illinois Aggregate Image Analyzer (E-UIAIA) is used to acquire the color RGB images of RAP particles from six different sources with sizes between 1/4 in. (6.35 mm) and 1/2 in. (12.5 mm). The influence of asphalt coating percentage on the RAP particle size and shape properties are quantified in this paper. Then, using the advanced color image thresholding scheme incorporated in the E-UIAIA, the corresponding segmented binary images of RAP particles are generated. A newly defined image mean property is used as an automatic variable threshold limit to segment the bright areas in the associated grayscale version of RAP images to detect the uncoated areas on each particle. A relationship was found between the results of the proposed image processing technique in terms of asphalt coating percentages and the asphalt content of the RAP. Furthermore, the asphalt surface coating percentages could be successfully correlated to the fracture energies of concrete specimens containing these RAP particles blended with other virgin aggregates.

Interferometric particle imaging (IPI) is a robust and popular technique for measuring particle size and velocity. A method based on a template matching algorithm and an auto-correlation method is proposed to simultaneously extract the location and the separation of doublet images of a particle from an IPI focused image. The position coordinate (x,y) of the particle can be determined with high accuracy, as evaluated by using a serial particle mask. Furthermore, the method can be employed to achieve sub-pixel spacing extraction when combined with Gaussian interpolation. The algorithm is tested using synthetic and experimental data. The results suggest that the method presented here is promising for its application to a high-density particle field, in accurately measuring both the particle size and its location.

Recently, considerable attention has been focused on the issue of large ice particles shattering on the inlets and tips of cloud particle probes, which produces copious ice particles that can be mistakenly measured as real ice particles. Currently two approaches are being used to mitigate the problem: (1) Based on recent high-speed video in icing tunnels, probe tips have been designed that reduce the number of shattered particles that reach the probe sample volume, and (2) Post processing techniques such as image processing and using the arrival time of each individual particle. This paper focuses on exposing suspected errors in measurements of ice particle size distributions due to shattering, and evaluation of the two techniques used to reduce the errors. Data from 2D-S probes constitute the primary source of the investigation, however, when available comparisons with 2D-C and CIP measurements are also included. Korolev et al. (2010b) report results from a recent field campaign (AIIE) and conclude that modified probe tips are more effective than an arrival time algorithm when applied to 2D-C and CIP measurements. Analysis of 2D-S data from the AIIE and SPARTICUS field campaigns shows that modified probe tips significantly reduce the number of shattered particles, but that a particle arrival time algorithm is more effective than the probe tips designed to reduce shattering. A large dataset of 2D-S measurements with and without modified probe tips was not available from the AIEE and SPARTICUS field campaigns. Instead, measurements in regions with large ice particles are presented to show that shattering on the 2D-S with modified probe tips produces large quantities of small particles that are likely produced by shattering. Also, when an arrival time algorithm is applied to the 2D-S data, the results show that it is more effective than the modified probe tips in reducing the number of small (shattered) particles. Recent results from SPARTICUS and MACPEX show that 2D-S ice particle concentration measurements are more consistent with physical arguments and numerical simulations than measurements with older cloud probes from previous field campaigns. The analysis techniques in this paper can also be used to estimate an upper bound for the effects of shattering. For example, the additional spurious concentration of small ice particles can be measured as a function of the mass concentration of large ice particles. The analysis provides estimates of upper bounds on the concentration of natural ice, and on the remaining concentration of shattered ice particles after application of the post-processing techniques. However, a comprehensive investigation of shattering is required to quantify effects that arise from the multiple degrees of freedom associated with this process, including different cloud environments, probe geometries, airspeed, angle of attack, particle size and type.

The size distribution of manufactured sand particles has a significant influence on the quality of concrete. To overcome the shortcomings of the traditional vibration-sieving method, a manufactured sand casting/dispersing system was developed, based on the characteristics of the sand particle contours (as determined by backlit image acquisition) and an extraction mechanism. Algorithms for eliminating particles from the image that had be repeatedly captured, as well as for identifying incomplete particles at the boundaries of the image, granular contour segmentation, and the determination of an equivalent particle size, are studied. The hardware and software for the image-based detection device were developed. A particle size repeatability experiment was carried out on the single-grade sands, grading the size fractions of the manufactured sand over a range of 0.6-4.75 mm. A method of particle-size correction is proposed to compensate for the difference in the results obtained by the image-based method and those obtained by the sieving method. The experimental results show that the maximum repeatability error of single-grade fractions is 3.46% and the grading size fraction is 0.51%. After the correction of the image method, the error between the grading size fractions obtained by the two methods was reduced from 7.22%, 6.10% and 5% to 1.47%, 1.65%, and 3.23%, respectively. The accuracy of the particle-size detection can thus satisfy real-world measuring requirements.

Precise particle size measurements are important in many aspects of engineering geology, e.g. in mineral processing and the study of methane hazard in coal mines. The volume of grains, estimated exclusively on the basis of dimensions of the grains differs tens of percent from the volume obtained from 3D digital measurements. This confirms that full three-dimensional automatic imaging can be used in the measurements of the particles. The paper discusses the technique of three-dimensional imaging performed with the use of three types of devices. Coal particles representing the 0.5–1.0 mm grain fraction were chosen as the research material. The measurements were performed by means of: X-ray computed tomography, confocal microscopy and optical profilometry. The last two techniques are less costly and more easily available than computed tomography. For the X-ray CT scanner, full three-dimensional imaging was performed. In the case of the other two techniques, only those parts of the particles that were captured by the heads of the relevant devices in question were measured. The invisible parts of the particle were reconstructed with the assumption that the bottom part of the particle is similar (in some scale) to the top part of the particle. The results obtained by means of X-ray CT scanner were used as the correct volume and size values. The results indicate that measurements carried out carefully and with use of the more easily available and less costly equipment, combined with careful data processing, can be used instead of methods that are expensive and harder to employ. The percentage deviation between the volume calculated with use of the data obtained with the X-ray CT scanner and the volume calculated with use of the data obtained with the other two devices varies from 0.2% to ca. 5%.

Traditional particle size analysis of chopped forage is done by mechanical sieving, thereby providing mass distribution of one dimension. Recent studies have shown that long and narrow particles can tip during shaking and slide through holes smaller than the longest particle dimension. Meanwhile, well calibrated image analysis is definitely more accurate than screening in measuring true dimensions of chopped particles. An experiment was carried out with chopped alfalfa and corn harvested at three theoretical lengths of cut (TLOC = 4.8, 9.5, and 11.1 mm). Particles were initially sorted by the ASABE standard screening method. Particles within each screen were spread on a flat surface and photographed. Pictures were processed with the Image Analysis Toolbox in MATLAB, providing total pixel area, vector length (greatest distance between two points on the periphery), and an estimate of width for individual particles. All particles per picture were weighed, providing an estimate of volume and the third dimension (thickness). The ASABE standard method underestimated particle length as measured by image analysis by an average of 31%. Width was not significantly different for alfalfa particles at three TLOC, as expected, but it increased for corn as TLOC increased, indicating breakage in two dimensions (length and width) due to the bulky nature of corn. Image analysis and mass measurements provided detailed information on total outer surface area per unit mass, with an average of 218 cm 2 g -1 dry matter (DM) for alfalfa particles and 133 cm 2 g -1 DM for corn particles. Combining image analysis and mechanical sieving improved the estimation of mass and dimensional parameters.

The size distribution of ore particles is an important basis for evaluating the crushing effect, and is also the main index for the optimal control of mineral processing equipment. Image processing technology is used to process the images collected by the camera on line, which can get the information of ore particle size in real time and feedback or adjust the parameters of the crushing machine in time. We first study the image pretreatment methods on ore images to separate foreground and background images. Then the improved watershed segmentation algorithm is used to segment the ore images. Finally, the distribution information of the ore particle can be obtained through the projected diameter of the ore particles. The experimental results show that the analysis results are effective in real applications, which can meet the needs of on-line detection of ore particles.

A speckle pattern is an interference pattern produced by coherent light scattered from an irregular particle. This pattern is observed in the out-of-focus plane of the particle and it can be used to obtain information about the particle size. When the particle is observed in focus, several bright spots known as glare points are observed on its surface. They correspond to the points from which the light is scattered in the direction of observation. Previous studies using the speckle pattern to obtain the size of irregular particles are based on the hypothesis that the glare points are distributed homogeneously over the whole surface of the particle. The research presented in this paper shows that in the case of opaque particles (such as ash particles), only the areas illuminated by the laser light are covered with glare points. This results in an underestimation of the particle size when characterizing opaque particles using their speckle pattern. In this paper, the speckle pattern is used to perform sizing measurements of translucent and opaque particles, and the results are compared with the ones obtained by image processing of their in-focus images.

Particle morphology, including size and shape, is an important factor that significantly influences the physical and chemical properties of biomass material. Based on image processing technology, a method was developed to process sample images, measure particle dimensions, and analyse the particle size and shape distributions of knife-milled wheat straw, which had been preclassified into five nominal size groups using mechanical sieving approach. Considering the great variation of particle size from micrometer to millimeter, the powders greater than 250 μm were photographed by a flatbed scanner without zoom function, and the others were photographed using a scanning electron microscopy (SEM) with high-image resolution. Actual imaging tests confirmed the excellent effect of backscattered electron (BSE) imaging mode of SEM. Particle aggregation is an important factor that affects the recognition accuracy of the image processing method. In sample preparation, the singulated arrangement and ultrasonic dispersion methods were used to separate powders into particles that were larger and smaller than the nominal size of 250 μm. In addition, an image segmentation algorithm based on particle geometrical information was proposed to recognise the finer clustered powders. Experimental results demonstrated that the improved image processing method was suitable to analyse the particle size and shape distributions of ground biomass materials and solve the size inconsistencies in sieving analysis.

In order to quantitatively analyze biological images and study underlying mechanisms of the cellular and subcellular processes, it is often required to track a large number of particles involved in these processes. Manual tracking can be performed by the biologists, but the workload is very heavy. In this paper, we present an automatic particle tracking method for analyzing an essential subcellular process, namely clathrin mediated endocytosis. The framework of the tracking method is an extension of the classical multiple hypothesis tracking (MHT), and it is designed to manage trajectories, solve data association problems, and handle pseudo-splitting/merging events. In the extended MHT framework, particle tracking becomes evaluating two types of hypotheses. The first one is the trajectory-related hypothesis, to test whether a recovered trajectory is correct, and the second one is the observation-related hypothesis, to test whether an observation from an image belongs to a real particle. Here, an observation refers to a detected particle and its feature vector. To detect the particles in 2D fluorescence images taken using total internal reflection microscopy, the images are segmented into regions, and the features of the particles are obtained by fitting Gaussian mixture models into each of the image regions. Specific models are developed according to the properties of the particles. The proposed tracking method is demonstrated on synthetic data under different scenarios and applied to real data.

Epoxy resins have wide applications in modern industries. To improve the properties of such resins the thermoplastic component is often used. This component dissolves in the epoxy resin at a high temperature. To determine the properties of the obtained cured epoxy matrix with thermoplastic particles it is important to estimate and classify this particle sizes. In this paper we investigate methods for solving these tasks automatically. The thermoplastic particle sizes are analyzed using the microscopy images of the cured epoxy matrix. The digital image processing methods for the thermoplastic particle detection are discussed. The Otsu’s method is implemented for microscopic images with homogeneous background. The Circular Hough Transform method is implemented for microscopic images with big visible particle radii. The results of both methods for the considered images are represented. The parameters of the Gaussian distribution for the thermoplastic particle sizes in a cured epoxy matrix are estimated from the analyzed microscopic images.

Reliable particles property distribution data is important in design of materials processing operation. With composite fines samples, such as the comminution fines in printed circuit board physical processing, having particles diverse in material type and morphology, size distribution data from some conventional techniques will not fully describe the sample. A review of basic analytical principles shows sieving, cyclosizing, and laser diffraction techniques will, in principle, return erroneous results. Rather, it was shown from test analysis carried out that detailed quantitative description of the particle size and shape distribution for such composite fines sample can be obtained by employing image processing and analysis of a good micrograph of the sample. Image processing with image analysis can be used to decompose the composite micrograph and separately characterize the constituent particle types. This is a good reference for composite fines size and shape distribution analysis, to narrow down appli...

On-line continuous monitoring of pneumatically conveyed particulate material is desirable in many industrial processes such as power generation, steel making, chemical processing and food handling. Co-firing of biomass with coal at existing coal-fired power plants has been widely adopted as one of the main technologies for reducing greenhouse gas emissions. Particle size and shape distributions of pulverised coal and biomass during pneumatic transportation are important parameters in relation to combustion efficiency and pollutant emissions and should therefore be monitored on-line continuously. In this paper, a novel instrumentation system incorporating a colour CCD camera and multi-wavelength laser sources is proposed to achieve on-line continuous measurement of particle size and shape distributions. Unlike the single-laser technique, three solid-state lasers of RGB (Red, Green and Blue) wavelengths are used to illuminate the particle flow field simultaneously whilst the camera captures the images of particles. The advantage of this approach is the simultaneous imaging of three different cross-sections of the pipe without the use of moving or scanning components. Quantitative information of the particles is obtained by decomposing the red, green and blue channels from the primary colour images. The equivalent field of view is estimated to be more than three times greater than that of the single-laser system. The size distribution of particles is determined by processing the particle images through contour-based image processing algorithms whilst the particle shape distribution is obtained by fitting the particles to the closest ellipses and determining their aspect ratios. Experimental results show that the multi-wavelength imaging system is capable of producing statistically more representative and more reliable measurement of particle size and shape distributions than the single-laser technique.

Studies of suspended particles and particle dynamics in aquatic environments increasingly rely on camera systems to characterize the particles. Numerous systems exist and all use different codes and practises to process the images from the systems. Here, a step-by-step guide to an image processing and particle characterization code for Matlab is presented with the aim of bringing the particle community towards standardized image processing techniques. The code uses morphological reconstruction and simple block processing to filter out noise, out-of-focus particles and light source inconsistencies. It has been implemented on a specific camera system but is applicable to numerous systems and on highly variable particle types due to the standardized setup.

Sensors Agrosup équipe BAP GEAPSI; International audience; Accurate spray characterization helps to better understand the pesticide spray application process. The goal of this research was to present the proof of principle of a droplet size and velocity measuring technique for different types of hydraulic spray nozzles using a high speed backlight image acquisition and analysis system. As only part of the drops of an agricultural spray can be in focus at any given moment, an in‐focus criterion based on the gray level gradient was proposed to decide whether a given droplet is in focus or not. In a first experiment, differentlysized droplets were generated with a piezoelectric generator and studied to establish the relationship between size and in‐focus characteristics. In a second experiment, it was demonstrated that droplet sizes and velocities from a real sprayer could be measured reliably in a non‐intrusive way using the newly developed image acquisition set‐up and image processing. Measured droplet sizes ranged from 24 μm to 543 μm, depending on the nozzle type and size. Droplet velocities ranged from around 0.5 m/s to 12 m/s. The droplet size and velocity results were compared and related well with the results obtained with a Phase Doppler Particle Analyzer (PDPA).

Morphology is important in industrial processes involving filamentous organisms because it affects the mixing and mass transfer and can be linked to productivity. Image analysis provides detailed information about the morphology but, in practice, it is often laborious including both collection of high quality images and image processing. Laser diffraction is rapid and fully automatic and provides a volume-weighted distribution of the particle sizes. However, it is based on a number of assumptions that do not always apply to samples. We have evaluated laser diffraction to measure cell clumps and pellets of Streptomyces coelicolor compare to image analysis. Samples, taken five times during fed-batch cultivation, were analyzed by image analysis and laser diffraction. The volume-weighted size distribution was calculated for each sample. Laser diffraction and image analysis yielded similar size distributions, i.e. unimodal or bimodal distributions. Both techniques produced similar estimations of the population means, whereas the estimates of the standard deviations were generally higher using laser diffraction compared to image analysis. Therefore, laser diffraction measurements are high quality and the technique may be useful when rapid measurements of filamentous cell clumps and pellets are required.

A new on-line remote particle analysis system based on image processing has been developed to measure microparticles. The system is composed of particle collector sensor (PCS), particle image sensor (PIS), image remote transmit module and image processing system. Then some details of image processing are discussed. The main advantage of this system is more convenient in particle sample collection and particle image acquisition. The particle size can be obtained using the system with a deviation abot less than 1 μm, and the particle number can be obtained without deviation. The developed system is also convenient and versatile for other analyses of microparticle for academic and industrial application.

Determination of particle size and shape factors of the corundum powders by SEM image analysis has been reported in our preceding work. However, the effect of sample preparation and image processing has not been investigated. In order to obtain appropriate images, samples was well-dispersed by vibrating particles manually up and down, and then thicker Pt coating was well sputter coated on the surface of samples; compared with the magnifications of 100 and 32, magnification of 50 was more reasonable; for distinguishing particles from image background properly, the gray threshold value of 70 was set to transform gray image to binary image; to decrease image noises, three image filters, i.e. Erosion Filter, Dilation Filter and Median Filter were investigated and the last one was the most effective. This method of sample preparation and image processing can be used in regular and irregular particles determination.

In this paper, an image processing system for estimating 3-D particle distributions from stereo light scatter images is described. The system incorporates measured, three-component velocity data to mitigate particle blur associated with instrument motion. An iterative background estimation algorithm yields a local threshold operator that dramatically reduces bias in particle counts over the full image field. Algorithms are tested on simulated particle distributions and data from an open-ocean profile collected near the Santa Barbara Channel Islands, CA. They yield over a 50% reduction in root-mean-squared error in particle size estimates, and a 30% reduction in the magnitude of the motion blur point spread function. In situ particle distributions are estimated and compared to several models. It is demonstrated that quantitative, 3-D particle distributions can be accurately estimated from these data for particles with diameter larger than 4 pixels (0.8 mm).

An on-line full scan inspection system is developed for particle size analysis. A particle image is first obtained through optical line scan technology and is then analyzed using digital image processing. The system is composed of a particle separation module, an image acquisition module, an image processing module, and an electric control module. Experiments are carried out using non-uniform 0.1 mm particles. The main advantage of this system consists of a full analysis of particles without any overlap or miss, thus improving the Area Scan Charge Coupled Device (CCD) acquisition problems. Particle size distribution, roundness, and sphericity can be obtained using the system with a deviation of repeated precision of around ±1%. The developed system is shown to be also convenient and versatile for any particle size and shape for academic and industrial users.

In order to develop catalysts it is important to characterize the system in terms of particle size and size distribution. TEM or STEM are used as the state-of-the-art methods. In this study monodispersed Pt3Co alloy nanoparticles supported on carbon black (CB) prepared by the nanocapsule method are investigated and the results are compared with a commercially available Pt3Co/C catalyst system. To avoid the obstacles of conventional bright-field TEM or STEM regarding the image analysis, an advanced computerized image processing procedure is introduced. The so-called local adaptive threshold (LAT) is a method for a precise determination of particle diameter and size distribution in the subnanometer scale. The results demonstrate the advantage of a completely computerized particle evaluation as compared to a conventional particle analysis.

Purpose This study aims to develop a characterization method for coating structure based on image analysis, which is particularly promising for the rational design of coated particles in the pharmaceutical industry. Methods The method applies the MATLAB image processing toolbox to images of coated particles taken with Confocal Laser Scanning Microscopy (CSLM). The coating thicknesses have been determined along the particle perimeter, from which a statistical analysis could be performed to obtain relevant thickness properties, e.g. the minimum coating thickness and the span of the thickness distribution. The characterization of the pore structure involved a proper segmentation of pores from the coating and a granulometry operation. Results The presented method facilitates the quantification of porosity, thickness and pore size distribution of a coating. These parameters are considered the important coating properties, which are critical to coating functionality. Additionally, the effect of the coating process variations on coating quality can straight-forwardly be assessed. Conclusions Enabling a good characterization of the coating qualities, the presented method can be used as a fast and effective tool to predict coating functionality. This approach also enables the influence of different process conditions on coating properties to be effectively monitored, which latterly leads to process tailoring.

This paper describes a system for the acquisition and analysis of 3D data from the surfaces of coarse aggregate particles. The technique uses laser triangulation to acquire data from the upper hemispheres of particles passing along a conveyor belt. Methods of determining particle size and form in three dimensions are described and a new approach to the quantification of angularity is presented. The algorithm uses mathematical morphology to provide a geometrically meaningful interpretation of particle shape. The relative advantages of 2D and 3D analysis of aggregate particles are discussed, and results are presented which demonstrate the validity of this approach.

Particle diagnostics involving three dimensional distributions are important topics in many engineering fields. The holographic system is a promising optical tool for measuring three dimensional features of particles. In this study, we developed a holographic particle diagnostic system with diffused illumination to measure the sizes and 3-D velocities of moving particles using automatic image processing. First, basic optical systems for pulse laser recording, continuous laser reconstruction, and image acquisition were constructed. One of inherent limitations of particle holography is its long depth of focus in particle images, which causes considerable difficulty in determination of particle positions in the optical axis. To solve this problem, three new auto-focusing parameters (AFPs) corresponding to particle sizes were introduced. The developed system was applied to spray droplets to validate its capabilities. Three dimensional positions of particles viewed from two sides were decided using AFPs and then three dimensional particle velocities were extracted using a particle tracking algorithm. Comparison of measured sizes and three dimensional velocities of particles with those obtained using a laser instrument, PDPA (Phase Doppler Particle Analyzer), showed that the developed holographic system produced consistent results.

Particle size plays a major role in segregation phenomena in mixing process. In pharmaceutical industry, it is essential to have a proper analysis to make sure that the good product is well mixed and to prevent segregation. In this study, Digital Image processing has been used as an alternative method to thief probe, to study the effect of particle size on mixing and segregation process. The experiment started with execution of image acquisition process of mixing process. The images taken were further analyzed using Image Processing Tool in MATLAB software. The experiments were done with two different arrangement of particle in the bed using air velocity of 0.94 ms−1. The mixture was considered to be well mixed when the intensity of red of colour histogram is approximately constant. For the layered position, the good mixing time for set 1, 2, 3 and 4 were achieved at 62, 84, 86 and 74 s respectively. Then, for random position, homogeneous mixture were achieved at 62, 76, 82 and 72 s for set 1, 2, 3 and 4 respectively. This finding has been quantified by using Lacey Mixing Index where the value obtained was nearly to 1.0 which showed uniform mixing.

Aiming to determine the size distribution and surface area per unit mass of catalyst nanoparticles for fuel cells, we have developed a novel method of digital image processing that quantitatively determines the size of individual nanoparticles from atomic-resolution transmission electron microscopy images. After introducing the basic concept of this new method, we demonstrate its efficiency using platinum nanoparticles on carbon support as a model system. Special attention is given to the error that can be introduced by estimating the surface area per unit mass from the distribution of particle diameters in the presence of asymmetry of the particle size about the mean.

For the purpose of choosing and designing measurement algorithms to characterize particle size and shape rotation-invariantly, reproducibly, simply, and reasonably, this paper presents an image analysis measurement algorithm-best-fit rectangle for particle size and shape. The best-fit rectangle approach is a combination of the Ferret method and the least 2nd moments minimization, only requiring calculation of three moments about the center of gravity, and maximum and minimum co-ordinates in a co-ordinate system oriented in the direction of the axis of the least 2nd moments, and a simple area ratio. It is a simple rotation-invariance method, reflecting shape (elongation and angularity). In this paper, the method that has been tested in a large number of aggregate particle samples in a laboratory is introduced theoretically in detail, analyzed and compared to other widely used methods. The test results show that by using this method, the results are very close to manual measurements (for size, elongation and angularity). The width accumulative curve is parallel to the curves of sieving and thickness, implying that sieving analysis and thickness measurement can be easily estimated by the width. The method combining other image processing algorithms in an online system does the processing in real time. It is more reasonable and useful than other traditionally used measurement methods in image analysis.

Char morphology is an important characteristic when attempting to understand coal behavior and coal burnout. In this study, an augmented algorithm has been proposed to identify char types using image analysis. On the basis of a series of image processing steps, a char image is singled out from the whole image, which then allows the important major features of the char particle to be measured, including size, porosity, and wall thickness. The techniques for automated char image analysis have been tested against char images taken from ICCP Char Atlas as well as actual char particles derived from pyrolyzed char samples. Thirty different chars were prepared in a drop tube furnace operating at 1300{sup o}C, 1% oxygen, and 100 ms from 15 different world coals sieved into two size fractions (53-75 and 106-125 {mu}m). The results from this automated technique are comparable with those from manual analysis, and the additional detail from the automated sytem has potential use in applications such as combustion modeling systems. Obtaining highly detailed char information with automated methods has traditionally been hampered by the difficulty of automatic recognition of individual char particles. 20 refs., 10 figs., 3 tabs.

Immediate release direct compression tablet formulations require a strict control of the particle characteristics (i.e. particle size (distribution) and shape) of both the active pharmaceutical ingredient (API) and the excipients. In this publication, the development of a dry dispersion laser diffraction (LD) method has been outlined. With this method, the chemical development of an API meant for the manufacturing of an immediate release direct compression tablet formulation can be supported. Comparison with static image analysis (SIA) and scanning electron microscopy (SEM) data often shows laser diffraction to generate different size data. However, since LD is fast and frequently shows an adequate precision over a wide particle size range, the technique is still considered as a valuable analytical tool in the screening of the particle size distribution of API batches. In the future, automated (static) image analysis and dynamic image analysis are believed to become more and more important, since these techniques will allow the fast analysis of large amounts of particles with a minimum intervention of the operator.

The feasibility of applying dynamic imaging analysis technology to particle characterization has been evaluated for application in the water sector. A system has been developed which captures in-situ images of suspended particles in a flowing sample stream and analyzes these images in real time to determine particle size and concentration. The technology can measure samples having a wide range of particle sizes (∼1.5 to 1,000 μm equivalent circular diameter) and concentrations (1 million/ml). The system also provides magnified images of particles for visual analysis of properties such as size, shape and grayscale level. There are no sample preparation requirements and statistically accurate results are produced in less than three minutes per sample. The overall system architecture is described. The major design challenges in developing a practical system include obtaining adequate contrast for the range of particle materials found in typical water samples and achieving this under operating conditions permitting an adequate sample processing rate for real time feedback of results. Performance of the instrument is reported in reference to industry accepted particle standards and applications as an analytical tool for the water industries are considered.

On-line particle sizing is desirable in many industries, particularly in the power generation industry. This paper presents the principle and design of a prototype, digital imaging based, instrumentation system that can measure size distribution of particles that are largely opaque. The system, which is intended as an industrial rather than a laboratory solution, operates on-line and is non-intrusive. The image processing sequence is discussed in some detail. Experimental results obtained using two common types of particulate material demonstrate the viability of the system. The use of static images has allowed the instrumentation system to be calibrated. The linearity and repeatability of the system are quantified in addition to direct comparisons between the measurements made and those from proven, off-line, industrial solutions. The effects of the illumination sub-system on the spatial sensitivity of the viewing field are discussed along with the turndown ratio. The discrepancies between reference and static measurements are also identified and discussed. The results indicate that the system has good linearity and excellent repeatability and that practical on-line measurement of particle size distribution can be achieved using this technique. As presented the system can be used with particles in the range 150µm–25 mm.

The interferometric particle imaging and the global phase Doppler techniques for multi-dimensional particle sizing are based on out-of-focus images of the particles illuminated in laser light sheets of finite width. For a given optical arrangement, an increase in particle number concentration will eventually lead to overlapping particle images on the recording medium, and this in turn will degrade or even prohibit the correct processing of individual particle images for size. The probability of overlapping images should therefore be minimized, which places constraints on the design of the optical system. These constraints are rigorously derived, using Poisson statistics to describe the particle spatial distribution in the flow. The main result consists of an overlap probability coefficient as a function of number density and the optical parameters of the system. However, the resolution of the third position coordinate normal to the laser sheet and the measurement size range also result from this analysis. These results are a prerequisite for designing such systems.